Use of Google Street View to Assess Environmental Contributions to Pedestrian Injury

Investigating the influence of streetscape environmental characteristics on pedestrian crashes at intersections using street view images and explainable machine learning

Examining the relationship between streetscape features and road traffic accidents is pivotal for enhancing roadway safety. While previous studies have primarily focused on the influence of street design characteristics, sociodemographic features, and land use features on crash occurrence, the impact of streetscape features on pedestrian crashes has not been thoroughly investigated. Furthermore, while machine learning models demonstrate high accuracy in prediction and are increasingly utilized in traffic safety research, understanding the prediction results poses challenges. To address these gaps, this study extracts streetscape environment characteristics from street view images (SVIs) using a combination of semantic segmentation and object detection deep learning networks. These characteristics are then incorporated into the eXtreme Gradient Boosting (XGBoost) algorithm, along with a set of control variables, to model the occurrence of pedestrian crashes at intersections. Subsequently, the SHapley Additive exPlanations (SHAP) method is integrated with XGBoost to establish an interpretable framework for exploring the association between pedestrian crash occurrence and the surrounding streetscape built environment. The results are interpreted from global, local, and regional perspectives. The findings indicate that, from a global perspective, traffic volume and commercial land use are significant contributors to pedestrian-vehicle collisions at intersections, while road, person, and vehicle elements extracted from SVIs are associated with higher risks of pedestrian crash onset. At a local level, the XGBoost-SHAP framework enables quantification of features' local contributions for individual intersections, revealing spatial heterogeneity in factors influencing pedestrian crashes. From a regional perspective, similar intersections can be grouped to define geographical regions, facilitating the formulation of spatially responsive strategies for distinct regions to reduce traffic accidents. This approach can potentially enhance the quality and accuracy of local policy making. These findings underscore the underlying relationship between streetscape-level environmental characteristics and vehicle-pedestrian crashes. The integration of SVIs and deep learning techniques offers a visually descriptive portrayal of the streetscape environment at locations where traffic crashes occur at eye level. The proposed framework not only achieves excellent prediction performance but also enhances understanding of traffic crash occurrences, offering guidance for optimizing traffic accident prevention and treatment programs.

The dynamic-static dual-branch deep neural network for urban speeding hotspot identification using street view image data

The visual information regarding the road environment can influence drivers' perception and judgment, often resulting in frequent speeding incidents. Identifying speeding hotspots in cities can prevent potential speeding incidents, thereby improving traffic safety levels. We propose the Dual-Branch Contextual Dynamic-Static Feature Fusion Network based on static panoramic images and dynamically changing sequence data, aiming to capture global features in the macro scene of the area and dynamically changing information in the micro view for a more accurate urban speeding hotspot area identification. For the static branch, we propose the Multi-scale Contextual Feature Aggregation Network for learning global spatial contextual association information. In the dynamic branch, we construct the Multi-view Dynamic Feature Fusion Network to capture the dynamically changing features of a scene from a continuous sequence of street view images. Additionally, we designed the Dynamic-Static Feature Correlation Fusion Structure to correlate and fuse dynamic and static features. The experimental results show that the model has good performance, and the overall recognition accuracy reaches 99.4%. The ablation experiments show that the recognition effect after the fusion of dynamic and static features is better than that of static and dynamic branches. The proposed model also shows better performance than other deep learning models. In addition, we combine image processing methods and different Class Activation Mapping (CAM) methods to extract speeding frequency visual features from the model perception results. The results show that more accurate speeding frequency features can be obtained by using LayerCAM and GradCAM-Plus for static global scenes and dynamic local sequences, respectively. In the static global scene, the speeding frequency features are mainly concentrated on the buildings and green layout on both sides of the road, while in the dynamic scene, the speeding frequency features shift with the scene changes and are mainly concentrated on the dynamically changing transition areas of greenery, roads, and surrounding buildings. The code and model used for identifying hotspots of urban traffic accidents in this study are available for access: https://github.com/gwt-ZJU/DCDSFF-Net.

Road Traffic Injuries and the Built Environment in Bogotá, Colombia, 2015-2019: A Cross-Sectional Analysis

Nine in 10 road traffic deaths occur in low- and middle-income countries (LMICs). Despite this disproportionate burden, few studies have examined built environment correlates of road traffic injury in these settings, including in Latin America. We examined road traffic collisions in Bogotá, Colombia, occurring between 2015 and 2019, and assessed the association between neighborhood-level built environment features and pedestrian injury and death. We used descriptive statistics to characterize all police-reported road traffic collisions that occurred in Bogotá between 2015 and 2019. Cluster detection was used to identify spatial clustering of pedestrian collisions. Adjusted multivariate Poisson regression models were fit to examine associations between several neighborhood-built environment features and rate of pedestrian road traffic injury and death. A total of 173,443 police-reported traffic collisions occurred in Bogotá between 2015 and 2019. Pedestrians made up about 25% of road traffic injuries and 50% of road traffic deaths in Bogotá between 2015 and 2019. Pedestrian collisions were spatially clustered in the southwestern region of Bogotá. Neighborhoods with more street trees (RR, 0.90; 95% CI, 0.82-0.98), traffic signals (0.89, 0.81-0.99), and bus stops (0.89, 0.82-0.97) were associated with lower pedestrian road traffic deaths. Neighborhoods with greater density of large roads were associated with higher pedestrian injury. Our findings highlight the potential for pedestrian-friendly infrastructure to promote safer interactions between pedestrians and motorists in Bogotá and in similar urban contexts globally.

Bicycle infrastructure and the incidence rate of crashes with cars: A case-control study with Strava data in Atlanta

Introduction

Bicycling has individual and collective health benefits. Safety concerns are a deterrent to bicycling. Incomplete data on bicycling volumes has limited epidemiologic research investigating safety impacts of bicycle infrastructure, such as protected bike lanes.

Methods

In this case-control study, set in Atlanta, Georgia, USA between 2016-10-01 and 2018-08-31, we estimated the incidence rate of police-reported crashes between bicyclists and motor vehicles (n = 124) on several types of infrastructure (off-street paved trails, protected bike lanes, buffered bike lanes, conventional bike lanes, and sharrows) per distance ridden and per intersection entered. To estimate underlying bicycling (the control series), we used a sample of high-resolution bicycling data from Strava, an app, combined with data from 15 on-the-ground bicycle counters to adjust for possible selection bias in the Strava data. We used model-based standardization to estimate effects of treatment on the treated.

