Elsevier

Health & Place

Volume 17, Issue 5, September 2011, Pages 1113-1121
Health & Place

The socio-spatial neighborhood estimation method: An approach to operationalizing the neighborhood concept

https://doi.org/10.1016/j.healthplace.2011.05.011Get rights and content

Abstract

The literature on neighborhoods and health highlights the difficulty of operationalizing “neighborhood” in a conceptually and empirically valid manner. Most studies, however, continue to define neighborhoods using less theoretically relevant boundaries, risking erroneous inferences from poor measurement. We review an innovative methodology to address this problem, called the socio-spatial neighborhood estimation method (SNEM). To estimate neighborhood boundaries, researchers used a theoretically informed combination of qualitative GIS and on-the-ground observations in Texas City, Texas. Using data from a large sample, we assessed the SNEM-generated neighborhood units by comparing intra-class correlation coefficients (ICCs) and multi-level model parameter estimates of SNEM-based measures against those for census block groups and regular grid cells. ICCs and criterion-related validity evidence using SF-36 outcome measures indicate that the SNEM approach to operationalization could improve inferences based on neighborhoods and health research.

Highlights

► We review issues with previous health research using neighborhood analysis. ► We conceptualize and then operationalize neighborhoods using a new approach. ► The socio-spatial neighborhood estimation method is explained and then evaluated. ► Validity evidence using a large sample and multi-level models support the method. ► We conclude that the method could improve inferences about neighborhoods and health.

Introduction

Research aimed at discovering how the social and physical environment affects health and well-being has increased rapidly during the last decade. A significant proportion of that inquiry focuses on the effects of so-called “neighborhoods.” Recognition of concomitant methodological difficulties in neighborhoods and health research, however, is growing. Further, such methodological difficulties ultimately impact the quality of inferences with regard to health outcomes. One of the most compelling challenges to the quality of inferences from this literature is the definition of the neighborhood as an ecological unit of analysis (Riva et al., 2007). In this paper, we argue that central among those challenges is the development of a methodology to create neighborhood boundaries that define areas in a conceptually valid manner (as opposed to census tracts, for example). Such a method would also help generate more reliable measures of social ecologies, which can then be linked to a host of well-being measures. Moreover, the challenge includes the need to balance the limitations inherent in any such method with the difficulty of use in research. The ultimate goal in addressing these issues in social epidemiology is to improve inferences about the relationship between a very important type of place in human experience—the neighborhood—and various dimensions of health and well-being.

Accordingly, colleagues involved in the Texas City Stress and Health Study developed a new methodology—the socio-spatial neighborhood estimation method (SNEM). SNEM was designed for creating conceptually informed neighborhood boundaries. This method became the basis for data collection and, eventually, multi-level modeling of ecological and individual variables. The SNEM approach employs principles of neighborhood boundary definition along with remote (aerial or satellite) imagery to draw boundaries and create neighborhoods for research purposes. Although innovative, the approach is relatively easy to employ by any researcher with access to a basic geographical information system in which to view aerial images of urban space.

In this report, we provide a rationale for this approach and explain its potential significance for the small area effects (on health) literature. We then describe and illustrate the method. To test the value of the approach, we report a two-step “ecometric” (Raudenbush and Sampson, 1999) comparison of ecological measures obtained using the SNEM areas versus the same measures obtained with census block groups and regular grid cells in the study area. Comparisons of intra-class correlation coefficients (ICCs) and establishment of criterion-related validity by way of multi-level models support the conclusion that the areas created with the SNEM approach are more neighborhood-like and provide a better basis for measurement and inferences about neighborhood effects than census boundaries or a regular grid matrix. We conclude with a discussion of the merits and limitations of the SNEM methodology for health research.

Section snippets

Background

The rise of social epidemiology and interest in neighborhoods and health was motivated by research on stress as well as the re-emergence of ecological thinking in epidemiology (Berkman and Kawachi, 2000). The synthesis of these two motivating factors indicated that neighborhoods are an important ecological unit that can act as a stressor, or complex of stressors, to affect health. Suggestions of causal links between neighborhoods and health have long extended beyond the stress model to include

The socio-spatial neighborhood estimation method

With these conceptual and operational issues in mind, we endeavored to develop an approach to define (bound) neighborhood areas within a study of stress and health. This section reports the context of the larger study, the goals of the method, the way the method was conducted, and the data and analysis used to evaluate the method.

