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

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Motlatso Godongwana, Khulu Gama, Vongani Maluleke, Lisa Micklesfield, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4310760/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Sep, 2024 Read the published version in Journal of Urban Health → Version 1 posted 5 You are reading this latest preprint version Abstract 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. Figures Figure 1 Figure 2 Key Message Familiarity with the local context improves the reliability of virtual audits of the built environment. Current ‘global’ auditing tools might not be entirely suitable for African cities, hence, contextual knowledge, local expertise and tailored training are necessary for effective virtual auditing of built environments. Thoughtful and transparent application of global tools with locally-specific adaptations is essential. INTRODUCTION By 2050, the majority urban growth is expected to occur in low-income and middle-income countries (LMICs) (UN-Habitat, 2022 ). South Africa exemplifies this trend, experiencing dramatic urban expansion in recent decades (Ritchie et al., 2024 ). This pivotal moment of expansion, transformation in urban planning, and informal urban development directly influences the built environment, which is closely linked to residents' physical activity levels (Bauman et al., 2012 ; Cerin et al., 2022 ). Understanding these dynamics is crucial for developing cities that promote healthier lifestyles (Lowe et al., 2022 ). Globally, one in four adults and four in five adolescents are insufficiently physically active despite the substantial health benefits of physical activity (Guthold et al., 2018 , 2020 ). It was estimated that only 60% of South Africans are meeting the World Health Organization (WHO) recommended standards for PA (Basu et al., 2022 ). Despite the WHO targets to reduce physical inactivity by 15% by 2030 (WHO 2020 ) and the ambitious policies to create healthy cities that will increase physical activity, several studies have found that there is a gap between what has been implemented and what is needed to achieve these targets (Cerin et al., 2022 ; Lowe et al., 2022 ). However, lack of data regarding the built environment in LMICs cities is a barrier for creating healthy environments that support physical activity (Dixon et al., 2021 ). Traditionally, the collection of the built environment data has been carried out through field audits whereby assessors walk a predetermined route through a specific area and use an observational form to assess predefined environmental characteristics (Dixon et al., 2021 ; Phillips et al., 2017 ). The Microscale Audit of Pedestrian Streetscapes Global version (MAPS-Global) is one of the tools used to measure and characterize features of the built environment such as street characteristics, sidewalks, intersections, streets aesthetics and other design features which may help to explain physical activity variation within a population (Cain et al., 2018 ). However, where these assessments are most relevant and needed, such as highly urbanized African cities, very little evidence exists on the nature of the built environment. Assessing the built environment as a first step is important for measuring the association between features of the built environment and physical activity. The use of virtual assessment tools has been advocated to reduce the time and resources required for conducting in-person audits (Fox et al., 2021 ; Mooney et al., 2017 ; Phillips et al., 2017 ). In high income countries, these tools have been found to be reliable assessments of the built environment (Andersen et al., 2021 ; Fox et al., 2021 ; Phillips et al., 2017 ; Zhu et al., 2017 ). Few researchers have explored the concept of contextual familiarity (living or working in the study area vs outside the study area) when assessing the reliability of the tools (Fox et al., 2021 ; Vanwolleghem et al., 2016 ; Zhu et al., 2017 ). This concept of familiarity becomes paramount when conclusions about the reliability of virtual tools are drawn from studies that do not contemplate African settings (Dixon et al., 2021 ; Rzotkiewicz et al., 2018 ). The African setting is unique because of its informality and factors including rapid urbanization. Thus, the aim of this study was to measure the reliability among raters with different levels of familiarity to a highly urbanized African city using the MAPS-Global tool. METHODS We conducted virtual audits of the built environment in Soweto, South Africa. Soweto is located in Johannesburg and was established in 1931 as a result of spatial segregation laws during the apartheid regime in South Africa. The region is now an urban settlement characterized by varying levels of socioeconomic deprivation with a population of approximately 1.9 million people living in a 200 km 2 area (SAHO, n.d.). We collected data from four small areas within Soweto: Chiawelo; Diepmeadow; Orlando East; and Protea Glen (Fig. 1 ). We purposively selected the areas to provide variation in socioeconomic deprivation (two areas of higher deprivation, two of lower deprivation). We determined deprivation level based on a methodology previously outlined (Prioreschi et al., 2022 ). Figure 1 : Reference map of the small areas audited and their deprivation level. To assess features of the built environment we used the global version of the Microscale Audit of Pedestrian Streetscapes (MAPS-Global) (Cain et al., 2018 ; Fox et al., 2021 ). The MAPS-Global tool was developed by researchers from the University of California San Diego and validated across countries with varying built environmental characteristics such as Australia, Belgium, Brazil, China, Spain (Queralt et al., 2021 ). The instrument comprises four sections, collected along a predefined route: (1) segment (measures block faces between intersections); (2) crossings (collects information on street intersections); (3) route (evaluates destinations and use, streetscape characteristics and aesthetic and social characteristics from a defined origin to a defined destination); (4) cul-de-sac (assesses amenities in dead ends). For this study, the routes were chosen using a geographically stratified sampling design. Specifically, the selection process involved three key features: firstly, random households (extracted from the OpenBuildings dataset (Sirko et al., 2021 )) were utilized as starting points; secondly, local points of interest (POIs) identified by the local team served as endpoints; and thirdly, the street network (from OpenStreetMaps (OpenStreetMap contributors, 2023 )) functioned as the connecting routes between these starting and ending points. Routes had a length between 400m and 700m. For each small area, sampled routes covered 25% of the total street network, which was considered to give an adequate representation of the built environment in that small area (McMillan et al., 2010 ). Data collection was conducted using the Google Street View (GSV) functionality within Google Earth Pro, where the designated routes were uploaded. The virtual audits were conducted between April and May 2023, and data collection took place in two phases. The auditing team consisted of 10 researchers collaborating with the Global Diet and Physical Activity (GDAR) network from five different countries (South Africa, Nigeria, Cameroon, the United States and United Kingdom). There were three categories of auditors, seven with no experience of the Soweto context (none), three who worked in the Soweto area (context), and two auditors from Soweto who had conducted field audits on the same streets nine months prior to the virtual assessment (field). The two field auditors are also within the context group. All auditors participated in an online training session to standardize the data collection methodology using the MAPS-Global material ( MAPS , n.d.). Subsequently, each auditor was tasked to assess seven routes in the phase one, and three routes in the phase two. All auditors assessed the same set of routes. Data entry was completed in REDCap, both via its online platform and mobile application. REDCap's functionality also enabled the upload of precise counts of segments and crossings (which varied by route), which were required for conducting the intraclass correlation coefficient (ICC) analysis (using R version 3.x). This study expands on the preliminary GDAR research assessing the built and food environments in four African cities (unpublished data), for which five items from MAPS global were incorporated into a different assessment of the food environment. Therefore, these five items were not scored in the current study. The list of items used in the current study can be found in supplementary table 1 . The inter-rater reliability of MAPS-Global was measured on several single-item indicators, sub-scales, valence scores (composite of positive or negative), and overall scores as described in Millstein et al., ( 2013 ). Numerical data was assessed with the ICC measurement and Cohen’s kappa coefficients for categorical data using the package “ pysch ” in R version 3.x. For this study the ICC and Cohen’s kappa were classified to indicate inter-rater reliability that was: ‘excellent’ (ICC ≥ 0.75), ‘good’ (0.60–0.74), ‘fair’ (0.40–0.59), and ‘poor’ (< 0.40) (Cicchetti, 2001 ). If the absence of features in the sub-scales, valence and overall scores creation was higher than 80% (i.e. places of worship, private recreational facilities, etc.), we excluded it from the analysis as there would not be enough variability for a correct interpretation of the ICC. RESULTS The feasibility and operational practicality of virtual assessments in Soweto We encountered two significant challenges in adhering to the MAPS-Global auditing procedures in phase 1 of data collection. Firstly, the coverage of several areas by GSV was incomplete, with GSV coverage for routes as low as 14.3%, limiting our ability to complete the audits in some streets. Secondly, inconsistencies in the number of segments and crossings recorded by different auditors hindered our ability to make comparisons. These challenges contributed to fluctuations in assessment times and inter-rater reliability. To address these issues in phase 2 of the data collection, we excluded routes with less than 75% GSV coverage. The challenge of discrepancies in the number of segments and crossings was partly derived from Soweto's street layouts, which differ from those assumed in MAPS-Global procedures. For example, some streets lack sidewalks, complicating the determination of safe pedestrian passages and crossing points. Acknowledging this limitation, the trainers standardized the number of segments and crossings for each route prior to audit, enabling consistent comparisons between auditors. In addition to the initial challenges, we also encountered significant network issues, as the auditors' limited broadband access delayed the auditing process. During the debriefing following the second phase of data collection, some auditors revealed they used Google searches to confirm or identify elements that were difficult to discern in the GSV images, particularly concerning land use, such as the presence of amenities along the route or the type of business present. In the second phase, a total of three routes, 19 segments and 16 crossings were analyzed virtually by all auditors. Most of the images used were approximately one year old, although some were as old as 10 years. The second phase of data collection showed marked improvement from the first phase, with GSV coverage rates reaching almost full coverage (96% vs. 76% in the first phase) for all routes. The data collection process was significantly more efficient, with the mean assessment time for routes at 7.7 ± 6 minutes (compared with 12.3 ± 11.6 min. in the first phase). Furthermore, the average time to assess segments and crossings was reduced to 4.1 ± 2.2 and 1.1 ± 0.1 minutes, respectively, (compared with 4.9 ± 6.3 min. and 2 ± 2.6 min. in the first phase) indicating a more consistent and streamlined auditing procedure. Data entries where image date coincided with the collection day were excluded from the analysis as they were considered a methodological error by auditors. Cul-de-sacs were not included in the analysis as there were none on the routes audited. Detailed descriptions of each route are delineated in the supplementary table 2 . The influence of familiarity in the IRR Overall, we found that contextual familiarity was associated with greater inter-rater reliability of virtual audits in Soweto. Figure 2 shows that for almost all the sub-scales, valence and overall scores, inter-rater reliability was higher when the online auditors were familiar with the context. We calculated measurements for only 30 out of 41 items or sub-scales, adhering to the criterion that required more than 80% presence for calculation. Figure 2 : Inter-rater reliability for virtual MAPS-Global assessments in Soweto, delineated by color-coded thresholds. Routes Detailed results for route reliability subscales and valence scores are presented in Table 1 . In overall, for the route section, reliability was markedly lower compared with segment and crossing, with no clear pattern by familiarity. Table 1 MAPS-Global - route section item-level and subscale inter-rater reliability and descriptive statistics. Route Section - Variable description # items (range of scores) Null Count (%) Mean (S.D) ICC. CI (95%) Sample items and overall subscale description Positive Destinations & Land Use Institutional-Service 3 (0–15) 12.5% f: 4.33 (2.25) 0.53 (-0.85–0.98) Bank, health-related professional, other service c: 4 (1.87) 0.31 (-0.35–0.97) n: 2.73 (2.05) 0.73 (0.21–0.99) Private Recreation 2 (0–10) 100% f: 0 NA Private indoor, private outdoor facility c: 0 n: 0 Public Recreation 4 (0–20) 83.3% f: 0.17 (0.41) NA Public indoor, public outdoor facility, park, trail c: 0.22 (0.44) n: 0.13 (0.35) Residential Mix 4 (0–3) 0.0% f: 1.5 (0.55) 0 Single family, multi-family, mixed, apartment over retail c: 1.33 (0.5) -0.5 n: 1 (0) 1.00 Restaurant-Entertainment 4 (0–20) 16.7% f: 2.67 (1.63) 0.75 (-0.70–0.99) Fast food, sit-down, café, entertainment c: 2.67 (2.65) 0.17 (-0.39–0.95) n: 2.27 (1.75) 0.83 (0.39–1.00) School 1 (0–5) 75% f: 0.33 (0.52) 1.00 (1.00–1.00) School land use c: 0.22 (0.44) 0.50 (-0.18–0.98) n: 0.27 (046) 0.33 (-0.09–0.96) Shops 8 (0–28) 0.0% f: 4.83 (2.04) 0.97 (0.22–1.00) Grocery, convenience store, bakery, drugstore, other retail, shopping mall, strip mall, open-air market c: 5.11 (1.83) 0.54 (-0.24–0.98) n: 4.27 (2.09) 0.35 (-0.09–0.97) Worship 1 (0–5) 100% f: 0 NA Place of worship c: 0 n: 0 Negative Destinations & Land Use Age-restricted bar or nightclub 1 (0–5) 100% f: 0 NA Age-restricted bar or nightclub c: 0 n: 0 Liquor or alcohol store 1 (0–5) 33.3% f: 0.67 (0.52) 1.00 (1.00–1.00) Liquor or alcohol store c: 0.67 (0.50) 1.00 (1.00–1.00) n: 0.73 (0.59) 0.86 (0.50–1.00) Valence & Overall Scores Positive DLU 28 (0-111) 0.0% f: 11.9 (5.12) 0.90 (-0.33–1.00) Sum of the positive DLU subscales c: 15.2 (3.93) 0.85 (0.20–1.00) n: 15.7 (3.20) 0.72 (0.21–0.99) Negative DLU 2 (0–10) 33.3% f: 0.67 (0.52) 1.00 (1.00–1.00) Sum of the negative DLU subscales c: 0.67 (0.50) 1.00 (1.00–1.00) n: 0.73 (0.59) 0.86 (0.50–1.00) Overall DLU 30 0.0% f: 15 (2.97) 0.88 (-0.42–1.00) Positive DLU - Negative