MCI (version 1.3.3)

DIY1: Distance matrix for DIY stores

Description

Preliminary stage of an interaction matrix: Distance matrix for 19 origins and six DIY (do-it-yourself) stores (\(i\) = 19 submarkets x \(j\) = 6 suppliers) in a German research area.

Usage

data("DIY1")

Format

A data frame with 114 observations on the following 3 variables.

i_origin

a factor with 19 levels representing the origins

j_destination

a factor with six levels representing the DIY stores

t_ij_min

a numeric vector containing the travel time (in minutes) from the origins to the stores

References

Wieland, T. (2015): “Raeumliches Einkaufsverhalten und Standortpolitik im Einzelhandel unter Beruecksichtigung von Agglomerationseffekten. Theoretische Erklaerungsansaetze, modellanalytische Zugaenge und eine empirisch-oekonometrische Marktgebietsanalyse anhand eines Fallbeispiels aus dem laendlichen Raum Ostwestfalens/Suedniedersachsens”. Geographische Handelsforschung, 23. 289 pages. Mannheim : MetaGIS.

Examples

# NOT RUN {
data(DIY1)
data(DIY2)
data(DIY3)
# Loading the three DIY store datasets

DIY_alldata <- merge (DIY1, DIY2, by.x = "j_destination", by.y = "j_destination")
# Add store data to distance matrix

huff_DIY <- huff.shares (DIY_alldata, "i_origin", "j_destination", "A_j_salesarea_sqm", 
"t_ij_min", gamma = 1, lambda = -2)
# Calculating Huff local market shares
# Gamma = 1, Lambda = -2

huff_DIY <- merge (huff_DIY, DIY3, by.x = "i_origin", by.y = "district")
# Add data for origins

huff_DIY_total <- shares.total (huff_DIY, "i_origin", "j_destination", "p_ij", 
"population")
# Calculating total market areas (=sums of customers)

colnames(DIY3) <- c("district", "pop")
# Change column name to "pop" (must be other name)

huff.lambda (huff_DIY, "i_origin", "j_destination", "A_j_salesarea_sqm", 
"t_ij_min", gamma = 1, atype = "pow", gamma2 = NULL,
lambda_startv = -1, lambda_endv = -2.5, dtype= "pow",
DIY3, "district", "pop", huff_DIY_total, "suppliers_single", "sum_E_j", 
method = "bisection", iterations = 10)
# Iterative search for the best lambda value using bisection
# Output: gamma and lambda

huff.lambda (huff_DIY, "i_origin", "j_destination", "A_j_salesarea_sqm", 
"t_ij_min", gamma = 1, atype = "pow", gamma2 = NULL,
lambda_startv = -1, lambda_endv = -2.5, dtype= "pow",
DIY3, "district", "pop", huff_DIY_total, "suppliers_single", "sum_E_j", 
method = "bisection", iterations = 10, output = "iterations", show_proc = TRUE)
# Same procedure, output: single iterations  

huff.lambda (huff_DIY, "i_origin", "j_destination", "A_j_salesarea_sqm", 
"t_ij_min", gamma = 1, atype = "pow", gamma2 = NULL,
lambda_startv = -1, lambda_endv = -2.5, dtype= "pow",
DIY3, "district", "pop", huff_DIY_total, "suppliers_single", "sum_E_j", 
method = "compare", iterations = 10, output = "iterations", show_proc = TRUE, plotVal = TRUE)
# Using compare method, output: single iterations and plot 
# }