Apply GIS and machine learning to predict urban growth

Authors: Jian Lange*, Esri, Witold Frazek, Esri, Carsten Lange, Cal Poly Pomona
Topics: Geographic Information Science and Systems, Urban and Regional Planning, Land Use and Land Cover Change
Keywords: GIS, Machine Learning, Urban Planning
Session Type: Virtual Paper
Day: 4/10/2021
Start / End Time: 4:40 PM / 5:55 PM
Room: Virtual 43
Presentation File: No File Uploaded

As population is growing, new areas need to be converted from their current land use types into urban land use. Which areas would be most suitable for urbanization? How probable is urban development in a specific area? The answers to these questions are critical for government agencies such as planning departments in need of better understanding of urban growth in order to make better policies. It is also beneficial for private investors who are searching for locations to make profitable investments. The presentation will showcase a collaboration project to apply GIS and machine learning algorithms to predict urban development in a study area known as the Research Triangle in North Carolina. The predictive model used in the project is the Random Forest machine learning algorithm, a popular supervised learning algorithm based on a multiple of decision trees. The study applies Esri’s advanced GIS software ArcGIS Pro, various R packages in Rstudio, and the R-ArcGIS Bridge, which is an open source R package from Esri that allows the passing of data between ArcGIS Pro and R. Factors that affect urban development such as the proximity to roads, urban centers, environmental protected areas, flood zones, as well as terrain characteristics and projected population growth are considered in the predictive analysis model. The goal of the project is to identify locations with a high probability of urban development in the study area. The prototype project shows promising prediction results.

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