Results

After adjustment for selection bias and confounding, estimated ratio effects on segments (excluding intersections) with protected bike lanes (incidence rate ratio [IRR] = 0.5 [95% confidence interval: 0.0, 2.5]) and buffered bike lanes (IRR = 0 [0,0]) were below 1, but were above 1 on conventional bike lanes (IRR = 2.8 [1.2, 6.0]) and near null on sharrows (IRR = 1.1 [0.2, 2.9]). Per intersection entry, estimated ratio effects were above 1 for entries originating from protected bike lanes (incidence proportion ratio [IPR] = 3.0 [0.0, 10.8]), buffered bike lanes (IPR = 16.2 [0.0, 53.1]), and conventional bike lanes (IPR = 3.2 [1.8, 6.0]), and were near 1 and below 1, respectively, for those originating from sharrows (IPR = 0.9 [0.2, 2.1]) and off-street paved trails (IPR = 0.7 [0.0, 2.9]).

Conclusions

Protected bike lanes and buffered bike lanes had estimated protective effects on segments between intersections but estimated harmful effects at intersections. Conventional bike lanes had estimated harmful effects along segments and at intersections.

Registry Data in Injury Research: Study Designs and Interpretation

Purpose of Review

Injury data is frequently captured in registries that form a census of 100% of known cases that meet specified inclusion criteria. These data are routinely used in injury research with a variety of study designs. We reviewed study designs commonly used with data extracted from injury registries and evaluated the advantages and disadvantages of each design type.

Recent Findings

Registry data are suited to 5 major design types: (1) Description, (2) Ecologic (with Ecologic Cohort as a particularly informative sub-type), (3) Case-control (with location-based and culpability studies as salient subtypes), (4) Case-only (including case-case and case-crossover subtypes), and (5) Outcomes.

Summary

Registries are an important resource for injury research. Investigators considering use of a registry should be aware of the advantages and disadvantages of available study designs.

The Weight of Place: Built Environment Correlates of Obesity and Diabetes

In recent decades, the prevalence of obesity and diabetes has risen substantially in North America and worldwide. To address these dual epidemics, researchers and policymakers alike have been searching for effective means to promote healthy lifestyles at a population level. As a consequence, there has been a proliferation of research examining how the "built" environment in which we live influences physical activity levels, by promoting active forms of transportation, such as walking and cycling, over passive ones, such as car use. Shifting the transportation choices of local residents may mean that more members of the population can participate in physical activity during their daily routine without structured exercise programs. Increasingly, this line of research has considered the downstream metabolic consequences of the environment in which we live, raising the possibility that "healthier" community designs could help mitigate the rise in obesity and diabetes prevalence. This review discusses the evidence examining the relationship between the built environment, physical activity, and obesity-related diseases. We also consider how other environmental factors may interact with the built environment to influence metabolic health, highlighting challenges in understanding causal relationships in this area of research.

Data science for pedestrian and high street retailing as a framework for advancing urban informatics to individual scales

Background

In this paper, we consider the applicability of the customer journey framework from retailing as a driver for urban informatics at individual scales within urban science. The customer journey considers shopper experiences in the context of shopping paths, retail service spaces, and touch-points that draw them into contact. Around this framework, retailers have developed sophisticated data science for observation, identification, and measurement of customers in the context of their shopping behavior. This knowledge supports broad data-driven understanding of customer experiences in physical spaces, economic spaces of decision and choice, persuasive spaces of advertising and branding, and inter-personal spaces of customer-staff interaction.

Method

We review the literature on pedestrian and high street retailing, and on urban informatics. We investigate whether the customer journey could be usefully repurposed for urban applications. Specifically, we explore the potential use of the customer journey framework for producing new insight into pedestrian behavior, where a sort of empirical hyperopia has long abounded because data are always in short supply.

Results

Our review addresses how the customer journey might be used as a structure for examining how urban walkers come into contact with the built environment, how people actively and passively sense and perceive ambient city life as they move, how pedestrians make sense of urban context, and how they use this knowledge to build cognition of city streetscapes. Each of these topics has relevance to walking studies specifically, but also to urban science more generally. We consider how retailing might reciprocally benefit from urban science perspectives, especially in extending the reach of retailers' insight beyond store walls, into the retail high streets from which they draw custom.

Conclusion

We conclude that a broad set of theoretical frameworks, data collection schemes, and analytical methodologies that have advanced retail data science closer and closer to individual-level acumen might be usefully applied to accomplish the same in urban informatics. However, we caution that differences between retailers' and urban scientists' viewpoints on privacy presents potential controversy.

Exploring environmental measures in disability: Using Google Earth and Street View to conduct remote assessments of access and participation in urban and rural communities

The Americans with Disabilities Act has been in place since 1990. Yet, we still do not know the actual levels of accessibility in the nation, how access varies across communities or over time, or how it influences participation in community life. The present two studies explored the use of Google Earth (GE) and Google Street View (GSV) imagery as a database for examining the accessibility of rural and urban cities and towns in the United States. We developed procedures for selecting places in a community to observe multiple access features. Study 1 reports the findings from assessments of 25 communities across 17 states. We observed ≈50,000 m (31 miles) of pathways through the observed places. The Combined Access Score (CAS) averaged 65% across these communities. In Study 2, we evaluated 22 towns and cities in a large rural state. We observed ≈77,000 m (48 miles) of pathways through the Central Business Districts observed as core areas connecting people to community life. The CAS averaged 83.9% across these communities. We noted a Rural Access Penalty (RAP), such that rural areas tended to be less accessible, leading to less community participation. The method for using GSV to examine accessibility is discussed. This study demonstrates an inexpensive and reliable method for evaluating the accessibility of communities and participation in them. Future research should be conducted to gather a larger sample of communities in order to create a baseline from which to monitor changes in accessibility of infrastructure over time.

Street View Imagery (SVI) in the Built Environment: A Theoretical and Systematic Review

Street view imagery (SVI) provides efficient access to data that can be used to research spatial quality at the human scale. The previous reviews have mainly focused on specific health findings and neighbourhood environments. There has not been a comprehensive review of this topic. In this paper, we systematically review the literature on the application of SVI in the built environment, following a formal innovation–decision framework. The main findings are as follows: (I) SVI remains an effective tool for automated research assessments. This offers a new research avenue to expand the built environment-measurement methods to include perceptions in addition to physical features. (II) Currently, SVI is functional and valuable for quantifying the built environment, spatial sentiment perception, and spatial semantic speculation. (III) The significant dilemmas concerning the adoption of this technology are related to image acquisition, the image quality, spatial and temporal distribution, and accuracy. (IV) This research provides a rapid assessment and provides researchers with guidance for the adoption and implementation of SVI. Data integration and management, proper image service provider selection, and spatial metrics measurements are the critical success factors. A notable trend is the application of SVI towards a focus on the perceptions of the built environment, which provides a more refined and effective way to depict urban forms in terms of physical and social spaces.

