This map's colors indicate which population is larger in each area: urban (green) or rural (yellow). The map's layers contain total population counts by sex, age, and race groups for Nation, State Legislative Districts Upper, State Legislative Districts Lower, Congressional District in the United States and Puerto Rico.The U.S. Census designates each census block as part of an urban area or as rural. Larger geographies in this map such as block group, tract, county and state can therefore have a mix of urban and rural population. This map illustrates the 100% urban areas in dark green, and 100% rural areas in dark yellow. Areas with mixed urban/rural population have softer shades of green or yellow, to give a visual indication of where change may be happening. From the Census:"The Census Bureau’s urban-rural classification is a delineation of geographic areas, identifying both individual urban areas and the rural area of the nation. The Census Bureau’s urban areas represent densely developed territory, and encompass residential, commercial, and other non-residential urban land uses. The Census Bureau delineates urban areas after each decennial census by applying specified criteria to decennial census and other data. Rural encompasses all population, housing, and territory not included within an urban area.For the 2020 Census, an urban area will comprise a densely settled core of census blocks that meet minimum housing unit density and/or population density requirements. This includes adjacent territory containing non-residential urban land uses. To qualify as an urban area, the territory identified according to criteria must encompass at least 2,000 housing units or have a population of at least 5,000." SourceAbout the dataYou can use this map as is and you can also modify it to use other attributes included in its layers. This map's layers contain total population counts by sex, age, and race groups data from the 2020 Census Demographic and Housing Characteristics. This is shown by Nation, State, County, Census Tract, Block Group boundaries. Each geography layer contains a common set of Census counts based on available attributes from the U.S. Census Bureau. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.Vintage of boundaries and attributes: 2020 Demographic and Housing Characteristics Table(s): P1, H1, H3, P2, P3, P5, P12, P13, P17, PCT12 (Not all lines of these DHC tables are available in this feature layer.)Data downloaded from: U.S. Census Bureau’s data.census.gov siteDate the Data was Downloaded: May 25, 2023Geography Levels included: Nation, State, County, Census Tract, Block GroupNational Figures: included in Nation layer The United States Census Bureau Demographic and Housing Characteristics: 2020 Census Results 2020 Census Data Quality Geography & 2020 Census Technical Documentation Data Table Guide: includes the final list of tables, lowest level of geography by table and table shells for the Demographic Profile and Demographic and Housing Characteristics.News & Updates This map is ready to be used in ArcGIS Pro, ArcGIS Online and its configurable apps, Story Maps, dashboards, Notebooks, Python, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the U.S. Census Bureau when using this data. Data Processing Notes: These 2020 Census boundaries come from the US Census TIGER geodatabases. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For Census tracts and block groups, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract and block group boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are unchanged and available as attributes within the data table (units are square meters).  The layer contains all US states, Washington D.C., and Puerto Rico. Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Block groups that fall within the same criteria (Block Group denoted as 0 with no area land) have also been removed.Percentages and derived counts, are calculated values (that can be identified by the "_calc_" stub in the field name). Field alias names were created based on the Table Shells file available from the Data Table Guide for the Demographic Profile and Demographic and Housing Characteristics. Not all lines of all tables listed above are included in this layer. Duplicative counts were dropped. For example, P0030001 was dropped, as it is duplicative of P0010001.To protect the privacy and confidentiality of respondents, their data has been protected using differential privacy techniques by the U.S. Census Bureau.

The 110th Congressional District Summary File (Sample) (110CDSAMPLE) contains the sample data, which is the information compiled from the questions asked of a sample of all people and housing units. Population items include basic population totals; urban and rural; households and families; marital status; grandparents as caregivers; language and ability to speak English; ancestry; place of birth, citizenship status, and year of entry; migration; place of work; journey to work (commuting); school enrollment and educational attainment; veteran status; disability; employment status; industry, occupation, and class of worker; income; and poverty status. Housing items include basic housing totals; urban and rural; number of rooms; number of bedrooms; year moved into unit; household size and occupants per room; units in structure; year structure built; heating fuel; telephone service; plumbing and kitchen facilities; vehicles available; value of home; monthly rent; and shelter costs. The file contains subject content identical to that shown in Summary File 3 (SF 3).

