Identification of contrastive and comparable school neighborhoods for childhood obesity and physical activity research
© Zhang et al; licensee BioMed Central Ltd. 2006
Received: 30 November 2005
Accepted: 30 March 2006
Published: 30 March 2006
The neighborhood social and physical environments are considered significant factors contributing to children's inactive lifestyles, poor eating habits, and high levels of childhood obesity. Understanding of neighborhood environmental profiles is needed to facilitate community-based research and the development and implementation of community prevention and intervention programs. We sought to identify contrastive and comparable districts for childhood obesity and physical activity research studies.
We have applied GIS technology to manipulate multiple data sources to generate objective and quantitative measures of school neighborhood-level characteristics for school-based studies. GIS technology integrated data from multiple sources (land use, traffic, crime, and census tract) and available social and built environment indicators theorized to be associated with childhood obesity and physical activity. We used network analysis and geoprocessing tools within a GIS environment to integrate these data and to generate objective social and physical environment measures for school districts. We applied hierarchical cluster analysis to categorize school district groups according to their neighborhood characteristics. We tested the utility of the area characterizations by using them to select comparable and contrastive schools for two specific studies.
We generated school neighborhood-level social and built environment indicators for all 412 Chicago public elementary school districts. The combination of GIS and cluster analysis allowed us to identify eight school neighborhoods that were contrastive and comparable on parameters of interest (land use and safety) for a childhood obesity and physical activity study.
The combination of GIS and cluster analysis makes it possible to objectively characterize urban neighborhoods and to select comparable and/or contrasting neighborhoods for community-based health studies.
An important decision when planning community-based health studies includes the choice of representative communities or neighborhoods. The communities selected for study will need to be few in number (for logistical and financial reasons), and must meet defined characteristics determined by the health condition under study (e.g. high or low disease rates), and the specific question being addressed (and related variables). The factors that will affect the selection of contrastive and/or comparable communities include the objectives of community program initiatives, the availability of community health indicators and health outcomes of interest, and the specific factors associated with those indicators and outcomes. Thus, research on community-based health programs requires methods for selection of representative communities or neighborhoods for interventions and investigation.
In our research, we have undertaken to detect the effects of school neighborhood social and built environments on child obesity and physical activity (and on associations between these). For this work, we seek to compare the environments of children attending defined groups of schools. The schools chosen for study are expected to be representative of groups of schools defined by environments and obesity rates, i.e.: 1) their social and built environments are contrastive between the selected school neighborhood groups and comparable within these groups and 2) their childhood obesity rate and/or physical activity levels are significantly different between neighborhood groups and are relatively similar within groups.
To achieve this, we needed to develop methods for objective characterization of neighborhood environments and to select representative neighborhoods or communities. In our large urban area, this was challenging.
In this paper, we report how we combined GIS and hierarchical cluster analysis to select contrastive and comparable school neighborhoods in Chicago for our pilot studies on child overweight and physical activity and school social and built environments.
The profiles of the selected school neighborhoods.
Race and Ethnicity
Black/African American (%)
population density (per acre)
block density (per acre)
distance to park (miles)
1997 violent crime rate (per 100000)
Comparison neighborhoods or communities are often used in the design of public health and epidemiology studies, in order to clarify the roles of environmental and demographic risk, and the effectiveness of community level interventions. This paper describes how we created school district characterizations and used them to identify contrastive and comparable school neighborhoods for community-based childhood obesity and physical activity studies. We used GIS to link multiple data sources to generate objective environmental measures for the school neighborhoods in Chicago, and conduct hierarchical cluster analysis to select the desirable school neighborhoods for health study. Using a combination of GIS-supported neighborhood characterization and cluster analysis, we successfully identified contrastive and comparable neighborhoods for our child physical activity and obesity research in Chicago, a large urban area.
These methods can be applied to in other urban settings to allow objective characterization of neighborhoods and the efficient and effective identification of contrastive and comparable neighborhoods for community-based health studies.
