rsocialwatcher: Usage Examples

Table of Contents

• Setup
• Querying Parameter IDs
  • User Type IDs
  • Location Key
  • Location Key with Coordinates/Geometries
• Get coordinates/geometries for location keys
• Querying Data
  • Different Location Types
    • Radius around lat/lon
    • Location IDs - Country, State, District, City, Neighborhood, etc.
      • Country Groups
      • Country
      • Region
      • Large Geographic Area
      • Medium Geographic Area
      • Small Geographic Area
      • City
      • Subcity
      • Neighborhood
      • Zip Code
      • Geo Market (U.S. Designated Market Areas)
      • Electoral Districts
  • Querying data using parameter IDs
    • Using one parameter ID per parameter type
    • And and OR Categories
      • Within parameter types
      • Across parameter types: Flexible Targetting
    • Excluding Categories
  • Multiple Queries
    • Simple Examples
    • And/Or Conditions

Setup

First, we load relevant packages and create variables for our Facebook API keys.

## Load packages
library(rsocialwatcher)
library(tidyverse)
library(dplyr)
library(purrr)
library(lubridate)
library(jsonlite)
library(httr)
library(stringr)
library(knitr)
library(kableExtra)
library(leaflet)
library(sf)

## Set Facebook Keys
TOKEN        <- "TOKEN-HERE"
CREATION_ACT <- "CREATION-ACT-HERE"
VERSION      <- "VERSION-HERE"

---

The rsocialwatcher package provides the following functions:

• get_fb_parameter_ids(): To obtain IDs for targeting users by different characteristics, including (1) different parameter types (eg, behaviors and interests) and (2) location keys (eg, city keys)
• get_location_coords(): To obtain coordinates and, when available, geometries of locations based on their location keys.
• query_fb_marketing_api(): Query daily and monthly active users, querying users for specific locations and by specific types.
• get_fb_suggested_radius(): Determine a suggested radius to reach enough people for a given coordinate pair.

Querying Parameter IDs

The get_fb_parameter_ids function facilitates querying IDs to target specific types of users (e.g., users with specific interests) and to target users in specific locations (eg, countries, cities, etc).

User Type IDs

Targeting specific types of users can be done along a number of categories, including by users’ interests, behaviors, job, etc. Using the get_fb_parameter_ids function, the type parameter is used to query IDs for different types of categories. The type parameter allows for the following inputs:

• behaviors
• demographics
• interests
• income
• industries
• life_events
• family_statuses
• locales
• work_positions
• work_employers
• education_statuses
• relationship_statuses
• education_majors
• education_schools

IDs can then be used in the query_fb_marketing_api function, which queries daily and monthly active Facebook users. For each type input, there is a parameter in the query_fb_marketing_api function to include the ID for the specific type. For example, query_fb_marketing_api has a parameter for behaviors to put behavior IDs.

behaviors_df <- get_fb_parameter_ids(type    = "behaviors",
                                     version = VERSION, 
                                     token   = TOKEN)

behaviors_df %>% 
  kable() %>% 
  kable_styling() %>% 
  scroll_box(width = "100%",
             height = "300px")

