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.

This map uses dot density patterns to indicate which population is larger in each area: urban (green) or rural (blue). Data is from the U.S. Census Bureau’s 2020 Census Demographic and Housing Characteristics. The map's layers contain total population counts by sex, age, and race groups for Nation, State, County, Census Tract, and Block Group 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 with all green dots, and 100% rural areas in dark blue dots. Areas with mixed urban/rural population have a proportional mix of green and blue dots 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 Federal Housing Enterprises Financial Safety and Soundness Act of 1992 establishes a duty for Fannie Mae and Freddie Mac (the Enterprises) to serve the housing needs of very low-, low-, and moderate-income families in rural areas. FHFA has issued a final rule that provides eligibility for Duty to Serve credit for Enterprise mortgage purchases and other activities in “rural areas,” as defined in the rule. Additionally, the final rule specifies supportfor high-needs rural regions as a Regulatory Activity that the Enterprises may consider when developing their plans for the Duty to Serve program. FHFA’s 2017 Rural Areas File designates census tracts in the Metropolitan Statistical Areas (MSAs) and outside of MSAs of the 50 states, the District of Columbia, and Puerto Rico that are considered rural areas or non-rural areas under the final rule. The File also identifies whether census tracts are located in “high-needs” counties in order to determine whether tracts meet the definition of “high-needs rural regions” in the final rule.This is a MD iMAP hosted service. Find more information at https://imap.maryland.gov.Feature Service Link:https://geodata.md.gov/imap/rest/services/BusinessEconomy/MD_HousingDesignatedAreas/FeatureServer/5

In 2023, there were approximately 55.94 million people living in rural areas in the United States, while about 278.98 million people were living in urban areas. Within the provided time period, the number of people living in urban U.S. areas has increased significantly since totaling only 126.46 million in 1960.

Population: Rural: Yunnan: Puer: Lancang data was reported at 452.791 Person th in 2017. This records an increase from the previous number of 451.200 Person th for 2016. Population: Rural: Yunnan: Puer: Lancang data is updated yearly, averaging 402.000 Person th from Dec 2004 (Median) to 2017, with 12 observations. The data reached an all-time high of 452.791 Person th in 2017 and a record low of 388.000 Person th in 2006. Population: Rural: Yunnan: Puer: Lancang data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GJ: Population: Rural: County Level Region.

Worldwide, Papua New Guinea was the country with the highest rural population in terms of share of the country's population. As of 2023, 86.28 percent of the Asian country's inhabitants lived in rural areas. Burundi followed in second with 85.22 percent, whereas 85.38 percent of Liechtenstein's population lived in rural areas that year. Over the past decades, the share of the global population living in rural areas decreased.

Population: Rural: Yunnan: Dali: Yangbi data was reported at 72.051 Person th in 2017. This records an increase from the previous number of 72.000 Person th for 2016. Population: Rural: Yunnan: Dali: Yangbi data is updated yearly, averaging 86.000 Person th from Dec 2004 to 2017, with 12 observations. The data reached an all-time high of 92.000 Person th in 2006 and a record low of 70.000 Person th in 2012. Population: Rural: Yunnan: Dali: Yangbi data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GJ: Population: Rural: County Level Region.

Population: Rural: Shandong: Heze: Shan data was reported at 1,085.000 Person th in 2012. This records an increase from the previous number of 1,080.000 Person th for 2011. Population: Rural: Shandong: Heze: Shan data is updated yearly, averaging 1,076.000 Person th from Dec 2004 to 2012, with 9 observations. The data reached an all-time high of 1,085.000 Person th in 2012 and a record low of 1,062.000 Person th in 2004. Population: Rural: Shandong: Heze: Shan data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GJ: Population: Rural: County Level Region.

Number of Household: Rural: Shandong: Dezhou: Leling data was reported at 151.920 Unit th in 2012. This records an increase from the previous number of 151.619 Unit th for 2011. Number of Household: Rural: Shandong: Dezhou: Leling data is updated yearly, averaging 143.120 Unit th from Dec 2004 (Median) to 2012, with 9 observations. The data reached an all-time high of 164.167 Unit th in 2009 and a record low of 141.000 Unit th in 2004. Number of Household: Rural: Shandong: Dezhou: Leling data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GJ: No of Household: Rural: County Level Region.

