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Spatial Temporal Modeling of Covid-19 Cases and Hospital Data Using Python and ArcMap
Spatial Temporal Modeling of Covid-19 Cases and Hospital Data Using Python and ArcMap
Fig.1 Seven day moving averages plotted as time series in HTML
Problem
Problem
The United States Center for Disease Control and Prevention (CDC) Covid-19 Case data and the Department of Health and Human Services (DHHS) Hospital Capacity data are both essential information for researchers and the public to track the spread of the virus. Most sites use the rRT-PCR raw test results from the CDC for geographical tracking, without a smoothing application to the data. My project merges data from CDC and DHHS, provides a moving average (MA) for trend analysis, and creates an ArcMap document for report or as basis for a Web App.
Results
Results
The resulting ArcMap document was published as a Web App for public consumption (Fig.2). Together with the time series plotted in HTML, users can see the exponential rise in new cases in matter of days, a component not able to be displayed in a 2D map. Covid-19 cases were shown increasing rapidly in California, Florida, Illinois, Michigan, Pennsylvania, Ohio, Indiana, and Georgia in September and October 2020.
Fig.2. ArcMap document was published as Web App
Analysis
Analysis
The Covid-19 case data and the data from hospitals were merged into a csv file and 7 day moving averages (MA) were done using Python. The Script tool was used to create a Graphical User Interface to allow users to select time period, state, working and output directory. Due to the inability of ArcMap to present time series in a space time cube format, this project can output the time series in HTML (Fig.1). Daily cases were aggregated monthly, converted into dBASE files. The selected two months were spatially joined to states polygon, class break values created, case count symbolized and output to a map document.
Reflection
Reflection
ArcMap cannot plot the time series based on location. There are three variables at play: time, case count and location. In order to map the three variables, the map needs to be in 3D. This project can be further developed by coding a 3D Space Time Cube in ArcPro with Python. This code can be modified to retrieve NO2 emission and map areas of pollution. The takeaway is Python excels in arrays and ArcPy excels in geoprocessing.
Customized Web App For Data Hosted On Server with REST Endpoints Using API for JavaScript
Customized Web App For Data Hosted On Server with REST Endpoints Using API for JavaScript
Fig.1. Portion of JavaScript shown utilizing API for JavaScript.
Problem
Problem
The challenge given was to construct a web app from the ground up without the use of ArcGIS Online, or ArcGIS Map, or ArcGIS Professional. The web app has to be coded with JavaScript. Consider challenge accepted.
Results
Results
A customized web app hosted on the same server as the data, allowing users to access the data via the Web App (Fig.2).
Fig.2. Web app constructed using JavaScript code.
Analysis
Analysis
The data was hosted on the server with ArcGIS Server installed. The REST end points were incorporated in JavaScript code using API for JavaScript (Fig.1), to generate a web app from the ground up.
Reflection
Reflection
This challenge can be taken a step further by hosting both the data and the web app on the cloud using either AWS or Azure.
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