Webinar recorded March 25, 2015

Summary by Meghan McMullen | Georgia Institute of Technology, MCRP ’16

A product of the Building Bridges Project, a partnership of the American Public Health Association, American Planning Association, and Georgia Tech’s Built Environment and Public Health Clearinghouse

Built Environment and Public Health Clearinghouse-solid-2lines-539+124


This webinar takes a deeper, practitioner-focused look at newly developed tools that can guide planners, architects, and public officials in generating innovative solutions to create healthy communities.  It features strategies for using three cutting-edge tools for creating healthy places: Urban Land Institute’s Building Healthy Places Toolkit, Georgia Tech’s Neighborhood Quality of Life and Health Project, and the U.S. Environmental Protection Agency’s EnviroAtlas.  These tools offer tangible applications for practitioners looking to incorporate human and environmental health into their development decisions, ranging from the site to the regional scale. Read a synopsis of the webinar content below and visit the Built Environment and Public Health Clearinghouse to view a recording of the webinar.

Moderated by:

Matthew Trowbridge, MD, MPH
Associate Professor, University of Virginia School of Medicine
Senior Research Fellow, U.S. Green Building Council

Urban Land Institute’s Building Healthy Places Toolkit

Presented by Sara Hammerschmidt, PhD | Senior Associate, Urban Land Institute

hammerschmidtLeading the way toward healthy building practices at the site level, the Urban Land Institute, in partnership with the Center for Active Design and Colorado Health Foundation, created the Building Healthy Places Toolkit as a resource for developers, designers, investors, and property managers to build places that promote the health of tenants, residents, and communities. The toolkit provides 21 specific, practical, evidence-based strategies for how design elements, programming strategies, materials, and other approaches can be leveraged to improve health and develop quality products.  These recommendations are broken into three categories of impact: 1) physical activity, 2) healthy food and drinking water, and 3) healthy environment and social well-being.  The complete list of recommendations is as follows:

  1. Incorporate a mix of land uses
  2. Design well-connected street networks at the human scale
  3. Provide sidewalks and enticing, pedestrian-oriented streetscapes
  4. Provide infrastructure to support biking
  5. Design visible, enticing stairs to encourage everyday use
  6. Install stair prompts and signage
  7. Provide high-quality spaces for multigenerational play and recreation
  8. Build play spaces for children
  9. Accommodate a grocery store
  10. Host a farmers market
  11. Promote healthy food retail
  12. Support on-site gardening and farming
  13. Enhance access to drinking water
  14. Ban smoking
  15. Use materials and products that support healthy indoor air quality
  16. Facilitate proper ventilation and airflow
  17. Maximize indoor lighting quality
  18. Minimize noise pollution
  19. Increase access to nature
  20. Facilitate social engagement
  21. Adopt pet-friendly policies

Insights from developers utilizing the recommended practices, photographs, links to additional resources, and schematic illustrations are interwoven throughout the toolkit, translating the recommendations into development realities.  In addition these recommended best practices, the toolkit gives an overview of related certification programs, marketing strategies to promote wellness, and how to build partnerships with foundations, universities, and health-focused nonprofits to take healthy building to the next level.

ECO Modern Flats in Fayetteville, Arkansas illustrates the potential of healthy building practices to enhance the value of a project for both the residents and the developer.  Inspired by the connection between interior environmental quality and asthma, developer Jeremy Hudson incorporated many of the toolkit recommendations in the rehabilitation of a 1960s apartment building.  The project used low- and no-VOC materials, a ductless heating and cooling system, and concrete floors to reduce the amount of pollutants and allergens to which residents are exposed. It also features a saltwater pool, visible staircases, a community garden, a roof deck, and a courtyard, elements that promote physical activity, healthy eating, and social wellbeing.  Hudson noted a significant challenge in working with contractors who did not have experience with or knowledge of healthier, nontraditional building materials, underscoring that we cannot achieve truly healthy places until there is a deep understanding of best practices at every level from the planner to the contractor.

To learn about all 21 recommendations, view diagrams, and read insight from industry leaders applying these practices, visit http://bhptoolkit.uli.org/

Georgia Tech’s Neighborhood Quality of Life and Health Project

Presented by Nisha Botchwey, PhD | Associate Professor, Georgia Tech School of City and Regional Planning

nishaResearchers at Georgia Tech sought to evaluate the link between built environment indicators, resident perceptions of quality of life, and resident health outcomes, but found the data necessary for their analysis was not readily available at the spatial scale where health and well-being occur: the neighborhood level.  Typical sources for this type of data, such as the County Health Rankings or Neighborhood Nexus, do not provide information at the sub-county level.  Responding to this gap, the team developed Atlanta’s Neighborhood Quality of Life and Health Project (NQoLH) as a portal for integrated health and quality of life information for each of the city’s 25 Neighborhood Planning Units (NPUs).

The NQoLH website is an interactive data resource designed for use by local governments, nonprofit organizations, and neighborhood residents.  Users can search for a specific NPU by name, click a location on a map, or enter a street address, intersection, zip code, major entity or neighborhood name and will be directed to the data set for the associated NPU.  For each NPU, a general description along with a selection of demographic data, quality of life indicators, and health indicators are available.  In addition to raw data for each neighborhood, the dashboard ranks neighborhoods categorically and provides composite indices for quality of life and health relative to the rest of the city.  The transportation ranking, for instance, uses a combination of mean travel time to work and transit access to score each neighborhood, then ranks all 25 neighborhoods according to their level of accessibility.  The indices provide composite scores for each neighborhood.  The quality of life index includes indicators related to the neighborhood amenities, economy, housing, public safety and transportation; the health index includes indicators related to resident nutrition, physical activity, mortality, and morbidity. These data are displayed in tabular form, as gradient maps, and on graphs displaying the relationship between categorical rankings and neighborhood socioeconomic status, allowing for simplified analysis of multiple indicators.  They are available for direct download as GIS files. The site also provides a set of external resources for users looking to take action to improve their neighborhoods.  This streamlined, integrated approach to neighborhood data can be applied to issues at a variety of scales, including a recent, citywide question of how and where Atlanta should equitably prioritize investments from a newly passed infrastructure bond.

The next step for Atlanta’s Neighborhood Quality of Life and Health Project will be an expansion to provide neighborhood-level data for the remaining 13 cities in Fulton County, funded by the Centers for Disease Control and Prevention’s Partnership to Improve Community Health grant.  This example of accessible, visual, joint-data systems at the sub-county level can be replicated in other municipalities as a resource for decision-making, presentations to the public, and community engagement based on objective measures.

Visit Atlanta’s Neighborhood Quality of Life and Health Project at http://www.cgis.gatech.edu/NQOLH/  

The Environmental Protection Agency’s EnviroAtlas

Presented by Laura Jackson, PhD | Biologist, Office of Research and Development, U.S. Environmental Protection Agency | Principal Investigator, Sustainable and Healthy Communities Research Program | Deputy Project Lead, EnviroAtlas

jacksonThe natural environment can buffer us from anthropogenic and natural hazards, provide food and materials, and facilitate healthy lifestyles and behaviors through proximity to natural areas. Recognizing the importance of the natural elements of the environment as health exposures, the U.S. Environmental Protection Agency (EPA) developed EnviroAtlas as an online tool for viewing, analyzing, and downloading geospatial data related to ecosystem services.  Data is available on multiple scales, including the watershed level for all 48 contiguous states and community-level data for 12 urban areas, which were selected to represent a diverse sample of urban typologies and environmental contexts.  The EPA expects to have data for 20 urban areas by the end of 2015 and to ultimately provide this service for 50 cities.

The platform provides a wealth of information organized into two primary functions: the Interactive Map and the Eco-Health Relationship Browser.  There are more than 300 map layers available within the EnviroAtlas Interactive Map, including reference data, such as demographic information, that can be overlaid with ecosystem data.  Nationally, more than 160 layers of environmental data are available within the categories of clean air, clean and plentiful water, natural hazard mitigation, climate stabilization, recreation, culture, aesthetics, food, fuel, materials, and biodiversity conservation.  More than 100 additional layers are available at the community level for select urban regions, with data summarized at the U.S. Census Block Group scale.  Land cover data at the community level is at a one meter resolution, enabling users to examine fine grain details such as individual street trees or tot lots.  Every data layer comes with a two-page fact sheet explaining where the data came from, why they are important, how they were created, and suggested uses.  Data layers can be exported for use in other applications.  The Eco-Health Browser is an extensive, interactive literature review on the ways ecosystem services are linked to health outcomes, providing context for the relationships between the environmental data and human impacts.

EnviroAtlas’ data and analysis tools can be used to incorporate environmental factors into community assessments or development decisions.  For example, planners could examine a heat map of the walking distance to the nearest park entrance throughout their communities to identify underserved areas when making a siting decision, or they might enter multiple potential transit corridor routes to analyze their impacts on wildlife habitats.  The City of Durham, the first pilot community, used EnviroAtlas to determine the allocation of newly planted trees throughout the city.  The Interactive Map allowed them to analyze which locations had the greatest impacts on their multiple objectives, including stormwater absorption and buffering alongside major roads to minimize vehicular emission dispersion.  They used that information to justify and visualize their options for managing the landscape using green infrastructure, and ultimately created an optimized scenario that strategically located trees to maximize outcomes for each objective.  Stories of other use cases that illustrate the powerful applications of EnviroAtlas’ analysis tools are available on the site.

