Type:	Package
Title:	Transport Accessibility Measures
Version:	1.4.0
Description:	A set of fast and convenient functions to help conducting accessibility analyses. Given a pre-computed travel cost matrix and a land use dataset (containing the location of jobs, healthcare and population, for example), the package allows one to calculate accessibility levels and accessibility poverty and inequality. The package covers the majority of the most commonly used accessibility measures (such as cumulative opportunities, gravity-based and floating catchment areas methods), as well as the most frequently used inequality and poverty metrics (such as the Palma ratio, the concentration and Theil indices and the FGT family of measures).
License:	MIT + file LICENSE
URL:	https://github.com/ipeaGIT/accessibility, https://ipeagit.github.io/accessibility/
BugReports:	https://github.com/ipeaGIT/accessibility/issues
Depends:	R (≥ 3.5.0)
Imports:	checkmate, data.table, Hmisc, Rdpack (≥ 0.7), stats, utils
Suggests:	covr, ggplot2, knitr, rmarkdown, sf, testthat, tibble
VignetteBuilder:	knitr
RdMacros:	Rdpack
Encoding:	UTF-8
NeedsCompilation:	no
RoxygenNote:	7.2.3
Packaged:	2024-03-06 20:44:17 UTC; dhersz
Author:	Rafael H. M. Pereira [aut], Daniel Herszenhut [aut, cre], Joey Reid [ctb], Ipea - Institute for Applied Economic Research [cph, fnd]
Maintainer:	Daniel Herszenhut <dhersz@gmail.com>
Repository:	CRAN
Date/Publication:	2024-03-06 23:40:06 UTC

accessibility: Transport accessibility measures

Description

A set of fast and convenient functions to help conducting accessibility analyses. Given a pre-computed travel cost matrix and a land use dataset (containing the location of jobs, healthcare and population, for example), the package allows one to calculate accessibility levels and accessibility poverty and inequality. The package covers the majority of the most commonly used accessibility measures (such as cumulative opportunities, gravity-based and floating catchment areas methods), as well as the most frequently used inequality and poverty metrics (such as the Palma ratio, the concentration and Theil indices and the FGT family of measures).

Usage

Please check the vignettes for more on the package usage:

• Introduction to accessibility: calculating accessibility measures. Run vignette("accessibility") or check it on the website.

• Decay functions. Run vignette("decay_functions", package = "accessibility") or check it on the website.

• Calculating accessibility inequality and poverty. Run vignette("inequality_and_poverty", package = "accessibility") or check it on the website.

Author(s)

Maintainer: Daniel Herszenhut dhersz@gmail.com (ORCID)

Authors:

• Rafael H. M. Pereira rafa.pereira.br@gmail.com (ORCID)

Other contributors:

• Joey Reid [contributor]

• Ipea - Institute for Applied Economic Research [copyright holder, funder]

See Also

Useful links:

• https://github.com/ipeaGIT/accessibility

• https://ipeagit.github.io/accessibility/

• Report bugs at https://github.com/ipeaGIT/accessibility/issues

Balancing cost accessibility measure

Description

Calculates the balancing cost measure, which is defined as the travel cost required to reach as many opportunities as the number of people in a given origin. Originally proposed by Barboza et al. (2021), under the name "balancing time".

This function is generic over any kind of numeric travel cost, such as distance, time and money.

Usage

balancing_cost(
  travel_matrix,
  land_use_data,
  opportunity,
  travel_cost,
  demand,
  cost_increment = 1,
  group_by = character(0),
  fill_missing_ids = TRUE
)

Arguments

Value

A data frame containing the accessibility estimates for each origin/destination (depending if active is TRUE or FALSE) in the travel matrix.

A data frame containing the accessibility estimates for each origin in the travel matrix. Origins marked with a NA balancing cost never reach as many opportunities as there is people residing in them, given the specified travel matrix.

References

Barboza MH, Carneiro MS, Falavigna C, Luz G, Orrico R (2021). “Balancing Time: Using a New Accessibility Measure in Rio de Janeiro.” Journal of Transport Geography, 90, 102924. ISSN 09666923, doi:10.1016/j.jtrangeo.2020.102924.

