Enhancing urban quality of life evaluation using spatial multi criteria analysis

Abstract

Quality of Life (QoL) in urban environments represents people's well-being and is influenced by various factors. This paper introduces an innovative approach to promote urban equity by integrating spatial analysis and Multi-Criteria Decision-Making (MCDM). The proposed method focuses mainly on accessibility to urban facilities and environmental health factors. In this research, data fusion techniques were utilized to generate criteria maps for urban air pollution and heat islands, while factor analysis, Principal Component Analysis (PCA), and entropy were employed to provide a comprehensive assessment of equity related to these environmental factors in the case study area. Expert opinions, collected through the Analytic Hierarchy Process (AHP) and Best-Worst Method (BWM), were refined using Dempster-Shafer theory to adjust the weights assigned to each evaluation criterion. The results highlight that access to educational facilities and green spaces significantly impacts urban equity, with respective weights of 0.189 and 0.149. Air pollution was also identified as a critical factor, with a weight of 0.152. These findings underscore the potential of this integrated approach in assessing urban livability, particularly in Tehran, Iran.

Keywords: Analytic hierarchy process; Best–Worst method; Dempster–Shafer theory; Principal component analysis; Urban quality of life.

Fig. 1

Case study/ The authors created the maps using ArcGIS 10.8.1 software (https://support.esri.com).

Fig. 2

Flowchart of the study/ Flowchart created by the authors using Microsoft Paint.

Fig. 3

The criteria maps for QoL evaluation/ The authors created the maps using ArcGIS 10.8.1 software (https://support.esri.com).

Fig. 4

Comparing generated weights from PCA, Entropy and combined weight/ Graph created by the authors using Python 3.9

Fig. 5

Comparing weights from DST and Combined weights from (PCA + Entropy)/ Graph created by the authors using Python 3.9

Fig. 6

Spatial representation of QoL based on calculated weights derived from (PCA + Enropy), AHP, BWM, and DST/ The authors created the maps using ArcGIS 10.8.1 software (https://support.esri.com).

Fig. 7

Comparing Weight from various methods in radar graph/ Graph created by the authors using Python 3.9. C1: Network distance to education facilities/ C2: Network distance to medical facilities. C3: Network distance to recreational facilities/ C4: Euclidean distance to parks. C5: Euclidean distance to industry facilities/ C6: Noise pollution/ C7: Urban heat island. C8: Air pollution/ C9: Literacy rates/ C10: Unemployment rates.

Fig. 8

A difference map between DST and (PCA + Entropy)/ The authors created the maps using ArcGIS 10.8.1 software (https://support.esri.com).