This study contains selected electoral and aggregate economic, ecological, and demographic data at the congressional district level for districts of the 87th and 88th Congresses in the period 1961-1965. Data are provided for the number of votes cast for the Democratic and the Republican parties, and the percentage of votes cast for the majority party in the biennial elections for United States Representatives in the period 1952-1962, as well as the total votes cast for the office of president, and the number of votes cast for each party's presidential candidate in the 1952, 1956, and 1960 election. Data are also provided for population and housing characteristics, including total population by household, group quarters, institutions, age group, gender, marital status, race, nationality, and urban and rural residency. Additional demographic variables describe the congressional districts in terms of education, income, employment status and occupation, veteran status, births, deaths, and marriages.

This edition of the Congressional District Atlas on DVD contains maps and tables that reflect the boundaries and geographic relationships for the 108th Congressional Districts. There are three map types included: individual congressional district maps, state-based congressional district maps, and a national congressional district map. The tables show the relationship of congressional districts to counties and county equivalents, incorporated places and census designated places (including cons olidated cities), county subdivisions (for 18 states), American Indian areas, census tracts, ZIP Code Tabulation Areas (ZCTAs), urban and rural population and land area, and school districts. The maps are in PDF format and tables are in both PDF and TEXT format. A browser-based interface for accessing maps and tables is included on the DVD.

Note to Users: This DVD is part of a collection located in the Data Archive of the Odum Institute for Research in Social Science, at the University of North Carolina at Chapel Hill. The collection is located in Room 10, Manning Hall. Users may check the CDs out subscribing to the honor system. Items can be checked out for a period of two weeks. Loan forms are located adjacent to the collection.

Summary File 1 (SF1) Urban/Rural Update contains summary statistics on population and housing subjects derived from the responses to the 2010 Census questionnaire. Population items include sex, age, race, Hispanic or Latino origin, household relationship, household type, household size, family type, family size, and group quarters. Housing items include occupancy status, vacancy status, and tenure (whether a housing unit is owner-occupied or renter-occupied). The summary statistics are presented in 333 tables, which are tabulated for multiple levels of observation (called "summary levels" in the Census Bureau's nomenclature), including, but not limited to, regions, divisions, states, metropolitan/micropolitan statistical areas, counties, county subdivisions, places, congressional districts, American Indian Areas, Alaska Native Areas, Hawaiian Home Lands, ZIP Code tabulation areas, census tracts, block groups, and blocks. There are 177 population tables and 58 housing tables shown down to the block level; 84 population tables and 4 housing tables shown down to the census tract level; and 10 population tables shown down to the county level. Some of the summary areas are iterated for "geographic components" or portions of geographic areas, e.g., the principal city of a metropolitan statistical area (MSA) or the urban and rural portions of a MSA. With one variable per table cell and additional variables with geographic information, the collection comprises 2,597 data files, 49 per state, the District of Columbia, Puerto Rico, and the National File. The Census Bureau released SF1 in three stages: initial release, National Update, and Urban/Rural Update. The National Update added summary levels for the United States, regions, divisions, and geographic areas that cross state lines such as Combined Statistical Areas. This update adds urban and rural population and housing unit counts, summary levels for urban areas and the urban/rural components of census tracts and block groups, geographic components involving urbanized areas and urban clusters, and two new tables (household type by relationship for the population 65 years and over and a new tabulation of the total population by race). The initial release and National Update is available as ICPSR 33461. ICPSR supplies this data collection in 54 ZIP archives. There is a separate archive for each state, the District of Columbia, Puerto Rico, and the National File. The last archive contains a Microsoft Access database shell and additional documentation files besides the codebook.

