A Reliable, Feasible Method to Observe Neighborhoods at High Spatial Resolution

Neighborhood conditions in a Swedish context-Two studies of reliability and validity of virtual systematic social observation using Google Street View

Introduction

The goal of these studies was to investigate the reliability and validity of virtual systematic social observation (virtual SSO) using Google Street View in a Swedish neighborhood context.

Methods

This was accomplished in two studies. Study 1 focused on interrater reliability and construct validity, comparing ratings conducted in-person to those done using Google Street View, across 24 study sites within four postal code areas. Study 2 focused on criterion validity of virtual SSO in terms of neighborhoods with low versus high income levels, including 133 study sites within 22 postal code areas in a large Swedish city. In both studies, assessment of the neighborhood context was conducted at each study site, using a protocol adapted to a Swedish context.

Results

Scales for Physical Decay, Neighborhood Dangerousness, and Physical Disorder were found to be reliable, with adequate interrater reliability, high consistency across methods, and high internal consistency. In Study 2, significantly higher levels of observed Physical Decay, Neighborhood Dangerousness, and signs of garbage or litter were observed in postal codes areas (site data was aggregated to postal code level) with lower as compared to higher income levels.

Discussion

We concluded that the scales within the virtual SSO with Google Street View protocol that were developed in this series of studies represents a reliable and valid measure of several key neighborhood contextual features. Implications for understanding the complex person-context interactions central to many theories of positive development among youth were discussed in relation to the study findings.

Association of the Neighborhood Built Environment With Incident and Prevalent Depression in the Rural South

INTRODUCTION

A neighborhood's built environment is associated with physical activity among its residents, and physical activity is associated with depression. Our study aimed to determine whether the built environment was associated with depression among residents of the rural South and whether observed associations were mediated by physical activity.

METHODS

We selected 2,000 participants from the Bogalusa Heart Study who had a valid residential address, self-reported physical activity (minutes/week), and a complete Center for Epidemiologic Study-Depression (CES-D) scale assessment from 1 or more study visits between 1998 and 2013. We assessed the built environment with the Rural Active Living Assessment street segment audit tool and developed built environment scores. The association between built environment scores and depression (CES-D ≥16) in geographic buffers of various radii were evaluated by using modified Poisson regression, and mediation by physical activity was evaluated with mixed-effects models.

RESULTS

Depression was observed in 37% of study participants at the first study visit. One-point higher physical security and aesthetic scores for the street segment of residence were associated with 1.07 times higher (95% CI, 1.02-1.11) and 0.96 times lower (95% CI, 0.92-1.00) baseline depression prevalence. One-point higher destination scores (ie, more commercial and civic facilities) in radius buffers of 0.25 miles or more were associated with 1.06 times (95% CI, 1.00-1.13) the risk of depression during follow-up. Neighborhood poverty (defined as percentage of residents with incomes below the federal poverty level and dichotomized at 28.3%) modified cross-sectional and longitudinal associations. Associations were not mediated by physical activity.

CONCLUSION

The built environment was associated with prevalence and risk of depression, and associations were stronger in high-poverty neighborhoods. Built environment improvements to promote physical activity should take neighborhood context into consideration to minimize negative side effects on mental health in high-poverty communities.

A dual-method approach toward measuring the built environment - sampling optimization, validity, and efficiency of using GIS and virtual auditing

In recent years, GIS and virtual auditing have been widely used to measure the built environment, and each method carries its strengths and weaknesses. To generate higher quality, more cost-effective, and less time-consuming measures, it is necessary to explore dual- or multi-method strategy toward sampling optimization, improvement of measurement, and enhancement of efficiency. To justify the proposed dual-method approach, the study has three major objectives. First, it examines the uncertainties associated with different sample sizes by using GIS to generate scenarios that contrast the validity of measurements to aid sampling optimization in auditing. Second, it compares the validity of GIS measures with those generated through Google Street View Auditing (GSVA) by human raters. Third, it further examines the efficiency of the proposed dual-method approach in comparison to the two individual methods. Such investigation generates several novel findings. First, the study presents important evidence to support that GIS measures can offer sampling guidance applicable to the GSVA method. It leads to a recommendation of sampling sizes (5%-20%) for cases in settings with a mixture of affluent and disadvantaged neighborhoods. Results further indicate that different communities and certain individual features and characteristics may demand different sampling practices. Second, the study found that while GSVA is trustworthy for most characteristic variables, especially those that required subjective input, GIS provides well-validated measures for certain objective environmental attributes. Furthermore, the study reports that a dual-method approach of GIS and GSVA had a lower financial and time burden than using GSVA alone and is thus recommended as a comprehensive solution for optimal measurement of an objective built environment in mixed urban neighborhoods.

The Effects of Neighborhood Built Environment on Walking for Leisure and for Purpose Among Older People

BACKGROUND AND OBJECTIVES

Characteristics of a neighborhood's built environment affect the walking behavior of older people, yet studies typically rely on small nonrepresentative samples that use either subjective reports or aggregate indicators from administrative sources to represent neighborhood characteristics. Our analyses examine the usefulness of a novel method for observing neighborhoods-virtual observations-and assess the extent to which virtual-based observations predict walking among older adults.