Findings

Table 2 shows the ICCs estimated for all measures and neighborhood types. The SNEM neighborhoods produced better ICCs for subjective measures in all cases except for the social embeddedness score, where block groups performed better. Objective measures results also indicate good performance by SNEM neighborhood units relative to the other two neighborhood types.

Table 3 reports the results of the criterion-related validity assessment. The boldface z-scores indicate statistically significant

Discussion and conclusion

The ICC comparison supports our hypothesis that the SNEM approach results in socio-spatial areas that are more reflective of the neighborhood concept. We should note, however, that the geographical basis for the neighborhood perception items—asking respondents to rate an area a few blocks around their home—probably created some error in those aggregated measures. However, the fact that our SNEM ICCs on the two objective measures were much larger than those for the other units of analysis is

Acknowledgments

The authors wish to acknowledge the contributions of Peter Dana and Joseph Forrest on GIS work related to this project, including imagery, maps, geocoding, and analysis. Tasanee Walsh also contributed earlier analysis that led to the work reported here. Steve Owen added statistical suggestions about neighborhoods and sampling as well as about validation. This work was supported by Grant P50 CA10563 from the National Cancer Institute, which funded the UTMB Center for Population Health and Health

References (51)

  • M.-P. Kwan

    From place-based to people-based exposure measures

    Social Science & Medicine

    (2009)
  • C.A. Mair et al.

    Allostatic load in an environmental riskscape: the role of stressors and gender

    Health & Place

    (2011)
  • M. Riva et al.

    Establishing the soundness of administrative spatial units for operationalising the active living potential of residential environments: an exemplar for designing optimal zones

    International Journal of Health Geographics

    (2008)
  • M. Riva et al.

    Disentangling the relative influence of built and socioeconomic environments on walking: the contribution of areas homogenous along exposures of interest

    Social Science & Medicine

    (2009)
  • N.A. Ross et al.

    Neighbourhood influences on health in Montreal, Canada

    Social Science & Medicine

    (2004)
  • S.E. Spielman et al.

    The spatial dimensions of neighborhood effects

    Social Science & Medicine

    (2009)
  • M. Stafford et al.

    Small area inequalities in health: are we underestimating them?

    Social Science & Medicine

    (2008)
  • L. Tarkiainen et al.

    Comparing the effects of neighbourhood characteristics on all-cause mortality using two hierarchical areal units in the capital region of Helsinki

    Health & Place

    (2010)
  • L.F. Berkman et al.

    A historical framework for social epidemiology

  • S.A. Bond Huie

    The concept of neighborhood in health and mortality research

    Sociological Spectrum

    (2001)
  • S.A. Bond Huie et al.

    Individual and contextual risks of death among race and ethnic groups in the United States

    Journal of Health and Social Behavior

    (2002)
  • R.J. Chaskin

    Perspectives on neighborhood and community: a review of the literature

    Social Service Review

    (1997)
  • M. Cope et al.

    Qualitative GIS: A Mixed Methods Approach

    (2009)
  • M.P. Cutchin et al.

    Concerns about petrochemical health risk before and after a refinery explosion

    Risk Analysis

    (2008)
  • B. Entwisle

    Putting people into place

    Demography

    (2007)
  • Cited by (45)

    • Developing a granular scale environmental burden index (EBI) for diverse land cover types across the contiguous United States

      2022, Science of the Total Environment
      Citation Excerpt :

      A contribution of our study is the development of a national environmental burden index (EBI) that is available and processed at the census tract level, a geographic level finer than that of other cumulative environmental burden indices, with tracts functioning as proxies for communities. Health studies have used census tracts as such and found associations with health outcomes at this geographic level (Cutchin et al., 2011; Owusu et al., 2018; K. Wang and Immergluck, 2018). Census tracts are often the geographic unit of choice for federally targeted interventions in impoverished areas (U.S. Library of Congress, 2020).

    • Stress and health behaviors as potential mediators of the relationship between neighborhood quality and allostatic load

      2018, Annals of Epidemiology
      Citation Excerpt :

      Neighborhood-level data were generated by matching U.S. Census 2000 block level data with TCSHS neighborhood boundaries (n = 48), defined by the sociospatial neighborhood estimation method, which incorporates (1) street patterns, (2) residential patterns including housing types and densities, (3) nonresidential land use, (4) landforms including barriers to passage and interaction, and (5) geographic spread. Details on data collection and the sociospatial neighborhood estimation method have been described in prior work with this data set [15–17]. Neighborhood quality was operationalized with three measures: neighborhood socioeconomic status, perceived neighborhood quality, and observed neighborhood quality.

    View all citing articles on Scopus
    View full text