Modeling Land Disaggregation in Urban Development Process (Case Study: Semnan City)

With the emergence of new urban development plans in different countries as well as the necessity of proper land use, land allocation has always been one of the most controversial issues in the context of new developments. The main objective of urban land segregation is to provide maximum efficiency of land and space and to maintain access between urban areas, thus creating a favorable urban environment. Several factors are effective in determining the size of the segments. Of these, the socio-economic characteristics of the households that are expected to reside on the horizon in the developed area are of great importance. Accordingly, in the present study four factors including household size, income level, land price and access to urban centers were selected as independent variables and land area as dependent variable and the results obtained from the importance of each Independent variables in explaining the dependent variable are presented in a regression equation.Accordingly, in the present study four factors including household size, income level, land price and access to urban centers were selected as independent variables and land area as dependent variable and the results obtained from the importance of each Independent variables in explaining the dependent variable are presented in a regression equation. The case study area is Semnan city which requires 86 Hectares based on forecasting urban development process to master plan horizon (1405). Land segmentation modeling process is done in six steps and basic tool used for land segmentation model. Designed by Dahal and Cho (2014), based on size, shape, and direction, it provides a fully automated large-scale segmentation scheme. Using this model, the plot of land is subdivided into 16 primary blocks. Each block has a different place or value to households depending on the price of land and their access to urban centers. By calculating the average of accessibility index and forecasting the price of land and income in the horizon and using the regression equation, the required land parcels for different income levels are obtained. On the other hand, by calculating the location value for each block and assuming that higher-income households tend to live in higher-value blocks, the obtained areas are allocated to the considered blocks. Finally, in order to achieve better results, the limitations of the automated land segmentation model as well as suggestions for achieving more accurate results are presented.