Help for package geodaData

Title:

Spatial Analysis Datasets for Teaching

Version:

0.1.0

Description:

Stores small spatial datasets used to teach basic spatial analysis concepts. Datasets are based off of the 'GeoDa' software workbook and data site https://geodacenter.github.io/data-and-lab/ developed by Luc Anselin and team at the University of Chicago. Datasets are stored as 'sf' objects.

Depends:

R (≥ 3.3.0)

License:

CC0

URL:

https://github.com/spatialanalysis/geodaData

BugReports:

https://github.com/spatialanalysis/geodaData/issues

Encoding:

UTF-8

LazyData:

true

RoxygenNote:

7.0.2

Suggests:

NeedsCompilation:

Packaged:

2020-05-20 01:07:05 UTC; angela

Author:

Angela Li

[aut, cre], Luc Anselin [ctb] (Creator of original spatial datasets)

Maintainer:

Angela Li <ali6@uchicago.edu>

Repository:

CRAN

Date/Publication:

2020-05-27 09:20:02 UTC

geodaData: Spatial Analysis Datasets for Teaching

Description

Stores small spatial datasets used to teach basic spatial analysis concepts. Datasets are based off of the 'GeoDa' software workbook and data site <https://geodacenter.github.io/data-and-lab/> developed by Luc Anselin and team at the University of Chicago. Datasets are stored as 'sf' objects.

Author(s)

Maintainer: Angela Li ali6@uchicago.edu (ORCID)

Other contributors:

Luc Anselin (Creator of original spatial datasets) [contributor]

Chicago Community Areas (2010).

Description

Population in Chicago community areas in 2010.

Usage

chicago_comm

Format

An sf data frame with 77 rows, 4 variables, and a geometry column:

community: Community name
area_num_1: Community ID
NID: Community ID (repeated)
POP2010: Population in 2010
geometry: MULTIPOLYGON

Details

Sf object, unprojected. EPSG 4326: WGS84.

Source

https://data.cityofchicago.org/Facilities-Geographic-Boundaries/Boundaries-Community-Areas-current-/cauq-8yn6

Examples

if (requireNamespace("sf", quietly = TRUE)) {
  library(sf)
  data(chicago_comm)

  plot(chicago_comm["community"])
}

Cleveland Home Sales (2015).

Description

Location and sales price of home sales in a core area of Cleveland, OH for the fourth quarter of 2015.

Usage

clev_pts

Format

An sf data frame with 205 rows, 9 variables, and a geometry column:

unique_id: unique parcel id
parcel: unique parcel number
x: point latitude
y: point longitude
sale_price: price paid for the house ($)
tract10int: License plate number and sometimes a description (state, color). Some entries did not include a plate number.
quarter: quarter of sale (4th for all)
year1: year of sale (2015 for all)
yrquarter: year and quarter of sale (4th quarter of 2015 for all)
geometry: POINT

Details

Sf object, units in ft. EPSG 3734: NAD83 / Ohio North (ftUS).

Source

Cuyahoga County Fiscal Office. https://geodacenter.github.io/data-and-lab//clev_sls_154_core/

Examples

if (requireNamespace("sf", quietly = TRUE)) {
  library(sf)
  data(clev_pts)

  plot(clev_pts["unique_id"])
}

Chicago Population Change (2000-2010).

Description

Change in population in Chicago community areas from 2000 to 2010.

Usage

commpop

Format

An sf data frame with 77 rows, 8 variables, and a geometry column:

community: Community name
NID: Community ID
POP2010: Population in 2010
POP2000: Population in 2000
POPCH: Population change, count
POPPERCH: Population percent change
popplus: 1 if area has positive population change (17 observations)
popneg: 1 if area has negative population change (60 observations)
geometry: MULTIPOLYGON

Details

Sf object, unprojected. EPSG 4326: WGS84.

Source

https://www.chicago.gov/city/en/depts/dcd/supp_info/community_area_2000and2010censuspopulationcomparisons.html

Examples

if (requireNamespace("sf", quietly = TRUE)) {
  library(sf)
  data(commpop)

  plot(commpop["community"])
}

Guerry "Moral Statistics" (1830s).

