Built Environment and Health: A Focus on Neighborhoods

A recent body of literature suggests that in addition to human exposures to chemical releases, a community’s land use pattern, layout, and design can influence behaviors and impact health.[381]  The term “built environment” describes the constructed places people use and consists of things like buildings, transportation systems, and open spaces.  Relationships between the built environment and health do not fit into the release-exposure-outcome model that has been presented thus far. However, several characteristics of neighborhood-level built environment features are considered in this report because they are very much a part of the physical environment around us. This section, therefore, presents the view that the places where we live, work, and play can be risk factors for multiple health outcomes like injuries, respiratory conditions, and obesity.  Figure 6 shows specific theorized connections between health outcomes and neighborhood-level built environment features.  Indoor environments also affect health but, as mentioned in the Introduction, are not discussed in this report.  However, an important consideration for neighborhoods are that they are not created equally; significant differences exist between rural, suburban, and urban areas.  In addition, predominantly low-income and/or minority communities suffer a disproportionate share of lower quality housing, unmaintained public spaces, and closer proximity to polluting sources.[382]

Figure 6: Examples of Health Outcomes that May be Related to Built Environments at the Neighborhood Level

Built Environment Characteristic

Consequence or Health Outcome

Residential Factors

Layout of community’s features including building heights and scale, street connectivity, density, and land use

May influence a person to drive, walk, or bike which affects  physical activity and stress levels as well as ambient air quality, which relates to the occurrence and severity of respiratory illnesses 

Appearance of local environment including vacant housing, graffiti, and litter

Presence and good condition of bike lanes, sidewalks

Business and Other Amenities

Access to trails, parks, public pools, recreation and senior centers

May influence individuals’ engagement in recreational physical activities to prevent obesity and other chronic conditions

Access to grocery stores, farmer’s markets, fast food restaurants, and other  food establishments

Influence nutrition (e.g., intake of fruits,  vegetables, and saturated fats)


Proper roadway lighting, pedestrian walk signals, pedestrian refuge islands

Prevention of pedestrian injures and roadway crashes

Roadway patterns and vehicle usage

May increase nearby residents’ exposure to exhaust pollutants


Many built environment-health linkages have been examined using GIS (Geographic Information Systems). A geographic information system is a tool for management, analysis, and display of features and attributes of places.  GIS is helpful because it visually captures the presence of features related to residential areas, business locations, and transportation systems, which can be analyzed in relation to income, health outcomes, or any other attribute that can be displayed spatially.

The following pages highlight selected literature and available data sources as they relate to the built environment.  To date, most research in this field has been in the form of cross-sectional studies that analyze conditions at a single point in time and can therefore indicate associations with health outcomes, but not causality.[383]  Other challenges of built environment research include (1) determining the relative impacts of multiple changes in an area over time,[384] (2) determining the relative impacts of local and regional factors, and (3) determining the spectrum of individual reactions to a given environment (e.g., women, children, and the elderly may be more affected by fear of crime or violence).[385]  Better available data is sure to aid the development of the evidence base relating the built environment to health.

Due to limitations of time and resources, the focus has been placed on information obtainable for the City of Pittsburgh. Although differences between urban and rural environments are important, they are not covered by the present report.  The remainder of the section is organized under three main headings:  Residential Characteristics, Business and Other Amenities, and Transportation.  For a listing of data sources for neighborhood level, built environment characteristics, see Figure 7 at the end of this section.   

Residential Characteristics

This section includes a discussion of urban sprawl, neighborhood appearance and walkability. 

Urban Sprawl

Urban sprawl refers to the mass suburbanization that has dominated the American landscape since the advent of the interstate highway system after World II.[386]  In contrast to urban areas, sprawl is characterized by low density (few people living on large parcels), low land use mix (large areas with similar zoning and use), low connectivity of roads (lack direct routes to destinations), and lack of a downtown center.[387] 

Evidence of the environmental and social impacts of sprawl is well established.[388]  However, recent research shows that urban sprawl may also be associated with poor health outcomes where those living in sprawling communities are likely to walk less, weigh more,[389] and have greater prevalence of hypertension than those living in compact counties.[390]  There may also be associations between urban sprawl and mental health.[391]