Examples

data_dir <- system.file("extdata", package = "accessibility")
travel_matrix <- readRDS(file.path(data_dir, "travel_matrix.rds"))
land_use_data <- readRDS(file.path(data_dir, "land_use_data.rds"))

bc <- balancing_cost(
  travel_matrix,
  land_use_data,
  opportunity = "jobs",
  travel_cost = "travel_time",
  demand = "population"
)
head(bc)

Concentration Index

Description

Calculates the Concentration Index (CI) of a given accessibility distribution. This measures estimates the extent to which accessibility inequalities are systematically associated with individuals' socioeconomic levels. CI values can theoretically vary between -1 and +1 (when all accessibility is concentrated in the most or in the least disadvantaged person, respectively). Negative values indicate that inequalities favor the poor, while positive values indicate a pro-rich bias. The function supports calculating the standard relative CI and the corrected CI, as proposed by Erreygers (2009).

Usage

concentration_index(
  accessibility_data,
  sociodemographic_data,
  opportunity,
  population,
  income,
  type,
  group_by = character(0)
)

Arguments

Value

A data frame containing the inequality estimates for the study area.

References

Erreygers G (2009). “Correcting the Concentration Index.” Journal of Health Economics, 28(2), 504–515. ISSN 0167-6296, doi:10.1016/j.jhealeco.2008.02.003.

See Also

Other inequality: gini_index(), palma_ratio(), theil_t()

Examples

data_dir <- system.file("extdata", package = "accessibility")
travel_matrix <- readRDS(file.path(data_dir, "travel_matrix.rds"))
land_use_data <- readRDS(file.path(data_dir, "land_use_data.rds"))

access <- cumulative_cutoff(
  travel_matrix,
  land_use_data,
  cutoff = 30,
  opportunity = "jobs",
  travel_cost = "travel_time"
)

ci <- concentration_index(
  access,
  sociodemographic_data = land_use_data,
  opportunity = "jobs",
  population = "population",
  income = "income_per_capita",
  type = "corrected"
)
ci

Minimum travel cost to closest N number of opportunities

Description

Calculates the minimum travel cost to the closest N number of opportunities.

This function is generic over any kind of numeric travel cost, such as distance, time and money.

Usage

cost_to_closest(
  travel_matrix,
  land_use_data,
  opportunity,
  travel_cost,
  n = 1,
  group_by = character(0),
  active = TRUE,
  fill_missing_ids = TRUE
)

Arguments

Value

A data frame containing the accessibility estimates for each origin/destination (depending if active is TRUE or FALSE) in the travel matrix.

Examples

data_dir <- system.file("extdata", package = "accessibility")
travel_matrix <- readRDS(file.path(data_dir, "travel_matrix.rds"))
land_use_data <- readRDS(file.path(data_dir, "land_use_data.rds"))

df <- cost_to_closest(
  travel_matrix,
  land_use_data,
  n = 1,
  opportunity = "schools",
  travel_cost = "travel_time"
)
head(df)

df <- cost_to_closest(
  travel_matrix,
  land_use_data,
  n = c(1, 2),
  opportunity = "schools",
  travel_cost = "travel_time"
)
head(df)

Cumulative access based on a travel cost cutoff

Description

Calculates the number of opportunities accessible under a given specified travel cost cutoff.

This function is generic over any kind of numeric travel cost, such as distance, time and money.

Usage

cumulative_cutoff(
  travel_matrix,
  land_use_data,
  opportunity,
  travel_cost,
  cutoff,
  group_by = character(0),
  active = TRUE,
  fill_missing_ids = TRUE
)

Arguments

Value

A data frame containing the accessibility estimates for each origin/destination (depending if active is TRUE or FALSE) in the travel matrix.

See Also

Other cumulative access: cumulative_interval()

Examples

data_dir <- system.file("extdata", package = "accessibility")
travel_matrix <- readRDS(file.path(data_dir, "travel_matrix.rds"))
land_use_data <- readRDS(file.path(data_dir, "land_use_data.rds"))

# active accessibility: number of schools accessible from each origin
df <- cumulative_cutoff(
  travel_matrix = travel_matrix,
  land_use_data = land_use_data,
  cutoff = 30,
  opportunity = "schools",
  travel_cost = "travel_time"
)
head(df)

df <- cumulative_cutoff(
  travel_matrix = travel_matrix,
  land_use_data = land_use_data,
  cutoff = c(30, 60),
  opportunity = "schools",
  travel_cost = "travel_time"
)
head(df)

