Bibliographic record
Abstract
INTRODUCTION Walkability captures the proximity between functionally complementary land uses (live, work, and play) and the directness of a route or the connectivity between destinations (Forsyth and Southworth 2008, Moudon et al. 2006). A walk score is an indicator of how friendly an area is for walking. This score is related to the benefits to society in terms of energy savings and improvement in health that a particular environment offers its residents. For example, a recently developed walk score Web site uses Google Maps, specifically Google's Local Search API (application programming interface), to find the stores, restaurants, bars, parks, and other amenities within walking distance of any address that is entered. Walk score currently includes addresses in the United States, Canada, and the United Kingdom. The algorithm behind this score indicates the walkability of a given route based on the fixed distance from one's home to nearby amenities. The number of amenities found nearby is the leading predictor of whether people will walk rather than take another travel mode. However, evaluating walkability is challenging because it requires the consideration of many subjective factors (Reid 2008). Moreover, all technical disciplines related to walkability have their own terminology and jargon (Abley 2005). During the urban and regional planning processes, the spaces and the environmental quality of neighborhoods are important factors that affect human health. Fortunately, spaces and neighborhood environmental quality can be improved through proper urban management. Thus, epidemiological studies have explored the relationship between access to nature and health. For example, a study in Sweden by Grahn and Stigsdotter (2003) demonstrated that the more often one visits areas, the less often one reports stress-related illness. One epidemiological study performed in the Netherlands (Maas et al. 2006) showed that residents of neighborhoods with abundant spaces tended, on average, to enjoy better general health. Another possible mechanism relating nature to health occurs during social interactions and social cohesion. Several studies conducted in Chicago suggest that spaces, especially trees, may facilitate positive social interactions between neighboring residents (Kweon, Sullivan, and Wiley 1998). Moreover, Pretty et al. (2007) summarized the effects of ten exercise case studies (including walking, cycling, horseback riding, fishing, canal boating, and conservation activities) in four regions of the United Kingdom on 260 participants. They determined that exercise (i.e., exercise in a area) led to significant improvements in self-esteem and in total mood. The results were not affected by the type, intensity, or duration of the exercise. Therefore, in many parts of the world, current urban planning activities are shifting toward a focus on green living. Many cities around the world now are developing integrated solutions to major environmental challenges and are transforming themselves into more sustainable and self-sufficient communities (Dizdaroglu, Yigitcanlar, and Dawes 2009). On the other hand, GIScience provides theory and methods that have the potential to facilitate the development of spatial analytical functions and various GIS data models, which improve the building of sophisticated GIS systems. Among them, the GIS road network data model is important for solving the problems in urban areas, such as transportation planning, retail market analysis, accessibility measurements, service allocation, etc. There are several network models in GIS, such as river networks, utility networks, and transportation networks or road networks. Understanding the road network patterns in urban areas is important for human mobility studies, because people live and move along the road networks. A network data model allows us to solve daily solutions, such as finding the shortest or quickest path between two locations, looking for the closest facilities within a specific distance, and estimating driving time. …
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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 | 0.001 | 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 | 0.002 |
| 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".