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.

Graph and download economic data for Regional Price Parities: Services: Housing: Nonmetropolitan Portion for District of Columbia (DCNMPRPPSERVERENT) from 2008 to 2023 about rural, DC, PPP, rent, services, price, and USA.

This data layer produced by the National Center for Education Statistics’ (NCES) Education Demographic and Geographic Estimates (EDGE) program provides a geographic locale framework that classifies all U.S. territory into twelve categories ranging from Large Cities to Remote Rural areas. NCES uses this framework to describe the type of geographic area where schools and school districts are located. The criteria for these classifications are defined by NCES, but they rely on standard geographic areas developed and maintained by the U.S. Census Bureau. The 2020 NCES Locale boundaries are based on geographic areas represented in Census TIGER/Line 2020. The NCES Education Demographic and Geographic Estimate (EDGE) program collaborates with the U.S. Census Bureau’s Education Demographic, Geographic, and Economic Statistics (EDGE) Branch to annually update the locale boundaries. For more information about the NCES locale framework, see: https://nces.ed.gov/programs/edge/Geographic/LocaleBoundaries. The classifications include: City - Large (11): Territory inside an Urbanized Area and inside a Principal City with population of 250,000 or more. City - Midsize (12): Territory inside an Urbanized Area and inside a Principal City with population less than 250,000 and greater than or equal to 100,000. City - Small (13): Territory inside an Urbanized Area and inside a Principal City with population less than 100,000. Suburb – Large (21): Territory outside a Principal City and inside an Urbanized Area with population of 250,000 or more. Suburb - Midsize (22): Territory outside a Principal City and inside an Urbanized Area with population less than 250,000 and greater than or equal to 100,000. Suburb - Small (23): Territory outside a Principal City and inside an Urbanized Area with population less than 100,000. Town - Fringe (31): Territory inside an Urban Cluster that is less than or equal to 10 miles from an Urbanized Area. Town - Distant (32): Territory inside an Urban Cluster that is more than 10 miles and less than or equal to 35 miles from an Urbanized Area. Town - Remote (33): Territory inside an Urban Cluster that is more than 35 miles of an Urbanized Area. Rural - Fringe (41): Census-defined rural territory that is less than or equal to 5 miles from an Urbanized Area, as well as rural territory that is less than or equal to 2.5 miles from an Urban Cluster. Rural - Distant (42): Census-defined rural territory that is more than 5 miles but less than or equal to 25 miles from an Urbanized Area, as well as rural territory that is more than 2.5 miles but less than or equal to 10 miles from an Urban Cluster. Rural - Remote (43): Census-defined rural territory that is more than 25 miles from an Urbanized Area and is also more than 10 miles from an Urban Cluster.All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

From April to August 2020 in India, the share of positive coronavirus COVID-19 cases has gradually been on the rise. From June to July 2020, the number of cases in the rural districts doubled. As of August 2020, more than half of India's share of COVID-19 cases were in the rural districts.

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.

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

Attitudes towards one´s own region.

Topics: Satisfaction with life; confidence about the future; importance of selected areas of life (children, good relationship with parents, secure job, interesting job, leisure time, friends, partner, earning a lot of money, getting to know the world, good education, good housing situation, voluntary work, career advancement, participation in public life in the place of residence, protection against crime); satisfaction with selected areas of life (housing situation, professional situation, financial situation, personal standard of living, contacts with neighbours, friends, acquaintances and work colleagues, family or partnership, medical care, opportunities for political participation, social safety net in Germany, politics of the Federal Government and the State Government, local politics, democracy in Germany, protection against crime).

Attitudes towards one´s own region: Advantages and disadvantages of one´s own place of residence (open); voluntary commitment; area of voluntary commitment (open); intention to move; general considerations or concrete plans to move; duration of residence at one´s current place of residence; development of one´s own region; closeness to a place (feeling of home); economic future prospects of one´s own region; provision of the region in various areas (childcare and schools, police stations, shopping facilities, offers for young people and the elderly, medical care, pharmacies, public transport, leisure facilities, road conditions, cultural offerings, job opportunities, fast Internet connections, service offerings, affordable housing, transport connections, banks, ATMs, post offices and parcel counters, care and nursing offers for the elderly); future prospects for the supply of the district in the aforementioned areas; assessment of living conditions in one´s own region in comparison with other counties in the state and federal government; personal worries (possible unemployment, not being able to maintain or achieve a standard of living, not being able to make professional progress, loneliness in old age, health, not being able to withstand the strains of everyday life); concerns with regard to the development in Germany (rising national debt, politics cannot solve Germany´s problems, rising unemployment, fixed-term employment relationships are becoming the rule, increasing crime and violence, more and more refugees in Germany, inadequate protection against illness, unemployment and old age, rising energy costs, living at the expense of future generations); personal expectations of the state (comprehensive protection of citizens against risks vs. creating framework conditions for protection by the citizens themselves); the state should continue to ensure equal living conditions in all regions of Germany in the future; opinion on compensation payments for weaker regions; areas in which the state would have to do more for the region (public transport, road construction and repairs, settlement of rural doctors, fast Internet, etc.); sufficient commitment of the state with regard to future challenges in the region.

