تحلیل فضایی حس تعلق مکانی در احیای بافت ناکارامد با استفاده از رگرسیون وزن‏ دار جغرافیایی: در منطقه سه تبریز

بافت‌های ناکارامد شهری به دلیل ارزش‏های تاریخی فرهنگی نیازمند دخالت و سامان‌دهی‌اند. امروزه، احیای این بافت‌ها بااصل قراردادن نیازساکنان دربرقراری ارتباط بافضای کالبدی ومباحثی چون حس تعلق به مکان مورد توجه بسیاری ازطراحان قرارمی‏گیرد. حس تعلق به مکان نشان‌دهنده علاقه و دلبستگی افراد به آن مکان است و به بازگشت فرد به آن مکان منجر می‌شود. هدف از این تحقیق تحلیل فضایی حس تعلق به مکان در احیای بافت ناکارامد با استفاده از روش رگرسیون وزن‏دار جغرافیایی (gwr) است. محدوده مورد مطالعه منطقه سه شهرداری تبریز شامل چهار ناحیه با 2707.96 هکتار مساحت و دویست‌وپنجاه هزار نفر جمعیت است. جمع‏آوری داده‏ها به‏صورت کتابخانه‏ای و میدانی و با استفاده ازابزارپرسش‌ نامه انجام گرفته است. برای تجزیه‏وتحلیل داده‏ها از نرم‏افزارهای spss وlisrelو برای بررسی نتایج و تحلیل فضایی حس تعلق مکانی از روش رگرسیون وزن‏دار جغرافیایی (gwr) در محیط نرم‏افزار arc gis استفاده‏شده است. نتایج رگرسیون وزن‏دار جغرافیایی نشان می‏دهد که در بین سه شاخص مورداستفاده (اجتماعی، کالبدی، و زیست‏محیطی) بیشترین تاثیررا شاخص اجتماعی دارد و دو شاخص کالبدی و زیست ‏محیطی به ترتیب در اولویت‏های بعدی قرار دارند. همچنین، نتایج تحلیل فضایی نشان می‏دهد که ناحیه‏های یک و دو (خیابان‏های چرنداب، لیل‌آباد، باغ شمال، حافظ، و همچنین ناحیه منظریه) ازنظر احیای بافت ناکارامد در وضعیت مناسب و مطلوبی قرار دارند.

Spatial Analysis of belonging ’sense to a place in Restoration of Deteriorated Texture using the Geographically Weighted Regression in the region three of Tabriz

Spatial Analysis of belonging ’sense to a place in Restoration of Deteriorated Texture using the Geographically Weighted Regression in the region three of TabrizHassan Mahmoudzadeh1, Osman soufi Bobakran2, Sohaila Normohammadi31. Associated Professor of Geography & Urban Planning, Tabriz University, Tabriz, Iran.Email: Hassan.mahmoudzadeh@yahoo.com Tel: 091431492522. MSc of Remote Sensing and Geographical Information System (GIS), Faculty of Planning and Environmental Sciences, University of Tabriz, Tabriz, IranEmail: osmansoufi@gmail.com Tel: 093826399093. MSc of Remote Sensing and Geographical Information System (GIS), Faculty of Planning and Environmental Sciences, University of Tabriz, Tabriz, IranEmail: nsoheila14@gmail.com Tel: 09335409265IntroductionThe sense of belonging is a dimension of the totality of the sense of place and the positive attachment that is created between the individual and the place. In the meantime, wornout textures and old neighborhoods, as the dominant beating heart of cities, are the most important public spaces that need to be taken care of and kept by residents within the texture. Today, the revitalization of these textures has been the focus of many planners and designers by prioritizing the needs of residents and communicating with the physical space as well as issues related to location. Considering the issues raised, attention to wornout urban contexts, both spatial and psychological, seems to be essential to increase residents’ satisfaction. For example, wornout urban contexts are one of the problems that, outside of the legal system, have diminished the appearance and quality of urban life, and have created many roads and densities of services, infrastructure, and urban facilities. The issues raised indicate that the need to intervene in aging tissues to improve their quality of life is crucial. In Tabriz, wornout textures account for one fifth of the city’s total area of 2,530 hectares, making it the second largest city in terms of wornout textures. According to the latest estimates, 400 to 500,000 Tabriz citizens live in these areas. Studies show that the rigid regulations of urban planning, lack of adequate financial resources, lack of ingenious and strategic management in the wornout textures over time, swallows Tabriz. On the other hand, the wornout texture of Tabriz as a vibrant, vibrant urban location has obvious physical, semantic, and functional differences among its neighborhoods, which has had a significant impact on the sense of belonging. Therefore, based on many scientific studies, research on the subject involves the use of numerical and statistical information that is influenced by the concept of space and environment. Spatial data are therefore the most basic and important data used by environmental and geoscientists in their research.MethodologyThe conventional global regression method assumes a constant relationship between spatial variables for modeling the area that does not take into account spatial instability of the variables. The major advantage of GWR geographic weighted regression over conventional regression models is its ability to investigate spatial instability. Spatial instability indicates that the measurement or estimation of relationships between variables varies from place to place. The GWR method is a regression technique that significantly improves ordinary regression for use in spatial data. Therefore, the maps generated from these analyzes play a key role in the nonstationary spatial description and interpretation of the variables. In this method, the coefficients of the explanatory variables are estimated using weighted matrices. The weight of each variable in this method is determined based on the distance of each observation to the estimated position of the variables. One of the methods of estimating model parameters when there is dependence between observations of each point in space is GWR. The main idea of geographic weighted regression is that the study of independent and dependent variables in the study area is done in places where their position is known.Result and discussionSince different indices can be used in regeneration of wornout tissue, in this study, sense of location is considered as dependent variable and other parameters in three social, physical and environmental indices as independent variables. They have also been used to obtain goodness of fit indices (R2). The VIF index was used to determine the linearity of the independent variables. VIF is a feature used to know whether or not there is a linear relationship between independent variables. This index shows the intensity of the linearity between the independent variables (multiple linearity).In fact, the index indicates how much of the change in the estimated coefficients for each end. The minimum value of this positive index is one and its maximum is infinite. As an empirical rule, if the VIF value is greater than 7.5, it represents a high multiplicity of linearity. Based on the results, the VIF index for the variables used is not so much that they cannot be involved in modeling (VHF> 1, VIF