Abstract:Social scientists investigating how context varies by geographical location and/or how macro-level phenomenon affects individual outcomes often make use of U.S. Census Bureau Public Use Microdata Sample (PUMS) files where microunits can only be geographically located to Public Use Microdata Area (PUMA) polygons. Most spatial analysis investigations with PUMAs ignore the fact that many of them are multipart polygons-spatially separated polygons that share the same attribute and are stored as a single feature in… Show more
“…Spatial continuity was evaluated by calculating the fragmentation index; this was 2.4% (n = 134) for the 5632 SU_analysis spatial units. This value is within the range of fragmentation indices (2–40%) reported for public use microdata areas (PUMAs) in the USA [ 37 ]. Fig.…”
Section: Application Of the Generic Method: An Illustrative Example Bsupporting
confidence: 70%
“…Lastly, validation ensured that the spatial units’ fragmentation index remains low. By way of an example, Siordia et al’s studies of the American PUMA database featured a high fragmentation index and thus encountered theoretical difficulties in the application of statistical models; the spatial position of a healthcare event was no longer coherent with that of a spatial unit [ 37 , 38 ]. This generic method may provide a structural framework so that researchers can provide a standardized description of the methods used to aggregate ecological and medical spatial data.…”
Section: Discussionmentioning
confidence: 99%
“…Hence, an error in the data’s spatial location (due to fragmented spatial units) might affect the findings and result in an erroneous conclusion [ 36 – 38 ]. In order to control for this eventuality, spatial continuity is evaluated by determining the fragmentation of the spatial units, defined as the number of discontinuous SU_analysis as a proportion of the total number of SU_analysis [ 37 , 38 ]. This index can be calculated using geographical information systems, such as QGIS and ArcGIS [ 39 , 40 ].…”
BackgroundThe availability of big data in healthcare and the intensive development of data reuse and georeferencing have opened up perspectives for health spatial analysis. However, fine-scale spatial studies of ecological and medical databases are limited by the change of support problem and thus a lack of spatial unit interoperability. The use of spatial disaggregation methods to solve this problem introduces errors into the spatial estimations. Here, we present a generic, two-step method for merging medical and ecological databases that avoids the use of spatial disaggregation methods, while maximizing the spatial resolution.MethodsFirstly, a mapping table is created after one or more transition matrices have been defined. The latter link the spatial units of the original databases to the spatial units of the final database. Secondly, the mapping table is validated by (1) comparing the covariates contained in the two original databases, and (2) checking the spatial validity with a spatial continuity criterion and a spatial resolution index.ResultsWe used our novel method to merge a medical database (the French national diagnosis-related group database, containing 5644 spatial units) with an ecological database (produced by the French National Institute of Statistics and Economic Studies, and containing with 36,594 spatial units). The mapping table yielded 5632 final spatial units. The mapping table’s validity was evaluated by comparing the number of births in the medical database and the ecological databases in each final spatial unit. The median [interquartile range] relative difference was 2.3% [0; 5.7]. The spatial continuity criterion was low (2.4%), and the spatial resolution index was greater than for most French administrative areas.ConclusionsOur innovative approach improves interoperability between medical and ecological databases and facilitates fine-scale spatial analyses. We have shown that disaggregation models and large aggregation techniques are not necessarily the best ways to tackle the change of support problem.Electronic supplementary materialThe online version of this article (doi:10.1186/s12942-017-0109-5) contains supplementary material, which is available to authorized users.
