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Increasing urban population and organizations have introduced many problems in urban life. In a city there are a lot of organizations like municipalities, Roads and Urban Development organizations, etc. Which have made their own segmentations for servicing their customers. All of these configurations have been made for responding to specific requirements and reaching specific goals. But Most of the time these segmentations have functional overlap and in many cases are in conflict with each other.This has caused inefficiency, and dissatisfaction of people, because they have to go to lots of organizations to get their work done. Therefore all the organizations in a city must clearly specify their responsibilities and territories.To do this, we aim to suggest a new system of regionalization using REDCAP algorithms in Tehran City. Research type is descriptive/analytical and the data has been collected from different organization. We designed 22 indexes to reshape new regions. These indices have been generated by aggregating raw data in primary spatial units. Primary spatial units have been designed by converting road network of Tehran city to polygons.After that we applied REDCAP algorithm by defining three parameters (number of regions, minimum population and REDCAP algorithms) to generate new regions. Then to prove our new regions are superior to formal regions of Tehran municipality we utilized Moran’s I index by measuring homogeneity of new regions in comparison with formal regions. Results showed us that new regions best fit our aims and are more homogenous than formal regions.

The explosive population growth and economic development in cities have led to continuous but diverse urban regionalization. The resulting urban agglomerations (UAs) exhibit differentiation at both regional and local scales. Few studies have documented the disparities in UAs regarding land use and geography. In this study, we investigated the county-level land use features of 19 China's planned UAs by using a land use intensity (LUI) metric. We employed the spatial autocorrelation, inequality statistics, and imbalance estimation models to estimate the agglomeration, inequalities, and coordination of land use, and we combined those indicators to distinguish the development pattern of all UAs. Our findings revealed that spatial pattern of LUIs and their autocorrelations over time are closely associated with the evolution of urban regionalization. The widening gaps of land use exist more within the UAs than among them. Based on the LUI inequality and its relations to population and economy, UAs were classified into core-driven, differentiated, coordinated, and mismatched ones. Our conclusions highlighted that policy incentives for sustainable urbanization help decouple the synchronism of socioeconomic and LUI inequality. • Spatial patterns of LUIs and their autocorrelation are closely associated with the evolution of urban regionalization. • The widening gaps of land use exist more within each UA than between UAs. • The LUI inequality and the imbalances between LUI and GDP/population jointly determine the development pattern of a UA. • Policy incentives for sustainable urbanization help decouple the synchronism of socioeconomic and LUI inequality. [ABSTRACT FROM AUTHOR]

A functional region is usually defined as a region characterized by not only interaction cohesiveness but also function diversity. The former indicates that places within a region should share a cohesive interacting pattern of human movements, such as commute or daily travel; the latter means that the places should possess diverse functions like residence, commerce, etc. Due to these two characteristics, people living and moving in one region can acquire almost all their daily needs, and they do not need to frequently move to other regions. In other words, a functional region resembles a life circle. Based on this definition, many previous studies have developed different methods to analyse human flow data so that they can properly profile different types of functional regions like Local labour market area (LLMA) or daily urban system (DUS), which helps people better understand how functional regions compose an area. Existing methods have considered interaction cohesiveness in a functional region, but function diversity is ignored. This is because these studies only used specific-purpose-trip data, such as journey to work or to shop. These kinds of trips describe people moving from their home to their office or to shopping malls, so each trip has an inherent distinction of functions between its origin and destination; thus, a functional region consisting of these trips indirectly owns function diversity. However, this framework has some problems. First, it cannot profile a functional region shaped by composite functions, for only one trip purpose can be taken into account each time. Second, collecting the data of specific-purpose trips needs either some questionnaires or some surveys, so it is often costly and time consuming. Finally, the purpose-unrecorded-trip data provided by rapidly developed smart card systems in several countries can be collected quickly, yet it cannot properly profile functional regions through the aforementioned framework due to the lack of trip purposes. Therefore, we propose a new spatial partitioning algorithm which can simultaneously consider both interaction cohesiveness and function diversity in profiling functional regions. Compared with previous methods, the proposed algorithm can better profile functional regions from many purpose-unrecorded-trip data based on the inclusion of land-use data. Our results provide deeper insights for understanding how various functions of lands and human movements together shape the boundary of a region. It could be significant reference for developing policies for urban planning or public transportation management. [ABSTRACT FROM AUTHOR]