Package 'aopdata'

Title: Data from the 'Access to Opportunities Project (AOP)'
Description: Download data from the 'Access to Opportunities Project (AOP)'. The 'aopdata' package brings annual estimates of access to employment, health, education and social assistance services by transport mode, as well as data on the spatial distribution of population, jobs, health care, schools and social assistance facilities at a fine spatial resolution for all cities included in the project. More info on the 'AOP' website <https://www.ipea.gov.br/acessooportunidades/en/>.
Authors: Rafael H. M. Pereira [aut, cre] , Daniel Herszenhut [aut] , Marcus Saraiva [aut] , Carlos Kaue Vieira Braga [aut] , Diego Bogado Tomasiello [ctb], Joao Bazzo [ctb], Ipea - Institute for Applied Economic Research [cph, fnd]
Maintainer: Rafael H. M. Pereira <[email protected]>
License: MIT + file LICENSE
Version: 1.1.0
Built: 2024-10-27 02:49:17 UTC
Source: https://github.com/ipeaGIT/aopdata

Help Index


aopdata data dictionary

Description

Opens aopdata data dictionary on a web browser. This function requires internet connection.

Usage

aopdata_dictionary(lang = "en")

Arguments

lang

Character. Language of data dictionary. It can be either "en" for English (default) or "pt" for Portuguese.

Value

Opens aopdata data dictionary on a web browser

Examples

# Data dictionary in English
aopdata_dictionary(lang='en')

# Data dictionary in Portuguese
aopdata_dictionary(lang='pt')

Download accessibility estimates with population and land use data

Description

Download estimates of access to employment, health, education and social assistance services by transport mode and time of the day for the cities included in the AOP project. See the documentation 'Details' for the data dictionary. The data set reports information for each heaxgon in a H3 spatial grid at resolution 9, with a side of 174 meters and an area of 0.10 km2. More information about H3 at https://h3geo.org/docs/core-library/restable/.

Usage

read_access(
  city = NULL,
  mode = "walk",
  peak = TRUE,
  year = 2019,
  geometry = FALSE,
  showProgress = TRUE
)

Arguments

city

Character. A city name or three-letter abbreviation. If city="all", the function returns data for all cities.

mode

Character. A transport mode. Modes available include 'public_transport', 'bicycle', or 'walk' (the default).

peak

Logical. If TRUE (the default), returns accessibility estimates during peak time, between 6am and 8am. If FALSE, returns accessibility during off-peak, between 2pm and 4am. This argument only takes effect when mode is either car or public_transport.

year

Numeric. A year number in YYYY format. Defaults to 2019.

geometry

Logical. If FALSE (the default), returns a regular data.table of aop data. If TRUE, returns an ⁠sf data.frame⁠ with simple feature geometry of spatial hexagonal grid H3. See details in read_grid.

showProgress

Logical. Defaults to TRUE display progress bar.

Value

A data.frame object

Data dictionary:

data_type column description values
temporal year Year of reference
transport mode Transport mode walk; bicycle; public_transport; car
transport peak Peak and off-peak 1 (peak); 0 (off-peak)

The name of the columns with accessibility estimates are the junction of three components: 1) Type of accessibility indicator 2) Type of opportunity / population 3) Time threshold

1) Type of accessibility indicator

Indicator Description Observation
CMA Cumulative opportunity measure (active)
CMP Cumulative opportunity measure (passive)
TMI Travel time to closest opportunity Value = Inf when travel time is longer than 2h (public transport or car) or 1,5h (walking or bicycle)

2) Type of opportunity / population

Type of opportunity Description Observation: available in combination with
TT All jobs CMA indicator
TB Jobs with primary education CMA indicator
TM Jobs with secondary education CMA indicator
TA Jobs with tertiary education CMA indicator
ST All healthcare facilities CMA and TMI indicators
SB Healthcare facilities - Low complexity CMA and TMI indicators
SM Healthcare facilities - Medium complexity CMA and TMI indicators
SA Healthcare facilities - High complexity CMA and TMI indicators
ET All public schools CMA and TMI indicators
EI Public schools - early childhood CMA and TMI indicators
EF Public schools - elementary schools CMA and TMI indicators
EM Public schools - high schools CMA and TMI indicators
MT All school enrollments CMA and TMI indicators
MI School enrollments - early childhood CMA and TMI indicators
MF School enrollments - elementary schools CMA and TMI indicators
MM School enrollments - high schools CMA and TMI indicators
CT All Social Assistance Reference Centers (CRAS) CMA and TMI indicators
People Description Observation: available in combination with
PT All population CMP indicator
PH Men CMP indicator
PM Women CMP indicator
PB White population CMP indicator
PA Asian-descendent population CMP indicator
PI Indigenous population CMP indicator
PN Back population CMP indicator
P0005I Population between 0 and 5 years old CMP indicator
P0614I Population between 6 and 14 years old CMP indicator
P1518I Population between 15 and 18 years old CMP indicator
P1924I Population between 19 and 24 years old CMP indicator
P2539I Population between 25 and 39 years old CMP indicator
P4069I Population between 40 and 69 years old CMP indicator
P70I Population with 70 years old or more CMP indicator