# passive accessibility: number of people that can reach each destination
df <- cumulative_cutoff(
  travel_matrix = travel_matrix,
  land_use_data = land_use_data,
  cutoff = 30,
  opportunity = "population",
  travel_cost = "travel_time",
  active = FALSE
)
head(df)

# using multiple travel costs
pareto_frontier <- readRDS(file.path(data_dir, "pareto_frontier.rds"))

df <- cumulative_cutoff(
  pareto_frontier,
  land_use_data = land_use_data,
  opportunity = "jobs",
  travel_cost = c("travel_time", "monetary_cost"),
  cutoff = list(c(20, 30), c(0, 5, 10))
)
head(df)

Cumulative access based on maximum travel time interval

Description

Calculates the average or median number of opportunities that can be reached considering multiple maximum travel cost thresholds within a given travel cost interval specified by the user. The time interval cumulative accessibility measures was originally proposed by Tomasiello et al. (2023).

This function is generic over any kind of numeric travel cost, such as distance, time and money.

Usage

cumulative_interval(
  travel_matrix,
  land_use_data,
  opportunity,
  travel_cost,
  interval,
  interval_increment = 1,
  summary_function = stats::median,
  group_by = character(0),
  active = TRUE
)

Arguments

Value

A data frame containing the accessibility estimates for each origin/destination (depending if active is TRUE or FALSE) in the travel matrix.

References

Tomasiello DB, Herszenhut D, Oliveira JLA, Braga CKV, Pereira RHM (2023). “A Time Interval Metric for Cumulative Opportunity Accessibility.” Applied Geography, 157, 103007. ISSN 0143-6228, doi:10.1016/j.apgeog.2023.103007.

See Also

Other cumulative access: cumulative_cutoff()

Examples

data_dir <- system.file("extdata", package = "accessibility")
travel_matrix <- readRDS(file.path(data_dir, "travel_matrix.rds"))
land_use_data <- readRDS(file.path(data_dir, "land_use_data.rds"))

df <- cumulative_interval(
  travel_matrix = travel_matrix,
  land_use_data = land_use_data,
  interval = c(20, 30),
  opportunity = "schools",
  travel_cost = "travel_time"
)
head(df)

df <- cumulative_interval(
  travel_matrix = travel_matrix,
  land_use_data = land_use_data,
  interval = c(40, 80),
  opportunity = "jobs",
  travel_cost = "travel_time"
)
head(df)

Binary (a.k.a. step) decay function

Description

Returns a binary weighting function (frequently used to calculate cumulative opportunities measures) to be used inside accessibility calculating functions.

This function is generic over any kind of numeric travel cost, such as distance, time and money.

Usage

decay_binary(cutoff)

Arguments

Value

A function that takes a generic travel cost vector (numeric) as an input and returns a list of weight vectors (a list of numeric vectors, named after the arguments passed to the decay function).

See Also

Other decay functions: decay_exponential(), decay_linear(), decay_logistic(), decay_power(), decay_stepped()

Examples

weighting_function <- decay_binary(cutoff = 30)

weighting_function(c(20, 35))

weighting_function <- decay_binary(cutoff = c(30, 45))

weighting_function(c(20, 35))

Negative exponential decay function

Description

Returns a negative exponential weighting function to be used inside accessibility calculating functions.

This function is generic over any kind of numeric travel cost, such as distance, time and money.

Usage

decay_exponential(decay_value)

Arguments

Value

A function that takes a generic travel cost vector (numeric) as an input and returns a list of weight vectors (a list of numeric vectors, named after the arguments passed to the decay function).

See Also

Other decay functions: decay_binary(), decay_linear(), decay_logistic(), decay_power(), decay_stepped()

Examples

weighting_function <- decay_exponential(decay_value = 0.1)

weighting_function(c(20, 30))

weighting_function <- decay_exponential(decay_value = c(0.1, 0.2))

weighting_function(c(20, 30))

Linear decay function

Description

Returns a linear weighting function to be used inside accessibility calculating functions.

This function is generic over any kind of numeric travel cost, such as distance, time and money.

