This dataset contains all the code, notebooks, datasets used in the study conducted for the research publication titled "Multi-scale CyberGIS Analytics for Detecting Spatiotemporal Patterns of COVID-19 Data". Specifically, this package include the artifacts used to conduct spatial-temporal analysis with space time kernel density estimation (STKDE) using COVID-19 data, which should help readers to reproduce some of the analysis and learn about the methods that were conducted in the associated book chapter.
## What’s inside – A quick explanation of the components of the zip file
* Multi-scale CyberGIS Analytics for Detecting Spatiotemporal Patterns of COVID-19.ipynb is a jupyter notebook for this project. It contains codes for preprocessing, space time kernel density estimation, postprocessing, and visualization.
* data is a folder containing all data needed for the notebook
* data/county.txt: US counties information and fip code from Natural Resources Conservation Service.
* data/us-counties.txt: County-level COVID-19 data collected from New York Times COVID-19 github repository on August 9th, 2020.
* data/covid_death.txt: COVID-19 death information derived after preprocessing step, preparing the input data for STKDE. Each record is if the following format (fips, spatial_x, spatial_y, date, number of death ).
* data/stkdefinal.txt: result obtained by conducting STKDE.
* wolfram_mathmatica is a folder for 3D visulization code.
* wolfram_mathmatica/Visualization.nb: code for visulization of STKDE result via weolfram mathmatica.
* img is a folder for figures.
* img/above.png: result of 3-D visulization result, above view.
* img/side.png: result of 3-D visulization, side view.
CyberGIS; COVID-19; Space-time kernel density estimation; Spatiotemporal patterns
| DOI | 10.13012/B2IDB-0299659_V1 |
|---|---|
| Publcation Date | 04-19-2021 |
| Title | Multi-scale CyberGIS Analytics for Detecting Spatiotemporal Patterns of COVID-19 |
| Author | Lyu, Fangzheng Kang, Jeon-Young Wang, Shaohua Han, Su Li, Zhiyu Wang, Shaowen Padmanabhan, Anand |
| Keywords | CyberGIS; COVID-19; Space-time kernel density estimation; Spatiotemporal patterns |
| Related Publication (Citation) | Lyu, Fangzheng; Kang, Jeon-Young; Wang, Shaohua; Han, Su; Li, Zhiyu; Wang, Shaowen; Padmanabhan, Anand (2021): Multi-scale CyberGIS Analytics for Detecting Spatiotemporal Patterns of COVID-19. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-0299659_V1 |
| Note |
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