Urban Design to Support Walking and Health

JAMA just published an editorial co-written by, Andrew Rundle, entitled “Can Walkable Urban Design Play a Role in Reducing the Incidence of Obesity-Related Conditions?”.  The editorial provides a perspective on a study published in JAMA by Creatore et al., that assessed the prevalence of obesity and incidence of diabetes from 2001 to 2012, by level neighborhood walkability across 15 municipalities in Canada.

Urban design to support active transport (walking and cycling) is an attractive avenue for public health interventions to increase population levels of physical activity and reduce the burden of obesity and diabetes. In the urban design/planning literature walkability is typically described in terms of the “D variables“: density of population, density of residences, density of public transit stops, design of street networks, and destination accessibility.  In many cities there is not enough undeveloped space to create new large urban parks that support exercise and recreational physical activity. Local governments have some policy mechanisms for influencing neighborhood retail food access: tax and loan incentives can be used to promote the development of new supermarkets, but efforts to ban new fast food outlets are controversial and have not been found to be effective. However, improvements in neighborhood walkability can be promoted through permitting, zoning, land use regulations, and street design, activities all under local governmental control. In addition, although public transit receives state and federal funding, key decisions about transit capital investment and operations are made at the local level.


Furthermore our work in NYC finds that variation in residential neighborhood walkability is more strongly associated with physical activity than is variation in residential neighborhood access to parks.  Similarly we find that variation in neighborhood walkability is more strongly associated with body mass index than is variation in neighborhood access to healthy food outlets, fast food outlets and park spaces.

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Can Big Data get us Better Estimates of Neighborhood Disorder?

Physical Disorder in Philadelphia estimated using Universal Kriging

Physical Disorder in Philadelphia estimated using Universal Kriging

At the Built Environment and Health group, we try hard to measure neighborhood characteristics accurately. We systematically audit Street View imagery, we use LiDAR scans to assess tree canopy, and we use business registration records to profile neighborhood retail. A lot of these measures are spatially interpolated. For example, it’s not feasible to collect pollen counts everywhere in the city, but we can take pollen count samples at a few locations and use those samples to estimate the pollen counts we would have measured in places we couldn’t measure.

One technique we use for spatial interpolation is ordinary kriging. Ordinary kriging uses the spatial correlation between sampled points – that is, on average, how similar are pairs of points at any given distance – to estimate, with confidence levels, measures at unobserved locations. Ordinary kriging was initially developed in geology – miners sampled minerals in locations that appeared promising, then analyzed the spatial variation in mineral content between the samples in order to identify potential gold deposits. We and others have borrowed this technique for neighborhood measures, like when we used ordinary kriging to estimate physical disorder levels throughout cities.

But a key assumption underlying an ordinary kriging model is that there’s a continuous correlation for the measures of interest – on average, mineral content at a given location looks more like the mineral content 50 feet away than the mineral content 100 feet away. We started wondering whether the assumption of continuity doesn’t hold for neighborhood disorder.  For example, if physical conditions are worse on the ‘wrong side of the tracks’, then a measure of nearby conditions that happens to be on the opposite side of the tracks might tell us less than a measure that’s further away but on the same side of the tracks. Maybe, we thought, if we pull external information like the side of the tracks into our interpolation models, we can interpolate more accurately. Continue reading

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Maintaining Human Subject’s Protections in Neighborhood Health Effects Research


Geocoding a study subject’s address with Google Maps or Earth transmits that personal identifier to Goolgle.

We recently published a commentary in the American Journal of Public Health describing the concerns we have for protecting study subject anonymity with the use of online geographic and data tools in neighborhood health effects research.  Examples of neighborhood data available from these tools include crime statistics from the New York Times and EveryBlock, neighborhood walkability scores from Walkscore.com, restaurant locations from Yelp and geocoding services from Google Maps. These online resources create new opportunities for medical geographic research but also create new ways in which study subject confidentiality can be broken.  Typically these web-tools allow a user to enter an address into an online interface and receive back data about the geographic area around that location.  We have seen study protocols, training materials, and published papers involving the submission of study subject’s home and/or work addresses to such web services.  The broad terms of service on most websites usually permit these service providers to freely use any data passed to them rather than hew to strict rules established by institutional review board (IRB) protocols to protect human subjects.  Furthermore, online advertising tracking cookies on the researcher’s (or research assistant’s) browser could be used to release respondent addresses to additional parties without the researcher’s knowledge.  In the Commentary we describe approaches to using these online services for neighborhood health effects research while maintaining human subjects protections.

