Skip to main content
SpringerLink
Log in

An Environmental Study of French Neighbourhoods

  • Conference paper
  • First Online:
Data Management Technologies and Applications (DATA 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1446))

  • 349 Accesses

Abstract

Neighbourhoods are key places for daily activities and many studies in social sciences, health or biology use this spatial concept as an impact factor. Conversely, the neighbourhood environment is rarely defined (e.g., in terms of landscape or main social class). In this paper, we propose six descriptive variables for this environment, and we provide a dataset of 270 annotated neighbourhoods. Next, we detail two methods (prediction and spatial computation) for describing environment of remaining neighbourhoods, and we show in our set of experiments an acceptable quality.

This paper is an extended version of a short paper published in the DATA 2020 proceedings [3]. This work has been partially funded by LABEX IMU (ANR-10-LABX-0088) from Université de Lyon, in the context of the program “Investissements d’Avenir” (ANR-11-IDEX-0007) from the French Research Agency (ANR), during the HiL project.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    HiL project, http://imu.universite-lyon.fr/projet/hil/.

  2. 2.

    Project mon quartier, mes voisins, http://mon-quartier-mes-voisins.site.ined.fr/.

  3. 3.

    DataFrance, http://datafrance.info/.

  4. 4.

    INSEE, http://www.insee.fr/en/.

  5. 5.

    IGN, http://www.ign.fr/.

  6. 6.

    L’express, http://www.lexpress.fr/.

  7. 7.

    Kelquartier, http://www.kelquartier.com/.

  8. 8.

    Home in Love company (in French), http://homeinlove.fr/.

  9. 9.

    Vivroù, http://www.vivrou.com/.

  10. 10.

    Cityzia, http://www.cityzia.fr/.

  11. 11.

    Ville idéale, http://www.ville-ideale.fr/.

  12. 12.

    IRIS definition, http://www.insee.fr/en/metadonnees/definition/c1523/.

  13. 13.

    The scale differences for analysing the environment make it difficult to automate the process, hence one of the objectives of this paper.

  14. 14.

    Gini coefficient, http://en.wikipedia.org/wiki/Gini_coefficient.

  15. 15.

    S80/20 ratio, http://www.insee.fr/en/metadonnees/definition/c1666.

  16. 16.

    Neighbourhoods in cities tend to be small while those in rural areas have a larger size.

  17. 17.

    Accommodation prices in France, http://app.dvf.etalab.gouv.fr/.

  18. 18.

    GeoJSON format, http://geojson.org/.

  19. 19.

    Federation of towns, http://en.wikipedia.org/wiki/Communes_of_France#Intercommunality.

  20. 20.

    Mongiris database, http://gitlab.liris.cnrs.fr/fduchate/mongiris.

  21. 21.

    Indicators from INSEE may not be filled in (empty or default value), especially for data provided by local communities (small towns may not have the resources to manage this task).

  22. 22.

    Predicting all variables at the same time is a multi-output classification problem, which is more complicated to manage and more adapted to correlated classes.

  23. 23.

    Predihood tool, http://gitlab.liris.cnrs.fr/fduchate/predihood.

  24. 24.

    Other algorithms such as Stochastic Gradient Descent or Nearest Centroid have been tested, but they mostly follow the same trend or achieve insufficient accuracy.

References

  1. Attard, J., Orlandi, F., Scerri, S., Auer, S.: A systematic review of open government data initiatives. Gov. Inf. Q. 32(4), 399–418 (2015)

    Article  Google Scholar 

  2. Jean-Yves, A., Bacque Marie-Hélène, G.P.F.: Le quartier. Enjeux scientifiques, actions politiques et pratiques sociales. La Découverte (2007). https://www.cairn.info/le-quartier-9782707150714.htm

  3. Barret, N., Duchateau, F., Favetta, F., Bonneval, L.: Predicting the environment of a neighborhood: a use case for France. In: International Conference on Data Management Technologies and Applications (DATA), pp. 294–301. SciTePress (2020)

    Google Scholar 

  4. Barret, N., Duchateau, F., Favetta, F., Miquel, M., Gentil, A., Bonneval, L.: À la recherche du quartier idéal. In: Extraction et Gestion des Connaissances, pp. 429–432 (2019)

