The 2013 Urban Influence Codes form a classification scheme that distinguishes metropolitan counties by population size of their metro area, and nonmetropolitan counties by size of the largest city or town and proximity to metro and micropolitan areas. The standard Office of Management and Budget (OMB) metro and nonmetro categories have been subdivided into two metro and 10 nonmetro categories, resulting in a 12-part county classification. This scheme was originally developed in 1993. This scheme allows researchers to break county data into finer residential groups, beyond metro and nonmetro, particularly for the analysis of trends in nonmetro areas that are related to population density and metro influence. An update of the Urban Influence Codes is planned for mid-2023.

{"definition": "12-level classification of counties by metro-micro-nonmetro status, location, and size of largest place", "availableYears": "2010 (Released May 2013)", "name": "Urban influence code, 2013", "units": "Classification", "shortName": "UrbanInfluenceCode2013", "geographicLevel": "County", "dataSources": "U.S. Department of Agriculture, Economic Research Service, using data from the U.S. Census Bureau"}

© UrbanInfluenceCode2013 This layer is sourced from gis.ers.usda.gov.

The 2013 Rural-Urban Continuum Codes form a classification scheme that distinguishes metropolitan counties by the population size of their metro area, and nonmetropolitan counties by degree of urbanization and adjacency to a metro area. The official Office of Management and Budget (OMB) metro and nonmetro categories have been subdivided into three metro and six nonmetro categories. Each county in the U.S. is assigned one of the 9 codes. This scheme allows researchers to break county data into finer residential groups, beyond metro and nonmetro, particularly for the analysis of trends in nonmetro areas that are related to population density and metro influence. The Rural-Urban Continuum Codes were originally developed in 1974. They have been updated each decennial since (1983, 1993, 2003, 2013), and slightly revised in 1988. Note that the 2013 Rural-Urban Continuum Codes are not directly comparable with the codes prior to 2000 because of the new methodology used in developing the 2000 metropolitan areas. See the Documentation for details and a map of the codes. An update of the Rural-Urban Continuum Codes is planned for mid-2023.

Urban-Rural Classifications by USDA, Economic Research Service - http://www.ers.usda.gov/topics/rural-economy-population/rural-classifications.aspxCode Urban Influence Description1 Large Metro >1M Population2 Small Metro <1M Population3 Micropolitan Near Large Metro4 Rural Near Large Metro5 Micropolitan Near Small Metro6 Rural Near Small Metro, town >2.5K7 Rural Near Small Metro, town <2.5K8 Micropolitan Not Near Metro9 Rural Near Micropolitan, town >2.5K10 Rural Near Micropolitan, town <2.5K11 Rural Not Near Metro or Micro, town >2.5K12 Rural Not Near Metro or Micro, town <2.5Khttp://www.ers.usda.gov/data-products/urban-influence-codes/documentation.aspx#.UYKQ2kpZRvYCode Rural-Urban Continuum Description1 Metro Area >1M Population2 Metro Area >250K-1M Population3 Metro Area <250K Population4 Urban >20K Near Metro5 Urban >20K Not Near Metro6 Urban 2.5K-20K Near Metro7 Urban 2.5K-20K Not Near Metro8 Rural, Near Metro9 Rural, Not Near Metrohttp://www.ers.usda.gov/data-products/rural-urban-continuum-codes.aspx#.UYJuVEpZRvY

Context & Content

This dataset consists of the unemployment rate and education level of adults in the USA by county. That is, for each county in the USA, this dataset provides the count and percentage of unemployed adults as well as the count and percentage of adults of various educational backgrounds. Each county was been assigned one of four locale categories (City, Suburb, Town, Rural) according to its 2013 Urban Influence Code and their descriptions provided in UIC_codes.csv. From the descriptions of each of the codes and the descriptions of the locales "City", "Suburb", "Town", and "Rural" provided on page 2 of the locale user manual (locale_user_manual.pdf), each county was assigned one of four locales.

