Examining the role of urban form In shaping people's accessibility to opportunities: An exploratory spatial data analysis
Bibliographic record
Abstract
This study employs a comprehensive suite of accessibility indices to investigate whether American cities are designed in such a way that the locations of goods, services, and other opportunities favor certain socio-economic groups over others. In so doing, the study’s findings contribute to pressing policy issues such as social exclusion. Seven counties of the Louisville, KY-IN MSA serve as the study area for the investigation. Data are derived from three sources: a geocoded travel diary survey that was conducted in the study area in 2000, a geocoded database of all urban opportunities in the study area, and a database containing shortest path travel times between the locations of households and urban opportunities. Accessibility indices (i.e., gravity, cumulative opportunity, and proximity) are computed for households found in the trip diary survey. Furthermore, these indices are defined for 34 types of opportunities: four aggregate types (i.e., retail, service, leisure, and religious) and 30 disaggregate types representing the 10 most popular destinations for trips for each of the first three aggregate types. Non-parametric Wilcoxon rank sum tests are used to compare the accessibilities of five socio-economic groups (i.e., individuals residing in rural communities, individuals residing in single-person and single-parent households, individuals residing in low-income households, women, and the elderly) to their counterparts. Except for individuals residing in rural areas, our findings indicate that groups, which conventional wisdom would suggest are at risk of social exclusion, are not disadvantaged in terms of accessibility.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".