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Modeling Quality of Urban Life Using a Geospatial Approach
The rapid global urbanization of the past century poses several challenges for planners and policy makers. In particular, the conflation of social and urban issues must be understood to create sustainable and livable urban places. In this regard, it was our aim to model and understand the relationship between urban characteristics and peoples’ perceived quality of urban life (QoUL) using statistical analysis and geospatial modeling. We selected objective variables representing urban characteristics based on literature and used principal components analysis to develop uncorrelated components. These components served as the independent variables in a multiple linear regression analysis. The subjective, dependent variables were extracted from a QoUL survey. Results indicated that only the Education/Income component is related to QoUL (R2 of 0.46). Using only single independent variables in a linear model explained 46% of the total variance—over 10% higher than any previously determined relationship between objective variables and subjective QoUL. Furthermore, we found that subjective high QoUL and subjective low QoUL were not strongly correlated, indicating that they are affected by different objective variables, respectively. This suggests that future efforts of increasing QoUL need to define their goals more precisely, as measures for increasing perceptions of high QoUL are likely different from measures for decreasing perceptions of low QoUL.
Modeling Quality of Urban Life Using a Geospatial Approach
The rapid global urbanization of the past century poses several challenges for planners and policy makers. In particular, the conflation of social and urban issues must be understood to create sustainable and livable urban places. In this regard, it was our aim to model and understand the relationship between urban characteristics and peoples’ perceived quality of urban life (QoUL) using statistical analysis and geospatial modeling. We selected objective variables representing urban characteristics based on literature and used principal components analysis to develop uncorrelated components. These components served as the independent variables in a multiple linear regression analysis. The subjective, dependent variables were extracted from a QoUL survey. Results indicated that only the Education/Income component is related to QoUL (R2 of 0.46). Using only single independent variables in a linear model explained 46% of the total variance—over 10% higher than any previously determined relationship between objective variables and subjective QoUL. Furthermore, we found that subjective high QoUL and subjective low QoUL were not strongly correlated, indicating that they are affected by different objective variables, respectively. This suggests that future efforts of increasing QoUL need to define their goals more precisely, as measures for increasing perceptions of high QoUL are likely different from measures for decreasing perceptions of low QoUL.
Modeling Quality of Urban Life Using a Geospatial Approach
Helena Merschdorf (author) / Michael E. Hodgson (author) / Thomas Blaschke (author)
2020
Article (Journal)
Electronic Resource
Unknown
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