Microscale pedestrian environment surrounding pedestrian injury sites in Washington state, 2015-2020

OBJECTIVE

While microscale pedestrian environment features such as sidewalks and crosswalks can affect pedestrian safety, it is challenging to assess microscale environment associated risk across locations or at scale. Addressing these challenges requires an efficient auditing protocol that can be used to assess frequencies of microscale environment features. For this reason, we developed an eight-item pedestrian environment virtual audit protocol and conducted a descriptive epidemiologic study of pedestrian injury in Washington State, USA.

METHODS

We used data from police reports at pedestrian-automotive collision sites where the pedestrian was seriously injured or died. At each collision site, high school students participating in an online summer internship program virtually audited Google Street View imagery to assess the presence of microscale pedestrian environment features such as crosswalks and streetlighting. We assessed inter-rater reliability using Cohen's kappa and explored prevalence of eight microscale environment features in relation to injury severity and municipal boundaries.

RESULTS

There were 2248 motor vehicle crashes eliciting police response and resulting in death or serious injury of a pedestrian in Washington State between January 1, 2015 and May 8, 2020. Of the crashes resulting in serious injury or death, 498 (22%) resulted in fatalities and 1840 (82%) occurred within municipal boundaries. Cohen's kappa scores for the eight pedestrian features that were audited ranged from 0.52 to 0.86. Audit results confirmed that features such as sidewalks and crosswalks were more common at collision sites within city limits.

CONCLUSIONS

High school student volunteers with minimal training can reliably audit microscale pedestrian environments using limited resources.

Machine Learning Approaches for Measuring Neighborhood Environments in Epidemiologic Studies

Purpose of review

Innovations in information technology, initiatives by local governments to share administrative data, and growing inventories of data available from commercial data aggregators have immensely expanded the information available to describe neighborhood environments, supporting an approach to research we call Urban Health Informatics. This review evaluates the application of machine learning to this new wealth of data for studies of the effects of neighborhood environments on health.

Recent findings

Prominent machine learning applications in this field include automated image analysis of archived imagery such as Google Street View images, variable selection methods to identify neighborhood environment factors that predict health outcomes from large pools of exposure variables, and spatial interpolation methods to estimate neighborhood conditions across large geographic areas.

Summary

In each domain, we highlight successes and cautions in the application of machine learning, particularly highlighting legal issues in applying machine learning approaches to Google’s geo-spatial data.

Development and validation of automated microscale walkability audit method

Measuring microscale factors of walkability has been labor-intensive and expensive. To reduce the cost, various efforts have been made including virtual audits (i.e., manual audits using street view images) and the introduction of computer vision techniques. Although studies have shown that virtual audits (i.e., manual audits using street view images) can reliably replicate in-person audits, they are still prohibitively expensive to be applied to a large geographic area. Past studies used computer vision techniques to help automate the audit process, but off-the-shelf models cannot detect some of the important microscale walkability characteristics, falling short of fully capturing the multi-facetted concept of walkability. This study is one of the earliest attempts to use the combination of custom-trained computer vision models, geographic information systems, and street view images to automatically audit a complete set of items of a validated microscale walkability audit tool. This study validates the reliability of the automated audit with virtual audit results. The automated audit results show high reliability, indicating automated audit can be a highly scalable and reliable replacement of virtual audit.

Panoramic Street-Level Imagery in Data-Driven Urban Research: A Comprehensive Global Review of Applications, Techniques, and Practical Considerations

The release of Google Street View in 2007 inspired several new panoramic street-level imagery platforms including Apple Look Around, Bing StreetSide, Baidu Total View, Tencent Street View, Naver Street View, and Yandex Panorama. The ever-increasing global capture of cities in 360° provides considerable new opportunities for data-driven urban research. This paper provides the first comprehensive, state-of-the-art review on the use of street-level imagery for urban analysis in five research areas: built environment and land use; health and wellbeing; natural environment; urban modelling and demographic surveillance; and area quality and reputation. Panoramic street-level imagery provides advantages in comparison to remotely sensed imagery and conventional urban data sources, whether manual, automated, or machine learning data extraction techniques are applied. Key advantages include low-cost, rapid, high-resolution, and wide-scale data capture, enhanced safety through remote presence, and a unique pedestrian/vehicle point of view for analyzing cities at the scale and perspective in which they are experienced. However, several limitations are evident, including limited ability to capture attribute information, unreliability for temporal analyses, limited use for depth and distance analyses, and the role of corporations as image-data gatekeepers. Findings provide detailed insight for those interested in using panoramic street-level imagery for urban research.

Development and Validation of a Google Street View Pedestrian Safety Audit Tool

BACKGROUND

Assessing aspects of intersections that may affect the risk of pedestrian injury is critical to developing child pedestrian injury prevention strategies, but visiting intersections to inspect them is costly and time-consuming. Several research teams have validated the use of Google Street View to conduct virtual neighborhood audits that remove the need for field teams to conduct in-person audits.

METHODS

We developed a 38-item virtual audit instrument to assess intersections for pedestrian injury risk and tested it on intersections within 700 m of 26 schools in New York City using the Computer-assisted Neighborhood Visual Assessment System (CANVAS) with Google Street View imagery.

RESULTS

Six trained auditors tested this instrument for inter-rater reliability on 111 randomly selected intersections and for test-retest reliability on 264 other intersections. Inter-rater kappa scores ranged from -0.01 to 0.92, with nearly half falling above 0.41, the conventional threshold for moderate agreement. Test-retest kappa scores were slightly higher than but highly correlated with inter-rater scores (Spearman rho = 0.83). Items that were highly reliable included the presence of a pedestrian signal (K = 0.92), presence of an overhead structure such as an elevated train or a highway (K = 0.81), and intersection complexity (K = 0.76).

CONCLUSIONS

Built environment features of intersections relevant to pedestrian safety can be reliably measured using a virtual audit protocol implemented via CANVAS and Google Street View.

Exploring convolutional neural networks and spatial video for on-the-ground mapping in informal settlements

BACKGROUND

The health burden in developing world informal settlements often coincides with a lack of spatial data that could be used to guide intervention strategies. Spatial video (SV) has proven to be a useful tool to collect environmental and social data at a granular scale, though the effort required to turn these spatially encoded video frames into maps limits sustainability and scalability. In this paper we explore the use of convolution neural networks (CNN) to solve this problem by automatically identifying disease related environmental risks in a series of SV collected from Haiti. Our objective is to determine the potential of machine learning in health risk mapping for these environments by assessing the challenges faced in adequately training the required classification models.

