Zoning of urban smart living spaces Case study: Five Municipalities area of Urmia city

Introduction
In 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.
Methodology
The 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 discussion
To 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. The region was also ranked first in smart living indicators. The last rank came in the 4th place with an entropy value of 0 which was in the last place in relation to the smart governance index. Overall, Region 3 was ranked as one of the most prosperous regions in terms of smart growth indices with economic and social structure, good accessibility, favourable environment, dynamic economy, proper urban infrastructure, proportional distribution of land uses and construction density. The mean of the integrated indices is 0.39 and the standard deviation is 0.39. Area one has the highest score above average and other areas are below average. Using the inequality coefficient, the coefficient of equilibrium in urban smart growth indices between urban areas for these indicators was calculated and a value of 1.01 was obtained, indicating heterogeneity and divergence between urban areas in terms of intelligence indicators. This inequality is affected by the inadequate distribution of facilities and services throughout the city. According to the calculated entropy and inequality coefficient, there are differences and inequality between the neighbourhoods of Urmia in terms of smart growth indices. In other words, this paper investigates and ranks the neighbourhoods of the five urban areas of Urmia for urban smart growth index using Shannon entropy model. The results of the ranking show that the neighbourhoods of Urmia city achieved different scores and scores in each of the indicators of a smart economy, smart people, smart governance, smart dynamics, and smart environment. This indicates significant inequality and differences in some indicators. The highest inequality between the indicators of smart governance and the lowest inequality between the indicators of intelligent life. All six indices (91 items) were combined and then tested for composite rank. Then the entropy of each index was calculated and classified using three clusters using cluster analysis. According to the consolidated results, smart growth index in neighbourhood 8 Shahrivar with entropy value 0.799 located in region 3 is in the first rank among smart neighbourhoods. The neighbourhoods of the school, Isarah, Imamate and Ayatollah Dastgheib are in second to fifth place, respectively. The last rank of this ranking is for the Kohnavard neighbourhood with an entropy value of 0.16 located in District 2 of Urmia.
Conclusion
Combined regression fitting shows that smart living variables have the greatest impact on predicting and developing the spatial structure of smart growth in urban neighbourhoods so that one unit change in the deviation of smart living indices will cause 0.680 units to change in integrated growth indices. The results emphasize the need for attention and prioritization of the Kohnavard neighbourhood in Zone 2 in future development and planning.