bridgecrew-perf7 / sgc-deployment-scenarios

Optimizing resilience hub deployment for overall population coverage, climate vulnerability, and more.

Overview

We first need to calculate distance matrices. This is done in the script calculate_distance_matrices.py which calls code from distance_matrix_functions.py

We then need to build many different types of models. All the code for building models are in deploment_models.py while the script results_analysis.py contains example code for how to run a model as well as a useful function for pulling statistics out of the result. vulnerability_risk_functions.py is a collection of functions in progress that is aimed at easily integrating different vulnerability and risk data.

Also, model_vis_functions.py has functions for visualizing the results of a model on open street maps.

sgc-deployment-scenarios

Optimizing resilience hub deployment for overall population coverage, climate vulnerability, and more.

• Uses Networkx package to perform network analysis: https://automating-gis-processes.github.io/2017/lessons/L7/network-analysis.html
• Resilience hub locations based on prior PSE analysis with geofabrik OSM data
• Coverage maximized for climate vulnerabilities using various public datasets on air, fire, EJ, etc.
• Users should save large datasets that must be stored locally one directory up from this repo and write code accordingly
• Useful tutorial for using pyomo framework: https://www.osti.gov/servlets/purl/1376827