NOTE: Data based on a sample except in P3, P4, H3, and H4. For.information on confidentiality protection, sampling error,.nonsampling error, definitions, and count corrections see.http://www.census.gov/prod/cen2000/doc/cd110s.pdf

Note: For information on data collection, confidentiality protection, nonsampling error, subject definitions, and guidance on using the data, visit the 2020 Census 118th Congressional District Summary File (CD118) Technical Documentation webpage..To protect respondent confidentiality, data have undergone disclosure avoidance methods which add "statistical noise" - small, random additions or subtractions - to the data so that no one can reliably link the published data to a specific person or household. The Census Bureau encourages data users to aggregate small populations and geographies to improve accuracy and diminish implausible results..Source: U.S. Census Bureau, 2020 Census 118th Congressional District Summary File (CD118)

Urban AreasThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Census Bureau (USCB), displays Urban Areas within the United States. Per USCB, "Urban areas represent densely developed territory, and encompass residential, commercial, and other non-residential urban land uses. The Census Bureau delineates urban areas after each decennial census by applying specified criteria to decennial census and other data."Washington/Arlington Urban AreaData currency: This cached Esri federal service is checked weekly for updates from its enterprise federal source (Urban Areas) and will support mapping, analysis, data exports and OGC API – Feature access.NGDAID: 61 (Series Information for 2020 Census Urban Area National TIGER/Line Shapefiles, Current)OGC API Features Link: Urban Areas copy this link to embed it in OGC Compliant viewersFor more information, please visit: Urban and Rural, 2020 Census Urban Areas FactsFor feedback please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Governmental Units, and Administrative and Statistical Boundaries Theme Community. Per the Federal Geospatial Data Committee (FGDC), this theme is defined as the "boundaries that delineate geographic areas for uses such as governance and the general provision of services (e.g., states, American Indian reservations, counties, cities, towns, etc.), administration and/or for a specific purpose (e.g., congressional districts, school districts, fire districts, Alaska Native Regional Corporations, etc.), and/or provision of statistical data (census tracts, census blocks, metropolitan and micropolitan statistical areas, etc.). Boundaries for these various types of geographic areas are either defined through a documented legal description or through criteria and guidelines. Other boundaries may include international limits, those of federal land ownership, the extent of administrative regions for various federal agencies, as well as the jurisdictional offshore limits of U.S. sovereignty. Boundaries associated solely with natural resources and/or cultural entities are excluded from this theme and are included in the appropriate subject themes."For other NGDA Content: Esri Federal Datasets

NOTE: For information on confidentiality protection, nonsampling error, and definitions, see https://www2.census.gov/programs-surveys/decennial/rdo/technical-documentation/CD115_TechnicalDocumentation.pdf..Source: U.S. Census Bureau, 2010 Census.

Abstract (en): This data collection contains information compiled from the questions asked of a sample of persons and housing units enumerated in Census 2000. Population items include sex, age, race, Hispanic or Latino origin, type of living quarters (household/group quarters), urban/rural status, household relationship, marital status, grandparents as caregivers, language and ability to speak English, ancestry, place of birth, citizenship status and year of entry into the United States, migration, place of work, journey to work (commuting), school enrollment and educational attainment, veteran status, disability, employment status, occupation and industry, class of worker, income, and poverty status. Housing items include vacancy status, tenure (owner/renter), number of rooms, number of bedrooms, year moved into unit, household size, occupants per room, number of units in structure, year structure was built, heating fuel, telephone service, plumbing and kitchen facilities, vehicles available, value of home, and monthly rent. With subject content identical to that provided in Summary File 3, the information is presented in 813 tables that are tabulated for every geographic unit represented in the data. There is one variable per table cell, plus additional variables with geographic information. The data cover more than a dozen geographic levels of observation (known as "summary levels" in the Census Bureau's nomenclature) based on the 110th Congressional Districts, e.g., the 110th Congressional Districts, themselves, Census tracts within the 110th Congressional Districts, and county subdivisions within the 110th Congressional Districts. There are 77 data files for each state, the District of Columbia, and Puerto Rico. The collection is supplied in 54 ZIP archives. There is a separate ZIP file for each state, the District of Columbia, and Puerto Rico, and for the convenience of those who need all of the data, a separate ZIP archive with all 4,004 data files. The codebook and other documentation are located in the last ZIP archive. All persons and housing units in the United States and Puerto Rico. Every person and housing unit in the United States was asked basic demographic and housing questions (for example, race, age, and relationship to householder). A sample of these people and housing units was asked more detailed questions. The sampling unit for Census 2000 was the housing unit, including all occupants. There were four different housing unit sampling rates, 1-in-8, 1-in-6, 1-in-4, and 1-in-2, designed to yield an overall average of about 1-in-6. mail questionnaireICPSR has not checked this data collection.

