Étude des quartiers : défis et pistes de recherche

Le projet Home In Love (HiL) s'interesse a la recommandation de biens immobiliers, en particulier dans le cas ou l'on ne connait pas sa future ville de residence (e.g., mutation professionnelle). Si le choix d'un logement est facilite par les nombreuses ressources disponibles (e.g., sites web avec photos, visites virtuelles), cela reste complique de se faire une idee concrete des quar-tiers ou se trouvent les logements disponibles. L'un des enjeux concerne donc la description et la comparaison de quartiers selon les domaines d'application (e.g., recherche immobiliere, etude sociale, recensement du patrimoine). Cet article decrit les defis et les pistes de recherche (en informatique) lies a cette etude des quartiers.

[1]  Chen Chen,et al.  BigGorilla: An Open-Source Ecosystem for Data Preparation and Integration , 2018, IEEE Data Eng. Bull..

[2]  A. D. Diez Roux,et al.  Neighborhood characteristics associated with the location of food stores and food service places. , 2002, American journal of preventive medicine.

[3]  Xiao Xiang Zhu,et al.  Building Instance Classification Using Street View Images , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[4]  Aniket Kittur,et al.  Crowdsourcing user studies with Mechanical Turk , 2008, CHI.

[5]  Jochen L. Leidner,et al.  Detecting geographical references in the form of place names and associated spatial natural language , 2011, SIGSPACIAL.

[6]  Jiawei Han,et al.  Geographic Data Mining and Knowledge Discovery , 2001 .

[7]  Guoliang Li,et al.  String similarity search and join: a survey , 2016, Frontiers of Computer Science.

[8]  Joann J. Ordille,et al.  Data integration: the teenage years , 2006, VLDB.

[9]  Jiawei Han,et al.  Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions , 2015, IEEE Transactions on Knowledge and Data Engineering.

[10]  Craig A. Knoblock,et al.  Karma: A System for Mapping Structured Sources into the Semantic Web , 2012, ESWC.

[11]  J. Sallis,et al.  Neighborhood-based differences in physical activity: an environment scale evaluation. , 2003, American journal of public health.

[12]  P. Mokhtarian,et al.  Neighborhood satisfaction in suburban versus traditional environments: An evaluation of contributing characteristics in eight California neighborhoods , 2010 .

[13]  Shihong Du,et al.  Semantic classification of urban buildings combining VHR image and GIS data: An improved random forest approach , 2015 .

[14]  Clodoveu A. Davis,et al.  Could Data from Location-Based Social Networks Be Used to Support Urban Planning? , 2017, WWW.

[15]  É. Maurin Le ghetto français : enquête sur le séparatisme social , 2005 .

[16]  Susanne Boll,et al.  Visual Overlay on OpenStreetMap Data to Support Spatial Exploration of Urban Environments , 2015, ISPRS Int. J. Geo Inf..

[17]  Erhard Rahm,et al.  Schema Matching and Mapping , 2013, Schema Matching and Mapping.

[18]  Cecilia Mascolo,et al.  Hoodsquare: Modeling and Recommending Neighborhoods in Location-Based Social Networks , 2013, 2013 International Conference on Social Computing.

[19]  Xing Xie,et al.  Discovering regions of different functions in a city using human mobility and POIs , 2012, KDD.

[20]  Hui Liu,et al.  Spatiotemporal Detection and Analysis of Urban Villages in Mega City Regions of China Using High-Resolution Remotely Sensed Imagery , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Xiaofang Yuan,et al.  Toward a user-oriented recommendation system for real estate websites , 2013, Inf. Syst..

[22]  Max L. Wilson,et al.  A data driven approach to mapping urban neighbourhoods , 2014, SIGSPATIAL/GIS.

[23]  Md. Monirul Islam,et al.  A review on automatic image annotation techniques , 2012, Pattern Recognit..

[24]  Emily Tang,et al.  Neighborhood and Price Prediction for San Francisco Airbnb Listings , 2015 .

[25]  Edmond Préteceille La ségrégation ethno-raciale a-t-elle augmenté dans la métropole parisienne ? , 2009 .

[26]  Mor Naaman,et al.  Generating diverse and representative image search results for landmarks , 2008, WWW.

[27]  Michael Gertz,et al.  Extraction and exploration of spatio-temporal information in documents , 2010, GIR.

[28]  Jean-Louis Pan Ké Shon La représentation des habitants de leur quartier: entre bien-être et repli , 2005 .

[29]  Peter Christen,et al.  Data Matching , 2012, Data-Centric Systems and Applications.

[30]  Aristides Gionis,et al.  Where Is the Soho of Rome? Measures and Algorithms for Finding Similar Neighborhoods in Cities , 2015, ICWSM.

[31]  Fabien Duchateau,et al.  À la recherche du quartier idéal , 2019, EGC.

[32]  Norman M. Sadeh,et al.  The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City , 2012, ICWSM.