To use EnviroAtlas and watch training videos to help you make the most of the tool, visit http://enviroatlas.epa.gov/

This webinar was recorded as the first in a three-part webinar series designed to provide professionals in the built environment and public health fields with advanced tools and techniques to improve the quality of life and health in the places in which they work.  See the second webinar on The Community Guide and Improving the Science of Built Environment and Public Health for additional information on how you can help build healthier places. The third webinar, Building the Bridge between Transportation and Health, is forthcoming.

Dr. Ray Pentecost to Launch New Institute for Research on how the Environment Impacts Health at TTU

RPentecost2jpgDr. Ray Pentecost is launching a new institute, jointly underwritten by the College of Architecture, Texas Tech University and the Department of Public Health, TTUHSC for research and multi-disciplinary studies on how the environment impacts health. He earned his doctorate in public health from the University of Texas, School of Public Health in Houston, and is a registered Architect, Board Certified in the healthcare architecture specialty. He has been named a Fellow in both the American Institute of Architects (FAIA) and the American College of Healthcare Architects (FACHA). He is a ‘Leadership in Energy and Environmental Design Accredited Professional’ (LEED AP), a former Licensed Long Term Care Administrator, and an Ordained Minister. Ray is the Immediate Past President of the International Academy for Design and Health (IADH) based in Stockholm and a two-time Past President of the AIA Academy of Architecture for Health. He is currently a member of the Board of Regents for the American College of Healthcare Architects (ACHA). In 2012 Ray was one of eight individuals named to Healthcare Design Magazine’s list of The Most Influential People in Healthcare Design.

Ray is the Chair of the Independent Review Panel on Military Medical Construction Standards (IRP) appointed by the Secretary of Defense to review military health facility construction activity in light of the world class characteristics recommended by the Defense Health Board and codified in the Military Construction Authorization Act for Fiscal Year 2009.


Address: Box 42091, College of Architecture, Texas Tech University, Lubbock, TX 79409-2091

Telephone: 806.834.6734

Email: ray.pentecost@ttu.edu

Austin Food Access: A Multimodal Approach to Identifying Food Deserts


By  Junfeng Jiao, jjiao@austin.utexas.edu

Assistant Professor of Community and Regional Planning
Director of the Urban Information Lab
The University of Texas at Austin


Food deserts and food accessibility represents an important bridge between public health and the built environment, especially in context of the relationship between quality food accessibility and obesity. While food deserts have been well-studied in terms of their relationship to obesity, the method for analyzing where obstacles occur as a function of urban form differs across studies. In addition, the city of Austin, Texas has not been formally evaluated as a site for food access inequality, despite its rapid growth and growing inequality.

This research solidifies a method for measuring the occurrence of food deserts using GIS through a pilot study in Austin, Texas. By including an analysis of transportation accessibility, this research provides a refined method that incorporates network mobility within a study area, rather than relying on a less specific food establishment radius. The combination of transit mobility and the presence of high quality food stores provides a better level of depth for investigating the relationship between the built environment and public health.


The connection between the built environment and public health is receiving new interest from a number of areas. A focus on active transportation and recreation, accessibility to health resources, and the density and quality of affordable food stores constitute the major areas of investigation, as researchers seek to understand how the form of one’s neighborhood may influence the ability of or induce a person to make healthy choices. A growing body of this related research investigates the link between food accessibility and public health issues, with a focus on understanding the relationship between obesity and where one lives. A frequent target of investigation is the effect of food desserts on obesity rates of a given area.

The United States Department of Agriculture (USDA) defines food deserts as “urban neighborhoods and rural towns without ready access to fresh, healthy, and affordable food” and considers these areas ripe for intervention (USDA 2010). In a report commissioned by the Food, Conservation, and Energy Act of 2008, populations living further than a mile from a supermarket were considered to have decreased accessibility to fresh food, especially when combined with another limiting factors such as not having access to a vehicle and having low-income (Ver Ploeg et al. 2010).  Because poor diet, in addition to lack of exercise, is associated with a host of illnesses, residential areas with limited accessibility to nutrient-rich, low-calorie foods is considered a potential public health threat.

Food deserts provide a prime example of the overlap between public health and urban planning, as the urban form can determine food and exercise accessibility (Adams et al., 2010). Many studies have shown that, compared to high-income areas, those living in low-income or predominately African American communities face lower levels of service by food enterprises, especially supermarkets, and greater distances to stores (Beaulac et al., 2009). In addition to low accessibility to healthy foods in vulnerable populations, instances of obesity also tend to be greater (Wardle et al., 2002; White, 2007). Because of this relationship, research on food deserts often centers on defining and identifying areas without health food choices in relation to the densities of vulnerable populations (Gordon et al., 2011; Smith & Morton, 2009; Raja et al., 2008). Other research also focusses on identifying food deserts in specific geographic areas, such as a particular city or state, in order to create a better understanding of phenomena occurring in the jurisdiction of a specific political or planning body (Jiao et al., 2012). Narrowing the scope of research to political boundaries provides an opportunity for analysis of food and urban form policies.

In addition to low food accessibility, mobility also plays a role in how food deserts are studied. While many studies do include an analysis of distance to supermarkets and healthy food stores, many of these base their measurements off of straight-lines or buffers, which is often impossible for actual transportation given street network conditions (Ver Ploeg et al., 2010; Andreyeva et al., 2008; Apparicio et al., 2007).

This research aims to map food access and identify food deserts in Austin, Texas based on multiple forms of transportation in order to provide insight into the relationship between the built environment and public health. By establishing the connection between food and transit access, this research imparts a dual level of understanding of how urban form is connected to health. The addition of transportation as an additional dimension of food access is important as many people in American cities are transit dependent and unable to access an area unless it is within walking distance, biking distance, or accessible by transit service. In Austin, most mobility takes the form of vehicular traffic, though bus transit is available. Large lot and block sizes and fragmented sidewalk networks provide barriers in some neighborhoods for engaging in pedestrian transportation, while the city’s low-density development pattern promotes car use. Because of these local characteristics, the study becomes much more meaningful with the examination of transit networks. The primary implication of this research is that it provides an alternative method for identifying food deserts so that areas with limited access to healthy food can be identified more precisely and addressed effectively. 


Three categories of spatial data comprise the basis for this analysis: food establishments, transportation networks, and demographics of vulnerable populations. Food population information was gathered in order to locate healthy and unhealthy food retailers in Austin. Furnished by the City of Austin, this publically available data contains all permitted food establishments within the city and within unincorporated areas within Travis County. Additional food retailers were located via Google Maps to provide a more up-to-date representation of available establishments. These establishments included entities such as farmer’s markets and newly opened supermarkets—generally enterprises providing healthy food options.

The City of Austin also furnished information on transportation networks through their GIS department. Transportation elements for this analysis included streets, bicycle infrastructure, and sidewalks. Capital Metro Transportation Authority, the local organization responsible for public transit, provided datasets on transit routes and stops. This data created the network datasets necessary to perform network analysis in ArcGIS.

Finally, vulnerable population data was obtained from the United States Census in the form of a block group GIS file. Joined with 2012 American Community Survey (ACS) 5-year data, this data pertained to vehicle ownership and income, with special attention to the amount below the poverty line and block group income relative to the Travis County media.

Together these data formed the bases for a multi-step methodology. Because the food establishment information from the City of Austin was not spatially coded for GIS application, it had to be geocoded with an address location developed for this analysis. After all addresses were matched to the City of Austin’s street shapefile to create the most complete and accurate dataset possible. Next, food establishment information was coded between those that offer healthy choices (“good”) and those that do not (“bad”). Good food sources included supermarkets, grocery stores, farmers markets, and small scale community markets selling produce. Meanwhile, convenience stores and fast food restaurants were coded as unhealthy, or bad food sources. These two point datasets (shown below in Figures 1 and 2) served as the basis for determining food access in the Austin area.

After the food establishment datasets were finalized, the transportation network datasets (NDs) were built in ArcGIS for the following modes: automobile, bicycle, pedestrian, and transit. The automobile ND was generated using the complete City of Austin streets shapefile. The bicycle and pedestrian NDs were also built using the City of Austin streets shapefile. However, highways, freeways, and their ramps were excluded from the bicycle ND because cyclists are not allowed to use those roadways; and only streets with sidewalks and/or a speed limit of 35 miles per hour or less were included in the pedestrian ND for safety reasons. The transit ND was generated from Cap Metro’s transit routes shapefile. This shapefile did not include travel time; therefore, in order to avoid using a distance proxy for the analysis, which may not have accurately represented transit travel time, average route circulation times were calculated from route schedules published on Cap Metro’s website and added to the dataset. Also, because the route shapefile was last updated in 2012, a few routes included in the dataset had been changed or discontinued and thus, did not have a schedule on the website. For those routes a speed of 13 miles per hour was used to calculate route time; this was the average speed of all routes provided in Cap Metro’s Service Plan 2010 (2010, p. 6-3). These NDs were then ready for utilization of ArcGIS’ network analyst.

