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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.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Barret, N.; Duchateau, F.; Favetta, F. and Bonneval, L. (2020). Predicting the Environment of a Neighborhood: A Use Case for France. In Proceedings of the 9th International Conference on Data Science, Technology and Applications - DATA; ISBN 978-989-758-440-4; ISSN 2184-285X, SciTePress, pages 294-301. DOI: 10.5220/0009885702940301

@conference{data20,
author={Nelly Barret. and Fabien Duchateau. and Franck Favetta. and Loïc Bonneval.},
title={Predicting the Environment of a Neighborhood: A Use Case for France},
booktitle={Proceedings of the 9th International Conference on Data Science, Technology and Applications - DATA},
year={2020},
pages={294-301},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009885702940301},
isbn={978-989-758-440-4},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Data Science, Technology and Applications - DATA
TI - Predicting the Environment of a Neighborhood: A Use Case for France
SN - 978-989-758-440-4
IS - 2184-285X
AU - Barret, N.
AU - Duchateau, F.
AU - Favetta, F.
AU - Bonneval, L.
PY - 2020
SP - 294
EP - 301
DO - 10.5220/0009885702940301
PB - SciTePress