id	name	type	path	description	real_time_cluster	audience_size_lower_bound	audience_size_upper_bound
6002714895372	Frequent travellers	behaviors	Travel , Frequent travellers	People whose activities on Facebook suggest that they are frequent travellers.	FALSE	1647123246	1937016938
6002714898572	Small business owners	behaviors	Digital activities , Small business owners	People who list themselves as small business owners or own small business Pages on Facebook	FALSE	38766796	45589753
6002764392172	Facebook Payments users (90 days)	behaviors	Digital activities , Facebook Payments users (90 days)	People who have used Facebook payments in the past 90 days	FALSE	551812	648932
6003808923172	Early technology adopters	behaviors	Digital activities , Early technology adopters	People who are likely to adopt new technologies earlier than others	FALSE	107522889	126446918
6003986707172	Facebook access (OS): Windows 7	behaviors	Digital activities , Operating system used , Facebook access (OS): Windows 7	People who primarily access Facebook using Windows 7.	FALSE	22318	26246
6003966451572	Facebook access (OS): Mac OS X	behaviors	Digital activities , Operating system used , Facebook access (OS): Mac OS X	People who primarily access Facebook using Mac OS X	FALSE	2329374	2739344
6003966450972	Facebook access (OS): Windows Vista	behaviors	Digital activities , Operating system used , Facebook access (OS): Windows Vista	People who primarily access Facebook using Windows Vista	FALSE	1088	1280
6003966466972	Facebook access (OS): Windows XP	behaviors	Digital activities , Operating system used , Facebook access (OS): Windows XP	People who primarily access Facebook using Windows XP	FALSE	375	442
6004386303972	Facebook access (mobile): iPhone 4S	behaviors	Mobile Device User , All Mobile Devices by Brand , Apple , Facebook access (mobile): iPhone 4S	People who primarily access Facebook using an iPhone 4S mobile device.	FALSE	8513	10012
6004383941372	Facebook access (mobile): iPhone 4	behaviors	Mobile Device User , All Mobile Devices by Brand , Apple , Facebook access (mobile): iPhone 4	People who primarily access Facebook using an iPhone 4 mobile device.	FALSE	3536	4159
6004386044572	Facebook access (mobile): Android devices	behaviors	Mobile Device User , All Mobile Devices by Operating System , Facebook access (mobile): Android devices	People who primarily access Facebook using any Android mobile device	FALSE	1726538017	2030408708
6004383149972	Facebook access (mobile): feature phones	behaviors	Mobile Device User , Facebook access (mobile): feature phones	People who primarily access Facebook using a feature phone	FALSE	3676947	4324090
6004383890572	Facebook access (mobile): iPod Touch	behaviors	Mobile Device User , All Mobile Devices by Brand , Apple , Facebook access (mobile): iPod Touch	People who primarily access Facebook using an iPod Touch mobile device	FALSE	53426	62830
6004385895772	Facebook access (mobile): Windows phones	behaviors	Mobile Device User , All Mobile Devices by Operating System , Facebook access (mobile): Windows phones	People who primarily access Facebook using a Windows mobile device	FALSE	23256	27350
6004384041172	Facebook access (mobile): Apple (iOS) devices	behaviors	Mobile Device User , All Mobile Devices by Operating System , Facebook access (mobile): Apple (iOS) devices	People who primarily access Facebook using an Apple (iOS) mobile device	FALSE	319822462	376111216
6004383806772	Facebook access (mobile): iPad 3	behaviors	Mobile Device User , All Mobile Devices by Brand , Apple , Facebook access (mobile): iPad 3	People who primarily access Facebook using an iPad 3 mobile device.	FALSE	78640	92481
6004383808772	Facebook access (mobile): iPad 2	behaviors	Mobile Device User , All Mobile Devices by Brand , Apple , Facebook access (mobile): iPad 2	People who primarily access Facebook using an iPad 2 mobile device	FALSE	2308	2715
6004383767972	Facebook access (mobile): iPad 1	behaviors	Mobile Device User , All Mobile Devices by Brand , Apple , Facebook access (mobile): iPad 1	People who primarily access Facebook using an iPad 1 mobile device	FALSE	1050528	1235422
6004386010572	Facebook access (mobile): Samsung Android mobile devices	behaviors	Mobile Device User , All Mobile Devices by Brand , Facebook access (mobile): Samsung Android mobile devices	People who primarily access Facebook using a Samsung Android mobile device	FALSE	479386047	563757992
6004385886572	Facebook access (mobile): HTC Android mobile devices	behaviors	Mobile Device User , All Mobile Devices by Brand , Facebook access (mobile): HTC Android mobile devices	People who primarily access Facebook using an HTC Android mobile device.	FALSE	481419	566149
6004385868372	Facebook access(mobile): LG Android mobile devices	behaviors	Mobile Device User , All Mobile Devices by Brand , Facebook access(mobile): LG Android mobile devices	People who primarily access Facebook using an LG Android mobile device	FALSE	8361310	9832901
6004385865172	Facebook access (mobile): Sony Android mobile devices	behaviors	Mobile Device User , All Mobile Devices by Brand , Facebook access (mobile): Sony Android mobile devices	People who primarily access Facebook using a Sony Android mobile device	FALSE	3469573	4080218
6004382299972	Facebook access (mobile): all mobile devices	behaviors	Mobile Device User , Facebook access (mobile): all mobile devices	People who primarily access Facebook using mobile devices	FALSE	1835336736	2158356002
6004383049972	Facebook access (mobile): smartphones and tablets	behaviors	Mobile Device User , Facebook access (mobile): smartphones and tablets	People who primarily access Facebook using a smartphone or tablet device	FALSE	1902422238	2237248552
6004385879572	Facebook access (mobile): Motorola Android mobile devices	behaviors	Mobile Device User , All Mobile Devices by Brand , Facebook access (mobile): Motorola Android mobile devices	People who primarily access Facebook using a Motorola Android mobile device	FALSE	64609022	75980211
6004854404172	Facebook access: older devices and OS	behaviors	Digital activities , Facebook access: older devices and OS	People who primarily access Facebook on older devices or operating systems before Windows 7, Mac OS X or Windows NT 6.2.	FALSE	680678837	800478313
6004883585572	Facebook access (mobile): iPhone 5	behaviors	Mobile Device User , All Mobile Devices by Brand , Apple , Facebook access (mobile): iPhone 5	People who primarily access Facebook using an iPhone 5 mobile device	FALSE	45032	52958
6004948896972	Facebook Payments users (30 days)	behaviors	Digital activities , Facebook Payments users (30 days)	People who have used Facebook Payments in the past 30 days	FALSE	224301	263779
6006298077772	Facebook access (OS): Windows 8	behaviors	Digital activities , Operating system used , Facebook access (OS): Windows 8	People who primarily access Facebook using Facebook using Windows 8	FALSE	29797	35042
6007078565383	New smartphone and tablet users	behaviors	Mobile Device User , New smartphone and tablet users	People who are new smartphone or tablet users.	FALSE	66962232	78747586
6007481031783	Owns: Galaxy S III devices	behaviors	Mobile Device User , All Mobile Devices by Brand, Samsung , Owns: Galaxy S III devices	People who likely own a Galaxy S III mobile device	FALSE	57835	68014
6007847947183	Console gamers	behaviors	Digital activities, Console gamers	People who have liked Pages related to console gaming or gaming systems	FALSE	48803738	57393196
6008261969983	Returned from travelling one week ago	behaviors	Travel , Returned from travelling one week ago	People whose activities on Facebook suggest that they returned from travelling within the past week	FALSE	134248774	157876559
6008297697383	Returned from travelling two weeks ago	behaviors	Travel , Returned from travelling two weeks ago	People whose activities on Facebook suggest that they returned from travelling within the past 2 weeks	FALSE	207718684	244277173
6008868254383	Owns: Kindle Fire	behaviors	Mobile Device User , All Mobile Devices by Brand, Amazon , Owns: Kindle Fire	People who are likely to own a Kindle Fire	FALSE	8701	10233
6010095777183	Facebook access (mobile): iPhone 5S	behaviors	Mobile Device User , All Mobile Devices by Brand , Apple , Facebook access (mobile): iPhone 5S	People who primarily access Facebook using an iPhone 5S mobile device.	FALSE	353159	415315
6010095794383	Facebook access (mobile): iPhone 5C	behaviors	Mobile Device User , All Mobile Devices by Brand , Apple , Facebook access (mobile): iPhone 5C	People who primarily access Facebook using an iPhone 5C mobile device	FALSE	19522	22959
6010231666183	Owns: LG G2 devices	behaviors	Mobile Device User , All Mobile Devices by Brand, LG , Owns: LG G2 devices	People who are likely to own LG G2 devices	FALSE	13947	16402
6011191254383	Owns: iPad 4	behaviors	Mobile Device User , All Mobile Devices by Brand, Apple , Owns: iPad 4	People who are likely to own an iPad 4	FALSE	322857	379681
6011191259183	Owns: iPad Mini 1	behaviors	Mobile Device User , All Mobile Devices by Brand, Apple , Owns: iPad Mini 1	People who are likely to own an iPad Mini 1	FALSE	178147	209502
6011244513583	Owns: iPad Air	behaviors	Mobile Device User , All Mobile Devices by Brand, Apple , Owns: iPad Air	People who are likely to own an iPad Air.	FALSE	843068	991449
6011244510983	Owns: iPad Mini 2	behaviors	Mobile Device User , All Mobile Devices by Brand, Apple , Owns: iPad Mini 2	People who are likely to own an iPad Mini 2.	FALSE	466277	548342
6011390261383	Owns: Huawei	behaviors	Mobile Device User , All Mobile Devices by Brand, Owns: Huawei	People who are likely to own a Huawei mobile device.	FALSE	57109294	67160530
6015852294783	Owns: Galaxy Y devices	behaviors	Mobile Device User , All Mobile Devices by Brand, Samsung , Owns: Galaxy Y devices	People who are likely to own a Galaxy Y mobile device.	FALSE	2386	2806
6013016790183	Owns: Galaxy S4	behaviors	Mobile Device User , All Mobile Devices by Brand, Samsung , Owns: Galaxy S4	People who are likely to own a Galaxy S4 mobile device	FALSE	167130	196545
6013017211983	Owns: Galaxy S III Mini	behaviors	Mobile Device User , All Mobile Devices by Brand, Samsung , Owns: Galaxy S III Mini	People who are likely to own a Galaxy S III Mini mobile device	FALSE	29620	34834
6013017235183	Galaxy Note II	behaviors	Mobile Device User , All Mobile Devices by Brand, Samsung , Galaxy Note II		FALSE	2403	2826
6013017236583	Owns: Galaxy Grand	behaviors	Mobile Device User , All Mobile Devices by Brand, Samsung , Owns: Galaxy Grand	People who are likely to own a Galaxy Grand mobile device	FALSE	62313	73281
6013017297383	Curve 9220	behaviors	Mobile Device User , All Mobile Devices by Brand, BlackBerry , Curve 9220		FALSE	188	222
6013017308783	Owns: Galaxy S 4 Mini	behaviors	Mobile Device User , All Mobile Devices by Brand, Samsung , Owns: Galaxy S 4 Mini	People who are likely to own a Galaxy S 4 Mini mobile device.	FALSE	20103	23642
6013279353983	Owns: Galaxy Note 3	behaviors	Mobile Device User , All Mobile Devices by Brand, Samsung , Owns: Galaxy Note 3	People who are likely to own a Galaxy Note 3 mobile device.	FALSE	216220	254275
6013516370183	Commuters	behaviors	Travel , Commuters	People who likely commute from their home to their workplace on weekdays	FALSE	301135354	354135177
6014808618583	Owns: Galaxy S5	behaviors	Mobile Device User , All Mobile Devices by Brand, Samsung , Owns: Galaxy S5	People who are likely to own a Samsung Galaxy S5 mobile device	FALSE	320046	376375
6014809400783	Owns: Nexus 5	behaviors	Mobile Device User , All Mobile Devices by Brand, Google , Owns: Nexus 5	People who are likely to own a Nexus 5 mobile device.	FALSE	218636	257116
6014809859183	Owns: HTC One	behaviors	Mobile Device User , All Mobile Devices by Brand, HTC , Owns: HTC One	People who are likely to own an HTC One mobile device	FALSE	100299	117952
6015235495383	Facebook access (network type): Wi-Fi	behaviors	Mobile Device User , Network Connection , Facebook access (network type): Wi-Fi	People who primarily access Facebook using a Wi-Fi network.	FALSE	979480774	1151869391
6015441244983	Owns: Galaxy Grand 2	behaviors	Mobile Device User , All Mobile Devices by Brand, Samsung , Owns: Galaxy Grand 2	People who are likely to own a Samsung Galaxy Grand 2 mobile device	FALSE	64236	75542
6015547847583	Facebook access (browser): Firefox	behaviors	Digital activities , Internet browser used , Facebook access (browser): Firefox	People who primarily access Facebook using Firefox	FALSE	12715828	14953814
6015547900583	Facebook access (browser): Chrome	behaviors	Digital activities , Internet browser used , Facebook access (browser): Chrome	People who primarily access Facebook using Google Chrome.	FALSE	274211097	322472251
6015559470583	Lives abroad	behaviors	Ex-pats , Lives abroad	People living outside their home country	FALSE	201652135	237142911
6015593608983	Facebook access (browser): Safari	behaviors	Digital activities , Internet browser used , Facebook access (browser): Safari	People who primarily access Facebook using Safari	FALSE	39145924	46035607
6015593652183	Facebook access (browser): Opera	behaviors	Digital activities , Internet browser used , Facebook access (browser): Opera	People who primarily access Facebook using Opera	FALSE	7603548	8941773
6015593776783	Facebook access (browser): Internet Explorer	behaviors	Digital activities , Internet browser used , Facebook access (browser): Internet Explorer	People who primarily access Facebook using Internet Explorer	FALSE	330398	388549
6015683810783	Facebook Page admins	behaviors	Digital activities , Facebook page admins, Facebook Page admins	People who are an admin of at least one Page on Facebook.	FALSE	553015092	650345749
6016286626383	Facebook access (mobile): tablets	behaviors	Mobile Device User , Facebook access (mobile): tablets	People who primarily access Facebook using a tablet.	FALSE	1682261446	1978339461
6016916298983	Lived in India (formerly Expats – India)	behaviors	Ex-pats , Lived in India (formerly Expats – India)	People who used to live in India who now live abroad	FALSE	13971504	16430489
6016925328983	Owns: Galaxy Tab S	behaviors	Mobile Device User , All Mobile Devices by Brand, Samsung , Owns: Galaxy Tab S	People who are likely to own a Samsung Galaxy Tab S mobile device.	FALSE	59821	70350
6016925394583	Owns: Galaxy Tab Pro	behaviors	Mobile Device User , All Mobile Devices by Brand, Samsung , Owns: Galaxy Tab Pro	People who are likely to own a Samsung Galaxy Tab Pro mobile device.	FALSE	10879	12794
6016925404783	Owns: Galaxy Tab 4	behaviors	Mobile Device User , All Mobile Devices by Brand, Samsung , Owns: Galaxy Tab 4	People who are likely to own a Samsung Galaxy Tab 4 mobile device	FALSE	245369	288555
6016925643983	Owns: Galaxy Tab 3	behaviors	Mobile Device User , All Mobile Devices by Brand, Samsung , Owns: Galaxy Tab 3	People who are likely to own a Galaxy Tab 3 mobile device.	FALSE	342591	402888
6016925657183	Owns: Galaxy Tab 2	behaviors	Mobile Device User , All Mobile Devices by Brand, Samsung , Owns: Galaxy Tab 2	People who are likely to own a Galaxy Tab 2 mobile device	FALSE	34091	40092
6016925667383	Galaxy Tab	behaviors	Mobile Device User , All Mobile Devices by Brand, Samsung , Galaxy Tab	People who are likely to own a Samsung Galaxy Tab	FALSE	238	281
6016926254583	Owns: Xperia M	behaviors	Mobile Device User , All Mobile Devices by Brand, Sony , Owns: Xperia M	People who are likely to own a Sony Xperia M mobile device	FALSE	2830	3329
6016926310383	Owns: Xperia SL	behaviors	Mobile Device User , All Mobile Devices by Brand, Sony , Owns: Xperia SL	People who are likely to own a Sony Xperia SLs	FALSE	275	324
6016926471583	Xperia T	behaviors	Mobile Device User , All Mobile Devices by Brand, Sony , Xperia T	People who are likely to own a Sony Xperia T	FALSE	325	383
6016926528983	Owns: Xperia Z	behaviors	Mobile Device User , All Mobile Devices by Brand, Sony , Owns: Xperia Z	People who are likely to own a Sony Xperia Z mobile device	FALSE	5346	6287
6016926651383	Owns: Xperia Z Ultra	behaviors	Mobile Device User , All Mobile Devices by Brand, Sony , Owns: Xperia Z Ultra	People who are likely to own a Sony Xperia Z Ultra mobile device	FALSE	1715	2017
6017253486583	Facebook access (network type): 2G	behaviors	Mobile Device User , Network Connection , Facebook access (network type): 2G	People who primarily access Facebook using a 2G network.	FALSE	7345473	8638277
6017253511583	Facebook access (network type): 3G	behaviors	Mobile Device User , Network Connection , Facebook access (network type): 3G	People who primarily access Facebook using a 3G network.	FALSE	90743383	106714219
6017253531383	Facebook access (network type): 4G	behaviors	Mobile Device User , Network Connection , Facebook access (network type): 4G	People who primarily access Facebook using a 4G network	FALSE	791357471	930636386
6017535283383	Owns: LG G3	behaviors	Mobile Device User , All Mobile Devices by Brand, LG , Owns: LG G3	People who are likely to own an LG G3 mobile device	FALSE	35312	41527
6017831560783	Owns: iPhone 6 Plus	behaviors	Mobile Device User , All Mobile Devices by Brand, Apple , Owns: iPhone 6 Plus	People who are likely to own an Apple iPhone 6 Plus mobile device	FALSE	1063623	1250821
6017831572183	Owns: iPhone 6	behaviors	Mobile Device User , All Mobile Devices by Brand, Apple , Owns: iPhone 6	People who are likely to own an iPhone 6 mobile device	FALSE	2195840	2582309
6018413514983	Anniversary (within 61-90 days)	behaviors	Anniversary , Anniversary (within 61-90 days)	People with an anniversary in 61-90 days	FALSE	7391487	8692389
6018796980983	Lived in Kenya (formerly Expats – Kenya)	behaviors	Ex-pats , Lived in Kenya (formerly Expats – Kenya)	People who used to live in Kenya who now live abroad	FALSE	1386561	1630596
6018797004183	Lived in Nigeria (formerly Expats – Nigeria)	behaviors	Ex-pats , Lived in Nigeria (formerly Expats – Nigeria)	People who used to live in Nigeria who now live abroad	FALSE	2482332	2919223
6018797036783	Lived in Cameroon (formerly Expats – Cameroon)	behaviors	Ex-pats , Lived in Cameroon (formerly Expats – Cameroon)	People who used to live in Cameroon who now live abroad	FALSE	481462	566200
6018797091183	Lived in Philippines (formerly Expats – Philippines)	behaviors	Ex-pats , Lived in Philippines (formerly Expats – Philippines)	People who used to live in the Philippines who now live abroad	FALSE	9472597	11139775
6018797127383	Lived in Cuba (formerly Expats – Cuba)	behaviors	Ex-pats , Lived in Cuba (formerly Expats – Cuba)	People who used to live in Cuba who now live abroad	FALSE	1798545	2115089
6018797165983	Lived in Ethiopia (formerly Expats – Ethiopia)	behaviors	Ex-pats , Lived in Ethiopia (formerly Expats – Ethiopia)	People who used to live in Ethiopia who now live abroad	FALSE	985661	1159138
6018797373783	Lived in Haiti (formerly Expats – Haiti)	behaviors	Ex-pats , Lived in Haiti (formerly Expats – Haiti)	People who used to live in Haiti who now live abroad	FALSE	1474987	1734585
6018995113183	Owns: iPad Air 2	behaviors	Mobile Device User , All Mobile Devices by Brand, Apple , Owns: iPad Air 2	People who are likely to own an iPad Air 2 mobile device	FALSE	1485676	1747156
6019221024783	Played Canvas games (yesterday)	behaviors	Digital activities , Canvas gaming , Played Canvas games (yesterday)	People who played a Canvas game yesterday.	FALSE	1798290	2114790
6019221038183	Played Canvas games (last 7 days)	behaviors	Digital activities , Canvas gaming , Played Canvas games (last 7 days)	People who played a Canvas game in the last 7 days.	FALSE	2693022	3166994
6019221046583	Played Canvas games (last 14 days)	behaviors	Digital activities , Canvas gaming , Played Canvas games (last 14 days)	People who played a Canvas game in the last 14 days.	FALSE	3127062	3677426
6019098117583	Owns: iPad Mini 3	behaviors	Mobile Device User , All Mobile Devices by Brand, Apple , Owns: iPad Mini 3	People who are likely to own an iPad Mini 3 mobile device.	FALSE	98511	115849
6019098214783	Owns: Galaxy Note 4	behaviors	Mobile Device User , All Mobile Devices by Brand, Samsung , Owns: Galaxy Note 4	People who are likely to own a Galaxy Note 4 mobile device.	FALSE	175571	206472
6019164544783	Owns: Karbonn	behaviors	Mobile Device User , All Mobile Devices by Brand, Owns: Karbonn	People who are likely to own a Karbonn mobile device.	FALSE	54010	63516
6019164586183	Owns: Micromax	behaviors	Mobile Device User , All Mobile Devices by Brand, Owns: Micromax	People who are likely to own a Micromax mobile device	FALSE	300462	353344
6019164630583	Owns: Xiaomi	behaviors	Mobile Device User , All Mobile Devices by Brand, Owns: Xiaomi	People who are likely to own a Xiaomi mobile device.	FALSE	15062213	17713163
6019246164583	Played Canvas games (last 3 days)	behaviors	Digital activities , Canvas gaming , Played Canvas games (last 3 days)	People who played a Canvas game in the last 3 days	FALSE	2292640	2696145
6019366943583	Lived in Spain (formerly Expats – Spain)	behaviors	Ex-pats , Lived in Spain (formerly Expats – Spain)	People who used to live in Spain who now live abroad	FALSE	1632858	1920242
6019367014383	Lived in France (formerly Ex-pats – France)	behaviors	Ex-pats , Lived in France (formerly Ex-pats – France)	People who used to live in France who now live abroad	FALSE	2963593	3485186
6019367052983	Lived in Germany (formerly Ex-pats – Germany)	behaviors	Ex-pats , Lived in Germany (formerly Ex-pats – Germany)	People who used to live in Germany who now live abroad	FALSE	1535034	1805201
6019377644783	Lived in Switzerland (formerly Ex-pats – Switzerland)	behaviors	Ex-pats , Lived in Switzerland (formerly Ex-pats – Switzerland)	People who used to live in Switzerland who now live abroad	FALSE	300822	353767
6019396649183	Lived in the United States (formerly Ex-pats – United States)	behaviors	Ex-pats , Lived in the United States (formerly Ex-pats – United States)	People who used to live in the United States who now live abroad	FALSE	16864520	19832676
6019396657183	Lived in Poland (formerly Ex-pats – Poland)	behaviors	Ex-pats , Lived in Poland (formerly Ex-pats – Poland)	People who used to live in Poland who now live abroad	FALSE	1413694	1662505
6019396654583	Lived in Italy (formerly Ex-pats – Italy)	behaviors	Ex-pats , Lived in Italy (formerly Ex-pats – Italy)	People who used to live in Italy who now live abroad	FALSE	1724471	2027978
6019396650783	Lived in Ireland (formerly Ex-pats – Ireland)	behaviors	Ex-pats , Lived in Ireland (formerly Ex-pats – Ireland)	People who used to live in Ireland who now live abroad	FALSE	284070	334067
6019396638383	Lived in Hungary (formerly Ex-pats – Hungary)	behaviors	Ex-pats , Lived in Hungary (formerly Ex-pats – Hungary)	People who used to live in Hungary who now live abroad	FALSE	335965	395095
6019396764183	Lived in Canada (formerly Expats – Canada)	behaviors	Ex-pats , Lived in Canada (formerly Expats – Canada)	People who used to live in Canada who now live abroad	FALSE	1300278	1529128
6019452369983	Lived in China (formerly Ex-pats – China)	behaviors	Ex-pats , Lived in China (formerly Ex-pats – China)	People who used to live in China who now live abroad	FALSE	2099995	2469595
6019520122583	Lived in Puerto Rico (formerly Ex-pats – Puerto Rico)	behaviors	Ex-pats , Lived in Puerto Rico (formerly Ex-pats – Puerto Rico)	People who used to live in Puerto Rico who now live abroad	FALSE	1135306	1335121
6019564340583	Lived in Brazil (formerly Ex-pats – Brazil)	behaviors	Ex-pats , Lived in Brazil (formerly Ex-pats – Brazil)	People who used to live in Brazil who now live abroad	FALSE	4013738	4720156
6019564344583	Lived in Indonesia (formerly Ex-pats – Indonesia)	behaviors	Ex-pats , Lived in Indonesia (formerly Ex-pats – Indonesia)	People who used to live in Indonesia who now live abroad	FALSE	4262948	5013227
6019564383383	Lived in South Africa (formerly Expats – South Africa)	behaviors	Ex-pats , Lived in South Africa (formerly Expats – South Africa)	People who used to live in South Africa who now live abroad	FALSE	1268147	1491341
6019673233983	Lived in Zimbabwe (formerly Ex-pats – Zimbabwe)	behaviors	Ex-pats , Lived in Zimbabwe (formerly Ex-pats – Zimbabwe)	People who used to live in Zimbabwe who now live abroad	FALSE	883955	1039532
6019673448383	Lived in Ghana (formerly Ex-pats – Ghana)	behaviors	Ex-pats , Lived in Ghana (formerly Ex-pats – Ghana)	People who used to live in Ghana who now live abroad	FALSE	744976	876092
6019673501783	Lived in Uganda (formerly Ex-pats – Uganda)	behaviors	Ex-pats , Lived in Uganda (formerly Ex-pats – Uganda)	People who used to live in Uganda who now live abroad	FALSE	624515	734430
6019673525983	Lived in Colombia (formerly Ex-pats – Colombia)	behaviors	Ex-pats , Lived in Colombia (formerly Ex-pats – Colombia)	People who used to live in Colombia who now live abroad	FALSE	2916567	3429883
6019673762183	Lived in Dominican Republic (formerly Ex-pats – Dominican Republic)	behaviors	Ex-pats , Lived in Dominican Republic (formerly Ex-pats – Dominican Republic)	People who used to live in the Dominican Republic who now live abroad	FALSE	1407819	1655596
6019673777983	Lived in El Salvador (formerly Expats – El Salvador)	behaviors	Ex-pats , Lived in El Salvador (formerly Expats – El Salvador)	People who used to live in El Salvador who now live abroad	FALSE	1473986	1733408
6019673808383	Lived in Guatemala (formerly Ex-pats – Guatemala)	behaviors	Ex-pats , Lived in Guatemala (formerly Ex-pats – Guatemala)	People who used to live in Guatemala who now live abroad	FALSE	2197619	2584400
6020530139383	Travel and tourism Page admins	behaviors	Digital activities , Facebook page admins , Travel and tourism Page admins	People who are an admin of a travel and tourism Page on Facebook	FALSE	19220526	22603339
6020530156983	Sport Page admins	behaviors	Digital activities , Facebook page admins, Sport Page admins	People who are an admin of a sport Page on Facebook	FALSE	7902853	9293756
6020530250383	Retail Page admins	behaviors	Digital activities , Facebook page admins, Retail Page admins	People who are an admin of a retail Page on Facebook	FALSE	11305724	13295532
6020568271383	Health and beauty Page admins	behaviors	Digital activities , Facebook page admins , Health and beauty Page admins	People who are an admin of a health and beauty Page on Facebook	FALSE	18209101	21413903
6020530269183	Food and restaurant Page admins	behaviors	Digital activities , Facebook page admins , Food and restaurant Page admins	People who are an admin of a food and restaurant Page on Facebook	FALSE	2089404	2457140
6020530280983	Community and club Page admins	behaviors	Digital activities , Facebook page admins , Community and club Page admins	People who are an admin of a community and club Page on Facebook	FALSE	15595613	18340442
6020530281783	Business Page admins	behaviors	Digital activities , Facebook page admins, Business Page admins	People who are an admin of a business Page on Facebook	FALSE	40704944	47869015
6021354152983	Lived in the UK (formerly Ex-pats – UK)	behaviors	Ex-pats , Lived in the UK (formerly Ex-pats – UK)	People who used to live in the United Kingdom who now live abroad	FALSE	4892666	5753776
6021354857783	Lived in Australia (formerly Ex-pats – Australia)	behaviors	Ex-pats , Lived in Australia (formerly Ex-pats – Australia)	People who used to live in Australia who now live abroad	FALSE	1339954	1575786
6021354882783	Lived in Portugal (formerly Ex-pats – Portugal)	behaviors	Ex-pats , Lived in Portugal (formerly Ex-pats – Portugal)	People who used to live in Portugal who now live abroad	FALSE	1003757	1180419
6022430911783	Owns: Xperia Z3	behaviors	Mobile Device User , All Mobile Devices by Brand, Sony , Owns: Xperia Z3	People who are likely to own a Sony Xperia Z3	FALSE	14199	16699
6022788483583	Frequent international travellers	behaviors	Travel , Frequent international travellers	People who have travelled abroad more than once in the past 6 months.	FALSE	644113401	757477360
6023287351383	Lived in Estonia (formerly Expats – Estonia)	behaviors	Ex-pats , Lived in Estonia (formerly Expats – Estonia)	People who used to live in Estonia who now live abroad	FALSE	80339	94479
6023287459983	Lived in Norway (formerly Ex-pats – Norway)	behaviors	Ex-pats , Lived in Norway (formerly Ex-pats – Norway)	People who used to live in Norway who now live abroad	FALSE	180323	212061
6023287455983	Lived in Denmark (formerly Ex-pats – Denmark)	behaviors	Ex-pats , Lived in Denmark (formerly Ex-pats – Denmark)	People who used to live in Denmark who now live abroad	FALSE	124342	146227
6023287438783	Lived in Czech Republic (formerly Ex-pats – Czech Republic)	behaviors	Ex-pats , Lived in Czech Republic (formerly Ex-pats – Czech Republic)	People who used to live in the Czech Republic who now live abroad	FALSE	205344	241485
6023287397383	Lived in Sweden (formerly Expats – Sweden)	behaviors	Ex-pats , Lived in Sweden (formerly Expats – Sweden)	People who used to live in Sweden who now live abroad	FALSE	267919	315073
6023287393783	Lived in the Netherlands (formerly Ex-pats – the Netherlands)	behaviors	Ex-pats , Lived in the Netherlands (formerly Ex-pats – the Netherlands)	People who used to live in the Netherlands who now live abroad	FALSE	439177	516473
6023356562783	Lived in Bangladesh (formerly Ex-pats – Bangladesh)	behaviors	Ex-pats , Lived in Bangladesh (formerly Ex-pats – Bangladesh)	People who used to live in Bangladesh who now live abroad	FALSE	6100326	7173984
6023356926183	Lived in Tanzania (formerly Expats – Tanzania)	behaviors	Ex-pats , Lived in Tanzania (formerly Expats – Tanzania)	People who used to live in Tanzania who now live abroad	FALSE	464745	546541
6023356955383	Lived in Nepal (formerly Ex-pats – Nepal)	behaviors	Ex-pats , Lived in Nepal (formerly Ex-pats – Nepal)	People who used to live in Nepal who now live abroad	FALSE	3473994	4085417
6023356956983	Lived in Jamaica (formerly Ex-pats – Jamaica)	behaviors	Ex-pats , Lived in Jamaica (formerly Ex-pats – Jamaica)	People who used to live in Jamaica who now live abroad	FALSE	691965	813752
6023356966183	Lived in Thailand (formerly Ex-pats – Thailand)	behaviors	Ex-pats , Lived in Thailand (formerly Ex-pats – Thailand)	People who used to live in Thailand who now live abroad	FALSE	1711204	2012377
6023356986383	Lived in Sierra Leone (formerly Expats – Sierra Leone)	behaviors	Ex-pats , Lived in Sierra Leone (formerly Expats – Sierra Leone)	People who used to live in Sierra Leone who now live abroad	FALSE	212931	250407
