shares.total: Total market shares/market areas
In MCI: Multiplicative Competitive Interaction (MCI) Model

Description Usage Arguments Details Value Author(s) References See Also Examples

This function calculates the total sales and market shares (or total market area) of the suppliers based on a given interaction matrix which already contains (local) market shares.

1
2
3

shares.total(mcidataset, submarkets, suppliers, shares, localmarket, 
plotChart = FALSE, plotChart.title = "Total sales", plotChart.unit = "sales", 
check_df = TRUE)

`mcidataset`	an interaction matrix which is a `data.frame` containing the submarkets, suppliers, the market shares and a variable for the local market potential (e.g. purchasing power, number of customers, population)
`submarkets`	the column in the interaction matrix `mcidataset` containing the submarkets
`suppliers`	the column in the interaction matrix `mcidataset` containing the suppliers
`shares`	the column in the interaction matrix `mcidataset` containing the the (local) market shares
`localmarket`	the column in the interaction matrix `mcidataset` containing the local market potential
`plotChart`	logical argument that indicates if the total values shall be visualized in a bar plot (default: `plotChart = FALSE`)
`plotChart.title`	If `plotChart = TRUE`: Title of the plot
`plotChart.unit`	If `plotChart = TRUE`: Unit of the plotted total values (e.g. a currency), used as plot subtitle
`check_df`	logical argument that indicates if the input (dataset, column names) is checked (default: `check_df = TRUE` (should not be changed, only for internal use))

If (local) market shares are observed and estimated, respectively, it is possible to link them to a (local) market potential to estimate the total sales and shares of the given suppliers. In this function, the input dataset (interaction matrix with local market shares) is used for the calculation of total sales (or total number of customers) and total market shares of all j regarded suppliers. Optionally, the function also returns a simple bar plot of the total values.

Returns a new data.frame with the total sales (sum_E_j) and the over-all market shares of the j suppliers (share_j).

Thomas Wieland

Huff, D. L./McCallum, D. (2008): “Calibrating the Huff Model Using ArcGIS Business Analyst”. ESRI White Paper, September 2008. https://www.esri.com/library/whitepapers/pdfs/calibrating-huff-model.pdf

Nakanishi, M./Cooper, L. G. (1974): “Parameter Estimation for a Multiplicative Competitive Interaction Model - Least Squares Approach”. In: Journal of Marketing Research, 11, 3, p. 303-311.

Nakanishi, M./Cooper, L. G. (1982): “Simplified Estimation Procedures for MCI Models”. In: Marketing Science, 1, 3, p. 314-322.

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.

mci.fit, mci.transmat, mci.transvar, mci.shares

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

mynewmatrix <- mci.shares(Freiburg1, "district", "store", "salesarea", 1, "distance", -2)
# Calculating shares based on two attractivity/utility variables

mynewmatrix_alldata <- merge(mynewmatrix, Freiburg2)
# Merge interaction matrix with district data (purchasing power)

shares.total (mynewmatrix_alldata, "district", "store", "p_ij", "ppower")
# Calculation of total sales

   suppliers_single  sum_E_j     share_j
1                 1  4057591 0.010759819
2                10  5809861 0.015406444
3                11  1289847 0.003420383
4                12  7103210 0.018836115
5                13  3476313 0.009218400
6                14  7264438 0.019263657
7                15  7667744 0.020333134
8                16  5836249 0.015476420
9                17  2093917 0.005552598
10               18  3717873 0.009858963
11               19  1836516 0.004870028
12                2  4163924 0.011041789
13               20  7193956 0.019076753
14               21  4587982 0.012166296
15               22  7219367 0.019144137
16               23  3511346 0.009311301
17               24  7529751 0.019967205
18               25  8959078 0.023757460
19               26 18002697 0.047739105
20               27  3118085 0.008268460
21               28  3247581 0.008611856
22               29 21478569 0.056956335
23                3  6936748 0.018394695
24               30  5087282 0.013490329
25               31  6333310 0.016794514
26               32  2555499 0.006776608
27               33  3659372 0.009703833
28               34  3660671 0.009707275
29               35  2439080 0.006467893
30               36  5843828 0.015496517
31               37  3687732 0.009779035
32               38  3329573 0.008829279
33               39  1412032 0.003744392
34                4  3890181 0.010315885
35               40  4837267 0.012827345
36               41  2996955 0.007947250
37               42  1747369 0.004633629
38               43  1307060 0.003466029
39               44  6414607 0.017010096
40               45 20704568 0.054903858
41               46 22209552 0.058894739
42               47  9658730 0.025612779
43               48 10347232 0.027438534
44               49  6232115 0.016526168
45                5  3272610 0.008678225
46               50 10234404 0.027139338
47               51 10456838 0.027729182
48               52  4877062 0.012932873
49               53 13217387 0.035049538
50               54  4402544 0.011674556
51               55  6433646 0.017060583
52               56  2437403 0.006463444
53               57  4097990 0.010866947
54               58  6495891 0.017225641
55               59  3502213 0.009287082
56                6  6880875 0.018246533
57               60  5747010 0.015239778
58               61  2760915 0.007321325
59               62  2458063 0.006518231
60               63  2860499 0.007585401
61                7  4027402 0.010679766
62                8  6245398 0.016561391
63                9  2241074 0.005942826