This paper discusses the calibration and assessment of a cellular automata model for urban growth modeling. A number of transition rules are introduced in the cellular automata model to consider the most influential urbanization factors, such as land-cover maps obtained from satellite images and population density from the census. The transition rules are calibrated both spatially and temporall...

Urban growth has been replaced by stagnation and shrinkage processes at many places in Europe during the last decades. Demographic changes and out-migration because of lack of jobs belong to the main impact factors. Urban planners are challenged by this new and dramatic development that impacts on housing markets, the utilization of infrastructure, local labor markets and the whole viability of...

This chapter considers the literature on urbanization and migration to cities from rural areas in developing and middle-income countries. The chapter assesses recent patterns, discusses traditional models, addresses more recent models of migration, presents some of the recent findings from the micro-econometric literature, and closes with a discussion of the role of cities in economic growth. C...

the earth is a stable feature but it is diminished due to different factors including: the extension and development of cities, emerging of villages and rural areas with the physical texture of cities, the spreading of industrial units and population growth, changing the patterns of land use and lack of good management. this paper is concerned with the global and national perspective of these p...

Human population continues to aggregate in urban centers. This inevitably increases the urban footprint with significant consequences for biodiversity, climate, and environmental resources. Urban growth prediction models have been extensively studied with the overarching goal to assist in sustainable management of urban centers. Despite the extensive body of research, these models are not frequ...

This paper presents an advanced method in urban growth modeling to discover transition rules of cellular automata (CA) using the artificial bee colony (ABC) optimization algorithm. Also, comparisons between the simulation results of CA models optimized by the ABC algorithm and the particle swarm optimization algorithms (PSO) as intelligent approaches were performed to evaluate the potential of ...

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...

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...

This article presents a general agent-based simulation approach that captures agents’ decision-making processes and dynamic relationships that determine urban shape. The article shows how to simulate urban growth patterns and land-use change dynamics using economic assumptions and by modeling the interactions of computational agents.