DLU c: 14.6 (3.75) 0.83 (0.33–1.00) n: 11.2 (4.74) 0.64 (0.11–0.99) Streetscape Characteristics Positive Streetscape 25 (0–22) 20.8% f: 2.83 (2.86) 0.40 (-0.89–0.98) Transit, traffic calming, trash bins, benches, bike racks, bike lockers, bike docking stations, kiosks, hawkers. c: 2.67 (2.55) -0.43 (-0.49–0.04) n: 2.07 (1.87) 0.15 (-0.16–0.94) Aesthetics & Social Characteristics Positive Aesthetics / Social 4 (0–4) 54.2% f: 1 (1.26) 0 (-0.95–0.95) Hardscape, water, softscape, landscaping c: 0.67 (1.12) 0 (-0.43–0.93) n: 0.60 (0.63) 0 (-0.2–0.88) Negative Aesthetics / Social 6 (0–5) 8.3% f: 0.67 (0.52) 0 (-0.95–0.95) Buildings not maintained, graffiti, litter, dog fouling, physical disorder, highway near c: 0.89 (0.60) -0.25 (-0.47–0.83) n: 1.33 (0.49) -0.17 (-0.21–0.64) Overall Aesthetics / Social 10 25% f: 0.33 (1.75) 0 (-0.95–0.95) Positive Aesthetics/Social - Negative Aesthetics/Social c: -0.22 (1.64) -0.06 (-0.44–0.91) n: -0.73 (0.88) -0.08 (-0.22–0.83) * Familiarity: f: Field, c: Context, n: None. Agreement: ‘excellent’ (ICC ≥ 0.75), ‘good’ (0.60–0.74), ‘fair’ (0.40–0.59), and ‘poor’ (< 0.40). DLU: Destinations and Land Use; ICC: intraclass correlation coefficient; CI: Confidence Interval; SD: Standard Deviation; NA: Not Applicable. Destinations and land use We evaluated five out of eight positive sub-scales and single items, because three (place of worship, public and private recreation) had over 80% zeros. Interestingly, in the destinations and land use section, unfamiliarity with the local context was associated with higher reliability, in contrast with the valence and overall scores. Notably high agreement between auditors who were not familiar with the context was observed in the assessment of residential mix (ICC = 1.00, 95% CI [1.00, 1.00]), and restaurants and entertainment (ICC = 0.83, 95% CI [0.39, 1.00]), and institutional services (ICC = 0.73, 95% CI [0.21, 0.99]). Conversely, agreement on the numbers of schools (ICC = 1.00, 95% CI [1.00, 1.00]) and shops (ICC = 0.97, 95% CI [0.22, 1.00]) was higher in field-experienced auditors compared to the other familiarity groups. Among negative subscales, none of the raters identified any age-restricted bar or nightclub. The presence of liquor or alcohol stores yielded near-perfect agreement between all familiarity groups, with the field and context team achieving a perfect score. Upon measuring positive and negative valences along with the overall scores, the influence of familiarity became evident, as the field group exhibited higher ICC values than the other groups. Streetscape characteristics The streetscape's positive subscale revealed no cycling infrastructure across audited routes. Notably, the mean count for all familiarity groups was below three streetscape features per route (out of a maximum of 22) noting a very low presence of amenities in the surveyed routes. The ICC for all familiarity groups was poor, or with negative values, implying that any agreement among raters was lower than what would be expected by chance alone. Aesthetics and Social Both the positive and negative subscales, as well as the overall aesthetics and social scale, demonstrated a lack of consensus among auditors, unaffected by their familiarity with the area. Notably, no measurements exceeded 0, suggesting either random variations in ratings or lower agreement than by chance. Segments Detailed results for segment reliability subscales, valence and overall scores are presented in Table 2 . Two categories, cycling infrastructure and informal path or shortcut, had over 80% zeros, indicating a lack of these features in the audited areas. Familiarity with the local context variably influenced agreement levels for the different segment sub-scales. The field group showed higher agreement between the auditors in both the positive (ICC = 0.85, 95% CI [0.64, 0.94]) and negative (ICC = 0.85, 95% CI [0.65, 0.94]) valence scores, as well as the overall score. All the subscales with exception of the buffer had the field or context group achieving the highest reliability. Table 2 MAPS-Global - route section item-level and subscale inter-rater reliability and descriptive statistics. Segments Section - Variable description # items (range of scores) Null Count (%) Mean (S.D) ICC. CI (95%) Sample items and overall subscale description Positive Segment Subscales Bicycle Infrastructure 3 (0–15) 100% f: 0 NA Bank, health-related professional, other service c: 0 n: 0 Buffer 2 (0–5) 46.10% f: 1.95 (0.32) 0 (-0.44–0.44) Parking along street, buffer c: 1.95 (0.32) 0.05 (-0.18–0.37) n: 0.96 (1.41) 0.35 (0.16–0.60) Building Aesthetics and Design 1 (0–2) 41.30% f: 1.63 (0.75) 0.52 (0.10–0.78) Street windows c: 1.39 (0.86) 0.43 (0.15–0.70) n: 0.63 (0.73) 0.40 (0.19–0.64) Building Height-Road Width Ratio 5 (0–3) 4.70% f: 2.81 (0.7) 1.00 (1.00–1.00) Building height, setback and road width c: 2.87 (0.58) 0.50 (0.23–0.74) n: 2.45 (0.86) 0.26 (0.08–0.52) Building Height-Setback 4 (0–10) 4.61% f: 3.34 (1.36) 0.47 (0.03–0.75) Building height, smallest and largest setback c: 3.74 (1.41) 0.46 (0.18–0.72) n: 3.41 (1.82) 0.31 (0.18–0.72) Hawkers/Shops 1 (0–2) 71.60% f: 0.27 (0.45) 0.23 (-0.24–0.61) Hawkers/shops on sidewalk/pedestrian zone c: 0.43 (0.57) 0.31 (0.03–0.61) n: 0.22 (0.42) 0.10 (-0.04–0.34) Informal Path or Shortcut 1 (0–1) 85.20% f: 0.13 (0.34) NA Informal path connecting to something else c: 0.11 (0.31) n: 0.17 (0.38) Pedestrian infrastructure 5 (0–5) 44.70% f: 0.42 (0.55) 0.48 (0.05–0.76) Mid-segment crossing, pedestrian bridge, covered place to walk, street lights c: 0.42 (0.53) 0.63 (0.38–0.82) n: 0.67 (0.53) 0.40 (0.19–0.64) Shade 3 (0–6) 36.80% f: 0.61 (0.64) 0.56 (0.16–0.80) Number of trees, sidewalk coverage, shade c: 0.63 (0.62) 0.59 (0.34–0.80) n: 0.74 (0.64) 0.58 (0.38–0.78) Sidewalk 2 (0–6) 2.63% f: 4.18 (0.98) 0.46 (0.03–0-75) Sidewalk presence and width c: 3.98 (1.17) 0.29 (0.02–0.60) n: 3.88 (0.94) 0.42 (0.22–0.66) Valence & Overall scores Positive Segment 27 (0–45) 0.00% f: 15.3 (2.97) 0.85 (0.64–0.94) Sum of the positive segment subscales c: 14.9 (2.99) 0.75 (0.54–0.88) n: 13.1(4.14) 0.51 (0.31–0.73) Negative Segment 7 (0–13) 0.00% f: 5.24 (1.82) 0.85 (0.65–0.94) Sum of the negative segment single items (non-continuous sidewalk, trip hazards, obstructions, cars blocking walkway, slope, gates, driveways) c: 5.09 (1.71) 0.65 (0.41–0.83) n: 4.2 (1.43) 0.48 (0.27–0.66) Overall Segment 34 0.00% f: 10 (3.05) 0.76 (0.48–0.90) Positive Segment - Negative Segment c: 9.84 (3.17) 0.67 (0.44–0.84) n: 8.89 (4.3) 0.59 (0.39–0.78) * Familiarity: f: Field, c: Context, n: None. Agreement: ‘excellent’ (ICC ≥ 0.75), ‘good’ (0.60–0.74), ‘fair’ (0.40–0.59), and ‘poor’ (< 0.40). DLU: Destinations and Land Use; ICC: intraclass correlation coefficient; CI: Confidence Interval; SD: Standard Deviation; NA: Not Applicable. Crossings Detailed results for crossing reliability subscales, valence and overall scores are presented in Table 3 . The only positive crossing subscale that did not have more than 80% zero’s was the intersection control and signage sub-scale and this still only reported the mean number of features as < 1/ crossing. Notably, crosswalk amenities, which are crucial for safe road crossing, had 90% zeros. In the negative subscale assessing road width, the group unfamiliar with the context showed no agreement, while those with context knowledge scored fair to excellent agreement. In the overall score, only the field auditors reached an excellent score and the other groups a fair reliability. Table 3 MAPS-Global - route section item-level and subscale inter-rater reliability and descriptive statistics. Crossing Section - Variable description # items (range of scores) Null Count (%) Mean (S.D) ICC. CI (95%) Sample items and overall subscale description Positive Crossing Subscales Bicycle Feature 3 (0–3) 99.2% f: 0 NA Waiting area, bike lane crossing the crossing, bike signal c: 0.01 n: 0 Crosswalk Amenities 7 (0–7) 90.6% f: 0 NA Crossing aids, marked crosswalk, high visibility striping, different material, curb extension, raised crosswalk, refuge islands c: 0.09 (0.33) n: 0.13 (0.33) Curb Quality & Presence 3 (0–5) 88.30% f: 0.13 (0.55) NA Curb presence, curb ramps lined up, tactile paving c: 0.34 (1.11) n: 0.42 (1.11) Intersection Control & Signage 7 (0–8) 39.80% f: 0.63 (0.49) 1.00 (1.00–1.00) Yield signs, stop signs, traffic signal, traffic circle, pedestrian walk signals, push buttons, countdown signal c: 0.61 (0.53) 0.64 (0.37–0.84) n: 0.63 (0.49) 0.57 (0.35–0.79) Overpass 1 (0–1) 100% f: 0 NA Crossing on pedestrian overpass, bridge c: 0 n: 0 Negative Crossing Subscales Road Width 1 (0–2) 60.90% f: 0.06 (0.25) 1.00 (1.00–1.00) Distance of crossing leg c: 0.61 (0.52) 0.50 (0.20–0.76) n: 0.04 (0.20) 0.00 ( -0.12–0.23) Valence & Overall scores Positive Crossing 21 (0–24) 39.80% f: 0.63 (0.49) 1.00 (1.00–1.00) Sum of the positive crossing subscales c: 0.61 (0.53) 0.64 (0.37–0.84) n: 0.63 (0.49) 0.57 (0.35–0.79) Negative Crossing 1 (0–2) 60.90% f: 0.06 (0.25) 1.00 (1.00–1.00) Sum of the negative crossing subscales c: 0.61 (0.52) 0.50 (0.20–0.76) n: 0.04 (0.20) 0 (-0.12–0.23) Overall 22 39.80% f: 0.56 (0.5) 1.00 (1.00–1.00) Positive Crossing - Negative Crossing c: 0.58 (0.5) 0.58 (0.28–0.81) n: 0.0 (0.68) 0.55 (0.32–0.77) * Familiarity: f: Field, c: Context, n: None. Agreement: ‘excellent’ (ICC ≥ 0.75), ‘good’ (0.60–0.74), ‘fair’ (0.40–0.59), and ‘poor’ (< 0.40). DLU: Destinations and Land Use; ICC: intraclass correlation coefficient; CI: Confidence Interval; SD: Standard Deviation; NA: Not Applicable. Grand score reliability Detailed results for the positive, negative, and final overall score are presented in Table 4 . Across all three scales we observe a familiarity gradient, with the field group having higher reliability than the other groups. However, the mean scores among rater familiarity groups are notably similar, indicating that the presence or degree of context familiarity does not markedly distinguish these groups. Table 4 MAPS-Global - route section item-level and subscale inter-rater reliability and descriptive statistics. Overall Section - Variable description # items (range of scores) Null Count (%) Mean (S.D) ICC. CI (95%) Sample items and overall subscale description Overall positive 101 (0-205) 0.0% f: 35 (3.63) 0.95 (0.02–1.00) Positive DLU, positive streetscape, positive aesthetics/social, positive segment (mean of all segments), positive crossing (mean of all segments). c: 33.6 (4.41) 0.67 (-0.12–0.99) n: 27.8 (7.85) 0.83 (0.39–1.00) Overall negative 16 (0–22) 0.0% f: 7.01 (2.09) 0.98 (0.36–1.00) Negative DLU, negative aesthetics/social, negative segment (mean of all segments), negative crossing (mean of all crossings). c: 6.96 (1.66) 0.52 (-0.25–0.98) n: 7.03 (0.95) 0.76 (0.26–0.99) Overall 113 0.0% f: 28 (4.28) 0.95 (0.00–1.00) Overall Positive – Overall Negative c: 26.7 (4.71) 0.89 (0.31- 1.00) n: 20.8 (7.96) 0.84 (0.41–1.00) * Familiarity: f: Field, c: Context, n: None. Agreement: ‘excellent’ (ICC ≥ 0.75), ‘good’ (0.60–0.74), ‘fair’ (0.40–0.59), and ‘poor’ (< 0.40). DLU: Destinations and Land Use; ICC: intraclass correlation coefficient; CI: Confidence Interval; SD: Standard Deviation; NA: Not Applicable. [Insert Tables 1 – 4 here] DISCUSSION To our knowledge, this is the first study assessing the inter-rater reliability of MAPS-Global in an African urban context, and our findings highlight the importance of local knowledge in applying research tools effectively. Our findings suggest that auditors with local familiarity yielded more reliable audits compared to their international peers. Despite the global accessibility of virtual platforms like Google Street View and Google Earth for environmental assessment, our results underscore the value of contextual familiarity in enhancing the meaningful application and rigor of research tools. Incorporating contextual familiarity in global health research is crucial and at the same time an ethical responsibility when the tools we use have not been validated in the contexts where we work (Canelas et al., 2024 ). This practice risks oversimplifying complex realities and may lead to wrong or misleading conclusions. Rzotkiewicz et al., ( 2018 ) highlighted this gap, noting the absence of studies using Google Street View in Africa and limited research in Latin America and Asia, challenging the assumption of universal applicability for virtual audits. This study contributes from an African setting to the limited and inconclusive research on rater familiarity in evaluating the built environment. Two studies, one utilizing the MAPS-Global in Belgium and the other applying S-VAT tool in Norway, demonstrated that auditors with greater contextual familiarity or those conducting audits in-person reported higher inter-rater reliability (Andersen et al., 2021 ; Vanwolleghem et al., 2016 ). On the other hand, Fox et al., ( 2021 ) using MAPS-Global in five HICs countries and Zhu et al., ( 2017 ) using MAPS-Global in the US suggested that familiarity does not significantly affect virtual audit outcomes. Most studies that have used virtual tools to characterize the built environment have been carried out in HICs with different environmental characteristics compared to LMICs (Andersen et al., 2021 ; Curtis et al., 2013 ; Fox et al., 2021 ; Kelly et al., 2013 ; Vanwolleghem et al., 2016 ; Zhu et al., 2017 ). Their studies present findings from well-planned cities making it difficult to draw similar conclusions to a highly urbanized and dynamic environment of the Soweto township. Not only are LMICs underrepresented in virtual auditing, but there are also acute spatial inequalities in the amount of GSV coverage within cities depending on deprivation levels (Fry et al., 2020 ). Several authors have stated that virtual audits are a reliable alternative to in-person street audits but with a caveat that there is the need for high coverage and updated images (Fox et al., 2021 ; Vanwolleghem et al., 2016 ). Our study tackled the variability in coverage by selecting routes with at least 75% visibility. However, assessing the recency of GSV images posed a challenge, as image dates can vary widely even within the same location, depending on the viewing angle. Incorporating insights from a local team regarding acceptable image year ranges can significantly enhance the relevance of urban assessments, especially in LMICs where rapid urbanization is prevalent (Ritchie et al., 2024 ; UN-Habitat, 2022 ). This dialogue with the local auditors is crucial due to the continuous and fast-paced urban changes, underscoring the necessity for up-to-date imagery in environmental audits of the built environment. Our auditors faced several challenges, including issues with image quality, outdated images, blurriness, and obstructions, similar to studies elsewhere (Andersen et al., 2021 ; Fox et al., 2021 ; Rzotkiewicz et al., 2018 ). An unexpected challenge emerged from feedback sessions: some resorted to using search engines to identify unclear elements in images. This practice potentially introduced inconsistencies in the auditing process, emphasizing the need for clearer guidelines in the training to ensure uniformity in virtual environmental assessments (Fox et al., 2021 ; Griew et al., 2013 ; Gullón et al., 2015 ). Although it is not unusual to find a high percentage of absence in some features of the microscale (Fox et al., 2021 ; Phillips et al., 2017 ), we found our study to lack many