This dataset is the definitive set of annually released urban rural boundaries for 2022 as defined by Stats NZ. This version contains 722 urban rural features.

The urban rural geography was introduced as part of the Statistical Standard for Geographic Areas 2018 (SSGA18) which replaced the New Zealand Standard Areas Classification (NZSAC92). The urban rural geography replaces the (NZSAC92) urban area geography.

Urban rural is an output geography that classifies New Zealand into areas that share common urban or rural characteristics and is used to disseminate a broad range of Stats NZ’s social, demographic, and economic statistics.

The urban rural indicator complements the urban rural geography and is an attribute in this dataset. Further information on the urban rural indicator is available on the Stats NZ classification and coding tool ARIA.

Names are provided with and without tohutō/macrons. The name field without macrons is suffixed ‘ascii’.

This generalised version has been simplified for rapid drawing and is designed for thematic or web mapping purposes.

Digital boundary data became freely available on 1 July 2007.

The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Urban Extents Grid distinguishes urban and rural areas based on a combination of population counts (persons), settlement points, and the presence of Nighttime Lights. Areas are defined as urban where contiguous lighted cells from the Nighttime Lights or approximated urban extents based on buffered settlement points for which the total population is greater than 5,000 persons. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT).

This dataset contains measures of the urban/rural characteristics of each census tract in the United States. These include proportions of urban and rural population, population density, rural/urban commuting area (RUCA) codes, and RUCA-based four- and seven- category urbanicity scales. A curated version of this data is available through ICPSR at https://www.icpsr.umich.edu/web/ICPSR/studies/38606/versions/V1

As of 2025, individuals from both urban and rural areas of Small Island Developing States (SIDS) had the highest internet penetration rate in comparison to other markets. Individuals in rural areas of Least Developed Countries (LDCs) had the lowest internet penetration rate at ** percent.

The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Urban Extent Polygons, Revision 02 is an update to Revision 01, which included new settlements and represented the first time that SEDAC released polygons (in Esri shapefile format) with the settlement name (or name of the largest city in the case of multi-city agglomerations). The shapefile consists of polygons defined by the extent of the nighttime lights and approximated urban extents (circles) based on buffered settlement points. Revision 01 also included new urban extents identified from multiple sources and corrected georeferencing for some settlements (see separate documentation for Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Settlement Points, Revision 01 for the data and methods). Revision 01 was produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with CUNY Institute for Demographic Research (CIDR). Revision 02 was produced by CIESIN.

Baltimore County's Urban Rural Demarcation Line (URDL) has divided the county into "urban" and "rural" areas since established by the Planning Board in 1967. Developed primarily as a growth management tool, it has influenced zoning, land-use, and infrustructure decisions, and was the baseline for the Baltimore County part of Maryland's Priority Funding Area. However, this boundary (digitized at a scale of 1:24,000 where 1" represents 2,000') was became obsolete as the county's GIS data increased in resolution (to 1:2,400 where 1" represents 200'). Until recently, determining a property's status as either urban or rural was of a highly interpretative nature. A new URDL was developed to more accurately match the 1:2,400 data (parcel, street centerline, zoning, etc). This version was reviewed and modified in a series of meetings with several interested county agencies. The new URDL removes much of the old one's ambiguity while keeping its original intent. The new URDL was reviewed, modified, and subsequently approved by the Planning Board on June 19, 2003. Minor revisions were effected 9/2/04, 10/21/04, 9/4/07, 5/21/09, 10/1/09 and 11/15/12. The URDL_poly feature class is one part of the URDL feature dataset.

While Spain’s population has increased slightly since the year 2000, the share of the rural and urban populations has remained relatively constant, with Spain being a highly urban country. This consistently high urbanization is a consequence of both economic and social factors. The Spanish wealth is generated in the cities to a large extent Two thirds of Spain’s economic output, as divided across economic sectors, comes from the service sector, with only ***** percent originating from agriculture. Naturally, service-based economies are easiest when people live closely, while agricultural practices need more land, and thus a rural population. Of course, this also brings economic costs, such as the high living and housing costs in Madrid. What draws people into cities? Social factors also drive people to cities. For some, it is being closer to family or culture, such as art museums. For others, it is finding a large city with green spaces, like Madrid. For others, it is the opportunity to watch a game in a world-class soccer stadium, perhaps FC Barcelona. These and other factors continue to keep Spaniards in their cities.