6023357000583	Lived in Senegal (formerly Ex-pats – Senegal)	behaviors	Ex-pats , Lived in Senegal (formerly Ex-pats – Senegal)	People who used to live in Senegal who now live abroad	FALSE	456789	537184
6023422105983	Lived in Côte d’Ivoire (formerly Ex-pats – Ivory Coast)	behaviors	Ex-pats , Lived in Côte d’Ivoire (formerly Ex-pats – Ivory Coast)	People who used to live in Côte d’Ivoire who now live abroad	FALSE	571107	671622
6023460563383	Owns: Alcatel	behaviors	Mobile Device User , All Mobile Devices by Brand, Owns: Alcatel	People who are likely to own an Alcatel mobile device	FALSE	2882607	3389947
6023460572383	Owns: ZTE	behaviors	Mobile Device User , All Mobile Devices by Brand, Owns: ZTE	People who are likely to own a ZTE mobile device	FALSE	7928835	9324311
6023460579583	Owns: Tecno	behaviors	Mobile Device User , All Mobile Devices by Brand, Owns: Tecno	People who are likely to own a Tecno mobile device.	FALSE	74934710	88123220
6023460590583	Owns: Cherry Mobile	behaviors	Mobile Device User , All Mobile Devices by Brand, Owns: Cherry Mobile	People who are likely to own a Cherry mobile device.	FALSE	108499	127595
6023516315983	Lived in Sri Lanka (formerly Ex-pats – Sri Lanka)	behaviors	Ex-pats , Lived in Sri Lanka (formerly Ex-pats – Sri Lanka)	People who used to live in Sri Lanka who now live abroad	FALSE	1031784	1213379
6023516338783	Lived in Morocco (formerly Ex-pats – Morocco)	behaviors	Ex-pats , Lived in Morocco (formerly Ex-pats – Morocco)	People who used to live in Morocco who now live abroad	FALSE	1992982	2343748
6023516430783	Lived in UAE (formerly Ex-pats – UAE)	behaviors	Ex-pats , Lived in UAE (formerly Ex-pats – UAE)	People who used to live in the United Arab Emirates who now live abroad	FALSE	1550009	1822811
6023516368383	Lived in New Zealand (formerly Ex-pats – New Zealand)	behaviors	Ex-pats , Lived in New Zealand (formerly Ex-pats – New Zealand)	People who used to live in New Zealand who now live abroad	FALSE	1332069	1566514
6023516373983	Lived in DR Congo (formerly Ex-pats – DR Congo)	behaviors	Ex-pats , Lived in DR Congo (formerly Ex-pats – DR Congo)	People who used to live in the Democratic Republic of the Congo who now live abroad	FALSE	2174757	2557515
6023516403783	Lived in Singapore (formerly Ex-pats – Singapore)	behaviors	Ex-pats , Lived in Singapore (formerly Ex-pats – Singapore)	People who used to live in Singapore who now live abroad	FALSE	85151	100138
6023620475783	Lived in the United States (formerly Ex-pats – United States)	behaviors	Ex-pats , Lived in the United States (formerly Ex-pats – United States)	People who used to live in the United States who now live abroad	FALSE	16863766	19831789
6023675997383	Lived in Austria (formerly Ex-pats – Austria)	behaviors	Ex-pats , Lived in Austria (formerly Ex-pats – Austria)	People who used to live in Austria who now live abroad	FALSE	142829	167967
6023676002183	Lived in Cyprus (formerly Ex-pats – Cyprus)	behaviors	Ex-pats , Lived in Cyprus (formerly Ex-pats – Cyprus)	People who used to live in Cyprus who now live abroad	FALSE	86221	101397
6068209522983	Lived in Finland (formerly Expats – Finland)	behaviors	Ex-pats , Lived in Finland (formerly Expats – Finland)	People who used to live in Finland who now live abroad	FALSE	108393	127471
6023676017583	Lived in Greece (formerly Ex-pats – Greece)	behaviors	Ex-pats , Lived in Greece (formerly Ex-pats – Greece)	People who used to live in Greece who now live abroad	FALSE	5742	6753
6023676022783	Lived in Hong Kong (formerly Ex-pats – Hong Kong)	behaviors	Ex-pats , Lived in Hong Kong (formerly Ex-pats – Hong Kong)	People who used to live in Hong Kong who now live abroad	FALSE	725337	852997
6023676028783	Lived in Japan (formerly Ex-pats – Japan)	behaviors	Ex-pats , Lived in Japan (formerly Ex-pats – Japan)	People who used to live in Japan who now live abroad	FALSE	1572051	1848733
6068613839383	Lived in Latvia (formerly Expats – Latvia)	behaviors	Ex-pats , Lived in Latvia (formerly Expats – Latvia)	People who used to live in Latvia who now live abroad	FALSE	151292	177920
6023676039183	Lived in Lithuania (formerly Ex-pats – Lithuania)	behaviors	Ex-pats , Lived in Lithuania (formerly Ex-pats – Lithuania)	People who used to live in Lithuania who now live abroad	FALSE	240238	282521
6023676044383	Lived in Luxembourg (formerly Ex-pats – Luxembourg)	behaviors	Ex-pats , Lived in Luxembourg (formerly Ex-pats – Luxembourg)	People who used to live in Luxembourg who now live abroad	FALSE	31769	37361
6023676045583	Lived in Malta (formerly Expats – Malta)	behaviors	Ex-pats , Lived in Malta (formerly Expats – Malta)	People who used to live in Malta who now live abroad	FALSE	35680	41960
6023676048183	Lived in Monaco (formerly Ex-pats – Monaco)	behaviors	Ex-pats , Lived in Monaco (formerly Ex-pats – Monaco)	People who used to live in Monaco who now live abroad	FALSE	25096	29514
6023676055383	Lived in Slovakia (formerly Ex-pats – Slovakia)	behaviors	Ex-pats , Lived in Slovakia (formerly Ex-pats – Slovakia)	People who used to live in Slovakia who now live abroad	FALSE	243781	286687
6023676060183	Lived in Slovenia (formerly Ex-pats – Slovenia)	behaviors	Ex-pats , Lived in Slovenia (formerly Ex-pats – Slovenia)	People who used to live in Slovenia who now live abroad	FALSE	61229	72006
6023676072183	Lived in Mexico (formerly Ex-pats – Mexico)	behaviors	Ex-pats , Lived in Mexico (formerly Ex-pats – Mexico)	People who used to live in Mexico who now live abroad	FALSE	11883433	13974918
6025000826583	Lived in Argentina (formerly Expats – Argentina)	behaviors	Ex-pats , Lived in Argentina (formerly Expats – Argentina)	People who used to live in Argentina who now live abroad	FALSE	1268125	1491315
6025000823583	Lived in Israel (formerly Ex-pats – Israel)	behaviors	Ex-pats , Lived in Israel (formerly Ex-pats – Israel)	People who used to live in Israel who now live abroad	FALSE	544188	639966
6025000815983	Lived in Russia (formerly Ex-pats – Russia)	behaviors	Ex-pats , Lived in Russia (formerly Ex-pats – Russia)	People who used to live in Russia who now live abroad	FALSE	1282211	1507881
6025000813183	Lived in Saudi Arabia (formerly Ex-pats – Saudi Arabia)	behaviors	Ex-pats , Lived in Saudi Arabia (formerly Ex-pats – Saudi Arabia)	People who used to live in the Kingdom of Saudi Arabia who now live abroad	FALSE	1098263	1291558
6025054896983	Lived in Chile (formerly Expats – Chile)	behaviors	Ex-pats , Lived in Chile (formerly Expats – Chile)	People who used to live in Chile who now live abroad	FALSE	543035	638610
6025670492783	Lived in Rwanda (formerly Ex-pats – Rwanda)	behaviors	Ex-pats , Lived in Rwanda (formerly Ex-pats – Rwanda)	People who used to live in Rwanda who now live abroad	FALSE	134193	157812
6025753961783	Family of those who live abroad	behaviors	Ex-pats , Family of those who live abroad	Family of people who now live abroad	FALSE	32474338	38189822
6026404871583	Lived in Venezuela (formerly Expats – Venezuela)	behaviors	Ex-pats , Lived in Venezuela (formerly Expats – Venezuela)	People who used to live in Venezuela who now live abroad	FALSE	5117986	6018752
6026660740983	Owns: Galaxy S6	behaviors	Mobile Device User , All Mobile Devices by Brand, Samsung , Owns: Galaxy S6	People who are likely to own a Samsung Galaxy S6 mobile device	FALSE	772850	908872
6027147160983	Lived in Malaysia (formerly Ex-pats – Malaysia)	behaviors	Ex-pats , Lived in Malaysia (formerly Ex-pats – Malaysia)	People who used to live in Malaysia who now live abroad	FALSE	1825450	2146730
6027148962983	Lived in Romania (formerly Ex-pats – Romania)	behaviors	Ex-pats , Lived in Romania (formerly Ex-pats – Romania)	People who used to live in Romania who now live abroad	FALSE	1774365	2086654
6027148973583	Lived in South Korea (formerly Ex-pats – South Korea)	behaviors	Ex-pats , Lived in South Korea (formerly Ex-pats – South Korea)	People who used to live in South Korea who now live abroad	FALSE	3098005	3643254
6027149004983	Lived in Serbia (formerly Expats – Serbia)	behaviors	Ex-pats , Lived in Serbia (formerly Expats – Serbia)	People who used to live in Serbia who now live abroad	FALSE	575873	677227
6027149006383	Lived in Vietnam (formerly Ex-pats – Vietnam)	behaviors	Ex-pats , Lived in Vietnam (formerly Ex-pats – Vietnam)	People who used to live in Vietnam who now live abroad	FALSE	4994960	5874073
6027149008183	Lived in Peru (formerly Ex-pats – Peru)	behaviors	Ex-pats , Lived in Peru (formerly Ex-pats – Peru)	People who used to live in Peru who now live abroad	FALSE	1757621	2066963
6028974370383	People in India who prefer high-value goods	behaviors	Consumer classification , India , People in India who prefer high-value goods	Aligned to (A) group, people in India who are predicted to prefer high-value goods	FALSE	88053823	103551297
6028974351183	People in India who prefer mid- and high-value goods	behaviors	Consumer classification , India , People in India who prefer mid- and high-value goods	Aligned to (A+B) group, people in India who are predicted to prefer mid to high-value goods	FALSE	149350418	175636092
6029587661983	Facebook access (OS): Windows 10	behaviors	Digital activities , Operating system used , Facebook access (OS): Windows 10	People who primarily access Facebook using Windows 10	FALSE	60842	71551
6031259562783	Owns: iPhone 6S	behaviors	Mobile Device User , All Mobile Devices by Brand, Apple , Owns: iPhone 6S	People who are likely to own an iPhone 6S mobile device	FALSE	3890994	4575809
6031259590183	Owns: iPhone 6S Plus	behaviors	Mobile Device User , All Mobile Devices by Brand, Apple , Owns: iPhone 6S Plus	People who are likely to own an iPhone 6S Plus mobile device.	FALSE	3268878	3844201
6041891177783	New Page admins	behaviors	Digital activities , Facebook page admins, New Page admins	People who have become Facebook Page admins within the past two weeks.	FALSE	316406417	372093947
6042330550783	Owns: Galaxy Note 5	behaviors	Mobile Device User , All Mobile Devices by Brand, Samsung , Owns: Galaxy Note 5	People who are likely to own a Samsung Galaxy Note 5 mobile device	FALSE	33830	39785
6043341245183	Owns: LG V10	behaviors	Mobile Device User , All Mobile Devices by Brand, LG , Owns: LG V10	People who are likely to own an LG V10 mobile device	FALSE	11840	13925
6043702804583	Lived in Belgium (formerly Expats – Belgium)	behaviors	Ex-pats , Lived in Belgium (formerly Expats – Belgium)	People who used to live in Belgium who now live abroad	FALSE	346538	407529
6043523344783	Owns: Galaxy S7	behaviors	Mobile Device User , All Mobile Devices by Brand, Samsung , Owns: Galaxy S7	People who are likely to own a Samsung Galaxy S7 mobile device	FALSE	1146183	1347912
6043522870783	Owns: Galaxy S7 Edge	behaviors	Mobile Device User , All Mobile Devices by Brand, Samsung , Owns: Galaxy S7 Edge	People who are likely to own a Samsung Galaxy S7 Edge mobile device	FALSE	1272721	1496720
6046095968983	People in South Africa who prefer high-value goods	behaviors	Consumer classification , South Africa , People in South Africa who prefer high-value goods	Aligned to (8,9,10) LSM group, people in South Africa who are predicted to prefer high-value goods	FALSE	2429703	2857331
6046096047583	People in South Africa who prefer mid- and high-value goods	behaviors	Consumer classification , South Africa , People in South Africa who prefer mid- and high-value goods	Aligned to (5,6,7) LSM group, people in South Africa who are predicted to prefer mid- to high-value goods	FALSE	3901518	4588186
6046096201583	People in Brazil who prefer high-value goods	behaviors	Consumer classification , Brazil , People in Brazil who prefer high-value goods	Aligned to (A+B) group, people in Brazil who are predicted to prefer high-value goods	FALSE	11757965	13827368
6054947014783	Owns: iPhone SE	behaviors	Mobile Device User , All Mobile Devices by Brand, Apple , Owns: iPhone SE	People who likely own an iPhone SE mobile device	FALSE	9845809	11578672
6047219032183	Lived in Zambia (formerly Ex-pats – Zambia)	behaviors	Ex-pats , Lived in Zambia (formerly Ex-pats – Zambia)	People who used to live in Zambia who now live abroad	FALSE	242234	284868
6055133998183	Facebook access (browser): Microsoft Edge	behaviors	Digital activities , Internet browser used , Facebook access (browser): Microsoft Edge	People who primarily access Facebook using Microsoft Edge	FALSE	21048470	24753001
6056265200983	Owns: Oppo	behaviors	Mobile Device User , All Mobile Devices by Brand, Owns: Oppo	People who are likely to own an Oppo mobile device	FALSE	174938451	205727619
6056265212183	Owns: VIVO devices	behaviors	Mobile Device User , All Mobile Devices by Brand, Owns: VIVO devices	People who are likely to own VIVO mobile devices	FALSE	165136462	194200480
6058034528983	Owns: Galaxy Note 7	behaviors	Mobile Device User , All Mobile Devices by Brand, Samsung , Owns: Galaxy Note 7	People who are likely to own a Samsung Galaxy Note 7 mobile device	FALSE	204	240
6059793664583	Lived in Honduras (formerly Ex-pats – Honduras)	behaviors	Ex-pats , Lived in Honduras (formerly Ex-pats – Honduras)	People who used to live in Honduras who now live abroad	FALSE	1576508	1853974
6060616578383	Owns: iPhone 7	behaviors	Mobile Device User , All Mobile Devices by Brand, Apple , Owns: iPhone 7	People who are likely to own an iPhone 7 mobile device	FALSE	6741051	7927477
6060616598183	Owns: iPhone 7 Plus	behaviors	Mobile Device User , All Mobile Devices by Brand, Apple , Owns: iPhone 7 Plus	People who are likely to own an iPhone 7 Plus mobile device.	FALSE	8629959	10148832
6061668174383	Owns: Google Pixel	behaviors	Mobile Device User , All Mobile Devices by Brand, Google , Owns: Google Pixel	People who are likely to own a Google Pixel mobile device	FALSE	33458	39347
6063136545383	Android: 360 degree media supported	behaviors	Mobile Device User , Android: 360 degree media supported	People whose primary mobile device is Android with support for 360-degree media (photos, videos).	FALSE	1209176853	1421991980
6063268655983	Facebook access (OS): Mac Sierra	behaviors	Digital activities , Operating system used , Facebook access (OS): Mac Sierra	People who primarily access Facebook using Mac Sierra	FALSE	1817304	2137150
6065048233383	Android: 360 degree media not supported	behaviors	Mobile Device User , Android: 360 degree media not supported	People whose primary mobile device is Android without support for 360 degree media (photos, videos).	FALSE	2092136	2460353
6068844014183	Lived in Lebanon (formerly Ex-pats – Lebanon)	behaviors	Ex-pats , Lived in Lebanon (formerly Ex-pats – Lebanon)	People who used to live in Lebanon who now live abroad	FALSE	762430	896618
6068843912183	Lived in Jordan (formerly Ex-pats – Jordan)	behaviors	Ex-pats , Lived in Jordan (formerly Ex-pats – Jordan)	People who used to live in Jordan who now live abroad	FALSE	763925	898376
6071631541183	Engaged shoppers	behaviors	Purchase behaviour, Engaged shoppers	People who have clicked on the call-to-action button “Shop Now” in the past week.	FALSE	672890008	791318650
6071248932383	Lived in Algeria (formerly Ex-pats – Algeria)	behaviors	Ex-pats , Lived in Algeria (formerly Ex-pats – Algeria)	People who used to live in Algeria who now live abroad	FALSE	1052940	1238258
6071248894383	Lived in Nicaragua (formerly Ex-pats – Nicaragua)	behaviors	Ex-pats , Lived in Nicaragua (formerly Ex-pats – Nicaragua)	People who used to live in Nicaragua who now live abroad	FALSE	902438	1061268
6071248981583	Lived in Kuwait (formerly Ex-pats – Kuwait)	behaviors	Ex-pats , Lived in Kuwait (formerly Ex-pats – Kuwait)	People who used to live in Kuwait who now live abroad	FALSE	148403	174523
6071249058983	Lived in Qatar (formerly Ex-pats – Qatar)	behaviors	Ex-pats , Lived in Qatar (formerly Ex-pats – Qatar)	People who used to live in Qatar who now live abroad	FALSE	115073	135327
6071590219583	Owns: Gionee	behaviors	Mobile Device User , All Mobile Devices by Brand, Owns: Gionee	People who likely own a Gionee mobile device	FALSE	845441	994239
6075237200983	Owns: Galaxy S8	behaviors	Mobile Device User , All Mobile Devices by Brand, Samsung , Owns: Galaxy S8	People who likely own a Galaxy S8 mobile device	FALSE	2512420	2954606
6075237226583	Owns: Galaxy S8+	behaviors	Mobile Device User , All Mobile Devices by Brand, Samsung , Owns: Galaxy S8+	People who are likely to own a Galaxy S8+ mobile device	FALSE	1642275	1931316
6080562616983	Football fans (high content engagement)	behaviors	Soccer , Football fans (high content engagement)	Interacted with content related to football five or more times over the past 90 days.	FALSE	9588252	11275785
6080562614783	Football fans (moderate content engagement)	behaviors	Soccer , Football fans (moderate content engagement)	Interacted with content related to football and sports fewer than five times over the past 90 days.	FALSE	94525839	111162387
6082317210583	People who prefer high-value goods in UAE	behaviors	Consumer classification , UAE , People who prefer high-value goods in UAE	Aligned to (A) group, people in UAE who are predicted to prefer high-value goods	FALSE	68737	80835
6082317378383	People who prefer mid and high-value goods in UAE	behaviors	Consumer classification , UAE , People who prefer mid and high-value goods in UAE	Aligned to (A+B) group, people in UAE who are predicted to prefer mid-to-high-value goods	FALSE	344455	405080
6082317392983	People who prefer high-value goods in the Kingdom of Saudi Arabia	behaviors	Consumer classification , Kingdom of Saudi Arabia , People who prefer high-value goods in the Kingdom of Saudi Arabia	Aligned to (A) group, people in the Kingdom of Saudi Arabia who are predicted to prefer high-value goods	FALSE	1066201	1253853
6082317405583	People who prefer mid to high-value goods in the Kingdom of Saudi Arabia	behaviors	Consumer classification , Kingdom of Saudi Arabia , People who prefer mid to high-value goods in the Kingdom of Saudi Arabia	Aligned to (A+B) group, people in the Kingdom of Saudi Arabia who are predicted to prefer mid to high-value goods	FALSE	2468528	2902990
6083036245383	Owns: Galaxy Note 8	behaviors	Mobile Device User , All Mobile Devices by Brand, Samsung , Owns: Galaxy Note 8	People who are likely to own a Samsung Galaxy Note 8 mobile device	FALSE	34844	40977
6085888747383	People in Mexico who prefer high-value goods	behaviors	Consumer classification , Mexico , People in Mexico who prefer high-value goods	Aligned to (AB) group, people in Mexico who are predicted to prefer high-value goods	FALSE	4811683	5658540
6085888777383	People in Mexico who prefer mid- and high-value goods	behaviors	Consumer classification , Mexico , People in Mexico who prefer mid- and high-value goods	Aligned to (ABC+) group, people in Mexico who are predicted to prefer mid to high-value goods	FALSE	15900290	18698742
6086568164383	Marketing API developers (last 90 days)	behaviors	More categories , Marketing API developers (last 90 days)	App developers that have used the Facebook marketing API in the last 90 days.	FALSE	49821	58590
6086326043983	People in Chile who prefer high-value goods	behaviors	Consumer classification , Chile , People in Chile who prefer high-value goods	Aligned to (ABC1) group, people in Chile who are predicted to prefer high-value goods	FALSE	754648	887467
6086326068183	People in Chile who prefer mid- and high-value goods	behaviors	Consumer classification , Chile , People in Chile who prefer mid- and high-value goods	Aligned to (A+B) group, people in Chile who are predicted to prefer mid to high-value goods	FALSE	2275689	2676211
6086326072983	People in Argentina who prefer high-value goods	behaviors	Consumer classification , Argentina , People in Argentina who prefer high-value goods	Aligned to (ABC1) group, people in Argentina who are predicted to prefer high-value goods	FALSE	939669	1105051
6086326078383	People in Argentina who prefer mid- and high-value goods	behaviors	Consumer classification , Argentina , People in Argentina who prefer mid- and high-value goods	Aligned to (ABC1+C2) group, people in Argentina who are predicted to prefer mid to high-value goods	FALSE	3562984	4190070
6089632523783	People in Turkey who prefer high-value goods	behaviors	Consumer classification , Turkey , People in Turkey who prefer high-value goods	Aligned to (A) SES group, people in Turkey who are predicted to prefer high-value goods	FALSE	1700222	1999462
6089632452783	People in Turkey who prefer mid- and high-value goods	behaviors	Consumer classification , Turkey , People in Turkey who prefer mid- and high-value goods	Aligned to (A+B) SES group, people in Turkey who are predicted to prefer mid- to high-value goods	FALSE	6958070	8182691
6091658707783	Uses a mobile device (less than 1 month)	behaviors	Mobile device user/device use time , Uses a mobile device (less than 1 month)	People who are likely to have used a mobile device for less than 1 month	FALSE	66962647	78748073
6091658708183	Uses a mobile device (1-3 months)	behaviors	Mobile device user/device use time, Uses a mobile device (1-3 months)	People who are likely to have used a mobile device for 1-3 months	FALSE	165829719	195015750
6091658512983	Uses a mobile device (4-6 months)	behaviors	Mobile device user/device use time, Uses a mobile device (4-6 months)	People who are likely to have used a mobile device for 4-6 months	FALSE	190109345	223568590
6091658512183	Uses a mobile device (7-9 months)	behaviors	Mobile device user/device use time, Uses a mobile device (7-9 months)	People who are likely to have used a mobile device for 7-9 months	FALSE	153962136	181059473
6091658540583	Uses a mobile device (10-12 months)	behaviors	Mobile device user/device use time , Uses a mobile device (10-12 months)	People who are likely to have used a mobile device for 10-12 months	FALSE	129175255	151910100
6091658562383	Uses a mobile device (13-18 months)	behaviors	Mobile device user/device use time , Uses a mobile device (13-18 months)	People who are likely to have used a mobile device for 13-18 months	FALSE	207472253	243987370
6091658651583	Uses a mobile device (19-24 months)	behaviors	Mobile device user/device use time , Uses a mobile device (19-24 months)	People who are likely to have used a mobile device for 19-24 months	FALSE	176642492	207731571
6091658683183	Uses a mobile device (25 months+)	behaviors	Mobile device user/device use time, Uses a mobile device (25 months+)	People who are likely to have used a mobile device for 25+ months	FALSE	600936921	706701820
6092145447383	People in Indonesia who prefer high-value goods	behaviors	Consumer classification , Indonesia , People in Indonesia who prefer high-value goods	Aligned to (Upper I) group, people in Indonesia who are predicted to prefer high-value goods	FALSE	16424498	19315210
6092512412783	Facebook access (mobile): iPhone 8	behaviors	Mobile Device User , All Mobile Devices by Brand , Apple , Facebook access (mobile): iPhone 8	People who are likely to own an iPhone 8 mobile device.	FALSE	5550073	6526886
6092512424583	Facebook access (mobile): iPhone 8 Plus	behaviors	Mobile Device User , All Mobile Devices by Brand , Apple , Facebook access (mobile): iPhone 8 Plus	People who are likely to own an iPhone 8 Plus mobile device	FALSE	8215284	9661174
6092512462983	Facebook access (mobile): iPhone X	behaviors	Mobile Device User , All Mobile Devices by Brand , Apple , Facebook access (mobile): iPhone X	People who are likely to own an iPhone X mobile device	FALSE	7432419	8740525
6100052630783	Interested in upcoming events	behaviors	More categories , Interested in upcoming events	People who have expressed interest in attending an upcoming Facebook event.	FALSE	6303151	7412506
6100406737783	People in Pakistan who prefer high-value goods	behaviors	Consumer classification , Pakistan , People in Pakistan who prefer high-value goods	Aligned to (A) SEC group, people in Pakistan who are predicted to prefer high-value goods	FALSE	1538664	1809470
6100407062383	People in Pakistan who prefer mid- and high-value goods	behaviors	Consumer classification , Pakistan , People in Pakistan who prefer mid- and high-value goods	Aligned to (A+B) SEC group, people in Pakistan who are predicted to prefer mid- to high-value goods	FALSE	7630532	8973506
6100407234583	People in Malaysia who prefer mid- and high-value goods	behaviors	Consumer classification , Malaysia , People in Malaysia who prefer mid- and high-value goods	Aligned to (AB+C1) SEC group, people in Malaysia who are predicted to prefer mid- to high-value goods	FALSE	6198394	7289312
6100407132383	People in Malaysia who prefer high-value goods	behaviors	Consumer classification , Malaysia , People in Malaysia who prefer high-value goods	Aligned to (AB) SEC group, people in Malaysia who are predicted to prefer high-value goods	FALSE	2354369	2768739
6106223987983	Owns: Galaxy S9	behaviors	Mobile Device User , All Mobile Devices by Brand, Samsung , Owns: Galaxy S9	People who are likely to own a Galaxy S9 mobile device	FALSE	3105319	3651856
6106224431383	Owns: Galaxy S9+	behaviors	Mobile Device User , All Mobile Devices by Brand, Samsung , Owns: Galaxy S9+	People who are likely to own a Galaxy S9+ mobile device	FALSE	3002565	3531017
6106805412383	Owns: OnePlus	behaviors	Mobile Device User, Owns: OnePlus	People who are likely to own a OnePlus mobile device	FALSE	17961699	21122959
6110446593183	People in Indonesia who prefer mid-value and high-value goods	behaviors	Consumer classification , Indonesia , People in Indonesia who prefer mid-value and high-value goods	Aligned to (upper I, upper II and middle I) group, people in Indonesia who are predicted to prefer high-value goods	FALSE	41063262	48290397
6110813675983	People in Brazil who prefer mid-value and high-value goods	behaviors	Consumer classification , Brazil , People in Brazil who prefer mid-value and high-value goods	Aligned to (A+B+C) group, people in Brazil who are predicted to prefer mid-value and high-value goods	FALSE	28592723	33625043
6110633547383	People who prefer high-value goods in the Philippines	behaviors	Consumer classification , Philippines , People who prefer high-value goods in the Philippines	Aligned to (A) group, people in the Philippines who are predicted to prefer high-value goods	FALSE	6207039	7299479
6110636171983	People who prefer mid-value and high-value goods in the Philippines	behaviors	Consumer classification , Philippines , People who prefer mid-value and high-value goods in the Philippines	Aligned to (A+B) group, people in the Philippines who are predicted to prefer mid to high-value goods	FALSE	27786753	32677222
6120699687383	Facebook access (mobile): iPhone XS	behaviors	Mobile Device User , All Mobile Devices by Brand , Apple , Facebook access (mobile): iPhone XS	People who are likely to own an iPhone XS mobile device	FALSE	4334244	5097072
6120699721983	Facebook access (mobile): iPhone XS Max	behaviors	Mobile Device User , All Mobile Devices by Brand , Apple , Facebook access (mobile): iPhone XS Max	People who are likely to own an iPhone XS Max mobile device	FALSE	6848094	8053359
6120699725783	Facebook access (mobile): iPhone XR	behaviors	Mobile Device User , All Mobile Devices by Brand , Apple , Facebook access (mobile): iPhone XR	People who are likely to own an iPhone XR mobile device	FALSE	14376830	16907153
6202657388783	People who have visited Facebook Gaming	behaviors	Digital activities , People who have visited Facebook Gaming	People who have recently accessed Facebook Gaming to watch videos, view posts, access tournaments or play games.	FALSE	13639459	16040004
6203619619383	Friends of football fans	behaviors	Soccer , Friends of football fans	Friends of anyone who is a moderately or highly engaged football fan. Excludes people who are already football fans.	FALSE	1541808271	1813166527
6203619967383	Friends of those who live abroad	behaviors	Ex-pats , Friends of those who live abroad	Friends of people who now live abroad	FALSE	1688480494	1985653061
6297846662583	Instagram business profile admins	behaviors	Digital activities , Instagram business profile admins	People who are an administrator of an Instagram business profile	FALSE	51334463	60369329
6273196847983	New active business (< 12 months)	behaviors	Digital activities , New active business (< 12 months)	Admins of engaged businesses that were created in the last 12 months.	FALSE	15557027	18295064
6273108079183	New active business (< 6 months)	behaviors	Digital activities , New active business (< 6 months)	Admins of engaged businesses that were created in the last 6 months.	FALSE	8845636	10402469
6273108107383	New active business (< 24 months)	behaviors	Digital activities , New active business (< 24 months)	Admins of engaged businesses that were created in the last 24 months.	FALSE	25967508	30537790
6320095608983	Recently detected devices	behaviors	Behaviours , Mobile Device User , All Mobile Devices by Operating System, Facebook access (mobile) , Recently detected devices	Users who have recently connected to Facebook on a new smartphone device	FALSE	582764518	685331074
6320095650783	Recently detected iPhone 14 devices	behaviors	Behaviours , Mobile Device User , All Mobile Devices by Operating System, Facebook access (mobile) , Recently detected iPhone 14 devices	Users who have recently connected to Facebook on an iPhone 14 device	FALSE	14018518	16485778
6378518542983	All creators	behaviors	Digital activities, All creators	People who are creators on Facebook and Instagram.	FALSE	19897946	23399985
6378552460983	Internet personality creators	behaviors	Digital activities , Internet personality creators	People who are Internet personality creators on Facebook and Instagram.	FALSE	5923459	6965988
6378532690183	Music creators	behaviors	Digital activitiesTeam, Music creators	People who are music creators on Facebook and Instagram.	FALSE	2081538	2447889
6377407066783	Food and drink creators	behaviors	Digital activities , Food and drink creators	People who are food and drink creators on Facebook and Instagram.	FALSE	1050746	1235678
6377406843183	Travel and outdoors creators	behaviors	Digital activities , Travel and outdoors creators	People who are travel and outdoors creators on Facebook and Instagram.	FALSE	821755	966385
6377407134383	Health and wellness creators	behaviors	Digital activities , Health and wellness creators	People who are health and wellness creators on Facebook and Instagram.	FALSE	324114	381159
6377178995383	Shops admins	behaviors	Digital activities, Shops admins	People who manage a shop on Facebook or Instagram.	FALSE	671257	789399
6356471865383	Facebook Lite app users	behaviors	Digital activities , Facebook Lite app users	People who use the Facebook Lite app.	FALSE	292864591	344408760

