shopping4: Grocery store data for the point-of-sale survey in Karlsruhe
In MCI: Multiplicative Competitive Interaction (MCI) Model

Description Usage Format Source See Also Examples

This dataset contains information about the regarded grocery stores in the east of Karlsruhe, based on the POS survey stored in the dataset shopping1 and the related information in shopping2..

1	data("shopping4")

A data frame with 11 observations on the following 4 variables.

location_code: a factor containing the grocery store codes
salesarea_qm: a numeric vector containing the sales area of the stores in sqm
storetype_dc: a numeric vector containing a dummy variable that indicates if the store is a discounter or not
store_chain: a factor containing the store chain

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

Mapping of grocery stores in the east of Karlsruhe in June 2016 with additional research

shopping1, shopping2, shopping3

# MCI analysis for the grocery store market areas based on the POS survey in shopping1 #

data(shopping1)
# Loading the survey dataset
data(shopping2)
# Loading the distance/travel time dataset
data(shopping3)
# Loading the dataset containing information about the city districts
data(shopping4)
# Loading the grocery store data

shopping1_KAeast <- shopping1[shopping1$resid_code %in% 
shopping3$resid_code[shopping3$KA_east == 1],]
# Extracting only inhabitants of the eastern districts of Karlsruhe

ijmatrix_gro_adj <- ijmatrix.create(shopping1_KAeast, "resid_code",
"gro_purchase_code", "gro_purchase_expen", remSing = TRUE, remSing.val = 1,
remSingSupp.val = 2, correctVar = TRUE, correctVar.val = 0.1)
# Removing singular instances/outliers (remSing = TRUE) incorporating
# only suppliers which are at least obtained three times (remSingSupp.val = 2)
# Correcting the values (correctVar = TRUE)
# by adding 0.1 to the absolute values (correctVar.val = 0.1)

ijmatrix_gro_adj <- ijmatrix_gro_adj[(ijmatrix_gro_adj$gro_purchase_code !=
"REFORMHAUSBOESER") & (ijmatrix_gro_adj$gro_purchase_code != "WMARKT_DURLACH")
& (ijmatrix_gro_adj$gro_purchase_code != "X_INCOMPLETE_STORE"),]
# Remove non-regarded observations

ijmatrix_gro_adj_dist <- merge (ijmatrix_gro_adj, shopping2, by.x="interaction",
by.y="route")
# Include the distances and travel times (shopping2)
ijmatrix_gro_adj_dist_stores <- merge (ijmatrix_gro_adj_dist, shopping4,
by.x = "gro_purchase_code", by.y = "location_code")
# Adding the store information (shopping4)

mci.transvar(ijmatrix_gro_adj_dist_stores, "resid_code", "gro_purchase_code", 
"p_ij_obs")
# Log-centering transformation of one variable (p_ij_obs)

ijmatrix_gro_transf <- mci.transmat(ijmatrix_gro_adj_dist_stores, "resid_code",
"gro_purchase_code", "p_ij_obs", "d_time", "salesarea_qm")
# Log-centering transformation of the interaction matrix

mcimodel_gro_trips <- mci.fit(ijmatrix_gro_adj_dist_stores, "resid_code",
"gro_purchase_code", "p_ij_obs", "d_time", "salesarea_qm")
# MCI model for the grocery store market areas
# shares: "p_ij_obs", explanatory variables: "d_time", "salesarea_qm"

summary(mcimodel_gro_trips)
# Use like lm

  [1]  0.5586409 -1.4456805  0.8119981  1.4633403 -1.4456805 -1.4456805
  [7] -0.4042878  0.1671033  0.7611454  0.8119981  0.1671033 -0.4297476
 [13]  0.6116451 -0.4297476  0.8924717 -0.4297476 -0.4297476 -0.4297476
 [19] -0.4297476  0.8924717  0.6116451 -0.4297476 -0.2148738 -0.2148738
 [25] -0.2148738  0.8265189 -0.2148738 -0.2148738 -0.2148738 -0.2148738
 [31]  1.1073455 -0.2148738 -0.2148738 -0.2148738 -0.2148738 -0.2148738
 [37]  1.1073455 -0.2148738 -0.2148738 -0.2148738 -0.2148738  0.8265189
 [43] -0.2148738 -0.2148738  0.1391845  0.1391845  0.4200111  0.8053619
 [49]  0.7105756 -0.9022082  0.5891535 -0.9022082  0.8053619 -0.9022082
 [55] -0.9022082 -0.1893441 -0.1893441 -0.1893441  0.8520486 -0.1893441
 [61] -0.1893441 -0.1893441 -0.1893441  0.8520486 -0.1893441 -0.1893441
 [67] -0.2768831 -0.2768831 -0.2768831  0.7645096 -0.2768831 -0.2768831
 [73] -0.2768831 -0.2768831  1.7274383 -0.2768831 -0.2768831 -0.1202018
 [79] -0.1202018 -0.1202018 -0.1202018 -0.1202018 -0.1202018 -0.1202018
 [85] -0.1202018  1.2020175 -0.1202018 -0.1202018 -0.3768671 -0.3768671
 [91]  0.6645256  1.2359167 -0.3768671 -0.3768671 -0.3768671 -0.3768671
 [97]  1.1144946 -0.3768671 -0.3768671 -0.3359608  0.7054318 -0.3359608
[103] -0.3359608 -0.3359608  0.7054318 -0.3359608 -0.3359608  1.2768230
[109] -0.3359608 -0.3359608 -0.2668185 -0.2668185 -0.2668185 -0.2668185
[115] -0.2668185  1.0554008 -0.2668185 -0.2668185  1.3459654 -0.2668185
[121] -0.2668185

Call:
lm(formula = mci_formula, data = mciworkfile)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.27457 -0.28725 -0.02391  0.32163  1.29351 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
d_time_t        -1.2443     0.2319  -5.367 4.02e-07 ***
salesarea_qm_t   0.9413     0.1158   8.132 4.59e-13 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.4458 on 119 degrees of freedom
Multiple R-squared:  0.4603,	Adjusted R-squared:  0.4512 
F-statistic: 50.74 on 2 and 119 DF,  p-value: < 2.2e-16