shopping3: Market area data for the point-of-sale survey in Karlsruhe

In MCI: Multiplicative Competitive Interaction (MCI) Model

Description

The dataset contains information about

Usage

data("shopping3")

Format

A data frame with 70 observations on the following 5 variables.

resid_name

a factor containing the customer origin (place of residence) as name of the corresponding city or city district

resid_name_offical

a factor containing the customer origin (place of residence) as official names of the corresponding city or city district

resid_pop2015

a numeric vector containing the population size of the area

KA_east

a numeric vector containing a dummy variable indicating whether the area belongs to the east of Karlsruhe or not

resid_code

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

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

Stadt Karlsruhe, Amt fuer Stadtentwicklung (2016): “Die Karlsruher Bevoelkerung im Dezember 2015”. Stadt Karlsruhe.

See Also

shopping1, shopping2, shopping4

Examples

# Market area analysis 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$freq_ij_abs_cor <- var.correct(ijmatrix_POS_data$freq_ij_abs, 
corr.mode = "inc", incby = 0.1)
# Correcting the absolute values (frequencies) by increasing by 0.1

data(shopping3)
ijmatrix_POS_data_residdata <- merge(ijmatrix_POS_data, shopping3)
# Adding the information about the origins (places of residence) stored in shopping3

ijmatrix_POS_data_residdata$visitper1000 <- (ijmatrix_POS_data_residdata$
freq_ij_abs_cor/ijmatrix_POS_data_residdata$resid_pop2015)*1000
# Calculating the dependent variable
# visitper1000: surveyed customers per 1.000 inhabitants of the origin

ijmatrix_POS_data_residdata <- 
ijmatrix_POS_data_residdata[(!is.na(ijmatrix_POS_data_residdata$
visitper1000)) & (!is.na(ijmatrix_POS_data_residdata$d_time)),]
# Removing NAs (data for some outlier origins and routes not available)

ijmatrix_POS_data_residdata_POS1 <- 
ijmatrix_POS_data_residdata[ijmatrix_POS_data_residdata$POS=="POS1",]
# Dataset for POS1 (town centre)

ijmatrix_POS_data_residdata_POS2 <- 
ijmatrix_POS_data_residdata[ijmatrix_POS_data_residdata$POS=="POS2",]
# Dataset for POS2 (out-of-town shopping centre)

huff.decay(ijmatrix_POS_data_residdata_POS1, "d_km", "visitper1000")
huff.decay(ijmatrix_POS_data_residdata_POS1, "d_time", "visitper1000")
huff.decay(ijmatrix_POS_data_residdata_POS2, "d_km", "visitper1000")
huff.decay(ijmatrix_POS_data_residdata_POS2, "d_time", "visitper1000")

Example output

   Model type Intercept p Intercept   Slope p Slope R-Squared Adj. R-squared
1      Linear    0.7354       2e-04 -0.0455  0.0038     0.216         0.1936
2       Power    1.9121      0.4434 -1.5267   2e-04     0.333         0.3139
3 Exponential    0.2788      0.0222 -0.1353  0.0044    0.2092         0.1866
4    Logistic    1.6999      0.0164  0.1823  0.0026    0.2311         0.2092
   Model type Intercept p Intercept   Slope p Slope R-Squared Adj. R-squared
1      Linear    1.2112           0 -0.0585   1e-04    0.3516         0.3331
2       Power   34.6019      0.0289 -2.2968   3e-04    0.3211         0.3017
3 Exponential     0.691      0.6322 -0.1432  0.0027      0.23          0.208
4    Logistic    0.2659      0.7795  0.2058   6e-04    0.2893         0.2689
   Model type Intercept p Intercept   Slope p Slope R-Squared Adj. R-squared
1      Linear    0.6734           0 -0.0316   7e-04    0.2812         0.2606
2       Power    5.7372      0.0348 -1.5978       0    0.3863         0.3688
3 Exponential    0.9161      0.8318 -0.1605       0    0.4086         0.3917
4    Logistic   -0.4868      0.4212  0.2043   1e-04    0.3444         0.3256
   Model type Intercept p Intercept   Slope p Slope R-Squared Adj. R-squared
1      Linear    0.9353           0 -0.0411   1e-04    0.3706         0.3526
2       Power  213.9932       7e-04 -2.7363       0    0.4213         0.4048
3 Exponential    2.3946        0.14  -0.184       0    0.4191         0.4025
4    Logistic   -1.8572       0.031  0.2441       0    0.3835         0.3659

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