Measuring Neighbourhood Spatial Accessibility to Urban Amenities: Does Aggregation Error Matter?
Why this work is in the frame
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Bibliographic record
Abstract
Neighbourhood spatial accessibility (NSA) refers to the ease with which residents of a given neighbourhood can reach amenities. NSA indicators have been used to inform urban policy issues, such as amenity provision and spatial equity. NSA measures are, however, susceptible to numerous methodological problems. We investigate one methodological issue, aggregation error, as it relates to the measurement of NSA. Aggregation error arises when, for the purpose of distance calculations, a single point is used to represent a neighbourhood, which in turn represents an aggregation of spatially distributed individuals. NSA to three types of recreational amenities (playgrounds, community halls, and leisure centres) in Edmonton, Alberta, Canada is used to assess whether aggregation error affects NSA measures. The authors use exploratory spatial data analysis techniques, including local indicators of spatial association, to examine aggregation-error effects on NSA. By integrating finer resolution data into NSA measures, we demonstrate that aggregation error does affect NSA indicators, but that the effect depends on the type of amenity under investigation. Aggregation error is particularly problematic when measuring NSA to amenities that are abundant and have highly localized service areas, such as playgrounds. We recommend that, when analyzing NSA to these types of amenities, researchers integrate finer resolution data to indicate the spatial distribution of individuals within neighbourhoods better, and hence reduce aggregation error.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 it