The neighbourhood built environment affects driving behaviours of older adults: a combined geographic information systems and machine learning method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Driving space is considered as the transaction between built environment features and driving behaviour. Driving keeps people active and engaged, particularly in later life. Using Geospatial Information Systems (GIS) and machine learning, this study examined the driving space of older drivers (aged ≥65; n = 134) living in St. Louis City, St. Louis County, USA from 1 January 2019, to 31 December 2019. Driving variables, such as total distance, trip frequency, ratio of short trips long trips, were analyzed. Built environment measures included transit accessibility, land use mix, and road network characteristics. Our findings indicate that the most important features predictive of driving space of older adults were public transit density and land use diversity within residential areas. This study demonstrates the non-linear relationship between built environment factors and driving space variables. Total distance has a complex relationship with each built environment variable. The differences in short-distance and long-distance driving are linked to varied land use types, balanced transport density, and intersection density. These findings highlight the value of using in-vehicle monitoring technologies to determine how specific characteristics of the built environment can influence everyday driving behaviours in later life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it