Using an audit tool (MAPS Global) to assess the characteristics of the physical environment related to walking for transport in youth: reliability of Belgian data

Virtual Assessment of Physical Activity-Related Built Environment in Soweto, South Africa: What Is the Role of Contextual Familiarity?

Understanding how urban environments shape physical activity is critical in rapidly urbanizing countries such as South Africa. We assessed the reliability of virtual audits for characterizing urban features related to physical activity in Soweto, South Africa. We used the Microscale Audit of Pedestrian Streetscapes Global tool to characterize pedestrian-related features from Google Street View images in four neighborhoods of Soweto. Neighborhoods were selected to represent different levels of deprivation. Inter-rater reliability was analyzed according to the rater's familiarity with the local area. The results show a higher inter-rater reliability was observed among auditors with greater contextual familiarity. Many measurements however generated inconclusive results due to either low variability in the raters' responses or the absence of the features in the streets. It is evident from our findings that virtual audits are efficient tools that can be used to assess the built environment. However, to ensure meaningful use of these tools in diverse settings, we recommend that auditors comprise of people with contextual familiarity.

Reliability assessment of the 'field audit for children's active transport routes to school' (FACTS) tool

BACKGROUND

Children's active travel to school is associated with physical activity and thus health. Safe Routes to School (SR2S) programs identify 'safe routes' to promote children's active travel to school. No field audit tool exists specifically to assess the microscale built environment of these 'safe routes' within Australia. This study describes the reliability assessment of the Field Audit for Children's Active Transport to School (FACTS) tool.

METHODS

The FACTS tool was developed using a multi-step process, including a literature search, expert opinion, and pilot testing. For the reliability assessment, two trained auditors collected data at eight schools over three weeks in April 2021. For inter-rater reliability, auditors conducted audits on the 'safe routes' for the same six schools. For intra-rater reliability, auditors conducted repeat measures on the 'safe routes' for four schools each (eight schools total), including three different schools each from the six used for inter-rater reliability and one additional school. Item-by-item reliability was assessed using Cohen's Kappa, Cohen's Weighted Kappa, and percentage agreement. The reliability of calculated domain scores was assessed using intraclass correlation coefficients.

RESULTS

For inter-rater reliability, 31 of the 45 (68.9%) items had moderate to almost perfect agreement, seven items (15.6%) had below moderate agreement, and a Kappa statistic could not be calculated for seven items (15.6%) due to constant values. For intra-rater reliability, 37 of the 45 (82.2%) items had moderate to almost perfect agreement, two items (4.4%) had below moderate agreement, and a Kappa statistic could not be calculated for six items (13.3%) due to constant values. For inter- and intra-rater reliability of the segment domain scores, three of the four domains had substantial to almost perfect agreement. For inter- and intra-rater reliability of the crossing domain scores, all four domains had moderate to almost perfect agreement. For inter- and intra-rater reliability of the segment, crossing, route, and school scores, all had substantial to perfect agreement.

CONCLUSIONS

The FACTS tool can reliably characterise the microscale built environment of promoted 'safe routes' for their use within SR2S programs, and should be considered for use in future SR2S programs within the suburban Australian context.

Meteorological gaps in audits of pedestrian environments: a scoping review

BACKGROUND

Weather and season are determinants of physical activity. Therefore, it is important to ensure built environments are designed to mitigate negative impacts of weather and season on pedestrians to prevent these losses. This scoping review aims to identify built environment audits of pedestrian environments developed for use during a specific weather condition or season. Secondly, this review aims to investigate gaps in the inclusion of relevant weather mitigating built environment features in pedestrian environment audit tools.

METHODS

Following a standard protocol, a systematic search was executed in CINAHL, Medline and Web of Science to identify built environment audit tools of pedestrian spaces. These databases were chosen since they are well-known to comprehensively cover health as well as multi-disciplinary research publications relevant to health. Studies were screened, and data were extracted from selected documents by two independent reviewers (e.g., psychometric properties and audit items included). Audit items were screened for the inclusion of weather mitigating built environment features, and the tool's capacity to measure temperature, precipitation, seasonal and sustainability impacts on pedestrians was calculated.