Usage

decay_linear(cutoff)

Arguments

Value

A function that takes a generic travel cost vector (numeric) as an input and returns a list of weight vectors (a list of numeric vectors, named after the arguments passed to the decay function).

See Also

Other decay functions: decay_binary(), decay_exponential(), decay_logistic(), decay_power(), decay_stepped()

Examples

weighting_function <- decay_linear(cutoff = 30)

weighting_function(c(20, 35))

weighting_function <- decay_linear(cutoff = c(30, 45))

weighting_function(c(20, 35))

Logistic decay function

Description

Returns a logistic weighting function (in which the weights follow the distribution of a reversed cumulative logistic curve) to be used inside accessibility calculating functions. The logistic curve is parameterized with the cutoff that sets its inflection point and the standard deviation that sets its steepness.

This function is generic over any kind of numeric travel cost, such as distance, time and money.

Usage

decay_logistic(cutoff, sd)

Arguments

Details

When using a function created with decay_logistic(), the output is named after the combination of cutoffs ("c") and standard deviations ("sd") - e.g. given the cutoffs c(30, 40) and the standard deviations c(10, 20), the first element of the output will be named "c30;sd10" and the second will be named "c40;sd20". This function uses the adjusted logistic decay curve proposed by Bauer and Groneberg (2016), in which the condition f(0) = 1 is met (i.e. the weight of an opportunity whose cost to reach is 0 is 1).

Value

A function that takes a generic travel cost vector (numeric) as input and returns a vector of weights (numeric).

References

Bauer J, Groneberg DA (2016). “Measuring Spatial Accessibility of Health Care Providers – Introduction of a Variable Distance Decay Function within the Floating Catchment Area (FCA) Method.” PLOS ONE, 11(7), e0159148. ISSN 1932-6203, doi:10.1371/journal.pone.0159148.

See Also

Other decay functions: decay_binary(), decay_exponential(), decay_linear(), decay_power(), decay_stepped()

Examples

weighting_function <- decay_logistic(cutoff = 30, sd = 5)

weighting_function(c(0, 30, 45, 60))

weighting_function <- decay_logistic(cutoff = c(30, 45), sd = c(5, 10))

weighting_function(c(0, 30, 45, 60))

Inverse power decay function

Description

Returns an inverse power weighting function to be used inside accessibility calculating functions.

This function is generic over any kind of numeric travel cost, such as distance, time and money.

Usage

decay_power(decay_value)

Arguments

Value

A function that takes a generic travel cost vector (numeric) as an input and returns a list of weight vectors (a list of numeric vectors, named after the arguments passed to the decay function).

See Also

Other decay functions: decay_binary(), decay_exponential(), decay_linear(), decay_logistic(), decay_stepped()

Examples

weighting_function <- decay_power(decay_value = 0.1)

weighting_function(c(20, 35))

weighting_function <- decay_power(decay_value = c(0.1, 0.2))

weighting_function(c(20, 35))

Stepped decay function

Description

Returns a stepped weighting function to be used inside accessibility calculating functions.

This function is generic over any kind of numeric travel cost, such as distance, time and money.

Usage

decay_stepped(steps, weights)

Arguments

Details

When both steps and weights parameters are given lists, their content are matched element-wise to define each stepped weighting function

• i.e. the first element of steps is matched to the first element of weights, the second element of steps is matched to the second of weights, etc. When using a function created with decay_stepped(), the output is named after the combination of steps ("s") and weights ("w")

• e.g. given the steps c(10, 20, 30) and the weights c(0.66, 0.33, 0), the output will be named "s(10,20,30);w(0.66,0.33,0)".

Value

A function that takes a generic travel cost vector (numeric) as an input and returns a vector of weights (numeric).

See Also

Other decay functions: decay_binary(), decay_exponential(), decay_linear(), decay_logistic(), decay_power()

Examples

weighting_function <- decay_stepped(
  c(10, 20, 30, 40),
  weights = c(0.75, 0.5, 0.25, 0)
)

weighting_function(c(5, 25, 35, 45))

weighting_function <- decay_stepped(
  list(c(10, 20, 30, 40), c(10, 20, 30, 40)),
  weights = list(c(0.75, 0.5, 0.25, 0), c(0.8, 0.6, 0.4, 0.2))
)

weighting_function(c(5, 25, 35, 45))

# intervals are open on the right, so the values change exactly at each step
weighting_function(c(0, 10, 20, 30, 40))

Foster-Greer-Thorbecke (FGT) poverty measures

Description

Calculates the FGT metrics, a family of poverty measures originally proposed by Foster et al. (1984) that capture the extent and severity of poverty within an accessibility distribution. The FGT family is composed of three measures that differ based on the \alpha parameter used to calculate them (either 0, 1 or 2) and which also changes their interpretation. Please see the details section for more information on the interpretation of the measures.

