This study applied logistic regression to model urban growth in the Atlanta Metropolitan Area of Georgia in a GIS environment and to discover the relationship between urban growth and the driving forces. Historical land use/cover data of Atlanta were extracted from the 1987 and 1997 Landsat TM images. Multi-resolution calibration of a series of logistic regression models was conducted from 50 m...

Due to the rapid growth of urban areas in the past decades, it has become increasingly important to model and monitor urban growth in mega cities. Although several researchers have proposed models for simulating urban growth, they have been primarily dependent on various manually selected spatial and nonspatial explanatory features for building models. A practical difficulty with this approach ...

The Change of Support Problem (COSP) reflects a possibility that the outcome of an urban analysis may critically depend on the researcher’s choice of territorial units. To verify this assumption, the present study examines the association between population growth and population size of localities, using population growth data for two levels of geographic resolution 4,667 local administrative u...

In the past, remote sensing has shown an ability to detect and describe urban growth patterns at different spatial scales. With new remote sensing devices (e.g. IKONOS, MODIS), with innovative image processing techniques and with further development of decision support tools (e.g. GIS and urban land use change models), remote sensing has new opportunities to provide useful information in suppor...

Globalization and subsequent opening up of Indian markets has given impetus to the urbanization process in most urban pockets in India leading to large scale irreversible land cover changes. Tier II cities in Karnataka with spurt in urban activities are trying to be infrastructural competitive. However this approach has marginalized the planned interventions through the understanding and visual...

This paper presents a linguistic approach for modeling urban growth. The authors developed a methodological framework which utilizes Fuzzy Set theory to capture and describe the effect of urban features on urban growth and applies Cellular Automata techniques to simulate urban growth. Although several approaches exist that combine Fuzzy Logic and Cellular Automata for urban growth modeling, the...