    Google Scholar 

  5. Barret, N., Duchateau, F., Favetta, F., Moncla, L.: Spatial entity matching with geoalign. In: ACM GIS SIGSPATIAL, p. 580–583. ACM (2019)

    Google Scholar 

  6. Bellahsène, Z., Bonifati, A., Rahm, E.: Schema Matching and Mapping. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-16518-4

  7. Bigot, R., Croutte, P., Müller, J., Osier, G.: Les classes moyennes en europe. Le CRÉDOC, Cahier de recherche 282 (2011)

    Google Scholar 

  8. Bonneval, L., et al.: Étude des quartiers : défis et pistes de recherche. In: Extraction et Gestion des Connaissances (2019). http://dahlia.egc.asso.fr/atelierDAHLIA-EGC2020.html

  9. Bourdieu, P.: What makes a social class? On the theoretical and practical existence of groups. Berkeley J. Sociol. 32, 1–17 (1987)

    Google Scholar 

  10. Bruce, P., Bruce, A.: Practical Statistics for Data Scientists: 50 Essential Concepts. O’Reilly (2017). https://books.google.fr/books?hl=fr&lr=&id=ldPTDgAAQBAJ

  11. Christen, P.: Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31164-2

  12. Coulondre, A.: Ouvrir la boîte noire des marchés du logement. Métropolitiques (2018). https://metropolitiques.eu/Ouvrir-la-boite-noire-des-marches-du-logement.html

  13. Cranshaw, J., Schwartz, R., Hong, J., Sadeh, N.: The livehoods project: utilizing social media to understand the dynamics of a city. In: ICWSM Conference (2012)

    Google Scholar 

  14. Dennis, G.: The American Class Structure. New York Wadsworth Publishing (1998)

    Google Scholar 

  15. Donoho, D.: 50 years of data science. J. Comput. Graph. Stat. 26(4), 745–766 (2017). https://doi.org/10.1080/10618600.2017.1384734

  16. Frank, L.D., Sallis, J.F., Saelens, B.E., Leary, L., Cain, K., Conway, T.L., Hess, P.M.: The development of a walkability index: application to the neighborhood quality of life study. Br. J. Sports Med. 44(13), 924–933 (2010)

    Article  Google Scholar 

  17. Galster, G.: On the nature of neighbourhood. Urban Stud. 38(12), 2111–2124 (2001)

    Article  Google Scholar 

  18. Garau, C., Pavan, V.M.: Evaluating urban quality: indicators and assessment tools for smart sustainable cities. Sustainability 10(3), 575 (2018)

    Article  Google Scholar 

  19. GIS, O.: Consortium Inc., OpenGIS simple features specification for SQL (1999). http://www.gismanual.com/relational/99-049_OpenGIS_Simple_Features_Specification_For_SQL_Rev_1.1.pdf

  20. Guest, A.M., Lee, B.A.: How urbanites define their neighborhoods. Population Environ. 7(1), 32–56 (1984). https://doi.org/10.1007/BF01257471

    Article  Google Scholar 

  21. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3(3), 1157–1182 (2003)

    MATH  Google Scholar 

  22. Halevy, A., Rajaraman, A., Ordille, J.: Data integration: the teenage years. In: Proceedings of the 32nd International Conference on Very Large Data Bases, pp. 9–16. VLDB Endowment (2006). http://portal.acm.org/citation.cfm?id=1164127.1164130

  23. Hoyt, H.: The Structure and Growth of Residential Neighborhoods in American Cities. Scholarly Press (1972)

    Google Scholar 

  24. Jenks, M., Dempsey, N.: Defining the neighbourhood: challenges for empirical research. Town Plann. Rev. 153–177 (2007)

    Google Scholar 

  25. Jordan, M.I., Mitchell, T.M.: Machine learning: trends, perspectives, and prospects. Science 349(6245), 255–260 (2015)

    Article  MathSciNet  Google Scholar 

  26. Le Falher, G., Gionis, A., Mathioudakis, M.: Where Is the Soho of Rome? Measures and algorithms for finding similar neighborhoods in cities. In: ICWSM, vol. 2, 3–2 (2015)

