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Local Kernel Density Estimation/Evidence
Method evidence record

Local Kernel Density Estimation

Local Kernel Density Estimation (Local KDE) is a non-parametric spatial method that estimates the density of point events at each location by applying a kernel function with a spatially adaptive bandwidth. Unlike global KDE, which uses a fixed bandwidth across the entire study area, Local KDE adjusts the smoothing window according to local data density, capturing fine-scale clustering where events are sparse or concentrated.

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Source record

Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.

Local Kernel Density Estimation
Taxonomic method record · regression-model / spatial-analysis
  • Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis. Chapman and Hall, London. · ISBN 978-0412246203
  • Diggle, P. J. (1985). A kernel method for smoothing point process data. Journal of the Royal Statistical Society: Series C (Applied Statistics), 34(2), 138-147. · URL
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Curated claims

Claims persisted in the evidence ledger, each with its own assessment.

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Related methods

Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.

Taxonomic bucketHot Spot Analysismachine-suggested · Relational suggestion, not evidence.Taxonomic bucketLocal Moran's Imachine-suggested · Relational suggestion, not evidence.Taxonomic bucketLocal Spatial Autocorrelationmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketNetwork-Based Spatial Analysismachine-suggested · Relational suggestion, not evidence.Taxonomic bucketSpatial Autocorrelationmachine-suggested · Relational suggestion, not evidence.

Evidence status

Sources recorded, not reviewed

Bibliographic sources are present. Claim-level evidence review has not been performed.

Sources

2 recorded citations, copied from the method source record.

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