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Record W6925550351 · doi:10.17605/osf.io/xytwv

Meteorological gaps in built environment audit tools: a scoping review

2023· other· en· W6925550351 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Science Framework · 2023
Typeother
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Property and Patents
Canadian institutionsnot available
Fundersnot available
KeywordsWalkabilityBuilt environmentPedestrianUrban planningUrban designScale (ratio)AuditPopulation

Abstract

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1.0 Overview: Walkability is a key concept in urban planning and public health, encompassing various aspects of the built environment that prioritize and promote pedestrian access (whether walking, wheeling or biking) in neighborhoods (Tobin et al., 2022). Extensive research has shown that neighborhoods with walkable built environments promote increased physical activity among residents which can prevent the development of chronic diseases such as diabetes, heart disease and cancer (Smith et al., 2017). Additionally, walkable neighborhoods offer numerous advantages, including increased mental wellbeing, environmental sustainability, community engagement, and economic vitality (Wilmut & Purcell, 2022). Given that a significant proportion of the population fails to meet the World Health Organization's recommended levels of physical activity (150 minutes of moderate to vigorous activity a week) (Guthold et al., 2018), enhancing neighborhood walkability and other non-motorized locomotion emerges as a valuable strategy to aid individuals reach this goal and enhance wellbeing on a community level. Walkability is often examined on a spectrum of microscale to macroscale elements. The microscale refers to specific features of the built environment that directly influence walkability (including pedestrian scale items such as the presence of benches, trees, art, and litter), while the macroscale considers broader contextual factors (including residential density and land use) (Frank et al., 2006). Notably, modifying microscale elements is often more cost-effective than making macroscale changes, while still yielding comparable or greater health benefits (Millington et al., 2009). Prioritizing improvements in microscale walkability is essential for city planners and policymakers to enhance community health through feasible, cost-effective methods. To enable effective microscale interventions, accurate measurement of microscale walkability within neighborhoods is crucial. One way this is achieved is through conducting built environment audits, which involve systematic evaluations of features that either facilitate or hinder pedestrian activity (Aghaabbasi et al., 2018; Brownson et al., 2009). However popular they may be, current built environment audit tools may have limitations in capturing walkability across different seasons and weather conditions (Curtis, 2017). Environmental variables such as weather, climate, and seasonal variations have been shown to significantly impact walkability, especially in regions with pronounced seasonal changes (Giles-Corti et al., 2005). For instance, active transportation often decreases during winter months due to icy or snowy sidewalks (Forsyth & Krizek, 2011). Considering the impending challenges of climate change, including more frequent extreme weather events, a consistent and comprehensive evaluation of walkability across diverse environments becomes even more critical (Sallis et al., 2016). The Housing for Health Research Team at the University of Alberta plans to conduct a scoping review to examine the inclusion of meteorological factors, such as seasons and weather, in microscale built environment audits. Firstly, to provide a comprehensive summary of microscale built environment audit tools that have been specifically developed for particular weather conditions or seasons; and secondly, to assess the extent to which current microscale audit tools incorporate environmental factors into their assessments.By leveraging this knowledge, the team aims to develop a winter audit tool that addresses the identified gaps. Additionally, they will evaluate the need for an audit tool that considers multiple environmental factors to enhance walkability across various weather and climate conditions. 2.0 Objectives and Questions: Objectives and corresponding research questions are as follows: Objectives Questions To identify, evaluate, and understand the methodologies of microscale-built environment audits designed for assessing the walkability of pedestrian environments in different seasons or extreme weather conditions 1.1 Have any microscale-built environment audits been specifically designed to assess the walkability of pedestrian environments during distinct seasons (e.g., winter), or periods of extreme weather conditions, such as heavy precipitation (e.g., rainstorms) or high winds? 1.2 What methodologies do microscale-built environment audits employ to measure the impact of specific meteorological conditions on the walkability of pedestrian environments? 1.3 Do current audits exhibit gaps in their inclusion of built environment variables, indicators, or dimensions that could potentially mitigate the impacts of extreme wind, temperatures, and precipitation on walkability? 