Challenges in Measuring the Built Environment

Assessment of physical neighborhood and the built environment is burdened by several problems. First, in Sampson’s (2001) words, “the tendency of research on child development has been to focus quickly and narrowly on poverty,” especially in high-poverty urban neighborhoods. Moreover, most of the research in this area conceptualizes the neighborhood only as a social construct, using metrics of residential stability, income, education, employment, family structure, and crime, while neglecting physical aspects of the built environment. Finally, most of this research focuses on adolescents, perhaps because they spend more time out of the home and are therefore more exposed to neighborhood factors. This produces large gaps in data collection on other aspects of the built environment and its effects on children’s health across the age span.

Addressing Gaps in Measuring the Built Environment

Further research and systematic assessment are necessary to ascertain how the built environment affects sense of community or social capital in ways that shape the development of younger children. In addition, to improve measurement of the built environment, standardized instruments need to be developed, validated, and implemented at geographic levels useful for local planning.

SOCIAL ENVIRONMENTS

As with our discussion of social influences themselves, we organize our measurement discussion into categories of family, community, culture, and discrimination.

Family Environment

The Current Population Survey and the Survey of Income and Program Participation are prime examples of high-quality surveys conducted by the Census Bureau that gather information about many of the components of family demography and process—in particular, family income, family composition, parental schooling, and occupation. These surveys typically contain very few data on children’s health. Surveys focused on children’s health and its influences often collect some data on the components of family socioeconomic status (SES), but these data are often too crude to serve most analytical purposes. For example, data on family income are sometimes gathered or recoded into such categories as poor, near-poor, or nonpoor, so that it is impossible to estimate social gradients in health at all levels of income.

Vital statistics data contain relatively little information on SES. Birth records contain educational level achieved by the mother but, starting in 1995, do not



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