Commit d23205b6 authored by Duchateau Fabien's avatar Duchateau Fabien

wrote summary in

parent 55a5a9f7
......@@ -33,28 +33,25 @@ For these reasons, we propose the package Mongiris, which includes integrated da
The package is composed of two modules: integration and API.
The integration module is responsible for extracting information from data sources
The `integration module` is responsible for extracting information from data sources. The module currently supports spreadsheets produced by [INSEE](
Since data evolve (e.g., statistics from INSEE are updated every few years), the integration module may be run. Note that new data may be stored in different database or collections so that the evolution can be studied.
For most users, there is no need to use the integration module since the dump of the database is provided. It is mainly based on INSEE files from 2014 and 2016.
The current dump contains roughly 37,000 IRIS with 375 indicators and 12,800 IRIS with 640 indicators.
<!-- {362: 36530, 650: 11738, 627: 1057, 385: 79} -->
discuss the integration (evolution of data) -> need to comment integrator + simplify the __main__
dire que integration = juste pour evolution , mais dum deja fourni - cf stats sur les indicateurs :
{350: 36530, 638: 11738, 615: 1057, 373: 79}
The `API module` includes common operations such as searching an IRIS (by IRIS code or according any field value), inserting, updating or deleting an IRIS.
It also provides geospatial operations useful in a research context: get IRIS given coordinates, get all adjacent or close IRIS from a given IRIS, find all IRIS in a given area, etc.
API: ... most important funct + spatial queries (adjacent, ex for computing average statistics by taking into account close neighborhoods)
used in MapIRIS, a tool for visualizing and searching for IRIS
The Mongiris package is currently used in Mapiris, a tool for visualizing and searching for IRIS.
![Screenshot of Mapiris.](mongiris/data/img/screenshot-mapiris.jpg)
also used in recommending neighborhhods according to start neighborhhodd [@egc19-demo]
It also powers VizLiris, a prototype for recommending or clustering neighborhoods [@egc19-demo].
![Screenshot of VizLiris - clustering.](mongiris/data/img/screenshot-vizliris-clustering.png)
![Screenshot of VizLiris - recommendation.](mongiris/data/img/screenshot-vizliris-recommandation.png)
# Acknowledgements
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).
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