“…Spatial continuity was evaluated by calculating the fragmentation index; this was 2.4% (n = 134) for the 5632 SU_analysis spatial units. This value is within the range of fragmentation indices (2–40%) reported for public use microdata areas (PUMAs) in the USA [ 37 ]. Fig.…”
Section: Application Of the Generic Method: An Illustrative Example Bsupporting
confidence: 70%
“…Lastly, validation ensured that the spatial units’ fragmentation index remains low. By way of an example, Siordia et al’s studies of the American PUMA database featured a high fragmentation index and thus encountered theoretical difficulties in the application of statistical models; the spatial position of a healthcare event was no longer coherent with that of a spatial unit [ 37 , 38 ]. This generic method may provide a structural framework so that researchers can provide a standardized description of the methods used to aggregate ecological and medical spatial data.…”
Section: Discussionmentioning
confidence: 99%
“…Hence, an error in the data’s spatial location (due to fragmented spatial units) might affect the findings and result in an erroneous conclusion [ 36 – 38 ]. In order to control for this eventuality, spatial continuity is evaluated by determining the fragmentation of the spatial units, defined as the number of discontinuous SU_analysis as a proportion of the total number of SU_analysis [ 37 , 38 ]. This index can be calculated using geographical information systems, such as QGIS and ArcGIS [ 39 , 40 ].…”
BackgroundThe availability of big data in healthcare and the intensive development of data reuse and georeferencing have opened up perspectives for health spatial analysis. However, fine-scale spatial studies of ecological and medical databases are limited by the change of support problem and thus a lack of spatial unit interoperability. The use of spatial disaggregation methods to solve this problem introduces errors into the spatial estimations. Here, we present a generic, two-step method for merging medical and ecological databases that avoids the use of spatial disaggregation methods, while maximizing the spatial resolution.MethodsFirstly, a mapping table is created after one or more transition matrices have been defined. The latter link the spatial units of the original databases to the spatial units of the final database. Secondly, the mapping table is validated by (1) comparing the covariates contained in the two original databases, and (2) checking the spatial validity with a spatial continuity criterion and a spatial resolution index.ResultsWe used our novel method to merge a medical database (the French national diagnosis-related group database, containing 5644 spatial units) with an ecological database (produced by the French National Institute of Statistics and Economic Studies, and containing with 36,594 spatial units). The mapping table yielded 5632 final spatial units. The mapping table’s validity was evaluated by comparing the number of births in the medical database and the ecological databases in each final spatial unit. The median [interquartile range] relative difference was 2.3% [0; 5.7]. The spatial continuity criterion was low (2.4%), and the spatial resolution index was greater than for most French administrative areas.ConclusionsOur innovative approach improves interoperability between medical and ecological databases and facilitates fine-scale spatial analyses. We have shown that disaggregation models and large aggregation techniques are not necessarily the best ways to tackle the change of support problem.Electronic supplementary materialThe online version of this article (doi:10.1186/s12942-017-0109-5) contains supplementary material, which is available to authorized users.
“…No attempts were made to include environmental measures. Although ACS data can only be geographically referenced to the Public Use Microdata Area (PUMA) geography, [15] PUMAs are for the most part very large and complex polygons. [16] The geographical attributes of PUMAs would render measurements of "environmental exposures" difficult to interpret in the likely presence of spatial mismatch.…”
Background: Sunlight has been linked the circadian rhythms that regulate sleep. Few studies have attempted to provide estimates on the size of the "daytime sleeper" population. Specific aims: Estimate prevalence of daytime sleepers in the labor force population and identify which demographic characteristics are risk factors for daytime sleeping. Methods: Cross-sectional, community-dwelling, nationally representative, observational study used information on 6,405,063 labor force participants representing 132,682,344 individuals in the contiguous United States. Data from the American Community Survey (ACS), Public Use Microdata Sample (PUMS), 2009-2013 (5-year) file was used to identify daytime sleepers (i.e., those who arrived at work between 7:00 PM and 2:59 AM). Findings: While nighttime sleepers represented 65.9% (n = 87,426,814) of those in the labor force population, daytime sleepers represent 3.3% (n = 4,344,311). Race-ethnic minority status, being disabled, and having low levels of educational attainment were found to be risk factors for daytime sleeping. Conclusions: Even though relatively small, the objectively large (≈ 4.3 million) number of daytime sleepers requires sleep research to invest resources in understanding how health varies in this population relative to those who primarily sleep in the absence of sunlight.
“…If in addition to this complexity, the use of non-probability driven allocation algorithms (e.g., logic driven fixes and not geographically based method using hot decks) may further aggravate the stability of quantifying MOE around population estimates. When attempting to create small area estimates (Siordia, 2013b), even more complex issues dealing with geographical uncertainty (Spielman, Folch, & Nagle, 2013) and polygon fragmentation (Siordia & Fox, 2013;Siordia & Wunneburger, 2013) need to be considered (see Sun & Wong, 2010). In short, the confidence interval may serve only as a tentative guide for where the true population characteristic may be found.…”
Section: Journal Of Sociological Researchmentioning
BACKGROUND:The American Community Survey (ACS) in the United States (US) collects detailed demographic information on the US population. Pressures to use year-to-year population estimates to analyze "trends" (i.e., between-year differences on the characteristics of interest) have motivated the need to explore how single-or multi-year estimates can be used to investigate changes in US population over time. OBJECTIVE: The specific aim of this manuscript is to provide empirical evidence that between-year differences in population characteristics have difference levels of uncertainty around point-estimates. METHODS: Six ACS Public Use Microdata Sample (PUMS) single year files from 2005 through 2010 are used to empirically show the heterogeneity of uncertainty in "between-year differences" on level of education, for a birth cohort born between 1960 and 1970 of non-Latino-whites and Mexican Latinos/as. RESULTS: The data show the precision of the education estimate decreases as the specificity of the population increases. For example, Mexican's 99% confidence intervals have wider and more time-varying bandwidths than non-Latino-whites. CONCLUSIONS: Inferring meaningful population change requires the challengeable assumption that between-year differences are not the product of data artifacts. Harvesting reputable ACS data demands further research before between-year differences can be treated as "real change."
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