Using network analyst, general ten-minute service areas were generated for each set of food establishments using each of the network datasets. Time impedance was used for automobile and transit NDs, while a distance proxy was used for bicycle and pedestrian modes; a two-mile bicycle ride and a half-mile walk represented 10 minutes of travel. For the transit mode, services areas were generated only for food establishments located within a half-mile, in street network distance, of a transit stop, ensuring they were serviceable by transit. This process created overall coverage areas for good and bad food sources in the Austin area for each mode in question.

Because access to food, good food in particular, can be more challenging for vulnerable populations, the last step of the analysis measured food access, based on the service areas produced in the previous step, for four categories of vulnerable population block groups in the study area. Using 2012 ACS data, block groups were designated as vulnerable population areas if they met one of the following conditions:

  • 20% or more of the population was below poverty,
  • 40% or more of the population was below double poverty,
  • 30% or more of households were without a vehicle, or
  • The median family income (MFI) for the block group was at or below 80% of Travis County’s MFI

After these block groups were identified and isolated, levels of access were calculated based on the percent of each block group covered by one or more food establishment service areas. For this analysis, the percent of acreage covered by a service area was used as a proxy for percentage of block group population with access to the food establishment(s).

Figure 1: Healthy Food Establishments

Figure 1

Figure 2: Unhealthy Food Establishments

Figure 2.jpg


Results of this study indicate that, as perhaps expected, drivers have the highest levels of access to all food sources, with access levels of these residents reaching 96 percent or higher for all groups. Though substantially lower than vehicle access, bicyclists have the second highest levels of access to food in the Austin area, followed by transit users. Pedestrians have the lowest levels of access to food, with up to 82% without access to a good food source. For both types of food, goo and bad, block groups with at least 40% of their population below double poverty had the lowest levels of access for all modes of transportation when compared to other vulnerable population categories. Additionally, levels of access for all non-auto modes were higher for bad food sources than for good food sources. The complete results are provided in Tables 1 and 2 and Figures 3 and 4 below.

Table 1: Percent of Vulnerable Populations Without Access to Healthy Food Establishments via Transportation Modes

Table 1


Table 2: Percent of Vulnerable Populations Without Access to Unhealthy Food Establishments via Transportation Modes

Table 2


Figure 3: Access to Healthy Food Sources

Figure 3.png

Figure 4: Access to Unhealthy Food Sources

Figure 4.png


This analysis yielded multiple conclusions. Results align with the auto-centric nature of central Texas, and highlight the potential challenges associated with accessing food without a vehicle. These are especially apparent when viewing results for the pedestrian mode. Because, as the results show, options are severely limited without a vehicle in this area, it makes sense that very few block groups met the vulnerable population measure of 30% or more households without a vehicle. Furthermore, the block groups that did fall within this category tended to be located more centrally, where, in general, food access is greater for non-auto modes of transportation. For these reasons, levels of food access for this group may not be as telling as the income categories.

Including different modes of transportation when mapping food access and identifying food deserts can be an important tool for future planning and policy efforts that attempt to effectively address the issues of urban mobility and food access. Policies that encourage automobile travel over pedestrian and transit may leave vulnerable communities without access to a vehicle.  Where Austin food policy is concerned with public health, this information should be taken into consideration, especially given the high levels of pedestrian reliability by people falling under the 40% poverty line in Austin.

This research comprises one element of bridging the divide between public health and urban form. The power of GIS technology in this analysis is especially pertinent in determining accessibility, as straight-line and buffer analyses do not portray the actual networks used by residents to travel between their homes and food establishments. Refining this technique to include a finer level of analysis is one way that planners and public health practitioners can work together; with the cooperation of public health scholars, who can provide insight as to which factors are most likely to influence healthy food choices, GIS-savvy planners can design better analyses and studies and use the best available information to determine urban form decisions.


Adams, A., Ulrich, M., & Coleman, A. (2010). Food Deserts. Journal of Applied Social Science, 4(2), 58-62.

Andreyeva, T., Blumenthal, D. M., Schwartz, M. B., Long, M. W., & Brownell, K. D. (2008). Availability and Prices of Foods across Stores and Neighborhoods: The Case of New Haven, Connecticut. Health Affairs27(5), 1381-1388.

Apparicio, P., Cloutier, M. S., & Shearmur, R. (2007). The Case Of Montreal’s Missing Food Deserts: Evaluation Of Accessibility To Food Supermarkets. International Journal of Health Geographics6(1), 4.

Beaulac, J., Kristjansson, E., & Cummins, S. (2009). Peer Reviewed: A Systematic Review of Food Deserts, 1966-2007. Preventing Chronic Disease, 6(3).

Cummins, S., & Macintyre, S. (2006). Food Environments and Obesity—Neighbourhood or Nation? International Journal of Epidemiology, (1), 100-104.

Gordon, C., Purciel-Hill, M., Ghai, N., Kaufman, L., Graham, R., & Van Wye, G. (2011). Measuring food deserts in New York City’s low-income neighborhoods. Health & Place, 17, 696-700.

Ghosh-Dastidar, B., Cohen, D., Hunter, G., Zenk, S. N., Huang, C., Beckman, R., & Dubowitz, T. (2014). Distance to store, food prices, and obesity in urban food deserts. American journal of preventive medicine47(5), 587-595.

Howlett, E., Davis, C., & Burton, S. (2015). From Food Desert to Food Oasis: The Potential Influence of Food Retailers on Childhood Obesity Rates. Journal of Business Ethics, 1-10.

Jiao, J., Moudon, A., Ulmer, J., Hurvitz, P., & Drewnowski, A. (2012). How to Identify Food Deserts: Measuring Physical and Economic Access to Supermarkets in King County, Washington. American Journal of Public Health, E32-E39.

Raja, S., Ma, C., & Yadav, P. (2008). Beyond Food Deserts: Measuring and Mapping Racial Disparities in Neighborhood Food Environments. Journal of Planning Education and Research, 27, 469-482.

Smith, C., & Morton, L. (2009). Rural Food Deserts: Low-income Perspectives on Food Access in Minnesota and Iowa. Journal of Nutrition Education and Behavior, 41(3), 176-187.

USDA. 2015. “Agricultural Marketing Service – Creating Access to Healthy, Affordable Food.” Retrieved 28 April 2015:  http:// apps.ams.usda.gov/food deserts/fooddeserts.aspx

Ver Ploeg, M. (Ed.). (2010). Access To Affordable And Nutritious Food: Measuring And Understanding Food Deserts And Their Consequences: Report To Congress. DIANE Publishing.

Wardle, J., Waller, J., & Jarvis, M. J. (2002). Sex Differences in the Association of Socioeconomic Status with Obesity. American Journal of Public Health92(8), 1299-1304.

White, M. (2007). Food Access and Obesity. Obesity Reviews8(S1), 99-107.

Cover Image Source


Bridging the Divide: Policymakers and Public Health Researchers


(Originally published on 10/23/15)

By Thomas K. Bias, PhD, Christiaan G. Abildso, PhD, Emily Vasile, MPAff, Jessica Coffman, MA


Over the past decade, great strides have been made to move the discussion of health promotion into the public policy realm. Despite some success, public health researchers have struggled at times to connect with policymakers. This manuscript describes how researchers conducting a Health Impact Assessment were able to work with city officials in West Virginia resulting in evidence-based policy changes and provides information on how to successfully bridge the policymaker and researcher divide. We find that policymakers respond well to local feedback, even when limited in generalizability and sample size. This feedback, however, is important in the policymaking process and, when combined with existing evidence based best-practices for health improvement, can lead to implementation of policies that enhance the health of communities.



Over the past decade, great strides have been taken by public health researchers to move discussion of health promotion into the public policy realm. This work is a natural output of public health theory, most notably discussion of the social-ecological model of health1 and the Health Impact Pyramid.2

These frameworks emphasize that interventions, which address underlying environmental and social determinants of health, have the greatest potential public health benefit.

Several examples of public health policy interventions have demonstrated the relationship between public policy and health including the well-known examples of tobacco3, widespread immunizations,3 and water fluoridation.4 Despite these successes, public health researchers and practitioners have struggled at times to connect with policymakers. Brownson et. al.5 describe this disconnect in great detail and give many recommendations about how to improve the use of research in policymaking, notably through stakeholder involvement and improving communication.5

Health Impact Assessments (HIAs) are a tool used to facilitate policymaker engagement and examine the health effects of potential policy choices in non-health sectors. This interactive process results in direct recommendations to policymakers on which policy decisions are likely to have the most positive or negative impact on health. In the past decade, there has been a wider call for the use of HIA across the policy spectrum in the United States and has been used in fields as diverse as agriculture, education, city planning, and energy6. The HIA process and its potential benefits for physical activity (PA) and built environment (BE) policymaking are well documented6.

Using the recommendations of Brownson and colleagues as a starting point5, this article describes an HIA which took place in Fairmont, West Virginia in 2014. Working directly with policymakers and stakeholders, an in-depth analysis of a proposed comprehensive Bicycle and Pedestrian Connectivity Plan (“Connectivity Plan”) resulted in HIA recommendations that would most impact PA. During this process, researchers learned important lessons about the importance of scientific data to policymakers which can be shared with public health in a broad way to enhance the impact of evidence-based practices on public policy design and implementation. Significant evidenced-based policy changes have begun to take shape since the public release of the HIA report.