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Measuring Pedestrian Activity Using GPS Logger Data

It has been suggested that GPS monitoring data can be used to estimate distances traveled and speeds of travel during active and non-active travel journeys and, that when combined with accelerometer monitoring, GPS data can be used to identify travel mode.  We tested whether the distances between successively captured GPS way points can be used to measure distances walked in varying environments in NYC. Students walked a series of structured routes in areas with high and low building bulk density and on streets with high and low tree canopy cover while wearing GPS monitors.  The sums of distances between successive GPS way points over estimated travel distances and over estimates were larger in areas with high building bulk density and on streets with high tree canopy cover. Algorithms using distances between successive GPS points to infer speed or travel mode may misclassify trips differentially across built environment contexts.  The abstract can be found HERE and the full paper will be available in the American Journal of Public Health.

Below is an image of the GPS data collected during walks along streets in low and high building bulk density.

GPS data collected during walks along streets in areas with low and high building bulk density. Image by Dan Sheehan.

GPS data collected during walks along streets in areas with low (left side) and high (right side) building bulk density. Image by Dan Sheehan.

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Maps of Neighborhood Physical Disorder

The Journal of Maps recently published our article showing a high resolution map of neighborhood physical disorder in New York City.

Physical disorder – the deterioration of urban spaces owing to social forces favoring neglect and abandonment – has long been of interest to social scientists [1, 2].  Criminologists and sociologists have debated the controversial ‘broken windows’ theory that disorder encourages violent crime [3, 4]. Separately, psychologists and psychiatric epidemiologists have investigated whether living amidst disorder negatively affects mental health, not only directly as stress induced by encountering a chaotic environment triggers earlier cognitive decline [5] but also indirectly as residents adopt coping mechanisms such as alcohol use that themselves trigger longer-term harms [6].

The data underlying the map was collected using neighborhood audits implemented via Street View. In addition to data collection in NYC, the team collected neighborhood physical disorder data from San Jose, California; Detroit, Michigan; and Philadelphia, Pennsylvania.  Below is a heat map of Philadelphia showing the distribution of neighborhood physical disorder across the city.

Neighborhood physical disorder in Philadelphia. Draker area within Philadelphia have higher levels of physical disorder.

Neighborhood physical disorder in Philadelphia. Draker area within Philadelphia have higher levels of physical disorder.

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Our Pedestrian Injury Research gets Further Coverage.

The Mailman School blog reached out to Steve Mooney to discuss our research on pedestrian injuries.  The post shows a series of Street Views of the key features that were associated with injuries.  The article is Here. And an article in New Scientist. And on Forbes.com.

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Using Google Street View to Understand Pedestrian Injury Risk

Street Viewing 125th Street

Street Viewing 125th Street

In 2013, an estimated 70 000 pedestrians were injured or killed by motor vehicles in the United States. In New York City more pedestrians than vehicle occupants have been killed by motor vehicles each year since at least 1910.  Pedestrian safety is not only vital for public health directly through reduced traffic-related morbidity and mortality, but also indirectly as the perception of increased safety from traffic encourages outdoor physical activity, with consequent mental and physical health benefits.

We just published an article in the American Journal of Public Health in which we use Google Street View to identify characteristics of streets and intersections associated with pedestrian injuries and fatalities.  Following up on our work using Street View to conduct virtual street audits (1, 2, 3), we used the CANVAS system to collect data on built environment characteristics at street intersections with varying numbers of pedestrian injuries.  Higher counts of pedestrian injuries at intersections were associated with the presence of nearby billboards and bus-stops.  Injury incidence per pedestrian was lower at intersections with higher estimated pedestrian volumes.

