Jupyter notebook for visualization of spatially distributed data in risk and resilience analysis

This project serves as the main repository for the Jupyter Notebook and complimentary input data required for the DesignSafe use case noted as “Jupyter notebooks for visualization of spatially distributed data in risk and resilience analysis”. The Jupyter Notebook leverages different Python packages and modules to visualize spatially distributed data that can be found as input (or output) data of the risk and resilience pipeline. The aim of this project is to share a Jupyter Notebook that can serve as a launching point for other researchers to create static, interactive, or dynamic maps for better visualization and communication of the risk and resilience pipeline intermediate and final outcomes. The Jupyter Notebook uses for illustrative purposes a highway bridge network hypothetically placed in the Memphis Metropolitan Statistical Area (MMSA) exposed to earthquake events. However, the visualization of spatial and temporal data has relevance to other hazards (e.g. floods, hurricanes, etc.) and other distributed infrastructure or lifelines (e.g., power, water, etc.). The Jupyter Notebook navigates various stages, from visualizing static hazard outputs to developing animated figures to understand the recovery of the highway system after an event occurrence. Some of the visualized products represent data that can be obtained through the Tools & Applications space in the DesignSafe cyberinfrastructure (for example using the SimCenter research tools) or other platforms for resilience measurement science (for example, the IN-CORE platform). Ideas presented in the Jupyter Notebook are useful for sanity checks, verification and validation of data and results, standardization of figures and maps for presentations, publishing results of resilience analyses, enabling interaction between stakeholders, among others. This Jupyter Notebook can be used as guidance, or as a launching point, to develop more effective communication tools for the risk and resilience analysis of distributed infrastructure, enabling a better understanding of the inputs and outcomes of the projects during the different intermediate or final stages of analysis.