GeoCA is a free software for the simulation and prediction of a large-scale pixel-based urban development process.

GeoCA has been well applied in the fields of urban development process analysis, urban ecological environment analysis and urban planning.

GeoCA supports multi-rule mining models, multi-spatial variable processing, geographic location alignment, and automatic memory control for large-scale urban development simulation.

Binary Files: Download or Download From Baidu Disk (Password: 4pc5)

User Manuals (in Chinese): Online Download

User Manuals (in English): Online Download

Important Notes:
Please download all GeoCA_V2.4.z01 ~ GeoCA_V2.4.z04 and GeoCA_V2.4.zip files before decompressing them, there are test data and executable files inside.

Please unzip the program to the full English file path. Double-click “GeoCA_UI.exe” in the program directory to start the GeoCA program.

If you encounter a program error, please install Microsoft Visual C++ 2015 Redistributable.
GeoCA v2.4 Functions:

Fig.1 About GeoCA v2.4.

Fig. 2. Simulation Data Settings UI for GeoCA v2.4.

Fig. 3. Output Data and Files Settings UI for GeoCA v2.4.

Fig. 4. Simulation and CA-Iteration Parameters Settings UI for GeoCA v2.4.

Fig. 5. Multi-rule Mining Models’ Parameters Setting UI for GeoCA v2.4.

Fig. 6. Independent CA-Validation Settings UI for GeoCA v2.4.

Fig. 7. A typical application: Mapping future eroded land (UI for GeoCA v2.4).

Fig. 8. Real-time Outputting and Saving the Log information data during GeoCA runtime.

Fig. 9. Output Result Files of GeoCA v2.4.

Fig. 10. Urban development probability map of Wuhan’s downtown area.

GeoCA is developed by the following laboratory:
High-Performance Spatial Computational Intelligence Laboratory,
School of Geography and Information Engineering,
China University of Geosciences, Wuhan, China.

Urban simulation algorithm developers:
Yao Yao, China University of Geosciences, Wuhan, China.
Guangzhao Chen, Sun Yat-sen University, Guangzhou, China.
Jinbao Zhang, Sun Yat-sen University, Guangzhou, China.
Xun Liang, China University of Geosciences, Wuhan, China.
Dongsheng Chen, Wuhan University, Wuhan, China.
Dachuan Zhang, Sun Yat-sen University, Guangzhou, China.
Hongzhi Cui, Chinese Academy of Sciences, Beijing, China.

High-performance computing platform developers:
Qingfeng Guan, China University of Geosciences, Wuhan, China.
Xiang Liu, China Aerospace Science and Technology Corporation, Beijing.
Yao Yao, China University of Geosciences, Wuhan, China.
Jinbao Zhang, Sun Yat-sen University, Guangzhou, China.
Penghua Liu, Alibaba Group, Hangzhou, China.
Zhewei Liang, China University of Geosciences, Wuhan, China.

User interface developers:
Yao Yao, China University of Geosciences, Wuhan, China.
Zhenhui Sun, China University of Geosciences, Wuhan, China.

Related references:
[1] Chen, D., Zhang, Y., Yao, Y., Hong, Y., Guan, Q., & Tu, W. (2019). Exploring the spatial differentiation of urbanization on two sides of the Hu Huanyong Line–based on nighttime light data and cellular automata. Applied Geography, 112, 102081.
[2] Zhang, D., Liu, X., Wu, X., Yao, Y., Wu, X., & Chen, Y. (2019). Multiple intra-urban land use simulations and driving factors analysis: a case study in Huicheng, China. GIScience & Remote Sensing, 56(2), 282-308.
[3] Yao, Y., Liu, X., Zhang, D., Liang, Z., & Zhang, Y. (2017). Simulation of Urban Expansion and Farmland Loss in China by Integrating Cellular Automata and Random Forest. arXiv preprint arXiv:1705.05651.
[4] He, Y., Ai, B., Yao, Y., & Zhong, F. (2015). Deriving urban dynamic evolution rules from self-adaptive cellular automata with multi-temporal remote sensing images. International Journal of Applied Earth Observation and Geoinformation, 38, 164-174.
[5] Li, X., & Yeh, A. G. O. (2002). Neural-network-based cellular automata for simulating multiple land use changes using GIS. International Journal of Geographical Information Science, 16(4), 323-343.

Q.E.D.