Most previous studies have been based on simple standards to select comparison neighborhoods, such as ethnic/racial mix, poverty level, urbanity in large-scale environmental settings (urban, suburban and rural area ), or the health intervention levels[3–6]. O'Camp et al. have presented the regression and principal component analysis (PCA) approaches to the identification of neighborhoods as intervention and control sites for community-based programs. These methods required that health outcomes be available in all neighborhoods. For obesity and physical activity research, local community or neighborhood-level health outcomes are often not available.
We faced a large number of potential neighborhoods in Chicago, and a long list of neighborhood variables related to child physical activity and obesity. In this context, the selection of contrastive and comparison neighborhoods for the assessment of neighborhood effects on child health became quite complex. If one environmental factor has multiple indicators, factor analysis could be used to reduce data dimensions.
In conclusion, this study shows a powerful methodology to select contrastive and/or comparable neighborhoods for community-based health studies and implement a logistically feasible and statistically valid sampling strategy. It appears that the combination of GIS and statistical tools provides a powerful approach to characterize neighborhood social structural context and built environment to facilitate community health programming and design.
Data and methods
In this report, we illustrate the methods that we have developed in applications to childhood obesity. To understand the applications, a bit of background about this condition is useful. Childhood overweight and obesity is an increasing public health problem and well known to have significant impact on both physical and psychological health. Childhood obesity has risen to unprecedented levels [8–11]. The United States 1999–2002 National Health and Nutrition Examination Survey (NHANES) indicates that, among children aged 6 through 19 years, 31.0% were at risk for overweight or overweight and 16.0% were overweight. The high levels of overweight among children become a major public health concern. Overweight and obesity are assumed to be the results of a decrease in physical activity and an increase in food intake. Environmental factors may play pivotal roles in the markedly rising prevalence of obesity in the last 2 decades. Social environment, such as the poverty associated with race/ethnicity, and the built environment, including landuse patterns, transportation network and community design features, are important for obesity prevention, as they may encourage or discourage physical activity and healthy food intake [12–19]. Thus population-based obesity prevention, especially for children, may be achieved through a variety of interventions targeting built environment relevant to physical activity and diet.
First we used GIS to integrate publicly available social and physical environmental data from multiple sources to generate objective neighborhood environment indicators. Those indicators were selected because they are considered to be potentially associated with school children's physical activities in Chicago. This process involved spatial operations between subject layers (school neighborhoods) and environmental factor layers within GIS, using appropriate spatial imputation algorithms for different types of environmental indicators. Here spatial imputation algorithms are the procedures for the partition and/or aggregation of spatial referenced data to generate the neighborhood level measures.
Second, we combined information from a Chicago public school population survey , school child overweight data , and 2000 census data to select contrastive school neighborhoods in terms of child overweight rates, built environment and/or sociodemographics (depending on what data were available). This yielded schools that were contrastive on the variables of interest.
Third, hierarchical cluster analysis was applied to identify comparable school neighborhoods corresponding to the selected contrastive ones in terms of their social and built environments. In the following sections, we first describe the data we used, then we illustrate in detail the generation of neighborhood measures of interest and cluster analysis for identifying comparable and/or contrastive neighborhoods.
Data sources for school neighborhood characterization.
