Freiburg1: Distance matrix for grocery stores in Freiburg

In MCI: Multiplicative Competitive Interaction (MCI) Model

Description

Preliminary stage of an interaction matrix: Distance matrix for all statistical 42 districts and all 63 grocery stores (i = 42 submarkets x j = 63 suppliers) in Freiburg (Germany) including the size of the grocery stores.

Usage

data("Freiburg1")

Format

A data frame with 2646 observations on the following 4 variables.

district

a numeric vector representing the 42 statistical districts of Freiburg

store

a numeric vector identifying the store code of the mentioned grocery store in the study area

salesarea

a numeric vector for the sales area of the grocery stores in sqm

distance

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

Source

Wieland, T. (2015): “Nahversorgung im Kontext raumoekonomischer Entwicklungen im Lebensmitteleinzelhandel - Konzeption und Durchfuehrung einer GIS-gestuetzten Analyse der Strukturen des Lebensmitteleinzelhandels und der Nahversorgung in Freiburg im Breisgau”. Projektbericht. Goettingen : GOEDOC, Dokumenten- und Publikationsserver der Georg-August-Universitaet Goettingen. http://webdoc.sub.gwdg.de/pub/mon/2015/5-wieland.pdf

References

Wieland, T. (2015): “Nahversorgung im Kontext raumoekonomischer Entwicklungen im Lebensmitteleinzelhandel - Konzeption und Durchfuehrung einer GIS-gestuetzten Analyse der Strukturen des Lebensmitteleinzelhandels und der Nahversorgung in Freiburg im Breisgau”. Projektbericht. Goettingen : GOEDOC, Dokumenten- und Publikationsserver der Georg-August-Universitaet Goettingen. http://webdoc.sub.gwdg.de/pub/mon/2015/5-wieland.pdf

Examples

data(Freiburg1)
data(Freiburg2)
data(Freiburg3)
# Loads the data

huff_mat <- huff.shares (Freiburg1, "district", "store", "salesarea", "distance")
# Market area estimation using the Huff Model with standard parameters
# (gamma = 1, lambda = -2)

huff_mat_pp <- merge (huff_mat, Freiburg2)
# Adding the purchasing power data for the city districts

huff_total <- shares.total (huff_mat_pp, "district", "store", "p_ij", "ppower")
# Total expected sales and shares

huff_total_control <- merge (huff_total, Freiburg3, by.x = "suppliers_single", 
by.y = "store")

model.fit(huff_total_control$annualsales, huff_total_control$sum_E_j, plotVal = TRUE)

Example output

$resids_sq_sum
[1] 2.125162e+15

$pseudorsq
[1] 0.5128422

$globerr
[1] 0.5210329

$mape
[1] 0.6383766

MCI documentation built on May 2, 2019, 6:02 a.m.