Youth living in remote rural communities face significant geographic barriers to college access. Even those living near to a postsecondary institution may not have the means for, or may not see the value of, pursuing a college degree within their local economy. This study uses 18 years of national county-level data to ask how local economic shocks affect the postsecondary enrollment and attainment of rural students, as compared to students in metropolitan and metropolitan-adjacent regions. Results from an instrumental variables analysis indicate that each 1 percentage point increase in local unemployment increases local college enrollment by 10.0 percent in remote rural areas, as compared to a 5.2 percent increase in metropolitan-adjacent areas and no detectable increase in metropolitan areas. The rise in rural college enrollment is driven primarily by students enrolling in or continuing in associate degree programs, and by students transferring from two-year to four-year programs.

In 2021, the online retail sales of household items and furnishings in China's county-level areas exceeded one trillion yuan and accounted for 23.6 percent of the total rural online sales. In comparison, around 736 billion yuan worth of apparel items were sold to Chinese rural residents online in 2021.

This utility file contains several geographic classifications which are useful when compiling statistics about counties in Tennessee. The classifications include data from three sources and can be joined to other county-level data using the GEOID field.

2020 Urban and Rural Counties; Tennessee Department of Economic and Community Development

According to TNECD policy, Rural counties have less than 50% of their population living within a 2020 Census Urbanized Area with a population of more 50,000. Urban area delineations were released by U.S. Census Bureau in January 2022. Tennessee has 17 urban counties and 78 rural counties in the delineations.

Development Districts

Each Tennessee county is assigned to one of nine Development Districts. They act as regional planning and economic development organizations. Cities and towns within each district provide oversight of district activities. Boundaries of Area Agencies on Aging and Disability coincide with the development districts but use different names.

2023 Metropolitan and Micropolitan Statistical Areas

Core-Based Statistical Areas (CBSAs) are county-based regions defined by the U.S. Office of Management and Budget and are used for statistical purposes. Metropolitan Statistical Areas include central counties with a Census Urbanized area of at least 50,000 people. Micropolitan Statistical Areas include counties with a central urbanized area of 10,000 to 50,000 people. Outlying counties with a high-degree economic integration, measured by commuting are also included in the delineation. 66 Tennessee counties are included in a core-based statistical area.

Name

Description

Type

GEOID

Geographic Identifier

Text

NAMELSAD

Name

Text

DEV_DIST_NAME

Development District Short

Text

DEV_DIST_ACRONYM

Development District Short

Text

CBSA_Code

CBSA Code

Text

CBSA_Title

CBSA Title

Text

CBSA_Type

CBSA Type

Text

CBSA_CType

CBSA County Type

Text

ECD_RURAL

TNECD Urban Rural

Text

The table below showcases the 10th, 25th, 50th, 75th, and 90th percentiles of property tax rates for each zip code in Quincy Rural, Washington. It's important to understand that tax rates can vary greatly and can change yearly.

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The All Roads Shapefile includes all features within the MTDB Super Class "Road/Path Features" distinguished where the MAF/TIGER Feature Classification Code (MTFCC) for the feature in MTDB that begins with "S". This includes all primary, secondary, local neighborhood, and rural roads, city streets, vehicular trails (4wd), ramps, service drives, alleys, parking lot roads, private roads for service vehicles (logging, oil fields, ranches, etc.), bike paths or trails, bridle/horse paths, walkways/pedestrian trails, and stairways.

Number of Household: Rural: Hunan: West Hunan: Guzhang data was reported at 31.400 Unit th in 2012. This records an increase from the previous number of 31.300 Unit th for 2011. Number of Household: Rural: Hunan: West Hunan: Guzhang data is updated yearly, averaging 30.300 Unit th from Dec 2004 (Median) to 2012, with 9 observations. The data reached an all-time high of 31.400 Unit th in 2012 and a record low of 30.100 Unit th in 2008. Number of Household: Rural: Hunan: West Hunan: Guzhang data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GJ: No of Household: Rural: County Level Region.