Usage

fgt_poverty(
  accessibility_data,
  sociodemographic_data,
  opportunity,
  population,
  poverty_line,
  group_by = character(0)
)

Arguments

Value

A data frame containing the three poverty estimates (FGT0, FGT1 and FGT2) for the study area.

Interpretation of FGT measures

The interpretation of each FGT measure depends on the \alpha parameter used to calculate it:

• with \alpha = 0 (FGT0) the measure captures the extent of poverty as a simple headcount - i.e. the proportion of people below the poverty line;

• with \alpha = 1 (FGT1) the measure, also know as the "poverty gap index", captures the severity of poverty as the average percentage distance between the poverty line and the accessibility of individuals below the poverty line;

• with \alpha = 2 (FGT2) the measure simultaneously captures the extent and the severity of poverty by calculating the number of people below the poverty line weighted by the size of the accessibility shortfall relative to the poverty line.

FGT values range from 0 to 1. A value of 0 indicates that every individual is above the poverty line. When every individual is below the poverty line, however, FGT0 value is 1 and FGT1 and FGT2 values approach 1.

References

Foster J, Greer J, Thorbecke E (1984). “A Class of Decomposable Poverty Measures.” Econometrica, 52(3), 761–766. ISSN 0012-9682, doi:10.2307/1913475, 1913475.

Examples

data_dir <- system.file("extdata", package = "accessibility")
travel_matrix <- readRDS(file.path(data_dir, "travel_matrix.rds"))
land_use_data <- readRDS(file.path(data_dir, "land_use_data.rds"))

access <- cumulative_cutoff(
  travel_matrix,
  land_use_data,
  cutoff = 30,
  opportunity = "jobs",
  travel_cost = "travel_time"
)

poverty <- fgt_poverty(
  access,
  opportunity = "jobs",
  sociodemographic_data = land_use_data,
  population = "population",
  poverty_line = 95368
)
poverty

Floating catchment area accessibility

Description

Calculates accessibility accounting for the competition of resources using a measure from the floating catchment area (FCA) family. Please see the details for the available FCA measures.

This function is generic over any kind of numeric travel cost, such as distance, time and money.

Usage

floating_catchment_area(
  travel_matrix,
  land_use_data,
  opportunity,
  travel_cost,
  demand,
  method,
  decay_function,
  group_by = character(0),
  fill_missing_ids = TRUE
)

Arguments

Value

A data frame containing the accessibility estimates for each origin/destination (depending if active is TRUE or FALSE) in the travel matrix.

Details

The package currently includes two built-in FCA measures:

• 2SFCA - the 2-Step Floating Catchment Area measure was the first accessibility metric in the FCA family. It was originally proposed by Luo and Wang (2003).

• BFCA - the Balanced Floating Catchment Area measure calculates accessibility accounting for competition effects while simultaneously correcting for issues of inflation of demand and service levels that are present in other FCA measures. It was originally proposed by Paez et al. (2019) and named in Pereira et al. (2021).

References

Luo W, Wang F (2003). “Measures of Spatial Accessibility to Health Care in a GIS Environment: Synthesis and a Case Study in the Chicago Region.” Environment and Planning B: Planning and Design, 30(6), 865–884. ISSN 0265-8135, 1472-3417, doi:10.1068/b29120.

Paez A, Higgins CD, Vivona SF (2019). “Demand and Level of Service Inflation in Floating Catchment Area (FCA) Methods.” PLOS ONE, 14(6), e0218773. ISSN 1932-6203, doi:10.1371/journal.pone.0218773.

Pereira RHM, Braga CKV, Servo LM, Serra B, Amaral P, Gouveia N, Paez A (2021). “Geographic Access to COVID-19 Healthcare in Brazil Using a Balanced Float Catchment Area Approach.” Social Science & Medicine, 273, 113773. ISSN 0277-9536, doi:10.1016/j.socscimed.2021.113773.