RESEARCH DESIGN AND METHODS

Using Google Street View, we observed the neighborhoods of 2,224 older people and examined how characteristics of the neighborhood built environments are associated with the amount of time older people spend walking for leisure and purpose.

RESULTS

Multilevel model analyses revealed that sidewalk characteristics had significant associations with both walking for purpose and leisure. Land use, including the presence of multifamily dwellings, commercial businesses, and parking lots were positively associated with walking for purpose and single-family detached homes were negatively associated with walking for purpose, but none of these characteristics were associated with leisure walking. Gardens/flowers were associated with walking for leisure but not purpose. Garbage/litter was not associated with either type of walking behavior.

DISCUSSION AND IMPLICATIONS

Virtual observations are a useful method that provides meaningful information about neighborhoods. Findings demonstrate how neighborhood characteristics assessed virtually differentially impact walking for leisure and purpose among older adults and are interpreted within a social-ecological model.

Cross-sectional associations between the neighborhood built environment and physical activity in a rural setting: the Bogalusa Heart Study

BACKGROUND

Insufficient physical activity (PA) is a common health risk and more prevalent in rural populations. Few studies have assessed relationships between the built environment and PA in rural settings, and community policy guidance to promote PA through built environment interventions is primarily based on evidence from urban studies.

METHODS

Participants in the Bogalusa Heart Study, a longitudinal study in rural Louisiana, with International Physical Activity Questionnaire data from 2012 to 2013 and a valid residential address (N = 1245) were included. PA was summarized as the number of weekly metabolic equivalent (MET)-minutes of total, transportation, and leisure time PA. The Rural Active Living Assessment street segment audit tool and Google Street View were used to assess features of the built environment overall and in six categories (path features, pedestrian safety features, aesthetics, physical security, destinations and land use) that influence PA. Scores for street segment built environment (overall and in categories) were calculated, for segments and buffers of 0.25, 0.50, 1.00 and 1.50 miles. Associations between built environment scores and PA were assessed with generalized estimating equations.

RESULTS

Participants reported little weekly total, leisure time, and transportation PA (mean 470, 230 and 43 MET-minutes per week, respectively). A 1-point increase in the overall built environment score was associated with 10.30 additional weekly leisure time MET-minutes within a 1.50 mile buffer (p-value 0.05), with a similar magnitude observed for a 1.00-mile buffer. A 1-point increase in the aesthetic score was associated with significantly higher leisure time PA for all geographic units (from 22.21 to 38.75 MET-minutes weekly) when adjusted for individual covariates, but was attenuated and only significant for the segment of the residence after accounting for other neighborhood characteristics.

CONCLUSIONS

Significant associations between features of the environment (overall and aesthetic scores) with leisure time PA were observed among adults in this rural population. Built environment interventions in rural settings face additional barriers of lower population density and greater distances for infrastructure projects, and it is important to identify approaches that are both feasible for rural communities and can promote PA.

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

Background

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

Methods

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

Results

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

Conclusions

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

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

INTRODUCTION

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Analyzing Associations Between Chronic Disease Prevalence and Neighborhood Quality Through Google Street View Images

Deep learning and, specifically, convoltional neural networks (CNN) represent a class of powerful models that facilitate the understanding of many problems in computer vision. When combined with a reasonable amount of data, CNNs can outperform traditional models for many tasks, including image classification. In this work, we utilize these powerful tools with imagery data collected through Google Street View images to perform virtual audits of neighborhood characteristics. We further investigate different architectures for chronic disease prevalence regression through networks that are applied to sets of images rather than single images. We show quantitative results and demonstrate that our proposed architectures outperform the traditional regression approaches.

Neighborhood Physical Disorder and Adverse Pregnancy Outcomes among Women in Chicago: a Cross-Sectional Analysis of Electronic Health Record Data

Adverse pregnancy outcomes increase infants' risk for mortality and future health problems. Neighborhood physical disorder may contribute to adverse pregnancy outcomes by increasing maternal chronic stress. Google Street View technology presents a novel method for assessing neighborhood physical disorder but has not been previously examined in the context of birth outcomes. In this cross-sectional study, trained raters used Google's Street View imagery to virtually audit a randomly sampled block within each Chicago census tract (n = 809) for nine indicators of physical disorder. We used an item-response theory model and spatial interpolation to calculate tract-level neighborhood physical disorder scores across Chicago. We linked these data with geocoded electronic health record data from a large, academic women's hospital in Chicago (2015-2017, n = 14,309 births). We used three-level hierarchical Poisson regression to estimate prevalence ratios for the associations of neighborhood physical disorder with preterm birth (overall and spontaneous), small for gestational age (SGA), and hypertensive disorder of pregnancy (HDP). After adjustment for maternal sociodemographics, multiparity, and season of birth, living in a neighborhood with high physical disorder was associated with higher prevalence of PTB, SGA, and HDP (prevalence ratios and 95% confidence intervals 1.21 (1.06, 1.39) for PTB, 1.13 (1.01, 1.37) for SGA, and 1.23 (1.07, 1.42) for HDP). Adjustment for neighborhood poverty and maternal health conditions (e.g., hypertension, diabetes, asthma, substance use) attenuated associations. Results suggest that an adverse neighborhood physical environment may contribute to adverse pregnancy outcomes. However, future work is needed to disentangle the unique contribution of physical disorder from other characteristics of disadvantaged neighborhoods.

