The concept of Volunteered Geographic Information (VGI) has recently emerged from the new Web 2.0 technologies. The OpenStreetMap project is currently the most significant example of a system based on VGI. It aims at producing free vector geographic databases using contributions from Internet users. Spatial data quality becomes a key consideration in this context of freely downloadable geographic databases. This article studies the quality of French OpenStreetMap data. It extends the work of Haklay to France, provides a larger set of spatial data quality element assessments (i.e. geometric, attribute, semantic and temporal accuracy, logical consistency, completeness, lineage, and usage), and uses different methods of quality control. The outcome of the study raises questions such as the heterogeneity of processes, scales of production, and the compliance to standardized and accepted specifications. In order to improve data quality, a balance has to be struck between the contributors' freedom and their respect of specifications. The development of appropriate solutions to provide this balance is an important research issue in the domain of user-generated content.

Little by little, co-existing geographical data sets are integrated into multi-representation databases, where the data sets represent different level of detail, or different point of views for the same geographical features. The ScaleMaster model makes it possible to formalize how to choose the features to map from the different data sets. The paper proposes an extension of the ScaleMaster model that drives automatic generalization rather than guidelines for manual mapmaking. The ScaleMaster 2.0 has been implemented and is tested for use with real data.

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Abstract. While the former part of this back-to-back paper dealt with the identification of multi-scale spatial patterns associated with the presence, abundance and dispersion of the insect vectors (Triatominae) of Chagas disease, this latter part examines the need for pattern characterisation by means of detailed data on environmental, residential, peri-domiciliary and human behaviour. The study site was, in both cases, a single village situated in Bahia, Brazil, wherefrom the data were collected through field observation and a standardised questionnaire, while the environmental characteristics were derived from satellite images and landscape characterisation. Following this, factorial analysis of mixed group (FAMG), an exploratory data analysis method, was applied to "mine" the huge dataset in a hierarchical way and to evaluate the relative impact of different factors such as the surrounding environment, the domiciliary/peri-domiciliary space properties and the presence of domestic animals. In the study village, five principal "districts" associated with different possible causes of infestation were identified. The results favour the role of depressions of the ground surface due to collapse of karstic subsoil (dolines) and open rock faces as infestation sources, vector attraction by outdoor lighting, risk of insect domiciliation in dwellings constructed without finishing materials and associated with apparent disorder. Ultimately, this study not only provides the basic information needed for decision-making and specification of vector control in the study village, but offers also a knowledge-base for more general control strategies in the region.