I share similar concerns as those raised by Suzanna Garcia's public comment about racial gerrymandering and competitiveness. Your proposed map essentially "cracks" the Denver metro Hispanic community stretching from Aurora to Berkley and Sherrelwood across 4 congressional districts. I believe this is a racial gerrymander meant to dilute the voting power of Hispanics in the Denver area. I am particularly concerned that Commerce City, which has one of the largest Hispanic populations in our state, is split in two with basically an "arm" shooting off from district 4 and into the eastern half of the city. District 4 is predominantly rural and yet this "arm" essentially pulls in half of Commerce City, which is a highly urban and industrial setting in the Denver metro. Commerce City has nothing in common with rural communities in Southern CO and I cannot see any reasonable justification for this. It is a racial gerrymander that I believe flagrantly violates the Voting Rights Act. Our community is facing serious issues around pollution and environmental racism because of the Suncor refinery. I believe it will basically be impossible for us to get our representative to address this if we are grouped mainly with rural counties up to 250 miles away from Commerce City. I agree with Ms. Garcia that a congressional district should be drawn that consolidates as much of the urban Hispanic community as possible by combining Aurora, northeast Denver, and the inner nothern suburbs including all of Commerce City. In my opinion, this is far more important than keeping the city of Denver whole- especially given that the airport neighborhoods are far more economically and culturally linked to Aurora and Commerce City than they are to downtown Denver.

I am also extremely concerned that the map doesn't seem to produce

competitive congressional districts. I thought a major purpose of the commission was to produce a competitive map. That was a huge part of the campaign for putting it in place and it was even in the language of the ballot measure we all voted on. It seems to me that there should be at least 3 districts that are competitive, if not more. I have drawn a map using Dave's Redistricting tool. This map is somewhat similar to the commission's map but I have made some adjustments to address the problems with racial gerrymandering and non-competitiveness. I have also restored Western Boulder county to district 2 and Castle Rock to district 4 because I saw in the public comments that these were frequent complaints. Now, district 6 is 37% Hispanic and 59% non-White. Additionally, district 3, 7, and 8 would all be competitive. In the 2018 AG's race, Brauchler would have won district 3 by 4% and district 7 by 2% while Weiser would have won district 8 by 3%. This is much more competitive than your current draft map. Thank you for reading my comment. MAP IS HERE: https://davesredistricting.org/join/f7835dfb-84af-41ec-aaa2-0081298fd57e

The State Legislative District Summary File (Sample) (SLDSAMPLE) contains the sample data, which is the information compiled from the questions asked of a sample of all people and housing units. Population items include basic population totals; urban and rural; households and families; marital status; grandparents as caregivers; language and ability to speak English; ancestry; place of birth, citizenship status, and year of entry; migration; place of work; journey to work (commuting); school enrollment and educational attainment; veteran status; disability; employment status; industry, occupation, and class of worker; income; and poverty status. Housing items include basic housing totals; urban and rural; number of rooms; number of bedrooms; year moved into unit; household size and occupants per room; units in structure; year structure built; heating fuel; telephone service; plumbing and kitchen facilities; vehicles available; value of home; monthly rent; and shelter costs. The file contains subject content identical to that shown in Summary File 3 (SF 3).

