تحلیل فضایی شاخص های رشد هوشمند شهری در شهرهای میان اندام (مطالعه موردی: شهر مرند)

در جهان امروز، شهرها با چالش های متعددی مواجه اند که ناشی از نیازهای اجتماعی جدید می باشد. با توجه به پیشرفت علم و صنعت، ضرورت استفاده بهینه از زمین و مدیریت توسعه شهری را بیش از پیش نمایان می سازد. رویکرد رشد هوشمند شهری در پی نظم بخشی به رشد و توسعه و تجدید حیات شهری است. هدف مقاله حاضر تحلیل کالبدی فضایی و اولویت بندی نواحی شهری مرند بر اساس شاخص های رشد هوشمند شهری است. روش تحقیق در این مقاله «توصیفی تحلیلی» و «کاربردی توسعه ایی» است. برای جمع آوری اطلاعات مورد نیاز از دو شیوه کتابخانه ای و میدانی استفاده شده است. روش نمونه گیری و جمع آوری داده ها با استفاده از پرسشنامه جامعه آماری شهروندان شهر مرند از روش کوکران 348 پرسشنامه می باشد. تجزیه و تحلیل داده ها با استفاده از تکنیک ویکور (vikor) و نرم افزارspss انجام شد است. نتایج تحقیق حاکی از آن است که ناحیه سه شهر با مقدار عددی q (صفر) در مجموع شاخص های مورد ارزیابی از وضعیت مطلوب تری نسبت به سایر نواحی قرار دارد. و سپس ناحیه یک شهر با مقدار q (0.128) در رتبه دوم و بعد از آن ناحیه چهار شهر با مقدار q (0.183) در رتبه سوم قرارگرفته و نواحی دو و پنج با توجه به شاخص های شهر هوشمند با مقدار q (0.454 و 0.736) در رتبه های چهارم و پنجم قرار دارد.  بر اساس مدل برازش رگرسیونی،  بخش کاربری اراضی کالبدی بیشترین تاثیر را در پیش بینی و توسعه ی ساختار فضایی رشد هوشمند در نواحی شهر مرند داشته است؛ به طوری که یک واحد تغییر در رشد هوشمند، به ترتیب 0.684 ، 0.352 و0.098 واحد در انحراف بخش کالبدی و کاربری اراضی، دسترسی و ارتباطات و زیست محیطی تغییر در شاخص های تلفیقی رشد هوشمند ایجاد کرده است. در ادامه یافته های حاصله از آزمون (t) نشان داد که مجموع شاخص ها شهرهوشمند در نواحی پنجگانه شهر مرند کمتر از میانگین انتخاب شده است.

Spatial analysis of urban smart growth indicators in middle Cities (Case Study: Marand city)

