@article{fdi:010067259,
  title = {{SPODT}: {A}n {R} package to perform spatial partitioning},
  author = {{G}audart, {J}. and {G}raffeo, {N}. and {C}oulibaly, {D}. and {B}arbet, {G}. and {R}ebaudet, {S}. and {D}essay, {N}adine and {D}oumbo, {O}.{K}. and {G}iorgi, {R}och},
  editor = {},
  language = {{ENG}},
  abstract = {{S}patial cluster detection is a classical question in epidemiology : are cases located near other cases ? {I}n order to classify a study area into zones of different risks and determine their boundaries, we have developed a spatial partitioning method based on oblique decision trees, which is called spatial oblique decision tree ({S}p{ODT}). {T}his non-parametric method is based on the classification and regression tree ({CART}) approach introduced by {L}eo {B}reiman. {A}pplied to epidemiological spatial data, the algorithm recursively searches among the coordinates for a threshold or a boundary between zones, so that the risks estimated in these zones are as different as possible. {W}hile the {CART} algorithm leads to rectangular zones, providing perpendicular splits of longitudes and latitudes, the {S}p{ODT} algorithm provides oblique splitting of the study area, which is more appropriate and accurate for spatial epidemiology. {O}blique decision trees can be considered as non-parametric regression models. {B}eyond the basic function, we have developed a set of functions that enable extended analyses of spatial data, providing: inference, graphical representations, spatio-temporal analysis, adjustments on covariates, spatial weighted partition, and the gathering of similar adjacent final classes. {I}n this paper, we propose a new {R} package, {SPODT}, which provides an extensible set of functions for partitioning spatial and spatio-temporal data. {T}he implementation and extensions of the algorithm are described. {F}unction usage examples are proposed, looking for clustering malaria episodes in {B}andiagara, {M}ali, and samples showing three different cluster shapes.},
  keywords = {{MALI} ; {BANDIAGARA}},
  booktitle = {},
  journal = {{J}ournal of {S}tatistical {S}cience},
  volume = {63},
  numero = {16},
  pages = {23},
  ISSN = {1548-7660},
  year = {2015},
  URL = {https://www.documentation.ird.fr/hor/fdi:010067259},
}