The use of virtual street audits allowed us to complete the research in a much shorter time period than comparable studies that use in-person audits to collect data at intersections. We are planning to expand this research to conduct a nationwide study of built environment risk factors for pedestrian injury.

Jerome Ave and Fordham Road in the Bronx, the intersection with the highest number of injuries in our study.

Jerome Ave and Fordham Road in the Bronx, the intersection with the highest number of injuries in our study.

Posted in CANVAS, Pedestrian Injury, Safety, Street View | 2 Comments

Using GPS and Accelerometers to Study Neighborhood Walkability and Physical Activity

We just published a paper in the American Journal of Preventive Medicine showing that differences in residential neighborhood walkability in New York City (NYC) are associated with how residents utilize neighborhood space and are associated with total weekly physical activity. Higher neighborhood walkability was associated with significantly more physical activity and differences in activity attributable to variation in urban design were substantial when compared to the recommended goal of achieving 150 minutes  of moderate intensity physical activity per week.

Examples of high and low walkability neighborhoods and a map of Neighborhood Walkability Index scores for all of NYC

Examples of high and low walkability neighborhoods and a map of neighborhood walkability for all of NYC

The research was conducted in collaboration with researchers from the NYC Department of Health and Mental Hygiene and analyzed Global Positioning System (GPS) and physical activity data from the Physical Activity and Transit Survey (PAT).  For a period of a week, PAT study participants wore an accelerometer to continuously measure physical activity and a GPS logger that recorded the participant’s location multiple times per minute.  In all, the PAT Survey collected over 8 million GPS location readings, known as waypoints, as the study participants (n=803) went about their daily lives.

Four illustrative examples of minimally convex polygons (white area) around GPS waypoints falling within 1Km of the residence (total area of circles).

Four illustrative examples showing how GPS logging data can be used to characterize which parts, and how much, of someone’s residential neighborhood is actually utilized by the person. The circle represents all neighborhood space within 1Km of a residence and the white area reflects a minimal convex polygon that encompasses GPS waypoints. They grey area represents space within 1Km of the residence that was not utilized.

To identify how much area within their residential neighborhood participants utilized during the monitoring period, we defined a minimally convex polygon around GPS waypoints falling within 1Km of each participant’s home.  This 1Km circular area around the home has commonly been used in prior research to define study participant’s residential neighborhoods.  The use of convex polygons around GPS waypoints to define the utilized residential area is similar to methods used in wildlife studies to define the home territory of animals. In NYC we see that residents vary considerably in how much of the total 1Km circular residential neighborhood area they actually use as judged by the area encompassed by the GPS data. Continue reading

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The CDC and DoT’s Transportation and Health Tool

Viewing 125th Street

Viewing 125th Street

The Centers for Disease Control and Prevention and Department of Transportation just released the new Transportation and Health Tool, which provides easy access to data that examines the health impacts of transportation systems. The Transportation and Health Tool provides data on 14 transportation and public health indicators for each state, metropolitan statistical area (MSA), and urbanized area (UZA). The indicators measure how the transportation environment affects health with respect to safety, active transportation, air quality, and connectivity to destinations.  You can use the tool to quickly see how a state, MSA, or UZA compares with others in addressing key transportation and health issues. The tool also provides information and resources to help agencies better understand the links between transportation and health and to identify strategies to improve public health through transportation planning and policy

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Tree Canopy Data for the Entire State of Pennsylvania

Jarlath O’Neil-Dunne at the University of Vermont just announced the release of a statewide, high-resolution tree canopy dataset for Pennsylvania.  The resolution of the data is 1 m which makes it 900 times more detailed than the National Land Cover Dataset; this is an amazing feat.

We previously worked with Jarlath to create tree canopy data for NYC and looked at the link between tree canopy coverage and asthma and allergic sensitization among children in NYC.


Tree canopy coverage in Washington Heights, NYC

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