Examples

data_dir <- system.file("extdata", package = "accessibility")
travel_matrix <- readRDS(file.path(data_dir, "travel_matrix.rds"))
land_use_data <- readRDS(file.path(data_dir, "land_use_data.rds"))

# 2SFCA with a step decay function
df <- floating_catchment_area(
  travel_matrix,
  land_use_data,
  method = "2sfca",
  decay_function = decay_binary(cutoff = 50),
  opportunity = "jobs",
  travel_cost = "travel_time",
  demand = "population"
)
head(df)


# BFCA with an exponential decay function
df <- floating_catchment_area(
  travel_matrix,
  land_use_data,
  method = "bfca",
  decay_function = decay_exponential(decay_value = 0.5),
  opportunity = "jobs",
  travel_cost = "travel_time",
  demand = "population"
)
head(df)

Gini Index

Description

Calculates the Gini Index of a given accessibility distribution.

Usage

gini_index(
  accessibility_data,
  sociodemographic_data,
  opportunity,
  population,
  group_by = character(0)
)

Arguments

Value

A data frame containing the inequality estimates for the study area.

See Also

Other inequality: concentration_index(), palma_ratio(), theil_t()

Examples

data_dir <- system.file("extdata", package = "accessibility")
travel_matrix <- readRDS(file.path(data_dir, "travel_matrix.rds"))
land_use_data <- readRDS(file.path(data_dir, "land_use_data.rds"))

access <- cumulative_cutoff(
  travel_matrix,
  land_use_data,
  cutoff = 30,
  opportunity = "jobs",
  travel_cost = "travel_time"
)

gini <- gini_index(
  access,
  sociodemographic_data = land_use_data,
  opportunity = "jobs",
  population = "population"
)
gini

Gravity-based accessibility measures

Description

Calculates gravity-based accessibility using a decay function specified by the user.

This function is generic over any kind of numeric travel cost, such as distance, time and money.

Usage

gravity(
  travel_matrix,
  land_use_data,
  opportunity,
  travel_cost,
  decay_function,
  group_by = character(0),
  active = TRUE,
  fill_missing_ids = TRUE
)

Arguments

Value

A data frame containing the accessibility estimates for each origin/destination (depending if active is TRUE or FALSE) in the travel matrix.

Examples

data_dir <- system.file("extdata", package = "accessibility")
travel_matrix <- readRDS(file.path(data_dir, "travel_matrix.rds"))
land_use_data <- readRDS(file.path(data_dir, "land_use_data.rds"))

df_linear <- gravity(
  travel_matrix,
  land_use_data,
  decay_function = decay_linear(cutoff = 50),
  opportunity = "schools",
  travel_cost = "travel_time"
)
head(df_linear)

df_exp <- gravity(
  travel_matrix,
  land_use_data,
  decay_function = decay_exponential(decay_value = 0.5),
  opportunity = "schools",
  travel_cost = "travel_time"
)
head(df_exp)

Palma Ratio

Description

Calculates the Palma Ratio of a given accessibility distribution. Originally defined as the income share of the richest 10% of a population divided by the income share of the poorest 40%, this measure has been adapted in transport planning as the average accessibility of the richest 10% divided by the average accessibility of the poorest 40%.

Usage

palma_ratio(
  accessibility_data,
  sociodemographic_data,
  opportunity,
  population,
  income,
  group_by = character(0)
)

Arguments

Value

A data frame containing the inequality estimates for the study area.

See Also

Other inequality: concentration_index(), gini_index(), theil_t()

Examples

data_dir <- system.file("extdata", package = "accessibility")
travel_matrix <- readRDS(file.path(data_dir, "travel_matrix.rds"))
land_use_data <- readRDS(file.path(data_dir, "land_use_data.rds"))

access <- cumulative_cutoff(
  travel_matrix,
  land_use_data,
  cutoff = 30,
  opportunity = "jobs",
  travel_cost = "travel_time"
)

palma <- palma_ratio(
  access,
  sociodemographic_data = land_use_data,
  opportunity = "jobs",
  population = "population",
  income = "income_per_capita"
)
palma

Spatial availability

Description

Calculates spatial availability, an accessibility measured proposed by Soukhov et al. (2023) that takes into account competition effects. The accessibility levels that result from using this measure are proportional both to the demand in each origin and to the travel cost it takes to reach the destinations.