RESULTS

The search returned 2823 documents. After screening and full text review, 27 articles were included. No tool was found that was developed specifically for use during a specific weather condition or season. Additionally, gaps in the inclusion of weather mitigating items were found for all review dimensions (thermal comfort, precipitation, seasonal, and sustainability items). Poorly covered items were: (1) thermal comfort related (arctic entry presence, materials, textures, and colours of buildings, roads, sidewalk and furniture, and green design features); (2) precipitation related (drain presence, ditch presence, hazards, and snow removal features); (3) seasonal features (amenities, pedestrian scale lighting, and winter destinations and aesthetics); and (4) sustainability features (electric vehicle charging stations, renewable energy, car share, and bike share facilities).

CONCLUSIONS

Current built environment audit tools do not adequately include weather / season mitigating items. This is a limitation as it is important to investigate if the inclusion of these items in pedestrian spaces can promote physical activity during adverse weather conditions. Because climate change is causing increased extreme weather events, a need exists for the development of a new built environment audit tool that includes relevant weather mitigating features.

Training Computers to See the Built Environment Related to Physical Activity: Detection of Microscale Walkability Features Using Computer Vision

The study purpose was to train and validate a deep learning approach to detect microscale streetscape features related to pedestrian physical activity. This work innovates by combining computer vision techniques with Google Street View (GSV) images to overcome impediments to conducting audits (e.g., time, safety, and expert labor cost). The EfficientNETB5 architecture was used to build deep learning models for eight microscale features guided by the Microscale Audit of Pedestrian Streetscapes Mini tool: sidewalks, sidewalk buffers, curb cuts, zebra and line crosswalks, walk signals, bike symbols, and streetlights. We used a train−correct loop, whereby images were trained on a training dataset, evaluated using a separate validation dataset, and trained further until acceptable performance metrics were achieved. Further, we used trained models to audit participant (N = 512) neighborhoods in the WalkIT Arizona trial. Correlations were explored between microscale features and GIS-measured and participant-reported neighborhood macroscale walkability. Classifier precision, recall, and overall accuracy were all over >84%. Total microscale was associated with overall macroscale walkability (r = 0.30, p < 0.001). Positive associations were found between model-detected and self-reported sidewalks (r = 0.41, p < 0.001) and sidewalk buffers (r = 0.26, p < 0.001). The computer vision model results suggest an alternative to trained human raters, allowing for audits of hundreds or thousands of neighborhoods for population surveillance or hypothesis testing.

International evaluation of the Microscale Audit of Pedestrian Streetscapes (MAPS) Global instrument: comparative assessment between local and remote online observers

OBJECTIVES

The use of online imagery by non-local observers to conduct remote, centralized collection of streetscape audit data in international studies has the potential to enhance efficiency of collection and comparability of such data for research on built environments and health. The objectives of the study were to measure (1) the consistency in responses between local in-field observers and non-local remote online observers and (2) the reliability between in-country online observers and non-local remote online observers using the Microscale Audit of Pedestrian Streetscapes Global tool to characterize pedestrian-related features along streets in five countries.

METHODS

Consistency and inter-rater reliability were analyzed between local and non-local observers on a pooled database of 200 routes in five study regions (Melbourne, Australia; Ghent, Belgium; Curitiba, Brazil; Hong Kong, China; and Valencia, Spain) for microscale environmental feature subscales and item-level variables using the intraclass correlation coefficient (ICC).

RESULTS

A local in-field versus remote online comparison had an ICC of 0.75 (95 % CI: 0.68-0.80) for the grand total score. An ICC of 0.91 (95 % CI: 0.88-0.93) was found for the local online versus remote online comparison. Positive subscales yielded stronger results in comparison to negative subscales, except for the similarly poor-performing positive aesthetics/social characteristics.

CONCLUSIONS

This study demonstrated remote audits of microscale built environments using online imagery had good reliability with local in-field audits and excellent reliability with local online audits. Results generally supported remote online environmental audits as comparable to local online audits. This identification of low-cost and efficient data acquisition methods is important for expanding research on microscale built environments and physical activity globally.

Reliability of streetscape audits comparing on-street and online observations: MAPS-Global in 5 countries

BACKGROUND

Microscale environmental features are usually evaluated using direct on-street observations. This study assessed inter-rater reliability of the Microscale Audit of Pedestrian Streetscapes, Global version (MAPS-Global), in an international context, comparing on-street with more efficient online observation methods in five countries with varying levels of walkability.