Demography: sex; age; employment; occupational status; household size; number of persons in the household aged 18 and over; number of school-age children in the household; education; self-rating of class; party sympathy; household net income.

Additionally coded: Respondent ID; region; survey region; federal state; city size; weighting factor.

Rural Urban ClassificationThe 2021 RUC is a statistical classification to provide a consistent and standardised method for classifying geographies as rural or urban. This is based on address density, physical settlement form, population size, and Relative Access to Major towns and cities (populations of over 75,000 people). The classification is produced by the Office for National Statistics (ONS) with advice from the Department for Environment, Food and Rural Affairs (Defra), the Welsh Government and colleagues from the Government Geography Profession (GGP).This is 2021 rural-urban classification (RUC) of 2024 Local Authority Districts in England and Wales. This means that the 2021 RUC methodology has been applied to the 2024 LAD boundaries. LAD classifications are divided into four categories based on their populations:1. Majority Rural: had at least 50% of their population residing in Rural OAs2. Intermediate Rural: 35-50% rural population3. Intermediate Urban: 20-35% rural population4. Urban: 20% or less of the population lived in rural OAs.Each 2024 LAD category is split into one of two Relative Access categories, using the same data as the 2021 Output Area RUC. If more than 50% of a LAD population lives in ‘Nearer a major town or city’ OAs, it is deemed ‘nearer a major town or city’; otherwise, it is classified as ‘further from a major town or city’.

Where data is unavailable for Super Output Area geographies, it may be appropriate for users to undertake analysis at the LAD level. At this level, the categorisation works slightly differently in that most areas will include a mix of both rural and urban areas - so the LA RUC categorisation is a reflection of this. A statistical geography may contain substantial portions of open countryside but still be given an ‘Urban’ classification if the majority of the population within the area live in settlements that are urban in nature. Users should take this into consideration to ensure correct interpretations of any analysis of RUC LAD categories.

In 2024, about 943.5 million people lived in urban regions in China and 464.8 million in rural. That year, the country had a total population of approximately 1.41 billion people. As of 2024, China was the second most populous country in the world. Urbanization in China Urbanization refers to the process by which people move from rural to urban areas and how a society adapts to the population shift. It is usually seen as a driving force in economic growth, accompanied by industrialization, modernization and the spread of education. Urbanization levels tend to be higher in industrial countries, whereas the degree of urbanization in developing countries remains relatively low. According to World Bank, a mere 19.4 percent of the Chinese population had been living in urban areas in 1980. Since then, China’s urban population has skyrocketed. By 2024, about 67 percent of the Chinese population lived in urban areas. Regional urbanization rates In the last decades, urbanization has progressed greatly in every region of China. Even in most of the more remote Chinese provinces, the urbanization rate surpassed 50 percent in recent years. However, the most urbanized areas are still to be found in the coastal eastern and southern regions of China. The population of Shanghai, the largest city in China and the world’s seventh largest city ranged at around 24 million people in 2023. China’s urban areas are characterized by a developing middle class. Per capita disposable income of Chinese urban households has more than doubled between 2010 and 2020. The emerging middle class is expected to become a significant driver for the continuing growth of the Chinese economy.

Abstract

The World Bank has launched a quick-deploying high-frequency phone-monitoring survey of households to generate near real-time insights on the socio-economic impact of COVID-19 on households which hence to be used to support evidence-based response to the crisis. At a moment when all conventional modes of data collection have had to be suspended, a phone-based rapid data collection/tracking tool can generate large payoffs by helping identify affected populations across the vast archipelago as the contagion spreads, identify with a high degree of granularity the mechanisms of socio-economic impact, identify gaps in public policy response as the Government responds, generating insight that could be useful in scaling up or redirecting resources as necessary as the affected population copes and eventually regains economic footing.