of the features of MAPS-Global. This highlights the lack of many essential amenities in these low-resourced settings but also raises the concern whether MAPS-Global was indeed the correct tool for our study site. The choice of MAPS-Global, was made collectively by the GDAR Network members ( GDAR , 2023). To enhance representativity, future studies using global audit tools should consider the differences within and between neighborhoods, regions, and countries. It is important to note that developmental patterns, urbanization levels, land uses, and socioeconomic statuses of residents have different definitions, interpretations, and representations across the world. Similar to other virtual audits of the built environment, the subjective features of the built environment such as the streets or building aesthetics had the lowest IRR across all the MAPS-Global measurements (Andersen et al., 2021 ; Fox et al., 2021 ; Gullón et al., 2015 ; Zhu et al., 2017 ). Our findings indicated inter-rater reliability was highest in the land use section of the routes, similar to results from Zhu et al., ( 2017 ). The high frequency of null responses in the crossing section (indicating a lack of infrastructure to facilitate road crossing) is notable in a country where pedestrians constitute almost 40% of road traffic fatalities (International Transport Forum, 2019 ). Additionally, a study in a low-income community in South Africa, showed that half of the children walking to school alone report experiences with pedestrian collisions (Koekemoer et al., 2017 ). We acknowledge the limitations of our study posed by a small sample size, due to a combination of challenges, such as image coverage and limited time resources. While certain measurements, such as the pedestrian buffers, showed high percentage of agreement across all auditors, the statistical measures of reliability, such as ICC or Kappa, indicated lower values. This discrepancy stems from limited variability in exposure, where we obtained low ICC or Kappa despite a high percent agreement (McHugh, 2012 ; Zhu et al., 2017 ). However, the adoption of virtual auditing markedly decreased the costs associated with conducting audits, offering a more economical alternative to traditional in-field methods. The auditors categorized as field familiarity for this study conducted in-field audits in the same area nine months prior, and the virtual audits were completed faster (unpublished data). Additionally, while in-field audits necessitated pairs of auditors for safety reasons, virtual audits allowed individuals to work solo, providing flexibility and the comfort of conducting audits from any location. CONCLUSION We advocate for the thoughtful application of virtual audits in highly urbanized African cities. Utilizing raters familiar with the local context will help to ensure the benefits of virtual audits, including efficiency, resource allocation and safety, are realized. However, key factors need to be considered including image coverage, the recency of images, dynamic and the suitability of global tools to capture local environments. Careful evaluation of these aspects would ensure that auditors are well placed to conduct accurate and effective virtual audits. Declarations Ethical Approval An ethics waiver was received from the University of the Witwatersrand Institutional Ethics Review Board (HRECNMW22/06/08) as the research study did not include any participants. Consent for Publication Not applicable Availability of data and materials The datasets during and/or analysed during the current study available from the corresponding author on reasonable request. Competing Interests None of the authors declares to have any conflict of interest Funding This study was funded by the National Institute for Health Research (NIHR) (NIHR133205) using the UK aid from the UK Government to support global health research. The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. Authors’ contributions MG, TC wrote the manuscript, TC analysed data and interpreted the results, LM and LF reviewed the manuscript. KG, VM, DO, YF, BE, EN, AL, MRB, EC, AM, and TO contributed read and approved the final manuscript. Acknowledgements Not applicable References Andersen OK, O’Halloran SA, Kolle E, Lien N, Lakerveld J, Arah OA, Gebremariam MK. Adapting the SPOTLIGHT Virtual Audit Tool to assess food and activity environments relevant for adolescents: A validity and reliability study. Int J Health Geogr. 2021;20(1):4. https://doi.org/10.1186/s12942-021-00258-0 . Basu D, Kibachio Mwangi J, Mahura L, Nemkula B, Ngako K, Ntsie R, Pinkney-Atkinson V, Sayed B, Setlhare I, Singh S, Tucker J. (2022). National Strategic Plan for the Prevention and Control of Non-Communicable Diseases, 2022–2027 (p. 72). Departmen of Health. https://www.sancda.org.za/wp-content/uploads/2022/05/NCDs-NSP-SA-2022-2027.pdf . Bauman AE, Reis RS, Sallis JF, Wells JC, Loos RJ, Martin BW. Correlates of physical activity: Why are some people physically active and others not? Lancet. 2012;380(9838):258–71. https://doi.org/10.1016/S0140-6736(12)60735-1 . Cain KL, Geremia CM, Conway TL, Frank LD, Chapman JE, Fox EH, Timperio A, Veitch J, Van Dyck D, Verhoeven H, Reis R, Augusto A, Cerin E, Mellecker RR, Queralt A, Molina-García J, Sallis JF. Development and reliability of a streetscape observation instrument for international use: MAPS-global. Int J Behav Nutr Phys Act. 2018;15(1):19. https://doi.org/10.1186/s12966-018-0650-z . Canelas T, Godongwana M, Wayas FA, Lambert EV, Wasnyo Y, Foley L. Charting a Course: Navigating Rigor and Meaning in Global Health Research. J Phys Activity Health. 2024;21(2):113–4. https://doi.org/10.1123/jpah.2023-0611 . Cerin E, Sallis JF, Salvo D, Hinckson E, Conway TL, Owen N, Van Dyck D, Lowe M, Higgs C, Moudon AV, Adams MA, Cain KL, Christiansen LB, Davey R, Dygrýn J, Frank LD, Reis R, Sarmiento OL, Adlakha D, Giles-Corti B. Determining thresholds for spatial urban design and transport features that support walking to create healthy and sustainable cities: Findings from the IPEN Adult study. Lancet Global Health. 2022;10(6):e895–906. https://doi.org/10.1016/S2214-109X(22)00068-7 . Cicchetti DV. Methodological Commentary The Precision of Reliability and Validity Estimates Re-Visited: Distinguishing Between Clinical and Statistical Significance of Sample Size Requirements. J Clin Exp Neuropsychol. 2001;23(5):695–700. https://doi.org/10.1076/jcen.23.5.695.1249 . Curtis JW, Curtis A, Mapes J, Szell AB, Cinderich A. Using google street view for systematic observation of the built environment: Analysis of spatio-temporal instability of imagery dates. Int J Health Geogr. 2013;12(1):53. https://doi.org/10.1186/1476-072X-12-53 . Dixon BN, Ugwoaba UA, Brockmann AN, Ross KM. Associations between the built environment and dietary intake, physical activity, and obesity: A scoping review of reviews. Obes Rev. 2021;22(4):e13171. https://doi.org/10.1111/obr.13171 . Fox EH, Chapman JE, Moland AM, Alfonsin NE, Frank LD, Sallis JF, Conway TL, Cain KL, Geremia C, Cerin E, Vanwolleghem G, Van Dyck D, Queralt A, Molina-García J, Hino AAF, Lopes AA, dos Salmon S, Timperio J, A., Kershaw SE. International evaluation of the Microscale Audit of Pedestrian Streetscapes (MAPS) Global instrument: Comparative assessment between local and remote online observers. Int J Behav Nutr Phys Activity. 2021;18(1):84. https://doi.org/10.1186/s12966-021-01146-3 . Fry D, Mooney SJ, Rodríguez DA, Caiaffa WT, Lovasi GS. Assessing Google Street View Image Availability in Latin American Cities. J Urb Health. 2020;97(4):552–60. https://doi.org/10.1007/s11524-019-00408-7 . Global Diet and Activity Research Network . (2023, September 21). Global Diet and Activity Research Network. https://gdarnet.org/. Griew P, Hillsdon M, Foster C, Coombes E, Jones A, Wilkinson P. Developing and testing a street audit tool using Google Street View to measure environmental supportiveness for physical activity. Int J Behav Nutr Phys Activity. 2013;10(1):103. https://doi.org/10.1186/1479-5868-10-103 . Gullón P, Badland HM, Alfayate S, Bilal U, Escobar F, Cebrecos A, Diez J, Franco M. Assessing Walking and Cycling Environments in the Streets of Madrid: Comparing On-Field and Virtual Audits. J Urb Health. 2015;92(5):923–39. https://doi.org/10.1007/s11524-015-9982-z . Guthold R, Stevens GA, Riley LM, Bull FC. Worldwide trends in insufficient physical activity from 2001 to 2016: A pooled analysis of 358 population-based surveys with 1·9 million participants. Lancet Global Health. 2018;6(10):e1077–86. https://doi.org/10.1016/S2214-109X(18)30357-7 . Guthold R, Stevens GA, Riley LM, Bull FC. Global trends in insufficient physical activity among adolescents: A pooled analysis of 298 population-based surveys with 1·6 million participants. Lancet Child Adolesc Health. 2020;4(1):23–35. https://doi.org/10.1016/S2352-4642(19)30323-2 . International Transport Forum. (2019). Road Safety Annual Report 2019 . OECD. https://doi.org/10.1787/2f0e33fe-en . Kelly CM, Wilson JS, Baker EA, Miller DK, Schootman M. Using Google Street View to Audit the Built Environment: Inter-rater Reliability Results. Ann Behav Med. 2013;45(S1):108–12. https://doi.org/10.1007/s12160-012-9419-9 . Koekemoer K, Van Gesselleen M, Van Niekerk A, Govender R, Van As AB. Child pedestrian safety knowledge, behaviour and road injury in Cape Town, South Africa. Accid Anal Prev. 2017;99:202–9. https://doi.org/10.1016/j.aap.2016.11.020 . Lowe M, Adlakha D, Sallis JF, Salvo D, Cerin E, Moudon AV, Higgs C, Hinckson E, Arundel J, Boeing G, Liu S, Mansour P, Gebel K, Puig-Ribera A, Mishra PB, Bozovic T, Carson J, Dygrýn J, Florindo AA, Giles-Corti B. City planning policies to support health and sustainability: An international comparison of policy indicators for 25 cities. Lancet Global Health. 2022;10(6):e882–94. https://doi.org/10.1016/S2214-109X(22)00069-9 . MAPS. (n.d.). Dr. James Sallis. Retrieved February 7. 2024, from https://www.drjimsallis.com/maps . McHugh ML. Interrater reliability: The kappa statistic. Biochemia Med. 2012;276–82. https://doi.org/10.11613/BM.2012.031 . McMillan TE, Cubbin C, Parmenter B, Medina AV, Lee RE. Neighborhood sampling: How many streets must an auditor walk? Int J Behav Nutr Phys Activity. 2010;7(1):20. https://doi.org/10.1186/1479-5868-7-20 . Millstein RA, Cain KL, Sallis JF, Conway TL, Geremia C, Frank LD, Chapman J, Van Dyck D, Dipzinski LR, Kerr J, Glanz K, Saelens BE. Development, scoring, and reliability of the Microscale Audit of Pedestrian Streetscapes (MAPS). BMC Public Health. 2013;13(1):403. https://doi.org/10.1186/1471-2458-13-403 . Mooney SJ, Bader MDM, Lovasi GS, Teitler JO, Koenen KC, Aiello AE, Galea S, Goldmann E, Sheehan DM, Rundle AG. Street Audits to Measure Neighborhood Disorder: Virtual or In-Person? Am J Epidemiol. 2017;186(3):265–73. https://doi.org/10.1093/aje/kwx004 . OpenStreetMap contributors. (2023). OpenStreetMap database [Available under the Open Database Licence]. openstreetmap.org. Phillips CB, Engelberg JK, Geremia CM, Zhu W, Kurka JM, Cain KL, Sallis JF, Conway TL, Adams MA. Online versus in-person comparison of Microscale Audit of Pedestrian Streetscapes (MAPS) assessments: Reliability of alternate methods. Int J Health Geogr. 2017;16(1):27. https://doi.org/10.1186/s12942-017-0101-0 . Prioreschi A, Ware LJ, Draper CE, Lye S, Norris SA. Contextualising individual, household and community level factors associated with sugar-sweetened beverage intake and screen time in Soweto, South Africa. J Hunger Environ Nutr. 2022;1–17. https://doi.org/10.1080/19320248.2022.2032901 . Queralt A, Molina-García J, Terrón-Pérez M, Cerin E, Barnett A, Timperio A, Veitch J, Reis R, Silva AAP, Ghekiere A, Van Dyck D, Conway TL, Cain KL, Geremia CM, Sallis JF. Reliability of streetscape audits comparing on-street and online observations: MAPS-Global in 5 countries. Int J Health Geogr. 2021;20(1):6. https://doi.org/10.1186/s12942-021-00261-5 . Ritchie H, Samborska V, Roser M. (2024). Urbanization. Our World in Data . https://ourworldindata.org/urbanization . Rzotkiewicz A, Pearson AL, Dougherty BV, Shortridge A, Wilson N. Systematic review of the use of Google Street View in health research: Major themes, strengths, weaknesses and possibilities for future research. Health Place. 2018;52:240–6. https://doi.org/10.1016/j.healthplace.2018.07.001 . SAHO. (n.d.). South African History Online, towards a people’s history. Retrieved February 6. 2024, from https://www.sahistory.org.za/place/soweto-johannesburg#:~:text=In%202023%2C%20the%20population%20of,aged%207%20to%2017%20years . Sirko W, Kashubin S, Ritter M, Annkah A, Bouchareb YSE, Dauphin Y, Keysers D, Neumann M, Cisse M, Quinn J. (2021). Continental-Scale Building Detection from High Resolution Satellite Imagery (arXiv:2107.12283). arXiv. http://arxiv.org/abs/2107.12283 . UN-Habitat. (2022). Envisaging the future of cities. World Cities Report 2022 (p. 422). UN Habitat. https://unhabitat.org/sites/default/files/2022/06/wcr_2022.pdf . Vanwolleghem G, Ghekiere A, Cardon G, De Bourdeaudhuij I, D’Haese S, Geremia CM, Lenoir M, Sallis JF, Verhoeven H, Van Dyck D. 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. Int J Health Geogr. 2016;15(1):41. https://doi.org/10.1186/s12942-016-0069-1 . WHO. (2020). WHO guidelines on physical activity and sedentary behaviour. WHO Report. https://iris.who.int/bitstream/handle/10665/336656/9789240015128-eng.pdf . Zhu W, Sun Y, Kurka J, Geremia C, Engelberg JK, Cain K, Conway T, Sallis JF, Hooker SP, Adams MA. Reliability between online raters with varying familiarities of a region: Microscale Audit of Pedestrian Streetscapes (MAPS). Landsc Urban Plann. 2017;167:240–8. https://doi.org/10.1016/j.landurbplan.2017.06.014 . 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1","display":"","copyAsset":false,"role":"figure","size":458076,"visible":true,"origin":"","legend":"\u003cp\u003eReference map of the small areas audited and their deprivation level.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4310760/v1/501d9ffbd1bfcc8ec6091f1b.jpg"},{"id":55633495,"identity":"9520b623-cb14-4b0e-9c3d-3b19eb8e700a","added_by":"auto","created_at":"2024-04-30 20:04:35","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":200443,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eInter-rater reliability for virtual MAPS-Global assessments in Soweto, delineated by color-coded thresholds.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e*red indicates poor or no reliability (\u0026lt;0.40), red to orange highlights fair reliability (0.40–0.59), yellow to green to good (0.60–0.74), and green signifies excellent reliability (≥0.75). DLU: Destination and Land Use.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4310760/v1/7e23640b17ab9e448e74901a.jpg"},{"id":64619513,"identity":"76956021-fe19-444b-af58-aab660c85363","added_by":"auto","created_at":"2024-09-16 16:15:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2001700,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4310760/v1/e629ef06-4f9a-4988-93ae-642807060e79.pdf"},{"id":55633493,"identity":"882eb501-0184-4cb8-b5a2-925ffc072ba5","added_by":"auto","created_at":"2024-04-30 20:04:35","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":21092,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4310760/v1/5ffd982177e7f7fc24cfbd5d.docx"},{"id":55633496,"identity":"872ae9f1-1620-4ba9-ade0-2539570dca86","added_by":"auto","created_at":"2024-04-30 20:04:36","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":17036,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-4310760/v1/d46218fb60f77158ad766d7d.docx"}],"financialInterests":"","formattedTitle":"Virtual Assessment of Physical Activity-Related Built Environment in Soweto, South Africa: What is the Role of Contextual Familiarity?","fulltext":[{"header":"Key Message","content":"\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eFamiliarity with the local context improves the reliability of virtual audits of the built environment.