Description

Classic social science foundational study by Andre-Michel Guerry on crime, suicide, literacy and other “moral statistics” in 1830s France. Data from the R package Guerry (Michael Friendly and Stephane Dray).

Usage

guerry

Format

An sf data frame with 85 rows, 23 variables, and a geometry column:

variable: Description
dept, code_de: Department ID: Standard numbers for the departments
region: Region of France (‘N’=’North’, ‘S’=’South’, ‘E’=’East’, ‘W’=’West’, ‘C’=’Central’). Corsica is coded as NA.
dprtmnt: Department name: Departments are named according to usage in 1830, but without accents. A factor with levels Ain Aisne Allier … Vosges Yonne
crm_prs: Population per Crime against persons.
crm_prp: Population per Crime against property.
litercy: Percent of military conscripts who can read and write.
donatns: Donations to the poor.
infants: Population per illegitimate birth.
suicids: Population per suicide.
maincty: Size of principal city (‘1:Sm’, ‘2:Med’, ‘3:Lg’), used as a surrogate for population density. Large refers to the top 10, small to the bottom 10; all the rest are classed Medium.
wealth: Per capita tax on personal property. A ranked index based on taxes on personal and movable property per inhabitant.
commerc: Commerce and Industry, measured by the rank of the number of patents / population.
clergy: Distribution of clergy, measured by the rank of the number of Catholic priests in active service population.
crim_prn: Crimes against parents, measured by the rank of the ratio of crimes against parents to all crimes – Average for the years 1825-1830.
infntcd: Infanticides per capita. A ranked ratio of number of infanticides to population – Average for the years 1825-1830.
dntn_cl: Donations to the clergy. A ranked ratio of the number of bequests and donations inter vivios to population – Average for the years 1815-1824.
lottery: Per capita wager on Royal Lottery. Ranked ratio of the proceeds bet on the royal lottery to population — Average for the years 1822-1826.
desertn: Military desertion, ratio of number of young soldiers accused of desertion to the force of the military contingent, minus the deficit produced by the insufficiency of available billets – Average of the years 1825-1827.
instrct: Instruction. Ranks recorded from Guerry’s map of Instruction. Note: this is inversely related to Literacy.
prsttts: Number of prostitutes registered in Paris from 1816 to 1834, classified by the department of their birth
distanc: Distance to Paris (km). Distance of each department centroid to the centroid of the Seine (Paris).
area: Area (1000 km^2).
pop1831: Population in 1831, in 1000s.
geometry: MULTIPOLYGON

Details

Sf object, units in m. EPSG 27572: NTF (Paris) / Lambert zone II.

Source

Angeville, A. (1836). Essai sur la Statistique de la Population française Paris: F. Doufour.
Guerry, A.-M. (1833). Essai sur la statistique morale de la France Paris: Crochard. English translation: Hugh P. Whitt and Victor W. Reinking, Lewiston, N.Y. : Edwin Mellen Press, 2002.
Parent-Duchatelet, A. (1836). De la prostitution dans la ville de Paris, 3rd ed, 1857, p. 32, 36

https://geodacenter.github.io/data-and-lab/Guerry/

Examples

if (requireNamespace("sf", quietly = TRUE)) {
  library(sf)
  data(guerry)

  plot(guerry["CODE_DE"])
}

Homicides & Socio-Economics (1960-90).

Description

Homicides and selected socio-economic characteristics for continental U.S. counties. Data for four decennial census years: 1960, 1970, 1980 and 1990.

