ارزیابی سطح هوشمندی محلات شهری ارومیه مورد مطالعه: مناطق پنج‏‏گانه‌ شهر ارومیه

هدف از این تحقیق بررسی و تحلیل فضایی شاخص ‏های شهر هوشمند و عوامل موثر بر آن از طریق  شش شاخص حکمروایی، مردم، زندگی، پویایی، محیط، و اقتصاد هوشمند است. روش پژوهش این تحقیق تحلیلی- توصیفی و آزمون همبستگی است. برای تجزیه ‏و تحلیل داده‏ ها نیز از مدل‏ های کمی تصمیم ‏گیری ‏های چندمعیاره‌ آنتروپی، ضریب پراکندگی، تحلیل خوشه ‏ای، و تحلیل رگرسیون استفاده‏ شده است. حجم نمونه با استفاده از فرمول کوکران 384 نفر انتخاب شد. روش نمونه ‏گیری طبقه ‏بندی تصادفی ساده و تخصیص متناسب است. بر اساس نتایج به ‏دست‏آمده، شاخص شهر هوشمند در محله‌ 8 شهریور با امتیاز تاپسیس 0.799 واقع در منطقه‌ 3 در رتبه‌ نخست هوشمندی در بین محله‏ های شهر است که بیشترین ضریب تاثیر در این محله مربوط به شاخص‏های زندگی هوشمند با امتیاز 0.29 است. رتبه‌ آخر این رده بندی مربوط به محله‌ کوهنورد با امتیاز 0.16 واقع در منطقه‌ 2 شهر ارومیه است. همچنین، میزان هوشمندی بین شاخص‏ های تلفیقی در رتبه ‏بندی مناطق پنج‏گانه، منطقه‌ 3 با وزن 1 و منطقه‌ 4 با وزن 0.001 به‏عنوان دو قطب متضاد برخوردار و محروم در سطح محله مطر‏ح ‏اند. بین حکمروایی و شاخص‏ های تلفیقی شهر هوشمند ارتباط همبستگی معناداری مشاهده نشد. شاخص ضریب پراکندگی نشان می ‏دهد در بین شاخص ‏های مختلف بیشترین میزان نابرابری در شاخص ‏های مردم هوشمند با مقدار 0.86 و کمترین میزان نابرابری در شاخص ‏های محیط هوشمند با مقدار 0.40 وجود دارد. نتایج حاصل از تحلیل رگرسیون توام نشان می‏دهد از بین شاخص ‏های شش‏گانه، شاخص ‏های زندگی و اقتصاد هوشمند بیشترین سطح معناداری را در تبیین و پیش‏بینی شهر هوشمند دارند.

zoning of urban smart living spaces case study: five municipalities area of urmia city

introductionin order to minimize the undesirable effects of unsteady growth of urmia and to apply the smart-growth model for this city, recognizing the characteristics of different areas and their inequality in planning, is the basis of the work. therefore, proper planning should be done to eliminate these inequalities and transform the optimal situation. areas need to be categorized in terms of &development& in order to plan for whether or not they are developed. in measuring smart growth indices, there are different types of statistical methods and techniques. using quantitative criteria and methods to classify the neighbourhoods and urban areas of urmia in terms of smart growth indices not only recognizes the differences between areas, but also these criteria for determining the types. the services needed and the adjustment of inequality between areas of the city. the present study tries to study the spatial distribution of urban smart growth components in the five urban areas of urmia and based on the obtained scores, the rate of urban smart growth indices in three levels of smart, semi-intelligent and less intelligent. therefore, the following objectives were considered for the study: identification of smart areas of urmia, prioritizing urban areas of urmia for future planning in line with urban smart growth pattern and identifying homogeneous neighborhoods of urmia in terms of urban smart growth indicators.methodologythe approach of the research method in this study is of applied type and it is descriptive-analytical and correlational. spatial statistics tests were used to model the spatial pattern of smart neighbourhood distribution. to identify the spatial pattern of intelligent living spaces and finally to identify the desired zones. first, library studies were used to identify smart city indices from different sources and databases. accordingly, six main indices (intelligent dynamics, intelligent people, intelligent living, intelligent environments, intelligent governance, and smart economics) with 91 items were used. the field was determined. regarding the research subject, random sampling was used in order to obtain the maximum accuracy coefficient in obtaining samples with a high degree of characteristics of the statistical population and the results of which can be generalized to the whole population. according to cochran’s formula, 384 people were selected as the sample population. simple random stratified sampling and distribution of samples for 30 neighbourhoods were done based on proportional allocation. questionnaires were collected based on field method through direct interviews with residents of five districts. in this study, cronbach’s alpha coefficient was calculated using spss software. the reliability of the research is significant since the reliability of the questionnaire was assigned to each of the answers 1 to 5 with a cronbach’s alpha of 0.785. in order to complete the questionnaire, a questionnaire was distributed in each of the five urban areas. according to the objectives of the study, shannon entropy (as a multi-criteria decision-making method) was used to evaluate and rank urmia metropolitan areas in terms of smart growth indices. regression analysis (pearson function and linear regression) were used in spss software.results and discussionto achieve definitive ranking in terms of smart growth indices, all 91 variables were computed using shannon entropy model and results were slightly different. in terms of integrated indices of region 3, with an entropy value of 1 was ranked first.