eprintid: 22618 rev_number: 28 eprint_status: archive userid: 1482 importid: 105 dir: disk0/00/02/26/18 datestamp: 2016-12-16 15:20:59 lastmod: 2023-08-31 07:47:18 status_changed: 2017-06-19 10:19:48 type: article succeeds: 22202 metadata_visibility: show creators_name: Chakir, Raja creators_name: Laurent, Thibault creators_name: Ruiz-Gazen, Anne creators_name: Thomas-Agnan, Christine creators_name: Vignes, Céline creators_idrefppn: 084585889 creators_idrefppn: 241586801 creators_idrefppn: 085824089 creators_idrefppn: 076657833 creators_affiliation: Economie Publique, AgroParisTech, INRA, Université Paris-Saclay creators_affiliation: Toulouse School of Economics; CNRS; University of Toulouse Capitole, 21 allée de Brienne, 31042 Toulouse, France creators_affiliation: Toulouse School of Economics; University of Toulouse Capitole, 21 allée de Brienne, 31042 Toulouse, France creators_affiliation: Toulouse School of Economics; University of Toulouse Capitole, 21 allée de Brienne, 31042 Toulouse, France creators_affiliation: Toulouse School of Economics; CNRS; University of Toulouse Capitole, 21 allée de Brienne, 31042 Toulouse, France title: Spatial scale in land use models: application to the Teruti-Lucas survey ispublished: pub subjects: subjects_ECO abstract: We consider the problem of land use prediction at di erent spatial scales using point level data such as the Teruti-Lucas (T-L hereafter1) survey and some explanatory variables. We analyze the components of the prediction error using a synthetic data set constructed from the Teruti-Lucas points in the Midi-Pyrénées region and a ve categories land use classi cation. The study rst shows that the number of points in the Teruti- Lucas survey is quite enough for estimating the probabilities of each land use category with a good quality. Furthermore it reveals that, contrary to usual practice, when the objective is to predict land use at aggregated levels, land use probabilities should be estimated at more locations where explanatory variables are available rather than restricting to the initial Teruti-Lucas locations. Indeed this strategy borrows strength from the knowledge of the explanatory variables which may be heterogeneous. Finally, guidelines for constructing the grid of locations for estimation are given from the analysis of the heterogeneity of each explanatory variable. date: 2016-11 date_type: published publisher: Elsevier id_number: 10.1016/j.spasta.2016.06.009 official_url: http://tse-fr.eu/pub/31272 faculty: tse divisions: tse keywords: land use models keywords: spatial scale keywords: Teruti-Lucas survey keywords: Gini-Simpson impurity index keywords: classication tree language: en has_fulltext: TRUE doi: 10.1016/j.spasta.2016.06.009 subjectsJEL: JEL_C21 subjectsJEL: JEL_C25 subjectsJEL: JEL_Q15 subjectsJEL: JEL_R14 view_date_year: 2016 full_text_status: public publication: Spatial Statistics volume: 18 pagerange: 246-262 refereed: TRUE issn: 2211-6753 oai_identifier: oai:tse-fr.eu:31272 harvester_local_overwrite: oai_set harvester_local_overwrite: issn harvester_local_overwrite: faculty harvester_local_overwrite: publisher harvester_local_overwrite: id_number harvester_local_overwrite: doi harvester_local_overwrite: creators_affiliation harvester_local_overwrite: creators_idrefppn harvester_local_overwrite: pending oai_lastmod: 2017-02-21T16:21:59Z oai_set: tse oai_set: ut1c site: ut1 citation: Chakir, Raja , Laurent, Thibault , Ruiz-Gazen, Anne , Thomas-Agnan, Christine and Vignes, Céline (2016) Spatial scale in land use models: application to the Teruti-Lucas survey. Spatial Statistics, 18. pp. 246-262. document_url: https://publications.ut-capitole.fr/id/eprint/22618/1/Chakir_22618.pdf