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, 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.

Posted in Active Transport, GPS, Physical Activity | Leave a comment

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

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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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Strategies to Refine Annual Business Establishment Data across More than Two Decades

Analyses of place and health have been largely cross-sectional, and new challenges are faced as we wrangle longitudinal geographic data.  Our group just published a manuscript detailing our work to clean and code data on all NYC metropolitan area businesses over the period 1990-2010.  Our goal was to use twenty years of business establishment data to characterize changes in neighborhoods in terms of the retail food environment, access to physical activity venues, access to medical facilities and access to other commercial and not-for-profit establishments.

Our process included re-geocoding 3,161,715 business locations to avoid disproportionately missing data on older businesses; identifying and coding health-relevant businesses such as food sources and fitness venues across the years; and collapsing potential duplicate business records by location, year, and business category.  Spot-checking was used, and the data are set up to allow for sensitivity analyses to check the robustness to these decisions as we move forward.

This effort was championed by lead author Tanya Kaufman, who has engaged in this effort since her MPH practicum project using these data.  Daniel Sheehan was the lead geographer on the project and developed the re-geocoding strategies and created time-lapse visualizations of businesses entering and existing the environment. One of Dan’s visualizations can be seen here.  It shows the location of Healthy Food Outlets from 1990 to 2010.

The focus of this project was not only to understand and improve the quality of data for future analysis, but also to develop scalable approaches that can be used with the larger national dataset.  We have recently been funded to purchase the nationwide business establishment data and to link these data to ongoing cohort studies of cardiovascular disease  (R01AG049970-01A1, PI: Lovasi).


Posted in Economic Development, Food Environment, Methods | Leave a comment

CDC Releases New Built Environment Assessment Tool

Viewing 125th Street

Viewing 125th Street

The CDC released a new direct systematic observation data collection instrument for measuring the core features and quality of the built environment related to behaviors that affect health, especially behaviors such as walking, biking, and other types of physical activity.  The core features assessed in the BE Tool include: built environment infrastructure (e.g., road type, curb cuts/ramps, intersections/crosswalks, traffic control, transportation), walkability (e.g. sidewalk/path features, walking safety, aesthetics & amenities), bikeability (e.g., bicycle lane/path features), recreational sites and structures, and the food environment (e.g., access to grocery stores, convenience stores, farmers markets, etc.).

Get the tool [HERE]


Posted in Community Needs Assessment, Methods, Walkability | Leave a comment