TY  - JOUR
ID  - publications22618
UR  - http://tse-fr.eu/pub/31272
A1  - Chakir, Raja
A1  - Laurent, Thibault
A1  - Ruiz-Gazen, Anne
A1  - Thomas-Agnan, Christine
A1  - Vignes, Céline
Y1  - 2016/11//
N2  - 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.
PB  - Elsevier
JF  - Spatial Statistics
VL  - 18
KW  - land use models
KW  - spatial scale
KW  - Teruti-Lucas survey
KW  - Gini-Simpson impurity index
KW  - classication tree
SN  - 2211-6753
TI  - Spatial scale in land use models: application to the Teruti-Lucas survey
SP  - 246
AV  - public
EP  - 262
ER  -