Open Data Métropole Européenne de Lille Open Data Awarness Project November 30th, 2020 La MEL, What’s that ? La MEL, What’s that ?
Métropole Européenne de Lille
EPCI
95 cities
VERLINGHEM WASQUEHAL LEERS MARQUETTE LYS-LEZ- Around 1.500 000 PÉRENCHIES LANNOY MARCQ-EN- CROIX LANNOY LOMPRET SAINT-ANDRÉ- BAROEUL inhabitants PRÉMESQUES LEZ-LILLE TOUFFLERS LA MADELEINE HEM LAMBERSART SAILLY-LEZ- th CAPINGHEM MONS-EN- LANNOY 4 French agglomeration BAROEUL FOREST-SUR- VILLENEUVE MARQUE WILLEMS ENGLOS LILLE D'ASCQ SEQUEDIN
HALLENNES-LEZ- HAUBOURDIN LOOS LEZENNES TRESSIN CHÉRENG BAISIEUX HAUBOURDIN RONCHIN ANSTAING
FACHES- EMMERIN THUMESNIL SAINGHIN-EN- GRUSON SANTES MÉLANTOIS LESQUIN WATTIGNIES BOUVINES
NOYELLES- HOUPLIN- LÈS-SECLIN VENDEVILLE ANCOISNE FRETIN TEMPLEMARS PÉRONNE-EN- MÉLANTOIS
SECLIN Why a data policy? Why a data policy? / A bit of history
opendata.lillemetropole.fr openned in november 2016 Why a data policy? / A bit of history
« Loi pour une Creation of a national public République service of data Numérique » called Loi « LEMAIRE » of Possibility to create a local octobre 7th, 2016 public service of data Where are datas from? Where are datas from?
• MEL • Direct from producers • Extract from apps and databases • Single public service delegation contract • Transport • Trash • Water production & distribution • Cities from MEL territory • Any other datasources with data on our territory • French government • Statistics authority • Electricity producer Road to a Metropolitan Data Policy ? What is the data strategy ? / Metropolitain Data Public Service
4 missions
Improve data lifecycle – data administration #Infrastructure #Gouvernance #DataManagement
Formation and mediation to data litteracy #Datalitteracy #Formation-Action #Ateliers
Be sure to respect an ethic and the Law (French and European) in administration and valorisation of data #Reglementation #Lobbying #Citizenship
The promotion of our datas from concret use cases #DigitalServices #Datascience #Dataviz Use cases Use case / Predict Air Quality Objective : Understand and improve ATMO prediction of air quality, if possible to d+2 or d+3
Work with ATMO Hauts de France
Project made by 2 student for 2 months this summer Development of algorithmes to predict ozone and fine particle concentrations.
=> ATMO will add this model to improve their predictions which are not based on IA yet Use Case / POC DATAMEL
Analysis: Data is seen as an expert subject for MEL agents
Goal : Develop an improvment program of our public policies by data and set data « user friendly »
2 « playgrounds » : bicycle policy and librairies policy
On bicycle: « We don’t have enough information on bicycle use to improve our amenagements »
-> Build a multi-actors datalake of bike using = MEL, citizens, associations, … share their datas (sensors, navigation apps, cyclability datas, …)
On librairies: Understand how it work in the 95 cities. Who go to library ? When ? What ? … Use case / Waze Datas
WAZE : Application mobile d’aide à la conduite et d’assistance de navigation
130 millions d’utilisateurs dans le monde
Programme « Waze for Cities » : share data program between Waze and Public organizations
We give roadworks and receive « waze alerts » (15 millions objects) and « traffic jam waze » (5 millions objects).
-> 2 students of engineering school work on these datas Jérôme Van Oost [email protected]