diff --git a/README.md b/README.md index 6b48508b0fc3abe68c7875548344f6bafa79fe7e..a6868a6963f2a3932e17d9973bb649c533ab8ae7 100644 --- a/README.md +++ b/README.md @@ -20,9 +20,9 @@ For installing Predihood, type in a terminal: python3 -m pip install -e predihood/ --process-dependency-links ``` -This command install dependencies, including [mongiris](https://gitlab.liris.cnrs.fr/fduchate/mongiris) which provide the querying of the MongoDB database containing information about neighbourhoods. +This command install dependencies, including [mongiris](https://gitlab.liris.cnrs.fr/fduchate/mongiris), a lightweight API which enables the querying of the MongoDB database containing information about French neighbourhoods. -Create this database is mandatory. To achieve this, execute this command (from the MongoDB's executables directory if needed): +Next, to install the database, execute this command (from the MongoDB's executables directory if needed): ``` ./mongorestore --archive=/path/to/dump-iris.bin diff --git a/paper.md b/paper.md index 4bbe2e7d86fad265f271344ca94b8d4b5bea822c..ea6b0df2b950785862ebbffb62b9c325e7914e0e 100644 --- a/paper.md +++ b/paper.md @@ -27,17 +27,19 @@ bibliography: paper.bib # Introduction -Finding a real estate in a new city is still a challenge. We often arrive in a city we don't know, thus finding the perfect living place becomes complex. Nearby public transport on one hand, a rural landscape on the other hand, an animated neighbourhood for some, far from urban hustle and bustle for others: there are many criteria for choosing your future neighbourhood. +Finding a real estate in a new city is a real challenge. We often arrive in a city we do not know, and finding the perfect living area becomes complex. Nearby public transportation on one hand, rural landscape on the other hand, an animated neighbourhood for some, far from urban hustle and bustle for others: there are many criteria for choosing a future neighbourhood. # Statement of need -Some projects focuses on qualifying neighbourhoods, such as Livehoods [@cranshaw2012livehoods], Hoodsquare [@zhang2013hoodsquare] and [DataFrance](https://datafrance.info/). The Livehoods project aims at defining and computing dynamics of neighbourhoods based on data gathered from social networks. The Hoodsquare project detects similar areas based on Foursquare check-ins. DataFrance is an interface that integrates data from several sources, such as indicators provided by the National Institute of Statistics ([INSEE](https://insee.fr/en/accueil)), geographical information from the National Geographic Institute ([IGN](http://www.ign.fr/institut/activites/geoservices-ign)) and surveys from newspapers for prices (L'Express). +Several projects focus on qualifying neighbourhoods. The Livehoods project aims at defining and computing dynamics of neighbourhoods based on data gathered from social networks [@cranshaw2012livehoods]. The Hoodsquare project detects similar areas based on Foursquare check-ins [@zhang2013hoodsquare]. Crowd-based systems are interesting but may be biased. [DataFrance](https://datafrance.info/) is an interface that integrates data from several sources, such as indicators provided by the National Institute of Statistics ([INSEE](https://insee.fr/en/)), geographical information from the National Geographic Institute ([IGN](http://www.ign.fr/institut/activites/geoservices-ign)) and surveys from newspapers for prices (L'Express). DataFrance enables the visualization of hundreds of indicators, but makes it difficult to judge on the environement of a neighbourhood. # Methodology Our approach Predihood aims at facilitating the comparison between neighbourhoods. It defines and predicts the environment of any neighbourhood in France using supervised learning. +contributions : either for adding new data (neighbourhoods from another country) or for adding predictive algorithms + ## Describing neighbourhoods In order to describe in the most accurate way the environment of a neighbourhood, social science researchers have defined six environment variables with a limited number of values for each one. These six variables are the _building type_, the _building usage_, the _landscape_, the _social class_, the _morphological position_ and the _geographical position_. As an example, the _landscape_ can be evaluated as _urban_, _green areas_, _forest_ or _countryside_ while the _social class_ have values from _lower_ to _upper_. These variables are commonly accepted and easily understandable.