Analysis unit

Household-level; Individual-level: household primary breadwinners, respondent, student, primary caregivers, and under-5 years old kids

Sampling procedure

The sampling frame of the Indonesia high-frequency phone-based monitoring of socio-economic impacts of COVID-19 on households was the list of households enumerated in three recent World Bank surveys, namely Urban Survey (US), Rural Poverty Survey (RPS), and Digital Economy Household Survey (DEHS). The US was conducted in 2018 with 3,527 sampled households living in the urban areas of 10 cities and 2 districts in 6 provinces. The RPS was conducted in 2019 with the sample size of 2,404 households living in rural areas of 12 districts in 6 provinces. The DEHS was conducted in 2020 with 3,107 sampled households, of which 2,079 households lived in urban areas and 1,028 households lived in rural areas in 26 districts and 31 cities within 27 provinces. Overall, the sampled households drawn from the three surveys across 40 districts and 35 cities in 27 provinces (out of 34 provinces). For the final sampling frame, six survey areas of the DEHS which were overlapped with the survey areas in the UPS were dropped from the sampling frame. This was done in order to avoid potential bias later on when calculating the weights (detailed below). The UPS was chosen to be kept since it had much larger samples (2,016 households) than that of the DEHS (265 households). Three stages of sampling strategies were applied. For the first stage, districts (as primary sampling unit (PSU)) were selected based on probability proportional to size (PPS) systematic sampling in each stratum, with the probability of selection was proportional to the estimated number of households based on the National Household Survey of Socio-economic (SUSENAS) 2019 data. Prior to the selection, districts were sorted by provincial code.

In the second stage, villages (as secondary sampling unit (SSU)) were selected systematically in each district, with probability of selection was proportional to the estimated number of households based on the Village Potential Census (PODES) 2018 data. Prior to the selection, villages were sorted by sub-district code. In the third stage, the number of households was selected systematically in each selected village. Prior to the selection, all households were sorted by implicit stratification, that is gender and education level of the head of households. If the primary selected households could not be contacted or refused to participate in the survey, these households were replaced by households from the same area where the non-response households were located and with the same gender and level of education of households’ head, in order to maintain the same distribution and representativeness of sampled households as in the initial design.

In the Round 8 survey where we focused on early nutrition knowledge and early child development, we introduced an additional respondent who is the primary caregiver of under 5 years old in the household. We prioritized the mother as the target of caregiver respondents. In households with multiple caregivers, one is randomly selected. Furthermore, only the under 5 children who were taken care of by the selected respondent will be listed in the early child development module.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire in English is provided for download under the Documentation section.

Response rate

The HiFy survey was initially designed as a 5-round panel survey. By end of the fifth round, it is expected that the survey can maintain around 3,000 panel households. Based on the experience of phone-based, panel survey conducted previously in other study in Indonesia, the response rates were expected to be around 60 percent to 80 percent. However, learned from other similar surveys globally, response rates of phone-based survey, moreover phone-based panel survey, are generally below 50 percent. Meanwhile, in the case of the HiFy, information on some of households’ phone numbers was from about 2 years prior the survey with a potential risk that the targeted respondents might not be contactable through that provided numbers (already inactive or the targeted respondents had changed their phone numbers). With these considerations, the estimated response rate of the first survey was set at 60 percent, while the response rates of the following rounds were expected to be 80 percent. Having these assumptions and target, the first round of the survey was expected to target 5,100 households, with 8,500 households in the lists. The actual sample of households in the first round was 4,338 households or 85 percent of the 5,100 target households. However, the response rates in the following rounds are higher than expected, making the sampled households successfully interviewed in Round 2 were 4,119 (95% of Round 1 samples), and in Rounds 3, 4, 5, 6, 7, and 8 were 4,067 (94%), 3,953 (91%), 3,686 (85%), 3,471 (80%), 3,435 (79%), 3,383 (78%) respectively. The number of balanced panel households up to Rounds 3, 4, 5, 6, 7, and 8 are 3,981 (92%), 3,794 (87%), 3,601 (83%), 3,320 (77%), 3,116 (72%), and 2,856 (66%) respectively.

Rural district (RD) polygons are a graphical representation of the rural district boundaries as defined in regulation 2022-45 under the Local Governance Act with an associated RD name attribute. Effective date is January 1, 2023. / Les polygones de district rural (DR) sont une représentation graphique des limites du district rural telles que définies dans le règlement 2022-45 en vertu de la Loi sur la gouvernance locale, avec un attribut de nom de DR associé. La date d'entrée en vigueur est le 1er janvier 2023.

This spreadsheet contains the data extracted from questionnaires administered for the study on the topic "FINANCIAL INCLUSION AND HOUSEHOLD POVERTY: EVIDENCE FROM THE RURAL WA WEST DISTRICT OF GHANA".

This data contains responses from 358 households statistically selected from a population of 23,615 households representing 222 communities within 5 area councils across Wa West District of the Upper West Region of Ghana. Wa West District is located in the western part of the region and shares physical boundaries with Nadowli-Kaleo District to the North, Sawla-Tuna-Kalba District to the South, Wa Municipal to the East, and the Republic of Burkina Faso to the West.

