CyberGIS-Vis integrates cutting-edge cyberGIS and online visualization capabilities into a suite of software modules for visualization and visual analytical approaches to knowledge discovery based on geospatial data. Key features of the current CyberGIS-Vis implementation include: (1) comparative visualization of spatiotemporal patterns through choropleth maps; (2) dynamic cartographic mapping linked with charts to explore high-dimensional data; (3) reproducible visual analytics through integration with CyberGIS-Jupyter; and (4) multi-language support including both Python and Javascript. Firefox is the recommended web browser for reaping the best performance of CyberGIS-Vis.

Example visaulizations are available in the two folders below:

• Quantitative_Data_Vis
  
• Categorical_Data_Vis
  

You can run CyberGIS-Vis in your Jupyter Notebook installed on your PC as well as in CybearGISX. We recommend that you use CyberGISX because all the required packages have been integrated in CyberGISX.

To use it in CyberGISX, follow steps below:

1. If you do not have a CyerGISX account, create a CyberGISX account with your GitHub id at https://cybergisxhub.cigi.illinois.edu
2. Begin by clicking the yellow 'Open with CyberGISX' button in this published notebook, available at this link.

To run in the loca environment, follow steps below.

1. Download and install Anaconda at https://www.anaconda.com/.
2. After installation is done, open "Anaconda Prompt" and enter command lines below to create an environment.

        conda create -n geo-env -c conda-forge geopandas
        conda activate geo-env
        conda install -c conda-forge jupyterlab
        jupyter lab

3. Open Python Script below.

        Quantitative_Data_Vis/Adaptive_Chropleth_Mapper.py
        Categorical_Data_Vis/Qualitative_Analysis_Mapper.py

4. Comment and uncomment out like below. These are related to create URLs in the Jupyter Server.

	#from jupyter_server import serverapp

	#jupyter_envs = {k: v for k, v in os.environ.items() if k.startswith('JUPYTER')}
	#temp_server = jupyter_envs['JUPYTER_INSTANCE_URL']
	
	#servers = list(serverapp.list_running_servers())
	#servers1 = temp_server+servers[0]["base_url"]+ 'view'
	#servers2 = temp_server+servers[0]["base_url"]+ 'edit'
	
	local_dir1 = cwd
	local_dir2 = cwd 
	
	#local_dir1 = servers1 + cwd + '/'
	#local_dir2 = servers2 + cwd + '/' 

5. Open Jupyter notebook below and run.

        Quantitative_Data_Vis/Adaptive_Chropleth_Mapper.ipynb
        Categorical_Data_Vis/Qualitative_Analysis_Mapper.ipynb

Start from the published notebook

Explore spatiotemporal patterns of your data using the Multiple Linked Chart

Explore spatiotemporal patterns of your data using the Comparison Line Chart

• Adaptive Choropleth Mapper (ACM)
• Click to see more information
• Adaptive Choropleth Mapper with Stacked Chart
  • The Stacked Chart visualizes the temporal change. Click to see demo.

• Adaptive Choropelth Mapper with Correlogram
  • Click to see Tutorial and Demo

• Adaptive Choropleth Mapper with Scatter Plot
  • Click to see Tutorial and Demo

• Adaptive Choropleth Mapper with Parallel Coordinate Plot (PCP)
  • Click to see Tutorial and Demo

• Adaptive Choropleth Mapper with Multiple Linked Chart (MLC)
  • Click to see Demo

• Adaptive Choropleth Mapper with Comparison Line Chart (CLC)
  • Click to see Demo

• Qualitative_Analysis_Mapper

• Qualitative_Analysis_Mapper with Stacked Chart
• The Stacked Chart visualizes the temporal change of categorical data in a quantitative way. Click to see demo.

• Qualitative_Analysis_Mapper with Parallel Categories Diagram
• Parallel Categories Diagram represents how the categorical data changes over time in quantity. Click to see demo.

• Qualitative_Analysis_Mapper with Chord Diagram
• The Chord Diagram quantifies changes of categorical data between the two periods. Click to see demo.

Visualizations created by CyberGIS-Vis are using following datasets:

1. A small subset of LTDB. The LTDB offers decennial socioeconomic and demographic data with harmonized boundaries from 1970 to 2010. For access to the complete dataset, please visit the official website for download
2. The New York Times. (2021). Coronavirus (Covid-19) Data in the United States. Retrieved [08/01/2024], from https://github.com/nytimes/covid-19-data
3. American Community Survey (ACS) - U.S. Census Bureau

• CyberGISX
• Leaflet
• PlotlyJS
• D3
• GEOSNAP VIZ
• geostats
• Simple Statistics

Dr. Su Yeon Han, a leading developer and faculty member in the Department of Geography and Environmental Studies at Texas State University. This repository is the advanced version of CyberGIS-Vis, which can be accessed at: https://github.com/cybergis/CyberGIS-Vis.

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

The CyberGIS-Vis project is supported by Department of Geography and Environmental Studies at Texas State University and CyberGIS Center for Advanced Digital and Spatial Studies at the University of Illinois at Urbana-Champaign.