Actionable Cultural Understanding for Support to Tactical Operations (ACUSTO): The Effect of Data Quality on Spatial Analysis Results

Galvis, Luis Armando; Guertin, Patrick J.; Meyer, William D.

doi:10.21236/ada522284

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…where the numerator is the expected number of events lying within distance h in an arbitrary event of process, and where the denominator λ is the intensity of the process (also see Diggle, 1983). So that if we are said to have a benchmark point pattern process is K(h) = πh 2 , then K(h) < πh 2 signals a "regular" point in the pattern, while K(h) > πh 2 indicates a "clustered" process (Galvis et al, 2009)-where the intensity in movement is over the threshold.…”

Section: Spatial Reference and Dependencementioning

confidence: 99%

Contiguity Principle for Geographic Units: Evidence on the Quantity, Degree, and Location of Public Use Microdata Area (Puma) Fragmentation

Siordia¹,

Wunneburger²

2013

hgeo

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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 a vector file. We briefly discuss the theoretical premises of how geographical boundaries are created for macro units and investigate the quantity, degree, and location of PUMA fragmentation. We argue that the basic contiguity principle (the assumption that spatial analysis uses polygon centroids for solid and contiguous geographic units) in spatial dependence analysis is being violated with many PUMAs in the U.S. mainlandwhere Texas, California, Tennessee, and Illinois merit special attention. Future research should outline a method for handling multipart polygons in spatial and hierarchical analyses.

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Section: Spatial Reference and Dependencementioning

confidence: 99%

Contiguity Principle for Geographic Units: Evidence on the Quantity, Degree, and Location of Public Use Microdata Area (Puma) Fragmentation

Siordia¹,

Wunneburger²

2013

hgeo

View full text Add to dashboard Cite

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Benefits of small area measurements: a spatial clustering analysis on medicare beneficiaries in the USA

Siordia¹

2013

hgeo

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Small area estimates on where services for potential Medicare beneficiaries may be needed, could provide unique research opportunities for improving the healthcare quality of the ageing U.S. population. The project described in this paper validates this argument by contrasting the spatial clustering results from an analysis that uses large geographical units with proxy measures to the results from an analysis using small area geographic units with direct measures. Largearea proxy measures come from county-level U.S. Census Bureau 2010 cross sectional data on the number of people aged 65 and over. Medicare beneficiary estimates in 2007 with Primary Care Service Areas (PCSAs) make up the small-area directmeasure analysis. Findings show that the latter offers a more geographically defined appraisal of where healthcare quality efforts should focus to aid potential Medicare beneficiary populations. Because the healthcare quality of an aging population will only increase in importance as their numbers grow in the US, further research is needed.

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Actionable Cultural Understanding for Support to Tactical Operations (ACUSTO): The Effect of Data Quality on Spatial Analysis Results

Cited by 2 publications

References 8 publications

Contiguity Principle for Geographic Units: Evidence on the Quantity, Degree, and Location of Public Use Microdata Area (Puma) Fragmentation

Contiguity Principle for Geographic Units: Evidence on the Quantity, Degree, and Location of Public Use Microdata Area (Puma) Fragmentation

Benefits of small area measurements: a spatial clustering analysis on medicare beneficiaries in the USA

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