\u003c/li\u003e\n \u003cli\u003eCurrent \u0026lsquo;global\u0026rsquo; auditing tools might not be entirely suitable for African cities, hence, contextual knowledge, local expertise and tailored training are necessary for effective virtual auditing of built environments.\u003c/li\u003e\n \u003cli\u003eThoughtful and transparent application of global tools with locally-specific adaptations is essential.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"INTRODUCTION","content":"\u003cp\u003eBy 2050, the majority urban growth is expected to occur in low-income and middle-income countries (LMICs) (UN-Habitat, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). South Africa exemplifies this trend, experiencing dramatic urban expansion in recent decades (Ritchie et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This pivotal moment of expansion, transformation in urban planning, and informal urban development directly influences the built environment, which is closely linked to residents' physical activity levels (Bauman et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Cerin et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Understanding these dynamics is crucial for developing cities that promote healthier lifestyles (Lowe et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGlobally, one in four adults and four in five adolescents are insufficiently physically active despite the substantial health benefits of physical activity (Guthold et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It was estimated that only 60% of South Africans are meeting the World Health Organization (WHO) recommended standards for PA (Basu et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Despite the WHO targets to reduce physical inactivity by 15% by 2030 (WHO \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and the ambitious policies to create healthy cities that will increase physical activity, several studies have found that there is a gap between what has been implemented and what is needed to achieve these targets (Cerin et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lowe et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, lack of data regarding the built environment in LMICs cities is a barrier for creating healthy environments that support physical activity (Dixon et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTraditionally, the collection of the built environment data has been carried out through field audits whereby assessors walk a predetermined route through a specific area and use an observational form to assess predefined environmental characteristics (Dixon et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Phillips et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The Microscale Audit of Pedestrian Streetscapes Global version (MAPS-Global) is one of the tools used to measure and characterize features of the built environment such as street characteristics, sidewalks, intersections, streets aesthetics and other design features which may help to explain physical activity variation within a population (Cain et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, where these assessments are most relevant and needed, such as highly urbanized African cities, very little evidence exists on the nature of the built environment. Assessing the built environment as a first step is important for measuring the association between features of the built environment and physical activity.\u003c/p\u003e \u003cp\u003eThe use of virtual assessment tools has been advocated to reduce the time and resources required for conducting in-person audits (Fox et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mooney et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Phillips et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In high income countries, these tools have been found to be reliable assessments of the built environment (Andersen et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Fox et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Phillips et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Few researchers have explored the concept of contextual familiarity (living or working in the study area vs outside the study area) when assessing the reliability of the tools (Fox et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Vanwolleghem et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zhu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This concept of familiarity becomes paramount when conclusions about the reliability of virtual tools are drawn from studies that do not contemplate African settings (Dixon et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rzotkiewicz et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The African setting is unique because of its informality and factors including rapid urbanization. Thus, the aim of this study was to measure the reliability among raters with different levels of familiarity to a highly urbanized African city using the MAPS-Global tool.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eWe conducted virtual audits of the built environment in Soweto, South Africa. Soweto is located in Johannesburg and was established in 1931 as a result of spatial segregation laws during the apartheid regime in South Africa. The region is now an urban settlement characterized by varying levels of socioeconomic deprivation with a population of approximately 1.9\u0026nbsp;million people living in a 200 km\u003csup\u003e2\u003c/sup\u003e area (SAHO, n.d.).\u003c/p\u003e \u003cp\u003eWe collected data from four small areas within Soweto: Chiawelo; Diepmeadow; Orlando East; and Protea Glen (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We purposively selected the areas to provide variation in socioeconomic deprivation (two areas of higher deprivation, two of lower deprivation). We determined deprivation level based on a methodology previously outlined (Prioreschi et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: Reference map of the small areas audited and their deprivation level.\u003c/p\u003e \u003cp\u003eTo assess features of the built environment we used the global version of the Microscale Audit of Pedestrian Streetscapes (MAPS-Global) (Cain et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Fox et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The MAPS-Global tool was developed by researchers from the University of California San Diego and validated across countries with varying built environmental characteristics such as Australia, Belgium, Brazil, China, Spain (Queralt et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The instrument comprises four sections, collected along a predefined route: (1) segment (measures block faces between intersections); (2) crossings (collects information on street intersections); (3) route (evaluates destinations and use, streetscape characteristics and aesthetic and social characteristics from a defined origin to a defined destination); (4) cul-de-sac (assesses amenities in dead ends).\u003c/p\u003e \u003cp\u003eFor this study, the routes were chosen using a geographically stratified sampling design. Specifically, the selection process involved three key features: firstly, random households (extracted from the OpenBuildings dataset (Sirko et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)) were utilized as starting points; secondly, local points of interest (POIs) identified by the local team served as endpoints; and thirdly, the street network (from OpenStreetMaps (OpenStreetMap contributors, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)) functioned as the connecting routes between these starting and ending points. Routes had a length between 400m and 700m. For each small area, sampled routes covered 25% of the total street network, which was considered to give an adequate representation of the built environment in that small area (McMillan et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Data collection was conducted using the Google Street View (GSV) functionality within Google Earth Pro, where the designated routes were uploaded.\u003c/p\u003e \u003cp\u003eThe virtual audits were conducted between April and May 2023, and data collection took place in two phases. The auditing team consisted of 10 researchers collaborating with the Global Diet and Physical Activity (GDAR) network from five different countries (South Africa, Nigeria, Cameroon, the United States and United Kingdom). There were three categories of auditors, seven with no experience of the Soweto context (none), three who worked in the Soweto area (context), and two auditors from Soweto who had conducted field audits on the same streets nine months prior to the virtual assessment (field). The two field auditors are also within the context group.\u003c/p\u003e \u003cp\u003eAll auditors participated in an online training session to standardize the data collection methodology using the MAPS-Global material (\u003cem\u003eMAPS\u003c/em\u003e, n.d.). Subsequently, each auditor was tasked to assess seven routes in the phase one, and three routes in the phase two. All auditors assessed the same set of routes.\u003c/p\u003e \u003cp\u003eData entry was completed in REDCap, both via its online platform and mobile application. REDCap's functionality also enabled the upload of precise counts of segments and crossings (which varied by route), which were required for conducting the intraclass correlation coefficient (ICC) analysis (using R version 3.x). This study expands on the preliminary GDAR research assessing the built and food environments in four African cities (unpublished data), for which five items from MAPS global were incorporated into a different assessment of the food environment. Therefore, these five items were not scored in the current study. The list of items used in the current study can be found in supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe inter-rater reliability of MAPS-Global was measured on several single-item indicators, sub-scales, valence scores (composite of positive or negative), and overall scores as described in Millstein et al., (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Numerical data was assessed with the ICC measurement and Cohen\u0026rsquo;s kappa coefficients for categorical data using the package \u0026ldquo;\u003cem\u003epysch\u003c/em\u003e\u0026rdquo; in R version 3.x. For this study the ICC and Cohen\u0026rsquo;s kappa were classified to indicate inter-rater reliability that was: \u0026lsquo;excellent\u0026rsquo; (ICC\u0026thinsp;\u0026ge;\u0026thinsp;0.75), \u0026lsquo;good\u0026rsquo; (0.60\u0026ndash;0.74), \u0026lsquo;fair\u0026rsquo; (0.40\u0026ndash;0.59), and \u0026lsquo;poor\u0026rsquo; (\u0026lt;\u0026thinsp;0.40) (Cicchetti, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). If the absence of features in the sub-scales, valence and overall scores creation was higher than 80% (i.e. places of worship, private recreational facilities, etc.), we excluded it from the analysis as there would not be enough variability for a correct interpretation of the ICC.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eThe feasibility and operational practicality of virtual assessments in Soweto\u003c/h2\u003e \u003cp\u003eWe encountered two significant challenges in adhering to the MAPS-Global auditing procedures in phase 1 of data collection. Firstly, the coverage of several areas by GSV was incomplete, with GSV coverage for routes as low as 14.3%, limiting our ability to complete the audits in some streets. Secondly, inconsistencies in the number of segments and crossings recorded by different auditors hindered our ability to make comparisons. These challenges contributed to fluctuations in assessment times and inter-rater reliability.\u003c/p\u003e \u003cp\u003eTo address these issues in phase 2 of the data collection, we excluded routes with less than 75% GSV coverage. The challenge of discrepancies in the number of segments and crossings was partly derived from Soweto's street layouts, which differ from those assumed in MAPS-Global procedures. For example, some streets lack sidewalks, complicating the determination of safe pedestrian passages and crossing points. Acknowledging this limitation, the trainers standardized the number of segments and crossings for each route prior to audit, enabling consistent comparisons between auditors.\u003c/p\u003e \u003cp\u003eIn addition to the initial challenges, we also encountered significant network issues, as the auditors' limited broadband access delayed the auditing process. During the debriefing following the second phase of data collection, some auditors revealed they used Google searches to confirm or identify elements that were difficult to discern in the GSV images, particularly concerning land use, such as the presence of amenities along the route or the type of business present.\u003c/p\u003e \u003cp\u003eIn the second phase, a total of three routes, 19 segments and 16 crossings were analyzed virtually by all auditors. Most of the images used were approximately one year old, although some were as old as 10 years. The second phase of data collection showed marked improvement from the first phase, with GSV coverage rates reaching almost full coverage (96% vs. 76% in the first phase) for all routes. The data collection process was significantly more efficient, with the mean assessment time for routes at 7.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6 minutes (compared with 12.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.6 min. in the first phase). Furthermore, the average time to assess segments and crossings was reduced to 4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2 and 1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 minutes, respectively, (compared with 4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3 min. and 2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6 min. in the first phase) indicating a more consistent and streamlined auditing procedure. Data entries where image date coincided with the collection day were excluded from the analysis as they were considered a methodological error by auditors. Cul-de-sacs were not included in the analysis as there were none on the routes audited. Detailed descriptions of each route are delineated in the supplementary table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eThe influence of familiarity in the IRR\u003c/h2\u003e \u003cp\u003eOverall, we found that contextual familiarity was associated with greater inter-rater reliability of virtual audits in Soweto. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that for almost all the sub-scales, valence and overall scores, inter-rater reliability was higher when the online auditors were familiar with the context. We calculated measurements for only 30 out of 41 items or sub-scales, adhering to the criterion that required more than 80% presence for calculation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e: \u003cem\u003eInter-rater reliability for virtual MAPS-Global assessments in Soweto, delineated by color-coded thresholds.