@article{fdi:010075715,
  title = {{I}mprovement of spatial autocorrelation, kernel estimation, and modeling methods by spatial standardization on distance},
  author = {{S}ouris, {M}arc and {D}emoraes, {F}.},
  editor = {},
  language = {{ENG}},
  abstract = {{I}n a point set in dimension superior to 1, the statistical distribution of the number of pairs of points as a function of distance between the points of the pair is not uniform. {T}his distribution is not considered in a large number of classic methods based on spatially weighted means used in spatial analysis, such as spatial autocorrelation indices, kernel interpolation methods, or spatial modeling methods (autoregressive, or geographically weighted). {I}t has a direct impact on the calculations and the results of indices and estimations, and by not taking into account this distribution of the distances, spatial analysis calculations can be biased. {I}n this article, we introduce a spatial standardization, which corrects and adjusts the calculations with respect to the distribution of point pairs distances. {A}s an example, we apply this correction to the calculation of spatial autocorrelation indices ({M}oran and {G}eary indices) and to trend surface calculation (by spatial kernel interpolation) on the results of the 2017 {F}rench presidential election.},
  keywords = {spatial analysis ; spatial autocorrelation ; spatial modeling ; spatial kernel interpolation ; standardization ; {SD}-correction},
  booktitle = {},
  journal = {{ISPRS} {I}nternational {J}ournal of {G}eo-{I}nformation},
  volume = {8},
  numero = {4},
  pages = {art. 199 [11 p.]},
  ISSN = {2220-9964},
  year = {2019},
  DOI = {10.3390/ijgi8040199},
  URL = {https://www.documentation.ird.fr/hor/fdi:010075715},
}