Each year, the Forecasting and Trends Office (FTO) publishes population estimates and future year projections based on the population estimates developed by the Bureau of Economic and Business Research (BEBR) at the University of Florida. This dataset contains boundaries for each county’s 2010 rural (non-urban) area in the State of Florida with 2020 census population and 2021 population estimates. The population estimates can be used for a variety of planning studies including statewide and regional transportation plan updates, subarea and corridor studies, and funding allocations for various planning agencies.Each year, the Forecasting and Trends Office (FTO) publishes population estimates and future year projections. The population estimates can be used for a variety of planning studies including statewide and regional transportation plan updates, subarea and corridor studies, and funding allocations for various planning agencies.The 2020 population estimates reported are based on the US Census Bureau 2020 Decennial Census. The 2021 population estimates are based on the population estimates developed by the Bureau of Economic and Business Research (BEBR) at the University of Florida. BEBR uses the decennial census count for April 1, 2020, as the starting point for state-level projections. More information is available from BEBR here.This dataset contains boundaries for each county’s 2010 rural (non-urban) area in the State of Florida with 2020 census population and 2021 population estimates. All legal boundaries and names in this dataset are from the US Census Bureau’s TIGER/Line Files (2021).For the 2010 Census, urban areas comprised a “densely settled core of census tracts and/or census blocks that meet minimum population density requirements, along with adjacent territory containing non-residential urban land uses as well as territory with low population density included to link outlying densely settled territory with the densely settled core.” “Rural” encompasses all population, housing, and territory not included within an urban area. Please see the Data Dictionary for more information on data fields. Data Sources:US Census Bureau 2020 Decennial CensusUS Census Bureau’s TIGER/Line Files (2021)Bureau of Economic and Business Research (BEBR) – Florida Estimates of Population 2021 Data Coverage: StatewideData Time Period: 2020 – 2021 Date of Publication: July 2022 Point of Contact:Dana Reiding, ManagerForecasting and Trends OfficeFlorida Department of TransportationDana.Reiding@dot.state.fl.us605 Suwannee Street, Tallahassee, Florida 32399850-414-4719

The rural-urban commuting area codes (RUCA) classify U.S. census tracts using measures of urbanization, population density, and daily commuting from the decennial census. The most recent RUCA codes are based on data from the 2000 decennial census. The classification contains two levels. Whole numbers (1-10) delineate metropolitan, micropolitan, small town, and rural commuting areas based on the size and direction of the primary (largest) commuting flows. These 10 codes are further subdivided to permit stricter or looser delimitation of commuting areas, based on secondary (second largest) commuting flows. The approach errs in the direction of more codes, providing flexibility in combining levels to meet varying definitional needs and preferences. The 1990 codes are similarly defined. However, the Census Bureau's methods of defining urban cores and clusters changed between the two censuses. And, census tracts changed in number and shapes. The 2000 rural-urban commuting codes are not directly comparable with the 1990 codes because of these differences. An update of the Rural-Urban Commuting Area Codes is planned for late 2013.

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 table contains the Veteran estimates by urban/rural, sex, age group, and ethnicity. Note: rounding to the nearest 1,000 is always appropriate for VetPop estimates.