Plan submitted by: AriBradshaw on 10/17/2021 USER DESCRIPTION: N/A USER PLAN OBJECTIVE: Dear public, commission, and mapping team:

This map follows many of the comments received at the latest IRC meeting in addition to a few parameters we feel must be followed to the best of the IRC's ability. The numbers we have used in this map are arbitrary and can be aligned closer with the current IRC numbers. Each district is equal in population.

The Phoenix-Mesa metro area holds 2/3 of the population. It holds that it should have 2/3 of the congressional districts without marginalizing the voices of rural counties.

Tucson can largely fit within one congressional district. Its suburbs can be split and its exurbs can be included into an "East Pima" district alongside a rural district.

We want to honor as best as possible the Latino coalition's map for a Phoenix district.

We have created two rural districts. CD1 combines the Colorado River counties with the Flagstaff-Prescott corridor. We exclude North Coconino and Mohave counties into CD2 as their towns of Fredonia and Colorado City are beyond the Grand Canyon and share more in common with the Mormon settlements of Apache and Navajo counties. The Kaibab reservation gets to have a voice alongside many other natives. CD1 combines the river natives with the Canyon natives and the Southern natives. The IRC made it clear that they wanted the Yuma natives and Tohono natives to be in the same district and so we have accomplished such with minimal compromise.

The East Valley is split into two districts. The far east includes San Tan Valley, Apache Junction, and much of the far east and south of the urban area. The other district includes all of Tempe south of the river alongside Ahwatukee and large portions of Mesa.

There exists a Northeast Valley district that consists of the rapidly changing downtown Phoenix, Uptown, Arcadia, Scottsdale, Fountain Hills, and the Salt River and Fort Mohave Natives. New River, Anthem, Cave Creek, and Carefree are included as well due to their community of interest with Northeast Phoenix and Scottsdale.

The Latino district is largely the same as that submitted by the coalition. We have traded a portion of downtown Phoenix that has much more in common with Arcadia and Uptown than it does with South Phoenix for more of Southwest Phoenix.

District 4 is a suburban and exurban district that covers most of Pinal county in addition to Buckeye, Goodyear, and Avondale. The borders lie across from retirement communities such as Sun City. The only major area of compromise on this entire map is found in this area and it is fixable with some effort - the town of Coolidge, AZ.

This map contains four competitive districts. By trading some blocks between District 5 and District 4 near Glendale, we can create five competitive districts.

This map contains a total of three minority-majority districts (CD3, 4, and 7) with one district at 50.95% non-hispanic white (CD2). By trading land between CD2 and CD3, we can achieve a total of four minority-majority districts.

Abraham and I highly urge the committee and mapping team to consider this map and its choice to leave Maricopan interests out of Rural districts and to consolidate Tucson into one district with its exurbs in another and its suburbs split.

Thank you so much for your time and dedication to the State of Arizona and all of its people. God Bless.

NOTE: For information on confidentiality protection,.nonsampling error, definitions, and count corrections see.http://www.census.gov/prod/cen2000/doc/cd110h.pdf

In 2025, the degree of urbanization worldwide was at 58 percent. North America, Latin America, and the Caribbean were the regions with the highest level of urbanization, with over four-fifths of the population residing in urban areas. The degree of urbanization defines the share of the population living in areas defined as "cities". On the other hand, less than half of Africa's population lives in urban settlements. Globally, China accounts for over one-quarter of the built-up areas of more than 500,000 inhabitants. The definition of a city differs across various world regions - some countries count settlements with 100 houses or more as urban, while others only include the capital of a country or provincial capitals in their count. Largest agglomerations worldwideThough North America is the most urbanized continent, no U.S. city was among the top ten urban agglomerations worldwide in 2023. Tokyo-Yokohama in Japan was the largest urban area in the world that year, with 37.7 million inhabitants. New York ranked 13th, with 21.4 million inhabitants. Eight of the 10 most populous cities are located in Asia. ConnectivityIt may be hard to imagine how the reality will look in 2050, with 70 percent of the global population living in cities, but some statistics illustrate the ways urban living differs from suburban and rural living. American urbanites may lead more “connected” (i.e., internet-connected) lives than their rural and/or suburban counterparts. As of 2021, around 89 percent of people living in urban areas owned a smartphone. Internet usage was also higher in cities than in rural areas. On the other hand, rural areas always have, and always will, attract those who want to escape the rush of the city.