RESULTS

We show that SV can be a suitable source for automatically identifying and extracting health risk features using machine learning. While well-defined objects such as drains, buckets, tires and animals can be efficiently classified, more amorphous masses such as trash or standing water are difficult to classify. Our results further show that variations in the number of image frames selected, the image resolution, and combinations of these can be used to improve the overall model performance.

CONCLUSION

Machine learning in combination with spatial video can be used to automatically identify environmental risks associated with common health problems in informal settlements, though there are likely to be variations in the type of data needed for training based on location. Success based on the risk type being identified are also likely to vary geographically. However, we are confident in identifying a series of best practices for data collection, model training and performance in these settings. We also discuss the next step of testing these findings in other environments, and how adding in the simultaneously collected geographic data could be used to create an automatic health risk mapping tool.

Towards activity-based exposure measures in spatial analysis of pedestrian-motor vehicle crashes

BACKGROUND

Although numerous efforts have been devoted to exploring the effects of area-wide factors on the frequency of pedestrian crashes in neighborhoods over the past two decades, existing studies have largely failed to provide a full picture of the factors that contribute to the incidence of zonal pedestrian crashes, due to the unavailability of reliable exposure data and use of less sound analytical methods.

METHODS

Based on a crowdsourced dataset in Hong Kong, we first proposed a procedure to extract pedestrian trajectories from travel-diary survey data. We then aggregated these data to 209 neighborhoods and developed a Bayesian spatially varying coefficients model to investigate the spatially non-stationary relationships between the number of pedestrian-motor vehicle (PMV) crashes and related risk factors. To dissect the role of pedestrian exposure, the estimated coefficients of models with population, walking trips, walking time, and walking distance as the measure of pedestrian exposure were presented and compared.

RESULTS

Our results indicated substantial inconsistencies in the effects of several risk factors between the models of population and activity-based exposure measures. The model using walking trips as the measure of pedestrian exposure had the best goodness-of-fit. We also provided new insights that in addition to the unstructured variability, heterogeneity in the effects of explanatory variables on the frequency of PMV crashes could also arise from the spatially correlated effects. After adjusting for vehicle volume and pedestrian activity, road density, intersection density, bus stop density, and the number of parking lots were found to be positively associated with PMV crash frequency, whereas the percentage of motorways and median monthly income had negative associations with the risk of PMV crashes.

CONCLUSIONS

The use of population or population density as a surrogate for pedestrian exposure when modeling the frequency of zonal pedestrian crashes is expected to produce biased estimations and invalid inferences. Spatial heterogeneity should also not be negligible when modeling pedestrian crashes involving contiguous spatial units.

Leveraging 31 Million Google Street View Images to Characterize Built Environments and Examine County Health Outcomes

OBJECTIVES

Built environments can affect health, but data in many geographic areas are limited. We used a big data source to create national indicators of neighborhood quality and assess their associations with health.

METHODS

We leveraged computer vision and Google Street View images accessed from December 15, 2017, through July 17, 2018, to detect features of the built environment (presence of a crosswalk, non-single-family home, single-lane roads, and visible utility wires) for 2916 US counties. We used multivariate linear regression models to determine associations between features of the built environment and county-level health outcomes (prevalence of adult obesity, prevalence of diabetes, physical inactivity, frequent physical and mental distress, poor or fair self-rated health, and premature death [in years of potential life lost]).

RESULTS

Compared with counties with the least number of crosswalks, counties with the most crosswalks were associated with decreases of 1.3%, 2.7%, and 1.3% of adult obesity, physical inactivity, and fair or poor self-rated health, respectively, and 477 fewer years of potential life lost before age 75 (per 100 000 population). The presence of non-single-family homes was associated with lower levels of all health outcomes except for premature death. The presence of single-lane roads was associated with an increase in physical inactivity, frequent physical distress, and fair or poor self-rated health. Visible utility wires were associated with increases in adult obesity, diabetes, physical and mental distress, and fair or poor self-rated health.

CONCLUSIONS

The use of computer vision and big data image sources makes possible national studies of the built environment's effects on health, producing data and results that may inform national and local decision-making.

The unknown denominator problem in population studies of disease frequency

Problems related to unknown or imprecisely measured populations at risk are common in epidemiologic studies of disease frequency. The size of the population at risk is typically conceptualized as a denominator to be used in combination with a count of disease cases (a numerator) to calculate incidence or prevalence. However, the size of the population at risk can take other epidemiologic properties in relation to an exposure of interest and the count outcome, including confounding, modification, and mediation. Using spatial ecological studies of injury incidence as an example, we identify and evaluate five approaches that researchers have used to address "unknown denominator problems": ignoring, controlling for a proxy, approximating, controlling by study design, and measuring the population at risk. We present a case example and recommendations for selecting a solution given the data and the hypothesized relationship between an exposure of interest, a count outcome, and the population at risk.

Not all protected bike lanes are the same: Infrastructure and risk of cyclist collisions and falls leading to emergency department visits in three U.S. cities

OBJECTIVE

Protected bike lanes separated from the roadway by physical barriers are relatively new in the United States. This study examined the risk of collisions or falls leading to emergency department visits associated with bicycle facilities (e.g., protected bike lanes, conventional bike lanes demarcated by painted lines, sharrows) and other roadway characteristics in three U.S. cities.

METHODS

We prospectively recruited 604 patients from emergency departments in Washington, DC; New York City; and Portland, Oregon during 2015-2017 who fell or crashed while cycling. We used a case-crossover design and conditional logistic regression to compare each fall or crash site with a randomly selected control location along the route leading to the incident. We validated the presence of site characteristics described by participants using Google Street View and city GIS inventories of bicycle facilities and other roadway features.

RESULTS

Compared with cycling on lanes of major roads without bicycle facilities, the risk of crashing or falling was lower on conventional bike lanes (adjusted OR = 0.53; 95 % CI = 0.33, 0.86) and local roads with (adjusted OR = 0.31; 95 % CI = 0.13, 0.75) or without bicycle facilities or traffic calming (adjusted OR = 0.39; 95 % CI = 0.23, 0.65). Protected bike lanes with heavy separation (tall, continuous barriers or grade and horizontal separation) were associated with lower risk (adjusted OR = 0.10; 95 % CI = 0.01, 0.95), but those with lighter separation (e.g., parked cars, posts, low curb) had similar risk to major roads when one way (adjusted OR = 1.19; 95 % CI = 0.46, 3.10) and higher risk when they were two way (adjusted OR = 11.38; 95 % CI = 1.40, 92.57); this risk increase was primarily driven by one lane in Washington. Risk increased in the presence of streetcar or train tracks relative to their absence (adjusted OR = 26.65; 95 % CI = 3.23, 220.17), on downhill relative to flat grades (adjusted OR = 1.92; 95 % CI = 1.38, 2.66), and when temporary features like construction or parked cars blocked the cyclist's path relative to when they did not (adjusted OR = 2.23; 95 % CI = 1.46, 3.39).