PLURAL (Place-level urban-rural indices) is a framework to create continuous classifications of "rurality" or "urbanness" based on the spatial configuration of populated places. PLURAL makes use of the concept of "remoteness" to characterize the level of spatial isolation of a populated place with respect to its neighbors. There are two implementations of PLURAL, including (a) PLURAL-1, based on distances to the nearest places of user-specified population classes, and (b) PLURAL-2, based on neighborhood characterization derived from spatial networks. PLURAL requires simplistic input data, i.e., the coordinates (x,y) and population p of populated places (villages, towns, cities) in a given point in time. Due to its simplistic input, the PLURAL rural-urban classification scheme can be applied to historical data, as well as to data from data-scarce settings. Using the PLURAL framework, we created place-level rural-urban indices for the conterminous United States from 1930 to 2018. Rural-urban classifications are essential for analyzing geographic, demographic, environmental, and social processes across the rural-urban continuum. Most existing classifications are, however, only available at relatively aggregated spatial scales, such as at the county scale in the United States. The absence of rurality or urbanness measures at high spatial resolution poses significant problems when the process of interest is highly localized, as with the incorporation of rural towns and villages into encroaching metropolitan areas. Moreover, existing rural-urban classifications are often inconsistent over time, or require complex, multi-source input data (e.g., remote sensing observations or road network data), thus, prohibiting the longitudinal analysis of rural-urban dynamics. We developed a set of distance- and spatial-network-based methods for consistently estimating the remoteness and rurality of places at fine spatial resolution, over long periods of time. Based on these methods, we constructed indices of urbanness for 30,000 places in the United States from 1930 to 2018. We call these indices the place-level urban-rural index (PLURAL), enabling long-term, fine-grained analyses of urban and rural change in the United States. The method paper has been peer-reviewed and is published in "Landscape and Urban Planning". The PLURAL indices from 1930 to 2018 are available as CSV files, and as point-based geospatial vector data (.SHP). Moreover, we provide animated GIF files illustrating the spatio-temporal variation of the different variants of the PLURAL indices, illustrating the dynamics of the rural-urban continuum in the United States from 1930 to 2018. Apply the PLURAL rural-urban classification to your own data: Python code is fully open source and available at https://github.com/johannesuhl/plural. Data sources: Place-level population counts (1980-2010) and place locations 1930 - 2018 were obtained from IPUMS NHGIS, (University of Minnesota, www.nhgis.org; Manson et al. 2022). Place-level population counts 1930-1970 were digitized from historical census records (U.S. Census Bureau 1942, 1964). References: Uhl, J.H., Hunter, L.M., Leyk, S., Connor, D.S., Nieves, J.J., Hester, C., Talbot, C. and Gutmann, M., 2023. Place-level urban–rural indices for the United States from 1930 to 2018. Landscape and Urban Planning, 236, p.104762. DOI: https://doi.org/10.1016/j.landurbplan.2023.104762 Steven Manson, Jonathan Schroeder, David Van Riper, Tracy Kugler, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 16.0 [dataset]. Minneapolis, MN: IPUMS. 2021. http://doi.org/10.18128/D050.V16.0 U.S. Census Bureau (1942). U.S. Census of Population: 1940. Vol. I, Number of Inhabitants. U.S. Government Printing Office, Washington, D.C. U.S. Census Bureau (1964). U.S. Census of Population: 1960. Vol. I, Characteristics of the Population. Part I, United States Summary. U.S. Government Printing Office, Washington, D.C.

This data set includes annual counts and percentages of Medicaid and Children’s Health Insurance Program (CHIP) enrollees by urban or rural residence. Results are shown overall; by state; and by four subpopulation topics: scope of Medicaid and CHIP benefits, race and ethnicity, disability-related eligibility category, and managed care participation. These results were generated using Transformed Medicaid Statistical Information System (T-MSIS) Analytic Files (TAF) Release 1 data and the Race/Ethnicity Imputation Companion File. This data set includes Medicaid and CHIP enrollees in all 50 states, the District of Columbia, Puerto Rico, and the U.S. Virgin Islands who were enrolled for at least one day in the calendar year, except where otherwise noted. Enrollees in Guam, American Samoa, and the Northern Mariana Islands are not included. Results shown overall (where subpopulation topic is "Total enrollees") and for the race and ethnicity subpopulation topic exclude enrollees in the U.S. Virgin Islands. Results shown for the race and ethnicity, disability category, and managed care participation subpopulation topics only include Medicaid and CHIP enrollees with comprehensive benefits. Results shown for the disability category subpopulation topic only include working-age adults (ages 19 to 64). Results for states with TAF data quality issues in the year have a value of "Unusable data." Some rows in the data set have a value of "DS," which indicates that data were suppressed according to the Centers for Medicare & Medicaid Services’ Cell Suppression Policy for values between 1 and 10. This data set is based on the brief: "Rural Medicaid and CHIP enrollees in 2020." Enrollees are assigned to an urban or rural category based on the 2010 Rural-Urban Commuting Area (RUCA) code associated with their home or mailing address ZIP code in TAF. Enrollees are assigned to the comprehensive benefits or limited benefits subpopulation according to the criteria in the "Identifying Beneficiaries with Full-Scope, Comprehensive, and Limited Benefits in the TAF" DQ Atlas brief. Enrollees are assigned to a race and ethnicity subpopulation using the state-reported race and ethnicity information in TAF when it is available and of good quality; if it is missing or unreliable, race and ethnicity is indirectly estimated using an enhanced version of Bayesian Improved Surname Geocoding (BISG) (Race and ethnicity of the national Medicaid and CHIP population in 2020). Enrollees are assigned to a disability category subpopulation using their latest reported eligibility group code and age in the year (Medicaid enrollees who qualify for benefits based on disability in 2020). Enrollees are assigned to a managed care participation subpopulation based on the managed care plan type code that applies to the majority of their enrolled-months during the year (Enrollment in CMC Plans). Please refer to the full brief for additional context about the methodology and detailed findings. Future updates to this data set will include more recent data years as the TAF data become available.

The Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 2 data set consists of country-level estimates of urban population, rural population, total population and land area country-wide and in LECZs for years 1990, 2000, 2010, and 2100. The LECZs were derived from Shuttle Radar Topography Mission (SRTM), 3 arc-second (~90m) data which were post processed by ISciences LLC to include only elevations less than 20m contiguous to coastlines; and to supplement SRTM data in northern and southern latitudes. The population and land area statistics presented herein are summarized at the low coastal elevations of less than or equal to 1m, 3m, 5m, 7m, 9m, 10m, 12m, and 20m. Additionally, estimates are provided for elevations greater than 20m, and nationally. The spatial coverage of this data set includes 202 of the 232 countries and statistical areas delineated in the Gridded Rural-Urban Mapping Project version 1 (GRUMPv1) data set. The 30 omitted areas were not included because they were landlocked, or otherwise lacked coastal features. This data set makes use of the population inputs of GRUMPv1 allocated at 3 arc-seconds to match the SRTM elevations, and at 30 arc-seconds resolution in order to reflect uncertainty levels in the product resulting from the interplay of input population data resolutions (based on census Units) and the elevation data. Urban and rural areas are differentiated by the GRUMPv1 Urban Extents. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).

The Rural Access Index (RAI) is a measure of access, developed by the World Bank in 2006. It was adopted as Sustainable Development Goal (SDG) indicator 9.1.1 in 2015, to measure the accessibility of rural populations. It is currently the only indicator for the SDGs that directly measures rural access.The RAI measures the proportion of the rural population that lives within 2 km of an all-season road. An all-season road is one that is motorable all year, but may be temporarily unavailable during inclement weather (Roberts, Shyam, & Rastogi, 2006). This dataset implements and expands on the most recent official methodology put forward by the World Bank, ReCAP's 2019 RAI Supplemental Guidelines. This is, to date, the only publicly available application of this method at a global scale.MethodologyReCAP's methodology provided new insight on what makes a road all-season and how this data should be handled: instead of removing unpaved roads from the network, the ones that are classified as unpaved are to be intersected with topographic and climatic conditions and, whenever there’s an overlap with excess precipitation and slope, a multiplying factor ranging from 0% to 100% is applied to the population that would access to that road. This present dataset developed by SDSN's SDG Transformation Centre proposes that authorities ability to maintain and remediate road conditions also be taken into account.Data sourcesThe indicator relies on four major items of geospatial data: land cover (rural or urban), population distribution, road network extent and the “all-season” status of those roads.Land cover data (urban/rural distinction)Since the indicator measures the acess rural populations, it's necessary to define what is and what isn't rural. This dataset uses the DegUrba Methodology, proposed by the United Nations Expert Group on Statistical Methodology for Delineating Cities and Rural Areas (United Nations Expert Group, 2019). This approach has been developed by the European Commission Global Human Settlement Layer (GHSL-SMOD) project, and is designed to instil some consistency into the definitions based on population density on a 1-km grid, but adjusted for local situations.Population distributionThe source for population distribution data is WorldPop. This uses national census data, projections and other ancillary data from countries to produce aggregated, 100 m2 population data. Road extentTwo widely recognized road datasets are used: the real-time updated crowd-sourced OpenStreetMap (OSM) or the GLOBIO’s 2018 GRIP database, which draws data from official national sources. The reasons for picking the latter are mostly related to its ability to provide information on the surface (pavement) of these roads, to the detriment of the timeliness of the data, which is restrained to the year 2018. Additionally, data from Microsoft Bing's recent Road Detection project is used to ensure completeness. This dataset is completely derived from machine learning methods applied over satellite imagery, and detected 1,165 km of roads missing from OSM.Roads’ all-season statusThe World Bank's original 2006 methodology defines the term all-season as “… a road that is motorable all year round by the prevailing means of rural