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The kernelSmoothing() function allows you to square and smooth geolocated data. It calculates a classical kernel smoothing (conservative) or a geographically weighted median. There are only two major call modes of the function. The smoothing with quantiles method is not available on the btbpy package.
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The first call mode is kernelSmoothing(obs, epsg, cellsize, bandwith) for a classical kernel smoothing and automatic grid.
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The second call mode is kernelSmoothing(obs, epsg, cellsize, bandwith, centroids) for a classical kernel smoothing and user grid.
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The `kernelSmoothing()` function allows you to square and smooth geolocated data. It calculates a classical kernel smoothing (conservative) or a geographically weighted median. There are only two major call modes of the function. The smoothing with quantiles method is not available on the `btbpy` package.
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The first call mode is `kernelSmoothing(obs, epsg, cellsize, bandwith)` for a classical kernel smoothing and automatic grid.
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The second call mode is `kernelSmoothing(obs, epsg, cellsize, bandwith, centroids)` for a classical kernel smoothing and user grid.
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Geographically weighted summary statistics : a framework for localised exploratory data analysis, C.Brunsdon & al., in Computers, Environment and Urban Systems C.Brunsdon & al. (2002) <doi:10.1016/S0198-9715(01)00009-6>,
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Statistical Analysis of Spatial and Spatio-Temporal Point Patterns, Third Edition, Diggle, pp. 83-86, (2003) <doi:10.1080/13658816.2014.937718>.
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