Author_Institution :
Dept. of Geogr., Florida State Univ., Tallahassee, FL, USA
Abstract :
Conventional thinking in urban remote sensing is to synchronize the dates of when satellite imagery is captured with the date a social survey, such as a population census, is collected. It´s assumed that identical dates, or dates as similar as possible, are intuitively important in order to make sure that structure, the physical configuration of a city as captured by remote sensing, relates as closely as possible to function, the socio-economic characteristics of a city, as represented by population censuses. However, this type of intuitive thinking is surely flawed because the structure of a city is hardly ever in harmony with the functionality of a city, at the same time. It is much more realistic to assume that there is a temporal lag between the socio-economic demands from a city´s population and the consequential physical ramifications of such demands. In other words, the tangible layout of a city, i.e. roads, houses, etc, is a result of prior decisions made by planners with respect to demands from the city´s population. This paper explores this temporal lag between structure and function by comparing the physical structure of cities from satellite images at T+1 with the functionality of the socio-economic characteristics from a population census in T-1. Initial results suggest an improved ldquoagreementrdquo between satellite images taken after a population census, in terms of predicting population growth and predicting residential demands. Further testing is needed in order to establish a theoretical link that can be used by planners in deciding where to develop cities, how sustainable such increased development can be contained and how best to reduce overcrowding, congestion and overall social deprivation.
Keywords :
geographic information systems; geophysics computing; pattern recognition; remote sensing; roads; socio-economic effects; town and country planning; GIS; city population; consequential physical ramifications; geographical information system; house; pattern recognition; road; satellite imagery; social survey; socio-economic characteristics; temporal lag function; temporal lag structure; urban planning; urban remote sensing; urban structure; Cities and towns; Educational institutions; Geographic Information Systems; Logistics; Nonlinear dynamical systems; Oceans; Predictive models; Principal component analysis; Remote sensing; Roads; Urban structure; temporal lag; urban function; urban planning;