transport, allowing for occasional interruptions of short duration”. ReCAP's 2019 methodology makes a case for passability equating to the all-season status of a road, along with the assumption that typically the wet season is when roads become impassable, especially so in steep roads that are more exposed to landslides.This dataset follows the ReCAP methodology by creating an passability index. The proposed use of passability factors relies on the following three aspects:• Surface type. Many rural roads in LICs (and even in large high-income countries including the USA and Australia) are unpaved. As mentioned before, unpaved roads deteriorate rapidly and in a different way to paved roads. They are very susceptible to water ingress to the surface, which softens the materials and makes them very vulnerable to the action of traffic. So, when a road surface becomes saturated and is subject to traffic, the deterioration is accelerated. • Climate. Precipitation has a significant effect on the condition of a road, especially on unpaved roads, which predominate in LICs and provide much of the extended connectivity to rural and poor areas. As mentioned above, the rainfall on a road is a significant factor in its deterioration, but the extent depends on the type of rainfall in terms of duration and intensity, and how well the roadside drainage copes with this. While ReCAP suggested the use of general climate zones, we argue that better spatial and temporal resolutions can be acquired through the Copernicus Programme precipitation data, which is made available freely at ~30km pixel size for each month of the year.• Terrain. The gradient and altitude of roads also has an effect on their accessibility. Steep roads become impassable more easily due to the potential for scour during heavy rainfall, and also due to slipperiness as a result of the road surface materials used. Here this is drawn from slope calculated from SRTM Digital Terrain data.• Road maintenance. The ability of local authorities to remediate damaged caused by precipitation and landslides is proposed as a correcting factor to the previous ones. Ideally this would be measured by the % of GDP invested in road construction and maintenance, but this isn't available for all countries. For this reason, GDP per capita is adopted as a proxy instead. The data range is normalized in such a way that a road maxed out in terms of precipitation and slope (accessibility score of 0.25) in a country at the top of the GDP per capita range is brought back at to the higher end of the accessibility score (0.95), while the accessibility score of a road meeting the same passability conditions in a country which GDP per capita is towards the lower end is kept unchanged.Data processingThe roads from the three aforementioned datasets (Bing, GRIP and OSM) are merged together to them is applied a 2km buffer. The populations falling exclusively on unpaved road buffers are multiplied by the resulting passability index, which is defined as the normalized sum of the aforementioned components, ranging from 0.25 to. 0.9, with 0.95 meaning 95% probability that the road is all-season. The index applied to the population data, so, when calculated, the RAI includes the probability that the roads which people are using in each area will be all-season or not. For example, an unpaved road in a flat area with low rainfall would have an accessibility factor of 0.95, as this road is designed to be accessible all year round and the environmental effects on its impassability are minimal.The code for generating this dataset is available on Github at: https://github.com/sdsna/rai

Rural Revitalisation Areas (RZs) are designed to assist the development of rural territories mainly through fiscal and social measures. Specific measures for economic development shall apply. The aim is to concentrate state aid measures for job-creating enterprises in the less populated rural areas most affected by demographic and economic decline. They were created by the Law of Orientation for the Planning and Development of the Territory (LOADT) of 4 February 1995.

The reform of the ZRRs, passed in the amending finance law for 2015 (Article 1465A of the General Tax Code), simplified the criteria for classifying the territories taken into account. The criteria are now examined at inter-municipal level and result in the ranking of all EPCI municipalities. To be classified as a RRA as of July 1, 2017, the EPCI must have both: — a population density of less than or equal to the median of densities per EPCI; — tax income per median consumption unit less than or equal to the median of median tax revenues. For the DOMs, the municipalities classified as ZRR are defined by law.