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRoutes\u003c/h3\u003e\n\u003cp\u003eDetailed results for route reliability subscales and valence scores are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In overall, for the route section, reliability was markedly lower compared with segment and crossing, with no clear pattern by familiarity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMAPS-Global - route section item-level and subscale inter-rater reliability and descriptive statistics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoute Section - Variable description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e# items (range of scores)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNull Count (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean (S.D)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eICC. CI (95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSample items and overall subscale description\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003ePositive Destinations \u0026amp; Land Use\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eInstitutional-Service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3 (0\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e12.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 4.33 (2.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.53 (-0.85\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBank, health-related professional, other service\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 4 (1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.31 (-0.35\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 2.73 (2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.73 (0.21\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePrivate Recreation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2 (0\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePrivate indoor, private outdoor facility\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePublic Recreation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e4 (0\u0026ndash;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e83.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 0.17 (0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePublic indoor, public outdoor facility, park, trail\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 0.22 (0.44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 0.13 (0.35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eResidential Mix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e4 (0\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 1.5 (0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSingle family, multi-family, mixed, apartment over retail\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 1.33 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 1 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eRestaurant-Entertainment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e4 (0\u0026ndash;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e16.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 2.67 (1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.75 (-0.70\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eFast food, sit-down, caf\u0026eacute;, entertainment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 2.67 (2.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.17 (-0.39\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 2.27 (1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.83 (0.39\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSchool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1 (0\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 0.33 (0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSchool land use\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 0.22 (0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.50 (-0.18\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 0.27 (046)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.33 (-0.09\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eShops\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e8 (0\u0026ndash;28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 4.83 (2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97 (0.22\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eGrocery, convenience store, bakery, drugstore, other retail, shopping mall, strip mall, open-air market\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 5.11 (1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.54 (-0.24\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 4.27 (2.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.35 (-0.09\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eWorship\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1 (0\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePlace of worship\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNegative Destinations \u0026amp; Land Use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge-restricted bar or nightclub\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1 (0\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge-restricted bar or nightclub\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLiquor or alcohol store\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1 (0\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e33.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 0.67 (0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLiquor or alcohol store\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 0.67 (0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 0.73 (0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.86 (0.50\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eValence \u0026amp; Overall Scores\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePositive DLU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e28 (0-111)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 11.9 (5.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90 (-0.33\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSum of the positive DLU subscales\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 15.2 (3.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.85 (0.20\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 15.7 (3.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.72 (0.21\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNegative DLU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2 (0\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e33.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 0.67 (0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSum of the negative DLU subscales\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 0.67 (0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 0.73 (0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.86 (0.50\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eOverall DLU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 15 (2.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.88 (-0.42\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePositive DLU - Negative DLU\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 14.6 (3.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.83 (0.33\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 11.2 (4.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.64 (0.11\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStreetscape Characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePositive Streetscape\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e25 (0\u0026ndash;22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e20.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 2.83 (2.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.40 (-0.89\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTransit, traffic calming, trash bins, benches, bike racks, bike lockers, bike docking stations, kiosks, hawkers.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 2.67 (2.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.43 (-0.49\u0026ndash;0.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 2.07 (1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15 (-0.16\u0026ndash;0.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAesthetics \u0026amp; Social Characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePositive Aesthetics / Social\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e4 (0\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e54.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 1 (1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (-0.95\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHardscape, water, softscape, landscaping\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 0.67 (1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (-0.43\u0026ndash;0.93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 0.60 (0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (-0.2\u0026ndash;0.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNegative Aesthetics / Social\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e6 (0\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e8.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 0.67 (0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (-0.95\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBuildings not maintained, graffiti, litter, dog fouling, physical disorder, highway near\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 0.89 (0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.25 (-0.47\u0026ndash;0.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 1.33 (0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.17 (-0.21\u0026ndash;0.64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eOverall Aesthetics / Social\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 0.33 (1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (-0.95\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePositive Aesthetics/Social - Negative Aesthetics/Social\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: -0.22 (1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.06 (-0.44\u0026ndash;0.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: -0.73 (0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.08 (-0.22\u0026ndash;0.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003e* Familiarity: f: Field, c: Context, n: None. Agreement: \u0026lsquo;excellent\u0026rsquo; (ICC\u0026thinsp;\u0026ge;\u0026thinsp;0.75), \u0026lsquo;good\u0026rsquo; (0.60\u0026ndash;0.74), \u0026lsquo;fair\u0026rsquo; (0.40\u0026ndash;0.59), and \u0026lsquo;poor\u0026rsquo; (\u0026lt;\u0026thinsp;0.40). DLU: Destinations and Land Use; ICC: intraclass correlation coefficient; CI: Confidence Interval; SD: Standard Deviation; NA: Not Applicable.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDestinations and land use\u003c/h2\u003e \u003cp\u003eWe evaluated five out of eight positive sub-scales and single items, because three (place of worship, public and private recreation) had over 80% zeros. Interestingly, in the \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003edestinations and land use\u003c/span\u003e section, unfamiliarity with the local context was associated with higher reliability, in contrast with the valence and overall scores. Notably high agreement between auditors who were not familiar with the context was observed in the assessment of residential mix (ICC\u0026thinsp;=\u0026thinsp;1.00, 95% CI [1.00, 1.00]), and restaurants and entertainment (ICC\u0026thinsp;=\u0026thinsp;0.83, 95% CI [0.39, 1.00]), and institutional services (ICC\u0026thinsp;=\u0026thinsp;0.73, 95% CI [0.21, 0.99]). Conversely, agreement on the numbers of schools (ICC\u0026thinsp;=\u0026thinsp;1.00, 95% CI [1.00, 1.00]) and shops (ICC\u0026thinsp;=\u0026thinsp;0.97, 95% CI [0.22, 1.00]) was higher in field-experienced auditors compared to the other familiarity groups. Among negative subscales, none of the raters identified any age-restricted bar or nightclub. The presence of liquor or alcohol stores yielded near-perfect agreement between all familiarity groups, with the field and context team achieving a perfect score. Upon measuring positive and negative valences along with the overall scores, the influence of familiarity became evident, as the field group exhibited higher ICC values than the other groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStreetscape characteristics\u003c/h2\u003e \u003cp\u003eThe streetscape's positive subscale revealed no cycling infrastructure across audited routes. Notably, the mean count for all familiarity groups was below three streetscape features per route (out of a maximum of 22) noting a very low presence of amenities in the surveyed routes. The ICC for all familiarity groups was poor, or with negative values, implying that any agreement among raters was lower than what would be expected by chance alone.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eAesthetics and Social\u003c/h2\u003e \u003cp\u003eBoth the positive and negative subscales, as well as the overall aesthetics and social scale, demonstrated a lack of consensus among auditors, unaffected by their familiarity with the area. Notably, no measurements exceeded 0, suggesting either random variations in ratings or lower agreement than by chance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSegments\u003c/h2\u003e \u003cp\u003eDetailed results for segment reliability subscales, valence and overall scores are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Two categories, cycling infrastructure and informal path or shortcut, had over 80% zeros, indicating a lack of these features in the audited areas. Familiarity with the local context variably influenced agreement levels for the different segment sub-scales. The field group showed higher agreement between the auditors in both the positive (ICC\u0026thinsp;=\u0026thinsp;0.85, 95% CI [0.64, 0.94]) and negative (ICC\u0026thinsp;=\u0026thinsp;0.85, 95% CI [0.65, 0.94]) valence scores, as well as the overall score. All the subscales with exception of the buffer had the field or context group achieving the highest reliability.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMAPS-Global - route section item-level and subscale inter-rater reliability and descriptive statistics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan refid=\"Sec10\" class=\"InternalRef\"\u003eSegments\u003c/span\u003e Section - Variable description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e# items (range of scores)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNull Count (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean (S.D)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eICC. CI (95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSample items and overall subscale description\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003ePositive Segment Subscales\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBicycle Infrastructure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3 (0\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBank, health-related professional, other service\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBuffer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2 (0\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e46.10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 1.95 (0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (-0.44\u0026ndash;0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eParking along street, buffer\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 1.95 (0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05 (-0.18\u0026ndash;0.