  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.