Project Background
The city of Fairmont has approximately 18,700 residents. Fairmont was awarded a grant from the West Virginia Development Office, in collaboration with the West Virginia Bureau for Public Health and the Claude Worthington Benedum Foundation, to create a comprehensive bicycle and pedestrian connectivity plan (“Connectivity Plan”) as part of their “Growing Healthy Communities” grant mechanism. A separate grant, from the Association of State and Territorial Health Officials (ASTHO) funded a rapid HIA to be conducted in parallel. The HIA investigated specific connectivity-related areas related to policy decisions including sidewalks, trail connections, perceptions of crime, etc.

Community Engagement

As per best HIA practices7 and the Brownson et. al article5 policymakers were involved in the HIA process alongside local champions to ensure adequate connection to the project. Weekly community meetings convened by Main Street Fairmont (MSF), were held to discuss the Connectivity Plan and gain insight into related issues. MSF was the primary grant recipient of the Growing Healthy Communities grant, in collaboration with the City Planner’s Office. Monthly meetings occurred with the City Planner’s Office to inform policymakers of HIA progress.

The research team followed the HIA process of screening, scoping, assessment, recommendations, reporting, and monitoring/evaluation7. At the screening phase, researchers completed a comprehensive review of evidence-based practices to help policymakers understand the literature around specific proposals (such as the potential health impact of building new sidewalks, community policing, etc.). This review helped demonstrate evidence driven policy solutions that could be implemented. It was also important to capture input from the citizens of Fairmont for the scoping and assessment phases. Feedback from key stakeholders through a very brief online survey in the scoping process helped identify priorities. The research team then designed a more in-depth survey to capture community input online and by mail about potential impacts of Connectivity Plan projects. The survey was designed with a scientific sampling plan for each neighborhood in the city, expecting each to have unique barriers and facilitators to pedestrian and bicyclist connectivity. City officials were involved at each stage of the HIA process as well. They helped design and structure the questionnaire to capture information most useful for policy planning purposes. These policymakers also participated in community meetings where neighborhood connectivity and health impact were discussed.

As a result of several data collection limitations experienced in the community (related to finite resources), researchers quickly realized that a randomized scientific sample was not going to be achieved. Because the HIA was funded as a small scale project, there were not financial resources to conduct a citywide randomized sample mailing with return postage. Surveys were instead collected via an online tool which was disseminated through city groups, postcards, and sent home via students in local schools (because many questions dealt with parks, school connectivity, and other activities for children). Only 240 surveys were collected city-wide (out of 18,700 residents), ranging from single digit results in one neighborhood to dozens in another. Disheartened, researchers reported the lack of a scientific, generalizable sample to policymakers at the city level.

Much to the researchers’ surprise, city personnel and policymakers were very excited to receive feedback from over 200 citizens. Policymakers described small attendance at general city council meetings, where input on any single issue may be small or nonexistent. They discussed that they rarely having access to public input of that scale, scientific or not. Residents reported both barriers and facilitators to active transportation and the BE. The top three issues reported were the lack of infrastructure, lack of activities downtown that people would want to visit, and concern about crime/safety.


An HIA report was released as an appendix to the Connectivity Report developed by the city and its engineering consultant. This report detailed the potential impact of various proposed connectivity measures grounded in the BE literature. Each project was given a separate health impact “grade”. Additionally, survey results were broken down by city-defined neighborhoods and presented individually to help refine findings to specific areas of the city. Findings from this HIA report were presented to various groups including policymakers and citizens.

Short-Term Policy Outcomes

There have been many important outcomes as a result of the concurrent Connectivity Plan and HIA. The City is using the projects identified in a Five-Year Action Plan as an addendum to the city’s Comprehensive Plan (Comprehensive Plans are required documents which serve as the main outline of city priorities and goals and are required to be updated at least every ten years in West Virginia). Based on community feedback gathered through the HIA process, the research team made specific suggestions to implement a combination of Encouragement, Engineering, and Enforcement activities. To address these, the City has initiated multiple projects.

Encouragement of PA, through events, promotions, and improvements to parks and downtown were endorsed by residents and highlighted in the HIA. To encourage active living, MSF, the City, and community members are working with Try This WV, a statewide campaign to encourage PA. Another initiative to encourage active living is the “Walkable Blocks” project. This project is an initiative based on a portion of the Connectivity Plan and data pulled from the HIA and will introduce painted mural crosswalks, sculpted bike racks, and “Share the Road” signage. MSF will host an early summer festival celebrating community wellness and will highlight the Connectivity Plan, the HIA, and the public art designed to encourage PA by providing a safe, aesthetically pleasing environment.

Key engineering activities recommended in the HIA were to improve sidewalks, intersections, and trails, especially within walking and cycling distances of key attractors. MSF used the HIA to start Friends of the Trail, which is a collaboration between local government, non-profits and citizens to connect the Mon River Trail that terminates just north of the City and the West Fork River Trail that terminates just south of the City. This group is currently using recommendations of the HIA to prioritize next steps and identify opportunities for small visible wins. Further engineering activities to improve sidewalks include a recommendation by the Fairmont Planning Commission to establish a Pedestrian Safety Board, whose will prioritize sidewalk repair and construction projects through a Sidewalk Improvement Plan. This recommendation will be made to Fairmont City Council for budget considerations.

Due to concerns voiced by residents about safety from crime, which were highlighted in the HIA, MSF has engaged the support of local law enforcement. The Fairmont Police Department has agreed to reinstate community policing. Finally, one intangible outcome of the HIA and Connectivity Plan is an increased understanding and focus city-wide on the impact of local-level policies on health. This relationship was not discussed in the past. The HIA served as a tool for relating local level decision-making to potential implications on the health of Fairmont citizens, effectively empowering local level decision makers to make evidenced based policy changes.


At the time of this manuscript, a year had passed since the release of the HIA report in July 2014. During that brief time, policymakers seriously considered the input given by residents of the city related to the BE, PA, and connectivity. Although this input was not a scientific, randomized sample, community concerns were paired with evidence-based practices to identify specific policies and programs in the HIA report. This idea of combining local data with evidence-based practices is in line with the idea of “ground truthing” recommended as a best-practice in HIAs.8 Policymakers have begun to implement changes to address issues identified in the HIA recommendations and the unique political context in their city. The involvement of community members at weekly meetings also created champions within the city that continue to advocate for healthy interventions. The description of this project is important, as it demonstrates a way to bridge the gap between science and policy implementation in a timely and relevant fashion. Using this strategy, proven and scientific interventions can be paired with community engagement and input.


The authors would like to thank Kathy Wyrosdick who is the director of Planning at the City of Fairmont and Kate Greene, Director of Main Street Fairmont for their willingness to keep the authors up to date on policy activities in the city and for their involvement with the Health Impact Assessment process.


1. Stokols D. Establishing and maintaining healthy environments: toward a social ecology of health promotion. American Psychologist. 1992;47(1):6.

2. Frieden TR. A framework for public health action: the health impact pyramid. Am. J. Public Health. 2010;100(4).

3. Collins J, Koplan JP. Health impact assessment: a step toward health in all policies.JAMA. 2009;302(3):315-317.

4. McDonagh MS, Whiting PF, Wilson PM, et al. Systematic review of water fluoridation.Bmj. 2000;321(7265):855-859.

5. Brownson RC, Royer C, Ewing R, McBride TD. Researchers and Policymakers:: Travelers in Parallel Universes. Am. J. Prev. Med. 2006;30(2):164-172.

6. de Nazelle A, Nieuwenhuijsen MJ, Antó JM, et al. Improving health through policies that promote active travel: A review of evidence to support integrated health impact assessment. Environment International. 2011.

7. National Research Council Committee on Health Impact Assessment. Improving Health in the United States: The Role of Health Impact Assessment. National Academies Press (US); 2011.

8. Stakeholder Participation Working Group. Guidance and best practices for stakeholder participation in health impact assessments. Paper presented at: 2010 HIA in the Americas Workshop, Oakland, CA2012.

Using Webcams and Crowds to Study Active Transportation


(Originally published on 09/23/15)

By J. Aaron Hipp, Alicia Manteiga, Amanda Burgess, Abby Stylaniou, and Robert Pless


Active transportation opportunities and infrastructure are an important component of a community’s design, livability, and health. Features of the built environment influence active transportation, but objective study of the effects of built environment improvements on active transportation is challenging. Outdoor temperature is known to be a common barrier to active transportation, yet there is limited information examining the interaction between temperature, built environment improvements, and active transportation. In this case study, 20,529 publicly available webcam images from two intersections in Washington, D.C., were used to examine the impact of an improved crosswalk on active transportation. A crowdsource, Amazon Mechanical Turk, analyzed image data. Temperature data was collected from the National Oceanic and Atmospheric Administration. Summary analyses demonstrated slight, bi-directional differences in the number of images with pedestrians and bicycles captured before and after the enhancement of the crosswalks. Chi-square analyses revealed these changes were not significant. In general, pedestrian presence increased in images captured during moderate temperatures compared to images captured during hot or cold temperatures. Chi-square analyses of one intersection indicated the crosswalk improvement may have encouraged walking in more uncomfortable outdoor temperatures. The methods employed provide an objective, cost-effective alternative to traditional means of examining the effects of built environment changes on active transportation. The use of webcams to collect active transportation data has applications for community policymakers, planners, and health professionals. Future research should work to validate this method in a variety of settings as well as across different built environment and community policy initiatives.