School attendance boundary
CPS (Chicago Public School Board) http://www.cps.k12.il.us/schools
CLOCC(Consortium to Lower Obesity in Chicago Children) http://www.clocc.net
Patch of various sizes
NIPC (Northern Illinois Planning Commission) http://www.nipc.org
AADT(Average Annual Daily Traffic)
IDOT(Illinois Department of Transportation) http://www.dot.state.il.us
Public park, playground
Patch of various sizes
Chicago Department of Information and Business Service http://www.cityofchicago.org
Violent crime rate
Chicago Police Department http://www.cityofchicago.org
US Census 2000 http://www.census.gov
Health outcomes for childhood obesity and physical activity data are usually not available. We have limited children's obesity data in term of Body Mass Index (BMI) data from public school children health exams . BMI is calculated as weight in kilograms divided by the square of height in meters. Chicago Public School (CPS) student height and weight data on 1208 3–7 year olds were collected from 25 schools in 19 different Chicago community areas. The 2000 Centers for Disease Control and Prevention Growth Charts for the United States were used to define overweight and at risk for overweight. The Growth Charts are sex- and age-specific and are based on national data (1963–1994). At risk of overweight is defined as between the 85th – 94th percentiles for sex- and age-specific BMI. Overweight is defined as ≥ 95th percentiles for sex- and age-specific BMI. The prevalence of overweight children was 23%, and the prevalence of children at risk of overweight 15% .
The majority of environmental measurements in physical activity research are subjective and/or survey-based . In order to characterize the neighborhood more accurately, we selected multidimensional and multilevel environmental variables relevant to child physical activity that can be measured objectively. Here we considered two general categories of environmental factors: built environment and social environment.
For the built environment, we started with factors associated with adults' participation in physical activity that are already reported in the literature [23, 24], including land use, accessibility, and neighborhood safety.
Land use refers to the spatial distribution of human activities in a defined space. The major land use types in an urban neighborhood include residential, commercial, industrial, institutional and others. Land use composition, diversity, and fragmentation are among its basic dimensions. Land use composition is described by the percentages of each land use types in a neighborhood. For example, a residential neighborhood could have more than 50% area classified as residential while in Chicago downtown will have more than 50% area as commercial. We used the number of land use types in a neighborhood to reflect the land use mix, and the number of land use patches to indicate land use fragmentation. Urban neighborhood land use configuration is associated with physical activity or travel behavior and a mixed land use in a neighborhood (locating different types of activities close together, such as shopping stores and schools within or adjacent to residential neighborhoods) may promote residents' physical activity[19, 25]. We generated 12 land use types for all areas within the school neighborhoods, including residential, commercial, industrial and urban open space.
Accessibility often refers to the spatial access to or from destinations or facilities in a neighborhood. Street density is a common measure for accessibility. Since Chicago is located on a flood plain, with a well developed grid street system, block density is closely related to street density and is an appropriate indicator of accessibility associated with pedestrian travel behaviors. We extend Eash's use of census blocks to use the block density in each school neighborhood to measure neighborhood accessibility as well as the pedestrian environmental suitability. For children, the proximity to public parks and playgrounds was used to assess accessibility of public areas for play or exercise. We derived the accessibility of schools to public parks and school playgrounds in term of distance for each school neighborhood.
Neighborhood safety in Chicago is significantly associated with the reduced children's physical activity level. According to our pilot focus group data (from a project called Transportation is Active and Safe for Kids (TASK)), safety related to both crime and traffic is the major concern of Chicago parents in allowing children to walk to school. Although schools are not far from homes and most elementary schools have an attendance boundary with a radius of less than half mile, many parents hesitate to let children walk to school alone due to fear of crime and/or wide and busy streets. Other studies also show that neighborhood safety is important for children's physical activity [29–31], more so than for adults' physical activity . We assessed two major aspects of urban neighborhood safety: traffic and violent crime.
Heavy urban neighborhood traffic in Chicago is a serious threat to school children and a major cause of injury to them . Most methods for acquiring neighborhood traffic information in larger urban areas were infeasible for our needs. For example, Chicago has 24,749 census blocks, 876 census tracts (within 77 Chicago community areas); as a result, direct field traffic survey is very difficult, both physically and economically. We adopted two indicators to measure the school neighborhood traffic status: 1) the number of arterial streets within local school areas and 2) the maximum average annual daily traffic (AADT), a number of vehicles on the arterial streets. We did not include the interstate highway passing by school neighborhood for traffic assessment, as the traffic on interstate highway are quite isolated from the local street systems.
