Detecting-Spatiotemporal-Clustering-of-COVID-19-in-the-United-States
Author: Fangzheng Lyu
Contact: [email protected]
This dataset contains all the code, notebooks, datasets used in the study conducted for the research Multi-scale CyberGIS Analytics for Detecting Spatiotemporal Patterns of COVID-19 Data. Specifically, the dataset and the codes conducted a spatial-temporal analysis with space time kernel estimation with COVID-19 data. The dataset includes codes, notebook and example LiDAR data for the users to be able to find pattern and conduct analysis with COVID-19 data.
This dataset includes:
● Multi-scale CyberGIS Analytics for Detecting Spatiotemporal Patterns of COVID-19.ipynb is a jupyter notebook for this project
● 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 4th
○ data/covid_death.txt: COVID-19 death information after processing us-counties.txt
○ data/stkdefinal.txt: result from conducting space-timne kernel density estimation
● 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: 3-D visulization, above view
○ img/side.png: 3-D visulization, side view
● STKDE is a folder containing all codes and data used for Space time kernel density estimation
○ `files` is a folder containing the data and parameter configuration
■ `new_data.txt` contains all county level COVID-19 data from New York Times GitHub COVID-19 repository
■ `new_parameterFile.txt` contains the parameters needed for space time kernel density estimation
○ `kde.py` is a python-based code used to conduct STKDE
○ `main.py` and `setting.py` is used to conduct STKDE onto COVID-19 data
● Clustering is a folder containing all codes and data used for postprocessing and further analysis based on the result from space time kernel estimation
○ `change projection.ipny` is used to change the project of the spatial data
○ `county.txt` and `us-counties.csv` include the information about counties in US
○ `final_stkde.txt` is output of the STKDE algorithm.
○ `correlation_stkde.txt` and `rt_new.txt` is used for conducting correlation analysis
○ `data_processing.ipny` is a notebook used for processing the STKDE result
○ `figure notebook1.ipny` and `figure notebook2.ipny` are used to generate figures used in the manuscript