The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): National Administrative Boundaries are derived from the land area grid to show the outlines of pixels (cells) that contain administrative Units in GRUMPv1 on a per-country/territory basis. They are derived from the pixels as polygons and thus have rectilinear boundaries at a large scale. The polygons that outline the countries and territories are not official representations; rather they represent the area covered by the statistical data as provided. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT).

Discover the booming Urban & Rural Planning and Design market. This comprehensive analysis reveals key trends, drivers, restraints, and regional insights for 2025-2033, including market size projections and top players like Foster + Partners and HOK. Learn about the impact of urbanization, sustainable development, and technological advancements on this dynamic sector.

CLICK ON THE ABOVE IMAGE TO LAUNCH THE MAP - Healthcare access issues vary greatly between urban and rural areas of New Mexico. Launch the map to explore alternate ways to classify geographies as urban or rural. These classifications are often used for food access as well as healthcare access.BIBLIOGRAPHY WITH LINKS:US Census Bureau, Urban Area - Urban Cluster FAQ - https://www2.census.gov/geo/pdfs/reference/ua/2010ua_faqs.pdfAre the problems with Rural areas actually just a result of definitions that change?: "When a rural county grows, it transmutes into an urban one." - The real (surprisingly comforting) reason rural America is doomed to decline, https://www.washingtonpost.com/business/2019/05/24/real-surprisingly-comforting-reason-rural-america-is-doomed-decline/ (See also the complete study - http://programme.exordo.com/2018annualmeeting/delegates/presentation/130/ )Rural Definitions for Health Policy, Harvey Licht, a presentation for the University of New Mexico Center for Health Policy: : http://nmcdc.maps.arcgis.com/home/item.html?id=7076f283b8de4bb69bf3153bc42e0402Rural Definitions for Health Policy, update of 2019, Harvey Licht, a presentation to the NMDOH Quarterly Epidemiology Meeting, November, 2019 - http://www.arcgis.com/home/item.html?id=a60a73f4e5614eb3ab01e2f96227ce4bNew Mexico Rural-Urban Counties Comparison Tables - October 2017, Harvey Licht, A preliminary compilation for the National Conference of State Legislators Rural Health Plan Taskforce : https://nmcdc.maps.arcgis.com/home/item.html?id=d3ca56e99f8b45c58522b2f9e061999eNew Mexico Rural Health Plan - Report of the Rural Health Planning Workgroup convened by the NM Department of Health 2018-2019 - http://nmcdc.maps.arcgis.com/home/item.html?id=d4b9b66a5ca34ec9bbe90efd9562586aFrontier and Remote Areas Zip Code Map - http://nmcdc.maps.arcgis.com/home/webmap/viewer.html?webmap=56b4005256244499a58f863c17bbac8aHOUSING ISSUES, RURAL & URBAN, 2017 - http://nmcdc.maps.arcgis.com/home/webmap/viewer.html?webmap=3e3aeabc04ac4672994e25a1ec94df83FURTHER READING:What is Rural? Rural Health Information Hub: https://www.ruralhealthinfo.org/topics/what-is-ruralDefining Rural. Research and Training Center on Disability in Rural Communities: http://rtc.ruralinstitute.umt.edu/resources/defining-rural/What is Rural? USDA: https://www.ers.usda.gov/topics/rural-economy-population/rural-classifications/what-is-rural/National Center for Health Statistics Urban–Rural Classification Scheme: https://www.cdc.gov/nchs/data_access/urban_rural.htm.Health-Related Behaviors by Urban-Rural County Classification — United States, 2013, CDC: https://www.cdc.gov/mmwr/volumes/66/ss/ss6605a1.htm?s_cid=ss6605a1_wExtending Work on Rural Health Disparities, The Journal of Rural Health: http://onlinelibrary.wiley.com/doi/10.1111/jrh.12241/fullMinority Populations Driving Community Growth in the Rural West, Headwaters Economics: https://headwaterseconomics.org/economic-development/trends-performance/minority-populations-driving-county-growth/ Methodology - https://headwaterseconomics.org/wp-content/uploads/Minorities_Methods.pdfThe Role of Medicaid in Rural America, Kaiser Family Foundation: http://www.kff.org/medicaid/issue-brief/the-role-of-medicaid-in-rural-america/The Future of the Frontier: Water, Energy & Climate Change in America’s Most Remote Communities: http://frontierus.org/wp-content/uploads/2017/09/FUTURE-OF-THE-FRONTIER_Final-Version_Spring-2017.pdfRural and Urban Differences in Passenger-Vehicle–Occupant Deaths and Seat Belt Use Among Adults — United States, 2014, CDC: https://www.cdc.gov/mmwr/volumes/66/ss/ss6617a1.htm

