I have access to several variables in geospatial research. Here I introduce the concept of the Dataverse and call my collection a "data-verse" because I am adopting the concept of having a data repository. The concept is to have a hub or repository of readily accessible and available data.
Having a Dataverse collection allows you to share, organize and archive your data, while also being able to display it on your site. It ensures that you receive credit for your data through formal scholarly data citations and helps satisfy data sharing requirements from funders and publishers. You also have full control of your datasets, from who you share your data with and when to publish. Your web visibility is also increased since it is searchable alongside other scholars’ research data. There are seven key characteristics of a dataverse entry:
Seven Characteristics of a dataverse entry:
author name(s)
year (date published in the Dataverse repository)
title
publisher (repository that published the dataset)
version number
universal numerical fingerprint (UNF): for tabular data
Below are the geographical variables with variable names highlighted in white:
state
Description: state name
county
Description: county name
FIPS
Description: county FIPS code
trump16, clinton16, otherpres16, romney12, obama12, otherpres12
Description: presidential candidate vote totals in 2012 and 2016 (republican, democrat, and non-major party)
Year/s: 2012, 2016
Source: MEDSL Election Returns Dataverse
demsen16, repsen16, othersen16, demhouse16, rephouse16, otherhouse16, demgov16, repgov16, othergov16
Description: senate, house, and governor candidate vote totals in 2016 (republican, democrat, and non-major party)
Year/s: 2016
Source: Stephen Pettigrew Dataverse
repgov14, demgov14, othergov14
Description: governor candidate vote totals in 2014 (republican, democrat, and non-major party)
Year/s: 2014
Source: Cengiz Zopluoglu
total_population
Description: total population
Year/s: 2012-2016 (ACS 5-Year Estimates)
cvap
Description: citizen voting-age population
Year/s: 2012-2016 (ACS 5-Year Estimates)
white_pct
Description: non-Hispanic whites as a percentage of total population
Year/s: 2012-2016 (ACS 5-Year Estimates)
black_pct
Description: non-Hispanic blacks as a percentage of total population
Year/s: 2012-2016 (ACS 5-Year Estimates)
hispanic_pct
Description: Hispanics or Latinos as a percentage of total population
Year/s: 2012-2016 (ACS 5-Year Estimates)
nonwhite_pct
Description: non-whites as a percentage of total population
Year/s: 2012-2016 (ACS 5-Year Estimates)
foreignborn_pct
Description: foreign-born population as a percentage of total population
Year/s: 2012-2016 (ACS 5-Year Estimates)
female_pct
Description: females as a percentage of total population
Year/s: 2012-2016 (ACS 5-Year Estimates)
age29andunder_pct
Description: population 29 years or under as a percentage of total population
Year/s: 2012-2016 (ACS 5-Year Estimates)
age65andolder_pct
Description: population 65 years or older as a percentage of total population
Year/s: 2012-2016 (ACS 5-Year Estimates)
median_hh_inc
Description: median household income in the past 12 months (in 2016 inflation-adjusted dollars)
Year/s: 2012-2016 (ACS 5-Year Estimates)
clf_unemploy_pct
Description: unemployed population in labor force as a percentage of total population in civilian labor force
Year/s: 2012-2016 (ACS 5-Year Estimates)
lesshs_pct
Description: population with an education of less than a regular high school diploma as a percentage of total population
Year/s: 2012-2016 (ACS 5-Year Estimates)
lesscollege_pct
Description: population with an education of less than a bachelor's degree as a percentage of total population
Year/s: 2012-2016 (ACS 5-Year Estimates)
lesshs_whites_pct
Description: white population with an education of less than a regular high school diploma as a percentage of total population
Year/s: 2012-2016 (ACS 5-Year Estimates)
lesscollege_whites_pct
Description: white population with an education of less than a bachelor's degree as a percentage of total population
Year/s: 2012-2016 (ACS 5-Year Estimates)
rural_pct
Description: rural population as a percentage of total population
Year/s: 2010
ruralurban_cc
Description: rural-urban continuum codes
Year/s: 2013
Source: USDA Economic Research Service
Coding:
CODE DESCRIPTION
1 Counties in metro areas of 1 million population or more
2 Counties in metro areas of 250,000 to 1 million population
3 Counties in metro areas of fewer than 250,000 population
4 Urban population of 20,000 or more, adjacent to a metro area
5 Urban population of 20,000 or more, not adjacent to a metro area
6 Urban population of 2,500 to 19,999, adjacent to a metro area
7 Urban population of 2,500 to 19,999, not adjacent to a metro area
8 Completely rural or less than 2,500 urban population, adjacent to a metro area
9 Completely rural or less than 2,500 urban population, adjacent to a metro area
zipcode The 5-digit zip code assigned by the U.S. Postal Service.
lat The latitude of the zip code (learn more).
lng The longitude of the zip code (learn more).
city The official USPS city name.
state_id The official USPS state abbreviation.
state_name The state's name.
zcta TRUE if the zip code is a Zip Code Tabulation area (learn more).
parent_zcta The ZCTA that contains this zip code. Only exists if zcta is FALSE. Useful for making inferences about a zip codes that is a point from the ZCTA that contains it.
population An estimate of the zip code's population. Only exists if zcta is TRUE.
density The estimated population per square kilometer. Only exists if zcta is TRUE.
county_fips The zip's primary county in the FIPS format.
county_name The name of the county_fips.
county_weights A JSON dictionary listing all county_fips and their weights (by population) associated with the zip code.
imprecise TRUE if the lat/lng has been geolocated using the city (rare).
military TRUE if the zip code is used by the US Military (lat/lng not available).
time zone The city's time zone in the tz database format. (e.g. America/Los_Angeles)