The Order of 22 February 2018 defines the new contours of the 2018 RRAs by incorporating the following changes compared to 2017: — outgoing municipalities in 2017 are reinstated.

Official classification of the municipalities of the department in a rural revitalisation zone

Rural Revitalisation Areas (RZs) are designed to assist the development of rural territories mainly through fiscal and social measures. Specific measures for economic development shall apply. The aim is to concentrate state aid measures for job-creating enterprises in the less populated rural areas most affected by demographic and economic decline.

The ZRRs were created by the Law of Orientation for the Planning and Development of the Territory (LOADT) of 4 February 1995. The Interministerial Committee on Territorial Planning and Development (CIADT) on 3 September 2003 set out new guidelines for adapting this tool to current needs. The corresponding provisions are contained in the Law on the Development of Rural Territories of 23 February 2005 and Decree No. 2005-1435 of 21 November 2005.

The list establishing the classification of municipalities in ZRR is drawn up and revised each year by order of the Prime Minister in the light of the creations, deletions and modifications of the scope of the EPCI with own taxation established on 31 December of the previous year

This national sheet corresponds to part of the metadata of the COVADIS ZRR standard. This standard has been developed in a generic and non-milled manner (its name and definitions do not mention any date as in previous records) to apply to different versions of the ZRR Order. The local GeoBase layers should be named N_ZRR_ZSUP_ddd_aaaa, aaaa corresponding to the data vintage.

The 1996 and 2005 RRAs remain described in National Fact Sheets #311 and #752.

Rural Revitalisation Areas (RZs) are designed to assist the development of rural territories mainly through fiscal and social measures. Specific measures for economic development shall apply. The aim is to concentrate state aid measures for job-creating enterprises in the less populated rural areas most affected by demographic and economic decline. They were created by the Law of Orientation for the Planning and Development of the Territory (LOADT) of 4 February 1995. The reform of the ZRRs, passed in the amending finance law for 2015 (Article 1465A of the General Tax Code), simplified the criteria for classifying the territories taken into account. The criteria are now examined at inter-municipal level and result in the ranking of all EPCI municipalities. To be classified as a RRA as of July 1, 2017, the EPCI must have both: — a population density of less than or equal to the median of densities per EPCI; — tax income per median consumption unit less than or equal to the median of median tax revenues. For the DOMs, the municipalities classified as ZRR are defined by law. The Order of 22 February 2018 defines the new contours of the 2018 RRAs by incorporating the following changes compared to 2017: — outgoing municipalities in 2017 are reinstated.

The Low Elevation Coastal Zone (LECZ) Urban-Rural Population Estimates consists of country-level estimates of urban, rural and total population and land area country-wide and in the LECZ, if applicable. Additionally, the data set provides the number of urban extents, their population and land area that intersect the LECZ, by city-size population classifications of less than 100,000, 100,000 to 500,000, 500,000 to 1,000,000, 1,000,000 to 5,000,000, and more than 5,000,000. All estimates are based on GRUMP Alpha data products. The LECZ was generated using SRTM Digital Elevation Model data and includes all land area that is contiguous with the coast and 10 meters or less in elevation. All grids used for population, land area, urban mask, and LECZ were of 30 arc-second (~1 km ) resolution. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Institute for Environment and Development (IIED).

Rural Revitalisation Areas (RZs) are designed to assist the development of rural territories mainly through fiscal and social measures. Specific measures for economic development shall apply. The aim is to concentrate state aid measures for job-creating enterprises in the less populated rural areas and those most affected by demographic and economic decline.The RRAs were created by the Law of 4 February 1995 of Guidance for the Development and Development of the Territory (LOADT). The Interministerial Committee on Territorial Planning and Development (CIADT) on 3 September 2003 set out new guidelines for adapting this tool to current needs. The corresponding provisions are laid down in the Law on the Development of Rural Territories of 23 February 2005 and Decree No. 2005-1435 of 21 November 2005.The list establishing the classification of municipalities as ZRR is drawn up and revised each year by order of the Prime Minister in the light of the creations, abolitions and modifications of the scope of EPCIs with own taxation established on 31 December of the previous year