Recent accounts of American politics focus heavily on urban-rural gaps in political behavior. Rural politics research is growing but may be stymied by difficulties defining and measuring which Americans qualify as “rural.” We discuss theoretical and empirical challenges to study- ing rurality. Much existing research has been inattentive to conceptualization and measurement of rural geography. We focus on improving estimation of different notions of rurality and provide a new dataset on urban-rural measurement of US state legislative districts. We scrutinize construct validity and measurement in two studies of rural politics. First, we replicate Flavin and Franko (2019) to demonstrate empirical results may be sensitive to measurement of rural residents. Second, we use Mummolo and Nall’s (2016) survey data to show rural self-identification is not well-captured with objective, place-based classifications, suggesting a rethinking of theoretical and empirical accounts of rural identity. We conclude with strategies for operationalizing rurality using readily available tools.

According to exit polls from the 2022 midterm election, voters living in urban areas were more likely to vote for Democrats, while rural and suburban voters were more likely to vote for Republican candidates. ** percent of people living in urban areas voted for Democrats in the 2022 midterms, compared to ** percent of rural voters who voted for Republican candidates.

These data describe the geographic relationships of the 103rd congressional districts to selected governmental and statistical geographic entities for the entire United States, American Samoa, Guam, Puerto Rico, and the Virgin Islands. Each record represents a census geographic tabulation unit (GTUB), a unique combination of geographic codes expressing specific geographic relationships. This file provides the following information: state, congressional district, county and county subdivision, place, American Indian/Alaska Native area, urbanized area, urban/rural descriptor, and Metropolitan Statistical Area/Primary Metropolitan Statistical Area (MSA/PMSA).

Disclaimer: These data are updated by the author and are not an official product of the Federal Reserve Bank of Cleveland.This project provides two sets of migration estimates for the major US metro areas. The first series measures net migration of people to and from the urban neighborhoods of the metro areas. The second series covers all neighborhoods but breaks down net migration to other regions by four region types: (1) high-cost metros, (2) affordable, large metros, (3) midsized metros, and (4) small metros and rural areas. These series were introduced in a Cleveland Fed District Data Brief entitled “Urban and Regional Migration Estimates: Will Your City Recover from the Pandemic?"The migration estimates in this project are created with data from the Federal Reserve Bank of New York/Equifax Consumer Credit Panel (CCP). The CCP is a 5 percent random sample of the credit histories maintained by Equifax. The CCP reports the census block of residence for over 10 million individuals each quarter. Each month, Equifax receives individuals’ addresses, along with reports of debt balances and payments, from creditors (mortgage lenders, credit card issuers, student loan servicers, etc.). An algorithm maintained by Equifax considers all of the addresses reported for an individual and identifies the individual’s most likely current address. Equifax anonymizes the data before they are added to the CCP, removing names, addresses, and Social Security numbers (SSNs). In lieu of mailing addresses, the census block of the address is added to the CCP. Equifax creates a unique, anonymous identifier to enable researchers to build individuals’ panels. The panel nature of the data allows us to observe when someone has migrated and is living in a census block different from the one they lived in at the end of the preceding quarter. For more details about the CCP and its use in measuring migration, see Lee and Van der Klaauw (2010) and DeWaard, Johnson and Whitaker (2019). DefinitionsMetropolitan areaThe metropolitan areas in these data are combined statistical areas. This is the most aggregate definition of metro areas, and it combines Washington DC with Baltimore, San Jose with San Francisco, Akron with Cleveland, etc. Metro areas are combinations of counties that are tightly linked by worker commutes and other economic activity. All counties outside of metropolitan areas are tracked as parts of a rural commuting zone (CZ). CZs are also groups of counties linked by commuting, but CZ definitions cover all counties, both metropolitan and non-metropolitan. High-cost metropolitan areasHigh-cost metro areas are those where the median list price for a house was more than $200 per square foot on average between April 2017 and April 2022. These areas include San Francisco-San Jose, New York, San Diego, Los Angeles, Seattle, Boston, Miami, Sacramento, Denver, Salt Lake City, Portland, and Washington-Baltimore. Other Types of RegionsMetro areas with populations above 2 million and house price averages below $200 per square foot are categorized as affordable, large metros. Metro areas with populations between 500,000 and 2 million are categorized as mid-sized metros, regardless of house prices. All remaining counties are in the small metro and rural category.To obtain a metro area's total net migration, sum the four net migration values for the the four types of regions.Urban neighborhoodCensus tracts are designated as urban if they have a population density above 7,000 people per square mile. High density neighborhoods can support walkable retail districts and high-frequency public transportation. They are more likely to have the “street life” that people associate with living in an urban rather than a suburban area. The threshold of 7,000 people per square mile was selected because it was the average density in the largest US cities in the 1930 census. Before World War II, workplaces, shopping, schools and parks had to be accessible on foot. Tracts are also designated as urban if more than half of their housing units were built before WWII and they have a population density above 2,000 people per square mile. The lower population density threshold for the pre-war neighborhoods recognizes that many urban tracts have lost population since the 1960s. While the street grids usually remain, the area also needs su

