MCI (version 1.3.3)

grocery2: Grocery store market areas in Goettingen

Description

Market areas of grocery stores in Goettingen, generated from a POS survey in Goettingen (Germany) from June 2015. The survey dataset contains 224 cases (\(i\) = 7 submarkets x \(j\) = 32 suppliers). The data is the result of a survey that is not representative (see grocery1) and also biased due to the data preparation. The data should be regarded as an example.

Usage

data("grocery2")

Format

A data frame with 224 observations on the following 8 variables.

plz_submarket

a factor with 7 levels (PLZ_37073, PLZ_37075, ...) representing the submarkets (places of residence based on the five-digit ZIP codes) in the study area

store_code

a factor with 32 levels (ALDI1, ALDI3, ..., EDEKA1, ... REWE1, ...), identifying the store code of the mentioned grocery store in the study area, data from Wieland (2011)

store_chain

a factor with 11 levels (Aldi, Edeka, ..., Kaufland, ...) for the store chain of the grocery stores in the study area, data from Wieland (2011)

store_type

a factor with 3 levels for the store type (Biosup = bio-supermarkt, Disc = discounter, Sup = supermarket)

salesarea_qm

a numeric vector for the sales area of the grocery stores in sqm, data from Wieland (2011)

pricelevel_euro

a numeric vector for the price level of the grocery chain (standardized basket in EUR), based on the data from DISQ (2015)

dist_km

a numeric vector for the distance from the places of residence (ZIP codes) to the grocery stores in km

p_ij_obs

a numeric vector for the empirically observed (and corrected) market shares (\(p_{ij}\)) of the stores in the submarkets

See Also

grocery1

Examples

# NOT RUN {
data(grocery2)
# Loads the data
mci.transmat (grocery2, "plz_submarket", "store_code", "p_ij_obs", "dist_km", "salesarea_qm")
# Applies the log-centering transformation to the dataset using the function mci.transmat
# }