Because there are serious environmental, social, economic, and health consequences of sprawl, it is important that we be able to measure it.  To do so, we need data.  On a regional scale, several indices have been developed that use similar data sources, but reveal different conclusions.  For example, The Brookings Institution’s “Back to Prosperity” used the indicator of land urbanized per new household to reveal the worrisome trend that the Pittsburgh Metropolitan Area is by far the worst sprawling area in the country.[392]  Smart Growth America developed the Metropolitan Sprawl Index, which integrates 22 variables describing residential density, land use mix, degree of centering, and street accessibility.  The corresponding county sprawl index uses only 6 of these variables, where Allegheny County was slightly more compact than the average county and Beaver, Fayette, Washington, and Westmoreland are at or slightly below the average score of 100. 

Both of these indicators use data from the US Census Bureau to assess population and household changes.  To determine land use patterns, both indicators also accessed data from the Natural Resources Inventory (NRI), which is a spatial survey of all non-federal U.S. lands.  It was conducted by the U.S. Department of Agriculture every five years between 1982 and 2000 but was changed to an annual survey in 2001.  Using photo-interpretation and other remote sensing methods, statistically sampled locations are labeled with a mutually exclusive category of use.  Data can be used to estimate land use trends for 1982, 1987, 1992, and 1997 for multi-county geographic areas.  Currently, the more recent 2001 and 2002 data are only available for larger geographic areas because of a smaller sample size of locations.[393]

Other data sources used by the Metropolitan Sprawl Index include the following:[394]

·         American Housing Survey is conducted by the US Census Bureau for the Department of Housing and Urban Development.  Pittsburgh is one of the 47 metropolitan areas surveyed for information about housing, household characteristics, equipment, fuels, recent movers, and neighborhood quality.  The most recent data available online are for 1995.  

·         Zip Code Business Patterns are extracted from the Standard Statistical Establishments List, a file of all single and multi-establishment companies created by the U.S. Census Bureau.  Data are provided on the total number of establishments, employment, and payroll for more than 40,000 zip codes nationwide.  The number of establishments is broken down into 9 employment size categories by detailed industry for each zip code. 

·         Census TIGER (Topologically Integrated Geographic Encoding and Referencing) files are a digital database of geographic features such as streets, railroads, lakes, and political boundaries.  To make use of the data, a user must have Geographic Information System (GIS) software that can import these files.  With the appropriate software, a user can produce digital street maps and generate measures of street density and block length.


Land use trends over time can also be analyzed using data extracted from satellite images.  For example, the Landsat Thematic Mapper can differentiate between 15 land use characteristics.[395]   Advantages of using this spatially detailed data source include the ability to examine the whole landscape, assess urban growth in all areas, and depict trends. To provide the most realistic representation of landscape patterns, information should be calculated to the smallest point (per pixel) and avoid spatial averaging over a large geographic area.   With this fine grained data, it is possible to not only identify how much and what kind of change has occurred but where it is in relation to other classes of urban development and existing land cover types.[396]

Neighborhood Appearance and Safety Concerns

There is some evidence that suggests neighborhood appearance influences certain behavioral and psychological responses.  Broken windows, abandoned buildings, graffiti, illegal drug sales, prostitution, and lack of green space have been associated with increased criminal activity.[397] [398]  . In addition, recent literature suggests that age of the housing stock[399] and other aesthetic features of local environments are correlated with levels of walking[400] and youth recreational activity.[401]  Age of the housing stock also serves as a useful indicator to measure the potential risk of childhood lead poisoning. 

Neighborhood appearance is a rather subjective term, but it can be measured using both objective data (e.g., locations of vacant housing) and perceptual data (e.g., residents’ feelings of safety).  Sources of objective measures may either come from direct observations of features in communities or from existing databases that may track certain maintenance activities or complaints.  The perceived physical environment may be assessed using population-based surveys and surveillance systems with responses aggregated to a small geographic area (e.g., census block or tract).[402]

There are limitations to both types of data sources.[403]  For example, resident’s self-reports of features in their neighborhood can display less variation than do objective assessments because few may want to admit that it’s substandard.   Regardless of potential bias, which can be minimized with proper survey design, perceptual data can reveal very different information than what is possible to measure objectively.[404] 

The U.S. Census Bureau is a popular source for housing characteristics such as age of household and vacancy rates because it contains detailed information for relatively small areas of geography and is also consistent across the country.  When analyzing trends over time with census data, adjustments in the tract boundaries should be identified because such adjustments can distort data presentation and conclusions.  Other limitations include its ten-year timeframe and lack of parcel level information, which can be helpful when analyzing connections between the built environment and health.   