Neighborhood crime events, especially violent crime events (homicide, aggravated assault, robbery and criminal sexual assault) may be a significant environmental barrier to outdoor physical activity and affect neighborhood safety perceptions [22, 32, 33]. A twenty-year Chicago violent crime study shows that neighborhood violent crime rates are quite stable over years. We used Chicago community area violent crime rates for incidents involving youth victims as our measure of neighborhood crime vulnerability .
Social environment factors
Neighborhood socioeconomic status (SES), such as poverty , education and employment are fundamental factors that influence health and well-being , including child overweight and physical activity [32, 38]. In Chicago, one of the most residentially segregated cities in the US, neighborhoods' physical layouts (street design) are closely related to their socioeconomical status. We selected census tract level data on racial and ethnic composition, educational attainment, unemployment and poverty rates to measure school neighborhood social and economic status.
Spatial analysis in GIS
We used GIS to generate school-level neighborhood environmental indicators from those originally created for other spatial units (e.g. census tract, street segment, and Chicago community area). Two spatial operations in GIS were used: spatial partition and spatial aggregation. Spatial partition here is defined as the process of linking environmental GIS layers with school boundary layer according to their spatial relationships. Spatial aggregation is the process of summarizing environmental measurements at the school level. Since the school neighborhood is a GIS layer in format of polygon or area, there are three types of spatial partition and aggregation between school GIS layer and environmental GIS data layers: polygon-point, polygon-line, and polygon-polygon.
If the original environment data are represented in point format in GIS, the spatial partition and aggregation are quite intuitive. For example, the block density is defined as the number of how many blocks per school neighborhood. Since a block centroid is only within only one school neighborhood, we assigned it to a school neighborhood, and then summarized the number of block centroid points within a school neighborhood, we divided the number of block centroid points by the school neighborhood area to generate the block density for each school neighborhood.
If the original environment data are represented in line format in GIS, spatial partition and aggregation are similar to those applied to point data. For example, neighborhood traffic was based on AADT on arterial street segments. We could assign the whole street segment or part street segment to a school neighborhood and then we perform the necessary spatial aggregation to generate the indicators of interest for each school. We obtained the number of arterials and maximum AADT for each school neighborhood.
If the original environment data are represented in polygon (or area) in GIS, the spatial partition and aggregation are a little more complex. We illustrate this in detail using the case of census tract to generate school neighborhood socioeconomic indicators.
• Spatial Aggregation: SES data were aggregated for school neighborhoods based on the census tract segments generated above. Two different algorithms for spatial imputation were used to calculate the school-level indicators from census tract-level ones. Sociodemographic measures were assumed to be evenly distributed within a census tract. Specifically, suppose that a ij is the area of a newly created polygon n ij within school j from census tract i, the area for school j, , and the area for census tract i, . Suppose r i is one SES indicator for tract i, then the aggregated SES indicator for school j, p j , is 1) the summary of r i , weighted by the proportion of the area of a newly created polygon within the school, if r i is non-summable indicator (e.g. family median income), or 2) the summary of r i , weighted by the proportion of the area of a newly created polygon to its census tract, if r i is summable indicator (e.g. population). For example, consider a school neighborhood with an area of 100 acres composed of only three census tracts: one tract with a population 2000 and family median income $35,000 has an area 20 of 40 acres within the school neighborhood, the other tract with a population 3,000 and family median income $45,000 has an area 30 of 50 acres within the school neighborhood, and the last tract of 50 acres with a population 1,500 and family median income $40,000 is nested in the school neighborhood. Now we need generate the school neighborhood family median income and population. Family median income is a non-summable indicator, and according to the first summary formula above, the neighborhood family median income equals to 40500(35000*20/100+45000*30/100+40000*50/100). Since Population is summable indicator, and according to the second summary formula, the neighborhood population equals to 43000(2000*20/40+3000*30/50+1500*50/50).
