Authors:
Nelly Barret
1
;
Fabien Duchateau
1
;
Franck Favetta
1
and
Loïc Bonneval
2
Affiliations:
1
LIRIS UMR5205, Université de Lyon, UCBL, Lyon, France
;
2
Centre Max Weber, Université de Lyon, France
Keyword(s):
Data Science, Machine Learning, Data Integration, Environment Prediction, Neighbourhood Study.
Abstract:
Notion of neighbourhoods is critical in many applications such as social studies, cultural heritage management, urban planning or environment impact on health. Two main challenges deal with the definition and representation of this spatial concept and with the gathering of descriptive data on a large area (country). In this paper, we present a use case in the context of real estate search for representing French neighbourhoods in a uniform manner, using a few environment variables (e.g., building type, social class). Since it is not possible to manually classify all neighbourhoods, our objective is to automatically predict this new information.