Description Usage Arguments Author(s) References See Also Examples

This function plots a Hoover curve from regions - industries matrices.

1 | ```
Hoover.curve(mat, pop, plot = TRUE, pdf = FALSE)
``` |

`mat` |
An incidence matrix with regions in rows and industries in columns. The input can also be a vector of industrial regional count (a matrix with n regions in rows and a single column). |

`pop` |
A vector of population regional count |

`plot` |
Logical; shall the curve be automatically plotted? Defaults to TRUE. If set to TRUE, the function will return x y coordinates that you can latter use to plot and customize the curve. |

`pdf` |
Logical; shall a pdf be saved to your current working directory? Defaults to FALSE. If set to TRUE, a pdf with all Hoover curves will be compiled and saved to your current working directory. |

Pierre-Alexandre Balland p.balland@uu.nl

Hoover, E.M. (1936) The Measurement of Industrial Localization, *The Review of Economics and Statistics* **18** (1): 162-171

`Hoover.Gini`

, `locational.Gini`

, `locational.Gini.curve`

, `Lorenz.curve`

, `Gini`

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 45 46 47 48 49 50 51 52 53 54 55 | ```
## generate vectors of industrial and population count
ind <- c(0, 10, 10, 30, 50)
pop <- c(10, 15, 20, 25, 30)
## run the function (30% of the population produces 50% of the industrial output)
Hoover.curve (ind, pop)
Hoover.curve (ind, pop, pdf = TRUE)
Hoover.curve (ind, pop, plot = F)
## generate a region - industry matrix
mat = matrix (
c (0, 10, 0, 0,
0, 15, 0, 0,
0, 20, 0, 0,
0, 25, 0, 1,
0, 30, 1, 1), ncol = 4, byrow = T)
rownames(mat) <- c ("R1", "R2", "R3", "R4", "R5")
colnames(mat) <- c ("I1", "I2", "I3", "I4")
## run the function
Hoover.curve (mat, pop)
Hoover.curve (mat, pop, pdf = TRUE)
Hoover.curve (mat, pop, plot = FALSE)
## run the function by aggregating all industries
Hoover.curve (rowSums(mat), pop)
Hoover.curve (rowSums(mat), pop, pdf = TRUE)
Hoover.curve (rowSums(mat), pop, plot = FALSE)
## run the function for industry #1 only
Hoover.curve (mat[,1], pop)
Hoover.curve (mat[,1], pop, pdf = TRUE)
Hoover.curve (mat[,1], pop, plot = FALSE)
## run the function for industry #2 only (perfectly proportional to population)
Hoover.curve (mat[,2], pop)
Hoover.curve (mat[,2], pop, pdf = TRUE)
Hoover.curve (mat[,2], pop, plot = FALSE)
## run the function for industry #3 only (30% of the pop. produces 100% of the output)
Hoover.curve (mat[,3], pop)
Hoover.curve (mat[,3], pop, pdf = TRUE)
Hoover.curve (mat[,3], pop, plot = FALSE)
## run the function for industry #4 only (55% of the pop. produces 100% of the output)
Hoover.curve (mat[,4], pop)
Hoover.curve (mat[,4], pop, pdf = TRUE)
Hoover.curve (mat[,4], pop, plot = FALSE)
Compare the distribution of the #industries
par(mfrow=c(2,2))
Hoover.curve (mat[,1], pop)
Hoover.curve (mat[,2], pop)
Hoover.curve (mat[,3], pop)
Hoover.curve (mat[,4], pop)
``` |

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