Cluster analysis was used to group school neighborhoods by characteristics of social and built environments. There are 412 public elementary school neighborhoods in Chicago. In order to select representative neighborhoods for our pilot studies, it is necessary to characterize these school neighborhoods into a small number of categories (clusters) according to the similarity of their neighborhood environments:
• First, we used public elementary school health exam data on 19 schools to identify 2 contrastive schools in terms of school student overweight rate. These schools had relatively larger sample sizes (N = 180, 113); one with a lower obese rate (20.6%) and the other with a higher one (33.3%). The schools are quite close to each geographically; one has > 90% Hispanic student, and the other has 50% black and 50% Hispanic students.
The selected socioeconomic indicators for school neighborhoods.
Population 25 years and over with high school graduate or over
average family median income
unemployment rate for population 16 years and over
region of birth of foreign born
the poverty rate of family with children under 5 years
• Third, we visualized the cluster analysis results in a map within GIS, which facilitated the selection of school neighborhoods that were not only environmentally contrastive and comparable, but also geographically close.
We would like to acknowledge Robert Hague from Department of School Demographics and Planning at Chicago Public Schools for providing school district boundary GIS data.
- Kegler MC, Malcoe LH: Results from a lay health advisor intervention to prevent lead poisoning among rural Native American children. American Journal of Public Health. 2004, 94 (10): 1730-1735.PubMedPubMed CentralView ArticleGoogle Scholar
- Huot I, Paradis G, Ledoux M: Effects of the Quebec Heart Health Demonstration Project on adult dietary behaviours. Preventive Medicine. 2004, 38 (2): 137-148. 10.1016/j.ypmed.2003.09.019.PubMedView ArticleGoogle Scholar
- Goff DC, Mitchell P, Finnegan J, Pandey D, Bittner V, Feldman H, Meischke H, Goldberg RJ, Luepker RV, Raczynski JM, Cooper L, Mann C, Grp RS: Knowledge of heart attack symptoms in 20 US communities. Results from the rapid early action for coronary treatment community trial. Preventive Medicine. 2004, 38 (1): 85-93. 10.1016/j.ypmed.2003.09.037.PubMedView ArticleGoogle Scholar
- Jiang B, Wang WZ, Wu SP, Du XL, Bao QJ: Effects of urban community intervention on 3-year survival and recurrence after first-ever stroke. Stroke. 2004, 35 (6): 1242-1247. 10.1161/01.STR.0000128417.88694.9f.PubMedView ArticleGoogle Scholar
- Gaglani MJ, Piedra PA, Herschler GB, Griffith ME, Kozinetz CA, Riggs MW, Fewlass C, Halloran ME, Longini IM, Glezen WP: Direct and total effectiveness of the intranasal, live-attenuated, trivalent cold-adapted influenza virus vaccine against the 2000-2001 influenza A(H1N1) and B epidemic in healthy children. Archives of Pediatrics & Adolescent Medicine. 2004, 158 (1): 65-73. 10.1001/archpedi.158.1.65.View ArticleGoogle Scholar
- Fisher EB, Strunk RC, Sussman LK, Sykes RK, Walker MS: Community organization to reduce the need for acute care for asthma among African American children in low-income neighborhoods: The Neighborhood Asthma Coalition. Pediatrics. 2004, 114 (1): 116-123. 10.1542/peds.114.1.116.PubMedView ArticleGoogle Scholar
- O'Campo P, Caughy MO, Aronson R, Xue XN: A comparison of two analytic methods for the identification of neighborhoods as intervention and control sites for community-based programs. Evaluation and Program Planning. 1997, 20 (4): 405-414. 10.1016/S0149-7189(97)00034-7.View ArticleGoogle Scholar
