Urban Design
F R; B Z
Abstract
Nowadays people’s health (physical, mental and social) is one of the main dilemmas of societies living in metropolitan areas. In this regard, urban design, as one of the influential disciplines among many others, seeks to find solutions to help solving these dilemmas by focusing on the physical ...
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Nowadays people’s health (physical, mental and social) is one of the main dilemmas of societies living in metropolitan areas. In this regard, urban design, as one of the influential disciplines among many others, seeks to find solutions to help solving these dilemmas by focusing on the physical and functional dimensions of the urban environment. The main questions of this paper are: what are the effective physical-environmental attributes on the social health of citizens and which of them are more effective? Accordingly, the main objective of this paper is to study and compare the effects of physical- environmental attributes on people’s social health. To achieve this aim, multiple regression method was applied to study, analyze and test the assumptions in order to (1) measure correlation among the mentioned indicators and social health and (2) to identify more effective indicators to predict social health. The study results demonstrate a few common indicators in both neighborhoods; some indicators in distinct neighborhoods have mutual correlation with social health. However, some of the indicators have no significant correlation with social health, these include age, job activities, suitable lighting of public spaces and car ownership. Some indicators have the most correlation in both neighborhoods; they have more certain correlation with social public life of people .These indicators include security and low crime rate in neighborhood, inclusiveness of public spaces, cohesion among neighborhood residents, safety and security of children in public spaces, sense of attachment to the neighborhood, education and townscape quality. In particular, security and low crime rates, sense of attachment to the neighborhood and inclusiveness of public spaces have a high correlation with resident’s social health. Results of the third assumption indicated that there were some differences among correlations between independent variables and social health in the two neighborhoods, one with a historical and traditional social and physical construction and another being a completely new grid neighborhood. Investigating the fourth assumption in the last stage, a limited number of indicators was extracted from the indicators with mutual correlation with social health to predict social health and form the regression equation. Regression results for Dardasht neighborhood indicate that only four indicators (among the indicators with a significant correlation with social health) are obtained as significant indicators in the regression results. Only the following indicators have enough significant predictive role for the dependent variable (social health): availability of gathering spaces, inclusiveness of public space, suitable security and low crime rate, social cohesion among neighborhood residents. However, there is not much difference between coefficients. The highest predicted effect is related to inclusiveness with 1.921 unstandardized coefficient B and the least predictive effect is related to social cohesion among community groups with 1.571 unstandardized coefficient B. The significant indicators in the regression equation for Mulla-Sadra neighborhood are more than those for Dardasht neighborhood, i.e. there are enough significant effect to predict the dependent variable. These indicators include security and crime rate, sense of attachment to the neighborhood, education level, inclusiveness of urban spaces, safety and security of children in public spaces, and suitable townscape of public spaces. Security and inclusiveness have the most coefficient value (2.345 and 1.939, respectively) and suitable townscape has the least coefficient value (1/374). The results indicate that only two indicators, security and inclusiveness, are common among regression results for the two neighborhoods, indicating the highest generalizability of these two indicators regarding different social and physical attributes of these two neighborhoods.
