نوع مقاله : مقاله علمی پژوهشی
نویسندگان
1 دانشیار گروه جغرافیا و برنامهریزی شهری، دانشگاه تبریز، تبریز
2 دانشجوی کارشناسی ارشد سنجش از دور و سیستم اطلاعات جغرافیایی، دانشگاه تبریز، تبریز
چکیده
کلیدواژهها
عنوان مقاله [English]
نویسندگان [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]
31. Canter, D., 1971, The Psychology of Place, Architectural Press, London.
32. De Magalha˜, es Claudio, 2015, Urban Regeneration, Vol. 24, http:// dx.doi.org/10.1016/B978-0-08-097086-8.74031.
33. Fotheringham, S. A. and Brunsdon, C., 1999, Local forms of spatial analysis. Geographical Analysis, 31.
34. Fotheringham, S. A.; Brunsdon, C. and Charlton, M., 2002, Geographically Weighted Regression the analysis of spatially. Varying relationships. John Wiley & Sons.
35. Gao, J. and Li, S., 2011, Detecting spatially nonstationary and scale-dependent relationships between urban landscape fragmentation and related factors using Geographically Weighted Regression. Applied Geography, No. 31, PP. 292-302.
36. Knox, P. and Pinch, S., 2000, Urban Social Geography: An Introduction. Prentice Hall: Harlow.
37. Kutner, M. H.; Nachtsheinm, C. and Neter, J., 2004, Applied linear regression models: McGraw-Hill/Irwin.
38. Knox, P. & Pinch, S. (2000). Urban Social Geography An Introduction, 6th ed, Published November 26, 2009 by Routledge.P 392.
39. La Rosa, D. et al., 2017, Assessing Spatial Benefits of Urban Regeneration Programs in a Highly Vulnerable Urban Context: A Case Study in Catania, Italy, Landscape and Urban Planning, Vol. 157, PP. 180-192.
40. Mennis, J., 2006, Mapping the Results of Geographically Weighted Regression. The Cartographic Journal, Vol. 43, No. 2, PP. 171-179.
41. Montgomery, D. C.; Peck, E. A. and Vining, G. G., 2001, Introduction to Linear Regression Analysis (Vol. 3). John Wiley & sons.
42. Morse, Ch. and Mudgett, J., 2017, Longing for Landscape: Homesickness and Place Attachment Among Rural Out-Migrants in the 19th and 21st Centuries, Journal of Rural Studies, Vol. 50, No. 3, PP. 95-103.
43. Propastin P. and Kappas M., 2008, Reducing uncertainty in modeling the NDVI–precipitation relationship: acomparative study using global and local regression techniques. GISci Remote Sens 45: 47.
44. Ramkinssoon, H.; Weiler, B. and Smith, G., 2012, Place Attachment and Proenvironmental Behavior in National Parks: The Development of a Conceptual Framework, Journal of Sustainable Tourism, Vol. 20, No. 2, PP. 257-276.
45. Relph, E., 1976, Place and Placelessness, Pion, London.
46. Rosental, Stuart, 2008, Old Home, Externalities, and Poor Neighborhoods, a Model of Urban Decline and Renewal, Journal of Urban Economic, Vol. 63, No. 3, PP. 816-840.
47. Sheng, J.; Han, X. and Zhou, H., 2016, Spatially Varying Patterns of Afforestation/ Reforestation and Socio-Economic Factors in China: A Geographically Weighted Regression Approach, Journal of Cleaner Production, Vol. 153, PP. 362-371.
48. Stedman, R. C, 2003, Is it really just a social construction? The construction of the physical environment to sense of place, Society & Natural Resources, Vol. 16, PP. 671-685.
49. Steele, Fritz, 1981, The Sense of Place, CBI Publishing Company, Boston.
50. Tan, S.; Kok, Y. and Choon, Sh., 2018, Sense of place and sustainability of intangible cultural heritage The case of George Town and Melaka. Tourism Management. No. 67, PP. 376-387.
51. Zhang, Honglei; Zhang, Jie; Lu, Shaojing; Cheng, Shaowen and Zhang, Jinhe, 2011, Modeling hotel room price with geographically weighted regression; Hospitality Management, No. 30, PP. 1036-1043.
)