estuary {ClustGeo}

estuary data

Description

Data refering to n=303 French municipalities of gironde estuary (a south-ouest French county). The data are issued from the French population census conducted by the National Institute of Statistics and Economic Studies. The dataset is an extraction of four quantitative socio-economic variables for a subsample of 303 French municipalities located on the atlantic coast between Royan and Mimizan. employ.rate.city is the employment rate of the municipality, that is the ratio of the number of individuals who have a job to the population of working age (generally defined, for the purposes of international comparison, as persons of between 15 and 64 years of age). graduate.rate refers to the level of education of the population that is the highest degree declared by the individual. It is defined here as the ratio for the whole population having completed a diploma equivalent or of upper level to two years of higher education (DUT, BTS, DEUG, nursing and social training courses, license, maitrise, master, DEA, DESS, doctorate, or Grande Ecole diploma). housing.appart is the ratio of apartment housing. agri.land is the part of agricultural area of the municipality.

Format

The R dataset estuary is a list of three objects:

• dat: a data frame with the description of the n=303 municipalities on p=4 socio-demographic variables.

• D.geo: a matrix with the geographical distances between the town hall of the n=303 municipalities.

• map: an object of class SpatialPolygonsDataFrame with the map of the gironde estuary.

Source

Original data are issued from the French population census of National Institute of Statistics and Economic Studies for year 2009. The agricultural surface has been calculated on data coming from the French National Institute of Geographical and Forestry Information. The calculation of the ratio and recoding of categories have been made by Irstea Bordeaux.

References

M. Chavent, V. Kuentz-Simonet, A. Labenne, J. Saracco. ClustGeo: an R package for hierarchical clustering with spatial constraints. Comput Stat (2018) 33: 1799-1822.

Examples

data(estuary)
names(estuary)
head(estuary$dat)