    Google Scholar 

  27. Leong, M., Dunn, R.R., Trautwein, M.D.: Biodiversity and socioeconomics in the city: a review of the luxury effect. Biol. Lett. 14(5), 20180082 (2018)

    Article  Google Scholar 

  28. Lillesand, T., Kiefer, R.W., Chipman, J.: Remote Sensing and Image Interpretation. Wiley, Hoboken (2015)

    Google Scholar 

  29. Liu, Y., Wei, W., Sun, A., Miao, C.: Exploiting geographical neighborhood characteristics for location recommendation. In: Conference on Information and Knowledge Management, pp. 739–748 (2014). https://doi.org/10.1145/2661829.2662002

  30. Mukaka, M.M.: A guide to appropriate use of correlation coefficient in medical research. Malawi Med. J. 24(3), 69–71 (2012)

    Google Scholar 

  31. Oberti, M., Préteceille, E.: La ségrégation urbaine. La Découverte (2016)

    Google Scholar 

  32. Pan Ké Shon, J.L.: La représentation des habitants de leur quartier: entre bien-être et repli. Économie et statistique 386(1), 3–35 (2005)

    Google Scholar 

  33. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  34. Reibel, M.: Classification approaches in neighborhood research: introduction and review. Urban Geogr. 32(3), 305–316 (2011)

    Article  Google Scholar 

  35. Salim, F.D., et al.: Modelling urban-scale occupant behaviour, mobility, and energy in buildings: a survey. Build. Environ. 183, 106964 (2020)

    Google Scholar 

  36. Sigaud, T.: Accompagner les mobilités résidentielles des salariés: l’épreuve de l’entrée en territoire. Espaces et sociétés 162(3), 129–145 (2015)

    Google Scholar 

  37. Tabard, N.: Des quartiers pauvres aux banlieues aisées: une représentation sociale du territoire. Economie et statistique 270(1), 5–22 (1993)

    Article  Google Scholar 

  38. Takada, M., Kondo, N., Hashimoto, H.: Japanese study on stratification, health, income, and neighborhood: study protocol and profiles of participants. J. Epidemiol. 24(4), 334–344 (2014)

    Article  Google Scholar 

  39. Tang, E., Sangani, K.: Neighborhood and price prediction for San Francisco Airbnb listings (2015). cs229.stanford.edu/proj2015/236report.pdf

  40. Yuan, X., Lee, J.H., Kim, S.J., Kim, Y.H.: Toward a user-oriented recommendation system for real estate websites. Inf. Syst. 38(2), 231–243 (2013). https://doi.org/10.1016/j.is.2012.08.004

    Article  Google Scholar 

  41. Zhang, A.X., Noulas, A., Scellato, S., Mascolo, C.: Hoodsquare: modeling and recommending neighborhoods in location-based social networks. In: Social Computing, pp. 69–74. IEEE (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. LIRIS UMR5205, Université de Lyon, UCBL, Lyon, France

    Nelly Barret, Fabien Duchateau & Franck Favetta

  2. Centre Max Weber, Université de Lyon, Lyon, France

    Aurélien Gentil & Loïc Bonneval

Authors
  1. Nelly Barret
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fabien Duchateau
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Franck Favetta
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Aurélien Gentil
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Loïc Bonneval
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fabien Duchateau .

Editor information

Editors and Affiliations

  1. MODESTE/ESEO, Angers, France

    Slimane Hammoudi

  2. Fraunhofer FIT and RWTH Aachen University, Aachen, Germany

    Christoph Quix

  3. University of Coimbra, Coimbra, Portugal

    Jorge Bernardino

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Barret, N., Duchateau, F., Favetta, F., Gentil, A., Bonneval, L. (2021). An Environmental Study of French Neighbourhoods. In: Hammoudi, S., Quix, C., Bernardino, J. (eds) Data Management Technologies and Applications. DATA 2020. Communications in Computer and Information Science, vol 1446. Springer, Cham. https://doi.org/10.1007/978-3-030-83014-4_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-83014-4_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-83013-7

  • Online ISBN: 978-3-030-83014-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation