4. Notebooks summary#
The jupyter notebooks are use cases where the deployment of the PAM-Toolkit is tested with different countires and configurations. Both Malawi (MWI) and Pakistan (PAK) are tested as country use cases for both the raster and vector-based accessibility disruption analysis. For Pakistan, conventional vector analysis is extremely slow due to the dimension of the country and of the road network dataset. Consequently, a parallel computing code has been developed for calculating the disrupted road network, and must be deployed as a stand-alone python code, given that multiprocessing libraries are not working on jupyter notebooks. The
Flood disruption (raster) - Country assessment
Function and libraries import
Dataset import and preprocessing
Flood impact of Health Facilities
Flood impact on friction surface
Travel time computation
Mapping accessibility to HF per flood scenario and ADM unit
Mapping accessibility to HF disaggregated per wealth quintile
Flood disruption (vector) - Country assessment
Function and libraries import
Dataset import and preprocessing
Flood impact of Health Facilities
Flood impact on road network
Road accessibility computation
Mapping accessibility to HF per flood scenario and ADM unit
Mapping accessibility to HF disaggregated per wealth quintile
Flood disruption (vector) - City assessment
Function and libraries import
Dataset import and preprocessing
Flood impact of Health Facilities
Flood impact on road network
Road accessibility computation
Betweeness centrality computation
Flood disruption (vector) - parallel computing
Function and libraries import
Dataset import and preprocessing
Parallelization of flood impact on road network Subsequent points for the mapping of the accessibility disruption are implemented as a jupyter notebook