Evaluating urban environmental quality using multi criteria decision making

Hassan Mahmoudzadeh; Asghar Abedini; Farshid Aram; A Mosavi

doi:10.1016/j.heliyon.2024.e24921

Evaluating urban environmental quality using multi criteria decision making

Heliyon. 2024 Jan 28;10(3):e24921. doi: 10.1016/j.heliyon.2024.e24921. eCollection 2024 Feb 15.

Authors

Hassan Mahmoudzadeh¹, Asghar Abedini², Farshid Aram², A Mosavi^{3

4}

Affiliations

¹ Department of Geography and Urban Planning, University of Tabriz, Tabriz, Iran.
² Urban Planning Department, Urmia University, Urmia, Iran.
³ John von Neumann Faculty of Informatics, Budapest, Hungary.
⁴ Ludovika University of Public Service, Budapest, Hungary.

Abstract

In the urban environment, the quality refers to the capacity that provides and fulfills the material and spiritual needs of inhabitants. In order to improve the quality of urban life and standard of living for their citizens, planners and managers strive to raise Urban Environmental Quality. The objective of this study is to evaluate the quality of urban environment through the spatial analysis of a multi-criteria decision making (MCDM) method utilizing CRITIC. This research is conducted in district 4 and district 2 of the Tabriz Metropolis Municipality. In order to determine the quality of an urban environment, air pollution, vegetation coverage, land surface temperature, production of waste, population density, noise pollution, health care per capita, green spaces per capita, recreational spaces per capita, and distance from fault lines are used. After evaluating and producing environmental quality maps in two separate districts, 10 indicators were tested for significance and a comparative evaluation of two districts was conducted in order to determine which district was in better condition based on a statistical analysis of the T-test results. In accordance with the CRITIC method, there are significant differences between averages of waste production, population density, noise pollution, distance from fault lines, Land Surface Temperature, Normalized difference vegetation index, and distance from fault lines between the two districts. It appears that recreational space, air pollution, health care per capita, and green space per capita are not meaningfully different on averages. The preparation of environmental quality maps reveals the importance of meaningful indicators at the neighborhood level in two urban districts. In both districts by strengthening the continuity of the landscape through the development of ecological corridors and an increase in per capita can contribute to the improvement of the quality of the urban environment.

Keywords: Big Data; Soft computing; Spatial modeling; Urban development; Urban environment quality.