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 0.96 (1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.35 (0.16\u0026ndash;0.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBuilding Aesthetics and Design\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1 (0\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e41.30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 1.63 (0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.52 (0.10\u0026ndash;0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eStreet windows\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 1.39 (0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.43 (0.15\u0026ndash;0.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 0.63 (0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.40 (0.19\u0026ndash;0.64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBuilding Height-Road Width Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e5 (0\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e4.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 2.81 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBuilding height, setback and road width\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 2.87 (0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.50 (0.23\u0026ndash;0.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 2.45 (0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.26 (0.08\u0026ndash;0.52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBuilding Height-Setback\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e4 (0\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e4.61%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 3.34 (1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.47 (0.03\u0026ndash;0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBuilding height, smallest and largest setback\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 3.74 (1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.46 (0.18\u0026ndash;0.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 3.41 (1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.31 (0.18\u0026ndash;0.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHawkers/Shops\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1 (0\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e71.60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 0.27 (0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.23 (-0.24\u0026ndash;0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHawkers/shops on sidewalk/pedestrian zone\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 0.43 (0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.31 (0.03\u0026ndash;0.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 0.22 (0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.10 (-0.04\u0026ndash;0.34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eInformal Path or Shortcut\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1 (0\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e85.20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 0.13 (0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eInformal path connecting to something else\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 0.11 (0.31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 0.17 (0.38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePedestrian infrastructure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e5 (0\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e44.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 0.42 (0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.48 (0.05\u0026ndash;0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMid-segment crossing, pedestrian bridge, covered place to walk, street lights\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 0.42 (0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.63 (0.38\u0026ndash;0.82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 0.67 (0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.40 (0.19\u0026ndash;0.64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eShade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3 (0\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e36.80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 0.61 (0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.56 (0.16\u0026ndash;0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNumber of trees, sidewalk coverage, shade\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 0.63 (0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.59 (0.34\u0026ndash;0.80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 0.74 (0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.58 (0.38\u0026ndash;0.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSidewalk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2 (0\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2.63%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 4.18 (0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.46 (0.03\u0026ndash;0-75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSidewalk presence and width\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 3.98 (1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.29 (0.02\u0026ndash;0.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 3.88 (0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.42 (0.22\u0026ndash;0.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eValence \u0026amp; Overall scores\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePositive Segment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e27 (0\u0026ndash;45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 15.3 (2.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.85 (0.64\u0026ndash;0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSum of the positive segment subscales\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 14.9 (2.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.75 (0.54\u0026ndash;0.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 13.1(4.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.51 (0.31\u0026ndash;0.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNegative Segment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e7 (0\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 5.24 (1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.85 (0.65\u0026ndash;0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSum of the negative segment single items (non-continuous sidewalk, trip hazards, obstructions, cars blocking walkway, slope, gates, driveways)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 5.09 (1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.65 (0.41\u0026ndash;0.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 4.2 (1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.48 (0.27\u0026ndash;0.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eOverall Segment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 10 (3.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.76 (0.48\u0026ndash;0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePositive Segment - Negative Segment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 9.84 (3.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67 (0.44\u0026ndash;0.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 8.89 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.59 (0.39\u0026ndash;0.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003e* Familiarity: f: Field, c: Context, n: None. Agreement: \u0026lsquo;excellent\u0026rsquo; (ICC\u0026thinsp;\u0026ge;\u0026thinsp;0.75), \u0026lsquo;good\u0026rsquo; (0.60\u0026ndash;0.74), \u0026lsquo;fair\u0026rsquo; (0.40\u0026ndash;0.59), and \u0026lsquo;poor\u0026rsquo; (\u0026lt;\u0026thinsp;0.40). DLU: Destinations and Land Use; ICC: intraclass correlation coefficient; CI: Confidence Interval; SD: Standard Deviation; NA: Not Applicable.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCrossings\u003c/h2\u003e \u003cp\u003eDetailed results for crossing reliability subscales, valence and overall scores are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The only positive crossing subscale that did not have more than 80% zero\u0026rsquo;s was the intersection control and signage sub-scale and this still only reported the mean number of features as \u0026lt;\u0026thinsp;1/ crossing. Notably, crosswalk amenities, which are crucial for safe road crossing, had 90% zeros. In the negative subscale assessing road width, the group unfamiliar with the context showed no agreement, while those with context knowledge scored fair to excellent agreement. In the overall score, only the field auditors reached an excellent score and the other groups a fair reliability.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMAPS-Global - route section item-level and subscale inter-rater reliability and descriptive statistics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrossing Section - Variable description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e# items (range of scores)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNull Count (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean (S.D)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eICC. CI (95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSample items and overall subscale description\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003ePositive Crossing Subscales\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBicycle Feature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3 (0\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e99.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eWaiting area, bike lane crossing the crossing, bike signal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCrosswalk Amenities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e7 (0\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e90.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCrossing aids, marked crosswalk, high visibility striping, different material, curb extension, raised crosswalk, refuge islands\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 0.09 (0.33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 0.13 (0.33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCurb Quality \u0026amp; Presence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3 (0\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e88.30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 0.13 (0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCurb presence, curb ramps lined up, tactile paving\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 0.34 (1.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 0.42 (1.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eIntersection Control \u0026amp; Signage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e7 (0\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e39.80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 0.63 (0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eYield signs, stop signs, traffic signal, traffic circle, pedestrian walk signals, push buttons, countdown signal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 0.61 (0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.64 (0.37\u0026ndash;0.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 0.63 (0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.57 (0.35\u0026ndash;0.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eOverpass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1 (0\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCrossing on pedestrian overpass, bridge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNegative Crossing Subscales\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eRoad Width\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1 (0\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e60.90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 0.06 (0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDistance of crossing leg\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 0.61 (0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.50 (0.20\u0026ndash;0.76)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 0.04 (0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00 ( -0.12\u0026ndash;0.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eValence \u0026amp; Overall scores\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePositive Crossing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e21 (0\u0026ndash;24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e39.80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 0.63 (0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSum of the positive crossing subscales\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 0.61 (0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.64 (0.37\u0026ndash;0.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 0.63 (0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.57 (0.35\u0026ndash;0.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNegative Crossing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1 (0\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e60.90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 0.06 (0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSum of the negative crossing subscales\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 0.61 (0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.50 (0.20\u0026ndash;0.76)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 0.04 (0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (-0.12\u0026ndash;0.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e39.80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 0.56 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePositive Crossing - Negative Crossing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 0.58 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.58 (0.28\u0026ndash;0.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 0.0 (0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.55 (0.32\u0026ndash;0.77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003e*\u003c/b\u003e\u003cem\u003eFamiliarity: f: Field, c: Context, n: None. Agreement: \u0026lsquo;excellent\u0026rsquo; (ICC\u0026thinsp;\u0026ge;\u0026thinsp;0.75), \u0026lsquo;good\u0026rsquo; (0.60\u0026ndash;0.74), \u0026lsquo;fair\u0026rsquo; (0.40\u0026ndash;0.59), and \u0026lsquo;poor\u0026rsquo; (\u0026lt;\u0026thinsp;0.40). DLU: Destinations and Land Use; ICC: intraclass correlation coefficient; CI: Confidence Interval; SD: Standard Deviation; NA: Not Applicable.