One source of local, parcel level data can be provided by the Community Information System (CIS) Project, mentioned earlier under the “Existing Endeavors” section.  Currently in its second phase of development, the project has surveyed nearly 10,000 parcels of real estate in approximately 18 neighborhoods throughout the City of Pittsburgh.  Eventually, the condition and vacancy status of all parcels in the city will be documented using a combination of data including observations from trained field workers and administrative records related to blight, disinvestment, investment, and land use.  These sources of secondary data include multiple departments within the City of Pittsburgh, Allegheny County Health Department, lien holders, and utility companies.  The project will not only centralize data from multiple data holders, but will also provide the ability for all stakeholders to understand how vacant housing impacts communities.  As the project moves forward, it will be possible to add other data sources to provide even greater utility to anyone interested in community and neighborhood level characteristics.[405]

Walkability and Bikeability

Walking and biking offer multiple health benefits.  Regular physical activity is associated with lower death rates for adults of any age, decreased risk of death from heart disease, lowered risk of developing diabetes, and decreased risk of colon cancer.  Children and adolescents need weight-bearing exercise for normal skeletal development, and young adults need such exercise to achieve and maintain peak bone mass. In addition, older adults can improve and maintain strength and agility with regular physical activity, which can reduce the risk of falling and help maintain independent living. [406]  

Interestingly, characteristics of the built environment found to be associated with walking for transportation differ from those associated with walking for exercise or recreational purposes.[407] Regardless, many of these environmental features have already been mapped by local universities and city, county, and state departments.  In addition, walkability and bikeability audits can reveal more detailed, street level information (e.g., width, gradient, and condition of surfaces).  However, collecting these data is time and labor intensive, but training interested community members as field workers can offset some costs, while also engaging those who have the most to gain from improvements to their neighborhoods.  These audits can also help identify unsafe conditions for pedestrian and bicyclists since walking is by far the most dangerous mode of travel per mile, especially in sprawling communities where streets are built for motor vehicle use only.  Streets without safe places to walk and bicycle put people at risk, particularly children who are especially vulnerable and minority populations who suffer a disproportionate share of traffic fatalities.[408]  There are a wide variety of data sources available to identify safety trends for motorists, pedestrians, and bicyclists for large geographic areas such as county, state, or nation. These include the Centers for Disease Control, Pennsylvania Department of Transportation, and National Highway Traffic Safety Administration.[409]   The Fatality Analysis Reporting System (FARS) is a nationwide reporting system that tracks all motor vehicle traffic crashes that occur on a public right of way and result in the death of a vehicle’s occupant or a non-motorist (pedestrian or bicyclist) within 30 days of the crash.  Information from multiple sources is compiled and standardized.  However, to protect individual privacy, no personal information, such as names, addresses, or specific crash locations is coded.  Race was recently added as a field to FARS database. [410]

The Pedestrian Danger Index referenced in the Mean Streets 2004 report, published by the Surface Transportation Policy Project, combines pedestrian fatality data from the FARS system with journey to work information from the U.S. Census.  The Pittsburgh Metropolitan Statistical Area was ranked in the top 50 for dangerous streets.  One limitation of the index is that it only includes fatalities, so accidents that result in minor or even serious injuries are omitted from the calculation.  The same datasets could potentially be used to develop a bicyclist danger index. 

