Development of Neighborhoods to Measure Spatial Indicators of Health
Bibliographic record
Abstract
INTRODUCTION In place-based research, geographic information systems (GIS) can be used to derive the context of place and further our understanding of whether place influences health. The external context may include the quality of the physical environment, resources (material and social), and infrastructures that can affect individual health (Pearce et al. 2006). These contextual factors act directly in some instances and indirectly in others (Evans and Stoddart 1994). A strong relationship exists between individual social economic status (SES) and the quality of the neighborhood environment; this may amplify the disparities in health between the richer and the more deprived (Yen and Syme 1999, Fiscella and Williams 2004, Braveman 2006). Researchers only recently have begun to study the impact of various neighborhood-level factors on individual health and health inequalities. In this research, natural neighborhoods within Ottawa, Canada, were delineated, using data from DMTI Spatial Inc., Statistics Canada, the City of Ottawa, the National Capital Commission, the Ottawa Real Estate Board, DigitalGlobe satellite imagery, field-based observations, and expert and community knowledge. These neighborhood units were used within a GIS to derive contextual health indicators in the natural environment, social environment, goods, services and amenities, and the built environment. These indicators were organized into a set of health-relevant domains inspired by Maslow's hierarchy of needs (Maslow 1968, 1970), which was the basis of the conceptual framework for this research. The ultimate goal was to determine which, if any, contextual indicators act as predictors of health outcomes. In the subsequent sections, research goals are described and the methodology used to delineate the neighborhoods and the conceptual framework and methods used to derive the indicators are provided. In conclusion, the initial results compare a measure of socioeconomic status within the neighborhoods and neighborhood health indicators. DESCRIPTION OF RESEARCH This study was initiated by a multidisciplinary team from the University of Ottawa who engage in collaborative community-based research aimed at reducing regional health inequalities. The practical objective was to work with city policy makers, planners, and program implementers to develop strategies and procedures to reduce health inequalities in Ottawa (Kristjansson et al. 2007). This project was focused on spatial inequalities in neighborhood resources for health, which can lead to inequities from a social justice perspective. More specifically, this project had four objectives: * To develop a methodology for defining natural* neighborhoods; * To gather data on a number of neighborhood social and physical resources/amenities; to essentially create a community inventory and subsequent measures of accessibility using GIS capabilities (c.f. Pearce et al. 2006); * To map the relationships between neighborhood socioeconomic status (SES), the distribution of resources necessary for health, and health outcomes; and * To share the evidence with decision-makers and relevant community organizations and to assess the usefulness of the GIS tools in a participatory process of neighborhood delineation. Because the project is still under way, an analysis of all community resource indicators with socioeconomic status (SES) is not yet completed. However, the preliminary results suggest clear intra-urban variations in neighborhood SES and relations with health indicators. As such, others should benefit from this experience and methods thus far. The health-outcome-indicator analysis is for a future publication. Study Area Ottawa, Ontario, is the national capital of Canada (see Figure 1), with a population of 846,802 and a population density per square kilometer of 258. …
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.
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 | 0.003 | 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 | 0.000 | 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) | 0.000 | 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".