CONCLUSIONS

Certain bicycle facilities are safer for cyclists than riding on major roads. Protected bike lanes vary in how well they shield riders from crashes and falls. Heavier separation, less frequent intersections with roads and driveways, and less complexity appear to contribute to reduced risk in protected bike lanes. Future research should systematically examine the characteristics that reduce risk in protected lanes to guide design. Planners should minimize conflict points when choosing where to place protected bike lanes and should implement countermeasures to increase visibility at these locations when they are unavoidable.

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.

The epidemiology of pedestrian fatalities and substance use in Georgia, United States, 2007-2016

Though U.S. motor vehicle crashes as a whole have decreased over the past few years, fatalities among vulnerable road users have increased. Pedestrian deaths rose nationally by 27% between 2007 and 2016 accounting for 16% of all motor vehicle fatalities. This increase continues to burden transportation specialists, public health professionals, and community stakeholders. Potential risk factors include characteristics of the built environment, distractions, and pedestrians' use of alcohol and drugs. Pedestrian deaths in Georgia, United States, increased 40% between 2014 and 2016 while drug overdose deaths have increased by 18% during the same period. Concurrent increases in mortality due to pedestrian fatalities and drug overdoses make Georgia a natural environment in which to describe the proximity of drugs among pedestrian fatalities, a topic largely overlooked by the literature. This study explores the epidemiology of pedestrian fatalities in Georgia over a 10-year period with an emphasis on reported substance use among cases. The study employed 10-year data from the Fatality Analysis Reporting System (FARS) administered by the National Highway Traffic Safety Administration. Descriptive methods were used to explore drug screens by person, place, and time. We also examined trends in total drug screens over the examination period. Between 2007 and 2016, 1781 pedestrian crashes were reported to FARS; the fatality rate for this period was 94.5%. Of these, most were male with Blacks and Whites equally represented. Ages 15-64 accounted for 81.1% of cases with most occurring in the Atlanta Metropolitan area. When adjusted for population, one finds higher rates in more rural areas of the state. Data revealed that testing for the presence of drugs occurred among half of reported cases. Of those testing positive, five drug categories emerged; stimulants (45.8%), cannabinoids (21.5%), narcotics (including opioids) (14.1%), depressants (12.1%), and "Other Drugs" (6.3%). Positive drug screens across all drug classifications increased by 178.1% between 2007 and 2016. These findings suggest the need for state-wide policies designed to promote more consistent screening among pedestrians involved in motor vehicle crashes as well as diligence in understanding the role played by drugs among this population. Additional investigation should be conducted to tease out the presence of category-specific drugs among pedestrians. Understanding the epidemiology of pedestrian fatalities in the state, especially in relation to substance use, serves as a first step toward implementing localized preventive efforts.

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.

Expanding Tools for Investigating Neighborhood Indicators of Drug Use and Violence: Validation of the NIfETy for Virtual Street Observation

A growing body of evidence suggests that characteristics of the neighborhood environment in urban areas significantly impact risk for drug use behavior and exposure to violent crime. Identifying areas of community need, prioritizing planning projects, and developing strategies for community improvement require inexpensive, easy to use, evidence-based tools to assess neighborhood disorder that can be used for a variety of research, urban planning, and community needs with an environmental justice frame. This study describes validation of the Neighborhood Inventory for Environmental Typology (NIfETy), a neighborhood environmental observational assessment tool designed to assess characteristics of the neighborhood environment related to violence, alcohol, and other drugs, for use with Google Street View (GSV). GSV data collection took place on a random sample of 350 blocks located throughout Baltimore City, Maryland, which had previously been assessed through in-person data collection. Inter-rater reliability metrics were strong for the majority of items (ICC ≥ 0.7), and items were highly correlated with in-person observations (r ≥ 0.6). Exploratory factor analysis and constrained factor analysis resulted in one, 14-item disorder scale with high internal consistency (alpha = 0.825) and acceptable fit indices (CFI = 0.982; RMSEA = 0.051). We further validated this disorder scale against locations of violent crimes, and we found that disorder score was significantly and positively associated with neighborhood crime (IRR = 1.221, 95% CI = (1.157, 1.288), p < 0.001). The NIfETy provides a valid, economical, and efficient tool for assessing modifiable neighborhood risk factors for drug use and violence prevention that can be employed for a variety of research, urban planning, and community needs.

Bayesian hierarchical spatial models: Implementing the Besag York Mollié model in stan

This report presents a new implementation of the Besag-York-Mollié (BYM) model in Stan, a probabilistic programming platform which does full Bayesian inference using Hamiltonian Monte Carlo (HMC). We review the spatial auto-correlation models used for areal data and disease risk mapping, and describe the corresponding Stan implementations. We also present a case study using Stan to fit a BYM model for motor vehicle crashes injuring school-age pedestrians in New York City from 2005 to 2014 localized to census tracts. Stan efficiently fit our multivariable BYM model having a large number of observations (n=2095 census tracts) with small outcome counts < 10 in the majority of tracts. Our findings reinforced that neighborhood income and social fragmentation are significant correlates of school-age pedestrian injuries. We also observed that nationally-available census tract estimates of commuting methods may serve as a useful indicator of underlying pedestrian densities.

