2016
DOI: 10.1016/j.envres.2016.06.010
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Application of the double kernel density approach to the analysis of cancer incidence in a major metropolitan area

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“…To eliminate the confines of arbitrary administrative boundaries, we followed the double kernel density (DKD) method [ 43 48 ] to calculate distributions of SGA and LBWT, normalized by all births. DKD involves kernel density estimation—a non-parametric method that spreads point values across a surface by calculating the magnitude-per-unit area from points (representing the counts of birth events), fitted to a smoothly tapered function that spreads the values within a specified distance (25 km for this study) around each point [ 49 ].…”
Section: Methodsmentioning
confidence: 99%
“…To eliminate the confines of arbitrary administrative boundaries, we followed the double kernel density (DKD) method [ 43 48 ] to calculate distributions of SGA and LBWT, normalized by all births. DKD involves kernel density estimation—a non-parametric method that spreads point values across a surface by calculating the magnitude-per-unit area from points (representing the counts of birth events), fitted to a smoothly tapered function that spreads the values within a specified distance (25 km for this study) around each point [ 49 ].…”
Section: Methodsmentioning
confidence: 99%