Demonstrating the applicability of using GPS and interview data to understand changes in use of space in response to new transport infrastructure: the case of the Cambridgeshire Guided Busway, UK
Bibliographic record
Abstract
Changes to the built environment can contribute to behavioural changes at the population level, including increases in physical activity. Evidence for how such interventions affect behaviour through qualitative understanding complements quantitative evidence of effectiveness of interventions, and may help to strengthen the basis for causal inference. We demonstrate the use of objective data to measure changes in spatial patterning of behaviour and physical activity in response to new transport infrastructure, as well as complementary interview data to understand why changes may have occurred. With a case study approach, we show how study design and a combination of data types can afford a stronger, more contextual package of evidence to meet methodological challenges of evaluating changes to the built environment. Longitudinal questionnaire, GPS, physical activity monitor, and interview data from the Commuting and Health in Cambridge study (2009–2012) were used to understand changes in mobility and physical activity in response to an environmental intervention, the opening of the Cambridgeshire Guided Busway. Firstly, aggregate maps were derived to explore the spatial patterning of physical activity before and after the Busway opened. Secondly, changes in the size of activity spaces were described and associations with personal and environmental characteristics investigated to understand whose mobility patterns changed. Lastly, narrative data and maps of movement for two individuals as case studies were used to investigate mechanisms behind use of the intervention and related behavioural changes. The Busway provided an alternative route for commuting, an additional space for leisure activity, and a new route for accessing greenspaces which may lead to potential changes in physical activity and wellbeing. Findings from studies which draw on multiple data types may be useful for informing the design and delivery of future public health interventions, an area where methods for evaluation and identification of plausible pathways to behavioural change remain underdeveloped.
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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 | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".