- Hedley AA, Ogden CL, Johnson CL, Carroll MD, Curtin LR, Flegal KM: Prevalence of overweight and obesity among US children, adolescents, and adults, 1999-2002. Jama. 2004, 291 (23): 2847-2850. 10.1001/jama.291.23.2847.PubMedView ArticleGoogle Scholar
- Ogden CL, Flegal KM, Carroll MD, Johnson CL: Prevalence and trends in overweight among US children and adolescents, 1999-2000. Jama. 2002, 288 (14): 1728-1732. 10.1001/jama.288.14.1728.PubMedView ArticleGoogle Scholar
- Flegal KM, Carroll MD, Kuczmarski RJ, Johnson CL: Overweight and obesity in the United States: prevalence and trends, 1960-1994. Int J Obes Relat Metab Disord. 1998, 22 (1): 39-47. 10.1038/sj.ijo.0800541.PubMedView ArticleGoogle Scholar
- Pawson IG, Martorell R, Mendoza FE: Prevalence of overweight and obesity in US Hispanic populations. Am J Clin Nutr. 1991, 53 (6 Suppl): 1522S-1528S.PubMedGoogle Scholar
- Dehghan M, Akhtar-Danesh N, Merchant AT: Childhood obesity, prevalence and prevention. Nutr J. 2005, 4 (1): 24-10.1186/1475-2891-4-24.PubMedPubMed CentralView ArticleGoogle Scholar
- Carter MA, Swinburn B: Measuring the 'obesogenic' food environment in New Zealand primary schools. Health Promot Int. 2004, 19 (1): 15-20. 10.1093/heapro/dah103.PubMedView ArticleGoogle Scholar
- Gordon-Larsen P, Nelson MC, Page P, Popkin BM: Inequality in the built environment underlies key health disparities in physical activity and obesity. Pediatrics. 2006, 117 (2): 417-424. 10.1542/peds.2005-0058.PubMedView ArticleGoogle Scholar
- King WC, Belle SH, Brach JS, Simkin-Silverman LR, Soska T, Kriska AM: Objective measures of neighborhood environment and physical activity in older women. Am J Prev Med. 2005, 28 (5): 461-469. 10.1016/j.amepre.2005.02.001.PubMedView ArticleGoogle Scholar
- Booth KM, Pinkston MM, Poston WS: Obesity and the built environment. J Am Diet Assoc. 2005, 105 (5 Suppl 1): S110-7. 10.1016/j.jada.2005.02.045.PubMedView ArticleGoogle Scholar
- Wakefield J: Fighting obesity through the built environment. Environ Health Perspect. 2004, 112 (11): A616-8.PubMedPubMed CentralView ArticleGoogle Scholar
- Levy JI, Welker-Hood LK, Clougherty JE, Dodson RE, Steinbach S, Hynes HP: Lung function, asthma symptoms, and quality of life for children in public housing in Boston: a case-series analysis. Environ Health. 2004, 3 (1): 13-10.1186/1476-069X-3-13.PubMedPubMed CentralView ArticleGoogle Scholar
- Frank LD, Andresen MA, Schmid TL: Obesity relationships with community design, physical activity, and time spent in cars. Am J Prev Med. 2004, 27 (2): 87-96. 10.1016/j.amepre.2004.04.011.PubMedView ArticleGoogle Scholar
- Chicago Public Schools : Office of Accountability, Department of Compliance, Student racial and ethnic survey, September 30. 2000Google Scholar
- CLOCC: Annual Report 2003, Mary Ann and J. Milburn Smith Child Health Research Program, Children's Memorial Institute for Education and Research, December 2003.Google Scholar
- Gomez JE, Johnson BA, Selva M, Sallis JF: Violent crime and outdoor physical activity among inner-city youth. Prev Med. 2004, 39 (5): 876-881. 10.1016/j.ypmed.2004.03.019.PubMedView ArticleGoogle Scholar
- Humpel N, Owen N, Leslie E: Environmental factors associated with adults' participation in physical activity: a review. Am J Prev Med. 2002, 22 (3): 188-199. 10.1016/S0749-3797(01)00426-3.PubMedView ArticleGoogle Scholar