This file provides a rural-urban view of 2001 Middle Layer Super Output Areas (MSOA) in England and Wales. The ZIP file contains the Rural Urban Classification in XLSX and CSV format and includes a user guide. The files were originally from the NeSS website. Click on the Download button to download the ZIP file.The classification of rural and urban areas is the outcome of a project co-sponsored by:Office for National Statistics (ONS);Department for Environment, Food and Rural Affairs (Defra);Office of the Deputy Prime Minister (now Communities and Local Government);Countryside Agency (CA); andNational Assembly for Wales (NAW).The classification was developed in 2004 by a consortium co-ordinated by Prof. John Shepherd from Birkbeck College. The technical work was lead by Peter Bibby of University of Sheffield and the project also involved the University of Glamorgan and Geowise. The rural and urban classification of Output Areas, Super Output Areas (this dataset) and Wards has been provided to enable datasets to be analysed according to the classification. This provides a powerful tool for the development and monitoring of rural and urban policies.Please Note: Super Output Areas do not have all the same codes as the OA level Dataset. For SOAs and Wards the classifications for ‘Villages, Hamlets and Isolated Dwellings’ have been combined.Similar procedures to those used to classify Output Areas apply to the classification to the 7,194 Middle Layer Super Output Areas in the dataset. However the morphological classification differs in the number of categories as very few MSOAs can be classified as predominantly dispersed settlements. MSOAs are categorised into just three domains: urban 10k, town and fringe and villages, hamlets and isolated dwellings, using the key below:2005 Rural and Urban morphology indicator1 - denotes predominantly urban >10k2 - denotes predominantly town and fringe3 - denotes other rural (including village, hamlet and isolated dwellings)2005 Rural and Urban context indicator0 - denotes less sparsely populated areas1 - denotes sparsely populated areas

How people feel about their neighbourhood across the UK. This dataset shows how people feel about their neighbourhood by looking at 5 measures of social capital and shows differences observed between regions,constituent countries and urban and rural areas by age

A******* share of inpatient discharges in rural hospitals were covered by Medicare compared to urban hospitals in the U.S. Meanwhile, a ******* share of discharges were covered by private insurance in rural hospitals. Typically, private insurers pay hospitals more than Medicare and Medicaid, but whether this is also true for rural regions is uncertain.

The Department of Veterans Affairs provides official estimates and projections of the Veteran population using the Veteran Population Projection Model (VetPop). Based on the latest model VetPop2023 and the most recent national survey estimates from the 2023 American Community Survey 1-Year (ACS) data, the projected number of Veterans living in the 50 states, DC and Puerto Rico for fiscal years, 2023 to 2025, are allocated to Urban and Rural areas. As defined by the Census Bureau, Rural encompasses all population, housing, and territory not included within an Urban area (https://www.census.gov/programs-surveys/geography/guidance/geo-areas/urban-rural.html).

This tables contains the Veteran estimates by urban/rural and period of service.

Note: rounding to the nearest 1,000 is always appropriate for VetPop estimates.

This dataset summarizes the number of registered enterprises in various county-level administrative units within Henan Province, and classifies them into urban and rural areas for statistical analysis. The data reflects the spatial distribution characteristics of enterprise registration in various regions and can be used to study the differences and trends in regional economic development, urban-rural industrial layout, market vitality, and other aspects in Henan Province. This dataset is one of the results of the Henan Province Soft Science Research Project "Research on the Entrepreneurial Effects of Digital Inclusive Finance from the Perspective of Urban Rural Differences (242400410047)".

This repository contains all publicly shareable contents of the replication kit for "Rural-Urban Migration, Structural Transformation, and Housing Markets in China."