Using data from the Crash Outcome Data Evaluation System (CODES) is a potential way to assess the severity of injuries resulting from a motor vehicle crash.  This dataset combines police reports from the crash scene with injury outcome data collected at the scene, en route to the emergency department, at the hospital or trauma center, and after discharge.  CODES data often contain information related to the crash location such as county, census tract, police beat, street name or address, distance from a milepost or nearest intersection which permits a spatial analysis.  Aggregate reports are available via the Internet, but due to confidentiality concerns, raw data are not readily available over the web.  The CODES Board of Directors makes all decisions related to management and release of the linked data. [411] 

Because it is difficult to obtain specific accident locations for a local level, a group of cyclists known as Ghost Bike has started to collect self-reported accident data from bicyclists using an online form.[412]  Unfortunately, due to limited financial and technical resources, they face multiple challenges with collection and analysis of this data.       

Businesses and Other Amenities

As already mentioned, stores, post offices, parks, and other amenities that are within walking distance increase the likelihood of getting out on foot instead of using a vehicle.  However, there are other health implications because certain amenities may be more convenient in some areas while less so in others.  An analysis of the locality of food retailers in relation to neighborhood wealth revealed that there are more supermarkets, fewer fast food restaurants, and fewer taverns in the wealthier neighborhoods compared to the poorest areas.   It has also been shown that urban dwellers pay 3% to 37% more for groceries compared to suburban residents.[413]  Thus, financial and transportation barriers may limit people’s ability to purchase nutritious foods.  This is especially relevant for those living in predominantly minority or low-income neighborhoods.  A 2003 Carnegie Mellon Heinz School graduate project mapping Allegheny County grocery stores over population and economic characteristics yielded similar findings.[414] 

Business locations as well as other features of the built environment including house size, street lights, and billboards are regulated by zoning guidelines.  In order to implement the new Urban Zoning Code adopted in 1999, The City of Pittsburgh’s Map Pittsburgh Project has collected land use information from approximately 1/3 of the city’s total parcels using trained community workers.  This information is useful in understanding the mixture of uses in a neighborhood.  Even though there are over 150 categories of land use, this inventory typically can’t differentiate between a shoe store or a coffee shop.  To obtain the specific type of business or amenities present, addresses need to be compiled using sources such as Reference USA or online yellow pages.  These addresses can be geocoded (entered into a GIS program), and then mapped using spatial software.  As shown in Figure 7, this process has already been completed for common amenities like grocery stores, farmer’s markets, schools, and recreational and senior centers.  Once mapped in this method, it is possible to assess the number and types of destinations in an area, as well as distance and shortest route to them. 


Mobile emissions from cars and other vehicles are a significant contributor to air pollution both on a regional scale and at the street level   Clean Air Task Force recently released a report documenting the health risks related to exposure to diesel exhaust.  Children and seniors are particularly vulnerable populations.  Those who operate diesel machinery, live near roadways which accommodate diesel vehicles, frequently ride on school or transit busses, and commute daily in heavy traffic also face potentially greater risks for respiratory and cardiovascular diseases, lung cancer, as well as premature death.[415]  

Maps of streets, bus routes, highways, busy roadways, railroad yards, busy roadways, bus depots, construction sites, and bridges can identify residential or workplace areas with potentially higher exposures.  Also, accessing historical maps and usage of roadways can be useful for identifying areas with contaminated soils from the once leaded gasoline. 

A combination of mechanical and human methods of data collection can determine the number of cars traveling on a road or people using a trail.  The Pennsylvania Department of Transportation’s Internet Traffic Monitoring System[416] collates data from partners who collect counts from state and federally funded local roads.  Users of the site can choose from 12 different reports for a municipality, zip codes, intersection, PennDot route, or street name.  Typically, data are updated on a 1-, 3-, or 5-year timeframe depending upon the road.  About 30% of this raw data distinguishes between cars and trucks, but estimates are made based on this information for other road segments.[417]  Numbers of bicycles on the road, on the other hand, are harder to obtain using mechanical counters and usually must rely on observational methods because there are fewer of them on the road.[418]

A variety of datasets can be utilized to help determine transportation patterns.   The U.S. Census provides data on means of transportation to work, but other destinations are not included.  Regional travel surveys have been conducted throughout the country as a means to fill in these gaps.  Locally, the Southwestern PA Commission (SPC)[419] recently completed a survey of 2,500 households to learn of transportation patterns.  Participants filled out travel diaries detailing all types of transportation modes (car, bike, foot) for all destination points (school, grocery stores, etc.).[420]  SPC has published a summary report and will be analyzing the data more closely to build a local origin-destination model for the 10-county region.  Accessing the raw data may be possible once quality issues have been addressed.[421]