interests_df <- get_fb_parameter_ids(type    = "interests",
                                     version = VERSION, 
                                     token   = TOKEN)

interests_df %>% 
  kable() %>% 
  kable_styling() %>% 
  scroll_box(width = "100%",
             height = "300px")

id	name	type	path	lifecycle	audience_size_lower_bound	audience_size_upper_bound
6002839660079	Cosmetics (personal care)	interests	Shopping and fashion , Beauty (social concept) , Cosmetics (personal care)	2	954268571	1122219840
6002866718622	Science (science)	interests	Business and industry, Science (science)	2	600334489	705993360
6002867432822	Beauty (social concept)	interests	Shopping and fashion , Beauty (social concept)	2	1271912474	1495769070
6002868021822	Adventure travel (travel & tourism)	interests	Hobbies and activities , Travel (travel & tourism) , Adventure travel (travel & tourism)	2	275203844	323639721
6002868910910	Organic food (food & drink)	interests	Food and drink (consumables), Food (food & drink) , Organic food (food & drink)	2	273132221	321203492
6002884511422	Small business (business & finance)	interests	Business and industry , Small business (business & finance)	2	177101195	208271006
6002920953955	Interior design (design)	interests	Business and industry , Design (design) , Interior design (design)	2	488461241	574430420
6002925538921	Acting (performing arts)	interests	Hobbies and activities , Arts and music (art) , Acting (performing arts)	2	195934863	230419400
6002926108721	Vacations (social concept)	interests	Hobbies and activities , Travel (travel & tourism) , Vacations (social concept)	2	312986868	368072557
6002929380259	Volleyball (sport)	interests	Sports and outdoors, Sports (sports) , Volleyball (sport)	2	342179787	402403430
6002936693259	Soft drinks (nonalcoholic beverage)	interests	Food and drink (consumables) , Beverages (food & drink) , Soft drinks (nonalcoholic beverage)	2	187387091	220367220
6002951587955	Classical music (music)	interests	Entertainment (leisure) , Music (entertainment & media), Classical music (music)	2	324272448	381344400
6002957026250	Theatre (performing arts)	interests	Entertainment (leisure) , Live events (entertainment), Theatre (performing arts)	2	512677397	602908620
6002960574320	Tablet computers (computers & electronics)	interests	Technology (computers & electronics) , Computers (computers & electronics) , Tablet computers (computers & electronics)	2	512619285	602840280
6002963523717	Aviation (air travel)	interests	Business and industry, Aviation (air travel)	2	164384736	193316450
6002964239317	Mexican cuisine (food & drink)	interests	Food and drink (consumables) , Cuisine (food & drink) , Mexican cuisine (food & drink)	8	102614047	120674120
6002964500317	Word games (games)	interests	Entertainment (leisure), Games (leisure) , Word games (games)	2	62499974	73499970
6002970406974	Concerts (music event)	interests	Entertainment (leisure) , Live events (entertainment), Concerts (music event)	2	259957134	305709590
6002971085794	Mobile phones (smart phone)	interests	Technology (computers & electronics) , Consumer electronics (computers & electronics), Mobile phones (smart phone)	2	1007046692	1184286910
6002971095994	Action games (video games)	interests	Entertainment (leisure) , Games (leisure) , Action games (video games)	2	152967687	179890000
6002979499920	Fishing (outdoors activities)	interests	Sports and outdoors , Outdoor recreation (outdoors activities), Fishing (outdoors activities)	2	278638503	327678880
6002984573619	Surfing (water sport)	interests	Sports and outdoors , Outdoor recreation (outdoors activities), Surfing (water sport)	2	126273336	148497444
6002985584323	Outdoor recreation (outdoors activities)	interests	Sports and outdoors , Outdoor recreation (outdoors activities)	2	581478290	683818470
6002986104968	Mystery fiction (entertainment & media)	interests	Entertainment (leisure) , Reading (communication) , Mystery fiction (entertainment & media)	2	147119005	173011950
6002991239659	Motherhood (children & parenting)	interests	Family and relationships , Motherhood (children & parenting)	2	698407040	821326680
6002991736368	Reading (communication)	interests	Entertainment (leisure), Reading (communication)	2	1279238928	1504384980
6002997799844	Singing (music)	interests	Hobbies and activities, Arts and music (art) , Singing (music)	2	425761938	500696040
6002998123892	Japanese cuisine (food & drink)	interests	Food and drink (consumables) , Cuisine (food & drink) , Japanese cuisine (food & drink)	8	135928681	159852130
6002998517244	Camcorders (consumer electronics)	interests	Technology (computers & electronics) , Consumer electronics (computers & electronics), Camcorders (consumer electronics)	2	18698377	21989292
6003012317397	Gambling (gambling)	interests	Entertainment (leisure), Games (leisure) , Gambling (gambling)	2	331443443	389777490
6003012461997	Beer (alcoholic drinks)	interests	Food and drink (consumables) , Alcoholic beverages (food & drink), Beer (alcoholic drinks)	2	333167653	391805160
6003020834693	Music (entertainment & media)	interests	Entertainment (leisure) , Music (entertainment & media)	2	1519764804	1787243410
6003025268985	Tattoos (body art)	interests	Shopping and fashion , Beauty (social concept), Tattoos (body art)	2	495843528	583111990
6003029869785	Arts and music (art)	interests	Hobbies and activities, Arts and music (art)	2	1390925697	1635728620
6003030029655	Chinese cuisine (food & drink)	interests	Food and drink (consumables) , Cuisine (food & drink) , Chinese cuisine (food & drink)	8	152438027	179267120
6003030519207	Online poker (gambling)	interests	Entertainment (leisure), Games (leisure) , Online poker (gambling)	2	151133044	177732460
6003049202156	Community issues (law & government)	interests	Hobbies and activities , Politics and social issues (politics), Community issues (law & government)	2	236059226	277605650
6003053056644	Gardening (outdoor activities)	interests	Hobbies and activities , Home and garden , Gardening (outdoor activities)	2	356451726	419187230
6003054884732	Coupons (coupons & discounts)	interests	Shopping and fashion , Shopping (retail) , Coupons (coupons & discounts)	2	577801581	679494660
6003059385128	Ecotourism (travel & tourism)	interests	Hobbies and activities , Travel (travel & tourism) , Ecotourism (travel & tourism)	2	124376713	146267015
6003059733932	First-person shooter games (video games)	interests	Entertainment (leisure) , Games (leisure) , First-person shooter games (video games)	2	558766989	657109980
6003062205328	Retail banking (banking)	interests	Business and industry , Banking (finance) , Retail banking (banking)	2	23764328	27946850
6003063638807	Investment banking (banking)	interests	Business and industry , Banking (finance) , Investment banking (banking)	2	32717426	38475693
6003064649070	Mountains (places)	interests	Hobbies and activities , Travel (travel & tourism), Mountains (places)	2	309836930	364368230
6003070122382	Toys (toys)	interests	Shopping and fashion, Toys (toys)	2	480501513	565069780
6003070856229	Games (leisure)	interests	Entertainment (leisure), Games (leisure)	2	1242865136	1461609400
6003074487739	E-books (publications)	interests	Entertainment (leisure), Reading (communication), E-books (publications)	2	366680399	431216150
6003074954515	Sales (business & finance)	interests	Business and industry , Sales (business & finance)	2	892383903	1049443470
6003076016339	Email marketing (marketing)	interests	Business and industry , Online (computing) , Email marketing (marketing)	2	14417554	16955044
6003083357650	Manga (anime & manga)	interests	Entertainment (leisure), Reading (communication), Manga (anime & manga)	2	226028885	265809969
6003087413192	Baseball (sport)	interests	Sports and outdoors, Sports (sports) , Baseball (sport)	2	450915484	530276610
6003088846792	Beauty salons (cosmetics)	interests	Shopping and fashion , Beauty (social concept) , Beauty salons (cosmetics)	2	618195365	726997750
6003090714101	Car rentals (transportation)	interests	Hobbies and activities , Travel (travel & tourism) , Car rentals (transportation)	2	153514217	180532720
6003092330156	Mountain biking (cycling)	interests	Sports and outdoors , Outdoor recreation (outdoors activities), Mountain biking (cycling)	2	94696265	111362808
6003092882217	Trucks (vehicles)	interests	Hobbies and activities , Vehicles (transportation), Trucks (vehicles)	2	272359982	320295340
6003096002658	Graphic design (visual art)	interests	Business and industry , Design (design) , Graphic design (visual art)	2	315060008	370510570
6003101323797	Fatherhood (children & parenting)	interests	Family and relationships , Fatherhood (children & parenting)	8	352672278	414742600
6003102729234	Italian cuisine (food & drink)	interests	Food and drink (consumables) , Cuisine (food & drink) , Italian cuisine (food & drink)	8	146667498	172480978
6003102988840	Latin American cuisine (food & drink)	interests	Food and drink (consumables) , Cuisine (food & drink) , Latin American cuisine (food & drink)	8	41796581	49152780
6003103108917	Boutiques (retailers)	interests	Shopping and fashion , Shopping (retail) , Boutiques (retailers)	2	532794960	626566874
6003105618835	Crafts (hobbies)	interests	Hobbies and activities, Arts and music (art) , Crafts (hobbies)	2	423784727	498370840
6003106813190	Hunting (sport)	interests	Sports and outdoors , Outdoor recreation (outdoors activities), Hunting (sport)	2	201917244	237454680
6003107699532	Soul music (music)	interests	Entertainment (leisure) , Music (entertainment & media), Soul music (music)	2	461193307	542363330
6003107902433	Association football (Soccer)	interests	Sports and outdoors , Sports (sports) , Association football (Soccer)	2	1239072202	1457148910
6003108411433	Rabbits (animals)	interests	Hobbies and activities, Pets (animals) , Rabbits (animals)	2	114737916	134931790
6003108649035	Spanish cuisine (food & drink)	interests	Food and drink (consumables) , Cuisine (food & drink) , Spanish cuisine (food & drink)	8	35717153	42003372
6003108826384	Music festivals (events)	interests	Entertainment (leisure) , Live events (entertainment), Music festivals (events)	2	293265824	344880610
6003115804542	Desktop computers (consumer electronics)	interests	Technology (computers & electronics) , Computers (computers & electronics) , Desktop computers (consumer electronics)	2	155917219	183358650
6003116038942	Computer monitors (computer hardware)	interests	Technology (computers & electronics) , Computers (computers & electronics) , Computer monitors (computer hardware)	2	145069931	170602240
6003120620858	Coffeehouses (coffee)	interests	Food and drink (consumables), Restaurants (dining) , Coffeehouses (coffee)	2	411936607	484437450
6003122958658	Boating (outdoors activities)	interests	Sports and outdoors , Outdoor recreation (outdoors activities), Boating (outdoors activities)	2	76591163	90071208
6003125064949	Electric vehicle (vehicle)	interests	Hobbies and activities , Vehicles (transportation) , Electric vehicle (vehicle)	2	98080261	115342387
6003125948045	Desserts (food & drink)	interests	Food and drink (consumables), Food (food & drink) , Desserts (food & drink)	2	398663415	468828177
6003126215349	Comics (comics & cartoons)	interests	Entertainment (leisure) , Reading (communication) , Comics (comics & cartoons)	2	284793789	334917497
6003126358188	TV game shows (television show)	interests	Entertainment (leisure) , TV (movies & television) , TV game shows (television show)	2	110719600	130206250
6003127206524	Digital marketing (marketing)	interests	Business and industry , Online (computing) , Digital marketing (marketing)	2	149444939	175747249
6003129926917	Animated movies (movies)	interests	Entertainment (leisure) , Movies (entertainment & media), Animated movies (movies)	2	418699651	492390790
6003132926214	Furniture (home furnishings)	interests	Hobbies and activities , Home and garden , Furniture (home furnishings)	2	522148358	614046470
6003133486214	Vehicles (transportation)	interests	Hobbies and activities , Vehicles (transportation)	2	847249855	996365830
6003133978408	Chocolate (food & drink)	interests	Food and drink (consumables), Food (food & drink) , Chocolate (food & drink)	2	475445765	559124220
6003134986700	Baking (cooking)	interests	Food and drink (consumables), Cooking (food & drink) , Baking (cooking)	2	356463045	419200542
6003137105590	Volunteering (social causes)	interests	Hobbies and activities , Politics and social issues (politics), Volunteering (social causes)	2	76758137	90267570
6003139266461	Movies (entertainment & media)	interests	Entertainment (leisure) , Movies (entertainment & media)	2	1420386760	1670374830
6003141785766	Mortgage loans (banking)	interests	Business and industry , Personal finance (banking), Mortgage loans (banking)	2	144190748	169568320
6003142705949	Computer processors (computer hardware)	interests	Technology (computers & electronics) , Computers (computers & electronics) , Computer processors (computer hardware)	2	186229379	219005750
6003142974961	Painting (visual art)	interests	Hobbies and activities, Arts and music (art) , Painting (visual art)	2	402405442	473228800
6003143720966	Personal finance (banking)	interests	Business and industry , Personal finance (banking)	2	729818596	858266670
6003146442552	Jazz music (music)	interests	Entertainment (leisure) , Music (entertainment & media), Jazz music (music)	2	462368469	543745320
6003146718552	Auto racing (motor sports)	interests	Sports and outdoors , Sports (sports) , Auto racing (motor sports)	2	316189499	371838851
6003146729229	Distilled beverage (liquor)	interests	Food and drink (consumables) , Alcoholic beverages (food & drink), Distilled beverage (liquor)	2	204502551	240495000
6003147868152	Parties (event)	interests	Entertainment (leisure) , Live events (entertainment), Parties (event)	2	384128267	451734843
6003148544265	Wine (alcoholic drinks)	interests	Food and drink (consumables) , Alcoholic beverages (food & drink), Wine (alcoholic drinks)	2	332466045	390980070
6003151951349	Computer servers (computing)	interests	Technology (computers & electronics), Computers (computers & electronics) , Computer servers (computing)	2	87445790	102836250
6003153672865	Online games (video games)	interests	Entertainment (leisure) , Games (leisure) , Online games (video games)	2	595532287	700345970
6003154043305	Performing arts (performing arts)	interests	Hobbies and activities , Arts and music (art) , Performing arts (performing arts)	2	431488095	507430000
6003155333705	Vegetarianism (diets)	interests	Food and drink (consumables), Food (food & drink) , Vegetarianism (diets)	8	207809507	244383981
6003156321008	Bars (bars, clubs & nightlife)	interests	Entertainment (leisure) , Live events (entertainment) , Bars (bars, clubs & nightlife)	2	294046380	345798543
6003157824284	Bollywood movies (movies)	interests	Entertainment (leisure) , Movies (entertainment & media), Bollywood movies (movies)	2	374775850	440736400
6003159378782	Cats (animals)	interests	Hobbies and activities, Pets (animals) , Cats (animals)	2	448420365	527342350
6003159413034	Fish (animals)	interests	Hobbies and activities, Pets (animals) , Fish (animals)	2	321510238	378096040
6003161475030	Comedy movies (movies)	interests	Entertainment (leisure) , Movies (entertainment & media), Comedy movies (movies)	2	1006576522	1183733990
6003162931434	College football (college sports)	interests	Sports and outdoors , Sports (sports) , College football (college sports)	2	106642491	125411570
6003166397215	Swimming (water sport)	interests	Sports and outdoors , Sports (sports) , Swimming (water sport)	2	228292881	268472429
6003172448161	TV talkshows (television show)	interests	Entertainment (leisure) , TV (movies & television) , TV talkshows (television show)	2	131425399	154556270
6003172932634	TV (movies & television)	interests	Entertainment (leisure) , TV (movies & television)	2	990365017	1164669260
6003176101552	Massively multiplayer online games (video games)	interests	Entertainment (leisure) , Games (leisure) , Massively multiplayer online games (video games)	2	111371562	130972958
6003176678152	Automobiles (vehicles)	interests	Hobbies and activities , Vehicles (transportation), Automobiles (vehicles)	2	677437057	796665980
6003179515414	Dance music (music)	interests	Entertainment (leisure) , Music (entertainment & media), Dance music (music)	2	285610654	335878130
6003120620858	Coffeehouses (coffee)	interests	Food and drink (consumables), Restaurants (dining) , Coffeehouses (coffee)	2	411936607	484437450
6003188355978	Dresses (apparel)	interests	Shopping and fashion , Fashion accessories (accessories), Dresses (apparel)	2	573380000	674294881
6003194056672	Fine art (visual art)	interests	Hobbies and activities, Arts and music (art) , Fine art (visual art)	2	140074631	164727767
6003195554098	Rhythm and blues music (music)	interests	Entertainment (leisure) , Music (entertainment & media) , Rhythm and blues music (music)	2	669180255	786955980
6003195797498	Cuisine (food & drink)	interests	Food and drink (consumables), Cuisine (food & drink)	2	589915858	693741050
6003198370967	Massively multiplayer online role-playing games (video games)	interests	Entertainment (leisure) , Games (leisure) , Massively multiplayer online role-playing games (video games)	2	109827267	129156867
6003198476967	Handbags (accessories)	interests	Shopping and fashion , Fashion accessories (accessories), Handbags (accessories)	2	413353863	486104143
6003200340482	Middle Eastern cuisine (food & drink)	interests	Food and drink (consumables) , Cuisine (food & drink) , Middle Eastern cuisine (food & drink)	8	28441849	33447615
6003206216430	Magazines (publications)	interests	Entertainment (leisure) , Reading (communication) , Magazines (publications)	2	613485722	721459210
6003206308286	Science fiction movies (movies)	interests	Entertainment (leisure) , Movies (entertainment & media) , Science fiction movies (movies)	2	363505918	427482960
6003207605030	Minivans (vehicle)	interests	Hobbies and activities , Vehicles (transportation), Minivans (vehicle)	2	48699184	57270241
6003210799924	Romance novels (publications)	interests	Entertainment (leisure) , Reading (communication) , Romance novels (publications)	2	229731352	270164070
6003211401886	Air travel (transportation)	interests	Hobbies and activities , Travel (travel & tourism) , Air travel (transportation)	2	305795457	359615458
6003217093576	Insurance (business & finance)	interests	Business and industry , Personal finance (banking) , Insurance (business & finance)	2	340092619	399948920
6003220634758	Discount stores (retail)	interests	Shopping and fashion , Shopping (retail) , Discount stores (retail)	2	393583676	462854404
6003224441249	Televisions (consumer electronics)	interests	Technology (computers & electronics) , Consumer electronics (computers & electronics), Televisions (consumer electronics)	2	1077339829	1266951640
6003225325061	Thriller movies (movies)	interests	Entertainment (leisure) , Movies (entertainment & media), Thriller movies (movies)	2	553071904	650412560
6003225556345	Hip hop music (music)	interests	Entertainment (leisure) , Music (entertainment & media), Hip hop music (music)	2	824386198	969478170
6003225930699	Cruises (travel & tourism business)	interests	Hobbies and activities , Travel (travel & tourism) , Cruises (travel & tourism business)	2	172715314	203113210
6003232518610	Parenting (children & parenting)	interests	Family and relationships , Parenting (children & parenting)	2	284294627	334330482
6003234413249	Home improvement (home & garden)	interests	Hobbies and activities , Home and garden , Home improvement (home & garden)	2	335748624	394840382
6003240742699	Seafood (food & drink)	interests	Food and drink (consumables), Food (food & drink) , Seafood (food & drink)	2	270206906	317763322
6003243058188	Diners (restaurant)	interests	Food and drink (consumables), Restaurants (dining) , Diners (restaurant)	2	117957833	138718412
6003243604899	Action movies (movies)	interests	Entertainment (leisure) , Movies (entertainment & media), Action movies (movies)	2	627385552	737805410
6003246168013	Simulation games (video games)	interests	Entertainment (leisure) , Games (leisure) , Simulation games (video games)	2	92506020	108787080
6003247127613	Ballet (dance)	interests	Entertainment (leisure) , Live events (entertainment), Ballet (dance)	2	100333394	117992072
6003247790075	Literature (publications)	interests	Entertainment (leisure) , Reading (communication) , Literature (publications)	2	372953690	438593540
6003247890613	Dancehalls (music)	interests	Entertainment (leisure) , Live events (entertainment), Dancehalls (music)	2	103399718	121598069
6003248338072	Casino games (gambling)	interests	Entertainment (leisure), Games (leisure) , Casino games (gambling)	2	43101736	50687642
6003252179711	Engineering (science)	interests	Business and industry, Engineering (science)	2	469516258	552151120
6003254590688	Spas (personal care)	interests	Shopping and fashion , Beauty (social concept), Spas (personal care)	2	588815610	692447158
6003255640088	Sunglasses (eyewear)	interests	Shopping and fashion , Fashion accessories (accessories), Sunglasses (eyewear)	2	370515170	435725841
6003257757682	Blues music (music)	interests	Entertainment (leisure) , Music (entertainment & media), Blues music (music)	2	453873086	533754750
6003263791114	Shopping (retail)	interests	Shopping and fashion, Shopping (retail)	2	1436935629	1689836300
6003266061909	Food (food & drink)	interests	Food and drink (consumables), Food (food & drink)	2	1204339217	1416302920
6003266225248	Jewelry (apparel)	interests	Shopping and fashion , Fashion accessories (accessories), Jewelry (apparel)	2	721781530	848815080
6003266266843	Fashion design (design)	interests	Business and industry , Design (design) , Fashion design (design)	2	347376821	408515142
6003268182136	TV reality shows (movies & television)	interests	Entertainment (leisure) , TV (movies & television) , TV reality shows (movies & television)	2	520518945	612130280
6003269553527	Sports (sports)	interests	Sports and outdoors, Sports (sports)	2	1446967517	1701633800
6003270811593	Higher education (education)	interests	Business and industry , Higher education (education)	2	563361079	662512630
6003274262708	Fiction books (publications)	interests	Entertainment (leisure) , Reading (communication) , Fiction books (publications)	2	407642219	479387250
6003277229371	Physical fitness (fitness)	interests	Fitness and wellness (fitness), Physical fitness (fitness)	2	678866522	798347030
6003279598823	Marketing (business & finance)	interests	Business and industry , Marketing (business & finance)	2	605494039	712060990
6003280676501	GPS devices (consumer electronics)	interests	Technology (computers & electronics) , Consumer electronics (computers & electronics), GPS devices (consumer electronics)	2	24952948	29344667