3) Time threshold (only applicable to CMA and CMP estimates)

Time threshold **Description ** Observation: only applicable to
15 Opportunities accessible within 15 min. Active transport modes
30 Opportunities accessible within 30 min. All transport modes
45 Opportunities accessible within 45 min. Active transport modes
60 Opportunities accessible within 60 min. All transport modes
90 Opportunities accessible within 90 min. Public transport and car
120 Opportunities accessible within 120 min. Public transport and car

4) Cities available

City name Three-letter abbreviation Transport modes
Belem bel Active
Belo Horizonte bho All
Brasilia bsb Active
Campinas cam All
Campo Grande cgr Active
Curitiba cur Active
Duque de Caxias duq Active
Fortaleza for All
Goiania goi All
Guarulhos gua Active
Maceio mac Active
Manaus man Active
Natal nat Active
Porto Alegre poa All
Recife rec All
Rio de Janeiro rio All
Salvador sal Active
Sao Goncalo sgo Active
Sao Luis slz Active
Sao Paulo spo All

Examples

# Read accessibility estimates of a single city
df <- read_access(city = 'Fortaleza', mode = 'public_transport', year = 2019, showProgress = FALSE)
df <- read_access(city = 'Goiania', mode = 'public_transport', year = 2019, showProgress = FALSE)

# Read accessibility estimates for all cities
all <- read_access(city = 'all', mode = 'walk', year = 2019, showProgress = FALSE)

Download spatial hexagonal grid H3

Description

Results of the AOP project are spatially aggregated on a H3 spatial grid at resolution 9, with a side of 174 meters and an area of 0.10 km2. More information about H3 at https://h3geo.org/docs/core-library/restable/. See the documentation 'Details' for the data dictionary.

Usage

read_grid(city = NULL, showProgress = FALSE)

Arguments

city

Character. A city name or three-letter abbreviation. If city="all", the function returns data for all cities.

showProgress

Logical. Defaults to TRUE display progress bar.

Value

An ⁠sf data.frame⁠ object

Data dictionary:

Data type column Description
geographic id_hex Unique id of hexagonal cell
geographic abbrev_muni Abbreviation of city name (3 letters)
geographic name_muni City name
geographic code_muni 7-digit code of each city

Cities available

City name Three-letter abbreviation
Belem bel
Belo Horizonte bho
Brasilia bsb
Campinas cam
Campo Grande cgr
Curitiba cur
Duque de Caxias duq
Fortaleza for
Goiania goi
Guarulhos gua
Maceio mac
Manaus man
Natal nat
Porto Alegre poa
Recife rec
Rio de Janeiro rio
Salvador sal
Sao Goncalo sgo
Sao Luis slz
Sao Paulo spo

Examples

# Read spatial grid of a single city
nat <- read_grid(city = 'Natal', showProgress = FALSE)

# Read spatial grid of all cities in the project
# all <- read_grid(city = 'all', showProgress = FALSE)

Download land use and population data

Description

Download data on the spatial distribution of population, jobs, schools, health care and social assitance facilities at a fine spatial resolution for the cities included in the AOP project. See the documentation 'Details' for the data dictionary. The data set reports information for each heaxgon in a H3 spatial grid at resolution 9, with a side of 174 meters and an area of 0.10 km2. More information about H3 at https://h3geo.org/docs/core-library/restable/.

Usage

read_landuse(city = NULL, year = 2019, geometry = FALSE, showProgress = TRUE)

Arguments

city

Character. A city name or three-letter abbreviation. If city="all", the function returns data for all cities.

year

Numeric. A year number in YYYY format. Defaults to 2019.

geometry

Logical. If FALSE (the default), returns a regular data.table of aop data. If TRUE, returns an ⁠sf data.frame⁠ with simple feature geometry of spatial hexagonal grid H3. See details in read_grid.

showProgress

Logical. Defaults to TRUE display progress bar.

Value

A data.frame object or an ⁠sf data.frame⁠ object

Data dictionary:

