Mining GIS Data to Predict Urban Sprawl

This paper addresses the interesting problem of processing and analyzing data in geographic information systems (GIS) to achieve a clear perspective on urban sprawl. The term urban sprawl refers to overgrowth and expansion of low-density areas with issues such as car dependency and segregation between residential versus commercial use. Sprawl has impacts on the environment and public health. In our work, spatiotemporal features related to real GIS data on urban sprawl such as population growth and demographics are mined to discover knowledge for decision support. We adapt data mining algorithms, Apriori for association rule mining and J4.8 for decision tree classification to geospatial analysis, deploying the ArcGIS tool for mapping. Knowledge discovered by mining this spatiotemporal data is used to implement a prototype spatial decision support system (SDSS). This SDSS predicts whether urban sprawl is likely to occur. Further, it estimates the values of pertinent variables to understand how the variables impact each other. The SDSS can help decision-makers identify problems and create solutions for avoiding future sprawl occurrence and conducting urban planning where sprawl already occurs, thus aiding sustainable development. This work falls in the broad realm of geospatial intelligence and sets the stage for designing a large scale SDSS to process big data in complex environments, which constitutes part of our future work.

💡 Research Summary

This paper explores the use of Geographic Information Systems (GIS) data to predict and understand urban sprawl, a phenomenon characterized by low-density expansion with issues such as car dependency and segregation between residential and commercial areas. Urban sprawl has significant impacts on both environmental sustainability and public health. The authors employ spatiotemporal features related to real GIS data, including population growth and demographic trends, to mine for insights that can support decision-making.

To achieve this, they adapt two data mining algorithms: Apriori for association rule mining and J4.8 for decision tree classification. These algorithms are integrated with the ArcGIS tool for mapping purposes. The knowledge extracted from spatiotemporal data is used to develop a prototype Spatial Decision Support System (SDSS). This SDSS aims to predict the likelihood of urban sprawl occurring, estimate the values of relevant variables, and understand how these variables interact.

The SDSS can assist decision-makers in identifying problems and creating solutions for preventing future sprawl or managing areas where sprawl has already occurred. It serves as a tool for sustainable urban development by providing insights into complex spatial dynamics. The work presented here is part of the broader field of geospatial intelligence, setting the stage for designing large-scale SDSS to process big data in complex environments. Future research will focus on expanding this system to handle larger datasets and more intricate scenarios, contributing further to the development of sustainable urban planning strategies.