- Saelens BE, Sallis JF, Frank LD: Environmental correlates of walking and cycling: findings from the transportation, urban design, and planning literatures. Ann Behav Med. 2003, 25 (2): 80-91. 10.1207/S15324796ABM2502_03.PubMedView ArticleGoogle Scholar
- Krizek K: Operationalizing neighborhood accessibility for land use - travel behavior research and regional modeling. Journal of Planning Education and Research. 2003, 22: 270-287. 10.1177/0739456X02250315.View ArticleGoogle Scholar
- Eash R: Incorporating Urban Design Variables in Metropolitan Planning organizations' Travel Demand Models, http://tmip.fhwa.dot.gov/clearinghouse/docs/udes/eash.pdf . 1997Google Scholar
- Molnar BE, Gortmaker SL, Bull FC, Buka SL: Unsafe to play? Neighborhood disorder and lack of safety predict reduced physical activity among urban children and adolescents. Am J Health Promot. 2004, 18 (5): 378-386.PubMedView ArticleGoogle Scholar
- Mason M, Christoffel KK, Longjohn M: The TASK collaborative: promoting safe & active travel for school aged children in 4 Chicago community areas. Poster presented at 7th World Conference on Injury Prevention and Safety Promotion in Vienna, Austria, June. 2004Google Scholar
- Petch R, Henson R: Child road safety in the urban environment. Journal of Transport Geography. 2000, 8: 197-211. 10.1016/S0966-6923(00)00006-5.View ArticleGoogle Scholar
- Timperio A, Crawford D, Telford A, Salmon J: Perceptions about the local neighborhood and walking and cycling among children. Prev Med. 2004, 38 (1): 39-47. 10.1016/j.ypmed.2003.09.026.PubMedView ArticleGoogle Scholar
- Timperio A, Salmon J, Telford A, Crawford D: Perceptions of local neighbourhood environments and their relationship to childhood overweight and obesity. Int J Obes Relat Metab Disord. 2005, 29 (2): 170-175. 10.1038/sj.ijo.0802865.View ArticleGoogle Scholar
- Wilson DK, Kirtland KA, Ainsworth BE, Addy CL: Socioeconomic status and perceptions of access and safety for physical activity. Ann Behav Med. 2004, 28 (1): 20-28. 10.1207/s15324796abm2801_4.PubMedView ArticleGoogle Scholar
- Marans S, Berkowitz SJ, Cohen DJ: Police and mental health professionals. Collaborative responses to the impact of violence on children and families. Child Adolesc Psychiatr Clin N Am. 1998, 7 (3): 635-651.PubMedGoogle Scholar
- Morenoff JD, Sampson RJ: Violent Crime and the Spatial Dynamics of Neighborhood Transition: Chicago, 1970-1990 . Social Forces. 1997, 76 (1): 31-64. 10.2307/2580317.View ArticleGoogle Scholar
- Child Health Data Lab: Child and Adolescent Well-Being in Chicago, The Mary Ann & J. Milburn Smith Child Health Research Program Children's Memorial Institute for Education and Research. 2002,Google Scholar
- Drewnowski A, Specter S: Poverty and obesity: the role of energy density and energy costs. American Journal of Clinical Nutrition. 2004, 79: 6-16.PubMedGoogle Scholar
- Northridge M, Sclar E, Biswas P: Sorting out the connections between the built environment and health: a conceptual framework for navigating pathways and planning healthy cities. Journal of Urban Health. 2003, 80 (4): 556-568.PubMedPubMed CentralView ArticleGoogle Scholar
- Giles-Corti B, Donovan RJ: Socioeconomic status differences in recreational physical activity levels and real and perceived access to a supportive physical environment. Prev Med. 2002, 35 (6): 601-611. 10.1006/pmed.2002.1115.PubMedView ArticleGoogle Scholar
- SAS Institute Inc.: SAS/STAT User's Guide, Version 8. 1999, Cary, NC , SAS Institute Inc., Volume 1: 3884-Google Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.