Active transportation, bicycling and walking between destinations, is associated with reduced rates of chronic disease and the promotion of healthier lifestyles in comparison to vehicle trips.1-5 Some characteristics of the built environment influence the adoption of behavioral changes, encouraging individuals to choose walking or biking over personal motor vehicle use.1-3, 5-7 Characteristics such as perceived safety, proximity to destinations, the presence of foliage and green spaces, and traffic control features like stop signs and speed bumps are mediating factors in this decision making process.1-3, 5-9

The improvement of infrastructure along transportation routes can impact the decision to walk or ride a bicycle in a community.5, 6, 8, 9 While these improvements are often associated with an increase in active transportation, there is little agreement on the specific elements that lead to this increase.9

Pedestrian safety has been at the forefront of a body of research evaluating built environment characteristics that aid or hinder the decision to walk.10-12 Most crosswalk enhancement studies focus on reducing pedestrian and vehicular collisions.10-12 Basic marked crosswalks are more effective than unmarked crosswalks at increasing pedestrian safety. Evidence indicates stand-alone crosswalks, independent of other interventions like speed limit reductions or speed bumps, reduced the number of intersection collisions across 30 cities in the United States .10 Beyond pedestrian safety, few studies have analyzed the effects of adding or enhancing crosswalks on pedestrian activity.

Permanent built environment features such as bicycle boulevards and improved bike lanes are associated with an increase in biking.7, 13-15 The relationship between factors influencing the use of biking spaces is complex, making it difficult to measure the effects of this infrastructure change on active transportation.7, 16

The decision to choose to walk or bike instead of drive is associated with weather as well as with features of the built environment.4, 6, 23, 24 Pedestrian activity decreases overall during snow, ice, and cold temperatures in winter seasons, but individuals are more likely to choose walking over biking when the temperature is cooler.4, 23-25  There is a lack of literature on the role temperature plays in the utilization of new built environment features in a community, which is likely due to the complex nature of the data needed to study such outcomes.

Understanding trends in pedestrian and bicyclist behaviors, especially in response to specific built environment interventions, allows key stakeholders to select policies for implementation that will have the greatest impact on their community’s specific active transportation needs. 4, 9, 17

The evaluation of built environment interventions requires active monitoring of these changes in outdoor spaces.4, 5, 9, 13, 17 The Archive of Many Outdoor Scenes (AMOS) is a database that has compiled images captured by publicly available webcams since 2006 (e.g., traffic cameras).14, 18 Images gathered from the AMOS database (over 735 million) can be analyzed to study changes in the built environment as well as changes in active transportation. The use of webcams to study active transportation can provide researchers and community stakeholders access to outcome data regarding the impact of built environment enhancements on active transportation.15, 19

The manual annotation of a large number of webcam images has the potential to be both time consuming and costly, but emerging technologies such as crowdsourcing can alleviate these constraints.20-22  Amazon’s Mechanical Turk (MTurk) is a crowdsourcing platform that allows anyone to design a human intelligence task (HIT) such as counting people in webcam images, and then post this HIT online for individuals over the age of 18 to complete via the internet.20, 22  Posting a HIT to the ‘crowd’ of human workers online allows researchers to obtain quality data quickly and inexpensively.20-22

The primary objective of this case study was to determine whether or not trends in active transportation are influenced by a change in the built environment. Additionally, the study aimed to explore interactions between built environment enhancements, temperature, and active transportation.  Finally, we sought to present a novel method for objectively and inexpensively measuring the effects of built environment changes on active transportation, thus building the bridge between health and community planning projects.


Active transportation data was from two webcams captured by the Archive of Many Outdoor Scenes (AMOS) dataset. Webcams used in this study are located at the intersections of Piney Branch Road NW and Eastern Avenue NW in Washington, D.C., 20012 (residential area, Figure 1), and Connecticut Avenue NW and Florida Avenue NW in Washington, D.C., 20009  (commercial area; Figure 1). The two intersection webcams were selected because they have a clear view of pedestrians, bicyclists, and vehicles, and because both captured the enhancement of crosswalks on November 20, 2007. Intersections were classified for general land use using Google Street View.26, 27

Using the AMOS dataset, images were collected every 30 minutes over an average of twelve hours per day, or approximately 24 images per day. In total, 20,529 webcam images were captured between May 7 and November 19, 2007, and between May 7 and November 19, 2008.

To examine the effects of temperature and precipitation on the use of crosswalks, images were matched with an hourly average temperature and precipitation status. Due to limited data availability, researchers only combined 8,067 images with hourly temperature data, and elected to ask MTurk workers about precipitation. Temperature and precipitation data was collected from the National Oceanic and Atmospheric Administration: National Centers for Environmental Information (http://www.ncep.noaa.gov/).

The Amazon Mechanical Turk (MTurk) website was used as the crowdsourcing platform to annotate the number of pedestrians and bicyclists in each captured image. Each image was annotated by four unique MTurk workers.15 MTurk workers were paid US $0.02 per image between September and December 2013. Some of the prompts MTurk workers responded to included:

  1. Please outline each bicycle or person riding a bicycle in the scene
  2. Please outline each pedestrian in the scene
  3. What is the weather in this image? Sunny, Cloudy, Rainy, Snowy

Each image was annotated at least four unique times (N=82,116), for a total cost of $1,642.32.

Counts per transportation mode were downloaded into SPSSv.22 (IBM) for analysis in March 2015.

Summaries of weekday, weekend, and overall presence of pedestrians and bicyclists were calculated at both intersections, and have been reported elsewhere.19 Chi-square analyses were performed to study differences in pedestrian and bicyclist presence in images before and after crosswalk enhancement.

Average and standard deviations of collected temperature data were calculated. Scatterplots were created to visually portray the relative frequency of pedestrians per intersection at various temperatures. Temperatures were divided into “normal” (within one standard deviation of the May-November mean temperature) and “non-normal” (outside one standard deviation of the mean temperature) categories. Summaries of the number of images with pedestrians and bicyclists, prior to and following crosswalk enhancement, at normal and non- normal temperatures were calculated. Chi-square analyses were performed to determine if there were differences in bicyclist and pedestrian presence when temperatures were normal versus non-normal.

Descriptive summaries of the number of images MTurk workers identified as rainy were compiled to determine the degree of agreement between crowdsource workers.


Descriptive Statistics

At the residential intersection, 4,959 images were captured prior to and 5,007 images were captured following the crosswalk enhancement. Pedestrians were present in 298 images (6.01 percent of all images) captured prior to the change and in 337 images (6.73 percent) captured following the change. Bicycles were present in 79 images (1.59 percent) captured before the change, and in 86 images (1.72 percent) captured after the change.

At the commercial intersection, 5,246 images were captured prior to the crosswalk enhancement and 5,317 images were captured following the crosswalk enhancement. Pedestrians were annotated in 3,658 images (69.73 percent) captured prior to the change, and in 3,615 images (67.99 percent) captured following the change. Bicycles were annotated in 581 images (11.08 percent) captured before the change, and in 565 images (10.63 percent) captured after the change (Table 1).

3,883 images at the residential intersection and 4,184 images at the commercial intersection were matched with temperature data. Temperatures ranged from 27 to 101 degrees Fahrenheit, with an average of 74 degrees, and a standard deviation of 12 degrees. In general, there were more pedestrians per image when temperatures were within one standard deviation of the mean (between 62-86 degrees) (Figure 2). Bicycle annotation patterns were not related to temperature at both intersections (Table 2).

Temperatures were divided into two categories: non-normal (less than 62 degrees or greater than 86 degrees) and normal (between 62 and 86 degrees, inclusive). At the residential intersection, before the enhancement of the crosswalk and when temperatures were non-normal, 32 images (6.61 percent) included pedestrians. After the enhancement when temperatures were non-normal, 55 images (8.44 percent) included pedestrians. There were 10 images (2.07 percent) and 13 images (1.99 percent) with bicycles before and after the change, respectively, when temperatures were non-normal.

At the commercial intersection prior to the crosswalk enhancement and when temperatures were non-normal, 373 images (67.70 percent) included pedestrians. This changed to 477 images (66.52 percent) after the change. Bicyclist annotation in images captured during periods of non-normal temperatures changed from 67 images (12.6 percent) to 78 images (11.61 percent) after the crosswalk enhancement (Table 3).



Researchers attempted to assess precipitation by asking MTurk workers the following question: What is the weather in this image: Sunny, Cloudy, Rainy, Snowy. These four options were collapsed into two categories, no precipitation or precipitation. Across only 55 percent of all images did all four MTurk workers agree on the image having precipitation or not. Therefore, researchers determined the wording of the question was not reliable, and elected to eliminate precipitation from analyses in this study.

Chi-Square Analyses

Chi-square tests of independence were performed to examine the relationship between presence of pedestrians and bicycles in images before and after the enhancement of crosswalks (Table 1). The overall (weekday and weekends combined) relationship between pedestrian presence and crosswalk enhancement at the residential intersection was not significant [X2(1, N= 9,966) = 2.17, p=.14]. The overall relationship between bicycle annotation and crosswalk enhancement was also not significant [X2(1, N= 9,966) = 0.24,p=0.63], though there was a significant decrease in bicycle annotation during weekends after the crosswalk enhancement [X2(1, N=2,735) = 4.04, p=0.04]. At the commercial intersection, the relationships between crosswalk enhancement and pedestrian presence, and crosswalk enhancement and bicycle presence were not significant overall [X2(1, N=10,563) = 3.73, p=0.05; X2(1, N= 10,563) = 0.55, p=0.46]. However, there was a significant decrease in pedestrian presence during weekdays after the crosswalk enhancement [X2(1,N=7,599) = 4.08, p=0.04].

