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        "author": "Julien Jamme, Arlindo Dos Santos, Fran\u00e7ois S\u00e9m\u00e9curbe",
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        "description": "## Beyond the Border (Python version) : Kernel Density Estimation for Urban Geography\n\n### btbpy (Beyond the Border for Python Users)\n\n`btbpy` is a partial transposition of R `btb` package, available on the [CRAN](https://cran.r-project.org/web/packages/btb/index.html).\n\n### Contributions\n\nDeveloper and maintainer of `btbpy` package\nJulien Jamme, <julien.jamme@protonmail.com>\n\nAuthors and Contributors of R `btb` package:\nArlindo Dos Santos [cre],\nFrancois Semecurbe [drt, aut],\nAuriane Renaud [ctb],\nFarida Marouchi [ctb]\nJoachim Timoteo [ctb]\n\n#### What do `btbpy` and `btb` do ?\n\nThe 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.\nThe first call mode is kernelSmoothing(obs, epsg, cellsize, bandwith) for a classical kernel smoothing and automatic grid.\nThe second call mode is kernelSmoothing(obs, epsg, cellsize, bandwith, centroids) for a classical kernel smoothing and user grid.\n        \nGeographically 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>, \nStatistical 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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        "keywords": "smooth greographic kernel smoothing",
        "license": "GPL3",
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