Reassessing Urban Health Interventions: Back to the Future with Google Street View Time Machine

INTRODUCTION

Validity of research linking built environments to health relies on the availability and reliability of data used to measure exposures. As cities transform, it is important to track when and where urban changes occur, to provide detailed information for urban health intervention research. This paper presents an online observation method of the implementation of traffic-calmingfeatures using Google Street View Time Machine. The method is used to validate an existingadministrative database detailing the implementation of curb extensions and speed bumps.

METHODS

Online observation of curb extensions and speed bumps was conducted for four boroughsin Montreal, Canada, in autumn 2016, and compared with administrative data documenting traffic-calming measures implemented between 2008 and 2014. All images available through the Time Machine function between 2007 and 2016 for 708 intervention sites were visualized online. Records in the administrative database were compared to real-world Google Street View observations and tested in terms of sensitivity, specificity, and positive predicted value.

RESULTS

Google Street View Time Machine allowed the visualization of a median of seven different dates per street intersection and six dates per street segment. This made it possible to analyze built environment changes within 3,973 distinct time periods at 708 locations. Validation of the administrative data regarding presence of an intervention showed 99% (95% CI=97%, 99%) sensitivity, 58% (95% CI=51%, 64%) specificity, and 77% (95% CI=73%, 81%) positive predictive value.

CONCLUSIONS

Google Street View Time Machine allowed past (2007-2016) online documentation of microscale urban interventions-curb extensions and speed bumps. The proposed method offers a new way to document historic changes to the built environment, which will be useful for urban health intervention research.

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

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

Neighborhood Disorder and Obesity-Related Outcomes among Women in Chicago

Neighborhood psychosocial stressors like crime and physical disorder may influence obesity-related outcomes through chronic stress or through adverse effects on health behaviors. Google Street View imagery provides a low-cost, reliable method for auditing neighborhood physical disorder, but few studies have examined associations of Street View-derived physical disorder scores with health outcomes. We used Google Street View to audit measures of physical disorder for residential census blocks from 225 women aged 18⁻44 enrolled from 4 Chicago neighborhoods. Latent neighborhood physical disorder scores were estimated using an item response theory model and aggregated to the block group level. Block-group level physical disorder scores and rates of police-recorded crime and 311 calls for service requests were linked to participants based on home addresses. Associations were estimated for 6 obesity-related outcomes: body mass index, obesity, total moderate-to-vigorous physical activity, and weekly consumption of sugar-sweetened beverages, fast food, and snacks. Hierarchical regression models estimated cross-sectional associations adjusting for individual sociodemographics and neighborhood poverty. Higher neighborhood physical disorder was associated with greater odds of obesity (OR: 1.43, 95% CI: 1.01, 2.02). Living in a neighborhood with a higher crime rate was associated with an increase in weekly snack consumption of 3.06 (95% CI: 1.59, 4.54).

Parental Perceptions of the Social Environment Are Inversely Related to Constraint of Adolescents' Neighborhood Physical Activity

Background: The current study examined relationships between the neighborhood social environment (parental perceived collective efficacy (PCE)), constrained behaviors (e.g., avoidance or defensive behaviors) and adolescent offspring neighborhood physical activity in low- versus high-incivility neighborhoods. Methods: Adolescents (n = 71; 11–18 years (14.2, SD ± 1.6); male = 37 (52%); non-white = 24 (33.8%); low-income = 20 (29%); overweight/obese = 40 (56%)) and their parents/guardians enrolled in the Molecular and Social Determinants of Obesity in Developing Youth study were included in the current study. Questionnaires measured parents’/guardians’ PCE, constrained outdoor play practices and offspring neighborhood physical activity. Systematic social observation performed at the parcel-level using Google Street View assessed neighborhood incivilities. t-tests and chi-square tests determined differences by incivilities. Multilevel regression models examined relationships between PCE and: (1) constrained behaviors; and (2) neighborhood physical activity. The Hayes (2013) macro determined the mediating role of constrained behaviors. Results: Parents who had higher PCE reported lower levels of avoidance (p = 0.04) and defensive (p = 0.05) behaviors. However, demographic variables (i.e., gender, race and annual household income) limited these results. The direct relationship between PCE and parent-reported neighborhood physical activity was statistically significant in high-incivility neighborhoods only. Neither avoidance nor defensive behavior mediated the relationship between PCE and neighborhood physical activity. Conclusions: PCE influences parenting behaviors related to youth physical activity. Community-based programs that seek to facilitate social cohesion and control may be needed to increase adolescents’ physical activity.