Chi-square tests of independence were then performed to examine the relationship between presence of pedestrians and bicycles in images before and after the enhancement of crosswalks at both normal and non-normal temperatures (Table 3). At the residential intersection prior to the crosswalk enhancement, there was no relationship between pedestrian annotation and temperature [X2(1, N=1,985) = 0.81, p>0.05], or between pedestrian annotation and temperature after the improvement of the crosswalk [X2(1,N=1,988) = 0.02, p>0.05]. There was no relationship between bicycle annotation and temperature, both prior to and following the crosswalk enhancement [X2(1, N=1,985) = 1.43, p>0.05; X2(1, N=1,988) = 0.22, p>0.05].

At the commercial intersection prior to the crosswalk enhancement, there were significantly more images with a pedestrian present during normal temperatures than during non-normal temperatures [X2(1, N=2,064) = 4.06, p<0.05]. However, after the completion of the crosswalk enhancement, there was no significant relationship between pedestrian presence and temperature [X2(1, N=2,120) = 0.22, p>0.05]. There was no relationship between bicycle annotation and temperature, both prior to and following the crosswalk enhancement [X2(1, N=2,064) = 0.13, p>0.05; X2(1, N=2,120) = 0.45, p>0.05].


The results of this study indicate that two webcams in Washington, D.C., were able to capture pedestrian and bicyclist activity before and after the enhancement of two crosswalks, and across a range of temperatures. Pedestrian and bicycle annotation was not significantly different before and after the crosswalk improvement at both locations. An improved crosswalk may signal to drivers that there are non-drivers present, including walkers and cyclists. Therefore, it is unclear why pedestrian and bicycle annotation did not increase after crosswalk improvement. Potential explanations include increased vehicular traffic due to other improvements, or unsafe crosswalks along the way to the improved crosswalks.28 Future research could include a more broad analysis of a network of crosswalks. Such research may help explain variations in pedestrian presence in crosswalks after improvements, as well as establish which types of improvements are associated with the greatest increase in pedestrian activity.

Webcam images reflected temperature-related differences in pedestrian activity. Fewer pedestrians were annotated in images captured when temperatures were cold or hot. At both intersections after the enhancement, more images contained pedestrians captured during non-normal temperatures compared to the year prior. Furthermore, at the commercial intersection, the relationship between pedestrian presence and non-ideal temperatures prior to the crosswalk enhancement was significant; following the improvement of the crosswalk, the relationship was not significant. This suggests that the crosswalk may have played a larger influence on pedestrian presence than ambient temperature. Or stated another way, the addition of the crosswalk diminished the change in pedestrians between ideal temperatures and non-ideal temperatures. This may be due to a sense of increased sense of pedestrian safety or speed in crossing when temperatures were less than ideal.

In this study, sufficient hourly precipitation data was not accessible. Researchers attempted to identify precipitation visually using MTurk workers. The collection of reliable precipitation annotation by MTurk workers in images was a challenge. Researchers should continue to develop and validate weather-related image questions for crowdsourcing tasks. The presence of pedestrian and bicycle activity during inclement weather are of interest to community stakeholders invested in safety and transportation.12

Limitations of the present analyses include the use of only two intersections. The images used for analyses only provide information on behaviors at two specific locations, restricting the external validity of the findings. This study was unable to determine whether or not pedestrians were changing their routes.

Despite these limitations, the ubiquity and unobtrusive nature of webcams presents an opportunity to understand the effects of a variety of built environment improvements, across time, and in a cost-effective manner. While the applications of this method are still being fully developed, there is great promise in its potential. Potential applications include understanding which populations are benefitting from built environment enhancements, as well as broader studies examining the synergistic effects of multiple built environment changes.

Proximity to a built environment intervention, such as a crosswalk addition, does not necessarily indicate an impact will be made on nearby community members.7, 13 Webcams could be used to examine the influence of built environment changes on specific population groups such as adolescents or older adults. These populations generally have different motivating factors for participation in active transportation, and may receive more benefits from tailored built environment features than the general population.1, 3, 5, 6, 8, 17, 23, 24, 29

Future webcam research should also include the simultaneous analysis of multiple (e.g., greater than two) webcam locations in order to establish the external validity of the method. Studies should not be restricted to crosswalk enhancement or bike lane addition,14 but could include speed bump additions, median enhancements, or other environmental improvements relevant to specific communities. There is also the opportunity to work with civic and community partners in identifying non-built pedestrian safety improvement efforts, such as neighborhood watch groups and speed limit reductions.

In conclusion, the use of webcams and crowdsourcing is a promising technique for evaluating the effects of built environment interventions and environmental factors such as temperature on active transportation. As the method continues to develop, it is crucial that researchers and practitioners across community health and planning fields collaborate to explore various environments, interventions, and healthy behaviors.



1.            Hino AA, Reis RS, Sarmiento OL, Parra DC, Brownson RC. Built environment and physical activity for transportation in adults from Curitiba, Brazil. J Urban Health. Jun 2014;91(3):446-462.

2.         Winters M, Brauer M, Setton EM, Teschke K. Built environment influences on healthy transportation choices: bicycling versus driving. J Urban Health. Dec 2010;87(6):969-993.

3.         Panter J, Griffin S, Dalton AM, Ogilvie D. Patterns and predictors of changes in active commuting over 12 months. Prev Med. Dec 2013;57(6):776-784.

4.         Yang Y, Diez Roux AV, Bingham CR. Variability and seasonality of active transportation in USA: evidence from the 2001 NHTS. Int J Behav Nutr Phys Act.2011;8:96.

5.         Frank LD, Sallis JF, Conway TL, Chapman JE, Saelens BE, Bachman W. Many Pathways from Land Use to Health: Associations between Neighborhood Walkability and Active Transportation, Body Mass Index, and Air Quality. Journal of the American Planning Association. 2006;72(1):75-87.

6.         Mitra R, Faulkner G. There’s No Such Thing as Bad Weather, Just the Wrong Clothing: Climate, Weather and Active School Transportation in Toronto, Canada. Canadian Journal of Public Health. 2012;103(Supplement 3):S35-S41.

7.         Dill J, McNeil N, Broach J, Ma L. Bicycle boulevards and changes in physical activity and active transportation: findings from a natural experiment. Prev Med. Dec 2014;69 Suppl 1:S74-78.

8.         Grow HM, Saelens BE, Kerr J, Durant NH, Norman GJ, Sallis JF. Where are youth active? Roles of proximity, active transport, and built environment. Med Sci Sports Exerc.Dec 2008;40(12):2071-2079.

9.         Saelens BE, Handy SL. Built environment correlates of walking: a review. Med Sci Sports Exerc. Jul 2008;40(7 Suppl):S550-566.

10.       Zeeger CV, Esse CT, Stewart JR, Huang H, Lagerwey P. Safety Analysis of Marked Versus Unmarked Crosswalks in 30 Cities. ITE Journal. 2004:34-41.

11.       Huang H. An Evaluation of Flashing Crosswalks in Gainesville and Lakeland. In: Transportation FDo, ed; 2000.

12.       Cafiso S, Garcia Garcia A, Cavarra R, Romero Rojas MA. Crosswalk Safety evaluation using a Pedestrian Risk Index as Traffic Conflict Measure. 3rd International Conference on Road Safety and Simulation. Indianapolis, IN, USA; 2011.

13.       Hipp JA, Eyler AA, Kuhlberg JA. Target population involvement in urban ciclovias: a preliminary evaluation of St. Louis open streets. J Urban Health. Dec 2013;90(6):1010-1015.

14.       Hipp JA, Adlakha D, Eyler AA, Chang B, Pless R. Emerging technologies: webcams and crowd-sourcing to identify active transportation. Am J Prev Med. Jan 2013;44(1):96-97.

15.       Hipp JA, Adlakha D, Gernes R, Kargol A, Pless R. Do You See What I See: Crowdsource Annotation of Captured Scenes. SenseCam 2013. San Diego, CA, USA; 2013.

16.       Fernhall B, Borghi-Silva A, Babu A. The Future of Physical Activity Research: Funding, Opportunities and Challenges. Progress in Cardiovascular Disease.2015;57(4):299-305.

17.       Marzoughi R. Teen travel in the Greater Toronto Area: A descriptive analysis of trends from 1986 to 2006 and the policy implications. Transport Policy. 2011;18(4):623-630.

18.       Pless R, Jacobs N. AMOS: The Archive of Many Outdoor Scenes. Available at: http://amos.cse.wustl.edu/.

19.       Manteiga A, Hipp A. Learning from Outdoor Webcams: Capturing Active Commuting Behavior Across Environments. Paper presented at: Active Living Research, 2015; San Diego, California.

20.       Buhrmester M, Kwang T, Gosling SD. Amazon’s Mechanical Turk: A New Source of Inexpensive, Yet High-Quality, Data? Perspectives on Psychological Science. 2011;6(1):3-5.

