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Spatial Analysis of Deterioration in Qom's Neighborhoods Using Geographical Weighted Regression

Journal: Geographical Urban Planning Research (GUPR) (Vol.6, No. 2)

Publication Date:

Authors : ; ;

Page : 361-383

Keywords : بافت فرسوده; تحلیل فضایی; رگرسیون وزنی جغرافیایی; شهر قم;

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Introduction   In just a few decades, urban areas across the world in both developed and developing countries have become increasingly deteriorated. In other words, it is a worldwide phenomenon. Deterioration and urban decay are created and aggravated by many factors and variables and considered as one of the most severe environmental and socio-economic problems of recent times. These areas were occupied by poor immigrants and low income groups. Thus, the areas are faced with physical deterioration, economic and environmental problems. Undesirable changes in urban environments make living conditions more difficult for citizens. What it is interesting in this research is the fabric that covers a decline in the area of 1074 hectares of Qom. This is allocated to about 6.8 percent of the city's legal limit and placed a population of over 220 thousand people. The process of physical, social and economic deterioration is confronted with inner city and central neighborhoods that can be explained in various ways. This process seems to be irreversible disclosing inequalities, poverty and serious environmental impacts that need to be systematically assessed. This study focuses on the causes and effects of urban decay. Therefore, the principal aim of this study is to explore the impacts of deterioration and suggest appropriate urban management interventions. Thus, analysis of spatial patterns of deterioration and spatial relationships between deterioration and its influencing factors is necessary for better understanding of effective factors and improving performance of urban renewal management. Hence, in this research we employed spatial statistical methods to analyze the spatial patterns of deterioration and its influencing factors. Materials and methods The research is conducted using descriptive and analytical approaches based on library research, documentation and data from Iran statistics Center (2011). In the present study, the city of Qom and 51 decline Neighborhoods have been investigated. In this regard, spatial statistics and geo-statistics methods are employed. The results obtained from Factor Analysis (FA) are used for identifying geographical patterns (Cluster, scattered and random) by using Moran’s spatial autocorrelation statistics. Also Getis-Ord general G statistics and Cluster and Outlier Analysis (Anselin Local Moran's I) statistics, respectively, are employed for detecting High/Low value clustering and mapping the cluster and outliers. All of the mentioned statistics are carried out in ArcMap 10.3.1 software.   Result and discussion The deterioration indicators in the current study are divided into several dimensions: economic, social, and physical aspects due to their extensity and the opinions of experts. The results of the application of Moran's index on the spatial distribution of deterioration show that this coefficient is positive and equal to 0.314. Representing the spatial distribution of the deterioration is cluster. Since Moran index can not identify spatial diverse patterns, General G Statistic analysis covered the defects. General G statistic showed that neighborhoods with high deterioration together have a high concentration of the cluster. About 6.29 percent of the deteriorated area is devoted to hot high-cluster and consists of five neighborhoods. However, medium clusters are 75.29 percent and includes 36 neighborhoods. Since the deterioration is dependent on the local and spatial variables, Geographically Weighted Regression (GWR) was used to investigate the influencing factors on the deterioration. The amount of deterioration were considered as dependent variables and physical, economic and social indices as independent variables. The results showed that the model with  R2= 0.92 and R2 adjusted equal to 0.84 has acceptable accuracy in modeling the spatial relationships of effective factors on urban deterioration. Moran’s Iof residuals GWR refers to insignificant autocorrelation. The results of the effectiveness of each of the indices on deterioration shows that the variables of impermeability, microlithic state, the quality of infrastructure, household density, land prices, and leased property have increasingly affecteddeterioration. Other variables were not significant and interpreted. According to research findings, physical, social and economic problems  in the mentioned old area are considered as the most affecting issues. Therefore, to organize the region, the mentioned factors can be helpful in urban improvement planning. Conclusion Urban old areas created a suitable living space for their dwellers, due to technological improvements and changes occurred in environmental, social and economic requirements. Thus, the areas can no longer have the same performance as they once did. Once, these old areas were the heart of wealth and power of cities, but under current conditions (in almost all cities) and because of having poor infrastructures and urban services, they are considered as disorganized urban neighborhoods, but still important in the lives of the residents. Therefore, it is important to find out how the recent factors can affect these neighborhoods. Based on the obtained results of the present study and also based on the spatial auto-correlation indices, the concentration of urban deterioration in Qom city is clustered. Factors affecting urban burnout include variables such as: impermeability, microlithic, the quality of infrastructure, household density, land prices and leased property. As a result, social, economic, and physical planning is necessary to the participation of citizens to improve the quality of life. Therefore, these findings can show scientific basis for policy in order to reduce deterioration and its effectiveness. Evaluation of the benefits of urban-regeneration programs with appropriate spatial indicators is a fundamental step for identifying specific planning measures in future urban transformations.

Last modified: 2019-09-02 02:53:02