Package 'divseg'

Title: Calculate Diversity and Segregation Indices
Description: Implements common measures of diversity and spatial segregation. This package has tools to compute the majority of measures are reviewed in Massey and Denton (1988) <doi:10.2307/2579183>. Multiple common measures of within-geography diversity are implemented as well. All functions operate on data frames with a 'tidyselect' based workflow.
Authors: Christopher T. Kenny [aut, cre]
Maintainer: Christopher T. Kenny <[email protected]>
License: MIT + file LICENSE
Version: 0.1.0
Built: 2024-08-22 05:03:10 UTC
Source: https://github.com/christopherkenny/divseg

Help Index

de_county

Description

This data contains 2010 Census data for each of the three counties in DE.

Usage

data("de_county")

Format

An sf dataframe with 3 observations

Examples

data('de_county')

de_tract

Description

This data contains 2010 Census data for each of the 218 tracts in DE.

Usage

data("de_tract")

Format

An sf dataframe with 218 observations

Examples

data('de_tract')

Compute Absolute Centralization

Description

Compute Absolute Centralization

Usage

ds_abs_cent(.data, .cols, .name)

abs_cent(..., .data = dplyr::across(everything()))

Arguments

.data

tibble with sf geometry
.cols

tidy-select Columns to compute the measure with. Must be at least 2 columns. If more than 2, treats first column as first group and sum of other columns as second.
.name

name for column with absolute centralization. Leave missing to return a vector.
...

arguments to forward to ds_abs_cent from abs_cent

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_abs_cent(de_county, c(pop_white, starts_with('pop_')))
ds_abs_cent(de_county, c(pop_white, starts_with('pop_')), 'abs_cent')

Compute Absolute Clustering

Description

Compute Absolute Clustering

Usage

ds_abs_clust(.data, .cols, .name)

abs_clust(..., .data = dplyr::across(everything()))

Arguments

.data

tibble with sf geometry
.cols

tidy-select Columns to compute the measure with. Must be at least 2 columns. If more than 2, treats first column as first group and sum of other columns as second.
.name

name for column with absolute clustering. Leave missing to return a vector.
...

arguments to forward to ds_abs_clust from abs_clust

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_abs_clust(de_county, c(pop_white, starts_with('pop_')))
ds_abs_clust(de_county, c(pop_white, starts_with('pop_')), 'abs_clust')

Compute Absolute Concentration

Description

Compute Absolute Concentration

Usage

ds_abs_conc(.data, .cols, .name)

abs_conc(..., .data = dplyr::across(everything()))

Arguments

.data

tibble with sf geometry
.cols

tidy-select Columns to compute the measure with. Must be at least 2 columns. If more than 2, treats first column as first group and sum of other columns as second.
.name

name for column with absolute concentration. Leave missing to return a vector.
...

arguments to forward to ds_abs_conc from abs_conc

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_abs_conc(de_county, c(pop_black, starts_with('pop_')))
ds_abs_conc(de_county, c(pop_black, starts_with('pop_')), 'abs_conc')

Compute Atkinson b Index

Description

Compute Atkinson b Index

Usage

ds_atkinson(.data, .cols, .name, b = 0.5)

atkinson(..., .data = dplyr::across(everything()))

Arguments

.data

tibble
.cols

tidy-select Columns to compute the measure with. Must be at least 2 columns. If more than 2, treats first column as first group and sum of other columns as second.
.name

name for column with Atkinson b index. Leave missing to return a vector.
b

Default 0.5. Exponent parameter b, where 0 <= b <= 1.
...

arguments to forward to ds_atkinson from atkinson

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_atkinson(de_county, c(pop_white, starts_with('pop_')))
ds_atkinson(de_county, starts_with('pop_'), 'atkinson')

Compute Blau's Index

Description

Compute Blau's Index

Usage

ds_blau(.data, .cols, .name)

blau(..., .data = dplyr::across(everything()))

Arguments

.data

tibble
.cols

tidy-select Columns to compute the measure with.
.name

name for column with Blau index. Leave missing to return a vector.
...

