Exploring Spatiotemporal Urbanization Through a Hybrid Remote Sensing-Geovisual Analytics Approach

This paper presents the findings derived from the design, implementation, and use of a geovisual analytics platform to explore the use of spatiotemporal data to study the evolution of urban sprawl using remotely sensed data. The ambitious project within which this research was developed aimed at using optical imagery to analyze the evolution of distinct features on the surface of a rapidly urbanizing area in central Mexico. Remotely sensed data were properly processed using Google Earth Engine to estimate the time of urbanization and results are displayed in a platform developed using open source tools and software. Its main goal is to seamlessly integrate both the spatial and temporal components of data to be able to follow the evolution of distinct features through both space and time, while presenting useful visualizations to help the viewer have a more complete understanding of the context of this evolution. Results from this case study show that subpixel mapping of urban coverage is possible using optical imagery, that time of urbanization can be estimated, and that its coupling with geovisual analytics is useful in finding trends and patterns over space and time.