shopping2 {MCI}

Distance matrix for the point-of-sale survey in Karlsruhe

Description

The dataset contains a distance matrix (OD matrix: Origins-Destinations matrix) including the street distance and the travel time from the customer origins to the shopping destinations, both stored in the dataset shopping1.

Usage

data("shopping2")

Format

A data frame with 3723 observations on the following 5 variables.

from

a factor containing the customer origin (place of residence) as internal code

to

a factor containing the shopping destination

d_km

a numeric vector containing the street distance from the origins to the destinations in km

d_time

a numeric vector containing the driving time from the origins to the destinations in km

route

a factor containing the interaction/route code between origins and destinations (from-to)

Source

Primary empirical sources: POS (point of sale) survey in the authors' course (“Praktikum Empirische Sozialforschung: Stadtteilzentren als Einzelhandelsstandorte - Das Fallbeispiel Karlsruhe-Durlach”, Karlsruhe Institute of Technology, Institute for Geography and Geoecology, May 2016), own calculations

The street distance and travel time was calculated using the package ggmap.

See Also

shopping1, shopping3, shopping4

Examples

# Market area segmentation based on the POS survey in shopping1 #

data(shopping1)
# The survey dataset
data(shopping2)
# Dataset with distances and travel times

shopping1_adj <- shopping1[(shopping1$weekday != 3) & (shopping1$holiday != 1) 
& (shopping1$survey != "pretest"),]
# Removing every case from tuesday, holidays and the ones belonging to the pretest

ijmatrix_POS <- ijmatrix.create(shopping1_adj, "resid_code", "POS", "POS_expen")
# Creates an interaction matrix based on the observed frequencies (automatically)
# and the POS expenditures (Variable "POS_expen" separately stated)

ijmatrix_POS_data <- merge(ijmatrix_POS, shopping2, by.x="interaction", by.y="route", 
all.x = TRUE)
# Adding the distances and travel times

ijmatrix_POS_data_segm_visit <- shares.segm(ijmatrix_POS_data, "resid_code", "POS",
"d_time", "freq_ij_abs", 0,10,20,30)
# Segmentation by travel time using the number of customers/visitors
# Parameters: interaction matrix (data frame), columns with origins and destinations,
# variable to divide in classes, absolute frequencies/expenditures, class segments

ijmatrix_POS_data_segm_exp <- shares.segm(ijmatrix_POS_data, "resid_code", "POS",
"d_time", "freq_ij_abs_POS_expen", 0,10,20,30)
# Segmentation by travel time using the POS expenditures