arguments to forward to ds_blau from blau

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_blau(de_county, starts_with('pop_'))
ds_blau(de_county, starts_with('pop_'), 'blau')

Compute Correlation Index

Description

Compute Correlation Index

Usage

ds_correlation(.data, .cols, .name)

correlation(..., .data = dplyr::across(everything()))

Arguments

.data

tibble
.cols

tidy-select Columns to compute the measure with. Must be at least 2 columns. If more than 2, treats first column as first group and sum of other columns as second.
.name

name for column with Correlation index. Leave missing to return a vector.
...

arguments to forward to ds_correlation from correlation

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_correlation(de_county, c(pop_white, starts_with('pop_')))
ds_correlation(de_county, starts_with('pop_'), 'correlation')

Compute Distance Decay Interaction

Description

Compute Distance Decay Interaction

Usage

ds_dd_interaction(.data, .cols, .name, .comp = FALSE)

dd_interaction(..., .data = dplyr::across(everything()))

Arguments

.data

tibble with sf geometry
.cols

tidy-select Columns to compute the measure with. Must be at least 2 columns. If more than 2, treats first column as first group and sum of other columns as second.
.name

name for column with distance decay interaction. Leave missing to return a vector.
.comp

Default is FALSE. FALSE returns the sum, TRUE returns the components.
...

arguments to forward to ds_dd_interaction from dd_interaction

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_dd_interaction(de_county, c(pop_black, starts_with('pop_')))
ds_dd_interaction(de_county, c(pop_black, starts_with('pop_')), 'dd_interaction')

Compute Distance Decay Isolation

Description

Compute Distance Decay Isolation

Usage

ds_dd_isolation(.data, .cols, .name, .comp = FALSE)

dd_isolation(..., .data = dplyr::across(everything()))

Arguments

.data

tibble with sf geometry
.cols

tidy-select Columns to compute the measure with. Must be at least 2 columns. If more than 2, treats first column as first group and sum of other columns as second.
.name

name for column with distance decay isolation. Leave missing to return a vector.
.comp

Default is FALSE. FALSE returns the sum, TRUE returns the components.
...

arguments to forward to ds_dd_isolation from dd_isolation

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_dd_isolation(de_county, c(pop_black, starts_with('pop_')))
ds_dd_isolation(de_county, c(pop_black, starts_with('pop_')), 'dd_isolation')

Compute Delta Index

Description

Compute Delta Index

Usage

ds_delta(.data, .cols, .name, .comp = FALSE)

delta(..., .data = dplyr::across(everything()))

Arguments

.data

tibble with sf geometry
.cols

tidy-select Columns to compute the measure with. Must be at least 2 columns. If more than 2, treats first column as first group and sum of other columns as second.
.name

name for column with delta index. Leave missing to return a vector.
.comp

Default is FALSE. FALSE returns the sum, TRUE returns the components.
...

arguments to forward to ds_delta from delta

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_delta(de_county, c(pop_white, starts_with('pop_')))
ds_delta(de_county, starts_with('pop_'), 'delta')

Compute Dissimilarity Index

Description

Compute Dissimilarity Index

Usage

ds_dissim(.data, .cols, .name, .comp = FALSE)

dissim(..., .data = dplyr::across(everything()))

Arguments

.data

tibble
.cols

tidy-select Columns to compute the measure with. Must be at least 2 columns. If more than 2, treats first column as first group and sum of other columns as second.
.name

name for column with dissimilarity index. Leave missing to return a vector.
.comp

Default is FALSE. FALSE returns the sum, TRUE returns the components.
...

arguments to forward to ds_dissim from dissim

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_dissim(de_county, c(pop_white, starts_with('pop_')))
ds_dissim(de_county, c(pop_white, starts_with('pop_')), .comp = TRUE)
ds_dissim(de_county, starts_with('pop_'), 'dissim')

Compute Diversity

Description

This is equivalent to perplexity.