The data is collected on; (i) Respondent/ Hosehold Demographics (ii) Ownership and usage of account (iii) Frequency of Savings (iv) Withdrawals (v) Access to and usage of Credit (vi) Access to and usage Insurance (vii) Financial Advice (viii) Household Consumption Expenditure (ix)Advancing Financial Inclusion to Reduce Poverty.

The questionnaire designed and used for the study is attached to the data.

Active loan characteristics aggregated at the Congressional District level of geography, including number of loans, average loan amount, average loan amount by 5 year ranges, number of loans to Section 523 Mutual Self Help Housing program participants, and number of leveraged loans.

As of December 2024, around 28.2 percent of internet users in China lived in rural areas. Meanwhile, Internet penetration in rural areas was 67.4 percent.

Graph and download economic data for Real Personal Income: Nonmetropolitan Portion for District of Columbia (DCNMPRPI) from 2008 to 2023 about rural, DC, personal income, personal, income, real, and USA.

Urban rural 2023 update

UR 2023 is the first major update of the geography since it was first created in 2018. The update is to ensure UR geographies are relevant and meet criteria before each five-yearly population and dwelling census. UR 2023 contains 13 new rural settlements and 7 new small urban areas. Updates were made to reflect real world change including new subdivisions and motorways, and to improve delineation of urban areas and rural settlements. The Wānaka urban area, whose population has grown to be more than 10,000 based on population estimates, has been reclassified to a medium urban area in the 2023 urban rural indicator.

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In the 2023 classification there are:

• 7 major urban areas
• 13 large urban areas
• 23 medium urban areas
• 152 small urban areas
• 402 rural settlements.

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This dataset is the definitive version of the annually released urban rural (UR) boundaries as at 1 January 2023 as defined by Stats NZ (the custodian), clipped to the coastline. This clipped version has been created for cartographic purposes and so does not fully represent the official full extent boundaries. This version contains 689 UR areas, including 195 urban areas and 402 rural settlements.

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Urban rural (UR) 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.

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The UR separately identifies urban areas, rural settlements, other rural areas, and water areas. Urban areas and rural settlements are form-based geographies delineated by the inspection of aerial imagery, local government land designations on district plan maps, address registers, property title data, and any other available information. However, because the underlying meshblock pattern is used to define the geographies, boundaries may not align exactly with local government land designations or what can be seen in aerial images. Other rural areas, and bodies of water represent areas not included within an urban area.

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Urban areas are built from the statistical area 2 (SA2) geography, while rural and water areas are built from the statistical area 1 (SA1) geography.

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Non-digitised

The following 4 non-digitised UR areas have been aggregated from the 16 non-digitised meshblocks/SA2s.

6901; Oceanic outside region, 6902; Oceanic oil rigs, 6903; Islands outside region, 6904; Ross Dependency outside region.

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UR numbering and naming

Each urban area and rural settlement is a single geographic entity with a name and a numeric code.

Other rural areas, inland water areas, and inlets are defined by territorial authority; oceanic areas are defined by regional council; and each have a name and a numeric code.

Urban rural codes have four digits. North Island locations start with a 1, South Island codes start with a 2, oceanic codes start with a 6 and non-digitised codes start with 69.

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Urban rural indicator (IUR)

The accompanying urban rural indicator (IUR) classifies the urban, rural, and water areas by type. Urban areas are further classified by the size of their estimated resident population:

• major urban area – 100,000 or more residents,
• large urban area – 30,000–99,999 residents,
• medium urban area – 10,000–29,999 residents,
• small urban area – 1,000–9,999 residents.

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This was based on 2018 Census data and 2021 population estimates. Their IUR status (urban area size/rural settlement) may change if the 2023 Census population count moves them up or down a category.

The indicators, by name, with their codes in brackets, are:

urban area – major urban (11), large urban (12), medium urban (13), small urban (14),

rural area – rural settlement (21), rural other (22),

water – inland water (31), inlet (32), oceanic (33).

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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.

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For more information please refer to the Statistical standard for geographic areas 2023.

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Clipped version

This clipped version has been created for cartographic purposes and so does not fully represent the official full extent boundaries.

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Macrons

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

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Digital data

Digital boundary data became freely available on 1 July 2007.

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To download geographic classifications in table formats such as CSV please use Ariā

Consumer expenditure at rural areas of Chukotka Autonomous District sank by 25.57% from 189,372 rubles in 2015 to 140,947 rubles in 2016. Since the 18.20% jump in 2014, consumer expenditure at rural areas plummeted by 29.78% in 2016. Indicator is based on the results of the sample survey of households' budgets. Households consumer expenditure is the part of money expenditure on purchasing consumer goods and services. Consumer expenditure does not include spending on purchasing pieces of art, antiques and jewelry made for the purpose of capital investment, it also excludes payments for materials and works for construction being classified as an investment. Consumer expenditure is classified by types of goods and services.