6003283801502	Thai cuisine (food & drink)	interests	Food and drink (consumables), Cuisine (food & drink) , Thai cuisine (food & drink)	8	57123875	67177678
6003286289697	Birds (animals)	interests	Hobbies and activities, Pets (animals) , Birds (animals)	2	353079693	415221720
6003288647527	Projectors (consumer electronics)	interests	Technology (computers & electronics) , Consumer electronics (computers & electronics), Projectors (consumer electronics)	2	31618071	37182852
6003289911338	Smartphones (consumer electronics)	interests	Technology (computers & electronics) , Consumer electronics (computers & electronics), Smartphones (consumer electronics)	2	789714659	928704440
6003290005325	Web development (websites)	interests	Business and industry , Online (computing) , Web development (websites)	2	45666865	53704234
6003290811111	Current events (politics)	interests	Hobbies and activities , Current events (politics)	2	879551573	1034352650
6003297396138	Banking (finance)	interests	Business and industry, Banking (finance)	2	428565144	503992610
6003299204611	Beverages (food & drink)	interests	Food and drink (consumables), Beverages (food & drink)	2	854926352	1005393390
6003302121228	Guitar (instruments)	interests	Hobbies and activities, Arts and music (art) , Guitar (instruments)	2	150172134	176602430
6003304473660	SUVs (vehicles)	interests	Hobbies and activities , Vehicles (transportation), SUVs (vehicles)	2	232294159	273177931
6003306084421	Yoga (fitness)	interests	Fitness and wellness (fitness), Yoga (fitness)	2	382445073	449755406
6003306415421	Greek cuisine (food & drink)	interests	Food and drink (consumables), Cuisine (food & drink) , Greek cuisine (food & drink)	8	31516505	37063410
6003324287371	Skiing (skiing & snowboarding)	interests	Sports and outdoors , Sports (sports) , Skiing (skiing & snowboarding)	2	141235696	166093179
6003325186571	Cameras (photography)	interests	Technology (computers & electronics) , Consumer electronics (computers & electronics), Cameras (photography)	2	435776598	512473280
6003332344237	Dogs (animals)	interests	Hobbies and activities, Pets (animals) , Dogs (animals)	2	491744251	578291240
6003332483177	Music videos (entertainment & media)	interests	Entertainment (leisure) , Music (entertainment & media) , Music videos (entertainment & media)	2	985474268	1158917740
6003341579196	Pop music (music)	interests	Entertainment (leisure) , Music (entertainment & media), Pop music (music)	2	994586870	1169634160
6003342470823	Board games (games)	interests	Entertainment (leisure), Games (leisure) , Board games (games)	2	87881277	103348382
6003343485089	Korean cuisine (food & drink)	interests	Food and drink (consumables) , Cuisine (food & drink) , Korean cuisine (food & drink)	8	102158375	120138250
6003343997689	Home Appliances (consumer electronics)	interests	Hobbies and activities , Home and garden , Home Appliances (consumer electronics)	2	265301547	311994620
6003346311730	Vietnamese cuisine (food & drink)	interests	Food and drink (consumables) , Cuisine (food & drink) , Vietnamese cuisine (food & drink)	8	48440493	56966020
6003346592981	Online shopping (retail)	interests	Shopping and fashion , Shopping (retail) , Online shopping (retail)	2	1347410025	1584554190
6003348453981	Shoes (footwear)	interests	Shopping and fashion, Clothing (apparel) , Shoes (footwear)	2	841455391	989551540
6003348604581	Fashion accessories (accessories)	interests	Shopping and fashion , Fashion accessories (accessories)	2	978194413	1150356630
6003348662930	Camping (outdoors activities)	interests	Sports and outdoors , Outdoor recreation (outdoors activities), Camping (outdoors activities)	2	240108356	282367427
6003349175527	Computer memory (computer hardware)	interests	Technology (computers & electronics), Computers (computers & electronics) , Computer memory (computer hardware)	2	34725485	40837171
6003349442621	Entertainment (leisure)	interests	Entertainment (leisure)	2	1762191547	2072337260
6003351312828	Musical theatre (performing arts)	interests	Entertainment (leisure) , Movies (entertainment & media) , Musical theatre (performing arts)	2	99099132	116540580
6003351764757	Triathlons (athletics)	interests	Sports and outdoors , Sports (sports) , Triathlons (athletics)	8	95150246	111896690
6003353550130	Motorcycles (vehicles)	interests	Hobbies and activities , Vehicles (transportation), Motorcycles (vehicles)	2	415968877	489179400
6003359996821	Nature (science)	interests	Hobbies and activities , Travel (travel & tourism), Nature (science)	2	815391156	958900000
6003361714600	Nightclubs (bars, clubs & nightlife)	interests	Entertainment (leisure) , Live events (entertainment) , Nightclubs (bars, clubs & nightlife)	2	334577993	393463720
6003369240775	Basketball (sport)	interests	Sports and outdoors, Sports (sports) , Basketball (sport)	2	714404481	840139670
6003369782940	Credit cards (credit & lending)	interests	Business and industry , Personal finance (banking) , Credit cards (credit & lending)	2	434819319	511347520
6003370636074	Search engine optimization (software)	interests	Business and industry , Online (computing) , Search engine optimization (software)	2	36242695	42621410
6003371567474	Entrepreneurship (business & finance)	interests	Business and industry , Entrepreneurship (business & finance)	2	382208471	449477162
6003372667195	Fast food restaurants (dining)	interests	Food and drink (consumables) , Restaurants (dining) , Fast food restaurants (dining)	2	169866686	199763223
6003373175581	Documentary movies (movies)	interests	Entertainment (leisure) , Movies (entertainment & media), Documentary movies (movies)	2	440272559	517760530
6003375422677	Drama movies (movies)	interests	Entertainment (leisure) , Movies (entertainment & media), Drama movies (movies)	2	473430323	556754060
6003376089674	American football (sport)	interests	Sports and outdoors , Sports (sports) , American football (sport)	2	417768477	491295730
6003380576181	Role-playing games (video games)	interests	Entertainment (leisure) , Games (leisure) , Role-playing games (video games)	2	152068967	178833106
6003381994165	Portable media players (audio equipment)	interests	Technology (computers & electronics) , Consumer electronics (computers & electronics), Portable media players (audio equipment)	2	6968758	8195260
6003382151137	Reptiles (animals)	interests	Hobbies and activities, Pets (animals) , Reptiles (animals)	2	49090909	57730910
6003384248805	Fitness and wellness (fitness)	interests	Fitness and wellness (fitness)	8	1083752219	1274492610
6003385141743	Racing games (video game)	interests	Entertainment (leisure) , Games (leisure) , Racing games (video game)	2	123480765	145213380
6003385609165	Recipes (food & drink)	interests	Food and drink (consumables), Cooking (food & drink) , Recipes (food & drink)	2	480500399	565068470
6003387418453	Web hosting (computing)	interests	Business and industry , Online (computing) , Web hosting (computing)	2	24069693	28305960
6003387633593	Drums (instruments)	interests	Hobbies and activities, Arts and music (art) , Drums (instruments)	2	116439379	136932710
6003388314512	Investment (business & finance)	interests	Business and industry , Personal finance (banking) , Investment (business & finance)	2	412508707	485110240
6003389760112	Social media marketing (marketing)	interests	Business and industry , Online (computing) , Social media marketing (marketing)	2	84384732	99236446
6003390752144	Shopping malls (retail)	interests	Shopping and fashion , Shopping (retail) , Shopping malls (retail)	2	588304336	691845900
6003392512725	Energy drinks (nonalcoholic beverage)	interests	Food and drink (consumables) , Beverages (food & drink) , Energy drinks (nonalcoholic beverage)	2	97544710	114712580
6003394580331	RVs (vehicle)	interests	Hobbies and activities , Vehicles (transportation), RVs (vehicle)	2	53278409	62655410
6003395414271	Construction (industry)	interests	Business and industry , Construction (industry)	2	466283290	548349150
6003397425735	Tennis (sport)	interests	Sports and outdoors, Sports (sports) , Tennis (sport)	2	329849081	387902520
6003397496347	Running (sport)	interests	Fitness and wellness (fitness), Running (sport)	2	295862363	347934139
6003398056603	Fast casual restaurants (restaurant)	interests	Food and drink (consumables) , Restaurants (dining) , Fast casual restaurants (restaurant)	2	127329706	149739735
6003402305839	Business (business & finance)	interests	Business and industry , Business (business & finance)	2	997456139	1173008420
6003402518839	Web design (websites)	interests	Business and industry, Online (computing) , Web design (websites)	2	53903690	63390740
6003404634364	Computers (computers & electronics)	interests	Technology (computers & electronics), Computers (computers & electronics)	2	1176187032	1383195950
6003409043877	Alcoholic beverages (food & drink)	interests	Food and drink (consumables) , Alcoholic beverages (food & drink)	2	533115918	626944320
6003409392877	Weddings (weddings)	interests	Family and relationships, Weddings (weddings)	2	304885943	358545870
6003415393053	Children’s clothing (apparel)	interests	Shopping and fashion , Clothing (apparel) , Children’s clothing (apparel)	2	261869502	307958535
6003416777039	Horses (animals)	interests	Hobbies and activities, Pets (animals) , Horses (animals)	2	258434243	303918670
6003417378239	Plays (performing arts)	interests	Entertainment (leisure) , Live events (entertainment), Plays (performing arts)	2	205761207	241975180
6003420024431	French cuisine (food & drink)	interests	Food and drink (consumables) , Cuisine (food & drink) , French cuisine (food & drink)	2	89071692	104748310
6003420644631	Non-fiction books (publications)	interests	Entertainment (leisure) , Reading (communication) , Non-fiction books (publications)	2	36691106	43148741
6003422719241	Charity and causes (social causes)	interests	Hobbies and activities , Politics and social issues (politics), Charity and causes (social causes)	2	64685429	76070065
6003423342191	Dance (art)	interests	Hobbies and activities, Arts and music (art) , Dance (art)	2	545935068	642019640
6003423416540	Free software (software)	interests	Technology (computers & electronics), Computers (computers & electronics) , Free software (software)	2	561457967	660274570
6003424404140	Marathons (running event)	interests	Sports and outdoors , Sports (sports) , Marathons (running event)	2	189882514	223301837
6003430600057	Lakes (body of water)	interests	Hobbies and activities , Travel (travel & tourism), Lakes (body of water)	2	168765374	198468080
6003430696269	Tourism (industry)	interests	Hobbies and activities , Travel (travel & tourism), Tourism (industry)	2	773164277	909241190
6003431201869	Beaches (places)	interests	Hobbies and activities , Travel (travel & tourism), Beaches (places)	2	413340857	486088848
6003434373937	Browser games (video games)	interests	Entertainment (leisure) , Games (leisure) , Browser games (video games)	2	51808472	60926764
6003435096731	Barbecue (cooking)	interests	Food and drink (consumables), Food (food & drink) , Barbecue (cooking)	2	348044897	409300800
6003436950375	Restaurants (dining)	interests	Food and drink (consumables), Restaurants (dining)	2	730478154	859042310
6003443805331	Fragrances (cosmetics)	interests	Shopping and fashion , Beauty (social concept), Fragrances (cosmetics)	2	557077516	655123159
6003445506042	Marriage (weddings)	interests	Family and relationships, Marriage (weddings)	2	233638750	274759170
6003446055283	Scooters (vehicle)	interests	Hobbies and activities , Vehicles (transportation), Scooters (vehicle)	2	116142112	136583124
6003456330903	Hair products (hair care)	interests	Shopping and fashion , Beauty (social concept) , Hair products (hair care)	2	725744795	853475880
6003456388203	Clothing (apparel)	interests	Shopping and fashion, Clothing (apparel)	2	1151133418	1353732900
6003461162225	Pet food (pet supplies)	interests	Hobbies and activities , Pets (animals) , Pet food (pet supplies)	2	108416370	127497652
6003462707303	Books (publications)	interests	Entertainment (leisure), Reading (communication), Books (publications)	2	581479795	683820240
6003466585319	Online banking (banking)	interests	Business and industry , Banking (finance) , Online banking (banking)	2	90556292	106494200
6003470511564	Do it yourself (DIY)	interests	Hobbies and activities, Home and garden , Do it yourself (DIY)	2	418387661	492023890
6003473077165	Weight training (weightlifting)	interests	Fitness and wellness (fitness) , Weight training (weightlifting)	2	191719338	225461942
6003476182657	Family (social concept)	interests	Family and relationships, Family (social concept)	2	1029906930	1211170550
6003491283786	Tea (nonalcoholic beverage)	interests	Food and drink (consumables), Beverages (food & drink) , Tea (nonalcoholic beverage)	2	395174719	464725470
6003493980595	Country music (music)	interests	Entertainment (leisure) , Music (entertainment & media), Country music (music)	2	470572865	553393690
6003494675627	Indian cuisine (food & drink)	interests	Food and drink (consumables) , Cuisine (food & drink) , Indian cuisine (food & drink)	8	91538323	107649069
6003510075864	Golf (sport)	interests	Sports and outdoors, Sports (sports) , Golf (sport)	2	264069122	310545288
6003512053894	Snowboarding (skiing & snowboarding)	interests	Sports and outdoors , Sports (sports) , Snowboarding (skiing & snowboarding)	2	109337720	128581159
6003526234370	Online advertising (marketing)	interests	Business and industry , Online (computing) , Online advertising (marketing)	2	156407143	183934801
6003540150873	Sports games (video games)	interests	Entertainment (leisure) , Games (leisure) , Sports games (video games)	2	167944091	197502252
6003572379887	Hotels (lodging)	interests	Hobbies and activities , Travel (travel & tourism), Hotels (lodging)	2	603371853	709565300
6003578086487	Real estate (industry)	interests	Business and industry , Real estate (industry)	2	424648647	499386810
6003582500438	Strategy games (games)	interests	Entertainment (leisure), Games (leisure) , Strategy games (games)	2	57279099	67360221
6003582732907	Rock music (music)	interests	Entertainment (leisure) , Music (entertainment & media), Rock music (music)	2	959474906	1128342490
6003584163107	Advertising (marketing)	interests	Business and industry , Advertising (marketing)	2	465943911	547950040
6003586608473	Writing (communication)	interests	Hobbies and activities , Arts and music (art) , Writing (communication)	2	341526028	401634610
6003605717820	Anime movies (movies)	interests	Entertainment (leisure) , Movies (entertainment & media), Anime movies (movies)	2	385607074	453473920
6003626773307	Coffee (food & drink)	interests	Food and drink (consumables), Beverages (food & drink) , Coffee (food & drink)	2	535770493	630066100
6003629266583	Hard drives (computer hardware)	interests	Technology (computers & electronics), Computers (computers & electronics) , Hard drives (computer hardware)	2	122281267	143802770
6003633122583	Heavy metal music (music)	interests	Entertainment (leisure) , Music (entertainment & media), Heavy metal music (music)	2	603639736	709880330
6003641846820	Veganism (diets)	interests	Food and drink (consumables), Food (food & drink) , Veganism (diets)	2	324563265	381686400
6003647522546	Card games (games)	interests	Entertainment (leisure), Games (leisure) , Card games (games)	2	272395153	320336700
6003648059946	Bodybuilding (sport)	interests	Fitness and wellness (fitness), Bodybuilding (sport)	2	196138179	230658499
6003649983713	Design (design)	interests	Business and industry, Design (design)	2	920081998	1082016430
6003654559478	Politics (politics)	interests	Hobbies and activities , Politics and social issues (politics), Politics (politics)	8	460704668	541788690
6003656112304	Economics (economics)	interests	Business and industry, Economics (economics)	2	308751105	363091300
6003656296104	Network storage (computers & electronics)	interests	Technology (computers & electronics) , Computers (computers & electronics) , Network storage (computers & electronics)	2	18677605	21964864
6003656922020	Horror movies (movies)	interests	Entertainment (leisure) , Movies (entertainment & media), Horror movies (movies)	2	376549897	442822680
6003659420716	Cooking (food & drink)	interests	Food and drink (consumables), Cooking (food & drink)	2	752634277	885097910
6003668857118	Pizza (food & drink)	interests	Food and drink (consumables), Food (food & drink) , Pizza (food & drink)	2	441634838	519362570
6003668975718	Puzzle video games (video games)	interests	Entertainment (leisure) , Games (leisure) , Puzzle video games (video games)	2	288117984	338826750
6003703762913	Law (law & legal services)	interests	Hobbies and activities , Politics and social issues (politics), Law (law & legal services)	8	477592806	561649140
6003703931713	Juice (nonalcoholic beverage)	interests	Food and drink (consumables) , Beverages (food & drink) , Juice (nonalcoholic beverage)	2	223882925	263286320
6003716669862	Consumer electronics (computers & electronics)	interests	Technology (computers & electronics) , Consumer electronics (computers & electronics)	2	1380321853	1623258500
6003717247746	Sculpture (art)	interests	Hobbies and activities, Arts and music (art) , Sculpture (art)	2	132166649	155427980
6003717914546	Hybrids (vehicle)	interests	Hobbies and activities , Vehicles (transportation), Hybrids (vehicle)	2	63554330	74739893
6003729124262	Audio equipment (electronics)	interests	Technology (computers & electronics) , Consumer electronics (computers & electronics), Audio equipment (electronics)	2	43983308	51724371
6003778400853	Retail (industry)	interests	Business and industry, Retail (industry)	2	713008299	838497760
6003779859852	Horseback riding (horse sport)	interests	Sports and outdoors , Outdoor recreation (outdoors activities), Horseback riding (horse sport)	2	102889171	120997666
6003780008652	Online (computing)	interests	Business and industry, Online (computing)	2	1148217236	1350303470
6003780025252	Drawing (visual art)	interests	Hobbies and activities, Arts and music (art) , Drawing (visual art)	2	198107763	232974730
6003840140052	Agriculture (industry)	interests	Business and industry , Agriculture (industry)	2	389563392	458126550
6003899195666	Photography (visual art)	interests	Hobbies and activities , Arts and music (art) , Photography (visual art)	2	1117351836	1314005760
6003902397066	Electronic music (music)	interests	Entertainment (leisure) , Music (entertainment & media), Electronic music (music)	2	763266700	897601640
6003902462066	Theme parks (leisure)	interests	Hobbies and activities , Travel (travel & tourism), Theme parks (leisure)	2	199474285	234581760
6003940339466	Video games (gaming)	interests	Entertainment (leisure), Games (leisure) , Video games (gaming)	2	946113469	1112629440
6003985771306	Technology (computers & electronics)	interests	Technology (computers & electronics)	2	1510784438	1776682500
6004030160948	Social media (online media)	interests	Business and industry , Online (computing) , Social media (online media)	2	633785255	745331460
6004037107009	Boats (watercraft)	interests	Hobbies and activities , Vehicles (transportation), Boats (watercraft)	2	145653103	171288050
6004037400009	Fast food (food & drink)	interests	Food and drink (consumables), Food (food & drink) , Fast food (food & drink)	2	445818967	524283106
6004037726009	Pets (animals)	interests	Hobbies and activities, Pets (animals)	2	895994081	1053689040
6004037932409	Management (business & finance)	interests	Business and industry , Management (business & finance)	2	332392721	390893840
6004043913548	Newspapers (publications)	interests	Entertainment (leisure) , Reading (communication) , Newspapers (publications)	2	815218979	958697520
6004094205989	German cuisine (food & drink)	interests	Food and drink (consumables) , Cuisine (food & drink) , German cuisine (food & drink)	8	29112772	34236620
6004100985609	Friendship (relationships)	interests	Family and relationships , Friendship (relationships)	2	716429413	842520990
6004115167424	Physical exercise (fitness)	interests	Fitness and wellness (fitness), Physical exercise (fitness)	2	646978494	760846710
6004140335706	Architecture (architecture)	interests	Business and industry , Architecture (architecture)	2	433894345	510259750
6004160395895	Travel (travel & tourism)	interests	Hobbies and activities , Travel (travel & tourism)	2	1172422168	1378768470
6005609368513	Software (computers & electronics)	interests	Technology (computers & electronics), Computers (computers & electronics) , Software (computers & electronics)	2	925583622	1088486340
6007828099136	Luxury goods (retail)	interests	Shopping and fashion , Shopping (retail) , Luxury goods (retail)	2	673653435	792216440
6008832464480	E-book readers (consumer electronics)	interests	Technology (computers & electronics) , Consumer electronics (computers & electronics), E-book readers (consumer electronics)	2	45626557	53656832
6009248606271	Food and drink (consumables)	interests	Food and drink (consumables)	2	1347509744	1584671460
6010924093432	Live events (entertainment)	interests	Entertainment (leisure) , Live events (entertainment)	2	955524030	1123696260
6011366104268	Women’s clothing (apparel)	interests	Shopping and fashion , Clothing (apparel) , Women’s clothing (apparel)	2	598187925	703469000
6011515350975	Politics and social issues (politics)	interests	Hobbies and activities , Politics and social issues (politics)	8	1046674914	1230889700
6011994253127	Men’s clothing (apparel)	interests	Shopping and fashion , Clothing (apparel) , Men’s clothing (apparel)	2	455533027	535706840

job_titles_df <- get_fb_parameter_ids(type    = "work_positions",
                                      version = VERSION, 
                                      token   = TOKEN,
                                      q = "data")

job_titles_df %>% 
  kable() %>% 
  kable_styling() %>% 
  scroll_box(width = "100%",
             height = "300px")

id	name	coverage_lower_bound	coverage_upper_bound	subtext
107995062561111	Data entry clerk	93060	109439	NA
431299473579193	Data science	14659	17240	NA
103088336397390	Data Architect	6984	8214	NA
6914363428	ADP	119	141	NA
117496231707441	NTT DATA North America	13	16	Plano, TX, US
268835395007	Datamatics	9	11	Datamatics · Mumbai, Maharashtra, India
112076745475038	Database design	6	8	NA
361773733939664	Tech Data Corporation	2	3	Singapore, Singapore
182402158477555	Dimension Data Asia Pacific	1	2	Singapore, Singapore
112383832111981	Hitachi Data Systems	1	2	NA

Location Key

Users can be targeted by different types of locations, including all users within a country, region (e.g., US state), city, neighborhood, etc. The get_fb_parameter_ids is used to obtain the location key for different places. The type parameter is used to define the type of location (e.g., "country", "region", etc.).