3.0 Methodology To conduct a comprehensive review of the relevant literature, a systematic approach will be employed. This process involves the joint efforts of our research team, a subject librarian from the University of Alberta, and subject matter experts, ensuring the selection of the most pertinent databases for our research questions. Search Strategy: The search strategy is developed in accordance with the PCC (Population, Concept, Context) criteria based on the JBI (Joanna Briggs Institute) guidelines (Aromataris & Munn, 2017) ● Population (P): Any user of a pedestrian environment (e.g., pedestrians, cyclists, wheelchair users). ● Concept (C): Microscale built environment audit tools that have been developed specifically for or consider environmental factors (weather/climate or seasonal factors) that may impact the walkability of pedestrian environments. We are interested in identifying these tools, their development, reliability and validity. ● Context (C): Open/Global, looking at all relevant studies/resources, regardless of geographical location or specific population. Eligibility Criteria Inclusion Criteria: ● Studies focusing on microscale pedestrian environments. ● Research with a primary aim of developing or discussing the measurement properties (e.g., validity, reliability, feasibility) of at least one audit tool designed for assessing impacts on pedestrians. ● Types of studies to be included are empirical research studies, observational studies, interventional studies, systematic reviews, and methodological papers. ● Full text studies available in English. ● Studies published since the inception of each database until the date of the literature search. Exclusion Criteria: ● Studies that do not concentrate on microscale audit tools. ● Research not related to pedestrian environments. ● Studies in which the audit tool was initially created for purposes other than assessing impacts on pedestrians. ● Non-English language studies. ● We are not including ● Research that does not provide the actual audit tool or access to it. ● Studies that do not discuss the development, reliability, validity, or other measurement aspects of at least one audit tool. Databases: ● Web of Science ● Medline ● CINAHL Keywords Concepts RELATED TERMS Assessment Tools ("evaluation tool*" or "assessment tool*" or "measurement instrument*" or "audit tool*" or "audit instrument*") AND Related Audit Tools ("Central corridor pedestrian environment" or "Systematic pedestrian and cycling environmental scan" or "Pedestrian environment data scan" or "active neighborhood checklist" or "analytic audit tool" or "Irvine Minnesota inventory" or "walking suitability assessment form" or "neighborhood audit tool" or "path environment audit tool") AND Active Transportation and Built Environment (pedestrian* or "active transport*" or bicycl* or cyclist* or cycling or "rollerblading" or wheelchair* or sidewalk* or walkab* or "built environment*" or "urban design" or "urban planning" or "active commuting" or neighborhood* or "street design") 4.0 Data management and Selection process: The search results from all databases will be exported into reference management software to eliminate duplicates. The unique references will then be imported into Covidence (www.covidence.org), a web-based software platform specifically designed to streamline the production of systematic reviews. The selection process will occur in two stages: ● Pilot Testing: Prior to the actual title and abstract screening, a pilot testing phase will be conducted to ensure consistency and agreement between the independent reviewers and to refine the inclusion and exclusion criteria if necessary. A subset of references (n=20) will be randomly selected from the complete reference list to be used for the pilot testing. Each independent reviewer will conduct a blinded screening of the titles and abstracts of the pilot sample references in Covidence. The reviewers will categorize the references as 'include', 'exclude', or 'unsure' based on the predetermined criteria. Following completion of the screening, the reviewers will compare their categorizations, discuss any discrepancies, and aim to reach a consensus. If consensus cannot be reached, a third reviewer will be consulted. The pilot testing phase will help ensure a common understanding of the screening process and enhance the reliability and consistency of the subsequent full-text review. ● Title and Abstract Screening: Two independent reviewers will conduct a blinded screening of titles and abstracts of all unique references in Covidence. Each reviewer will categorize the references as 'include', 'exclude', or 'unsure' based on the predetermined inclusion and exclusion criteria. References categorized as 'include' or 'unsure' by either reviewer will proceed to the next stage. ● Full-text Review: The full text of all references included after the title and abstract screening will be retrieved and uploaded into Covidence. Two independent reviewers will assess each full-text article for eligibility based on the same inclusion and exclusion criteria. Each reviewer will categorize the full-text articles as

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.213
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0030.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0250.017

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.

Opus teacher head0.214
GPT teacher head0.333
Teacher spread0.119 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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