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eGrand score reliability\u003c/h2\u003e \u003cp\u003eDetailed results for the positive, negative, and final overall score are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Across all three scales we observe a familiarity gradient, with the field group having higher reliability than the other groups. However, the mean scores among rater familiarity groups are notably similar, indicating that the presence or degree of context familiarity does not markedly distinguish these groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMAPS-Global - route section item-level and subscale inter-rater reliability and descriptive statistics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall Section - Variable description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e# items (range of scores)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNull Count (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean (S.D)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eICC. CI (95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSample items and overall subscale description\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eOverall positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e101 (0-205)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 35 (3.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.95 (0.02\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePositive DLU, positive streetscape, positive aesthetics/social, positive segment (mean of all segments), positive crossing (mean of all segments).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 33.6 (4.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.67 (-0.12\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 27.8 (7.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.83 (0.39\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eOverall negative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e16 (0\u0026ndash;22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 7.01 (2.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.98 (0.36\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNegative DLU, negative aesthetics/social, negative segment (mean of all segments), negative crossing (mean of all crossings).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 6.96 (1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.52 (-0.25\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 7.03 (0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.76 (0.26\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef: 28 (4.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.95 (0.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eOverall Positive \u0026ndash; Overall Negative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec: 26.7 (4.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.89 (0.31- 1.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en: 20.8 (7.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.84 (0.41\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e* \u003cem\u003eFamiliarity: f: Field, c: Context, n: None. Agreement: \u0026lsquo;excellent\u0026rsquo; (ICC\u0026thinsp;\u0026ge;\u0026thinsp;0.75), \u0026lsquo;good\u0026rsquo; (0.60\u0026ndash;0.74), \u0026lsquo;fair\u0026rsquo; (0.40\u0026ndash;0.59), and \u0026lsquo;poor\u0026rsquo; (\u0026lt;\u0026thinsp;0.40). DLU: Destinations and Land Use; ICC: intraclass correlation coefficient; CI: Confidence Interval; SD: Standard Deviation; NA: Not Applicable.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Insert Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e here]\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eTo our knowledge, this is the first study assessing the inter-rater reliability of MAPS-Global in an African urban context, and our findings highlight the importance of local knowledge in applying research tools effectively. Our findings suggest that auditors with local familiarity yielded more reliable audits compared to their international peers. Despite the global accessibility of virtual platforms like Google Street View and Google Earth for environmental assessment, our results underscore the value of contextual familiarity in enhancing the meaningful application and rigor of research tools.\u003c/p\u003e \u003cp\u003eIncorporating contextual familiarity in global health research is crucial and at the same time an ethical responsibility when the tools we use have not been validated in the contexts where we work (Canelas et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This practice risks oversimplifying complex realities and may lead to wrong or misleading conclusions. Rzotkiewicz et al., (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) highlighted this gap, noting the absence of studies using Google Street View in Africa and limited research in Latin America and Asia, challenging the assumption of universal applicability for virtual audits.\u003c/p\u003e \u003cp\u003eThis study contributes from an African setting to the limited and inconclusive research on rater familiarity in evaluating the built environment. Two studies, one utilizing the MAPS-Global in Belgium and the other applying S-VAT tool in Norway, demonstrated that auditors with greater contextual familiarity or those conducting audits in-person reported higher inter-rater reliability (Andersen et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Vanwolleghem et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). On the other hand, Fox et al., (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) using MAPS-Global in five HICs countries and Zhu et al., (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) using MAPS-Global in the US suggested that familiarity does not significantly affect virtual audit outcomes. Most studies that have used virtual tools to characterize the built environment have been carried out in HICs with different environmental characteristics compared to LMICs (Andersen et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Curtis et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Fox et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kelly et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Vanwolleghem et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zhu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Their studies present findings from well-planned cities making it difficult to draw similar conclusions to a highly urbanized and dynamic environment of the Soweto township.\u003c/p\u003e \u003cp\u003eNot only are LMICs underrepresented in virtual auditing, but there are also acute spatial inequalities in the amount of GSV coverage within cities depending on deprivation levels (Fry et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Several authors have stated that virtual audits are a reliable alternative to in-person street audits but with a caveat that there is the need for high coverage and updated images (Fox et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Vanwolleghem et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Our study tackled the variability in coverage by selecting routes with at least 75% visibility. However, assessing the recency of GSV images posed a challenge, as image dates can vary widely even within the same location, depending on the viewing angle. Incorporating insights from a local team regarding acceptable image year ranges can significantly enhance the relevance of urban assessments, especially in LMICs where rapid urbanization is prevalent (Ritchie et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; UN-Habitat, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This dialogue with the local auditors is crucial due to the continuous and fast-paced urban changes, underscoring the necessity for up-to-date imagery in environmental audits of the built environment.\u003c/p\u003e \u003cp\u003eOur auditors faced several challenges, including issues with image quality, outdated images, blurriness, and obstructions, similar to studies elsewhere (Andersen et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Fox et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rzotkiewicz et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). An unexpected challenge emerged from feedback sessions: some resorted to using search engines to identify unclear elements in images. This practice potentially introduced inconsistencies in the auditing process, emphasizing the need for clearer guidelines in the training to ensure uniformity in virtual environmental assessments (Fox et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Griew et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Gull\u0026oacute;n et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Although it is not unusual to find a high percentage of absence in some features of the microscale (Fox et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Phillips et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), we found our study to lack many of the features of MAPS-Global. This highlights the lack of many essential amenities in these low-resourced settings but also raises the concern whether MAPS-Global was indeed the correct tool for our study site. The choice of MAPS-Global, was made collectively by the GDAR Network members (\u003cem\u003eGDAR\u003c/em\u003e, 2023). To enhance representativity, future studies using global audit tools should consider the differences within and between neighborhoods, regions, and countries. It is important to note that developmental patterns, urbanization levels, land uses, and socioeconomic statuses of residents have different definitions, interpretations, and representations across the world.\u003c/p\u003e \u003cp\u003eSimilar to other virtual audits of the built environment, the subjective features of the built environment such as the streets or building aesthetics had the lowest IRR across all the MAPS-Global measurements (Andersen et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Fox et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gull\u0026oacute;n et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Our findings indicated inter-rater reliability was highest in the land use section of the routes, similar to results from Zhu et al., (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The high frequency of null responses in the crossing section (indicating a lack of infrastructure to facilitate road crossing) is notable in a country where pedestrians constitute almost 40% of road traffic fatalities (International Transport Forum, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Additionally, a study in a low-income community in South Africa, showed that half of the children walking to school alone report experiences with pedestrian collisions (Koekemoer et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe acknowledge the limitations of our study posed by a small sample size, due to a combination of challenges, such as image coverage and limited time resources. While certain measurements, such as the pedestrian buffers, showed high percentage of agreement across all auditors, the statistical measures of reliability, such as ICC or Kappa, indicated lower values. This discrepancy stems from limited variability in exposure, where we obtained low ICC or Kappa despite a high percent agreement (McHugh, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Zhu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, the adoption of virtual auditing markedly decreased the costs associated with conducting audits, offering a more economical alternative to traditional in-field methods. The auditors categorized as field familiarity for this study conducted in-field audits in the same area nine months prior, and the virtual audits were completed faster (unpublished data). Additionally, while in-field audits necessitated pairs of auditors for safety reasons, virtual audits allowed individuals to work solo, providing flexibility and the comfort of conducting audits from any location.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eWe advocate for the thoughtful application of virtual audits in highly urbanized African cities. Utilizing raters familiar with the local context will help to ensure the benefits of virtual audits, including efficiency, resource allocation and safety, are realized. However, key factors need to be considered including image coverage, the recency of images, dynamic and the suitability of global tools to capture local environments. Careful evaluation of these aspects would ensure that auditors are well placed to conduct accurate and effective virtual audits.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn ethics waiver was received from the University of the Witwatersrand Institutional Ethics Review Board (HRECNMW22/06/08) as the research study did not include any participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets during and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone of the authors declares to have any conflict of interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded\u0026nbsp;by the National Institute for Health Research (NIHR) (NIHR133205) using the UK aid from the UK Government to support global health research. The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMG, TC wrote the manuscript, TC analysed data and interpreted the results, LM and LF reviewed the manuscript. KG, VM, DO, YF, BE, EN, AL, MRB, EC, AM, and TO contributed read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAndersen OK, O\u0026rsquo;Halloran SA, Kolle E, Lien N, Lakerveld J, Arah OA, Gebremariam MK. Adapting the SPOTLIGHT Virtual Audit Tool to assess food and activity environments relevant for adolescents: A validity and reliability study. Int J Health Geogr. 2021;20(1):4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12942-021-00258-0\u003c/span\u003e\u003cspan address=\"10.1186/s12942-021-00258-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBasu D, Kibachio Mwangi J, Mahura L, Nemkula B, Ngako K, Ntsie R, Pinkney-Atkinson V, Sayed B, Setlhare I, Singh S, Tucker J. (2022). \u003cem\u003eNational Strategic Plan for the Prevention and Control of Non-Communicable Diseases, 2022\u0026ndash;2027\u003c/em\u003e (p. 72). Departmen of Health. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.sancda.org.za/wp-content/uploads/2022/05/NCDs-NSP-SA-2022-2027.pdf\u003c/span\u003e\u003cspan address=\"https://www.sancda.org.za/wp-content/uploads/2022/05/NCDs-NSP-SA-2022-2027.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBauman AE, Reis RS, Sallis JF, Wells JC, Loos RJ, Martin BW. Correlates of physical activity: Why are some people physically active and others not? Lancet. 2012;380(9838):258\u0026ndash;71. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(12)60735-1\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(12)60735-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCain KL, Geremia CM, Conway TL, Frank LD, Chapman JE, Fox EH, Timperio A, Veitch J, Van Dyck D, Verhoeven H, Reis R, Augusto A, Cerin E, Mellecker RR, Queralt A, Molina-Garc\u0026iacute;a J, Sallis JF. Development and reliability of a streetscape observation instrument for international use: MAPS-global. Int J Behav Nutr Phys Act. 2018;15(1):19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12966-018-0650-z\u003c/span\u003e\u003cspan address=\"10.1186/s12966-018-0650-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCanelas T, Godongwana M, Wayas FA, Lambert EV, Wasnyo Y, Foley L. Charting a Course: Navigating Rigor and Meaning in Global Health Research. J Phys Activity Health. 