Case Study #5: The Data-Gathering Process as a Path to a Healthy Neighborhood[422]


The Healthy Neighborhoods Project (HNP), a partnership between public housing residents living in Pittsburg, CA and the local health department, exemplifies how innovative methods of data collection can in themselves yield positive outcomes.  An initial step of this project was to train interested residents in skills such as interviewing, public speaking, and community building.  The group then developed an asset/capacity map as a way of collecting and displaying information about the physical and social characteristics of the neighborhood.  In order to gather qualitative and perceptual data, a door-to-door survey was conducted by residents to assess local people’s abilities (e.g., political involvement, event organization experience, etc.).  In addition, a community mapping day was organized wherein both positive and negative features of built environment would be inventoried.  On this day, teams of youth and adults identified places selling tobacco, alcohol, and nutritious foods, as well as local businesses, transportation networks, parks, and other amenities. Specific findings included the identification of parks that needed to be made safe for kids to play, a building that could potentially be renovated for a new community center and churches that could be mobilized to improve the neighborhoods. 

HNP’s organizing efforts produced a variety of both social and environmental changes in the community.  For example, as a result of this project, tobacco billboards within the neighborhood were removed and funding was secured to develop workforce development programs, drug elimination activities, and establish a children’s soccer league.  

This brief case study illustrates the following points:

·         A combination of observational and perceptual data may sometimes better reveal the eco-social context of community environmental health issues.

·         The participatory community appraisal process can itself be an intervention. For example, through this process, community members may learn organizing and other skills, may gain ability to identify problems and to take action, and may even begin to feel more a part of their own communities. The participatory process can also help public agencies to build partnerships with communities to improve health.

·         While a focus that is solely related to data about problems and needs may be disempowering, identification of data about community assets and capacity may empower local residents to identify solutions that they themselves can implement.


Figure 7: Data Sources for Neighborhood Level, Built Environment Characteristics



Data Type (Examples of Source)



Regional land use






Statistical survey of lands (U.S. Natural Resources Inventory[423])

Satellite images (Landsat Thematic Images[424] and Keyhole interactive images[425] )

Population survey (Census Transportation Planning Package[426])

Zip Code Business Patterns (US Economic Census[427])

Housing density

Zoning and building footprint maps (City of Pittsburgh[428] and County GIS Dept. [429])

Population density and trends

Population survey (U.S. Census Bureau[430])

Street connectivity

Maps (City of Pittsburgh-City Planning Dept. and US Census TIGER files[431])

Neighborhood Appearance

General appearance of neighborhood

Complaints or violations for rodent, garbage, abandoned vehicles etc.  (City of Pittsburgh-Citistats[432] and Allegheny County Health Dept.(ACHD)[433])

Experiential inventory (Carnegie Mellon University’s (CMU)’s Studio for Creative Inquiry MapHub Project[434])

Population Survey (American Housing Survey for the Pittsburgh metropolitan area but no known local data source[435])

Condition of housing

Population Survey (American Housing Survey for the Pittsburgh metropolitan area but no known local data source)

Observational inventory (Community Technical Assistance Center[436] and Community Information System (CIS)[437])

Real estate records (County Real Estate Dept.[438] and CIS)

Housing complaints, building/environmental violations, or condemnation health hazard reports (City of Pittsburgh-Citistats, ACHD, and CIS)

Vacant housing

Population Survey (US Census Bureau)

Combination of observational inventory and secondary data sources (CIS)

Vacant lots

Demolition records (City of Pittsburgh-BBI[439] and CIS)

Observational inventory (CIS and possible data forthcoming from Greenlots[440])

Real estate records (County Real Estate and GIS Depts.)

Local green space

Maps of parks and cemeteries (City of Pittsburgh-City Planning and County GIS Depts.)

