DIY3: Data for origins (DIY store customers' places of residence)
In MCI: Multiplicative Competitive Interaction (MCI) Model
Description
Usage
Format
Source
References
Examples
Description
The 19 origins and the resident population.
Usage
1
data("DIY3")
Format
A data frame with 19 observations on the following 2 variables.
districta factor with 19 levels representing the origins
populationa numeric vector containing the resident population (2012)
Source
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.
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
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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
Example output
$gamma
[1] 1
$lambda
[1] -2.000488
Iteration 1 of 10 ...
Iteration 2 of 10 ...
Iteration 3 of 10 ...
Iteration 4 of 10 ...
Iteration 5 of 10 ...
Iteration 6 of 10 ...
Iteration 7 of 10 ...
Iteration 8 of 10 ...
Iteration 9 of 10 ...
Iteration 10 of 10 ...
Iteration Lambda
1 1 -1.750000
2 2 -2.125000
3 3 -1.937500
4 4 -2.031250
5 5 -1.984375
6 6 -2.007812
7 7 -1.996094
8 8 -2.001953
9 9 -1.999023
10 10 -2.000488
Iteration 1 of 151 ...
Iteration 2 of 151 ...
Iteration 3 of 151 ...
Iteration 4 of 151 ...
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Iteration 6 of 151 ...
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Iteration 144 of 151 ...
Iteration 145 of 151 ...
Iteration 146 of 151 ...
Iteration 147 of 151 ...
Iteration 148 of 151 ...
Iteration 149 of 151 ...
Iteration 150 of 151 ...
Iteration 151 of 151 ...
Iteration Lambda
1 1 -1.00
2 2 -1.01
3 3 -1.02
4 4 -1.03
5 5 -1.04
6 6 -1.05
7 7 -1.06
8 8 -1.07
9 9 -1.08
10 10 -1.09
11 11 -1.10
12 12 -1.11
13 13 -1.12
14 14 -1.13
15 15 -1.14
16 16 -1.15
17 17 -1.16
18 18 -1.17
19 19 -1.18
20 20 -1.19
21 21 -1.20
22 22 -1.21
23 23 -1.22
24 24 -1.23
25 25 -1.24
26 26 -1.25
27 27 -1.26
28 28 -1.27
29 29 -1.28
30 30 -1.29
31 31 -1.30
32 32 -1.31
33 33 -1.32
34 34 -1.33
35 35 -1.34
36 36 -1.35
37 37 -1.36
38 38 -1.37
39 39 -1.38
40 40 -1.39
41 41 -1.40
42 42 -1.41
43 43 -1.42
44 44 -1.43
45 45 -1.44
46 46 -1.45
47 47 -1.46
48 48 -1.47
49 49 -1.48
50 50 -1.49
51 51 -1.50
52 52 -1.51
53 53 -1.52
54 54 -1.53
55 55 -1.54
56 56 -1.55
57 57 -1.56
58 58 -1.57
59 59 -1.58
60 60 -1.59
61 61 -1.60
62 62 -1.61
63 63 -1.62
64 64 -1.63
65 65 -1.64
66 66 -1.65
67 67 -1.66
68 68 -1.67
69 69 -1.68
70 70 -1.69
71 71 -1.70
72 72 -1.71
73 73 -1.72
74 74 -1.73
75 75 -1.74
76 76 -1.75
77 77 -1.76
78 78 -1.77
79 79 -1.78
80 80 -1.79
81 81 -1.80
82 82 -1.81
83 83 -1.82
84 84 -1.83
85 85 -1.84
86 86 -1.85
87 87 -1.86
88 88 -1.87
89 89 -1.88
90 90 -1.89
91 91 -1.90
92 92 -1.91
93 93 -1.92
94 94 -1.93
95 95 -1.94
96 96 -1.95
97 97 -1.96
98 98 -1.97
99 99 -1.98
100 100 -1.99
101 101 -2.00
102 102 -2.01
103 103 -2.02
104 104 -2.03
105 105 -2.04
106 106 -2.05
107 107 -2.06
108 108 -2.07
109 109 -2.08
110 110 -2.09
111 111 -2.10
112 112 -2.11
113 113 -2.12
114 114 -2.13
115 115 -2.14
116 116 -2.15
117 117 -2.16
118 118 -2.17
119 119 -2.18
120 120 -2.19
121 121 -2.20
122 122 -2.21
123 123 -2.22
124 124 -2.23
125 125 -2.24
126 126 -2.25
127 127 -2.26
128 128 -2.27
129 129 -2.28
130 130 -2.29
131 131 -2.30
132 132 -2.31
133 133 -2.32
134 134 -2.33
135 135 -2.34
136 136 -2.35
137 137 -2.36
138 138 -2.37
139 139 -2.38
140 140 -2.39
141 141 -2.40
142 142 -2.41
143 143 -2.42
144 144 -2.43
145 145 -2.44
146 146 -2.45
147 147 -2.46
148 148 -2.47
149 149 -2.48
150 150 -2.49
151 151 -2.50
MCI documentation built on May 2, 2019, 6:02 a.m.