METHODS

Data were collected along likely walking routes of study participants, from residential starting points toward commercial clusters in Melbourne (Australia), Ghent (Belgium), Curitiba (Brazil), Hong Kong (China), and Valencia (Spain). In-person on the street and online using Google Street View audits were carried out by two independent trained raters in each city. The final sample included 349 routes, 1228 street segments, 799 crossings, and 16 cul-de-sacs. Inter-rater reliability analyses were performed using Kappa statistics or Intraclass Correlation Coefficients (ICC).

RESULTS

Overall mean assessment times were the same for on-street and online evaluations (22 ± 12 min). Only a few subscales had Kappa or ICC values < 0.70, with aesthetic and social environment variables having the lowest overall reliability values, though still in the "good to excellent" category. Overall scores for each section (route, segment, crossing) showed good to excellent reliability (ICCs: 0.813, 0.929 and 0.885, respectively), and the MAPS-Global grand score had excellent reliability (ICC: 0.861) between the two methods.

CONCLUSIONS

MAPS-Global is a feasible and reliable instrument that can be used both on-street and online to analyze microscale environmental characteristics in diverse international urban settings.

Transit environments for physical activity: Relationship between micro-scale built environment features surrounding light rail stations and ridership in Houston, Texas

INTRODUCTION

Health professionals promote transport-related physical activity because travelers oftentimes walk or bike to and from transit stops or stations. Although previous studies have examined the associations between macro-scale built environment features surrounding light rail transit (LRT) stations (e.g., density) and LRT ridership, this study examined the associations between numerous micro-scale features (e.g., street-level noise pollution) and ridership.

METHODS

This analysis originated from the Houston Travel-Related Activity in Neighborhoods (TRAIN) Study, a project evaluating how an LRT extension impacted adult physical activity in Houston, Texas. In 2014, researchers used the Analytic Audit Tool to quantify 58 micro-scale built environment features within six categories: Land Use Environment, Transportation Environment, Facilities, Aesthetics, Signage, and Social Environment. Feature data were obtained from 590 street segments within 0.25 miles of 22 LRT stations. For each station, separate composite indices were created per category by averaging the computed feature scores (1-7) within each category, with higher scores signifying more physical activity-promoting features. Station-level LRT ridership data were obtained from monthly ridership reports for the 12 months following station opening. Linear mixed models were constructed to examine the associations of the six built environment categories with ridership, adjusting for season, weekday vs. weekend day, and station as a random intercept.

RESULTS

Holding all other variables constant, every one-unit increase in composite index scores for Transportation Environment and Social Environment was associated with an increase in daily ridership by 425 and 488 riders, respectively (p < 0.05). Every one-unit increase in composite index score for Signage was associated with a decrease of 722 riders daily (p < 0.05). The relations of Land Use Environment, Facilities, and Aesthetics with ridership were statistically null (p > 0.05).

CONCLUSIONS

Enhancements to the Transportation Environment and Social Environment may slightly increase overall LRT ridership, and consequently, utilitarian physical activity.

Spatial predictive properties of built environment characteristics assessed by drop-and-spin virtual neighborhood auditing

Background

Virtual neighborhood audits have been used to visually assess characteristics of the built environment for health research. Few studies have investigated spatial predictive properties of audit item responses patterns, which are important for sampling efficiency and audit item selection. We investigated the spatial properties, with a focus on predictive accuracy, of 31 individual audit items related to built environment in a major Metropolitan region of the Northeast United States.

Methods

Approximately 8000 Google Street View (GSV) scenes were assessed using the CANVAS virtual audit tool. Eleven trained raters audited the 360^° view of each GSV scene for 10 sidewalk-, 10 intersection-, and 11 neighborhood physical disorder-related characteristics. Nested semivariograms and regression Kriging were used to investigate the presence and influence of both large- and small-spatial scale relationships as well as the role of rater variability on audit item spatial properties (measurement error, spatial autocorrelation, prediction accuracy). Receiver Operator Curve (ROC) Area Under the Curve (AUC) based on cross-validated spatial models summarized overall predictive accuracy. Correlations between predicted audit item responses and select demographic, economic, and housing characteristics were investigated.

Results

Prediction accuracy was better within spatial models of all items accounting for both small-scale and large- spatial scale variation (vs large-scale only), and further improved with additional adjustment for rater in a majority of modeled items. Spatial predictive accuracy was considered ‘Excellent’ (0.8 ≤ ROC AUC < 0.9) for full models of all but four items. Predictive accuracy was highest and improved the most with rater adjustment for neighborhood physical disorder-related items. The largest gains in predictive accuracy comparing large- + small-scale to large-scale only models were among intersection- and sidewalk-items. Predicted responses to neighborhood physical disorder-related items correlated strongly with one another and were also strongly correlated with racial-ethnic composition, socioeconomic indicators, and residential mobility.