Epidemiology of alcohol and drug screening among pedestrian fatalities in the United States, 2014-2016

Objective: U.S. pedestrian fatalities increased by 25% between 2010 and 2015. Risk factors include distractions, the built environment, urbanization, economic variables, and weather conditions. Of interest is the role of alcohol and drugs in premature death among pedestrians. This study sought to explore the prevalence of substance use screenings among pedestrian fatalities in the United States between 2014 and 2016. Methods: Data were collected from the Fatality Analysis Reporting System provided by the NHTSA. Pedestrian crash variables included demographics as well as information regarding alcohol or drug testing status. Frequency and cross-tabulation tables were constructed to assess the prevalence of screening by person, place, and time. Log-linear analyses were completed to explore age, race, and sex differences. A 3-year examination period was used to control for yearly fluctuations and to incorporate an increasing trend in cases. Results: Pedestrian fatalities accounted for 84% of all deaths among vulnerable road users during the examination period. Those most at risk were white males between the ages of 45 and 64. Over all states, 74.7% of fatalities were tested for alcohol and 67.1% were tested for drugs; further, 66.5% of cases were tested for both alcohol and drugs and 24.8% were tested for neither substance. Cases screened for both alcohol and drugs ranged from 2.9% in North Carolina to 95.7% in Nevada and those testing for neither substance ranged from a high of 68.9% in Indiana to a low of 1.1% in Maryland. Log-linear regression revealed significant differences in alcohol screening by age and race but not by sex. Differences in drug screening were not identified for any demographic variable. Fatalities tested for alcohol were significantly more likely to be tested for drugs; only 8.2% were screened solely for alcohol and 0.05% were screened for drugs alone. Conclusions: Preventive strategies become more important as pedestrian crashes and fatalities increase. Risk reduction in the form of policy change, alterations to the built environment, or interdisciplinary approaches to injury prevention is dependent upon best evidence supported in part by more deliberate and consistent screening.

Comparative Associations of Street Network Design, Streetscape Attributes and Land-Use Characteristics on Pedestrian Flows in Peripheral Neighbourhoods

Research has sufficiently documented the built environment correlates of walking. However, evidence is limited in investigating the comparative associations of micro- (streetscape features) and macro-level (street network design and land-use) environmental measures with pedestrian movement. This study explores the relative association of street-level design-local qualities of street environment-, street network configuration -spatial structure of the urban grid- and land-use patterns with the distribution of pedestrian flows in peripheral neighbourhoods. Street design attributes and ground-floor land-uses are obtained through field surveys while street network configuration is evaluated through space syntax measures. The statistical models indicate that the overall spatial configuration of street network proves to be a stronger correlate of walking than local street-level attributes while only average sidewalk width appears to be a significant correlate of walking among the streetscape measures. However, the most significant and consistent correlate of the distribution of flows is the number of recreational uses at the segment-level. This study contributes to the literature by offering insights into the comparative roles of urban design qualities of the street environment and street network layout on pedestrian movement. The findings also offer evidence-based strategies to inform specific urban design and urban master planning decisions (i.e., the provision of more generous sidewalks on streets with relatively higher directional accessibility) in creating lively, walkable environments.

Bayesian approach to model pedestrian crashes at signalized intersections with measurement errors in exposure

This study intended to identify the potential factors contributing to the occurrence of pedestrian crashes at signalized intersections in a densely populated city, based on a comprehensive dataset of 898 pedestrian crashes at 262 signalized intersections during 2010-2012 in Hong Kong. The detailed geometric design, traffic characteristics, signal control, built environment, along with the vehicle and pedestrian volumes were elaborately collected. A Bayesian measurement errors model was introduced as an alternative method to explicitly account for the uncertainties in volume data. To highlight the role played by exposure, models with and without pedestrian volume were estimated and compared. The results indicated that the omission of pedestrian volume in pedestrian crash frequency models would lead to reduced goodness-of-fit, biased parameter estimates, and incorrect inferences. Our empirical analysis demonstrated the existence of moderate uncertainties in pedestrian and vehicle volumes. Six variables were found to have a significant association with the number of pedestrian crashes at signalized intersections. The number of crossing pedestrians, the number of passing vehicles, the presence of curb parking, and the presence of ground-floor shops were positively related with pedestrian crash frequency, whereas the presence of playgrounds near intersections had a negative effect on pedestrian crash occurrences. Specifically, the presence of exclusive pedestrian signals for all crosswalks was found to significantly reduce the risk of pedestrian crashes by 43%. The present study is expected to shed more light on a deeper understanding of the environmental determinants of pedestrian crashes.

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.

Blight Abatement of Vacant Land and Crime in New Orleans

OBJECTIVES

In 2005, Hurricane Katrina caused damage in New Orleans, Louisiana, and much of the land in low-resource neighborhoods became vacant and blighted. In 2014, New Orleans launched a program, Fight the Blight, which remediated properties in 6 neighborhoods. Our objective was to examine changes in crime rates near lots that were remediated (ie, debris removed and vegetation mowed).

METHODS

We used a quasi-experimental design to test whether crime rates changed from preremediation (January 2013-October 2014) to postremediation (July 2016-March 2017) near 204 vacant lots that were remediated compared with 560 control vacant lots that were not remediated between October 2014 and July 2016. We also examined differences between remediated lots that received 1 treatment (n = 64) and those that received ≥2 treatments (n = 140).

RESULTS

We found no significant differences between remediated and control lots in levels of violent, property, and domestic crimes from preremediation to postremediation. However, the number of drug crimes per square mile decreased significantly near all remediated lots (5.7% lower; P < .001) compared with control lots, largely driven by the significant decrease (6.4% lower; P < .001) in drug crimes found near lots that received ≥2 treatments.

CONCLUSIONS

Investing in programs that improve neighborhood environments affected by high rates of physical disorder and vacancy may be a way to decrease violence. However, routine remediation may be needed to increase the public health impact of blight abatement programs in warmer climates, where weeds and vegetation grow rapidly.

"Complete Streets" and Adult Bicyclist Fatalities: Applying G-Computation to Evaluate an Intervention That Affects the Size of a Population at Risk

"Complete streets" policies require transportation engineers to make provisions for pedestrians, bicyclists, and mass transit users. These policies may make bicycling safer for individual cyclists while increasing the overall number of bicycle fatalities if more people cycle due to improved infrastructure. We merged county-level records of complete streets policies with Fatality Analysis Reporting System counts of cyclist fatalities occurring between January 2000 and December 2015. Because comprehensive county-level estimates of numbers of cyclists were not available, we used bicycle commuter estimates from the American Community Survey and the US Census as a proxy for the cycling population and limited analysis to 183 counties (accounting for over half of the US population) for which cycle commuting estimates were consistently nonzero. We used G-computation to estimate the effect of complete streets policies on overall numbers of cyclist fatalities while also accounting for potential policy effects on the size of the cycling population. Over a period of 16 years, 5,254 cyclists died in these counties, representing 34 fatalities per 100,000 cyclist-years. We estimated that complete streets policies made cycling safer, averting 0.6 fatalities per 100,000 cyclist-years (95% confidence interval: -1.0, -0.3) by encouraging a 2.4% increase in cycling but producing only a 0.7% increase in cyclist fatalities. G-computation is a useful tool for understanding the impact of policy on risk and exposure.