This function is generic over any kind of numeric travel cost, such as distance, time and money.

Usage

spatial_availability(
  travel_matrix,
  land_use_data,
  opportunity,
  travel_cost,
  demand,
  decay_function,
  alpha = 1,
  group_by = character(0),
  fill_missing_ids = TRUE,
  detailed_results = FALSE
)

Arguments

Value

A data frame containing the accessibility estimates for each origin/destination (depending if active is TRUE or FALSE) in the travel matrix.

References

Soukhov A, Páez A, Higgins CD, Mohamed M (2023). “Introducing Spatial Availability, a Singly-Constrained Measure of Competitive Accessibility.” PLOS ONE, 18(1), e0278468. ISSN 1932-6203, doi:10.1371/journal.pone.0278468.

Examples

# the example below is based on Soukhov et al. (2023) paper

travel_matrix <- data.table::data.table(
  from_id = rep(c("A", "B", "C"), each = 3),
  to_id = as.character(rep(1:3, 3)),
  travel_time = c(15, 30, 100, 30, 15, 100, 100, 100, 15)
)
land_use_data <- data.table::data.table(
  id = c("A", "B", "C", "1", "2", "3"),
  population = c(50000, 150000, 10000, 0, 0, 0),
  jobs = c(0, 0, 0, 100000, 100000, 10000)
)

df <- spatial_availability(
  travel_matrix,
  land_use_data,
  opportunity = "jobs",
  travel_cost = "travel_time",
  demand = "population",
  decay_function = decay_exponential(decay_value = 0.1)
)
df

detailed_df <- spatial_availability(
  travel_matrix,
  land_use_data,
  opportunity = "jobs",
  travel_cost = "travel_time",
  demand = "population",
  decay_function = decay_exponential(decay_value = 0.1),
  detailed_results = TRUE
)
detailed_df

Theil T Index

Description

Calculates the Theil T Index of a given accessibility distribution. Values range from 0 (when all individuals have exactly the same accessibility levels) to the natural log of n, in which n is the number of individuals in the accessibility dataset. If the individuals can be classified into mutually exclusive and completely exhaustive groups, the index can be decomposed into a between-groups inequaliy component and a within-groups component.

Usage

theil_t(
  accessibility_data,
  sociodemographic_data,
  opportunity,
  population,
  socioeconomic_groups = NULL,
  group_by = character(0)
)

Arguments

Value

If socioeconomic_groups is NULL, a data frame containing the total Theil T estimates for the study area. If not, a list containing three dataframes: one summarizing the total inequality and the between- and within-groups components, one listing the contribution of each group to the between-groups component and another listing the contribution of each group to the within-groups component.

See Also

Other inequality: concentration_index(), gini_index(), palma_ratio()

Examples

data_dir <- system.file("extdata", package = "accessibility")
travel_matrix <- readRDS(file.path(data_dir, "travel_matrix.rds"))
land_use_data <- readRDS(file.path(data_dir, "land_use_data.rds"))

access <- cumulative_cutoff(
  travel_matrix,
  land_use_data,
  cutoff = 30,
  opportunity = "jobs",
  travel_cost = "travel_time"
)

ti <- theil_t(
  access,
  sociodemographic_data = land_use_data,
  opportunity = "jobs",
  population = "population"
)
ti

# to calculate inequality between and within income deciles, we pass
# "income_decile" to socioeconomic_groups.
# some cells, however, are classified as in the decile NA because their
# income per capita is NaN, as they don't have any population. we filter
# these cells from our accessibility data, otherwise the output would include
# NA values (note that subsetting the data like this doesn't affect the
# assumption that groups are completely exhaustive, because cells with NA
# income decile don't have any population)

na_decile_ids <- land_use_data[is.na(land_use_data$income_decile), ]$id
access <- access[! access$id %in% na_decile_ids, ]
sociodem_data <- land_use_data[! land_use_data$id %in% na_decile_ids, ]

ti <- theil_t(
  access,
  sociodemographic_data = sociodem_data,
  opportunity = "jobs",
  population = "population",
  socioeconomic_groups = "income_decile"
)
ti