Locations of public gardens (Western PA Conservancy[441])

Mapped inventory of street trees (City of Pittsburgh-City Planning Dept., forthcoming)

Satellite images (Landsdat Thematic Mapper and keyhole.com)

Safety concerns

Crime occurrences from police reports (City of Pittsburgh[442]  and CIS)

Population survey of perceptions of safety (No known local source)

Walkability and Bikeability

Location and maintenance of pedestrian crosswalks and crossing signals

Addresses and maintenance reports (City of Pittsburgh-Dept. of Public Works[443])

Roadway and sidewalk lighting

Location of telephone poles (Utility companies[444])

Location of sidewalks, bike lanes, and city steps

Maps (City of Pittsburgh-City Planning Dept.)

Condition of sidewalks, bike lanes, and city steps


Walkability and bikeability audits (No known local source)

Maintenance reports for street surfaces (City of Pittsburgh-Public Works)

Topography of land

Maps (City of Pittsburgh-City Planning Dept. and County GIS Dept.)



Data Type (Examples of Source)

Business and Other Amenities

Local land use

Zoning maps (City of Pittsburgh-City Planning Dept.)

Land use inventory (City of Pittsburgh-City Planning Dept.and CIS)

Location of public recreational facilities including trails, fields, parks, pools, and city recreation and senior centers

Maps (City of Pittsburgh-City Planning Dept. and County GIS Dept.)

Use of trails


Electronic Counters (Old data for selected trails available from Port Authority[445])

Observational Data (Forthcoming from Friends of the Riverfront Summer 2005[446])

Population based surveys (No known local data source)

Condition of fields

Report with maps (City of Pittsburgh[447])

Condition of pools and parks

Inspection results (ACHD)

Location of other recreational facilities such as YMCA's, fitness gyms, etc.

Addresses  (Reference USA[448] or online yellow pages)

Location of open lots appropriate for community gardens or urban farms

See sources for vacant lots

Observational inventory (Forthcoming from Greenlots)

Topological maps (City of Pittsburgh-City Planning Dept. and County GIS Dept.)

Location of food service establishments including restaurants, fast food places, grocery stores, and food pantries 

Addresses and violations (ACHD[449])

Maps (CMU’s Heinz School[450])

Location of bars and liquor stores

Addresses (Reference USA and online yellow pages)

Location of post offices and curbside collection  boxes

Addresses (US Postal Service[451])

Current and historical locations of industrial sources

Zoning maps and land use data (City of Pittsburgh-City Planning Dept.)

Addresses of TRI facilities (EPA[452]) or non TRI facilities such as dry cleaners, gas stations (Reference USA or online yellow pages)

Location of public, private, and parochial schools

Maps (Dept. of City Planning)

Location of health care providers

Maps of hospitals (City of Pittsburgh-City Planning Dept. and County GIS Dept.)

Addresses of clinics and physicians (PA Dept. of Health[453])

Location of major roadways

Maps (City of Pittsburgh, ESRI[454], Southwestern PA Commission (SPC)[455])

Street traffic (automobile)

Counts of cars (Penn Dot Internet Traffic Monitoring System,[456] SPC)

Street traffic (diesel)

Location or maps of bus depots (Port Authority), maps of railroad tracks (City Planning and County GIS Depts.), location of construction projects (Penn DOT[457])

Maps of bus routes (City of Pittsburgh, SPC, Port Authority) and scheduling information (Port Authority, SPC, and CMU’s Heinz School)

Motor vehicle related fatalities (Regional)

Aggregated number of fatalities ( PA Department of Transportation[458], National Highway Traffic Safety Administration[459], and ACHD)

Motor vehicle related fatalities (local)

Police reports (City of Pittsburgh)

Motor vehicle related injuries (Regional)

Info from police reports and health related sources (PA Dept of Health’s CODES[460])

Injuries (Local)

Self reported bike accidents (Ghost Bike[461])

Biker’s perceptions of safety

Qualitative data from bikers (Bike Pittsburgh[462])

Commuting patterns to work

Population survey (US Census Bureau and Census Transportation Planning Package[463] )

All commuting patterns

Origin-destination surveys (SPC)

Location of surface and structure parking lots, permit areas, and off street parking

Maps (City of Pittsburgh, Parking Authority[464])