Usage

ds_diversity(.data, .cols, .name, q = 1)

diversity(..., .data = dplyr::across(everything()))

ds_perplexity(.data, .cols, .name, q = 1)

perplexity(..., .data = dplyr::across(everything()))

Arguments

.data

tibble
.cols

tidy-select Columns to compute the measure with.
.name

name for column with diversity. Leave missing to return a vector.
q

exponent parameter. Default 0. Can not be 1.
...

arguments to forward to ds_diversity from diversity

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_diversity(de_county, starts_with('pop_'))
ds_diversity(de_county, starts_with('pop_'), 'diversity')

Compute Entropy Index

Description

Compute Entropy Index

Usage

ds_entropy(.data, .cols, .name, .comp = FALSE)

entropy(..., .data = dplyr::across(everything()))

Arguments

.data

tibble
.cols

tidy-select Columns to compute the measure with. Must be at least 2 columns. If more than 2, treats first column as first group and sum of other columns as second.
.name

name for column with entropy index. Leave missing to return a vector.
.comp

Default is FALSE. FALSE returns the sum, TRUE returns the components.
...

arguments to forward to ds_entropy from entropy

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_entropy(de_county, c(pop_white, starts_with('pop_')))
ds_entropy(de_county, starts_with('pop_'), 'entropy')

Compute Gini Index

Description

Compute Gini Index

Usage

ds_gini(.data, .cols, .name, .comp = FALSE)

gini(..., .data = dplyr::across(everything()))

Arguments

.data

tibble
.cols

tidy-select Columns to compute the measure with. Must be at least 2 columns. If more than 2, treats first column as first group and sum of other columns as second.
.name

name for column with gini index. Leave missing to return a vector.
.comp

Default is FALSE. FALSE returns the sum, TRUE returns the components.
...

arguments to forward to ds_gini from gini

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_gini(de_county, c(pop_white, starts_with('pop_')))
ds_gini(de_county, starts_with('pop_'), 'gini')

Compute Herfindahl-Hirshman Index

Description

This is equivalent to the Simpson Index.

Usage

ds_hhi(.data, .cols, .name)

hhi(..., .data = dplyr::across(everything()))

ds_simpson(.data, .cols, .name)

simpson(..., .data = dplyr::across(everything()))

Arguments

.data

tibble
.cols

tidy-select Columns to compute the measure with.
.name

name for column with HHI. Leave missing to return a vector.
...

arguments to forward to ds_hhi from hhi

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_hhi(de_county, starts_with('pop_'))
ds_hhi(de_county, starts_with('pop_'), 'blau')

Compute Interaction Index

Description

Compute Interaction Index

Usage

ds_interaction(.data, .cols, .name, .comp = FALSE)

interaction(..., .data = dplyr::across(everything()))

Arguments

.data

tibble
.cols

tidy-select Columns to compute the measure with. Must be at least 2 columns. If more than 2, treats first column as first group and sum of other columns as second.
.name

name for column with Interaction index. Leave missing to return a vector.
.comp

Default is FALSE. FALSE returns the sum, TRUE returns the components.
...

arguments to forward to ds_interaction from interaction

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_interaction(de_county, c(pop_white, starts_with('pop_')))
ds_interaction(de_county, starts_with('pop_'), 'interaction')

Compute Simpson Index

Description

Compute Simpson Index

Usage

ds_inv_simpson(.data, .cols, .name)

inv_simpson(..., .data = dplyr::across(everything()))

Arguments

.data

tibble
.cols

tidy-select Columns to compute the measure with.
.name

name for column with Simpson Index Leave missing to return a vector.
...

arguments to forward to ds_inv_simpson from inv_simpson

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_inv_simpson(de_county, starts_with('pop_'))
ds_inv_simpson(de_county, starts_with('pop_'), 'blau')

Compute Isolation Index

Description

Compute Isolation Index

Usage

ds_isolation(.data, .cols, .name, .comp = FALSE)

isolation(..., .data = dplyr::across(everything()))

Arguments

.data

tibble
.cols

tidy-select Columns to compute the measure with. Must be at least 2 columns. If more than 2, treats first column as first group and sum of other columns as second.
.name

name for column with Isolation index. Leave missing to return a vector.
.comp

Default is FALSE. FALSE returns the sum, TRUE returns the components.
...

