Remote Sensing Derived Mapping to Support Urban Growth Theory

This paper presents an addition to urban theory through a time-series analysis of remotely sensed imagery using spatial metrics. Results from the research are used to support the theory that urban areas are formed through an oscillatory growth process that switches between phases of urban coalescence and diffusion. In testing for the presence of this theory in a real-world context, the urban evolution of the Central Valley of California (USA) was recreated through the use of historical remotely sensed imagery. To test hypotheses about variation over geographical scale, multiple spatial extents were used in examining a set of spatial metric values including an index of contagion, the mean nearest neighbor distance, urban patch density and edge density. Through changes in these values a general temporal oscillation between phases of diffusion and coalescence in urban growth was revealed. Additionally a simple model of urban dynamics is presented, which has the ability to replicate some of the changes in urban form observed within imagery of urban areas. While the results are still preliminary, the research demonstrates the importance of urban remote sensing in the formulation and evaluation of urban theory.