Modeling Urban Land Use Allocation using Spatial Justice Approach Based on the Multi-Objective Optimization Methods

This research was conducted mainly to provide a method based on artificial intelligence algorithms in order to obtain optimal urban land use allocation, subjected to mathematical modeling from the perspective of spatial justice using multi-criteria decision analysis concepts, both parcel-based and floor-based. For that purpose, objective functions were considered for land use allocation, including mixed use, suitability, dependency, compatibility, and access to services, in terms of both floors and neighbors. For achievement of the main purpose, parameters effective on urban land use planning were examined to optimize urban land use allocation from the spatial justice perspective. This led to a presentation and classification of the spatial parameters effective on land use allocation based on sustainability and, more specifically, spatial justice concerns. The main part of the research, named “land use allocation modeling with NSGA-II” focused on development of a land use allocation model and its optimization. The main stage in the modeling procedure was to adapt multi-objective optimization algorithms to land use allocation and, then, to define it as desired. The adaptation of the multi-objective optimization algorithm involved the definition of the solution structure, objective functions, and problem constraints and their calculation for use in the second version of the Non-dominant Genetic Ranking Algorithm (NSGA-II). The objective functions were defined based on criteria and indexes extracted in the second part of the research, including maximization of accessibility to the facilities, of service efficiency (compatibility), of mixed uses, of land suitability, and of spatial dependency. Moreover, seven constraints were introduced for land use allocation, including avoidance of allocating wasteland to the first floor of the parcel, allocation of a land use to the third floor provided that the second one already has a defined land use, possibility of land use allocation to various floors of the same parcel, consideration of maximum and minimum land parceling, avoidance of exceeding the per capita standards in neighborhoods and districts, and, finally, allocation of the necessary land uses to each of the neighborhoods. The models were then implemented, where the main purpose was to optimize urban land use allocation according to all the criteria and constraints. In other words, these criteria had to be defined in terms of the model objectives and with an extensive search space (many of the possible land use allocations) at the same time. Therefore, a set of solutions, which included Pareto front, i.e. optimal, solutions, was obtained rather than a single solution to the problem. As decision-makers prefer to examine the corresponding scenarios after introducing their own decision priorities, the AHP method was used finally after the optimal Pareto solutions were obtained for selection of one of the four possible land use allocations and its presentation in a land use plan given spatial justice concerns. For that purpose, weights were assigned to the objective functions based on spatial justice. Once the objective function values were normalized, the desired weights were multiplied by the normal values. After the calculation of total weight, the solutions corresponding to the objective functions being addressed were converted to land use plans in ArcGIS. The results of land use allocation in floors were compared to the actual conditions. It could be stated in general that the most important achievement of the study involved the introduction and presentation of an efficient model that was appropriate for addressing multi-criteria decision-making problems for allocation of urban land use. The presented model performs simultaneous optimization, and helps decision makers to select one desired solution from among multiple optimized ones according to their priorities, although it is in contradiction with some of the objective functions. The results of the model designed for land use allocation given spatial justice concerns improved the spatial distribution of facilities at the level of the area under study. Moreover, a quantitative evaluation of the allocation results demonstrated that the commercial, academic, health, sport, and cultural land uses were allocated properly per capita, increasing the level of enjoyment in neighborhoods across the area under investigation.