Alcohol Outlets, Neighborhood Retail Environments, and Pedestrian Injury Risk

BACKGROUND

Alcohol outlet density has been associated with increased pedestrian injury risk. It is unclear whether this is because alcohol outlets are located in dense retail areas with heavy pedestrian traffic or whether alcohol outlets contribute a unique neighborhood risk. We aimed to compare the pedestrian injury rate around alcohol outlets to the rate around other, similar retail outlets that do not sell alcohol.

METHODS

A spatial analysis was conducted on census block groups in Baltimore City. Data included pedestrian injury emergency medical services (EMS) records from January 1, 2014 to April 15, 2015 (n = 848); locations of alcohol outlets licensed for off-premise (n = 726) and on-premise consumption (n = 531); and corner (n = 398) and convenience stores (n = 192) that do not sell alcohol. Negative binomial regression was used to determine the relationship between retail outlet count and pedestrian injuries, controlling for key confounding variables. Spatial autocorrelation was also assessed and variable selection adjusted accordingly.

RESULTS

Each additional off-premise alcohol outlet was associated with a 12.3% increase in the rate of neighborhood pedestrian injury when controlling for convenience and corner stores and other confounders (incidence rate ratio [IRR] = 1.123, 95% confidence interval [CI] = 1.065, 1.184, p < 0.001). The attributable risk was 4.9% (95% CI = 0.3, 8.9) or 41 additional injuries. On-premise alcohol outlets were not significant predictors of neighborhood pedestrian injury rate in multivariable models (IRR = 0.972, 95% CI = 0.940, 1.004, p = 0.194).

CONCLUSIONS

Off-premise alcohol outlets are associated with pedestrian injury rate, even when controlling for other types of retail outlets. Findings reinforce the importance of alcohol outlets in understanding neighborhood pedestrian injury risk and may provide evidence for informing policy on liquor store licensing, zoning, and enforcement.

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.

Local vs. national: Epidemiology of pedestrian injury in a mid-Atlantic city

OBJECTIVE

Understanding pedestrian injury trends at the local level is essential for program planning and allocation of funds for urban planning and improvement. Because we hypothesize that local injury trends differ from national trends in significant and meaningful ways, we investigated citywide pedestrian injury trends to assess injury risk among nationally identified risk groups, as well as identify risk groups and locations specific to Baltimore City.

METHODS

Pedestrian injury data, obtained from the Baltimore City Fire Department, were gathered through emergency medical services (EMS) records collected from January 1 to December 31, 2014. Locations of pedestrian injuries were geocoded and mapped. Pearson's chi-square test of independence was used to investigate differences in injury severity level across risk groups. Pedestrian injury rates by age group, gender, and race were compared to national rates.

RESULTS

A total of 699 pedestrians were involved in motor vehicle crashes in 2014-an average of 2 EMS transports each day. The distribution of injuries throughout the city did not coincide with population or income distributions, indicating that there was not a consistent correlation between areas of concentrated population or concentrated poverty and areas of concentrated pedestrian injury. Twenty percent (n = 138) of all injuries occurred among children age ≤14, and 22% (n = 73) of severe injuries occurred among young children. The rate of injury in this age group was 5 times the national rate (Incident Rate Ratio [IRR] = 4.81, 95% confidence interval [CI], [4.05, 5.71]). Injury rates for adults ≥65 were less than the national average.

CONCLUSIONS

As the urban landscape and associated pedestrian behavior transform, continued investigation of local pedestrian injury trends and evolving public health prevention strategies is necessary to ensure pedestrian safety.

Novel Methods for Environmental Assessment of Pedestrian Injury: Creation and Validation of the Inventory for Pedestrian Safety Infrastructure

Nationally, 80% of pedestrian fatalities occur in urban environments, yet the distribution of injuries across urban areas is not uniform. Identifying street-level risk factors for pedestrian injury is essential for urban planning and improvement projects, as well as targeted injury prevention efforts. However, creating and maintaining a comprehensive database of a city's traffic safety infrastructure can be cumbersome and costly. The purpose of this study was to create and validate a neighborhood environmental observational assessment tool to capture evidence-based pedestrian safety infrastructure using Google Street View (GSV)-The Inventory for Pedestrian Safety Infrastructure (IPSI). We collected measures in-person at 172 liquor stores in Baltimore City from June to August 2015 to assess the tool's reliability; we then collected IPSI measures at the same 172 locations using GSV from February to March 2016 to assess IPSI reliability using GSV. The majority of items had good or excellent levels of inter-rater reliability (ICC ≥ 0.8), with intersection features showing the highest agreement across raters. Two scales were also developed using exploratory factor analysis, and both showed strong internal consistency (Cronbach's alpha ≥ 0.6). The IPSI provides a valid, economically efficient tool for assessing pedestrian safety infrastructure that can be employed for a variety of research and urban planning needs. It can also be used for in-person or GSV observation. Reliable and valid measurement of pedestrian safety infrastructure is essential to effectively prevent future pedestrian injuries.

Neighbourhood alcohol environment and injury risk: a spatial analysis of pedestrian injury in Baltimore City

OBJECTIVES

The purpose of this study was to investigate the contribution of neighbourhood disorder around alcohol outlets to pedestrian injury risk.

METHODS

A spatial analysis was conducted on census block groups in Baltimore City. Data included pedestrian injury EMS records from 1 January 2014 to 15 April 2015 (n=858), off-premise alcohol outlet locations for 2014 (n=693) and neighbourhood disorder indicators and demographics. Negative binomial regression models were used to determine the relationship between alcohol outlet count and pedestrian injuries at the block group level, controlling for other neighbourhood factors. Attributable risk was calculated by comparing the total population count per census block group to the injured pedestrian count.

RESULTS

Each one-unit increase in the number of alcohol outlets was associated with a 14.2% (95% CI 1.099 to 1.192, P<0.001) increase in the RR of neighbourhood pedestrian injury, adjusting for traffic volume, pedestrian volume, population density, per cent of vacant lots and median household income. The attributable risk was 10.4% (95% CI 7.7 to 12.7) or 88 extra injuries. Vacant lots was the only significant neighbourhood disorder indicator in the final adjusted model (RR=1.016, 95% CI 1.007 to 1.026, P=0.003). Vacant lots have not been previously investigated as possible risk factors for pedestrian injury.

CONCLUSIONS

This study identifies modifiable risk factors for pedestrian injury previously unexplored in the literature and may provide evidence for alcohol control strategies (eg, liquor store licencing, zoning and enforcement).

Rethinking safety in numbers: are intersections with more crossing pedestrians really safer?

