From 30708c173c8b72cb22c92cf70a95f37aa50f2970 Mon Sep 17 00:00:00 2001
From: Duchateau Fabien <fabien.duchateau@univ-lyon1.fr>
Date: Mon, 28 Sep 2020 12:35:53 +0200
Subject: [PATCH] [M] updated incomplete ref data2020
---
paper.bib | 12 ++++++++----
paper.md | 4 ++--
2 files changed, 10 insertions(+), 6 deletions(-)
diff --git a/paper.bib b/paper.bib
index 76d214a4..5aef0bea 100644
--- a/paper.bib
+++ b/paper.bib
@@ -15,8 +15,12 @@
organization={IEEE}
}
-@article{barretpredicting,
- title={Predicting the enviornment of a neighbourhood: a use case for France},
- author={Barret, Nelly and Duchateau, Fabien and Favetta, Franck and Bonneval, Loic},
- year={2020}
+@INPROCEEDINGS{data20,
+ title = {Predicting the Environment of a Neighborhood: a Use Case for France},
+ publisher = {SciTePress},
+ year = {2020},
+ isbn = {978-989-758-440-4},
+ author = {Nelly Barret and Fabien Duchateau and Franck Favetta and Loïc Bonneval},
+ booktitle = {International Conference on Data Management Technologies and Applications (DATA)},
+ pages = {294-301},
}
\ No newline at end of file
diff --git a/paper.md b/paper.md
index 48c7857f..55e6cc98 100644
--- a/paper.md
+++ b/paper.md
@@ -35,7 +35,7 @@ Several projects focus on qualifying neighbourhoods using social networks. For i
# Methodology
-In order to describe in the most accurate way the environment of a neighbourhood, social science researchers have defined six environment variables, each with a limited number of values [@barretpredicting]. 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.
+In order to describe in the most accurate way the environment of a neighbourhood, social science researchers have defined six environment variables, each with a limited number of values [@data2020]. 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.
Predihood provides the following functionnalities:
- adding new neighbourhoods and indicators to describe them;
@@ -111,7 +111,7 @@ In addition, Predihood provides an interface for easily tuning and testing algor
# Mentions of Predihood
-Our Predihood tool has been presented during the DATA conference [@barretpredicting]. Prediction results using 6 algorithms from Scikit-learn range from 30% to 65% depending on the environment variable, and designing new algorithms could help improving these results.
+Our Predihood tool has been presented during the DATA conference [@data2020]. Prediction results using 6 algorithms from Scikit-learn range from 30% to 65% depending on the environment variable, and designing new algorithms could help improving these results.
The project is available here: [https://gitlab.liris.cnrs.fr/fduchate/predihood](https://gitlab.liris.cnrs.fr/fduchate/predihood).
--
GitLab