CyberGIS-Vis is an open-source software tool for interactive geospatial visualization and scalable visual analytics.
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:
- If you do not have a CyerGISX account, create a CyberGISX account with your GitHub id at https://cybergisxhub.cigi.illinois.edu
- Open up the CyberGIX, click the "new" button on the top right corner, and select python3 and enter the command line below to download CyberGIS-Vis.
!git clone https://github.com/cybergis/CyberGIS-Vis- Open Jupyter notebook below and run.
Quantitative_Data_Vis/Adaptive_Chropleth_Mapper.ipynb
Categorical_Data_Vis/Qualitative_Analysis_Mapper.ipynb
To run in the loca environment, follow steps below.
- Download and install Anaconda at https://www.anaconda.com/.
- 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- Open Python Script below.
Quantitative_Data_Vis/Adaptive_Chropleth_Mapper.py
Categorical_Data_Vis/Qualitative_Analysis_Mapper.py- 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 + '/' - Open Jupyter notebook below and run.
Quantitative_Data_Vis/Adaptive_Chropleth_Mapper.ipynb
Categorical_Data_Vis/Qualitative_Analysis_Mapper.ipynb- Adaptive Choropleth Mapper (ACM)
- Adaptive Choropleth Mapper with Stacked Chart
- The Stacked Chart visualizes the temporal change. Click to see demo.
- Adaptive Choropelth Mapper with Correlogram
- Adaptive Choropleth Mapper with Scatter Plot
- Adaptive Choropleth Mapper with Parallel Coordinate Plot (PCP)
- 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 a small subset of LTDB. LTDB provides socioeconomic and demographic data with harmonized boundaries from 1970 to 2010 decennially. If you need the entire dataset, visit this website to download.
The lead developer of CyberGIS-Vis is Dr. Su Yeon Han at the CyberGIS Center for Advanced Digital and Spatial Studies (CyberGIS Center) and the Principal Investigator of CyberGIS-Vis is Dr. Shaowen Wang at CyberGIS Center. This software repository is primarily maintained by CyberGIS Center. Please email any questions to [email protected].
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
The CyberGIS-Vis project is supported by the CyberGIS Center for Advanced Digital and Spatial Studies at the University of Illinois at Urbana-Champaign.