REFLEXIONES EN TORNO A LA ENSEÑANZA DE LA LECTURA EN PREESCOLAR Y PRIMERO EN PREESCOLAR Y PRIMERO

Among NYC participants, the concordance was 77% or higher for UHF, PP, CD, and SD areas, with similar variance across units (SD 21 to 26%), but only 14% for CTs (Table 15, next page). Among PGH participants, self-defined neighborhood areas were more strongly concordant with DCPN areas (76%) than with CTs (45%). We did not find significant differences in socio- demographic characteristics between participants in the 25th _{versus 75}th _{percentile of}

concordance, in either city.

Table 15. Summary statistics - Self-defined neighborhoods compared to Administrative areas

New York City (n=92) Mean % Concordance (SD)

Census Tracts (n=2116) 14.3% (16.1)

United Health Fund Areas (n=34) 84.8% (20.6)

Police Precincts (n=78) 77.6% (26.4) School Districts (n=32) 81.8% (24.0) Community Districts (n=59) 84.8% (22.7) Pittsburgh (n=67) Census Tracts (n=139) 45.2% (32.9) DCP Neighborhoods (n=94) 76.4% (33.9)

5.3 DISCUSSION

Developing a flexible tool for describing perceived neighborhood geography can enable specification of neighborhood-level exposure pathways and interventions. Moving beyond analytic challenges of interpreting administrative areas and areal aggregations, to quantitatively characterizing perceived neighborhood area, for individuals and groups, enables more refined understanding of overlapping operational scales within and among neighborhoods. Furthermore, information on population sub-groups that may differ in neighborhood perceptions, and thus systematic misspecification of neighborhood effects, can help identify mechanism for persistent health disparities. Here, we provided a reproducible quantitative and qualitative approach for assessing self-defined neighborhood areas and clarifying neighborhood conceptualization. We used narrative boundaries to validate the accuracy of the mapping tool, and then used perceived neighborhood areas to evaluate spatial concordance with administrative boundaries, in each city. Systematically assessing the accuracy of self-report mapped neighborhood geography in two distinct cities demonstrated the feasibility of collecting perceived neighborhood information through a mapping interface embedded in an online survey. Geographic concordance between mapped areas and narrative boundaries did not differ by individual-level socio-demographic characteristics in our sample, and was similar in magnitude and variance across cities. We assumed that providing narrative boundaries would be more accessible for participants, compared to mapping perceived boundaries in a Google Maps-based interface, and, thus, that narrative descriptions would better represent “true” perceived neighborhood boundaries,

compared to the mapped area. Further, participants reported high levels of ease and perceived accuracy of their mapped neighborhoods. This apparent greater facility with a mapping interface than narrative reporting of boundaries could be a function of familiarity with internet mapping

platforms among our samples, relative to orientation on the ground. Alternatively, lower completion rates for narrative descriptions could be an artifact of conservative manual transcription protocols (e.g., requiring closed polygon). Similar online platforms for collecting self-report neighborhood information in digital form, such as VERITAS (Chaix et al. 2012), have relied on in-person survey interviews, where the interviewer input geographic boundaries and the participant confirmed accuracy. Our validation suggests that unassisted online survey mapping items may be a reliable alternative to in-person administration, which could minimize costs, increase sample size, and avert potential response bias from in-person administration.

Evaluating exposure misclassification induced by using administrative area as neighborhood proxies is useful for identifying optimal units of aggregation for population-level investigations. Given previous findings for perceived neighborhoods being smaller than administrative areas (e.g., Yonas et al. 2007) and best GIS-based assessment practice to use the finest unit of population aggregation [especially for assessing disparities (Maantay 2002)], we were surprised to find that perceived neighborhoods in both cities were more quantitatively concordant with relatively coarse administrative units, compared to census tracts. Assessing individual-level exposures to discrete pollution sources, hazards, or assets (e.g., roadways, dry cleaners, alcohol outlets, healthy food vendors), or to continuous processes (e.g., model-based air pollution concentrations, elevation), is amenable to aggregation at multiple geographic scales, including self-report neighborhood areas. However, important data describing the physical and social environment are generally only available in aggregate (e.g., violent crime rates, socioeconomic conditions). As such, population-level neighborhood effects research has largely utilized distance-based metrics (i.e., radial buffers) or census tracts as proxies for neighborhood areas. While these approaches have to some extent facilitated comparisons across studies and

locations, the limited interpretability of neighborhood construct, and potential for unmeasured spatial confounding or misspecification, require refined assessment approaches. Our findings suggest that census tracts are not necessarily the best administrative proxy for perceived neighborhood areas, and propose a metrics for identifying which areal units may best match (i.e., minimize Type 1 error) perceived neighborhood geography.

Qualitative information about factors that influence perceived neighborhood geography, and types of activities conducted within and outside of these boundaries, strengthened interpretability of self-defined neighborhood areas and quantitative analyses. Some reported factors influencing perceived suggest activity patterns and physical exposure pathways, such as land use and topographic features (e.g., major roads, landmarks, rivers, cemeteries), distance (e.g., walking distance), and utilization (e.g., area covered running errands). This conceptualization of neighborhood was echoed by participant reports of spending ‘most’ of their weekend time in their residential neighborhoods, and ‘some’ (NYC) or ‘most’ (PGH) of

weekday time. Other neighborhood delineation factors were based on more social notions of comfort, belonging, and perceived differences from neighboring areas. This self-definition relative to other places or people resonated with previous focus groups findings of perceptions of neighborhood stressors characterized relative to other areas (Carr et al. 2012). Likewise, the approach of delineating neighborhood areas – empirically (Chaix et al. 2009) or subjectively (e.g., Weiss et al. 2007) - based on socio-demographic homogeneity has been used before; however, these factors were only reported by NYC residents in our sample, indicating that this approach may be appropriate for some places and not others.

The richness of qualitative definitions of neighborhood reveals the range of factors that contribute to “neighborhood,” and future assessments could provide structured survey item

(rather than open-response) for respondents to rate what is their primary reason. In future applications, queries targeting specific neighborhood definitions (e.g., physical structures and borders, versus community social dynamics) could aid in developing mechanism-specific investigations.

5.3.1 Strengths

The primary strength of this analysis was the utilization of quantitative and qualitative methods to describe individual-level perceived neighborhood geography. While there is no “gold standard” metric for assessing geographic precision or misclassification, our approach to quantifying concordance, assessing differential concordance by socio-demographic characteristics, and qualitatively assessing self-rated accuracy and neighborhood definition support the viability of online mapping survey instruments for public health research. We piloted the online mapping tool in two cities with distinct urban design, transportation patterns, and residential mobility. We utilized low-cost, broadly-recognizable Google Maps interface to maximize accessibility of the tool for future public health and community applications.

5.3.2 Limitations

The results of neighborhood geography validation, and the definitions and activities associated with residential neighborhoods, are not generalizable, and reflect the sample population (e.g., majority white race, employed, high educational attainment). Analytically, our validation method calling for narrative boundary transcription is time-consuming and computationally intensive. In the context of neighborhood effects research, our focus on residential neighborhood, to the

exclusion of other potentially important places (e.g., work, school neighborhoods) is an important limitation for fully characterizing exposure pathways, however, the tool is sufficiently flexible that future applications could query perceptions of multiple lived environments.