Have a personal or library account? Click to login
Using spatial and machine learning analyses to assess satisfaction with life in an urban historical downtown area Cover

Using spatial and machine learning analyses to assess satisfaction with life in an urban historical downtown area

Open Access
|Sep 2025

Figures & Tables

Figure 1.

Study area and sampling pointsSource: own elaboration

Figure 2.

Local Moran's I for the satisfaction with life variableSource: own elaboration

Figure 3.

Satisfaction with life (artificial neural network predictions)Source: own elaboration

Figure 4.

Decision treeSource: own elaboration

Variables used for the analyses

Variable
Satisfaction with life
Education
Health
Socioeconomic status
Household conditions
Work
Interaction with neighbours
Environmental conditions

Results of the regression models

Artificial Neural NetworkDecision Tree
RMSE: 0.804R2: 0.366RMSE: 0.822R2: 0.367
VariableRelative ImportanceVariableRelative Importance
Health23.90Work26.06
Work18.20Health18.55
Socioeconomic status14.20Socioeconomic status17.21
Interaction with neighbours12.90Household conditions15.35
Environmental conditions11.30Interaction with neighbours11.18
Household conditions10.50Education9.42
Education9.00Environmental conditions0.23
DOI: https://doi.org/10.2478/mgrsd-2025-0027 | Journal eISSN: 2084-6118 | Journal ISSN: 0867-6046
Language: English
Page range: 257 - 266
Submitted on: Nov 28, 2024
|
Accepted on: May 20, 2025
|
Published on: Sep 14, 2025
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2025 Jefferson Revelo, Pablo Cabrera-Barona, published by Faculty of Geography and Regional Studies, University of Warsaw
This work is licensed under the Creative Commons Attribution 4.0 License.