Usage

ncovr

Format

An sf data frame with 3085 rows, 69 variables, and a geometry column:

name: county name
state_name: state name
state_fips: state fips code (character)
cnty_fips: county fips code (character)
fips: combined state and county fips code (character)
stfips: state fips code (numeric)
cofips: county fips code (numeric)
fipsno: fips code as numeric variable
south: dummy variable for Southern counties (South = 1)
hr: homicide rate per 100,000 (1960, 1970, 1980, 1990)
hc: homicide count, three year average centered on 1960, 1970, 1980, 1990
po: county population, 1960, 1970, 1980, 1990
rd: resource deprivation 1960, 1970, 1980, 1990 (principal component, see Codebook for details)
ps: population structure 1960, 1970, 1980, 1990 (principal component, see Codebook for details)
ue: unemployment rate 1960, 1970, 1980, 1990
dv: divorce rate 1960, 1970, 1980, 1990 (percent males over 14 divorced)
ma: median age 1960, 1970, 1980, 1990
pol: log of population 1960, 1970, 1980, 1990
dnl: log of population density 1960, 1970, 1980, 1990
mfil: log of median family income 1960, 1970, 1980, 1990
fp: percent families below poverty 1960, 1970, 1980, 1990 (see Codebook for details)
blk: percent black 1960, 1970, 1980, 1990
gi: Gini index of family income inequality 1960, 1970, 1980, 1990
fh: percent female headed households 1960, 1970, 1980, 1990
geometry: MULTIPOLYGON

Details

Sf object, unprojected. EPSG 4326: WGS84.

Source

S. Messner, L. Anselin, D. Hawkins, G. Deane, S. Tolnay, R. Baller (2000). An Atlas of the Spatial Patterning of County-Level Homicide, 1960-1990. Pittsburgh, PA, National Consortium on Violence Research (NCOVR). https://geodacenter.github.io/data-and-lab/ncovr/

Examples

if (requireNamespace("sf", quietly = TRUE)) {
  library(sf)
  data(ncovr)

  plot(ncovr["NAME"])
}

Rental Housing and Demographics in NYC (2000s), non-spatial.

Description

Demographic and housing data for New York City’s 55 sub-boroughs (2000s).

Usage

nyc

Format

A data frame with 55 rows and 34 variables:

CODE: sub-borough code, 1XX Bronx, 2XX Brooklyn, 3XX Manhattan, 4XX Queens, 5XX Staten Island
FORHIS06: percentage of hispanic population, not born in US, 2006
FORHIS07: percentage of hispanic population, not born in US, 2007
FORHIS08: percentage of hispanic population, not born in US, 2008
FORHIS09: percentage of hispanic population, not born in US, 2009
FORWH06: percentage of white population, not born in US, 2006
FORWH07: percentage of white population, not born in US, 2007
FORWH08: percentage of white population, not born in US, 2008
FORWH09: percentage of white population, not born in US, 2009
HHSIZ1990: average number of people per household, 1990
HHSIZ00: average number of people per household, 2000
HHSIZ02: average number of people per household, 2002
HHSIZ05: average number of people per household, 2005
HHSIZ08: average number of people per household, 2008
KIDS2000: percentage households w kids under 18, 2000
KIDS2005: percentage households w kids under 18, 2005
KIDS2006: percentage households w kids under 18, 2006
KIDS2007: percentage households w kids under 18, 2007
KIDS2008: percentage households w kids under 18, 2008
KIDS2009: percentage households w kids under 18, 2009
NAME: name of borough, one of five
RENT2002: median monthly contract rent, 2002
RENT2005: median monthly contract rent, 2005
RENT2008: median monthly contract rent, 2008
RENTPCT02: percentage of housing stock that is market rate rental units, 2002
RENTPCT05: percentage of housing stock that is market rate rental units, 2005
RENTPCT08: percentage of housing stock that is market rate rental units, 2008
SUBBOROUGH: name of sub-borough
PUBAST90: percentage of households receiving public assistance, 1990
PUBAST00: percentage of households receiving public assistance, 2000
YRHOM02: average number of years living in current residence, 2002
YRHOM05: average number of years living in current residence, 2005
YRHOM08: average number of years living in current residence, 2008
bor_subb: sub-borough code, repeated

Details

Dataframe, no spatial components.

Source

https://geodacenter.github.io/data-and-lab/nyc/

Rental Housing and Demographics in NYC (2000s).