In addition to type, the country_code, region_id, key and q parameters can be used to further refine searchers for locations. Providing an input for country_code, region_id, and key (e.g., city key), will search for locations within those larger locations. Providing an input to the q parameter limits the search by name. For smaller geographic regions, an input for q is required.

country_group_df <- get_fb_parameter_ids(type    = "country_group",
                                         version = VERSION, 
                                         token   = TOKEN)

country_df <- get_fb_parameter_ids(type    = "country",
                                   version = VERSION, 
                                   token   = TOKEN)

us_states_df <- get_fb_parameter_ids(type    = "region",
                                     version = VERSION, 
                                     token   = TOKEN,
                                     country_code = "US")

ny_key <- us_states_df %>% filter(name == "New York") %>% pull(key) 
ny_cities_df <- get_fb_parameter_ids(type    = "city",
                                     version = VERSION, 
                                     token   = TOKEN,
                                     region_id = ny_key,
                                     q = "New York")

ny_cities_df %>% 
  kable() %>% 
  kable_styling() %>% 
  scroll_box(width = "100%",
             height = "300px")

key	name	type	country_code	country_name	region	region_id	supports_region	supports_city	geo_hierarchy_level	geo_hierarchy_name
2490299	New York	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490629	Otego	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488053	Delanson	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491852	Summit	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488965	Hadley	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492286	Wellsburg	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491500	Sinclairville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489582	Lake Success	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490154	Morristown	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490567	Old Westbury	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490076	Millwood	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490463	North Salem	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487428	Brushton	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491249	Rushville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490095	Modena	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490334	Nissequogue	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488037	Deansboro	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491482	Shushan	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492587	Woodbourne	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491349	Schenevus	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490040	Middleville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488915	Greenwood	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490148	Morris	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492015	Troupsburg	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488236	East Greenbush	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490930	Port Jervis	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489403	Johnson City	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488187	East Aurora	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488309	East Setauket	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492599	Woodridge	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489120	Henderson	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488733	Gates	subcity	US	United States	New York	3875	TRUE	TRUE	SUBCITY	CITY
2487596	Cayuga	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487589	Caton	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492233	Wassaic	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489375	Jasper	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492466	Westtown	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490695	Parishville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487849	Constable	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487456	Burdett	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487011	Ava	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488687	Fultonville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488119	Downsville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488558	Floral Park	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487574	Cassville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487277	Bloomingburg	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491064	Redfield	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491251	Russell	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488767	Gilbertsville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487531	Canaseraga	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487599	Cayuta	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487851	Constantia	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491829	Stratford	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487081	Barneveld	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488658	Freeville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489038	Harford	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491730	Spring Valley	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489685	Limestone	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2673869	Fort Drum	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489459	Kennedy	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2480661	Grand Island	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491384	Sea Cliff	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488269	East Massapequa	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489935	Masonville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491277	Saint James	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491248	Rushford	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487960	Croton-on-Hudson	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488253	East Islip	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488184	East Amherst	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491515	Sleepy Hollow	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489009	Hampton Bays	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490597	Oran	subcity	US	United States	New York	3875	TRUE	TRUE	SUBCITY	CITY
2487637	Central Square	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490360	North Bellport	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488279	East Northport	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487580	Castorland	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488449	Esperance	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491822	Stony Point	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487334	Bradford	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489938	Massapequa Park	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488951	Guilford	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488288	East Patchogue	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490561	Old Forge	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489347	Island Park	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490824	Pine Bush	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489565	Lake Grove	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489747	Locust Valley	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488274	East Moriches	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490070	Millport	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488301	East Rochester	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491001	Putnam Valley	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489877	Mannsville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490351	North Babylon	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488643	Franklin Square	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490088	Mineville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491818	Stony Brook	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490569	Olivebridge	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487366	Briarcliff Manor	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492453	Westernville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490177	Mount Kisco	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490957	Pound Ridge	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490002	Memphis	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492376	West Islip	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489047	Harpursville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489080	Hastings-on-Hudson	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487057	Ballston Spa	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488829	Gorham	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489782	Loudonville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489330	Ireland Corners	subcity	US	United States	New York	3875	TRUE	TRUE	SUBCITY	CITY
2492370	West Hempstead	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490039	Middletown, Orange County	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489581	Lake Ronkonkoma	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487742	Clayville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489496	Kings Point	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491325	Saranac Lake	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492251	Watkins Glen	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492220	Wappingers Falls	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490741	Pearl River	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490765	Penn Yan	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487718	Circleville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488874	Great Neck	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491634	South Huntington	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489160	Highland Falls	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488520	Fire Island	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490038	Middlesex	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487592	Cattaraugus	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491267	Sag Harbor	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490732	Pavilion	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491334	Savona	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488673	Friendship	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488510	Fillmore	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489213	Honeoye	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487775	Clymer	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490547	Odessa	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490920	Port Chester	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488970	Hague	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491068	Redwood	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488732	Gasport	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488224	East Fishkill	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490595	Oppenheim	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491378	Scottsville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490631	Otisco	subcity	US	United States	New York	3875	TRUE	TRUE	SUBCITY	CITY
2488757	Ghent	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487168	Bellerose	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488754	Gerry	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489385	Jeffersonville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487370	Bridgeport	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489915	Marion	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487850	Constableville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488371	Edmeston	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492465	Westport	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492460	Westmoreland	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491332	Savannah	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487160	Belfast	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487573	Cassadaga	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490236	Nedrow	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487774	Clyde	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488317	East Syracuse	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492302	West Babylon	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490786	Petersburg	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490766	Pennellville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487549	Carle Place	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488141	Dundee	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492425	West Seneca	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491749	Stafford	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489414	Jordan	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487669	Chazy	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487348	Brant	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487755	Clifton Park	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489470	Kerhonkson	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488473	Falconer	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489183	Hinsdale	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491463	Shokan	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490508	Norwood	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492401	West Nyack	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491363	Scio	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487383	Bristol	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490445	North New Hyde Park	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488101	Dix Hills	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490045	Milan	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488565	Floyd	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2486900	Altmar	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2486961	Arkport	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490086	Minerva	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489203	Holley	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492148	Verona	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492110	Valley Cottage	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489352	Italy	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492549	Willsboro	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489830	Machias	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489186	Hobart	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488775	Glasco	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489948	Maybrook	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487747	Cleveland	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489058	Harrisville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489815	Lyndonville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488967	Hagaman	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487723	Clarendon	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491739	Springwater	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489592	Lakeview	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488103	Dobbs Ferry	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489004	Hammond	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489294	Hurleyville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491837	Stuyvesant	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491474	Shortsville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491747	Staatsburg	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491967	Tillson	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490607	Orient	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490614	Orleans	subcity	US	United States	New York	3875	TRUE	TRUE	SUBCITY	CITY
2490202	Mumford	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487482	Busti	subcity	US	United States	New York	3875	TRUE	TRUE	SUBCITY	CITY
2490554	Old Bethpage	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489298	Hyde Park	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489068	Hartwick	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490645	Ovid	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490965	Prattsville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487883	Corfu	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489699	Lindley	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489381	Jefferson	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489962	McDonough	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487713	Churchville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2486920	Ancram	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488084	Dexter	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488054	Delevan	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492024	Truxton	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487090	Barryville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487821	Colton	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488552	Fleischmanns	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487765	Clintondale	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487110	Bay Shore	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491077	Remsenburg	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487831	Conesus	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492617	Worcester	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487393	Brocton	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490335	Niverville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489738	Locke	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489544	Lacona	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489655	Leon	subcity	US	United States	New York	3875	TRUE	TRUE	SUBCITY	CITY
2492152	Verplanck	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487619	Celoron	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489135	Heuvelton	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488941	Groveland	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490222	Napanoch	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488768	Gilboa	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487056	Ballston Lake	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489639	Leeds	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488398	Ellington	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491193	Romulus	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490967	Preble	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491134	Ripley	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2486876	Alexandria Bay	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489495	Kings Park	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489577	Lake Placid	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488455	Evans Mills	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492324	West Carthage	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492443	West Winfield	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490929	Port Jefferson Station	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491547	Snyder	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490926	Port Henry	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490919	Port Byron	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491796	Stewart Manor	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487727	Clark Mills	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488118	Dover Plains	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488609	Fort Montgomery	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491601	South Corning	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490835	Pine Plains	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490356	North Bay Shore	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489867	Manhasset Hills	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492083	Upper Brookville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491765	Star Lake	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491606	South Dayton	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488713	Garden City South	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487794	Cold Spring Harbor	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487255	Black River	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488712	Garden City Park	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491629	South Hempstead	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490446	North Norwich	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488531	Fishers Island	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491054	Red Creek	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492312	West Bloomfield	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490612	Oriskany Falls	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489756	Lonelyville	subcity	US	United States	New York	3875	TRUE	TRUE	SUBCITY	CITY
2489733	Lloyd Harbor	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491617	South Glens Falls	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488879	Great Valley	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487757	Clifton Springs	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487185	Bemus Point	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2486840	Adams Center	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490924	Port Ewen	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490555	Old Brookville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489736	Loch Sheldrake	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491234	Rouses Point	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492073	Union Springs	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489477	Keuka Park	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489621	Laurel Hollow	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490366	North Boston	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492445	Westbrookville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491716	Speonk	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487579	Castleton-on-Hudson	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491819	Stony Creek	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491555	Sodus Point	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489539	La Fargeville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492494	White Lake	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492346	West Elmira	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492089	Upper Nyack	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490730	Paul Smiths	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491651	South Nyack	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488597	Fort Covington	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487464	Burlington Flats	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491612	South Fallsburg	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489585	Lake View	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2486994	Atlantic Beach	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489202	Holland Patent	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488828	Gordon Heights	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491512	Slate Hill	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491493	Silver Creek	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491614	South Floral Park	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492111	Valley Falls	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492421	West Sand Lake	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492306	West Batavia	subcity	US	United States	New York	3875	TRUE	TRUE	SUBCITY	CITY
2489139	Hewlett Harbor	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487284	Blue Mountain Lake	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490825	Pine City	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490454	North Pole	subcity	US	United States	New York	3875	TRUE	TRUE	SUBCITY	CITY
2489817	Lyon Mountain	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487676	Chenango Forks	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487003	Au Sable Forks	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491035	Raquette Lake	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2486879	Alfred Station	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492441	West Webster	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489282	Huntington Bay	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490188	Mount Upton	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488843	Grand Gorge	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488196	East Bethany	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487132	Beaver Falls	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491503	Skaneateles Falls	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492422	West Saugerties	subcity	US	United States	New York	3875	TRUE	TRUE	SUBCITY	CITY
2490943	Porter Corners	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491162	Rock City Falls	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491842	Sugar Loaf	subcity	US	United States	New York	3875	TRUE	TRUE	SUBCITY	CITY
2486929	Angola on the Lake	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492366	West Hampton Dunes	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491957	Three Mile Bay	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490485	North White Plains	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491443	Shelter Island Heights	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491081	Rensselaer Falls	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492439	West Valley	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488275	East Nassau	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492202	Walker Valley	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487760	Clinton Corners	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491496	Silver Springs	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490231	Natural Bridge	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487352	Brasher Falls	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488333	East Williston	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487344	Branchport	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490478	North Valley Stream	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492011	Tribes Hill	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490672	Palatine Bridge	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492043	Tuxedo Park	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491678	South Valley Stream	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491297	Salt Point	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490921	Port Crane	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2486933	Annandale-on-Hudson	subcity	US	United States	New York	3875	TRUE	TRUE	SUBCITY	CITY
2490558	Old Chatham	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490460	North Rose	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491986	Tomkins Cove	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490832	Pine Island	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490932	Port Leyden	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490657	Oyster Bay Cove	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487681	Cherry Grove	subcity	US	United States	New York	3875	TRUE	TRUE	SUBCITY	CITY
2489563	Lake Erie Beach	subcity	US	United States	New York	3875	TRUE	TRUE	SUBCITY	CITY
2489626	Lawrence, Nassau County	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488607	Fort Johnson	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488015	Davis Park	subcity	US	United States	New York	3875	TRUE	TRUE	SUBCITY	CITY
2490938	Port Washington North	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487792	Cold Brook	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490889	Point Lookout	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488280	East Norwich	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488417	Elmira Heights	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489965	McGraw	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490449	North Patchogue	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489313	Indian Lake	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488247	East Hills	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491746	St. Bonaventure	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487626	Center Moriches	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489051	Harris	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490103	Mohegan Lake	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487962	Crown Heights	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491526	Smallwood	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489263	Hudson Falls	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492535	Williston Park	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488878	Great River	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488302	East Rockaway	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489764	Long Lake	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491613	South Farmingdale	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491110	Richfield Springs	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492235	Water Mill	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492034	Tupper Lake	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491887	Sylvan Beach	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489557	Lake Carmel	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492423	West Sayville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487285	Blue Point	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487142	Bedford Hills	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490389	North Creek	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489228	Hopewell Junction	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492479	Wheatley Heights	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490542	Ocean Beach	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489578	Lake Pleasant	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489214	Honeoye Falls	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487956	Cross River	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487793	Cold Spring	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488385	Elizaville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490059	Miller Place	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488445	Erin	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490186	Mount Sinai	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491182	Rocky Point	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490441	North Massapequa	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488787	Glen Head	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491118	Richville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491353	Schodack Landing	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489676	Lido Beach	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490031	Middle Island	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487169	Bellerose Terrace	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487690	Chestnut Ridge	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492652	Yorktown Heights	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488197	East Bloomfield	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490175	Mount Hope	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489943	Mastic Beach	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492187	Wading River	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489384	Jefferson Valley	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488916	Greenwood Lake	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491427	Sharon Springs	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492457	Westhampton Beach	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491220	Roslyn Heights	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490348	North Amityville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491645	South Lockport	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488727	Garrison	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490379	North Chili	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492078	University Gardens	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489893	Maple View	subcity	US	United States	New York	3875	TRUE	TRUE	SUBCITY	CITY
2488656	Freetown	subcity	US	United States	New York	3875	TRUE	TRUE	SUBCITY	CITY
2489407	Johnsonville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492375	West Hurley	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489224	Hoosick Falls	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490125	Montour Falls	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487963	Crown Point	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492514	Whitney Point	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491359	Schroon Lake	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488612	Fort Plain	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488296	East Quogue	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488599	Fort Edward	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492095	Upton	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488464	Fair Haven	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487519	Campbell Hall	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492184	Waccabuc	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490999	Putnam Lake	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487278	Bloomingdale	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492039	Tuscarora	subcity	US	United States	New York	3875	TRUE	TRUE	SUBCITY	CITY
2491566	Sound Beach	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489572	Lake Luzerne	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490729	Pattersonville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487975	Cuddebackville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487049	Baldwin Harbor	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489153	High Falls	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492396	West Monroe	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489376	Java Center	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488283	East Otto	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489513	Knapp Creek	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491312	Sands Point	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487047	Baldwin, Nassau County	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491164	Rock Hill	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488661	French Creek	subcity	US	United States	New York	3875	TRUE	TRUE	SUBCITY	CITY
2490875	Pleasant Valley	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487012	Averill Park	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487036	Baiting Hollow	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488614	Fort Salonga	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491185	Rodman	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490755	Pelham Manor	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491360	Schuyler Falls	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489820	Lyons Falls	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491263	Sackets Harbor	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489569	Lake Katrine	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489343	Irving	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487295	Bolton Landing	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487098	Batavia, Genesee County	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491108	Richburg	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491816	Stone Ridge	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487026	Bagdad	subcity	US	United States	New York	3875	TRUE	TRUE	SUBCITY	CITY
2488820	Goldens Bridge	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490442	North Merrick	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489440	Keene Valley	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488697	Gainesville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488217	East Durham	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489463	Kent	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490671	Painted Post	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488841	Grahamsville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491926	Texas	subcity	US	United States	New York	3875	TRUE	TRUE	SUBCITY	CITY
2487722	Clarence Center	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489351	Islip Terrace	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488812	Glenwood Landing	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489728	Livingston Manor	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488886	Green Island	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492510	Whitesville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487206	Berlin	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489050	Harriman	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2703980	Manhattan	subcity	US	United States	New York	3875	TRUE	TRUE	SUBCITY	BOROUGH
2491561	Somers	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487052	Baldwinsville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490247	Nesconset	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487886	Corning	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487787	Cohoes	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487369	Bridgehampton	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489232	Hornell	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488485	Farmingdale	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487492	Byron	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491341	Sayville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489209	Holtsville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487876	Copiague	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487117	Bayport	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487363	Brewster	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489078	Hastings	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488742	Geneseo	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487313	Boston	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487506	Calverton	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489506	Kirkwood	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2486966	Armonk	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492495	White Plains	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488469	Fairport	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488007	Darien	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490728	Patterson	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492250	Watervliet	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491966	Ticonderoga	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491174	Rockland	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489855	Malta	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492630	Wyandanch	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491112	Richland	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488997	Hamilton	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491217	Roslyn	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489992	Medford	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490114	Montauk	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487717	Cincinnatus	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489389	Jericho	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490577	Oneida	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492446	Westbury	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489543	Lackawanna	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488815	Gloversville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2486914	Amherst	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491398	Selden	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487877	Coram	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490320	Niagara Falls	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487705	Chittenango	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487390	Brockport	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488808	Glenville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487432	Buchanan	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492533	Williamsville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488143	Dunkirk	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490617	Orwell	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492228	Warwick	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489196	Holbrook	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488534	Fishkill	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487819	Colonie	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488711	Garden City	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490273	New Haven	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487613	Cedarhurst	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487674	Chemung	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488437	Endicott	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487403	Brookhaven	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490003	Menands	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488418	Elmont	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489993	Medina	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490123	Monticello	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490004	Mendon	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488419	Elmsford	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488438	Endwell	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492552	Wilmington	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491099	Rhinebeck	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489905	Marcy	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489882	Manorville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489845	Maine	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491560	Solvay	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489919	Marlboro	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489988	Mechanicville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491784	Sterling	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491455	Sherrill	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492592	Woodhull	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492108	Valhalla	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488448	Esopus	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490225	Napoli	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491691	Southold	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487783	Coeymans	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489499	Kingsbury	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490760	Pendleton	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491738	Springville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490851	Pittsford	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487650	Chappaqua	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491373	Scotia	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490307	Newcomb	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490984	Pulaski	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488408	Elma	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490550	Ohio	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490495	Northport	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490276	New Hyde Park	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488486	Farmington	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487874	Copenhagen	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489348	Islandia	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488074	Depew	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490878	Pleasantville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491306	Sanborn	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489370	Jamesville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487689	Chestertown	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488745	Georgetown	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488648	Fredonia	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488429	Elwood	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492644	Yaphank	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487630	Centereach	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487426	Brunswick	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488744	Genoa	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490144	Moriah	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489304	Ilion	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491423	Shandaken	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489726	Liverpool	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490118	Montebello	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491236	Roxbury	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489613	Larchmont	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2486855	Akron	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489327	Inwood	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489636	Ledyard	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487095	Barton	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491388	Searingtown	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488982	Halfmoon	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492557	Wilton	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489667	Lewiston	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490868	Plattsburgh	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490955	Poughkeepsie	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492611	Woodstock	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488416	Elmira	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491538	Smithtown	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489122	Henrietta	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487004	Auburn	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491343	Scarsdale	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489117	Hempstead	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490625	Oswego	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490000	Melville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2673555	Staten Island	subcity	US	United States	New York	3875	TRUE	TRUE	SUBCITY	BOROUGH
2491889	Syosset	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489083	Hauppauge	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490252	New Baltimore	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2790125	Westchester County	medium_geo_area	US	United States	New York	3875	TRUE	TRUE	MEDIUM_GEO_AREA	COUNTY
2488349	Eaton	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492075	Uniondale	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492219	Wantagh	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488654	Freeport	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487715	Cicero	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492246	Watertown	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2790627	Erie County	medium_geo_area	US	United States	New York	3875	TRUE	TRUE	MEDIUM_GEO_AREA	COUNTY
2790617	Rockland County	medium_geo_area	US	United States	New York	3875	TRUE	TRUE	MEDIUM_GEO_AREA	COUNTY
2490084	Mineola	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487672	Cheektowaga	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489811	Lynbrook	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491991	Tonawanda	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489281	Huntington	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489369	Jamestown	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487556	Carmel	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492019	Troy	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490260	New City	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492649	York	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487404	Brooklyn	subcity	US	United States	New York	3875	TRUE	TRUE	SUBCITY	BOROUGH
2491013	Queens	subcity	US	United States	New York	3875	TRUE	TRUE	SUBCITY	BOROUGH
2491158	Rochester	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487444	Buffalo	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490287	New Rochelle	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491890	Syracuse	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2733673	East New York	neighborhood	US	United States	New York	3875	TRUE	TRUE	NEIGHBORHOOD	NEIGHBORHOOD
2490286	New Paltz	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2486858	Albany	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490300	New York Mills	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492648	Yonkers	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489355	Ithaca	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490279	New Lebanon	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489632	Lebanon	subcity	US	United States	New York	3875	TRUE	TRUE	SUBCITY	CITY
2492099	Utica	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489056	Harrison	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487241	Binghamton	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489500	Kingston	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2790622	Kings County	medium_geo_area	US	United States	New York	3875	TRUE	TRUE	MEDIUM_GEO_AREA	COUNTY
2490259	New Cassel	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491024	Ramapo	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491192	Rome	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490305	Newburgh	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490297	New Windsor	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491348	Schenectady	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2486917	Amityville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492232	Washingtonville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488831	Goshen	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491229	Rotterdam	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491904	Tarrytown	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490221	Nanuet	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491195	Ronkonkoma	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488995	Hamburg	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490301	Newark	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492651	Yorktown	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489933	Maryland	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490271	New Hampton	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490993	Purchase	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490752	Peekskill	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488743	Geneva	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490544	Oceanside	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488341	Eastchester	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489662	Levittown	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490156	Morrisville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491385	Seaford	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487214	Bethel	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489615	Latham	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491080	Rensselaer	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2790113	Onondaga County	medium_geo_area	US	United States	New York	3875	TRUE	TRUE	MEDIUM_GEO_AREA	COUNTY
2487868	Cooperstown	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491141	Riverhead	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487402	Brookfield	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491256	Rye	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490516	Nyack	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2673772	Cornwall	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490011	Merrick	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2790628	Ulster County	medium_geo_area	US	United States	New York	3875	TRUE	TRUE	MEDIUM_GEO_AREA	COUNTY
2490549	Ogdensburg	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490763	Penfield	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492155	Vestal	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489147	Hicksville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490856	Plainview	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491840	Suffern	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487124	Beacon	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487360	Brentwood	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490568	Olean	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488949	Guilderland	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489740	Lockport	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487399	Bronxville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2486918	Amsterdam	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489344	Irvington	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490691	Paris	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489937	Massapequa	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487738	Clay	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487219	Bethpage	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487023	Babylon	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492598	Woodmere	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491014	Queensbury	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2789899	Orange County	medium_geo_area	US	United States	New York	3875	TRUE	TRUE	MEDIUM_GEO_AREA	COUNTY
2487893	Cortland	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491462	Shirley	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489698	Lindenhurst	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491701	Sparkill	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487826	Commack	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489337	Irondequoit	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488442	Ephratah	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490723	Patchogue	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489866	Manhasset	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487291	Bohemia	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490583	Oneonta	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488487	Farmingville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489001	Hamlin	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487009	Austerlitz	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487786	Cohocton	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487737	Claverack	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2486944	Aquebogue	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492104	Valatie	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491253	Russia	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489200	Holland	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488514	Fine	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488723	Garnerville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491331	Sauquoit	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492531	Williamson	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491809	Stockton	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491976	Tivoli	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488590	Forestville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491902	Tannersville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490037	Middleport	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489908	Margaretville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490309	Newfield	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490674	Palenville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488345	Eastport	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492627	Wurtsboro	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490226	Narrowsburg	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487524	Canaan	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490945	Portland	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489096	Hawthorne	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491519	Slingerlands	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490153	Morrisonville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489445	Keeseville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491663	South Salem	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490655	Oxford	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2486990	Athens	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490126	Montrose	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489006	Hammondsport	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491731	Springfield	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491039	Ravena	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490781	Peru	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492212	Walton	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490189	Mount Vernon	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488271	East Meadow	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492172	Virgil	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491056	Red Hook	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490937	Port Washington	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488589	Forestport	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488807	Glens Falls	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488803	Glenmont	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492239	Waterford	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487192	Bennington	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490778	Persia	subcity	US	United States	New York	3875	TRUE	TRUE	SUBCITY	CITY
2488836	Gowanda	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489323	Inlet	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489704	Lisbon	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492194	Wainscott	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490249	Neversink	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487154	Beekmantown	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2486927	Angola	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2486911	Amenia	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488636	Frankfort	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492277	Weedsport	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491708	Speculator	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490676	Palisades	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487798	Colden	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491202	Roscoe	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492563	Windham	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488709	Gansevoort	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490928	Port Jefferson	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492249	Waterville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490106	Moira	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490437	North Lindenhurst	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491088	Rexford	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492114	Valley Stream	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490803	Phoenicia	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490852	Pittstown	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492638	Wyoming	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487635	Central Islip	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488042	Deer Park	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492179	Voorheesville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491470	Shoreham	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491269	Sagaponack	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487204	Berkshire	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492554	Wilson	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488180	Earlville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487518	Campbell	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487666	Chaumont	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489587	Lakeland	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488649	Freedom	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492532	Williamstown	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490680	Panama	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491323	Saranac	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489454	Kendall	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488223	East Farmingdale	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488670	Frewsburg	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489284	Huntington Station	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491111	Richford	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491019	Quogue	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492636	Wynantskill	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488011	Davenport	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488546	Flanders	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488076	Derby	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487657	Charlton	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489008	Hampton	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491523	Sloatsburg	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490611	Oriskany	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487207	Berne	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487296	Bombay	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488782	Glen Cove	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489836	Madrid	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2486937	Apalachin	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487182	Belmont	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491521	Sloan	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489061	Hartford	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490801	Philmont	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492452	Westerlo	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491782	Stephentown	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490476	North Tonawanda	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490067	Millerton	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492650	Yorkshire	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491033	Ransomville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488133	Duanesburg	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489727	Livingston	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490953	Pottersville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491178	Rockville Centre	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488107	Dolgeville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488450	Essex	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489541	LaFayette	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490553	Olcott	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491354	Schoharie	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490902	Pompey	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489879	Manorhaven	subcity	US	United States	New York	3875	TRUE	TRUE	SUBCITY	CITY
2487265	Blauvelt	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487668	Chautauqua	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489171	Hillsdale	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487501	Calcium	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491735	Springs	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488044	Deerfield	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490206	Munnsville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487871	Copake	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491196	Roosevelt	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487362	Brewerton	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492507	Whitesboro	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487811	Collins	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488359	Eden	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488900	Greenlawn	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488050	Defreestville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487664	Chatham	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489175	Hilton	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491372	Scotchtown	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490758	Pembroke	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490964	Prattsburgh	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491327	Saratoga Springs	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2486857	Alabama	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489065	Hartsdale	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490215	Muttontown	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490333	Niskayuna	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488860	Granville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491714	Spencerport	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490633	Otisville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490900	Pomona	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490956	Poughquag	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492196	Walden	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490599	Orangeburg	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487838	Congers	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487721	Clarence	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492271	Webster	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491399	Selkirk	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490524	Oakdale	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492570	Wingdale	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489137	Hewlett	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487532	Canastota	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488996	Hamden	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488492	Fayetteville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488057	Delmar	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489552	Lagrangeville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489401	Johnsburg	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487176	Bellport	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2486964	Arlington	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489942	Mastic	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489112	Hector	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489125	Herkimer	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491213	Rosendale	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2486854	Airmont	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490809	Piermont	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487414	Brookville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490054	Millbrook	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488475	Fallsburg	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489945	Mattituck	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489522	Knox	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489600	Lancaster	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488468	Fairmount	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490146	Moriches	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491246	Rush	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492021	Trumansburg	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488572	Fonda	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491362	Schuylerville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492177	Volney	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492659	Youngstown	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487070	Bardonia	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492283	Wells	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492456	Westhampton	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490588	Ontario	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487068	Barcelona	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489194	Hogansburg	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492240	Waterloo	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2486835	Accord	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491328	Sardinia	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492135	Varysburg	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490845	Piseco	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488370	Edinburg	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488838	Grafton	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489505	Kirkville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489168	Hillburn	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487638	Central Valley	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492414	West Point	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487263	Blasdell	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487379	Brightwaters	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2492214	Walworth	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491346	Schaghticoke	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489913	Marilla	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489368	Jamesport	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489244	Houghton	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491380	Scriba	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487840	Conklin	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489955	Mayville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491955	Thornwood	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487120	Bayville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490804	Phoenix	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2486860	Albertson	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2488393	Ellenville	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489946	Mattydale	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489324	Interlaken	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2486904	Amagansett	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491121	Ridge	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490112	Monsey	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489433	Katonah	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2487986	Cutchogue	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489378	Jay	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490854	Plainedge	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2489858	Malverne	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2790636	Lewis County	medium_geo_area	US	United States	New York	3875	TRUE	TRUE	MEDIUM_GEO_AREA	COUNTY
2488907	Greenvale	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2491692	Southport	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490780	Perth	city	US	United States	New York	3875	TRUE	TRUE	NA	NA
2490675	Palermo	city	US	United States	New York	3875	TRUE	TRUE	NA	NA