N            Nowadays ,cities face numerous challenges which arise from social needs. By  growth of science and industry, the need for optimal  land use and urban development management. gets more and more obvious. Urban smart growth approach necessitates regularizing growth, development, and urban revitalization.                                                                                            The aim of this research is spatialphysical analysis and it aims to  prioritize urban areas of Marand via urban smart growth indicator. The methodology used in this research is descriptiveanalytical and developmentalapplied. In order to collect data both survey and documental (librarian) methods are used. Sampling and data collection are carried out   by means of questionnaire (348 cochran) and the population is the citizens of Marand . The technique VIKOR and the software SPSS is used in this data analysis. The results shows that area 3 of this city by amount of Q (0) among surveyed indicators seems to be more ideal compared to other areas .It shows that the 1 area by amount of Q (0.128) is second and also area four with Q (0.183) stands the third. According to indicators it is mentioned that area 2 and 5 stand fourth and fifth by amounts of respectively)(0.454) and (0.736. According to regression fit model, it is shown that used areas of physical lands are mostly affected in planning and developing spatial structure of smart growth in Marand ; so that a unit of change in smart growth causes respectively 0.684 , 0.352, 0.098 unit of deviation of physical area  and land use, accessibility, communication, and environmental changes in the integrated indicators of smart growth. And finally T test results showed that the sum of urban smart indicators of 5areas Marand were chosen something less that the average. Extended Abstract Introduction:         Nowadays, cities face numerous challenges which arise from social needs.    With the advancement of science and industry, the need to optimize land use and urban development management becomes increasingly evident. The idea of smart city as a resolution of most of the problems of current cities is given by authors and designers of big cities in world (Modiri, et.al 97:12). Smart city growth approach necessitates regularizing growth, development, and urban revitalization. The term smart city and its origin can be pursued in smart growth movement which was formed in late 1980s and in early 1990 swhich supported new policies of urban planning. The term that for the first time was used about Brisbane in Australia and Blacksburg in USA, where information and communication technology supported social participation, decrease of digital gap, and availability of information and services (Pour Ahmad, et.al.1397:9). Smart growth is for integration of transportation system and land use which supports compact developments and complex uses in city areas and it opposes carbased and scattered developments in peripheral parts of cities. It also aims to create accessible land use models, to improve transportation opportunities, to create livable societies, and to decrease the costs of general services(AnnaMorad Nejhad et.al,1397:23). Smart city consists of 6 key bases that can be done by substructures of communication and information technology. They are as follows:                                                                                   Smart economy: Refers to cities with smart industries, especially industries with information and communication technologies and other industries with information and communication technologies in their manufacturing processes. Smart people: the discriminating factor between digital and smart city is the existence of  smart people. They are defined according to their proficiency level.(Hyeok Yang.2012:8). Smart government: It is consist of active and political participation, citizen services, and smart use of electronic government. Smart environment: It refers to the use of new technologies to preserve environment.Smart mobility: It means preparing the needs to obtain general access to new technologies and their use.Smart living: It refers to collecting different aspects which improve living of citizen; for example, culture ,tourism, housing (Peiser.2001:278).The aim of this research is spatialphysical analysis and it aims to prioritize urban areas of Marand via urban smart growth indicator. Methods:          The methodology used in this research is descriptiveanalytical and developmentalapplied. In order to collect data both survey and documental (librarian) methods are used.  Sampling and data collection are carried out by means of questionnaire and a regular random sampling of 348 of people. In this research in order to identify the indicators of smart city, different resources and data bases were used through survey. Hence 6 main indicators; Smart economy, Smart people, Smart government, Smart environment, Smart mobility, Smart living, were recognized. In order to analyze data a questionnaire including 30 questions was prepared. For every indicator 5 questions were allocated. Every question contained 4 choices and the answers scored from 1 to 4.According to obtained data the quantity of Alpha Cronbach equated 0.786.          As this quantity is more than 0.6, then  stability of the instrument was reliable. Because of the technique Vikor used in this research, 84 questionnaires equally were distributed. Considering amount of effectiveness (meaningfulness)  of extracted  indicators on growth of smart city of Marand; according to the nature of data and variables, the regression analysis (Pierson Function and Linear Regression) in SPSS software environment was used. In order to analyze compute, get output informationof areas of Marand  from view points of smart city indicators the multifunctional decision making method vikor was used. Also according to the nature of the research  and the questions using deductive statistics, T test was used. Results:          The results shows that area 3 of this city by amount of Q (0) among surveyed indicators seems to be more ideal compared to other areas. And then 1 city area with Q (0.128) comes in second and then 4 city area with Q (0.183) comes in third. According to indicators it is mentioned that area 2 and 5 stand fourth and fifth by amounts of (0.454), (0.736). According to regression model, it is shown that used areas of physical lands are mostly affected in planning and developing spatial structure of smart growth in Marand; so that a unit of change in smart growth causes respectively 0.684, 0.352 and 0.098 unit of deviation of   physical area  and land use, accessibility, communication, and environmental changes. And finally T test results showed that the sum of urban smart indicators of 5area Marand were chosen something less that the mean. Conclusion:       In fact, the smart city is not a reality but a strategy of urban development, in which technology is the center of future development. Therefore, urban development should develop smart city indicators to improve the lives of citizens.