Description

Demographic and housing data for New York City’s 55 sub-boroughs (2000s).

Usage

nyc_sf

Format

An sf data frame with 55 rows, 34 variables, and a geometry column:

forhis06-09: percentage of hispanic population, not born in US
forwh06-09: percentage of white population, not born in US
hhsiz1990: average number of people per household
hhsiz00: average number of people per household
hhsiz02-05-08: average number of people per household
kids2000, kids2005-2009: percentage households w kids under 18
rent2002,2005,2008: median monthly contract rent
rentpct02,05,08: percentage of housing stock that is market rate rental units
pubast90,00: percentage of households receiving public assistance
yrhom02,05,08: average number of years living in current residence
geometry: MULTIPOLYGON

Details

Sf object, units in ft. EPSG 2263: NAD83 / New York Long Island (ftUS).

Source

https://geodacenter.github.io/data-and-lab/nyc/

Examples

if (requireNamespace("sf", quietly = TRUE)) {
  library(sf)
  data(nyc_sf)

  plot(nyc_sf["bor_subb"])
}

Ohio Lung Cancer Mortality (1960s-80s).

Description

Ohio lung cancer data for 1968, 1978 and 1988.

Usage

ohio_lung

Format

An sf data frame with 88 rows, 42 variables, and a geometry column:

county_id: Sequential county ID (alphabetic order)
name: County name
fipsno: Fips code as numeric
lg_ryy: Lung cancer cases for gender G (M or F) and race R (W or B) in year yy (1968, 1978, 1988)
popg_ryy: Population at risk for gender G (M or F) and race R (W or B) in year yy (1968, 1978, 1988)
l_gyy: Total male and female lung cancer cases for each year
pop_gyy: Total population at risk by gender
geometry: POLYGON

Details

Sf object, units in m. EPSG 32617: WGS 84 / UTM Zone 17N.

Source

https://geodacenter.github.io/data-and-lab/ohiolung/

Examples

if (requireNamespace("sf", quietly = TRUE)) {
  library(sf)
  data(ohio_lung)

  plot(ohio_lung["FIPSNO"])
}

Abandoned Vehicles (2016).

Description

Point locations of abandoned vehicles in Chicago in September 2016.

Usage

vehicle_pts

Format

An sf data frame with 2635 rows, 10 variables, and a geometry column:

CreationDt: Date created
Address: Address of abandoned vehicle
ZIPCode: Zip code of abandoned vehicle
X: Projected X, EPSG 32616
Y: Projected Y, EPSG 32616
Ward: Ward ID
PoliceD: Police district ID
Comm: Community area ID
Latitude: Latitude of vehicle
Longitude: Longitude of vehicle
geometry: POINT

Details

Sf object, unprojected. EPSG 4326: WGS84.

Source

https://data.cityofchicago.org/Service-Requests/311-Service-Requests-Abandoned-Vehicles/3c9v-pnva

Examples

if (requireNamespace("sf", quietly = TRUE)) {
  library(sf)
  data(vehicle_pts)

  plot(vehicle_pts["CreationDt"])
}

geodaData: Spatial Analysis Datasets for Teaching

Description

Author(s)

See Also

Chicago Community Areas (2010).

Description

Usage

Format

Details

Source

Examples

Cleveland Home Sales (2015).

Description

Usage

Format

Details

Source

Examples

Chicago Population Change (2000-2010).

Description

Usage

Format

Details

Source

Examples

Guerry "Moral Statistics" (1830s).

Description

Usage

Format

Details

Source

Examples

Homicides & Socio-Economics (1960-90).

Description

Usage

Format

Details

Source

Examples

Rental Housing and Demographics in NYC (2000s), non-spatial.

Description

Usage

Format

Details

Source

Rental Housing and Demographics in NYC (2000s).

Description

Usage

Format

Details

Source

Examples

Ohio Lung Cancer Mortality (1960s-80s).

Description

Usage

Format

Details

Source

Examples

Abandoned Vehicles (2016).

Description

Usage

Format

Details

Source

Examples