Spatial modeling of urban land use change using NSGA-II algorithm and clustering of the Pareto-front for urban dynamic plans

In urban space, the need to different facilities and diverse land uses increases continuously. Continuous changes in the demands of the citizens results in rapid and frequent land use changes. Therefore, dynamic characteristics of urban environment should be considered in urban planning. On the other hand, land uses have different effects on each other. In other words, any change in the land use of a parcel or zone, will results in tendency of its neighboring parcels for land use change. Therefore, proposing of new land use arrangements after any occurred land use change could be a proper response to such tendencies. The main goal of this study is to propose a method, based on GIS and NSGAII optimization algorithm, for generating optimum land use arrangements after any occurred land use change.Usually, the output of a multi-objective optimization algorithm is a collection of optimum solutions. Selection of appropriate solution from such a collection needs extra efforts and processes. Therefore, another goal of this research is to use an appropriate clustering method that helps the user to select the most preferred solution. With such a method, the decision maker can introduce his planning priorities, perceive the resulted scenario and select accordingly. In this research, ant colony clustering algorithm is used for clustering, first because of its high speed, and then because the representative of the clusters are selected from the Pareto front solutions. In this research, for modelling of the aspects of land use change and its factors, four objective functions are considered, which are: the maximization of land use compatibility, the maximization of land use dependency, the maximization of land use suitability, and the maximization of land use compactness. Finally, the providence of per capita for different land uses is considered as constraints. The ant colony clustering algorithm is used for clustering of the found solutions (land use arrangements). The developed method is implemented and tested using the data related to some districts of region 7 of Tehran.Different evaluations are considered and carried out for the results of optimization. These include the convergence trend, repeatability test, and the comparison of the previous land use arrangement with the optimized ones. In the resulted optimized land use arrangements, the levels of objective functions are much better than the previous arrangement. Furthermore, the required per capita for different land uses are much better satisfied. The highest improvement in the objective functions is 36%, which is related to land suitability. In addition, the required per capita is improved by 18.5% of. The results of clustering using ant colony clustering algorithm are compared with those of K-means and Fuzzy K-means. The comparison showed that the ant colony clustering algorithm is faster. In addition, the results of this clustering method are exactly the original solutions of the land use arrangement optimization.Finally, the developed method can help urban planners and decision makers to correct and change the detailed urban plans according to any occurred land use change. One of the limitations of the detailed urban land uses plans is that they are not flexible and cannot opt to the deviations from the plan. This research is one step in the development of a general approach to dynamic urban planning. Such a planning approach can respond to the continuous and dynamic changes of the land uses in urban space.