نوع مقاله : مقاله علمی پژوهشی
1 دانشیار دانشکدۀ جغرافیا، قطب برنامهریزی روستایی
2 کارشناس ارشد سنجشازدور و GIS دانشگاه تهران
3 دانشجوی کارشناسی ارشد برنامهریزی شهری، دانشگاه آزاد اسلامی واحد علوم و تحقیقات تهران
4 دانشیار دانشگاه زنجان
عنوان مقاله [English]
Urban planners require a deep understanding of city and urban planning for detection and resolving of urban problems. Because cities are composed of a wide variety of societies and infinite number of dimensions, urban planners couldn’t devise and apply a similar plan for whole city. Thus, the city must be divided into spatial units based on social, economic and physical aspects. This segmentation is used for some constraints like specific land use or construction density to control urban development. This kind of segmentation can be defined through lots of urban plans such as detailed plans and comprehensive plan. On the other hand, in city there are lots of organizations like municipalities, roads and urban development organizations, Tavanir companies, Abfa companies and etc. with their own segmentations for servicing their clients. All of these configurations have been made to respond specific requirements and achieve some goals. However, most of the times these segmentations have functional overlap and in many cases conflict with each other. They aren’t, sometimes, efficient enough to do their functions properly. They usually make people unsatisfied because they oblige to explore lots of organizations to do their work. On the other hand, the number of units and divisions of all municipals, organizations and agencies are not fixed. It has been increased through the time due to the increasing population and urban expansion. Such a situation with inconsistent spatial structure and lack of public participation, are some reasons which make it difficult for urban sustainable management. Therefore, the aim of this article is to apply new approaches and methodologies to solve these problems. The AZP is one of the many different methods to do that. AZP is an algorithm used extensively for delimitation of multidimensional units or detection of spatial scale to study specific relationships in space of a city. In this article, we want to apply this model in urban areas of Iran. Zanjan city is case study of this research.
Spatial clustering methods (such as AZP) have been developed over time. They could be used for making zonation by aggregation and interchange of basic spatial units in each other with optimization of objective functions. These regionalization algorithms have some basic features. All of them integrate basic spatial units into predefined number of regions with optimization of an aggregation function. The basic spatial units assigned to a region must be spatially connected and the maximum number of regions should be one less than basic spatial units. A basic spatial unit could be aggregated to only one region and minimum number of basic spatial units should be assigned to a region. They have a supervise capability. Thus, relevant variables can define number of regions and types of objective function. The AZP algorithm can work with any type of objective function that is sensitive to the aggregation of data for N basic spatial units into M regions; for example functions extracted directly from the data (for instance sum of squared deviations from average zone size) or functions that can represent the goodness of a fit of a model applied to the data (fit of a linear regression model or the performance of a spatial interaction model). Output regions will not change over time and can make urban management more sustainable and more coordinated. These configurations could be used as basic directorial units of different organizations. Type of this research is applied and the purpose is development of unified multidimensional regionalization. This can assist sustainable management of city. We used census block and land use types of Zanjan city as basic data. Then, we extract 26 indicators from those data and aggregate them into fishnet with cell size of 300 meter. In the next step, to decrease the heterogeneity and discovering general trends, we applied Principal Component Analysis (PCA) on the indicators. Therefore, five PCs are extracted which control 76 percent of variance. Regionalization has been conducted based on these PSs. Objective functions are an intra-area correlation and a shape function to optimize output regions. The homogeneity of the regions can be evaluated based on a direct measure of within-area homogeneity, the intra-area correlation (IAC). We measured shape compactness by comprising the squared perimeter. Finally, after running the algorithm and exporting results, we need to test our output. Moran's I statistic is a measure of spatial autocorrelation in which Negative (positive) values indicate negative (positive) spatial autocorrelation. The values range from −1 (indicating perfect dispersion) to +1 (perfect correlation) and a zero value indicates a random spatial pattern.
Results and Discussion
We used 13 factors for validation of our work. Resulted regionalization has been compared with regionalization of detailed plan of Zanjan city for validation of AZP algorithm based on Moran's I statistics. The Moran's I showed detailed plan regionalization which have clustered spatial pattern. AZP algorithm could create regions with random spatial pattern which indicate homogenous regionalization.
This research showed that unknown pattern should be recognized by new methodologies so urban planners could do planning more efficiently. We should not limit our research to earlier constructed regions because every regionalization have been made for specific purpose and certainly would affect the results of the analysis. Therefore, at first step there is a need to make our objective-related regions. These regions, then, could be statistically used in the meaning of spatial units which are free from MAUP and very reliable.
This research showed that unknown pattern should be recognized by new methodologies for more efficiently planning by urban planners. We should not limit our research to earlier constructed regions because every regionalization have been made for specific purpose and certainly would affect the result of analysis. Thus, at first step, there is a need to make our objective-related regions.
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