21.       Kim AE, Lieberman AJ, Dench D. Crowdsourcing data collection of the retail tobacco environment: case study comparing data from crowdsourced workers to trained data collectors. Tob Control. Mar 2015;24(e1):e6-9.

22.       Amazon Mechanical Turk: Artificial Artifical Intelligence. Available at: https://www.mturk.com/mturk/welcome.

23.       Li Y, Hsu JA, Fernie G. Aging and the use of pedestrian facilities in winter-the need for improved design and better technology. J Urban Health. Aug 2013;90(4):602-617.

24.       Saneinejad S, Roorda MJ, Kennedy C. Modelling the impact of weather conditions on active transportation travel behaviour. Transportation Research Part D: Transport and Environment. 2012;17(2):129-137.

25.       Academies TRBotN. National Cooperative Highway Research Program: Guidebook on Pedestrian and Bicycle Volume Data Collection. 2014;797.

26.       Rundle A, Teitler J, Bader M, Lovasi L, Richards C, Lovasi G. Using Google Street View to Implement Community Audit Tools: The Pedestrian Environment Data Scan. Presentation at: Active Living Research, 2010; San Diego, California.

27.       Wilson JK, Kelly C. Navigating Google Street View: A Guide to Conducting Audits of the Built Environment Using Google Street View. Paper presented at: Active Living Research, 2011; San Diego, California.

28.       Fitzpatrick K, Turner S, Brewer M, et al. Improving Pedestrian Safety at Unsignalized Crossings:TCRP Report 112/ NCHRP Report 562. Washington, D.C. 2006.

29.       Graham DJ, Hipp JA. Emerging technologies to promote and evaluate physical activity: cutting-edge research and future directions. Frontiers in Public Health. 2014;2.

Food Systems for Healthy Places


(Originally published on 11/24/14)

By Alfonso Morales, PhD

Systems guide the food we eat from the fields all the way to our tables.  The built environment made possible the food system we have today, from canals, rail and storage infrastructure to refrigeration, retail forms, and use of the built environment to recover historical practices.  My work examines the multifaceted and reciprocal relationship between the built environment, health and livable communities.  In this post, I will highlight three projects currently underway that aim to maximize positive outcomes for food systems on local and national scales:

  1. The Oneida Nation:  Modern Tribal Food Systems
  2. Farmers Market Coalition:  Metrics for Market Success
  3. Food Systems Wiki:  A Guide to Understanding Food Systems

The Oneida Nation:  Modern Tribal Food Systems

Belief in food-founded health for people, and people who believe in their food, drive much of my work with the Oneida Tribe of North Central Wisconsin.  The Oneida Nation, like most Indian nations in the present-day United States, has undergone a long history of discrimination and cultural suppression.  The Oneida as a community suffer particularly from diabetes.  However, there are many efforts in place to reverse this trend. The resurgence of traditional agriculture, tsyunhehkwa^, is one such effort, as well as health and wellness programs, such as Just Move It Oneida.  A food system that is able to increase the supply of locally produced, healthy foods will be culturally meaningful, and supportive of tribal economic and health related goals.

In the fall of 2014, I began working with Joanie Buckley, internal services division director of the Oneida Tribe, on two four-month projects for the tribe: a food systems plan for the Nation, and a food center building design project to support the renewed cultural interest in food, provide an environment for entrepreneurs, and stimulate economic activity and educational and recreational opportunities.  The Oneida Planning Department, the U.S. Department of Agriculture, and the University of Wisconsin’s Nelson Institute and Center for Nonprofits have collaborated on these efforts.

Students and faculty at the University of Wisconsin began by combining land use maps, zoning overlays, and demographics with cultural insights from public participation processes at the Tribe, culminating in a trip to the Tribe to meet Fidel Delgado, the USDA architect for the food center building, and for feedback on our interim concept designs. By December, the Tribe will have completed cleared the food center site of an old structure, and the UW will deliver conceptual design for a new food center building and a food system plan that describes culturally sensitive economic and social opportunities to the tribe.

The new food center building incorporates the Oneida brand, culture and history, while performing the functions of food processing, distribution and sales. In addition to enhancing the existing Oneida food production and processing system, the building will also promote the economic revitalization of this area.


Farmers Market Coalition Research:  Metrics for Market Success

More than 8,100 farmers markets across the country play integral roles in their local food systems and economies, providing an avenue for farmers to sell their food directly to consumers.  Valuing their contributions, the federal government supports farmers markets by making parking lots and plazas available as market sites, and requiring building managers to accommodate the needs of market managers.

The Farmers Market Coalition, a nonprofit organization dedicated to strengthening farmers markets across the U.S., recently partnered with the University of Wisconsin to launch a new project called, “Indicators for Impact.”  In this project, we are pilot testing indicators associated with 27 metrics that will advance our understanding of the relationship between marketplaces and other actors in their built environments, such as the economic impact on surrounding businesses, and the ecological impacts of the farms.  We are working with markets in the Gulf Coast, Chesapeake Bay, and Northern Appalachia areas to provide market managers with the tools for data collection, analysis and reporting, which will help them advance their goals and better articulate the contributions of markets to the community’s quality of life.  The data collected will then be aggregated to create an online database on farmers markets across the country.

We worked from the literature on marketplaces and directly with market managers to select metrics of interest, which we are now operationalizing.  The metrics address a variety of indicators, such as, “acres in organic or third party certified ‘sustainable’ production,” and, “percentage of visitors using non-auto modalities to access the market.”  Many are concerned with food access and food security, such as how low-income or senior citizens access federal food benefits programs at marketplaces.  Managers will learn how to collect and analyze data on these metrics, and use that analysis to support market and community goals and relationships. 

See prototypes of the project’s graphic metrics reports.

Marketplaces are multifunctional spaces that activate existing environments and stimulate economic activity.  They have been and continue to be the scaffolding for many economic, health and social purposes, providing spaces for health screenings, school food education, and other services beyond the traditional scope of food provision.  Because of these linkages, the data provided by the Indicators for Impact project will be a valuable resource for public health practitioners, nonprofit organizations, schools, and municipalities.


The Food System Wiki:  A Guide to Understanding Food Systems

The Food System Wiki and its companion documents, an annotated bibliography and a resource for extension professionals, were initiated in 2010 as a project for my class, Markets and Food Systems, at the University of Wisconsin-Madison, with support from the Journal of Agriculture, Food Systems, and Community Development (JAFSCD), and the U.S. Department of Agriculture (USDA).  The wiki aims to present a comprehensive guide to food systems and agricultural development–related terms. It is a collection of scientific, political, and popular words, terms, and acronyms: all things food systems-related. We believe that these terms provide an accurate overview of both everyday and not-so-common phrases about this growing field, and will support the work of a wide range of students, academics and professionals.

Visit the Food System Wiki.

The work being done with the Oneida Nation, the Farmers Market Coalition and the Food System Wiki illustrates some of the many facets of the reciprocal relationship between the built environment and different food system activities. I have found the importance of strong relationships in this work to be clear– relationships between ideas and behavior, as well as relationships between organizations.  Advancing health through food requires more than advancing healthy food; it requires advancing a healthy built environment, good governance, and healthy inter-organizational relationships.  Through further collaboration, we can continue to improve the quality of our food systems and the health of our people.


I would like to acknowledge the following individuals for their invaluable support:  Joanie Buckley, internal services division director for the Oneida Tribe; Barbara Dickson, Stacie Danforth, and Barbara Webster, of the Oneida government, Mary Beth Collins and Natalie Feggestad from the Center for NonProfits in the University of Wisconsin (UW) School of Human Ecology; Hope Simon of the Nelson Institute at the UW; two excellent graduate students, Riley Balikian (Nelson Institute and URPL) and Jessica Buechler ​(URPL), as well as an undergraduate student, Tony Castagnoli, of Landscape Architecture; USDA architect Fidel Delgado; Jen Cheek, executive director of the Farmers Market Coalition; Sara Padilla, Stacy Miller and Dar Wolnik, all of FMC; and two of my PhD students, Lauren Suerth and Yuni Jeong. A very special thanks to Paul Robbins, director of the Nelson Institute at the UW, and Anne Alonzo, Administrator of the USDA Agricultural Marketing Service.

Related Resources

Read more by Dr. Morales

Dawson, Julie and Alfonso Morales eds. Under Contract.  Cities of Farmers: Problems, Possibilities and Processes of Producing Food in Cities. University of Iowa Press.

Day Farnsworth, Lindsay and Alfonso Morales. 2011. Scaling up for Regional Food Distribution. Journal of Agriculture, Food Systems and Community Development. 2(1): 1-21.

Morales, Alfonso. 2011. “Public Markets: Prospects for Social, Economic, and Political Development.” Journal of Planning Literature. 26(3): 3-17.

Morales, Alfonso. 2010. “Planning and the Self-Organization of Marketplaces.” Journal of Planning Education and Research. 30(2): 182-197.

Morales, Alfonso and Gregg Kettles. 2009. “Healthy Food Outside: Farmers’ Markets, Taco Trucks, and Sidewalk Fruit Vendors.” Journal of Contemporary Health Law and Policy. 26(1): 20-48.