This dataset provides information for the analysis of rural housing, rural-urban comparisons in housing and leadership within local government. The material provided covers services and actions associated with the provision and maintenance of housing, such as land-use planning, sewerage and water services, house building finance, dwelling improvement and modernisation, slum clearance, overcrowding, under-occupation, second homes, caravans, homes for employees, civilian accommodation in military huts and camps, labour and materials supply, dwelling costs and cost yardsticks, building consortia, building by direct labour forces, non-traditional building methods, new and expanding towns, prefabricated dwellings, flats and bungalows, temporary dwellings, waiting lists, tenant selection, council regulation of tenants, the regulation of private landlords, and issues of geographical distribution nationally, within regions and within localities. The dataset focuses on 17 rural districts, with seven explored over the time period 1900-1974 (primarily 1919-1974) and a further 10 for 1945-1974, for which period a large body of statistical data are available on many aspects of housing conditions and supply, along with local socioeconomic conditions, for all 1,165 local authorities in England (as existed in 1971, with the data covering 1945-1973). The period focused on is before the huge middle-class inflows into the countryside that have since occurred; where the primary issue in rural housing was poor quality and insufficient supply at acceptable standards. A central resource in the dataset is insight on the provision of social housing, for those unable to afford house purchases. Key insights are embedded on local leadership and central-local government relations. For 13 rural districts, the dataset has substantive information from county record offices on council decisions, decision rationale and indicators of decision outcomes. There are complementary insights from newspaper accounts of council actions, both for these 13 and for a further four rural councils, along with material from Medical Officer of Health reports. The rural districts in the detailed 13 council focus are: Ampthill and Biggleswade (both Bedfordshire), Braughing and Hatfield (Hertfordshire), Elham and West Ashford (Kent), Erpingham, Smallburgh and Walsingham (Norfolk), Richmond and Settle (North Yorkshire) and Dorking & Horley and Godstone (Surrey), with less detailed supplementary information on Sturminster and Wimborne & Cranborne (Dorset) and Droitwich and Evesham (Worcestershire). This material is supplemented with extensive coverage of national government decision-making as revealed in files in The National Archives, alongside coverage of national (and rural) debates on housing, with considerable statistical information accompanying this, from House of Commons Hansard reports.