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تحلیل فضایی حس تعلق مکانی در احیای بافت ناکارامد با استفاده از رگرسیون وزن دار جغرافیایی: در منطقة سه تبریز | ||
| پژوهشهای جغرافیای انسانی | ||
| مقاله 10، دوره 53، شماره 1، فروردین 1400، صفحه 157-171 اصل مقاله (1.16 M) | ||
| نوع مقاله: مقاله علمی پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/jhgr.2019.281752.1007935 | ||
| نویسندگان | ||
| حسن محمودزاده* 1؛ عثمان صوفی بوبکران2؛ سهیلا نورمحمدی2 | ||
| 1دانشیار گروه جغرافیا و برنامه‌ریزی شهری، دانشگاه تبریز، تبریز | ||
| 2دانشجوی کارشناسی ارشد سنجش از دور و سیستم اطلاعات جغرافیایی، دانشگاه تبریز، تبریز | ||
| چکیده | ||
| بافتهایناکارامدشهریبهدلیلارزشهایتاریخی-فرهنگینیازمنددخالت و سامان‌دهی‌اند. امروزه، احیایاینبافت‌ها بااصلقراردادننیازساکناندربرقراریارتباطبافضایکالبدی ومباحثیچونحس تعلقبهمکانموردتوجهبسیاریازطراحان قرارمیگیرد. حس تعلق به مکان نشان‌دهندة 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. محدودةموردمطالعه منطقة سه شهرداری تبریز شامل چهار ناحیه با 96/2707 هکتار مساحت و دویست‌وپنجاه هزار نفر جمعیت است. جمعآوری دادهها بهصورتکتابخانهایومیدانیو با استفادهازابزارپرسش‌نامه انجامگرفتهاست. 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 تأثیر را شاخص اجتماعی دارد و دو شاخص کالبدی و زیستمحیطی به ترتیب در اولویتهای بعدی قرار دارند. همچنین، نتایج تحلیل فضایی نشان میدهد که ناحیههای یک و دو (خیابانهای چرنداب، لیل‌آباد، باغشمال، حافظ، و همچنین ناحیةمنظریه) ازنظر احیای بافت ناکارامد در وضعیت مناسب و مطلوبی قرار دارند. | ||
| کلیدواژه‌ها | ||
| بافت ناکارامد؛ تحلیل فضایی؛ حس تعلق مکانی؛ رگرسیون وزن دار جغرافیایی؛ منطقة سه تبریز | ||
| عنوان مقاله [English] | ||
| Spatial Analysis of Sense of Belonging to a Place in Restoration of Deteriorated Texture, Using Geographically Weighted Regression in District Three of Tabriz | ||
| نویسندگان [English] | ||
| Hassan Mahmoudzadeh1؛ Osman Soufi Bobakran2؛ Sohaila Normohammadi2 | ||
| 1Associated Professor of Geography & Urban Planning, Tabriz University, Tabriz, Iran | ||
| 2MSc of Remote Sensing and Geographical Information System (GIS), Faculty of Planning and Environmental Sciences, University of Tabriz, Tabriz, Iran | ||
| چکیده [English] | ||
| Introduction Sense of belonging is a dimension of the sum of the sense of place and positive attachment, formed between the individual and the place. At the same time, worn-out 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, many planners and designers have emphasized revitalizion of these textures by prioritizing the residents’ needs and communicating with not only the physical space but also the issues related to location. Considering the issues raised, it seems essential to pay attention to worn-out urban contexts, both spatial and psychological so as to increase residents' satisfaction. For example, worn-out urban contexts are one of the problems that, in addition to the legal system, have diminished the appearance and quality of urban life, leading to the establishment of 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, worn-out textures account for one fifth of the city's total area of 2,530 hectares, making it the second largest city in terms of worn-out textures. According to the latest estimates, 400 to 500,000 Tabriz citizens live in such areas. Studies show that the rigid regulations of urban planning, lack of adequate financial resources, and lack of ingenious and strategic management in the worn-out textures will swallow Tabriz over time. On the other hand, the worn-out texture of Tabriz as a vibrant urban location has obvious physical, semantic, and functional differences from its neighborhoods. This 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, thus, the most basic and important data, used by environmental analysts and geoscientists in their research. Methodology The conventional global regression method assumes a constant correlation 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 relations between variables varies from place to place. The GWR method is a regression technique that significantly improves ordinary regression to be used in spatial data. Therefore, the maps generated from these analyses play a key role in the non-stationary spatial description and interpretation of the variables. In this method, the coefficients of the explanatory variables are estimated, using weighted matrices, where each variable’s weight is determined based on the distance of each observation to the estimated position of the variable. GWR is one of the methods to estimate model parameters when there is a dependence among observations of each point. The main idea of GWR is that the study of independent and dependent variables in the study area is done in places where their position is known. Result and Discussion Since different indices can be used in regeneration of worn-out tissue, this study considered the sense of location as a dependent variable and the other parameters in three social, physical, and environmental indices as independent ones. They were also used to obtain goodness of fit indices (R2). The VIF index helped determining the linearity of the independent variables. VIF is a feature, used to know whether there is a linear correlation between independent variables or not. It shows the intensity of the linearity among independent variables (multiple linearity). In fact, the index indicates the amount of change in the estimated coefficients for each end. The minimum value of this positive index is one and its maximum is infinite. As a rule of thumb, if VIF is greater than 7.5, it represents a high multiplicity of linearity. Here, according to the results, the VIF index for the variables, used, was a lot, making them incapable of getting involved in modeling (VHF> 1, VIF <7.5). Therefore, the assumption that the input variables are independent seemed to be correct for all used variables. The sense of belonging among the three studied indices had the most impact on social indicators, followed by environmental and physical indicators. Conclusion Results from this research show that the role of social and environmental factors is more important in restoring the worn out texture. Regarding the status of the sense of belonging to locality in the studied area, three findings show that Maralan Neighborhood in which Montazeri, New Pasteur, Ashrafi, Hafez, and Laklar Streets were located along with Hakimi Neighborhood did enjoy the highest sense of belonging within Shariati, Taleghani, Azadi Boulevard, and Martyr Fathi Vend Regions as well as the parish. These neighborhoods are located in the northern, central, and eastern parts of the Third District. In order to raise the sense of belonging to the area, it is possible to modernize the worn-out tissues, especially in public spaces and interiors of residential blocks. On the other hand, by providing welfare facilities, creating sufficient parking lots, and observing visual spatial proportions one can keep on promoting the sense of belonging to the location among the residents of the region. | ||
| کلیدواژه‌ها [English] | ||
| geographically weighted regression, Tabriz, spatial analysis, sense of belonging | ||
| مراجع | ||
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