Seattle Rated the Most Sustainable City in the Nation


(Originally published on 09/11/14)

STAR Communities awarded Seattle a 5-STAR Community Rating for national leadership in sustainability, making it the top-scoring community to date, and one of only two cities in the nation to achieve the 5-STAR rating.

STAR (Sustainability Tools for Assessing and Rating) Communities is a nonprofit organization that provides local leaders with a framework for assessing their sustainability, setting targets for moving forward and measuring progress to goal. Its rating system identifies seven sustainability goal areas: Built Environment, Climate & Energy, Economy & Jobs, Education, Arts & Community, Equity & Empowerment, Health & Safety, and Natural Systems.

The report cites the city’s commitment to carbon neutrality, leading edge energy efficiency programs, transportation choices, and the Green Seattle Partnership as noteworthy efforts to promote citywide sustainability.

In the Health and Safety category, Seattle received a score of 90.9 out of 100, reflecting strong performance on a robust set of indicators for active living, community health, emergency prevention and response, food access and nutrition, indoor air quality, natural and human hazards and safe communities.  The city earned high marks for increasing access to healthful food, improvements to and use of bicycle and pedestrian amenities, high quality health systems, and other initiatives that have made Seattle a healthy place to live.

Read the announcement and the full report on the STAR Communities website.

(Image source)

Green Health Tools for “Back to School”


By. Nisha Botchwey, PhD, MCRP, MPH, and Kirsten Cook

(Originally published on 09/05/14)

This month, over 55,000,000 K-12 students across the nation return to school, and less than 16% of them will walk or bike to get there each day (National Center for Education Reform Statistics 2013; US EPA 2003).  That means a huge majority will be driven to and from their school buildings five days a week. With that many cars on the road, back-to-school travel affects many more households than just those with school-aged children.

The importance of schools for planning extends beyond  their effects on travel patterns. Schools also intersect with a number of other planning-related considerations in cities. Physical activity, community development and engagement, food consumption, land use, and the environment are just a few aspects of the planning arena affected by the ripple effects of schools.  These myriad issues means that planners and planning scholars need to think more about how schools impact our ability to create safe and healthy communities.

In particular, we need to start talking about how schools can promote Green Health. Green Health is the practice of place-making at a variety of scales that integrates environmental sustainability with health promotion.  The Summer 2014 issue of the Journal of Planning Education and Research is a compilation of conversations and research on this topic. TheGreen Health Symposium presents a number of policies, strategies, and recommendations to help guide these issues.

Banerjee, Uhm, and Bahl examine student safety and travel as it relates to the built environment and social milieu environmental risks of walking to school. Their piece, Walking to School: The Experience of Children in Inner City Los Angeles and Implications for Policyprovides crucial information about student travel for safe walking trips. Based on their analyses of student and parent perceptions, they present some very useful suggestions for researchers, planners and policymakers to consider in making similar evaluations in other regions. For example, walking to school policies and interventions need to address children’s concerns about crime, aesthetics and destinations, in addition to traffic and infrastructure. Also, policies impacting the trips to and from school need to be considered individually as the impetus behind each differs; time demands dominate the morning commute versus parental perception of youth competence, preference and social support in the afternoon.

Wineman et al. further the conversation on walkability and physical activity in general in Designing Healthy Neighborhoods: Contributions of the Built Environment to Physical Activity in DetroitThis piece extends the discussion to the perspective of the low-income resident in studying the interrelationships between the built environment, physical activity, and health outcomes. Their findings demonstrate that in neighborhoods with g well-connected street networks, residents report higher levels of walking than  in less well-connected neighborhoods.

Several pieces in the Green Health Symposium focus on the policy perspective as it pertains to environmental sustainability and health promotion across various community design sectors including transportation. McAndrews and Marcus consider the integration of public health in transportation policy in Community-Based Advocacy at the Intersection of Public Health and Transportation: The Challenges of Addressing Local Health Impacts within a Regional Policy ProcessA significant policy implication from their research is that action is required from multiple scales in order to effectively integrate health and transportation issues at the local level. They demonstrate that the issues that pose regional-local conflict require complex solutions, and should include forums, institutions, and advocacy tools for the implementation of health in all policies.

Veering away from the focus on travel, McDonald et al. examine another significant impact that schools can have on Green Health, which is the issue of physical space and size. The Impact of Changes in State Minimum Acreage Policies on School Siting Practices highlights that that schools are now a driver of sprawl, with parcels occupying an average of 20 to 50 acres of land. Given that there are over 132,000 schools across the nation (National Center for Education Reform Statistics 2013), the sheer size and space of school land suggests the importance of schools in Green Health. They recommend eliminating minimum acreage requirements as a way of ensuring that school districts have the flexibility to meet unique educational and infrastructure needs. They further recommend that states better educate and inform practitioners and decision makers about school siting and successful implementation of policy, noting that there is a gap between emerging research and policy making at the local level The alignment of state guidance and state policy will also lead to successful change in school siting practice.

Another important policy consideration on this issue relates to the actual use of school grounds, which Vincent explores in Joint Use of Public Schools: A Framework for Promoting Healthy Communities. Schools have enormous potential to provide community infrastructure, but are empty much of the day.  The currently available research on this topic is rather limited, and Vincent shows some new and important insights to help guide the successful implementation of joint use in schools to promote a range of physical activity and community programing. He also presents a framework on the varying joint use terminology in order to aid practitioners and policymakers in their collaboration across sectors.

Finally, the issue discusses the sustainability perspective of schools and Green Healthprimarily through Rao and Ross’ Health Impact Assessments and Healthy SchoolsStudents spend an average of 180 days each year in school (Bush, Ryan, and Rose 2011). Therefore, the design and operation of the physical space in which they spend this time likely has a significant impact on their health. This piece makes a connection between the social environment, spatial distribution, and the built environment in its consideration of the influence of factors like school location, exterior landscape, interior environment, and programs.  The authors present the current Decision Support Tools that are useful in the creation of healthy schools. They also evaluate existing Health Impact Assessments (HIAs) relating to schools in order to suggest the need for a more comprehensive, multidisciplinary, and participatory approach to HIA for healthier schools.

As Botchwey et al. state in Green Health: Urban Planning and the Development of Healthy and Sustainable Neighborhoods and Schools, Green Health opens up new ways of strategically assessing the current state of schools and their surrounding communities. Given the significant impact of schools on society, the rich collection of tools, policies, and recommendations embedded in this Green Health Symposium offer the potential for significant positive health and environmental impacts. Applied to the right context in the consideration of factors such as location, timing, resources, and need, this toolkit is a very valuable resource for planners.



Bush, M., M. Ryan, and S. Rose. 2011. “Number of Instructional Days/Hours in the School Year.” Education Commission of the States. http://www.ecs.org/clearinghouse/95/05/9505.pdf

National Center for Education Reform Statistics. 2013. http://www.edreform.com (accessed November 21, 2013).

U.S. EPA (Environmental Protection Agency). 2003. Travel and Environmental Implications of School Siting. Washington, DC: U.S. Environmental Protection Agency.


* This Blog was originally published on Planetizen.

** Photo credit: Trailnet/Flickr

Office of the Surgeon General: Community Planning & Design Matter!


(Originally published on 08/29/14)

The Building Bridges project offers cross-sector professional development opportunities for people in public health, planning, architecture, transportation engineering, and related fields, with the goal of increasing the workforce’s capacity to promote healthy community design. With funding and support from the Centers for Disease Control and Prevention (CDC), the Building Bridges partners – American Public Health Association, American Planning Association, National Network of Public Health Institutes, and Georgia Institute of Technology – have worked together to foster networking and collaboration among leaders in these fields, as well as develop extensive online resources and model curricula. The Building Bridges project will support health and planning professionals in the effort to achieve healthy and safe community environments, as recommended in the National Prevention Strategy.

Visit the Built Environment and Public Health Clearinghouse to find lessons and resources that students and professors can use in the academic setting, as well as training materials and resources for practitioners who wish to enhance their skills. To learn about other organizations advancing the National Prevention Strategy across the country, go to the Surgeon General’s website at www.surgeongeneral.gov/initiatives/prevention/partners/. For information on CDC’s Healthy Community Design initiative, visit http://www.cdc.gov/healthyplaces/.

2014 NACCHO Annual Conference Recap


By Tina Yuen

(Originally published on 08/29/14)

Health officials from across the country convened at the National Association of County and City Health Officials (NACCHO) Conference in Atlanta this summer to discuss the theme of, “The New Era of Public Health: Science, Innovation and Policy.”   The annual meeting took place on July 8-10 and gave approximately 730 participants an opportunity to learn about healthy community design and how communities, cities and counties are implementing policy, system and environmental changes that enable healthier choices and improved health outcomes.  Jimmy Hills, a research associate at the Georgia Health Policy Center, led a one-day Health Impact Assessment (HIA) 101 training to equip public health professionals to assess local planning projects and propose solutions that will promote positive health effects and minimize adverse health effects.  Additional sessions discussed local health department participation in healthy community design decision-making, health equity implications of climate change, healthy corner stores, healthy homes, and brownsfields redevelopment.  Engagement in sessions pertaining to the impact of the built environment on public health showed an interest amongst local health departments in addressing these upstream determinants of health and equity.  For more information about NACCHO’s ongoing work and upcoming events, please visit their website.