CR-SITE: An Infrastructure Siting Tool for Crisis Response
Ehren Hill & Frank Hardisty
Department of Geography, Pennsylvania State University
Department of Geography, Pennsylvania State University
Abstract:
Many crisis response and recovery efforts require choosing locations in order to deliver needed materials and services. Automated methods can help choose optimal locations for relief camps, field hospitals, command centers, and other critical relief infrastructure. However, current information technology tools for siting relief infrastructure suffer from exposing too much complexity to the user. We have developed a tool, CR-Site, which we hope will serve as an exemplar of an emergency siting tool that eliminates unnecessary complexity, while exposing necessary parameters.
Background:
We describe on this website the means by which CR-Site arrives at an optimal location, then describe our development process and outputs, and finally our future plans. Briefly, CR-Site uses spatial analysis operations to find the most suitable places for crisis response infrastructure. Input layers for the spatial analysis include aspects of the built environment (roads, ports, etc.), natural features (elevation, land cover) and user-defined inputs, as well as an exclusion area. CR-Site was developed as a model builder application, before being transitioned to a stand-alone Python script. Finally, we developed a GUI to allow users to easily specify input layers and parameters. Our future plans include modifying the user interface to make it more appealing to novice users, investigating the possibilities for turning CR-Site into a web-based tool, and turning CR-Site into an open source project.
Many crisis response and recovery efforts require choosing locations in order to deliver needed materials and services. Automated methods can help choose optimal locations for relief camps, field hospitals, command centers, and other critical relief infrastructure. However, current information technology tools for siting relief infrastructure suffer from exposing too much complexity to the user. We have developed a tool, CR-Site, which we hope will serve as an exemplar of an emergency siting tool that eliminates unnecessary complexity, while exposing necessary parameters.
Background:
We describe on this website the means by which CR-Site arrives at an optimal location, then describe our development process and outputs, and finally our future plans. Briefly, CR-Site uses spatial analysis operations to find the most suitable places for crisis response infrastructure. Input layers for the spatial analysis include aspects of the built environment (roads, ports, etc.), natural features (elevation, land cover) and user-defined inputs, as well as an exclusion area. CR-Site was developed as a model builder application, before being transitioned to a stand-alone Python script. Finally, we developed a GUI to allow users to easily specify input layers and parameters. Our future plans include modifying the user interface to make it more appealing to novice users, investigating the possibilities for turning CR-Site into a web-based tool, and turning CR-Site into an open source project.
Methodology:
![Picture](/uploads/1/1/9/8/11986673/1802456.png?634)
CR-Site uses up to eight geospatial input layers to calculate the most suitable areas for crisis response infrastructure. All of the input layers are optional and they include five pre-defined inputs (elevation, roads, ports, streams, and land use), two user-defined inputs, and one user-defined exclusion area.
This map grid and table highlight how the suitability values are calculated for the output layer. The first step in the process is to calculate the ‘Ranking Value’ for each cell by analyzing the spatial relationship between each of the input layers and the cell. If a cell meets the user defined parameter for an input, e.g., within 200 meters of a stream, it will receive a high ranking value. The ‘Ranking Value’ is then multiplied by the normalized user-defined weight for each of the inputs, which are then summed to calculate the total suitability for the selected cell. This process is repeated for every cell in the project area of interest.