2024;21(2):113\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1123/jpah.2023-0611\u003c/span\u003e\u003cspan address=\"10.1123/jpah.2023-0611\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCerin E, Sallis JF, Salvo D, Hinckson E, Conway TL, Owen N, Van Dyck D, Lowe M, Higgs C, Moudon AV, Adams MA, Cain KL, Christiansen LB, Davey R, Dygr\u0026yacute;n J, Frank LD, Reis R, Sarmiento OL, Adlakha D, Giles-Corti B. Determining thresholds for spatial urban design and transport features that support walking to create healthy and sustainable cities: Findings from the IPEN Adult study. Lancet Global Health. 2022;10(6):e895\u0026ndash;906. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S2214-109X(22)00068-7\u003c/span\u003e\u003cspan address=\"10.1016/S2214-109X(22)00068-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCicchetti DV. Methodological Commentary The Precision of Reliability and Validity Estimates Re-Visited: Distinguishing Between Clinical and Statistical Significance of Sample Size Requirements. J Clin Exp Neuropsychol. 2001;23(5):695\u0026ndash;700. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1076/jcen.23.5.695.1249\u003c/span\u003e\u003cspan address=\"10.1076/jcen.23.5.695.1249\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCurtis JW, Curtis A, Mapes J, Szell AB, Cinderich A. Using google street view for systematic observation of the built environment: Analysis of spatio-temporal instability of imagery dates. Int J Health Geogr. 2013;12(1):53. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1476-072X-12-53\u003c/span\u003e\u003cspan address=\"10.1186/1476-072X-12-53\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDixon BN, Ugwoaba UA, Brockmann AN, Ross KM. Associations between the built environment and dietary intake, physical activity, and obesity: A scoping review of reviews. Obes Rev. 2021;22(4):e13171. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/obr.13171\u003c/span\u003e\u003cspan address=\"10.1111/obr.13171\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFox EH, Chapman JE, Moland AM, Alfonsin NE, Frank LD, Sallis JF, Conway TL, Cain KL, Geremia C, Cerin E, Vanwolleghem G, Van Dyck D, Queralt A, Molina-Garc\u0026iacute;a J, Hino AAF, Lopes AA, dos Salmon S, Timperio J, A., Kershaw SE. International evaluation of the Microscale Audit of Pedestrian Streetscapes (MAPS) Global instrument: Comparative assessment between local and remote online observers. Int J Behav Nutr Phys Activity. 2021;18(1):84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12966-021-01146-3\u003c/span\u003e\u003cspan address=\"10.1186/s12966-021-01146-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFry D, Mooney SJ, Rodr\u0026iacute;guez DA, Caiaffa WT, Lovasi GS. Assessing Google Street View Image Availability in Latin American Cities. J Urb Health. 2020;97(4):552\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11524-019-00408-7\u003c/span\u003e\u003cspan address=\"10.1007/s11524-019-00408-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cem\u003eGlobal Diet and Activity Research Network\u003c/em\u003e. (2023, September 21). Global Diet and Activity Research Network. https://gdarnet.org/.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGriew P, Hillsdon M, Foster C, Coombes E, Jones A, Wilkinson P. Developing and testing a street audit tool using Google Street View to measure environmental supportiveness for physical activity. Int J Behav Nutr Phys Activity. 2013;10(1):103. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1479-5868-10-103\u003c/span\u003e\u003cspan address=\"10.1186/1479-5868-10-103\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGull\u0026oacute;n P, Badland HM, Alfayate S, Bilal U, Escobar F, Cebrecos A, Diez J, Franco M. Assessing Walking and Cycling Environments in the Streets of Madrid: Comparing On-Field and Virtual Audits. J Urb Health. 2015;92(5):923\u0026ndash;39. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11524-015-9982-z\u003c/span\u003e\u003cspan address=\"10.1007/s11524-015-9982-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuthold R, Stevens GA, Riley LM, Bull FC. Worldwide trends in insufficient physical activity from 2001 to 2016: A pooled analysis of 358 population-based surveys with 1\u0026middot;9 million participants. Lancet Global Health. 2018;6(10):e1077\u0026ndash;86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S2214-109X(18)30357-7\u003c/span\u003e\u003cspan address=\"10.1016/S2214-109X(18)30357-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuthold R, Stevens GA, Riley LM, Bull FC. Global trends in insufficient physical activity among adolescents: A pooled analysis of 298 population-based surveys with 1\u0026middot;6 million participants. Lancet Child Adolesc Health. 2020;4(1):23\u0026ndash;35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S2352-4642(19)30323-2\u003c/span\u003e\u003cspan address=\"10.1016/S2352-4642(19)30323-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInternational Transport Forum. (2019). \u003cem\u003eRoad Safety Annual Report 2019\u003c/em\u003e. OECD. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1787/2f0e33fe-en\u003c/span\u003e\u003cspan address=\"10.1787/2f0e33fe-en\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKelly CM, Wilson JS, Baker EA, Miller DK, Schootman M. Using Google Street View to Audit the Built Environment: Inter-rater Reliability Results. Ann Behav Med. 2013;45(S1):108\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12160-012-9419-9\u003c/span\u003e\u003cspan address=\"10.1007/s12160-012-9419-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoekemoer K, Van Gesselleen M, Van Niekerk A, Govender R, Van As AB. Child pedestrian safety knowledge, behaviour and road injury in Cape Town, South Africa. Accid Anal Prev. 2017;99:202\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.aap.2016.11.020\u003c/span\u003e\u003cspan address=\"10.1016/j.aap.2016.11.020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLowe M, Adlakha D, Sallis JF, Salvo D, Cerin E, Moudon AV, Higgs C, Hinckson E, Arundel J, Boeing G, Liu S, Mansour P, Gebel K, Puig-Ribera A, Mishra PB, Bozovic T, Carson J, Dygr\u0026yacute;n J, Florindo AA, Giles-Corti B. City planning policies to support health and sustainability: An international comparison of policy indicators for 25 cities. Lancet Global Health. 2022;10(6):e882\u0026ndash;94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S2214-109X(22)00069-9\u003c/span\u003e\u003cspan address=\"10.1016/S2214-109X(22)00069-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMAPS. (n.d.). Dr. James Sallis. Retrieved February 7. 2024, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.drjimsallis.com/maps\u003c/span\u003e\u003cspan address=\"https://www.drjimsallis.com/maps\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcHugh ML. Interrater reliability: The kappa statistic. Biochemia Med. 2012;276\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.11613/BM.2012.031\u003c/span\u003e\u003cspan address=\"10.11613/BM.2012.031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcMillan TE, Cubbin C, Parmenter B, Medina AV, Lee RE. Neighborhood sampling: How many streets must an auditor walk? Int J Behav Nutr Phys Activity. 2010;7(1):20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1479-5868-7-20\u003c/span\u003e\u003cspan address=\"10.1186/1479-5868-7-20\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMillstein RA, Cain KL, Sallis JF, Conway TL, Geremia C, Frank LD, Chapman J, Van Dyck D, Dipzinski LR, Kerr J, Glanz K, Saelens BE. Development, scoring, and reliability of the Microscale Audit of Pedestrian Streetscapes (MAPS). BMC Public Health. 2013;13(1):403. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1471-2458-13-403\u003c/span\u003e\u003cspan address=\"10.1186/1471-2458-13-403\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMooney SJ, Bader MDM, Lovasi GS, Teitler JO, Koenen KC, Aiello AE, Galea S, Goldmann E, Sheehan DM, Rundle AG. Street Audits to Measure Neighborhood Disorder: Virtual or In-Person? Am J Epidemiol. 2017;186(3):265\u0026ndash;73. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/aje/kwx004\u003c/span\u003e\u003cspan address=\"10.1093/aje/kwx004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOpenStreetMap contributors. (2023). \u003cem\u003eOpenStreetMap database\u003c/em\u003e [Available under the Open Database Licence]. openstreetmap.org.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePhillips CB, Engelberg JK, Geremia CM, Zhu W, Kurka JM, Cain KL, Sallis JF, Conway TL, Adams MA. Online versus in-person comparison of Microscale Audit of Pedestrian Streetscapes (MAPS) assessments: Reliability of alternate methods. Int J Health Geogr. 2017;16(1):27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12942-017-0101-0\u003c/span\u003e\u003cspan address=\"10.1186/s12942-017-0101-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrioreschi A, Ware LJ, Draper CE, Lye S, Norris SA. Contextualising individual, household and community level factors associated with sugar-sweetened beverage intake and screen time in Soweto, South Africa. J Hunger Environ Nutr. 2022;1\u0026ndash;17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/19320248.2022.2032901\u003c/span\u003e\u003cspan address=\"10.1080/19320248.2022.2032901\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQueralt A, Molina-Garc\u0026iacute;a J, Terr\u0026oacute;n-P\u0026eacute;rez M, Cerin E, Barnett A, Timperio A, Veitch J, Reis R, Silva AAP, Ghekiere A, Van Dyck D, Conway TL, Cain KL, Geremia CM, Sallis JF. Reliability of streetscape audits comparing on-street and online observations: MAPS-Global in 5 countries. Int J Health Geogr. 2021;20(1):6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12942-021-00261-5\u003c/span\u003e\u003cspan address=\"10.1186/s12942-021-00261-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRitchie H, Samborska V, Roser M. (2024). Urbanization. \u003cem\u003eOur World in Data\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ourworldindata.org/urbanization\u003c/span\u003e\u003cspan address=\"https://ourworldindata.org/urbanization\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRzotkiewicz A, Pearson AL, Dougherty BV, Shortridge A, Wilson N. Systematic review of the use of Google Street View in health research: Major themes, strengths, weaknesses and possibilities for future research. Health Place. 2018;52:240\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.healthplace.2018.07.001\u003c/span\u003e\u003cspan address=\"10.1016/j.healthplace.2018.07.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSAHO. (n.d.). South African History Online, towards a people\u0026rsquo;s history. Retrieved February 6. 2024, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.sahistory.org.za/place/soweto-johannesburg#:~:text=In%202023%2C%20the%20population%20of,aged%207%20to%2017%20years\u003c/span\u003e\u003cspan address=\"https://www.sahistory.org.za/place/soweto-johannesburg#:~:text=In%202023%2C%20the%20population%20of,aged%207%20to%2017%20years\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSirko W, Kashubin S, Ritter M, Annkah A, Bouchareb YSE, Dauphin Y, Keysers D, Neumann M, Cisse M, Quinn J. (2021). \u003cem\u003eContinental-Scale Building Detection from High Resolution Satellite Imagery\u003c/em\u003e (arXiv:2107.12283). arXiv. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://arxiv.org/abs/2107.12283\u003c/span\u003e\u003cspan address=\"http://arxiv.org/abs/2107.12283\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUN-Habitat. (2022). \u003cem\u003eEnvisaging the future of cities. World Cities Report 2022\u003c/em\u003e (p. 422). UN Habitat. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://unhabitat.org/sites/default/files/2022/06/wcr_2022.pdf\u003c/span\u003e\u003cspan address=\"https://unhabitat.org/sites/default/files/2022/06/wcr_2022.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVanwolleghem G, Ghekiere A, Cardon G, De Bourdeaudhuij I, D\u0026rsquo;Haese S, Geremia CM, Lenoir M, Sallis JF, Verhoeven H, Van Dyck D. 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. Int J Health Geogr. 2016;15(1):41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12942-016-0069-1\u003c/span\u003e\u003cspan address=\"10.1186/s12942-016-0069-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. (2020). \u003cem\u003eWHO guidelines on physical activity and sedentary behaviour.\u003c/em\u003e WHO Report. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://iris.who.int/bitstream/handle/10665/336656/9789240015128-eng.pdf\u003c/span\u003e\u003cspan address=\"https://iris.who.int/bitstream/handle/10665/336656/9789240015128-eng.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu W, Sun Y, Kurka J, Geremia C, Engelberg JK, Cain K, Conway T, Sallis JF, Hooker SP, Adams MA. Reliability between online raters with varying familiarities of a region: Microscale Audit of Pedestrian Streetscapes (MAPS). Landsc Urban Plann. 2017;167:240\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.landurbplan.2017.06.014\u003c/span\u003e\u003cspan address=\"10.1016/j.landurbplan.2017.06.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-urban-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jurh","sideBox":"Learn more about [Journal of Urban Health](https://www.springer.com/journal/11524)","snPcode":"11524","submissionUrl":"https://www.editorialmanager.com/jurh","title":"Journal of Urban Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4310760/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4310760/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding 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.\u003c/p\u003e","manuscriptTitle":"Virtual Assessment of Physical Activity-Related Built Environment in Soweto, South Africa: What is the Role of Contextual Familiarity?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-30 20:04:30","doi":"10.21203/rs.3.rs-4310760/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revise and resubmit","date":"2024-07-18T15:21:56+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-07-03T19:17:46+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-22T23:41:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-24T14:25:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Urban Health","date":"2024-04-24T03:05:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-urban-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jurh","sideBox":"Learn more about [Journal of Urban Health](https://www.springer.com/journal/11524)","snPcode":"11524","submissionUrl":"https://www.editorialmanager.com/jurh","title":"Journal of Urban Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a6de66af-9e9f-4af6-83e8-eda1d4a30c35","owner":[],"postedDate":"April 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-16T16:08:38+00:00","versionOfRecord":{"articleIdentity":"rs-4310760","link":"https://doi.org/10.1007/s11524-024-00914-3","journal":{"identity":"journal-of-urban-health","isVorOnly":false,"title":"Journal of Urban Health"},"publishedOn":"2024-09-10 15:57:50","publishedOnDateReadable":"September 10th, 2024"},"versionCreatedAt":"2024-04-30 20:04:30","video":"","vorDoi":"10.1007/s11524-024-00914-3","vorDoiUrl":"https://doi.org/10.1007/s11524-024-00914-3","workflowStages":[]},"version":"v1","identity":"rs-4310760","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4310760","identity":"rs-4310760","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}