The unemployment rate data includes the count and percentage of unemployed adults for each county in the USA for each year from 2000-2020. The median household income for 2019 is also included. The education level data includes the count and percentage of adults with less than a high school diploma, a high school diploma only, some college, and a bachelor's degree/four years of college or more for the years 1970, 1980, 1990, 2000, and 2019. The Urban Influence Code data includes the UIC and locale description of each county in the USA and the locale user manual has been included as a PDF as strictly a reference file, to understand how each county was assigned a locale within the unemployment.csv and education.csv files.

Data Sources

Source for the unemployment rate and education level data by county: "County-level Data Sets." USDA Economic Research Service, US Department of Agriculture. Access date: Sept 8, 2021. URL: https://www.ers.usda.gov/data-products/county-level-data-sets/

Source for Urban Influence Codes by county: "Urban Influence Codes." USDA Economic Research Service, US Department of Agriculture. Access date: Sept 8, 2021. URL: https://www.ers.usda.gov/data-products/urban-influence-codes/#:~:text=The%202013%20Urban%20Influence%20Codes,to%20metro%20and%20micropolitan%20areas.&text=An%20update%20of%20the%20Urban,is%20planned%20for%20mid%2D2023.

Inspiration

This dataset was created to be used as an additional data source for the LearnPlatform COVID-19 Impact on Digital Learning Kaggle competition, but is suitable for other analyses related to unemployment rate and education level in the USA.

The Index of Relative Rurality (IRR) is a continuous, threshold-free, and unit-free measure of rurality.

The original version of the IRR was proposed by Waldorf (2006, http://ageconsearch.umn.edu/handle/21383) as an alternative to the traditional discrete threshold-based classifications, such as the Rural-urban Continuum Code and the Urban Influence Code. Waldorf and Kim (2015) re-designed measuring the index and applied it to publish improved county-level IRR for 2000 and 2010. IRR 2020 was measured by the same method suggested in 2015 except for the network data (North American Roads*) due to the data availability. (* Bureau of Transportation Statistics (BTS), https://geodata.bts.gov/datasets/usdot::north-american-roads/about).

The IRR has three significant advantages over typology-based rurality measures. (1) It is spatially flexible in that it can be designed for any spatial units; (2) it is a relative measure and thus embeds rurality in the broader system of settlements; (3) it is analytically more easily handled than threshold-based typologies.

The IRR ranges between 0 (low level of rurality, i.e., urban) and 1 (most rural). Four steps are involved in its design:

1. Identifying the dimensions of rurality: population size, density, remoteness, and built-up area.
2. Selecting measurable variables to adequately represent each dimension:
a. Size: logarithm of population size
b. Density: logarithm of population density.
c. Remoteness: network distance.
d. Built-up area: urban area (as defined by the US Census Bureau) as a percentage of total land area.
3. Re-scaling the variables onto bounded scales that range from 0 to 1.
4. Selecting a link function: unweighted average of the four re-scaled variables.

IRR 2020 - County-level Map

Please cite this work:

DOI: 10.5281/zenodo.7675745

For more information:
Waldorf, Brigitte, and Ayoung Kim. 2015. "Defining and Measuring Rurality in the US: From Typologies to Continuous Indices." Commissioned paper prepared for the National Academies of Sciences Workshop on Rationalizing Rural Classifications, April 2015, Washington, DC. http://sites.nationalacademies.org/cs/groups/dbassesite/documents/webpage/dbasse_168031.pdf

Acknowledgment:

** This project was supported by the Agricultural and Food Research Initiative Competitive Program of the USDA National Institute of Food and Agriculture (NIFA), grant number 2020-67019-30772.

Contact:

Ayoung Kim | a.kim@msstate.edu | Dept. of Agricultural Economics | Mississippi State University

Brigitte Waldorf | bwaldorf@purdue.edu

RC_IUA 2018 contains urban accessibility classes by regional council geography. The dataset uses geographic boundaries (urban accessibility indicator (IUA), regional council (RC) as at 1 January 2018.

