Data

Intent

The purpose of this visual interface is to paint a portrait of Rennes Metropolis from public data. It strives to stimulate curiosity and dialogue around what these public statistics reveal.

Browser support

For an optimal experience, we recommend using Chrome. Otherwise, a recent version of Safari, Firefox or Internet Explorer (version 9 or above).

Source

Our interface presents data from the 2017 census results published by INSEE.

We used the file “Individus localisés au canton-ou-ville” (2017_INDCVI), which contains a sampling of the census results by individual. It is downloadable here.

We used the following indicators:

Cross-referencing indicators

We cross referenced INSEE data to create new indicators for our interface.

In the section « Highest diploma », we cross referenced the indicators TACT and DIPL in order to isolate students and children. We then created a separate category for them within this section.

Cross referencing the TACT indicator with other INSEE indicators also allowed us to isolate unemployed individuals or stay-at-home spouses. This new data was used in the «Working Conditions» and «Means of Transport» sections.

To group individuals by household, we cross referenced the CANTVILLE and NUMMI indicators. The SFM indicator most often determined the household type. When this was not accurate enough, we used the LPRM indicator: household type was then extrapolated from the LPRM results for the household’s members.

Households composed of several families were included in the category «Group». Furthermore, we chose to assign the same «household type» regardless of whether or not individuals such as friends or extended family members also lived in the household.

Geographic coverage:

The zone covered by this application includes 13 sectors. Each corresponds to a pseudo-canton belonging, at least in part, to Rennes Metropolis:

Pseudo-cantons are not to be confused with « cantons », as used in French everyday language.Pseudo-cantons are the result of an INSEE-specific geographic breakdown of the country. Each sector (pseudo-canton) is comprised of one or several French municipalities.

INSEE’s définition of a pseudo-canton, otherwise known as a «canton-ou-ville», is available here: ici.

Adjusting data to the scale of the sectors and INSEE sampling

In France the population census is completed through the aggregation of yearly samplings . The data file published by INSEE provides detailed data for each individual sampled in 2017. Although all individuals of each sector (pseudo-canton) are not included in our dataset, we still have a representative sample for each sector (pseudo-canton) to work with. To scale our statistics appropriately, the figures extrapolated from the dataset were adjusted to cover the scope of each sector's (pseudo-canton's) total population.

Aggregating data at the scale of the Metropolis

The borders of Rennes Metropolis do not coincide with the exterior borders of the 13 sectors (pseudo-cantons) listed above. 5 out of 13 sectors (pseudo-canton) are cut in two by Rennes Metropolis’s borders. Consequently, part of these sectors' (pseudo-cantons') population cannot be included in the metropolis.

INSEE publishes also data by municipality. However, because such data is not available for every municipality nor for every indicator, we could not aggregate those statistics in order to create data at the scale of the metropolis. We consequently continued to use data at the scale of the sector (pseudo-canton).

Within each sector (pseudo-canton), we knew which municipalities are included in the metropolis and which aren’t. Thanks to INSEE data tables, we determined the population of each municipality. The data tables for each municipality are availible here. From this, we calculated the population fraction included in the Metropolis for each sector (pseudo-canton).

We then determined that the eight sectors (pseudo-cantons) entirely included in the Metropolis represent 85% of Rennes Metropolis’s population.

Consequently, the five other sectors (pseudo-cantons) only contribute towards 15% of the global statistics for Rennes Metropolis.

Since that figure is relatively low, we allowed ourselves to make the following hypothesis: in these five pseudo cantons, the statistical behavior of inhabitants living in municipalities within the metropolis is the same as that of inhabitants living in municipalities outside the metropolis.

By applying the statistical distribution observed at the scale of the sector (pseudo-canton) to the population of the metropolis’s municipalities, we were able to reconstruct data which was representative of the metropolis as a whole.

Determination of under/over representation

For a given filter ( Age/Sexe combination or Household Type) and a given sector (pseudo-canton), we indicate the over/under representation of the subpopulations characterized by the different socio-demographic indicators of our interface.

In order to estimate this over/under representation, we calculate the fraction that this subpopulation represents with respect to the filtered population (ie. the population determined by the Age/Sexe combination or Household Type)

Once applied to each of the thirteen sectors (pseudo-cantons), this operation leaves us with a list of thirteen percentages, from which we extrapolate a standard deviation and an average. These two figures are then used to create a normal distribution model. The over/under representation index for each subpopulation is calculated with the distribution’s cumulative distribution function.

Credits

Publisher

Rennes Métropole
and Ville de Rennes
 Direction de l'information
et de la communication
4, avenue Henri Fréville
CS 20723 -35207 Rennes cedex 2

Design and execution

Interactive data visualisation : Dataveyes

Library

This interactive visualisation
uses the d3.js library.