We propose in this paper models that allow to predict land use (urban, agriculture, forests, natural grasslands and soil) at the points of the Teruti-Lucas survey from easily accessible covariates. Our approach involves two steps : first we model land use at the Teruti Lucas point level and second, we propose a method to aggregate land use on regular meshes. The model of the first stage provides fine level predictions. The second step aggregates these predictions on the tiles of the mesh comparing several methods. We are considering various regular meshes of the territory to study the prediction quality depending on the resolution. We show that with easily accessible variables we have an acceptable prediction quality at the point level and that the quality of prediction is improved from the very first stage of aggregation.
land use models; Teruti-Lucas survey; classication tree;
- C21: Cross-Sectional Models • Spatial Models • Treatment Effect Models • Quantile Regressions
- C25: Discrete Regression and Qualitative Choice Models • Discrete Regressors • Proportions
- Q15: Land Ownership and Tenure • Land Reform • Land Use • Irrigation • Agriculture and Environment
- R14: Land Use Patterns
Raja Chakir, Thibault Laurent, Anne Ruiz-Gazen, Christine Thomas-Agnan, and Céline Vignes, “Prédiction de l’usage des sols sur un zonage régulier à différentes résolutions et à partir de covariables facilement accessibles : Land use predictions on a regular grid at different scales and with easily accessible covariates”, Revue Économique, vol. 68, March 2017, pp. 435–469.
Raja Chakir, Thibault Laurent, Anne Ruiz-Gazen, Christine Thomas-Agnan, and Céline Vignes, “Land use predictions on a regular grid at different scales and with easily accessible covariates”, TSE Working Paper, n. 16-666, July 2016.
TSE Working Paper, n. 16-666, July 2016