arguments to forward to ds_isolation from isolation

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_isolation(de_county, c(pop_white, starts_with('pop_')))
ds_isolation(de_county, starts_with('pop_'), 'isolation')

Compute Relative Centralization

Description

Compute Relative Centralization

Usage

ds_rel_cent(.data, .cols, .name)

rel_cent(..., .data = dplyr::across(everything()))

Arguments

.data

tibble with sf geometry
.cols

tidy-select Columns to compute the measure with. Must be at least 2 columns. If more than 2, treats first column as first group and sum of other columns as second.
.name

name for column with relative centralization. Leave missing to return a vector.
...

arguments to forward to ds_rel_cent from rel_cent

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_rel_cent(de_county, c(pop_white, starts_with('pop_')))
ds_rel_cent(de_county, c(pop_white, starts_with('pop_')), 'rel_cent')

Compute Relative Clustering

Description

Compute Relative Clustering

Usage

ds_rel_clust(.data, .cols, .name)

rel_clust(..., .data = dplyr::across(everything()))

Arguments

.data

tibble with sf geometry
.cols

tidy-select Columns to compute the measure with. Must be at least 2 columns. If more than 2, treats first column as first group and sum of other columns as second.
.name

name for column with relative clustering. Leave missing to return a vector.
...

arguments to forward to ds_rel_clust from rel_clust

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_rel_clust(de_county, c(pop_black, starts_with('pop_')))
ds_rel_clust(de_county, c(pop_black, starts_with('pop_')), 'rel_clust')

Compute Relative Concentration

Description

Compute Relative Concentration

Usage

ds_rel_conc(.data, .cols, .name)

rel_conc(..., .data = dplyr::across(everything()))

Arguments

.data

tibble with sf geometry
.cols

tidy-select Columns to compute the measure with. Must be at least 2 columns. If more than 2, treats first column as first group and sum of other columns as second.
.name

name for column with relative concentration. Leave missing to return a vector.
...

arguments to forward to ds_rel_conc from rel_conc

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_rel_conc(de_county, c(pop_black, starts_with('pop_')))
ds_rel_conc(de_county, c(pop_black, starts_with('pop_')), 'rel_conc')

Compute Reyni Entropy

Description

Compute Reyni Entropy

Usage

ds_reyni(.data, .cols, .name, q = 0)

reyni(..., .data = dplyr::across(everything()))

Arguments

.data

tibble
.cols

tidy-select Columns to compute the measure with.
.name

name for column with Reyni entropy. Leave missing to return a vector.
q

exponent parameter. Default 0. Can not be 1.
...

arguments to forward to ds_reyni from reyni

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_reyni(de_county, starts_with('pop_'))
ds_reyni(de_county, starts_with('pop_'), 'reyni')

Compute Shannon Index

Description

Compute Shannon Index

Usage

ds_shannon(.data, .cols, .name)

shannon(..., .data = dplyr::across(everything()))

Arguments

.data

tibble
.cols

tidy-select Columns to compute the measure with.
.name

name for column with Shannon index. Leave missing to return a vector.
...

arguments to forward to ds_shannon from shannon

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_shannon(de_county, starts_with('pop_'))
ds_shannon(de_county, starts_with('pop_'), 'shannon')

Compute Spatial Proximity

Description

Compute Spatial Proximity

Usage

ds_spat_prox(.data, .cols, .name)

spat_prox(..., .data = dplyr::across(everything()))

Arguments

.data

tibble with sf geometry
.cols

tidy-select Columns to compute the measure with. Must be at least 2 columns. If more than 2, treats first column as first group and sum of other columns as second.
.name

name for column with spatial proximity. Leave missing to return a vector.
...

arguments to forward to ds_spat_prox from spat_prox

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_spat_prox(de_county, c(pop_black, starts_with('pop_')))
ds_spat_prox(de_county, c(pop_black, starts_with('pop_')), 'spat_prox')