Improving the Accuracy of Urban Environmental Quality Assessment Using Geographically-Weighted Regression Techniques
Bibliographic record
Abstract
Population growth around the world may cause an adverse impact on the environment and the human life. Thus, modeling the Urban Environmental Quality (UEQ) becomes indispensable for a better city planning and an efficient urban sprawl control. To evaluate the impact of city development, this study aims to utilize remote sensing and Geographic Information System (GIS) techniques to assess the UEQ in two major cities in Ontario, Canada. The main objectives of this research are: 1) to examine the relationship of multiple UEQ parameters derived from remote sensing, GIS and socio-economic data; 2) to evaluate some of the existing methods (e.g. linear regression, GIS overlay and Principal Component Analysis (PCA)) for assessing and integrating multiple UEQ parameters; 3) to propose a new method to weight urban and environmental parameters obtained from different data sources; 4) to develop a new method to validate the UEQ results with respect to three socio-economic indicators. Remote sensing, GIS and census data were first obtained to calculate various environmental, urban parameters and socio-economic indicators. The derived parameters and indicators were tested to emphasize their relationship to UEQ. Three geographically-Weighted Regression (GWR) techniques were used to integrate all these environmental, urban parameters and socio-economic indicators. Three key indicators including family income, the level of education and land value were used as a reference to validate the outcomes derived from the integration techniques. The results were evaluated by assessing the relationship between the extracted UEQ results and the three indicators. The findings showed that the GWR with spatial lag model represents an improved precision and accuracy up to 20% with respect to GIS overlay and PCA techniques. The final outcomes of the research can help the authorities and decision makers to understand the empirical relationships among regional science, urban morphology, real estate economics and economic geography.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".