Location Key with Coordinates/Geometries

When querying locations using get_fb_parameter_ids, setting add_location_coords to TRUE adds coordinates and, when available, geometries.

ny_cities_coords_df <- get_fb_parameter_ids(type    = "city",
                                            version = VERSION, 
                                            token   = TOKEN,
                                            region_id = ny_key,
                                            q = "New York",
                                            add_location_coords = T)

leaflet() %>%
  addTiles() %>%
  addCircles(data = ny_cities_coords_df,
              popup = ~name)

Get coordinates/geometries for location keys

Above we show that, when using get_fb_parameter_ids, coordinates/geometries of locations can be added by setting add_location_coords to TRUE. However, coordinates/geometries can also be queried using location keys. Retrieving locations/coordinates for many locations can take a long time. Consequently, we may want to (1) use get_fb_parameter_ids to obtain location keys, (2) filter keys to relevant keys, then (3) query coordinates/geometries for relevant locations.

For valid location keys, the function will return coordinates (latitude and longitude). When available, the function will also return the geometry.

Below we show an example for obtaining geometries for Washington, DC zip codes

## All Washington, DC zip codes start with 20
zip_df <- get_fb_parameter_ids(type = "zip", 
                               q = "20", 
                               country_code = "US",
                               version = VERSION, 
                               token = TOKEN)

zip_dc_df <- zip_df %>%
  filter(region == "Washington, District of Columbia")

zip_dc_sf <- get_location_coords(location_unit_type = "zip",
                                 location_keys = zip_dc_df$key,
                                 version = VERSION,
                                 token = TOKEN)

leaflet() %>%
  addTiles() %>%
  addPolygons(data = zip_dc_sf,
              popup = ~name)

Querying Data

The query_fb_marketing_api function enables querying daily and monthly active users for specific locations and by specific types. Many parameters rely on IDs obtained from the get_fb_parameter_ids function. For example, get_fb_parameter_ids can be used to obtain the location key for New York City and the interest parameter ID for “Music (entertainment & media)”; these IDs can then be used in query_fb_marketing_api to obtain the number of Facebook users interested in music that live in New York City.

Different Location Types

The number of daily and monthly active Facebook users can be queried (1) around a specific point (setting the latitude and longitude and the radius around this point), and (2) for a specific geographic (e.g., country, region, city, neighborhood, etc.). For small geographies, such as neighborhoods, a radius can also be set.

Radius around lat/lon

## All Facebook Users by Radius (Near NYC)
query_fb_marketing_api(location_unit_type = "coordinates",
                                  lat_lon            = c(40.712, -74.006),
                                  radius             = 5,
                                  radius_unit        = "kilometer",
                                  version            = VERSION, 
                                  creation_act       = CREATION_ACT, 
                                  token              = TOKEN) %>%
  kable() %>% 
  kable_styling() %>%
  scroll_box(width = "100%")

estimate_dau	estimate_mau_lower_bound	estimate_mau_upper_bound	location_unit_type	location_types	radius	radius_unit	gender	age_min	age_max	latitude	longitude	api_call_time_utc
1778265	2300000	2700000	coordinates	home or recent	5	kilometer	1 or 2	18	65	40.712	-74.006	2024-05-06 17:01:19

Facebook enables querying a specific location to determine a suggested radius to reach enough people (see Facebook documentation here). We can use the get_fb_suggested_radius function to get the suggested radius. Below shows the querying the suggested radius for Paris, France and Paris, Kentucky.

# Paris, France
get_fb_suggested_radius(location = c(48.856667, 2.352222),
                        version = VERSION,
                        token = TOKEN) %>%
  kable() %>% 
  kable_styling() %>%
  scroll_box(width = "100%")

suggested_radius	distance_unit
1	kilometer

# Paris, Kentucky
get_fb_suggested_radius(location = c(38.209682, -84.253915),
                        version = VERSION,
                        token = TOKEN) %>%
  kable() %>% 
  kable_styling() %>%
  scroll_box(width = "100%")

suggested_radius	distance_unit
25	kilometer

Location IDs - Country, State, District, City, Neighborhood, etc.

We can also query using different location types, including countries, cities, etc. The get_fb_parameter_ids() function can be used to extract location keys for different location types; the location keys can then be used in the query_fb_marketing_api(). The below table indicates all the location types supported by the package.

• Location Type: This is specified (1) in the type parameter in the get_fb_parameter_ids() function and in the location_unit_type parameter in the query_fb_marketing_api() function
• Radius Can be Specified: For these location types, a radius can optionally be specified; for example, to specify a location and it’s surrounding area.
• US Only: These location types can only be used for the US

Location Type	Radius Can be Specified	US Only	Example
country_group	No	No	World Wide
country	No	No	United States
region	No	No	New York
large_geo_area	No	No	Akkar, Lebanon
medium_geo_area	No	No	Henrico County, Virginia, USA
small_geo_area	No	No	El Rosario, Spain
city	Yes	No	New York City
subcity	No	No	Manhattan
neighborhood	No	No	East Village, Manhattan
zip	No	Yes	90210
geo_market	No	Yes	New York
electoral_district	No	Yes	New York’s 18th District, 2020 Census

Below shows examples for querying Facebook users for each type of location type. Note that when entering the type in get_fb_parameter_ids, the singular is used (e.g., "country"), while when entering the type in query_fb_marketing_api, the plural is used (e.g., "countries"). Using the singular type to obtain the IDs and the plural to query the number of Facebook users mimics how parameters are entered in the Facebook Marketing API.

Country Groups

#### Country Group: World Wide
loc_df <- get_fb_parameter_ids(type = "country_group", 
                               version = VERSION, token = TOKEN)

query_fb_marketing_api(
  location_unit_type = "country_groups",
  location_keys      = loc_df %>% 
    filter(name == "Worldwide") %>% 
    pull(key),
  version            = VERSION, 
  creation_act       = CREATION_ACT, 
  token              = TOKEN) %>%
  kable() %>% 
  kable_styling() %>%
  scroll_box(width = "100%")

estimate_dau	estimate_mau_lower_bound	estimate_mau_upper_bound	location_unit_type	location_types	location_keys	gender	age_min	age_max	api_call_time_utc
0	2740800000	3224400000	country_groups	home or recent	worldwide	1 or 2	18	65	2024-05-06 17:01:20

Country

#### Country: United States
loc_df <- get_fb_parameter_ids(type = "country", 
                               version = VERSION, token = TOKEN)

query_fb_marketing_api(
  location_unit_type = "countries",
  location_keys      = loc_df %>% 
    filter(name == "United States") %>% 
    pull(key),
  version            = VERSION, 
  creation_act       = CREATION_ACT, 
  token              = TOKEN) %>%
  kable() %>% 
  kable_styling() %>%
  scroll_box(width = "100%")

estimate_dau	estimate_mau_lower_bound	estimate_mau_upper_bound	location_unit_type	location_types	location_keys	gender	age_min	age_max	api_call_time_utc
219374690	234900000	276400000	countries	home or recent	US	1 or 2	18	65	2024-05-06 17:01:21

Region

#### Region: New York (state)
region_df <- get_fb_parameter_ids(type = "region", country_code = "US", 
                                  version = VERSION, token = TOKEN)

query_fb_marketing_api(
  location_unit_type = "regions",
  location_keys      = region_df %>% 
    filter(name == "New York") %>% 
    pull(key),
  version            = VERSION, 
  creation_act       = CREATION_ACT, 
  token              = TOKEN) %>%
  kable() %>% 
  kable_styling() %>%
  scroll_box(width = "100%")

estimate_dau	estimate_mau_lower_bound	estimate_mau_upper_bound	location_unit_type	location_types	location_keys	gender	age_min	age_max	api_call_time_utc
13758866	14700000	17300000	regions	home or recent	3875	1 or 2	18	65	2024-05-06 17:01:22

Large Geographic Area

The Facebook Marketing Targeting Search provides the following description about large geographic areas: “Known commonly as a district or governate covering hundreds of square kilometers or more. Example: Akkar in Lebanon.”

#### Large Metro Area
loc_df <- get_fb_parameter_ids(type = "large_geo_area", 
                               country_code = "LB", 
                               q = "Akkar",
                               version = VERSION, token = TOKEN)

query_fb_marketing_api(
  location_unit_type = "large_geo_areas",
  location_keys      = loc_df %>% 
    filter(name == "Akkar") %>% 
    pull(key),
  version            = VERSION, 
  creation_act       = CREATION_ACT, 
  token              = TOKEN) %>%
  kable() %>% 
  kable_styling() %>%
  scroll_box(width = "100%")

estimate_dau	estimate_mau_lower_bound	estimate_mau_upper_bound	location_unit_type	location_types	location_keys	gender	age_min	age_max	api_call_time_utc
94014	130800	153800	large_geo_areas	home or recent	1321105	1 or 2	18	65	2024-05-06 17:01:23

Medium Geographic Area

The Facebook Marketing Targeting Search provides the following description about medium geographic areas: “Known commonly as a county, covering more than one city. Example: Henrico county in the state of Virginia in United States.”

#### Large Metro Area
loc_df <- get_fb_parameter_ids(type = "medium_geo_area", 
                               country_code = "US", 
                               q = "Henrico",
                               version = VERSION, token = TOKEN)

query_fb_marketing_api(
  location_unit_type = "medium_geo_areas",
  location_keys      = loc_df %>% 
    filter(name == "Henrico") %>% 
    pull(key),
  version            = VERSION, 
  creation_act       = CREATION_ACT, 
  token              = TOKEN) %>%
  kable() %>% 
  kable_styling() %>%
  scroll_box(width = "100%")

estimate_dau	estimate_mau_lower_bound	estimate_mau_upper_bound	location_unit_type	location_types	location_keys	gender	age_min	age_max	api_call_time_utc
159379	197600	232500	medium_geo_areas	home or recent	2701973	1 or 2	18	65	2024-05-06 17:01:24

Small Geographic Area

The Facebook Marketing Targeting Search provides the following description about small geographic areas: “Known commonly as a residential area near a city or town. Example: El Rosario near Marbella in Spain.”

#### Large Metro Area
loc_df <- get_fb_parameter_ids(type = "small_geo_area", 
                               country_code = "ES", 
                               q = "El",
                               version = VERSION, token = TOKEN)

query_fb_marketing_api(
  location_unit_type = "small_geo_areas",
  location_keys      = loc_df %>% 
    filter(name == "El Rosario") %>% 
    pull(key),
  version            = VERSION, 
  creation_act       = CREATION_ACT, 
  token              = TOKEN) %>%
  kable() %>% 
  kable_styling() %>%
  scroll_box(width = "100%")

estimate_dau	estimate_mau_lower_bound	estimate_mau_upper_bound	location_unit_type	location_types	location_keys	gender	age_min	age_max	api_call_time_utc
6884	7900	9300	small_geo_areas	home or recent	2713574	1 or 2	18	65	2024-05-06 17:01:25

City

#### City: New York City
loc_df <- get_fb_parameter_ids(type = "city", 
                               region_id = region_df %>% 
                                 filter(name == "New York") %>% 
                                 pull(key),
                               q = "New York",
                               version = VERSION, token = TOKEN)

## Facebook users in NYC
query_fb_marketing_api(
  location_unit_type = "cities",
  location_keys      = loc_df %>% filter(name == "New York") %>% pull(key),
  version            = VERSION, 
  creation_act       = CREATION_ACT, 
  token              = TOKEN) %>%
  kable() %>% 
  kable_styling() %>%
  scroll_box(width = "100%")

estimate_dau	estimate_mau_lower_bound	estimate_mau_upper_bound	location_unit_type	location_types	location_keys	gender	age_min	age_max	api_call_time_utc
7057416	7900000	9300000	cities	home or recent	2490299	1 or 2	18	65	2024-05-06 17:01:29

## Facebook users in and 10 miles around NYC
city_r_df <- query_fb_marketing_api(
  location_unit_type = "cities",
  location_keys      = loc_df %>% filter(name == "New York") %>% pull(key),
  radius             = 10,
  radius_unit        = "mile",
  version            = VERSION, 
  creation_act       = CREATION_ACT, 
  token              = TOKEN) %>%
  kable() %>% 
  kable_styling() %>%
  scroll_box(width = "100%")

Subcity

#### Subcity: Manhattan
loc_df <- get_fb_parameter_ids(type = "subcity", 
                               region_id = region_df %>% 
                                 filter(name == "New York") %>% 
                                 pull(key),
                               q = "Manhattan",
                               version = VERSION, token = TOKEN)

query_fb_marketing_api(
  location_unit_type = "subcities",
  location_keys      = loc_df %>% filter(name == "Manhattan") %>% pull(key),
  version            = VERSION, 
  creation_act       = CREATION_ACT, 
  token              = TOKEN) %>%
  kable() %>% 
  kable_styling() %>%
  scroll_box(width = "100%")

estimate_dau	estimate_mau_lower_bound	estimate_mau_upper_bound	location_unit_type	location_types	location_keys	gender	age_min	age_max	api_call_time_utc
2050996	2600000	3e+06	subcities	home or recent	2703980	1 or 2	18	65	2024-05-06 17:01:31

Neighborhood

loc_df <- get_fb_parameter_ids(type = "neighborhood", 
                               region_id = region_df %>% 
                                 filter(name == "New York") %>% 
                                 pull(key),
                               q = "East Village",
                               version = VERSION, token = TOKEN)

query_fb_marketing_api(
  location_unit_type = "neighborhoods",
  location_keys      = loc_df %>% 
    filter(name == "East Village, Manhattan") %>% 
    pull(key),
  version            = VERSION, 
  creation_act       = CREATION_ACT, 
  token              = TOKEN) %>%
  kable() %>% 
  kable_styling() %>%
  scroll_box(width = "100%")

estimate_dau	estimate_mau_lower_bound	estimate_mau_upper_bound	location_unit_type	location_types	location_keys	gender	age_min	age_max	api_call_time_utc
48063	65800	77400	neighborhoods	home or recent	2728364	1 or 2	18	65	2024-05-06 17:01:32

Zip Code

## Zip codes can be directly entered as "US:[Zip Code]"
query_fb_marketing_api(
  location_unit_type = "zips",
  location_keys      = "US:90210",
  version            = VERSION, 
  creation_act       = CREATION_ACT, 
  token              = TOKEN) %>%
  kable() %>% 
  kable_styling() %>%
  scroll_box(width = "100%")

estimate_dau	estimate_mau_lower_bound	estimate_mau_upper_bound	location_unit_type	location_types	location_keys	gender	age_min	age_max	api_call_time_utc
31834	39600	46600	zips	home or recent	US:90210	1 or 2	18	65	2024-05-06 17:01:32

## Zip codes can also be searched for using: "get_fb_parameter_ids"
loc_df <- get_fb_parameter_ids(type = "zip", 
                               q = "9", # Search using numbers
                               version = VERSION, token = TOKEN)

query_fb_marketing_api(
  location_unit_type = "zips",
  location_keys      = loc_df %>% filter(name == "90210") %>% pull(key),
  version            = VERSION, 
  creation_act       = CREATION_ACT, 
  token              = TOKEN) %>%
  kable() %>% 
  kable_styling() %>%
  scroll_box(width = "100%")

estimate_dau	estimate_mau_lower_bound	estimate_mau_upper_bound	location_unit_type	location_types	location_keys	gender	age_min	age_max	api_call_time_utc
31834	39600	46600	zips	home or recent	US:90210	1 or 2	18	65	2024-05-06 17:01:42

Geo Market (U.S. Designated Market Areas)

loc_df <- get_fb_parameter_ids(type = "geo_market", 
                               version = VERSION, token = TOKEN)

query_fb_marketing_api(
  location_unit_type = "geo_markets",
  location_keys      = loc_df %>% 
    filter(name == "New York") %>% 
    pull(key),
  version            = VERSION, 
  creation_act       = CREATION_ACT, 
  token              = TOKEN) %>%
  kable() %>% 
  kable_styling() %>%
  scroll_box(width = "100%")

estimate_dau	estimate_mau_lower_bound	estimate_mau_upper_bound	location_unit_type	location_types	location_keys	gender	age_min	age_max	api_call_time_utc
15658746	16800000	19800000	geo_markets	home or recent	DMA:501	1 or 2	18	65	2024-05-06 17:01:42

Electoral Districts

loc_df <- get_fb_parameter_ids(type = "electoral_district", 
                               q = "New York", 
                               version = VERSION, token = TOKEN)

query_fb_marketing_api(
  location_unit_type = "electoral_districts",
  location_keys      = loc_df %>% 
    filter(name == "New York's 18th District - 2020 Census") %>% 
    pull(key),
  version            = VERSION, 
  creation_act       = CREATION_ACT, 
  token              = TOKEN) %>%
  kable() %>% 
  kable_styling() %>%
  scroll_box(width = "100%")

estimate_dau	estimate_mau_lower_bound	estimate_mau_upper_bound	location_unit_type	location_types	location_keys	gender	age_min	age_max	api_call_time_utc
434981	528800	622100	electoral_districts	home or recent	US:NY18:2020	1 or 2	18	65	2024-05-06 17:01:44

Querying data using parameter IDs

Using one parameter ID per parameter type

We use parameter IDs to query the number of Facebook users by behaviors, interests, etc. Below we show examples querying data by behaviors and interests. An AND condition is used when parameters are entered for multiple parameter types. For example, the third example shows querying users by a behavior and an interest—setting a value in query_fb_marketing_api for the behaviors (commuters) and interests (music) parameters. In this case, the function determines the number of Facebook users that are commuters AND have an interest in music.