A PDF map showing the Rural Urban Classification (2011) of the MSOAs in the North West Region. (File Size - 745 KB)

The Census Bureau’s urban-rural classification is fundamentally a delineation of geographical areas, identifying both individual urban areas and the rural areas of the nation. The Census Bureau’s urban areas represent densely developed territory, and encompass residential, commercial, and other non-residential urban land uses. To qualify as an urban area, the territory identified according to criteria must have at least 5,000 people or 2,000 housing units. The 2020 Census changed how urban areas are determined from the 2010 criteria. The population requirement was increased to 5,000 people from 2,500 in 2010. This value is now determined by housing unit density instead of population density. Urban areas can now also be defined by the number of housing units present. Finally, the 2020 Census does not distinguish different types of urban areas. Areas are simply urban or rural.This layer was originally downloaded from the US Census Bureau website and clipped to the Stark County boundary. For more information on urban and rural classification and criteria, visit Redefining Urban Areas following the 2020 Census.

This data contains all the essential data in the form of % with respect to rural and urban Indian states . This dataset is highly accurate as this is taken from the Indian govt. it is updated till 2021 for all states and union territories. source of data is data.gov.in titled - ******All India and State/UT-wise Factsheets of National Family Health Survey******

it is advised to you pls search the data keywords you need by using (Ctrl+f) , as it will help to avoid time wastage. States/UTs

Different columns it contains are Area

Number of Households surveyed Number of Women age 15-49 years interviewed Number of Men age 15-54 years interviewed

Female population age 6 years and above who ever attended school (%)

Population below age 15 years (%)

Sex ratio of the total population (females per 1,000 males)

Sex ratio at birth for children born in the last five years (females per 1,000 males)

Children under age 5 years whose birth was registered with the civil authority (%)

Deaths in the last 3 years registered with the civil authority (%)

Population living in households with electricity (%)

Population living in households with an improved drinking-water source1 (%)

Population living in households that use an improved sanitation facility2 (%)

Households using clean fuel for cooking3 (%) Households using iodized salt (%)

Households with any usual member covered under a health insurance/financing scheme (%)

Children age 5 years who attended pre-primary school during the school year 2019-20 (%)

Women (age 15-49) who are literate4 (%)

Men (age 15-49) who are literate4 (%)

Women (age 15-49) with 10 or more years of schooling (%)

Men (age 15-49) with 10 or more years of schooling (%)

Women (age 15-49) who have ever used the internet (%)

Men (age 15-49) who have ever used the internet (%)

Women age 20-24 years married before age 18 years (%)

Men age 25-29 years married before age 21 years (%)

Total Fertility Rate (number of children per woman) Women age 15-19 years who were already mothers or pregnant at the time of the survey (%)

Adolescent fertility rate for women age 15-19 years5 Neonatal mortality rate (per 1000 live births)

Infant mortality rate (per 1000 live births) Under-five mortality rate (per 1000 live births)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Any method6 (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Any modern method6 (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Female sterilization (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Male sterilization (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - IUD/PPIUD (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Pill (%)  

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Condom (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Injectables (%)

Total Unmet need for Family Planning (Currently Married Women Age 15-49 years)7 (%)

Unmet need for spacing (Currently Married Women Age 15-49 years)7 (%)

Health worker ever talked to female non-users about family planning (%)

Current users ever told about side effects of current method of family planning8 (%)

Mothers who had an antenatal check-up in the first trimester (for last birth in the 5 years before the survey) (%)

Mothers who had at least 4 antenatal care visits (for last birth in the 5 years before the survey) (%)

Mothers whose last birth was protected against neonatal tetanus (for last birth in the 5 years before the survey)9 (%)

Mothers who consumed iron folic acid for 100 days or more when they were pregnant (for last birth in the 5 years before the survey) (%)

Mothers who consumed iron folic acid for 180 days or more when they were pregnant (for last birth in the 5 years before the survey} (%)

Registered pregnancies for which the mother received a Mother and Child Protection (MCP) card (for last birth in the 5 years before the survey) (%)

Mothers who received postnatal care from a doctor/nurse/LHV/ANM/midwife/other health personnel within 2 days of delivery (for last birth in the 5 years before the survey) (%)

Average out-of-pocket expenditure per delivery in a public health facility (for last birth in the 5 years before the survey) (Rs.)

Children born at home who were taken to a health facility for a check-up within 24 hours of birth (for last birth in the 5 years before the survey} (%)

Children who received postnatal care from a doctor/nurse/LHV/ANM/midwife/ other health personnel within 2 days of delivery (for last birth in the 5 years before the survey) (%)

Institutional births (in the 5...