Conclusions

Audits of sidewalk and intersection characteristics exhibit pronounced variability, requiring more spatially dense samples than neighborhood physical disorder audits do for equivalent accuracy. Incorporating rater effects into spatial models improves predictive accuracy especially among neighborhood physical disorder-related items.

A Conceptual Framework for Modelling Safe Walking and Cycling Routes to High Schools

Active transport to or from school presents an opportunity for adolescents to engage in daily physical activity. Multiple factors influence whether adolescents actively travel to/from school. Creating safe walking and cycling routes to school is a promising strategy to increase rates of active transport. This article presents a comprehensive conceptual framework for modelling safe walking and cycling routes to high schools. The framework has been developed based on several existing relevant frameworks including (a) ecological models, (b) the “Five Es” (engineering, education, enforcement, encouragement, and evaluation) framework of transport planning, and (c) a travel mode choice framework for school travel. The framework identifies built environment features (land use mix, pedestrian/cycling infrastructure, neighbourhood aesthetics, and accessibility to local facilities) and traffic safety factors (traffic volume and speed, safe road crossings, and quality of path surface) to be considered when modelling safe walking/cycling routes to high schools. Future research should test this framework using real-world data in different geographical settings and with a combination of tools for the assessment of both macro-scale and micro-scale built environment features. To be effective, the modelling and creation of safe routes to high schools should be complemented by other interventions, including education, enforcement, and encouragement in order to minimise safety concerns and promote active transport.

School Neighbourhood Built Environment Assessment for Adolescents' Active Transport to School: Modification of an Environmental Audit Tool and Protocol (MAPS Global-SN)

School neighbourhood built environments (SN-BE) can influence adolescents' active transport to school habits. Typically, SN-BE assessment has involved micro-scale (i.e., environmental audits) or macro-scale (Geographic Information Systems (GIS)) assessment tools. However, existing environmental audits are time/resource-intensive and not specific to school neighbourhoods, while GIS databases are not generally purposed to include micro-scale data. This study evaluated the inter-rater reliability and feasibility of using a modified audit tool and protocol (Microscale Audit of Pedestrian Streetscapes Global-School Neighbourhood (MAPS Global-SN)) to assess the SN-BE of twelve secondary schools in Dunedin, New Zealand. Correlations between MAPS Global-SN and GIS measures of the SN-BE were also examined. Specifically, MAPS Global-SN audit and GIS spatial analysis (intersection density, residential density, land use mix, walkability) was conducted within a 0.5 km street-network buffer-zone around all twelve schools. Based on investigator and expert consultation, MAPS Global-SN included eight modifications to both auditing processes and items. Inter-rater reliability data was collected from two independent auditors across two schools. The feasibility of a condensed audit protocol (auditing one side of each street segment in the neighbourhood, compared to both sides) was also assessed. Results indicated the modified MAPS Global-SN tool had good to excellent inter-rater reliability and the condensed MAPS Global-SN audit protocol appeared to sufficiently represent the micro-scale SN-BE. Results also highlighted the complementary nature of micro- and macro-scale assessments. Further recommendations for SN-BE assessment are discussed.

Drop-And-Spin Virtual Neighborhood Auditing: Assessing Built Environment for Linkage to Health Studies

INTRODUCTION

Various built environment factors might influence certain health behaviors and outcomes. Reliable, resource-efficient methods that are feasible for assessing built environment characteristics across large geographies are needed for larger, more robust studies. This paper reports the item response prevalence, reliability, and rating time of a new virtual neighborhood audit protocol, drop-and-spin auditing, developed for assessment of walkability and physical disorder characteristics across large geographic areas.

METHODS

Drop-and-spin auditing, a method where a Google Street View scene was rated by spinning 360° around a point location, was developed using a modified version of the virtual audit tool Computer Assisted Neighborhood Visual Assessment System. Approximately 8,000 locations within Essex County, New Jersey were assessed by 11 trained auditors. Using a standardized protocol, 32 built environment items per a location within Google Street View were audited. Test-retest and inter-rater κ statistics were from a 5% subsample of locations. Data were collected in 2017-2018 and analyzed in 2018.