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Description Usage Format Source References Examples
Description
The 19 origins and the resident population.
Usage
1 | data("DIY3")
|
Format
A data frame with 19 observations on the following 2 variables.
districta factor with 19 levels representing the origins
populationa numeric vector containing the resident population (2012)
Source
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.
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | 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
|
Example output
$gamma
[1] 1
$lambda
[1] -2.000488
Iteration 1 of 10 ...
Iteration 2 of 10 ...
Iteration 3 of 10 ...
Iteration 4 of 10 ...
Iteration 5 of 10 ...
Iteration 6 of 10 ...
Iteration 7 of 10 ...
Iteration 8 of 10 ...
Iteration 9 of 10 ...
Iteration 10 of 10 ...
Iteration Lambda
1 1 -1.750000
2 2 -2.125000
3 3 -1.937500
4 4 -2.031250
5 5 -1.984375
6 6 -2.007812
7 7 -1.996094
8 8 -2.001953
9 9 -1.999023
10 10 -2.000488
Iteration 1 of 151 ...
Iteration 2 of 151 ...
Iteration 3 of 151 ...
Iteration 4 of 151 ...
Iteration 5 of 151 ...
Iteration 6 of 151 ...
Iteration 7 of 151 ...
Iteration 8 of 151 ...
Iteration 9 of 151 ...
Iteration 10 of 151 ...
Iteration 11 of 151 ...
Iteration 12 of 151 ...
Iteration 13 of 151 ...
Iteration 14 of 151 ...
Iteration 15 of 151 ...
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Iteration 30 of 151 ...
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Iteration 33 of 151 ...
Iteration 34 of 151 ...
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Iteration 36 of 151 ...
Iteration 37 of 151 ...
Iteration 38 of 151 ...
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Iteration 40 of 151 ...
Iteration 41 of 151 ...
Iteration 42 of 151 ...
Iteration 43 of 151 ...
Iteration 44 of 151 ...
Iteration 45 of 151 ...
Iteration 46 of 151 ...
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Iteration 48 of 151 ...
Iteration 49 of 151 ...
Iteration 50 of 151 ...
Iteration 51 of 151 ...
Iteration 52 of 151 ...
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Iteration 54 of 151 ...
Iteration 55 of 151 ...
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Iteration 60 of 151 ...
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Iteration 68 of 151 ...
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Iteration 90 of 151 ...
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Iteration 114 of 151 ...
Iteration 115 of 151 ...
Iteration 116 of 151 ...
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Iteration 118 of 151 ...
Iteration 119 of 151 ...
Iteration 120 of 151 ...
Iteration 121 of 151 ...
Iteration 122 of 151 ...
Iteration 123 of 151 ...
Iteration 124 of 151 ...
Iteration 125 of 151 ...
Iteration 126 of 151 ...
Iteration 127 of 151 ...
Iteration 128 of 151 ...
Iteration 129 of 151 ...
Iteration 130 of 151 ...
Iteration 131 of 151 ...
Iteration 132 of 151 ...
Iteration 133 of 151 ...