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The All Roads Shapefile includes all features within the MTDB Super Class "Road/Path Features" distinguished where the MAF/TIGER Feature Classification Code (MTFCC) for the feature in MTDB that begins with "S". This includes all primary, secondary, local neighborhood, and rural roads, city streets, vehicular trails (4wd), ramps, service drives, alleys, parking lot roads, private roads for service vehicles (logging, oil fields, ranches, etc.), bike paths or trails, bridle/horse paths, walkways/pedestrian trails, and stairways.

Population: Rural: Sichuan: Yibin: Pingshan data was reported at 267.000 Person th in 2012. This records a decrease from the previous number of 270.000 Person th for 2011. Population: Rural: Sichuan: Yibin: Pingshan data is updated yearly, averaging 268.000 Person th from Dec 2004 (Median) to 2012, with 9 observations. The data reached an all-time high of 270.000 Person th in 2011 and a record low of 260.000 Person th in 2005. Population: Rural: Sichuan: Yibin: Pingshan data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GJ: Population: Rural: County Level Region.

Kenya’s population has nearly tripled in the last 35 years, from 16.3 million in 1980 to about 47 million today yet majority of the population are below the poverty line. poverty in Kenya is a widespread problem concentrated in the rural areas. This data set shows poverty rates within the Kenyan counties.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

Abstract

According to the Food and Agricultural Organization (FAO) 123 million Chinese remained undernourished in 2003-2005. That represents 14% of the global total. UNICEF states that 7.2 million of the world's stunted children are located in China. In absolute terms, China continues to rank in the top countries carrying the global burden of under-nutrition. China must-and still can reduce under-nutrition, thus contributing even further to the global attainment of MDG1. In this context that the United Nations Joint Programme, in partnership with the Chinese government, has conducted this study. The key objective is to improve evidence of household food security through a baseline study in six pilot counties in rural China. The results will be used to guide policy and programmes aimed at reducing household food insecurity in the most vulnerable populations in China. The study is not meant to be an exhaustive analysis of the food security situation in the country, but to provide a demonstrative example of food assessment tools that may be replicated or scaled up to other places.

Geographic coverage

Six rural counties

Analysis unit

• Household
• Village

Universe

The survey covered household heads and women between 15-49 years resident of that household. A household is defined as a group of people currently living and eating together "under the same roof" (or in same compound if the household has 2 structures).

Kind of data

Sample survey data [ssd]

Sampling procedure

The required sample size for the survey was calculated using standard sample size calculations with each county representing a stratum. After the sample size was calculated, a two-stage clustering approach was applied. The first stage is the selection of villages using the probability proportional to size (PPS) method to create a self-weighted sample in which larger population clusters (villages) have a greater chance of selection, proportional to their size. Following the selection of the villages, 12 households within the village were selected using simple random selection.

Sampling deviation

Floods and landslides prevented the team from visiting two of the selected villages, one in Wuding and one in Panxian, so they substituted them with replacement villages.

Mode of data collection

Face-to-face [f2f]

Research instrument

The household questionnaire was administered to all households in the survey and included modules on demography, education, migration and remittances, housing and facilities, household assets, agricultural, income activities, expenditure, food sources and consumption, shocks and coping strategies.

The objective of the village questionnaire was to gather contextual information on the six counties for descriptive purposes. In each village visited, a focus group discussion took place on topics including: population of the village, migrants, access to social services such as education and health, infrastructure, access to markets, difficulties facing the village, information on local agricultural practices.

The questionnaires were developed by WFP and Chinese Academy of Agricultural Sciences (CAAS) with inputs from partnering agencies. They were originally formulated in English and then translated into Mandarin. They were pilot tested in the field and corrected as needed. The final interviews were administered in Mandarin with translation provided in the local language when needed.

All questionnaires and modules are provided as external resources.

Cleaning operations

After data collection, data entry was carried out by CAAS staff in Beijing using EpiData software. The datasets were then exported into SPSS for analysis. Data cleaning was an iterative process throughout the data entry and analysis phases.

Descriptive analysis, correlation analysis, principle component analysis, cluster analysis and various other forms of analyses were conducted using SPSS.