RESULTS

Roughly 70% of Google Street View scenes had sidewalks. Among those, two thirds were in good condition. At least 5 obvious items of garbage or litter were present in 41% of Google Street View scenes. Maximum test-retest reliability indicated substantial agreement (κ ≥0.61) for all items. Inter-rater reliability of each item, generally, was lower than test-retest reliability. The median time to rate each item was 7.3 seconds.

CONCLUSIONS

Compared with segment-based protocols, drop-and-spin virtual neighborhood auditing is quicker and similarly reliable for assessing built environment characteristics. Assessment of large geographies may be more feasible using drop-and-spin virtual auditing.

Reliability and validity of environmental audits using GigaPan® technology in parks

Park quality and features can contribute to more engaging places for play and recreation. However, assessing park characteristics remains a challenge. This study measured the reliability of GigaPan® as a method for assessing park characteristics as well as the validity of GigaPan® compared to Google Street View (GSV) and direct observation (DO). A total of 65 target areas (16 parks total) in Pittsburgh, PA were assessed using GigaPan®, GSV, and DO from July 2015–January 2016. For reliability and validity, 14 and 28 variables were examined, respectively. Cohen's kappa was used to assess inter-rater reliability. Sensitivity and specificity were used to measure validity. Of the 14 variables included in the inter-rater reliability analysis, five variables had almost perfect reliability (kappa > 0.80) and three variables had substantial reliability (kappa > 0.60). Of the 28 variables included in the validity analysis, GigaPan® was able to correctly classify 17 of the 28 variables and GSV was able to correctly classify 15 of the 28 variables with a sensitivity >80%. There were no significant differences between sensitivity and specificity between GSV and GigaPan®. GigaPan® performed similarly to GSV with DO being used as the gold standard. Further, GigaPan overall had high reliability among the features measured. A strength of GigaPan® is the ability to be implemented quickly in the field, making it a viable alternative to GSV particularly when temporality is an important factor.

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GigaPan®, a robot system for taking panoramic photos, had high reliability among the park attributes measured

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GigaPan® is a valid method to assess park attributes and offers some advantages over other methods

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GigaPan® had comparable validity to Google Earth for assessing features, amenities and incivilities

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GigaPan® should be used in future studies to help identify the association between park attributes and health outcomes

Systematic review of the use of Google Street View in health research: Major themes, strengths, weaknesses and possibilities for future research

We systematically reviewed the current use of Google Street View (GSV) in health research and characterized major themes, strengths and weaknesses in order to highlight possibilities for future research. Of 54 qualifying studies, we found that most used GSV to assess the neighborhood built environment, followed by health policy compliance, study site selection, and disaster preparedness. Most studies were conducted in urban areas of North America, Europe, or New Zealand, with few studies from South America or Asia and none from Africa or rural areas. Health behaviors and outcomes of interest in these studies included injury, alcohol and tobacco use, physical activity and mental health. Major strengths of using GSV imagery included low cost, ease of use, and time saved. Identified weaknesses were image resolution and spatial and temporal availability, largely in developing regions of the world. Despite important limitations, GSV is a promising tool for automated environmental assessment for health research. Currently untapped areas of health research using GSV include identification of sources of air, soil or water pollution, park design and usage, amenity design and longitudinal research on neighborhood conditions.

Validity of environmental audits using GigaPan® and Google Earth Technology

Background

Health behaviors are shaped by the context in which people live. However, documenting environmental context has remained a challenge. More specifically, direct observation techniques require large investments in time and resources and auditing the environment through web-based platforms has limited stability in spatio-temporal imagery. This study examined the validity of a new methodology, using GigaPan^® imagery, where we took photos locally and, stitched them together using GigaPan^® technology, and quantified environmental attributes from the resulting panoramic photo. For comparison, we examined validity using Google Earth imagery.

Methods

A total of 464 street segments were assessed using three methods: GigaPan^® audits, Google Earth audits, and direct observation audits. Thirty-seven different attributes were captured representing three broad constructs: land use, traffic and safety, and amenities. Sensitivity (i.e. the proportion of true positives) and specificity (i.e. the proportion of true negatives) were used to estimate the validity of GigaPan^® and Google Earth audits using direct observation audits as the gold standard.