Iteration 134 of 151 ...
Iteration 135 of 151 ...
Iteration 136 of 151 ...
Iteration 137 of 151 ...
Iteration 138 of 151 ...
Iteration 139 of 151 ...
Iteration 140 of 151 ...
Iteration 141 of 151 ...
Iteration 142 of 151 ...
Iteration 143 of 151 ...
Iteration 144 of 151 ...
Iteration 145 of 151 ...
Iteration 146 of 151 ...
Iteration 147 of 151 ...
Iteration 148 of 151 ...
Iteration 149 of 151 ...
Iteration 150 of 151 ...
Iteration 151 of 151 ...
Iteration Lambda
1 1 -1.00
2 2 -1.01
3 3 -1.02
4 4 -1.03
5 5 -1.04
6 6 -1.05
7 7 -1.06
8 8 -1.07
9 9 -1.08
10 10 -1.09
11 11 -1.10
12 12 -1.11
13 13 -1.12
14 14 -1.13
15 15 -1.14
16 16 -1.15
17 17 -1.16
18 18 -1.17
19 19 -1.18
20 20 -1.19
21 21 -1.20
22 22 -1.21
23 23 -1.22
24 24 -1.23
25 25 -1.24
26 26 -1.25
27 27 -1.26
28 28 -1.27
29 29 -1.28
30 30 -1.29
31 31 -1.30
32 32 -1.31
33 33 -1.32
34 34 -1.33
35 35 -1.34
36 36 -1.35
37 37 -1.36
38 38 -1.37
39 39 -1.38
40 40 -1.39
41 41 -1.40
42 42 -1.41
43 43 -1.42
44 44 -1.43
45 45 -1.44
46 46 -1.45
47 47 -1.46
48 48 -1.47
49 49 -1.48
50 50 -1.49
51 51 -1.50
52 52 -1.51
53 53 -1.52
54 54 -1.53
55 55 -1.54
56 56 -1.55
57 57 -1.56
58 58 -1.57
59 59 -1.58
60 60 -1.59
61 61 -1.60
62 62 -1.61
63 63 -1.62
64 64 -1.63
65 65 -1.64
66 66 -1.65
67 67 -1.66
68 68 -1.67
69 69 -1.68
70 70 -1.69
71 71 -1.70
72 72 -1.71
73 73 -1.72
74 74 -1.73
75 75 -1.74
76 76 -1.75
77 77 -1.76
78 78 -1.77
79 79 -1.78
80 80 -1.79
81 81 -1.80
82 82 -1.81
83 83 -1.82
84 84 -1.83
85 85 -1.84
86 86 -1.85
87 87 -1.86
88 88 -1.87
89 89 -1.88
90 90 -1.89
91 91 -1.90
92 92 -1.91
93 93 -1.92
94 94 -1.93
95 95 -1.94
96 96 -1.95
97 97 -1.96
98 98 -1.97
99 99 -1.98
100 100 -1.99
101 101 -2.00
102 102 -2.01
103 103 -2.02
104 104 -2.03
105 105 -2.04
106 106 -2.05
107 107 -2.06
108 108 -2.07
109 109 -2.08
110 110 -2.09
111 111 -2.10
112 112 -2.11
113 113 -2.12
114 114 -2.13
115 115 -2.14
116 116 -2.15
117 117 -2.16
118 118 -2.17
119 119 -2.18
120 120 -2.19
121 121 -2.20
122 122 -2.21
123 123 -2.22
124 124 -2.23
125 125 -2.24
126 126 -2.25
127 127 -2.26
128 128 -2.27
129 129 -2.28
130 130 -2.29
131 131 -2.30
132 132 -2.31
133 133 -2.32
134 134 -2.33
135 135 -2.34
136 136 -2.35
137 137 -2.36
138 138 -2.37
139 139 -2.38
140 140 -2.39
141 141 -2.40
142 142 -2.41
143 143 -2.42
144 144 -2.43
145 145 -2.44
146 146 -2.45
147 147 -2.46
148 148 -2.47
149 149 -2.48
150 150 -2.49
151 151 -2.50
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