Simulating Urban Expansion through Remote Sensing, Geographic Information System and Agent-based Modeling: Integrating Spatial Data and Spatial Process Model

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Oliver T. Macapinlac

Thesis (MS in Remote Sensing)--University of the Philippines Diliman-2008


This research is an attempt to integrate three technologies , remote sensing (RS), geographic information systems (GIS), and agent based modeling (ABM), to create simulation of an urban expansion model- the MarikinaSim (named after the city where the study was conducted). It incorporates spatial data and spatial process models offering a new way to see the environment. In remote sensing, change detection analysis of satellite images is a technique used in monitoring land use / and land cover changes and urban growth. Remotely sensed images of areas are obtained at different times and thereby spatial and temporal analyses are made possible for researchers to identify patterns of changes and view the different phenomena that take place on the earth’s surface. Integration of spatially referenced data is usually done in a GIS model which shows rich emphasis on topology and spatial relationship. Unfortunately, representation of these spatial data is static in nature, as they are mostly obtained in a single data and then stored. The full potential of both remote sensing and geographic information system in describing urban growth is realized in integrating the two technologies (Jensen, 1996). With RS and GIS, researchers can ?see? changes associated with urban expansion over vast areas of the earth’s surface at different times.

In the temporal analysis of space, RS and GIS can only ,see, snapshots of the surface patterns. The patterns are a result of many underlying processes. Process models which express theories predicting the nature of exchange of energy and mass within a system over time can not be portrayed by RS and GIS (Brown et all., 2004). More recent developments in modeling have included computer base simulations such as agent-based modeling in understanding environmental change like urban expansion. There have been many studies-SprawISim by Torrens et all 2004, ILUMASS by Wegener et all 2003, UrbanSim by Waddell et all 2002, and works by other scientists which attempt to investigate urban processes through statistics and object-oriented programming and explicitly using space to operate their model.

Through MarikinaSim, this research attempts to use RS and GIS’s spatially-rich data model of the environment and ABM’s process model of residential and firm location to create a simulation of urban expansion. Information comes from different sources and various scales and is integrated in GIS which acts as the base map and display platform for the study. Although the primary output of the research is creating a simulation, it can be extended to understanding the process by exploring different parameters of the ABM and visualizing their effects on the system. MarikinaSim becomes a scenario builder where different situations and events can be expressed through manipulation of the attributes of the spatial data.