Spatial Distribution of Socioeconomic Factors and Its Impact on Urban Land Use Dynamics: An Agent Based Modeling Approach

In spite of existing development guidelines, the current policy cannot accommodate the increasing resource demand. Therefore, leading to haphazard growth of the cities, especially in the developing nations. The unplanned advancement leading to extreme heterogeneity and complex socioeconomic profiles pose a great challenge in modeling their growth. To address these challenges, a novel Land-use Simulation and Decision-Support system (LSDS) for cities has been designed. The system comprehensively explores the interference and impact of natural environment, socioeconomic factors, spatial neighborhoods, individual choice, and national macro-policies on land use and cover change. LSDS applies the cellular automata (CA) model to examine land use land cover (LULC) changes over time, and agent-based model (ABM) to observe the role of different socioeconomic drivers in the LULC prediction process. When applied to the Dehradun city of India, quantitative analysis of spatial patterns achieved an overall built-up area accuracy of 10.62% (relative error) and Kappa accuracy value 79.73%. It was observed that most of the urban agriculture areas converted to medium compact residential areas occupied by higher and middle-income groups. An expansion of low-density residential in the southern and northeastern parts of the city was observed. Most of the land in the northeast part of Dehradun city was predicted to be owned by high-income group. The study can benefit the decision-makers in generating various redevelopment scenarios and efficient resource planning.