data_type column description values
temporal year Year of reference
geographic id_hex Unique id of hexagonal cell
geographic abbrev_muni Abbreviation of city name (3 letters)
geographic name_muni City name
geographic code_muni 7-digit code of each city
sociodemographic P001 Total number of residents
sociodemographic P002 Number of white residents
sociodemographic P003 Number of black residents
sociodemographic P004 Number of indigenous residents
sociodemographic P005 Number of asian-descendents residents
sociodemographic P006 Number of men
sociodemographic P007 Number of women
sociodemographic P010 Number of people between 0 and 5 years old
sociodemographic P011 Number of people between 6 and 14 years old
sociodemographic P012 Number of people between 15 and 18 years old
sociodemographic P013 Number of people between 19 and 24 years old
sociodemographic P014 Number of people between 25 and 39 years old
sociodemographic P015 Number of people between 40 and 69 years old
sociodemographic P016 Number of people with 70 years old or more
sociodemographic R001 Average household income per capita R$ (Brazilian Reais), values in 2010
sociodemographic R002 Income quintile group 1 (poorest), 2, 3, 4, 5 (richest)
sociodemographic R003 Income decile group 1 (poorest), 2, 3, 4, 5, 6, 7, 8, 9, 10 (richest)
land use T001 Total number of formal jobs
land use T002 Number of formal jobs with primary education
land use T003 Number of formal jobs with secondary education
land use T004 Number of formal jobs with tertiary education
land use E001 Total number of public schools
land use E002 Number of public schools - early childhood
land use E003 Number of public schools - elementary schools
land use E004 Number of public schools - high schools
land use M001 Total number of school enrollments
land use M002 Number of school enrollments - early childhood
land use M003 Number of school enrollments - elementary schools
land use M004 Number of school enrollments - high schools
land use S001 Total number of healthcare facilities
land use S002 Number of healthcare facilities - low complexity
land use S003 Number of healthcare facilities - medium complexity
land use S004 Number of healthcare facilities - high complexity
land use C001 Total number of Social Assistance Reference Centers (CRAS)

Cities available

City name Three-letter abbreviation
Belem bel
Belo Horizonte bho
Brasilia bsb
Campinas cam
Campo Grande cgr
Curitiba cur
Duque de Caxias duq
Fortaleza for
Goiania goi
Guarulhos gua
Maceio mac
Manaus man
Natal nat
Porto Alegre poa
Recife rec
Rio de Janeiro rio
Salvador sal
Sao Goncalo sgo
Sao Luis slz
Sao Paulo spo

Examples

# a single city
bho <- read_landuse(city = 'Belo Horizonte', year = 2019, showProgress = FALSE)
bho <- read_landuse(city = 'bho', year = 2019, showProgress = FALSE)

# all cities
all <- read_landuse(city = 'all', year = 2019)

Download population and socioeconomic data

Description

Download population and socioeconomic data from the Brazilian Census at a fine spatial resolution for the cities included in the AOP project. See the documentation 'Details' for the data dictionary. The data set reports information for each heaxgon in a H3 spatial grid at resolution 9, with a side of 174 meters and an area of 0.10 km2. More information about H3 at https://h3geo.org/docs/core-library/restable/.

Usage

read_population(
  city = NULL,
  year = 2010,
  geometry = FALSE,
  showProgress = TRUE
)

Arguments

city

Character. A city name or three-letter abbreviation. If city="all", the function returns data for all cities.

year

Numeric. A year number in YYYY format. Defaults to 2019.

geometry

Logical. If FALSE (the default), returns a regular data.table of aop data. If TRUE, returns an ⁠sf data.frame⁠ with simple feature geometry of spatial hexagonal grid H3. See details in read_grid.

showProgress

Logical. Defaults to TRUE display progress bar.

Value

A data.frame object or an ⁠sf data.frame⁠ object

Data dictionary:

data_type column description values
temporal year Year of reference
geographic id_hex Unique id of hexagonal cell
geographic abbrev_muni Abbreviation of city name (3 letters)
geographic name_muni City name
geographic code_muni 7-digit code of each city
sociodemographic P001 Total number of residents
sociodemographic P002 Number of white residents
sociodemographic P003 Number of black residents
sociodemographic P004 Number of indigenous residents
sociodemographic P005 Number of asian-descendents residents
sociodemographic P006 Number of men
sociodemographic P007 Number of women
sociodemographic P010 Number of people between 0 and 5 years old
sociodemographic P011 Number of people between 6 and 14 years old
sociodemographic P012 Number of people between 15 and 18 years old
sociodemographic P013 Number of people between 19 and 24 years old
sociodemographic P014 Number of people between 25 and 39 years old
sociodemographic P015 Number of people between 40 and 69 years old
sociodemographic P016 Number of people with 70 years old or more
sociodemographic R001 Average household income per capita R$ (Brazilian Reais), values in 2010
sociodemographic R002 Income quintile group 1 (poorest), 2, 3, 4, 5 (richest)
sociodemographic R003 Income decile group 1 (poorest), 2, 3, 4, 5, 6, 7, 8, 9, 10 (richest)

Cities available

City name Three-letter abbreviation
Belem bel
Belo Horizonte bho
Brasilia bsb
Campinas cam
Campo Grande cgr
Curitiba cur
Duque de Caxias duq
Fortaleza for
Goiania goi
Guarulhos gua
Maceio mac
Manaus man
Natal nat
Porto Alegre poa
Recife rec
Rio de Janeiro rio
Salvador sal
Sao Goncalo sgo
Sao Luis slz
Sao Paulo spo

Examples

# a single city
bho <- read_population(city = 'Belo Horizonte', year = 2010, showProgress = FALSE)
bho <- read_population(city = 'bho', year = 2010, showProgress = FALSE)

# all cities
all <- read_population(city = 'all', year = 2010)