Urban growth modeling and assesment using agent-based model

Municipal development authorities, primarily in metropolitan areas, prepare perspective and other development plans for future prediction using statistically-based urban growth models, guidelines, and drivers-based approaches. However, some results showed that the intersection of population distribution, housing parcels, and location of proposed housing parcels are not focused. Urban growth computational models can now be used to fill in gaps in planning and implementation processes, such as the significance of small-scale changes, bottom-up modeling, and decision-making during policy formulation. A decision-making system was developed for Hyderabad city(zone-1) to help with proposed land use and land cover. Hexagonal-planning-based and computational, statistical-based approaches, including the vector-cellular-automata technique were used to put this theory into practice to understand the behavior, impact, and potential development of housing parcels. Rule-based modeling was used to measure, update, and test various scenarios of the city regulations on spatial growth aspects. Use of logistic regression, various suitable scenarios (suitability maps) for the growth of various land uses and drivers (variables) were developed to determine the link between dependent and independent variables. These housing parcels along with their suitability scores and significance factors are included in an agent-based model resulting in growth potential areas and housing parcel locations of the city.