Objective

To advance the interpretation of the ‘safety in numbers’ effect by addressing the following three questions. How should the safety of pedestrians be measured, as the safety of individual pedestrians or as the overall safety of road facilities for pedestrians? Would intersections with large numbers of pedestrians exhibit a favourable safety performance? Would encouraging people to walk be a sound safety countermeasure?

Methods

We selected 288 signalised intersections with 1003 pedestrian crashes in Hong Kong from 2010 to 2012. We developed a Bayesian Poisson-lognormal model to calculate two common indicators related to pedestrian safety: the expected crash rate per million crossing pedestrians and the expected excess crash frequency. The ranking results of these two indicators for the selected intersections were compared.

Results

We confirmed a significant positive association between pedestrian volumes and pedestrian crashes, with an estimated coefficient of 0.21. Although people who crossed at intersections with higher pedestrian volumes experienced a relatively lower crash risk, these intersections may still have substantial potential for crash reduction.

Conclusions

Conclusions on the safety in numbers effect based on a cross-sectional analysis should be reached with great caution. The safety of individual pedestrians can be measured based on the crash risk, whereas the safety of road facilities for pedestrians should be determined by the environmental hazards of walking. Intersections prevalent of pedestrians do not always exhibit favourable safety performance. Relative to increasing the number of pedestrians, safety strategies should focus on reducing environmental hazards and removing barriers to walking.

Validation of a Google Street View-Based Neighborhood Disorder Observational Scale

Recently, there has been a growing interest in developing new tools to measure neighborhood features using the benefits of emerging technologies. This study aimed to assess the psychometric properties of a neighborhood disorder observational scale using Google Street View (GSV). Two groups of raters conducted virtual audits of neighborhood disorder on all census block groups (N = 92) in a district of the city of Valencia (Spain). Four different analyses were conducted to validate the instrument. First, inter-rater reliability was assessed through intraclass correlation coefficients, indicating moderated levels of agreement among raters. Second, confirmatory factor analyses were performed to test the latent structure of the scale. A bifactor solution was proposed, comprising a general factor (general neighborhood disorder) and two specific factors (physical disorder and physical decay). Third, the virtual audit scores were assessed with the physical audit scores, showing a positive relationship between both audit methods. In addition, correlations between the factor scores and socioeconomic and criminality indicators were assessed. Finally, we analyzed the spatial autocorrelation of the scale factors, and two fully Bayesian spatial regression models were run to study the influence of these factors on drug-related police interventions and interventions with young offenders. All these indicators showed an association with the general neighborhood disorder. Taking together, results suggest that the GSV-based neighborhood disorder scale is a reliable, concise, and valid instrument to assess neighborhood disorder using new technologies.

Contextual Correlates of Physical Activity among Older Adults: A Neighborhood Environment-Wide Association Study (NE-WAS)

Background: Few older adults achieve recommended physical activity levels. We conducted a "neighborhood environment-wide association study (NE-WAS)" of neighborhood influences on physical activity among older adults, analogous, in a genetic context, to a genome-wide association study.Methods: Physical Activity Scale for the Elderly (PASE) and sociodemographic data were collected via telephone survey of 3,497 residents of New York City aged 65 to 75 years. Using Geographic Information Systems, we created 337 variables describing each participant's residential neighborhood's built, social, and economic context. We used survey-weighted regression models adjusting for individual-level covariates to test for associations between each neighborhood variable and (i) total PASE score, (ii) gardening activity, (iii) walking, and (iv) housework (as a negative control). We also applied two "Big Data" analytic techniques, LASSO regression, and Random Forests, to algorithmically select neighborhood variables predictive of these four physical activity measures.Results: Of all 337 measures, proportion of residents living in extreme poverty was most strongly associated with total physical activity [-0.85; (95% confidence interval, -1.14 to -0.56) PASE units per 1% increase in proportion of residents living with household incomes less than half the federal poverty line]. Only neighborhood socioeconomic status and disorder measures were associated with total activity and gardening, whereas a broader range of measures was associated with walking. As expected, no neighborhood meaZsures were associated with housework after accounting for multiple comparisons.Conclusions: This systematic approach revealed patterns in the domains of neighborhood measures associated with physical activity.Impact: The NE-WAS approach appears to be a promising exploratory technique. Cancer Epidemiol Biomarkers Prev; 26(4); 495-504. ©2017 AACRSee all the articles in this CEBP Focus section, "Geospatial Approaches to Cancer Control and Population Sciences."

Using Virtual Street Audits to Understand the Walkability of Older Adults' Route Choices by Gender and Age

Walking for physical activity can bring important health benefits to older adults. In this population, walking has been related to various urban design features and street characteristics. To gain new insights into the microscale environmental details that might influence seniors’ walking, details which might be more amenable to change than neighbourhood level factors, we employed a reliable streetscape audit tool, in combination with Google Street View^™, to evaluate the ‘walkability’ of where older adults choose to walk. Analysis of the routes selected by a purposive sample of independently mobile adults aged 65 years and over living in Edinburgh, UK, revealed a preference to walk in more walkable environments, alongside a willingness to walk in less supportive settings. At times, factors commonly considered important for walking, including wayfinding and legibility, user conflict, kerb paving quality, and lighting appeared to have little impact on older adults’ decisions about where to walk. The implications for policy, practice, and the emerging technique of virtual auditing are considered.

Spatial analysis of the association of alcohol outlets and alcohol-related pedestrian/bicyclist injuries in New York City

BACKGROUND

Pedestrian and bicyclist injury is an important public health issue. The retail environment, particularly the presence of alcohol outlets, may contribute the the risk of pedestrian or bicyclist injury, but this association is poorly understood.

METHODS

This study quantifies the spatial risk of alcohol-related pedestrian injury in New York City at the census tract level over a recent 10-year period using a Bayesian hierarchical spatial regression model with Integrated Nested Laplace approximations. The analysis measures local risk, and estimates the association between the presence of alcohol outlets in a census tract and alcohol-involved pedestrian/bicyclist injury after controlling for social, economic and traffic-related variables.

RESULTS

Holding all other covariates to zero and adjusting for both random and spatial variation, the presence of at least one alcohol outlet in a census tract increased the risk of a pedestrian or bicyclist being struck by a car by 47 % (IDR = 1.47, 95 % Credible Interval (CrI) 1.13, 1.91).

CONCLUSIONS

The presence of one or more alcohol outlets in a census tract in an urban environment increases the risk of bicyclist/pedestrian injury in important and meaningful ways. Identifying areas of increased risk due to alcohol allows the targeting of interventions to prevent and control alcohol-related pedestrian and bicyclist injuries.