Webinar Online – Urban Informatics: Studying How Urban Design Influences Health in New York City

Dr. Rundle’s March 2nd webinar for the ISBNPA webinar has been posted online at ISBNPA’s web site (Here and embedded below).

His talk covered different approaches to assessing neighborhood walkability and the link between urban design and resident’s physical activity using New York City as a case study.  He highlighted the challenges to measuring neighborhood form across multiple municipal jurisdictions and retrospectively over the past three decades.

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Webinar – Urban Informatics: Studying How Urban Design Influences Health in New York City

Neighborhood walkability in NYC

Neighborhood walkability in NYC

On Thursday March 2nd at 3pm EST, Dr. Rundle will give a webinar entitled “Urban Informatics: Studying How Urban Design Influences Health in New York City” for the International Society of Behavioral Nutrition and Physical Activity. You can register for the webinar HERE.

Dr. Rundle will discuss different approaches to assessing neighborhood walkability and the link between urban design and resident’s physical activity using New York City as a case study. His talk covers the usage of large administrative and commercial datasets and geospatial tools to characterize neighborhood built environment features; global positioning systems (GPS) and accelerometers to measure individual’s behaviors; and epidemiologic methods to understand how differences in neighborhood characteristics influence the health of residents. He will highlight challenges to measure neighborhood form across multiple municipal jurisdictions and retrospectively over the past three decades.

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Neighborhood Physical Disorder and Physical Activity Among Older Adults in NYC

Elements that are incorporated into the overall Neighborhood Disorder Scale score.

Elements that are incorporated into the overall Neighborhood Disorder Scale score.

Through the years, we have done a fair amount of work to collect and validate measures of neighborhood physical disorder – urban deterioration – using our CANVAS/Google Street View system. Neighborhood disorder is controversial construct and measure, not only because perceptions of what constitutes disorder can vary sharply between people – one person’s chaotic urban jungle is another person’s lively street scene – but also because the impacts of disorder – does disorder induce crime or is it just correlated with crime because the two share common causes such as neighborhood disadvantage? – are unclear.

One possible impact of disorder may be deterring physical activity, especially walking. In walk-along studies with older adults – wherein a researcher takes a walk with a study participant and asks about the neighborhood characteristics and how they affect the walker’s experience –many participants report that they don’t like or are threatened by indicators of disorder, such as graffiti, poor building maintenance, and similar signs of abandonment. But there has been relatively little rigorous longitudinal, population-based research on the extent to which disorder is a barrier to physical activity in practice. This is important, because if disorder is a major barrier to activity, then removing disorder, perhaps through aggressive blight removal and related clean-up programs, may have substantially positive implications for the health of aging populations trying to maintain active lifestyles.

We recently published a study using our disorder measure with data from the New York City Neighborhood and Mental Health in the Elderly Study (NYCNAMES-II), a three-wave cohort study of about 3,500 adults aged 65-75 at baseline, to see whether disorder seems to be impeding physical activity among older New York City residents. Too few study participants moved over the two years of follow-up for us to reliably assess the impact of moving from a neighborhood at one level of disorder to another, but we did find that, comparing across subjects, each standard deviation increase in neighborhood disorder (the difference between a neighborhood with no litter or graffiti to one where both were prevalent) was associated with an decrease in self-reported activity equivalent to about 6 min of walking per day. However, neighborhood physical disorder was not related to changes in physical activity over the two years of follow-up.

Ultimately, it seems that there is some meaningful association between living in a more disordered place and being less physically active, but that neighborhood disorder was not a major cause of decline in physical activity among these older adults. We hope to explore the relationship between disorder and physical activity more deeply in future research using datasets with longer follow-up and more dynamic neighborhood conditions.

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Commandments for Variable Naming and Data Management

Mel Brooks and the 15 Commandments from History of the World Part 1

Mel Brooks and the 15 Commandments from History of the World Part 1

As we launched another multifaceted geographic data linkage study our multi-institution team, that includes researchers at Drexel University, Columbia University and the University of Washington, has developed a set of commandments to streamline and harmonize our data management, variable naming and data coding processes.

 

  1. Thou shalt not transmit HIPAA/IRB protected data, nor data protected by licensing agreement without PI approval.

Clearly, we both want to be responsible custodians of the data entrusted to us, and avoid getting into trouble.  For additional discussion of cautions around the common practice of using online tools to characterize addresses, see our recent commentary.

  1. Thou shalt always use YYYYMMDD when formatting date variable values, stored as a string.

The date storage was much discussed by our group, but ultimately we wanted a solution that would sort chronologically, be readable to humans, and be usable seamlessly across software that use a different sentinel date.

  1. Thou shalt always use YYYY when using a year in a variable name.

Given that our studies of adult health frequently span both the 1990s and 2000s, using 4 digits (versus 2 digit) for year when possible allows for easier conversion from wide to long format, and sorting in chronological order.

  1. Thou shalt prefer use of tall rather than wide data formats to avoid storing empty data and simplify query expressions.

As we move to using longitudinal data on where people live, and how their environment has changed over time, the structure of data becomes more complex.  Long format avoids storing fields for which many observations have no data.  However, the overarching goal is efficiency and usability, which may at times favor a wide format instead.

  1. Thou shalt always use lowercase for variable names to avoid case sensitivity issues when jumping between software.

Inconsistent capitalization in variable names is a source of frustration for users of software such as STATA.  A typical scenario is that you have working syntax, receive an updated dataset with differences in capitalization (which a user of less case sensitive software packages such as SAS may not be attentive to), and have to spend time troubleshooting and editing to get it to work again.  While conventions vary, we decided the simplest thing would be to use only lowercase in our variable names. Continue reading

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JAMA on Walking and Walkability

High and low walkability neighborhoods in NYC

High and low walkability neighborhoods in NYC

Following up on its two recent articles about neighborhood walkability, including an editorial co-authored by Andrew Rundle, JAMA today published a Medical News and Perspectives article entitled “As Walking Movement Grows, Neighborhood Walkability Gains Attention”.  The article notes the various Federal Agencies that are working on improving neighborhood walkability including: the US Department of Health and Human Services which launched an initiative “Step It Up! The Surgeon General’s Call to Action to Promote Walking and Walkable Communities.”; the CDC funded National Physical Activity Plan Alliance’s forthcoming (expected early 2017) “Walking and Walkability Report Card”; and the collaboration of the USDOT, the CDC, and the American Public Health Association to release the online Transportation and Health Tool, which provides access to data on the health effects of transportation systems and includes a focus on active transport.

In regards to the lack of randomized trial data on neighborhood walkability and the paucity of longitudinal studies in the literature, the article quotes Jim Sallis Sallis saying that even without direct evidence of causality, “the correlational evidence is really piling up.” and that “the risk of improving walkability appears very low, whereas the benefits could be very substantial.”

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Steve Mooney receives Poster Award at Epidemiology Congress of the Americas 2016

HomeSteve Mooney, a recently minted PhD who did his doctoral work with the BEH group, won a best poster presentation award at the 2016 Epidemiology Congress of the Americas for his work on the Neighborhood Environment-Wide Association Study design. Dr. Mooney’s poster, available as a PDF here, explored the potential to apply theory-agnostic empirical approaches to finding the neighborhood measures most predictive of physical activity among older adults in the NYCNAMES-II cohort.

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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.

NHood_walkability_2

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

Map_Pin

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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