An agent-based approach to model future residential pressure on a regional landscape

作者：Fontaine Corentin M^*; Rounsevell Mark D A

来源：Landscape Ecology, 2009, 24(9): 1237-1254.

DOI：10.1007/s10980-009-9378-0

• Agent-based modelling
• Multi-agent systems
• Urban growth
• Land-use change
• Regional landscape
• Residential demand
• Household behaviours
• Location choices
• Preferences

This paper presents a framework to model future residential demand for housing in a polycentric region. The model, called HI-LIFE (Household Interactions through LIFE cycle stages), builds on Agent-Based Modelling (ABM) paradigms. In contrast to traditional equilibrium-based urban economics models that assume a homogenous population of rational actors, ABM focuses on the diversity of heterogeneous household agents and their behaviour in time and in space. The model simulates land-use patterns at the regional scale by integrating qualitative knowledge of agent location preferences with quantitative analysis of urban growth dynamics within a high resolution spatial modelling framework. The model was calibrated for the region of East Anglia in the UK using a semi-quantitative procedure. Simulation of urban dynamics for the future was undertaken for a 25 year period with the assumption of a continuation of baseline behavioural trends. The results demonstrated non-uniform, spatial patterns of urban sprawl with some locations experiencing greater urban development pressure than others. The town of Brundall, in particular, has a large potential demand for residential housing because of its proximity to the principle city, Norwich. As Brundall is also located close to a national park and a river, new housing development in this area would increase the risk of ecological impacts and flood damage. By modelling explicitly agent behaviour and interactions, ABM can simulate the response and adaptation strategies of a population to changing circumstances. This makes ABM especially well suited to the analysis of environmental change and landscape ecology pressures through scenario modelling.

• 出版日期2009-11