Results

Using GigaPan^®, sensitivity was 80% or higher for 6 of 37 items and specificity was 80% or higher for 31 of 37 items. Using Google Earth, sensitivity was 80% or higher for 8 of 37 items and specificity was 80% or higher for 30 of 37 items. The validity of GigaPan^® and Google Earth was similar, with significant differences in sensitivity and specificity for 7 items and 2 items, respectively.

Conclusion

GigaPan^® performed well, especially when identifying features absent from the environment. A major strength of the GigaPan^® technology is its ability to be implemented quickly in the field relative to direct observation. GigaPan^® is a method to consider as an alternative to direct observation when temporality is prioritized or Google Earth imagery is unavailable.

Estimating city-level travel patterns using street imagery: A case study of using Google Street View in Britain

Background

Street imagery is a promising and growing big data source providing current and historical images in more than 100 countries. Studies have reported using this data to audit road infrastructure and other built environment features. Here we explore a novel application, using Google Street View (GSV) to predict travel patterns at the city level.

Methods

We sampled 34 cities in Great Britain. In each city, we accessed 2000 GSV images from 1000 random locations. We selected archived images from time periods overlapping with the 2011 Census and the 2011–2013 Active People Survey (APS). We manually annotated the images into seven categories of road users. We developed regression models with the counts of images of road users as predictors. The outcomes included Census-reported commute shares of four modes (combined walking plus public transport, cycling, motorcycle, and car), as well as APS-reported past-month participation in walking and cycling.

Results

We found high correlations between GSV counts of cyclists (‘GSV-cyclists’) and cycle commute mode share (r = 0.92)/past-month cycling (r = 0.90). Likewise, GSV-pedestrians was moderately correlated with past-month walking for transport (r = 0.46), GSV-motorcycles was moderately correlated with commute share of motorcycles (r = 0.44), and GSV-buses was highly correlated with commute share of walking plus public transport (r = 0.81). GSV-car was not correlated with car commute mode share (r = –0.12). However, in multivariable regression models, all outcomes were predicted well, except past-month walking. The prediction performance was measured using cross-validation analyses. GSV-buses and GSV-cyclists are the strongest predictors for most outcomes.

Conclusions

GSV images are a promising new big data source to predict urban mobility patterns. Predictive power was the greatest for those modes that varied the most (cycle and bus). With its ability to identify mode of travel and capture street activity often excluded in routinely carried out surveys, GSV has the potential to be complementary to new and traditional data. With half the world’s population covered by street imagery, and with up to 10 years historical data available in GSV, further testing across multiple settings is warranted both for cross-sectional and longitudinal assessments.

Online versus in-person comparison of Microscale Audit of Pedestrian Streetscapes (MAPS) assessments: reliability of alternate methods

Background

An online version of the Microscale Audit of Pedestrian Streetscapes (Abbreviated) tool was adapted to virtually audit built environment features supportive of physical activity. The current study assessed inter-rater reliability of MAPS Online between in-person raters and online raters unfamiliar with the regions.

Methods

In-person and online audits were conducted for a total of 120 quarter-mile routes (60 per site) in Phoenix, AZ and San Diego, CA. Routes in each city included 40 residential origins stratified by walkability and SES, and 20 commercial centers. In-person audits were conducted by raters residing in their region. Online audits were conducted by raters in the alternate location using Google Maps (Aerial and Street View) images. The MAPS Abbreviated Online tool consisted of four sections: overall route, street segments, crossings and cul-de-sacs. Items within each section were grouped into subscales, and inter-rater reliability (ICCs) was assessed for subscales at multiple levels of aggregation.

Results

Online and in-person audits showed excellent agreement for overall positive microscale (ICC = 0.86, 95% CI [0.80, 0.90]) and grand scores (ICC = 0.93, 95% CI [0.89, 0.95]). Substantial to near-perfect agreement was found for 21 of 30 (70%) subscales, valence, and subsection scores, with ICCs ranging from 0.62, 95% CI [0.50, 0.72] to 0.95, 95% CI [0.93, 0.97]. Lowest agreement was found for the aesthetics and social characteristics scores, with ICCs ranging from 0.07, 95% CI [−0.12, 0.24] to 0.27, 95% CI [0.10, 0.43].

Conclusions

Results support use of the MAPS Abbreviated Online tool to reliably assess microscale neighborhood features that support physical activity and may be used by raters residing in different geographic regions and unfamiliar with the audit areas.