The past three decades have witnessed the rapid development of geosimulation technology, such as cellular automata (CA) and agent-based modelling (ABM), for solving various geographical problems in rapidly growing regions. This chapter summarizes the main applications of CA for researchers and decision-makers to solve the environmental and planning problems associated with urban sprawl, illegal development and improper facility sitting. An integrated CA-based model, the Geographical Simulation and Optimization System (GeoSOS), has been applied to tackle a wide spectrum of environmental and resource management issues, such as the zoning of basic farmland protection areas, coupling land-use dynamics with facility sitting, and delineation of urban growth boundaries. Our studies have shown that CA models are capable of evaluating land-use policies and predicting illegal developments for early warning purposes. The simulation of using CA provides valuable experiences for urban researchers and planners to solve a series of simulation and planning problems in fast-growing regions.
Xia Li and Anthony G.O. Yeh
Anthony G.O. Yeh and Fiona F. Yang
Anthony G.O. Yeh, Yang Yue, Xingang Zhou and Qi-Li Gao
Many countries have been implementing plans for smart-city development in recent years. However, how to plan a smart city effectively is still a big issue deserving further exploration. This chapter reviews the use of big data, such as mobile-phone data and transit smart-card data, which can capture spatial–temporal movement of people in a city to support the planning of smart cities. The chapter reviews how big data are captured, pre-processed and analysed. It then discusses how big data can advance our knowledge in the planning of smart cities with three examples: the study of urban structure, jobs–housing balance and low-to-moderate income group spatial distribution relating to housing policy.