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Using Land Cover Data to Characterize Living Environments of Geocoded Addresses: Estes et al. Respond

Environmental Health Perspectives
Environmental Health Perspectives
Publication Date
DOI: 10.1289/ehp.0901863r
  • Perspectives
  • Correspondence


Perspectives | Correspondence A 108 volume 118 | number 3 | March 2010 • Environmental Health Perspectives Methodologic Issues in Using Land Cover Data to Characterize Living Environments of Geocoded Addresses doi:10.1289/ehp.0901863 Estes et al. (2009) presented an interesting analysis of the relationship between blood pressure levels of individuals in four metro­ politan regions and their living environ ments. Remotely sensed data was used to determine urban, suburban, and rural living environments as well as day/night land surface tempera tures (LST). These remotely sensed data sets are readily available nationally, increasing the repli­ cability and consistency of the methods. Estes et al. (2009) characterized living environments using the 2001 National Land Cover Dataset (NLCD; Homer et al. 2004). Detailed land cover classes were reclassified into broad categories of urban, suburban, and rural, and the original 30­m resolution raster data was resampled to a 1­km grid using a majority filter to match the resolu­ tion of the LST data. Residential addresses were geocoded and their location compared to the 1­km grid cell values to establish the living environment variables. There are sev­ eral problems that result from this particular methodology, which I address below. First, Estes et al. (2009) geocoded the resi­ dential addresses using SAS/GIS geocoding software which employs TIGER data (SAS 2010) from the U.S. Census Bureau for street geocoding. The positional accuracy of TIGER data is not very good (e.g., Zandbergen 2008), and street geocoding in general is not very accurate (Cayo and Talbot 2003; Zandbergen 2009). The street geocoded location of the residence of a particular individual is therefore not very likely to fall inside the same 30­m grid cell as the true location of the residence. For example, the median error of typical street geocoding is in the order of 30–60 m for urban areas, about double that for suburban areas and much larger

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