Spatial scale in land use models: application to the Teruti-Lucas survey

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

Fiches méthodologiques, méthodes statistiques d’allocation spatiale : interpolation de données surfaciques

When socio-economic data have been collected on several separate partitions of a given zone into administrative units its statistical analysis implies the reallocation to a common spatial resolution level called target spatial units. We consider the case of areal-to-areal change of support with a particular attention to disaggregation for continuous data and we describe in details the implementation of the proportional weighting schemes also called dasymetric methods

Fiches méthodologiques, méthodes statistiques d’allocation spatiale : interpolation de données surfaciques

When socio-economic data have been collected on several separate partitions of a given zone into administrative units its statistical analysis implies the reallocation to a common spatial resolution level called target spatial units. We consider the case of areal-to-areal change of support with a particular attention to disaggregation for continuous data and we describe in details the implementation of the proportional weighting schemes also called dasymetric methods

Land use predictions on a regular grid at different scales and with easily accessible covariates

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 predictions on a regular grid at different scales and with easily accessible covariates

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

Socio-demographic and lifestyle factors associated with overweight in a representative sample of 11-15 year olds in France: Results from the WHO-Collaborative Health Behaviour in School-aged Children (HBSC) cross-sectional study

<p>Abstract</p> <p>Background</p> <p>The prevalence of overweight in children and adolescents is high and overweight is associated with poor health outcomes over short- and long-term. Lifestyle factors can interact to influence overweight. Comprehensive studies linking overweight concomitantly with several demographic and potentially-modifiable lifestyle factors and health-risk behaviours are limited in adolescents - an age-group characterized by changes in lifestyle behaviours and high prevalence of overweight. Thus, the objective of the current study was to examine the association of overweight with several socio-demographic and lifestyle variables simultaneously in a representative sample of adolescents.</p> <p>Methods</p> <p>A nationally representative sample of 11-15 year-olds (n = 7154) in France participated as part of the WHO-Collaborative Health Behaviour in School-aged Children (HBSC) study. Students reported data on their age, height, weight, socio-demographic variables, lifestyle factors including nutrition practices, physical activity at two levels of intensity (moderate and vigorous), sedentary behaviours, as well as smoking and alcohol consumption patterns using standardized HBSC protocols. Overweight (including obesity) was defined using the IOTF reference. The multivariate association of overweight with several socio-demographic and lifestyle factors was examined with logistic regression models.</p> <p>Results</p> <p>The adjusted odds ratios for the association with overweight were: 1.80 (95% CI: 1.37-2.36) for low family affluence; 0.73 (0.60-0.88) for eating breakfast daily; 0.69 (0.56-0.84) for moderate to vigorous physical activity (MVPA); and 0.71 (0.59-0.86) for vigorous physical activity (VPA). Significant interactions between age and gender as well as television (TV) viewing and gender were noted: for boys, overweight was not associated with age or TV viewing; in contrast, for girls overweight correlated negatively with age and positively with TV viewing. Fruit and vegetable intake, computer and video-games use, smoking and alcohol consumption were not associated with overweight.</p> <p>Conclusions</p> <p>In multivariate model, family affluence, breakfast consumption and moderate to vigorous as well as vigorous physical activity were negatively associated with overweight. These findings extend previous research to a setting where multiple risk and protective factors were simultaneously examined and highlight the importance of multi-faceted approaches promoting physical activity and healthy food choices such as breakfast consumption for overweight prevention in adolescents.</p

Fiches méthodologiques, méthodes statistiques d’allocation spatiale : interpolation de données surfaciques

When socio-economic data have been collected on several separate partitions of a given zone into administrative units its statistical analysis implies the reallocation to a common spatial resolution level called target spatial units. We consider the case of areal-to-areal change of support with a particular attention to disaggregation for continuous data and we describe in details the implementation of the proportional weighting schemes also called dasymetric methods

Prédiction de l’usage des sols sur un zonage régulier à différentes résolutions et à partir de covariables facilement accessibles

Codes JEL : C21, C25, C38, Q15, R14In this paper, we propose 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.Les auteurs évaluent dans quelle mesure ils peuvent prédire l’usage des sols (usage urbain, usage agricole, forêts, prairies et sols naturels) au niveau des points de l’enquête Teruti-Lucas à partir de covariables facilement accessibles. Leur approche comporte deux étapes : la première permet de modéliser l’usage du sol au niveau des points Teruti-Lucas et la deuxième propose une méthode pour en déduire l’utilisation des sols sur un maillage défini par des carreaux. Le modèle de la première étape fournit des prédictions à un niveau fin. La deuxième étape agrège ces prédictions sur les carreaux du maillage en comparant plusieurs méthodes. Sont envisagés différents maillages réguliers du territoire en carreaux pour étudier la qualité de restitution en fonction de la résolution. Les auteurs montrent qu’avec des variables facilement accessibles on obtient une qualité de prédiction acceptable au niveau point et que l’amélioration de la qualité est importante dès la première étape d’agrégation