# Likely commute to work in US
beh_comm_id <- behaviors_df %>% filter(name == "Commuters") %>% pull(id)
query_fb_marketing_api(location_unit_type = "countries",
                       location_keys      = "US",
                       behaviors          = beh_comm_id,
                       version            = VERSION, 
                       creation_act       = CREATION_ACT, 
                       token              = TOKEN)
#>   estimate_dau estimate_mau_lower_bound estimate_mau_upper_bound
#> 1     20968885                 22000000                 25900000
#>   location_unit_type location_types location_keys     behaviors gender age_min
#> 1          countries home or recent            US 6013516370183 1 or 2      18
#>   age_max   api_call_time_utc
#> 1      65 2024-05-06 17:01:45

# Interested in music in US
int_music_id <- interests_df %>% filter(name == "Music (entertainment & media)") %>% pull(id)
query_fb_marketing_api(location_unit_type = "countries",
                       location_keys      = "US",
                       interests          = int_music_id,
                       version            = VERSION, 
                       creation_act       = CREATION_ACT, 
                       token              = TOKEN)
#>   estimate_dau estimate_mau_lower_bound estimate_mau_upper_bound
#> 1    187777990                178600000                210100000
#>   location_unit_type location_types location_keys     interests gender age_min
#> 1          countries home or recent            US 6003020834693 1 or 2      18
#>   age_max   api_call_time_utc
#> 1      65 2024-05-06 17:01:46

# Likely commute to work AND interested in music in US
query_fb_marketing_api(location_unit_type = "countries",
                       location_keys      = "US",
                       behaviors          = beh_comm_id,
                       interests          = int_music_id,
                       version            = VERSION, 
                       creation_act       = CREATION_ACT, 
                       token              = TOKEN)
#>   estimate_dau estimate_mau_lower_bound estimate_mau_upper_bound
#> 1     18508523                 17400000                 20500000
#>   location_unit_type location_types location_keys     interests     behaviors
#> 1          countries home or recent            US 6003020834693 6013516370183
#>   gender age_min age_max   api_call_time_utc
#> 1 1 or 2      18      65 2024-05-06 17:01:47

And and OR Categories

The function allows for specifying and and or conditions; for example, identifying Facebook users that are commuters and/or are frequent travelers. Vectors and lists are used to distinguish between AND and OR conditions:

• Vectors c() are used to specify OR conditions
• Lists list() are used to specify AND conditions

However, the following parameters can only except one input (a list or vector is not allowed).

• location_unit_type
• radius
• radius_unit

Within parameter types

To specify AND or OR conditions for the same parameter type:

• Enter multiple parameter IDs within vectors c() to specify OR conditions
• Enter multiple parameter IDs within lists list() to specify AND conditions

beh_freqtrvl_id  <- behaviors_df %>% filter(name == "Frequent travellers") %>% pull(id)
beh_comm_id      <- behaviors_df %>% filter(name == "Commuters") %>% pull(id)
beh_sb_id        <- behaviors_df %>% filter(name == "Small business owners") %>% pull(id)

# Users who are: Commuters or Travelers
query_fb_marketing_api(location_unit_type = "countries",
                      location_keys      = "US",
                      behaviors          = c(beh_comm_id, beh_freqtrvl_id),
                      version            = VERSION, 
                      creation_act       = CREATION_ACT, 
                      token              = TOKEN)
#>   estimate_dau estimate_mau_lower_bound estimate_mau_upper_bound
#> 1    110796644                116200000                136700000
#>   location_unit_type location_types location_keys
#> 1          countries home or recent            US
#>                        behaviors gender age_min age_max   api_call_time_utc
#> 1 6013516370183 or 6002714895372 1 or 2      18      65 2024-05-06 17:01:48

# Users who are: Commuters and Travelers
query_fb_marketing_api(location_unit_type = "countries",
                      location_keys      = "US",
                      behaviors          = list(beh_comm_id, beh_freqtrvl_id),
                      version            = VERSION, 
                      creation_act       = CREATION_ACT, 
                      token              = TOKEN)
#>   estimate_dau estimate_mau_lower_bound estimate_mau_upper_bound
#> 1     20053536                 20600000                 24200000
#>   location_unit_type location_types location_keys
#> 1          countries home or recent            US
#>                         behaviors gender age_min age_max   api_call_time_utc
#> 1 6013516370183 and 6002714895372 1 or 2      18      65 2024-05-06 17:01:48

# Users who are (Commuters or Small Business Owners) and Travelers
query_fb_marketing_api(location_unit_type = "countries",
                       location_keys      = "US",
                       behaviors          = list(c(beh_comm_id,
                                                   beh_sb_id), 
                                                 beh_freqtrvl_id),
                       version            = VERSION, 
                       creation_act       = CREATION_ACT, 
                       token              = TOKEN)
#>   estimate_dau estimate_mau_lower_bound estimate_mau_upper_bound
#> 1     23764960                 24200000                 28500000
#>   location_unit_type location_types location_keys
#> 1          countries home or recent            US
#>                                            behaviors gender age_min age_max
#> 1 (6013516370183 or 6002714898572) and 6002714895372 1 or 2      18      65
#>     api_call_time_utc
#> 1 2024-05-06 17:01:49

Across parameter types: Flexible Targeting

Flexible tageting, using the flex_target parameter, allows for more complicated parameter specification—including AND conditions across parameter types (eg, behavior X AND interest Y). The input for flex_target follows the following logic:

• flex_target uses named lists to specify the (1) parameter type and (2) parameter ID
• All parameters within the same list index are grouped using an OR condition
• All parameter separated across list indices are spearated by an AND condition

The below shows examples using dummy IDs

Example 1: OR Condition The below shows an example setting an OR condition for interest ID 2 and behavior ID 2. Both interest:1 and behavior:2 are in the same list index, so are grouped using an OR condition (interests:1 OR behavior 2)

flex_param <- list("interests" = 1, "behaviors" = 2)

Example 2: AND Condition The below shows an example setting an AND condition for interest ID 2 and behavior ID 2. interest:1 and behavior:2 are separated across list indices, so are separated by an AND condition (interests:1 AND behavior:2)

flex_param <- list(list("interests" = 1), list("behaviors" = 2))

Example 3: OR and AND Conditions The below shows a more complicated example, which grabs all users that meet (EITHER interests:1 or behaviors:3) AND (behaviors:2). Everything within flex_param[[1]] are grouped within OR conditions, but different indices (flex_param[[1]] and flex_param[[2]]) are separated by AND conditions.

flex_param <- list(list("interests" = 1, "behaviors" = 3), list("behaviors" = 2))

Example 4: OR and AND Conditions The below shows a more complicated example, which grabs all users that meet (EITHER interests:1 or behaviors:3) AND (EITHER behaviors:2 or behaviors:4). Everything within flex_param[[1]] are grouped within OR conditions, but different indices (flex_param[[1]] and flex_param[[2]]) are separated by AND conditions.

flex_param <- list(list("interests" = 1, "behaviors" = 3), list("behaviors" = c(2,4)))

int_music_id <- interests_df %>% filter(name == "Music (entertainment & media)") %>% pull(id)
beh_comm_id  <- behaviors_df %>% filter(name == "Commuters") %>% pull(id)

## OR CONDITION
query_fb_marketing_api(location_unit_type = "countries",
                       location_keys      = "US",
                       flex_target        = list("interests" = int_music_id,
                                                 "behaviors" = beh_comm_id),
                       version            = VERSION, 
                       creation_act       = CREATION_ACT, 
                       token              = TOKEN)
#>   estimate_dau estimate_mau_lower_bound estimate_mau_upper_bound
#> 1    191099644                184300000                216800000
#>   location_unit_type location_types location_keys gender age_min age_max
#> 1          countries home or recent            US 1 or 2      18      65
#>                                            flex_target   api_call_time_utc
#> 1 (interests:6003020834693 or behaviors:6013516370183) 2024-05-06 17:01:50

## AND CONDITION
query_fb_marketing_api(location_unit_type = "countries",
                       location_keys      = "US",
                       flex_target        = list(list("interests" = int_music_id),
                                                 list("behaviors" = beh_comm_id)),
                       version            = VERSION, 
                       creation_act       = CREATION_ACT, 
                       token              = TOKEN)
#>   estimate_dau estimate_mau_lower_bound estimate_mau_upper_bound
#> 1     18508523                 17400000                 20500000
#>   location_unit_type location_types location_keys gender age_min age_max
#> 1          countries home or recent            US 1 or 2      18      65
#>                                           flex_target   api_call_time_utc
#> 1 interests:6003020834693 and behaviors:6013516370183 2024-05-06 17:01:50

## AND and OR CONDITION
query_fb_marketing_api(location_unit_type = "countries",
                       location_keys      = "US",
                       flex_target        = list(list("interests" = int_music_id),
                                                 list("behaviors" = c(beh_comm_id, beh_freqtrvl_id))),
                       version            = VERSION, 
                       creation_act       = CREATION_ACT, 
                       token              = TOKEN)
#>   estimate_dau estimate_mau_lower_bound estimate_mau_upper_bound
#> 1     95506433                 90000000                105900000
#>   location_unit_type location_types location_keys gender age_min age_max
#> 1          countries home or recent            US 1 or 2      18      65
#>                                                                        flex_target
#> 1 interests:6003020834693 and (behaviors:6013516370183 or behaviors:6002714895372)
#>     api_call_time_utc
#> 1 2024-05-06 17:01:51

Excluding Categories

Many parameters come with an alternate input that starts with excl_ to exclude types of users. For example, there are both behaviors and interests parameters, as well as excl_behaviors and excl_interests parameters. The below code shows an example querying users who likely commute to work and are not tagged as interested in music.

beh_comm_id  <- behaviors_df %>% filter(name == "Commuters") %>% pull(id)
int_music_id <- interests_df %>% filter(name == "Music (entertainment & media)") %>% pull(id)

# Like to commute
query_fb_marketing_api(location_unit_type = "countries",
                       location_keys      = "US",
                       behaviors          = beh_comm_id,
                       version            = VERSION, 
                       creation_act       = CREATION_ACT, 
                       token              = TOKEN)
#>   estimate_dau estimate_mau_lower_bound estimate_mau_upper_bound
#> 1     20968885                 22000000                 25900000
#>   location_unit_type location_types location_keys     behaviors gender age_min
#> 1          countries home or recent            US 6013516370183 1 or 2      18
#>   age_max   api_call_time_utc
#> 1      65 2024-05-06 17:01:52

# Likely commute to work AND NOT interested in music in US
query_fb_marketing_api(location_unit_type = "countries",
                       location_keys      = "US",
                       behaviors          = beh_comm_id,
                       excl_interests     = int_music_id,
                       version            = VERSION, 
                       creation_act       = CREATION_ACT, 
                       token              = TOKEN)
#>   estimate_dau estimate_mau_lower_bound estimate_mau_upper_bound
#> 1      2347682                  4700000                  5600000
#>   location_unit_type location_types location_keys     behaviors excl_interests
#> 1          countries home or recent            US 6013516370183  6003020834693
#>   gender age_min age_max   api_call_time_utc
#> 1 1 or 2      18      65 2024-05-06 17:01:54

Multiple Queries

The function allows making multiple queries within one function call. All parameters in the map_param function will be iterated as separate calls. For example, when location_unit_type = "countries", location_key = map_param("US", "MX") will make one query for the US, and a second query for Mexico. The map_param functions creates an object of the class map_param, which instructs the query_fb_marketing_api() to make separate queries for different items in map_param.

Below shows examples. Assume hat we are using map_param to create separate queries for interests, for interest ID 1 and 2.

Example 1: Simple example, making one query to determining the number of users with an interest in 1, and a separate query to determine the number of users with an interest in 2.

map_param(1, 2)
#> [[1]]
#> [1] 1
#> 
#> [[2]]
#> [1] 2
#> 
#> attr(,"class")
#> [1] "map_param"

Example 2: Note that below will only make one query, querying users with an interest in 1 OR 2. There is only one item: c(1,2) – which the function interprets using an OR condition.

interests_vec <- c(1, 2)

map_param(interests_vec)
#> [[1]]
#> [1] 1 2
#> 
#> attr(,"class")
#> [1] "map_param"

Example 3: To map over a vector, we can use the map_param_vec() function—which creates a separate query for each item in the vector.

interests_vec <- c(1, 2)

map_param_vec(interests_vec)
#> [1] 1 2
#> attr(,"class")
#> [1] "map_param"

Simple Examples

Below we show simple examples using map_param.

## Number of Users in US or Mexico, as separate queries
df_out <- query_fb_marketing_api(location_unit_type = "countries",
                                 location_key       = map_param("US", "MX"),
                                 version            = VERSION, 
                                 creation_act       = CREATION_ACT, 
                                 token              = TOKEN)

df_out %>% 
  kable() %>% 
  kable_styling() %>% 
  scroll_box(width = "100%")

estimate_dau	estimate_mau_lower_bound	estimate_mau_upper_bound	location_unit_type	location_types	location_keys	gender	age_min	age_max	api_call_time_utc
219374690	234900000	276400000	countries	home or recent	US	1 or 2	18	65	2024-05-06 17:01:55
88830897	95600000	112400000	countries	home or recent	MX	1 or 2	18	65	2024-05-06 17:01:56

## Same query as above, but using a pre-defined vector of countries
# Because we use a vector, we need to use map_param_vec instead of map_param
countries_vec <- c("US", "MX")
df_out <- query_fb_marketing_api(location_unit_type = "countries",
                                 location_key       = map_param_vec(countries_vec),
                                 version            = VERSION, 
                                 creation_act       = CREATION_ACT, 
                                 token              = TOKEN)

df_out %>% 
  kable() %>% 
  kable_styling() %>% 
  scroll_box(width = "100%")

estimate_dau	estimate_mau_lower_bound	estimate_mau_upper_bound	location_unit_type	location_types	location_keys	gender	age_min	age_max	api_call_time_utc
219374690	234900000	276400000	countries	home or recent	US	1 or 2	18	65	2024-05-06 17:01:57
88830897	95600000	112400000	countries	home or recent	MX	1 or 2	18	65	2024-05-06 17:01:57

## Iterate across countries and across behaviors. Use `NA` for not filtering by behavior. The below call will give users for (1) all US, (2) all Mexico, (3) frequent travelers in US, and (4) frequenty travelers in Mexico.
df_out <- query_fb_marketing_api(location_unit_type = "countries",
                                 location_key       = map_param("US", "MX"),
                                 behaviors          = map_param(NA, beh_freqtrvl_id),
                                 version            = VERSION, 
                                 creation_act       = CREATION_ACT, 
                                 token              = TOKEN)

df_out %>% 
  kable() %>% 
  kable_styling() %>% 
  scroll_box(width = "100%")

estimate_dau	estimate_mau_lower_bound	estimate_mau_upper_bound	location_unit_type	location_types	location_keys	gender	age_min	age_max	api_call_time_utc	behaviors
219374690	234900000	276400000	countries	home or recent	US	1 or 2	18	65	2024-05-06 17:01:57	NA
88830897	95600000	112400000	countries	home or recent	MX	1 or 2	18	65	2024-05-06 17:01:58	NA
110149161	115100000	135400000	countries	home or recent	US	1 or 2	18	65	2024-05-06 17:01:59	6002714895372
66342947	67800000	79700000	countries	home or recent	MX	1 or 2	18	65	2024-05-06 17:02:00	6002714895372

## Separate query for number of users in US and Mexico and commuters and frequent travelers. Parameters not in
## map_param are applied across all queries. 
beh_comm_id      <- behaviors_df %>% filter(name == "Commuters") %>% pull(id)
beh_freqtrvl_id  <- behaviors_df %>% filter(name == "Frequent Travelers") %>% pull(id)
beh_sb_id        <- behaviors_df %>% filter(name == "Small business owners") %>% pull(id)
int_music_id     <- interests_df %>% filter(name == "Music (entertainment & media)") %>% pull(id)

df_out <- query_fb_marketing_api(location_unit_type = "countries",
                                 location_key       = map_param("US", "MX"),
                                 behaviors          = map_param(beh_comm_id, beh_freqtrvl_id),
                                 interests          = int_music_id,
                                 version            = VERSION, 
                                 creation_act       = CREATION_ACT, 
                                 token              = TOKEN)

df_out %>% 
  kable() %>% 
  kable_styling() %>% 
  scroll_box(width = "100%")

estimate_dau	estimate_mau_lower_bound	estimate_mau_upper_bound	location_unit_type	location_types	location_keys	interests	behaviors	gender	age_min	age_max	api_call_time_utc
18508523	17400000	20500000	countries	home or recent	US	6003020834693	6013516370183	1 or 2	18	65	2024-05-06 17:02:01
10274875	9700000	11400000	countries	home or recent	MX	6003020834693	6013516370183	1 or 2	18	65	2024-05-06 17:02:01
187777990	178600000	210100000	countries	home or recent	US	6003020834693	NA	1 or 2	18	65	2024-05-06 17:02:02
78563650	74900000	88100000	countries	home or recent	MX	6003020834693	NA	1 or 2	18	65	2024-05-06 17:02:02

And/Or Conditions

And/or conditions work as before: vectors c() specify OR conditions, while lists list() specify and conditions.

beh_freqtrvl_id  <- behaviors_df %>% filter(name == "Frequent travellers") %>% pull(id)
beh_comm_id      <- behaviors_df %>% filter(name == "Commuters") %>% pull(id)
beh_sb_id        <- behaviors_df %>% filter(name == "Small business owners") %>% pull(id)

df_out <- query_fb_marketing_api(
  location_unit_type = "countries",
  location_key       = "US",
  behaviors          = map_param(
    # Commuters
    beh_comm_id, 
    
    # Frequent Travelers
    beh_freqtrvl_id, 
    
    # Commuters OR Frequent Travelers
    c(beh_comm_id, beh_freqtrvl_id),
    
    # Commuters AND Frequent Travelers
    list(beh_comm_id, beh_freqtrvl_id),
    
    # Commuters AND (Frequent Travelers OR Small Business Owners)
    list(beh_comm_id, c(beh_freqtrvl_id, beh_sb_id))
  ),
  
  version            = VERSION, 
  creation_act       = CREATION_ACT, 
  token              = TOKEN)

df_out %>% 
  kable() %>% 
  kable_styling() %>% 
  scroll_box(width = "100%")

estimate_dau	estimate_mau_lower_bound	estimate_mau_upper_bound	location_unit_type	location_types	location_keys	behaviors	gender	age_min	age_max	api_call_time_utc
20968885	22000000	25900000	countries	home or recent	US	6013516370183	1 or 2	18	65	2024-05-06 17:02:03
110149161	115100000	135400000	countries	home or recent	US	6002714895372	1 or 2	18	65	2024-05-06 17:02:04
110796644	116200000	136700000	countries	home or recent	US	6013516370183 or 6002714895372	1 or 2	18	65	2024-05-06 17:02:05
20053536	20600000	24200000	countries	home or recent	US	6013516370183 and 6002714895372	1 or 2	18	65	2024-05-06 17:02:05
20047486	20600000	24200000	countries	home or recent	US	6013516370183 and (6002714895372 or 6002714898572)	1 or 2	18	65	2024-05-06 17:02:06