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rdf:type |
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lifeskim:mentions |
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pubmed:dateCreated |
2010-8-13
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pubmed:abstractText |
Geographic information systems have advanced the ability to both visualize and analyze point data. While point-based maps can be aggregated to differing areal units and examined at varying resolutions, two problems arise 1) the modifiable areal unit problem and 2) any corresponding data must be available both at the scale of analysis and in the same geographic units. Kernel density estimation (KDE) produces a smooth, continuous surface where each location in the study area is assigned a density value irrespective of arbitrary administrative boundaries. We review KDE, and introduce the technique of utilizing an adaptive bandwidth to address the underlying heterogeneous population distributions common in public health research.
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pubmed:grant |
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pubmed:commentsCorrections |
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pubmed:language |
eng
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pubmed:journal |
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pubmed:citationSubset |
IM
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pubmed:status |
MEDLINE
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pubmed:issn |
1476-072X
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pubmed:author |
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pubmed:issnType |
Electronic
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pubmed:volume |
9
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pubmed:owner |
NLM
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pubmed:authorsComplete |
Y
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pubmed:pagination |
39
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pubmed:dateRevised |
2011-6-14
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pubmed:meshHeading |
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pubmed:year |
2010
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pubmed:articleTitle |
Density estimation and adaptive bandwidths: a primer for public health practitioners.
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pubmed:affiliation |
Norris Cotton Cancer Center, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA.
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pubmed:publicationType |
Journal Article,
Research Support, N.I.H., Extramural
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