To download census and other data for this classification go to '2018 Census selected variables by Regional Council Urban Accessibility Indicator 2018'.

The urban accessibility (IUA) indicator measures the degree of urban influence New Zealand’s urban areas have on surrounding rural areas. It classifies the geographic accessibility of rural statistical area 1s (SA1s) and small urban areas according to their proximity, or degree of remoteness, to larger urban areas. The full classification is: 111 Major urban area 112 Large urban area 113 Medium urban area 221 High urban accessibility 222 Medium urban accessibility 223 Low urban accessibility 224 Remote 225 Very remote 331 Inland water 332 Inlet 333 Oceanic.

Regional council (RC) boundaries for 2018 are defined by the regional councils and/or Local Government Commission and maintained by Stats NZ. This version contains 16 regional councils. The full classification is: 01 Northland Region 02 Auckland Region 03 Waikato Region 04 Bay of Plenty Region 05 Gisborne Region 06Hawke's Bay Region 07 Taranaki Region 08 Manawatu-Wanganui Region 09 Wellington Region 12 West Coast Region 13 Canterbury Region 14 Otago Region 15 Southland Region 16 Tasman Region 17 Nelson Region 18 Marlborough Region 99 Area Outside Region.

The RC_IUA codes and names combine the RC and IUA codes and names, e.g. 01112 Northland Region_Major urban area.There are 140 classes. Some regional council areas do not contain all IUA classes but each region has at least four IUA classes.

The aim of this study was to provide a systematic empirical assessment of three basic organizational premises of Community-Oriented Policing (COP). This study constructed a comprehensive data set by synthesizing data available in separate national data sets on police agencies and communities. The base data source used was the 1999 Law Enforcement Management and Administrative Statistics (LEMAS) survey [LAW ENFORCEMENT MANAGEMENT AND ADMINISTRATIVE STATISTICS (LEMAS), 1999 (ICPSR 3079)], which contained data on police organizational characteristics and on adoption of community-oriented policing procedures. The 1999 survey was supplemented with additional organizational variables from the 1997 LEMAS survey [LAW ENFORCEMENT MANAGEMENT AND ADMINISTRATIVE STATISTICS (LEMAS), 1997 (ICPSR 2700)] and from the 1996 Directory of Law Enforcement Agencies [DIRECTORY OF LAW ENFORCEMENT AGENCIES, 1996: UNITED STATES]. Data on community characteristics were extracted from the 1994 County and City Data Book, from the 1996 to 1999 Uniform Crime Reports [UNIFORM CRIME REPORTING PROGRAM DATA. [UNITED STATES]: OFFENSES KNOWN AND CLEARANCES BY ARREST (1996-1997: ICPSR 9028, 1998: ICPSR 2904, 1999: ICPSR 3158)], from the 1990 and 2000 Census Gazetteer files, and from Rural-Urban Community classifications. The merging of the separate data sources was accomplished by using the Law Enforcement Agency Identifiers Crosswalk file [LAW ENFORCEMENT AGENCY IDENTIFIERS CROSSWALK [UNITED STATES], 1996 (ICPSR 2876)]. In all, 23 data files from eight separate sources collected by four different governmental agencies were used to create the merged data set. The entire merging process resulted in a combined final sample of 3,005 local general jurisdiction policing agencies. Variables for this study provide information regarding police organizational structure include type of government, type of agency, and number and various types of employees. Several indices from the LEMAS surveys are also provided. Community-oriented policing variables are the percent of full-time sworn employees assigned to COP positions, if the agency had a COP plan, and several indices from the 1999 LEMAS survey. Community context variables include various Census population categories, rural-urban continuum (Beale) codes, urban influence codes, and total serious crime rate for different year ranges. Geographic variables include FIPS State, county, and place codes, and region.