Rural Revitalisation Areas (RZs) are designed to assist the development of rural territories mainly through fiscal and social measures. Specific measures for economic development shall apply. The aim is to concentrate state aid measures for job-creating enterprises in the less populated rural areas most affected by demographic and economic decline. The ZRRs were created by the Law of Orientation for the Planning and Development of the Territory (LOADT) of 4 February 1995. The Interministerial Committee on Territorial Planning and Development (CIADT) on 3 September 2003 set out new guidelines for adapting this tool to current needs. The corresponding provisions are contained in the Law on the Development of Rural Territories of 23 February 2005 and Decree No. 2005-1435 of 21 November 2005. The list establishing the classification of municipalities in ZRR is drawn up and revised each year by order of the Prime Minister in the light of the creations, deletions and modifications of the scope of the EPCI with own taxation established on 31 December of the previous year — source: interministerial stop of 16 March 2017 — validite: 16/03/2017

Rural Revitalisation Areas (RZs) are designed to assist the development of rural territories mainly through fiscal and social measures. Specific measures for economic development shall apply. The aim is to concentrate state aid measures for job-creating enterprises in the less populated rural areas most affected by demographic and economic decline. The ZRRs were created by the Law of Orientation for the Planning and Development of the Territory (LOADT) of 4 February 1995. The Interministerial Committee on Territorial Planning and Development (CIADT) on 3 September 2003 set out new guidelines for adapting this tool to current needs. The corresponding provisions are laid down in the Law on the Development of Rural Territories of 23 February 2005 and Decree No. 2005-1435 of 21 November 2005.The list establishing the classification of municipalities as ZRR is drawn up and revised each year by order of the Prime Minister in the light of the creations, abolitions and modifications of the scope of EPCIs with own taxation established on 31 December of the previous year

Knowing where people are is crucial for policymakers, particularly for the efficient allocation of resources in their country and the development of effective, people-centred policies. However, rural population distribution maps suffer from biases related to the type of dataset used to predict population density, such as the use of nighttime lights datasets in areas without electricity. This renders widely used datasets irrelevant in rural areas and biases nationwide models towards urban areas. To compensate for such biases, we aim at understanding the importance and relationship between water-related covariates and population densities in a random forest model across the urban-rural gradient. By extending a recursive feature elimination framework, we show that commonly used covariates are only selected when modelling the whole country. However, once the highest density areas are removed, water-related characteristics (especially distance to boreholes) become important covariates of population density outside of densely populated areas. This has important implications for modelling population in rural areas, including for a better estimation of the size of remote communities. When seeking to produce country-level population maps, we encourage further studies to explicitly account for rural areas by considering the urban-rural gradient and encourage the use of water-related datasets.

Rural Revitalisation Areas (RZs) are designed to assist the development of rural territories mainly through fiscal and social measures. Specific measures for economic development shall apply. The aim is to concentrate state aid measures for job-creating enterprises in the less populated rural areas most affected by demographic and economic decline. The ZRRs were created by the Law of Orientation for the Planning and Development of the Territory (LOADT) of 4 February 1995. The Interministerial Committee on Territorial Planning and Development (CIADT) on 3 September 2003 set out new guidelines for adapting this tool to current needs. The corresponding provisions are contained in the Law on the Development of Rural Territories of 23 February 2005 and Decree No. 2005-1435 of 21 November 2005. The list establishing the classification of municipalities in ZRR is drawn up and revised each year by order of the Prime Minister in the light of the creations, abolitions and modifications of the scope of the EPCI with own taxation established on 31 December of the previous year. The municipal boundaries are that of the BD CARTO of 2016. The right of way is that of the department of Côte-d’Or. All municipalities are taken over in order to be able to highlight developments. Updated to the Order of 22/02/2018

Official classification of the municipalities of the department of Ariège in a rural revitalisation zone following the decree of 16/03/2017. Rural Revitalisation Areas (RZs) are designed to assist the development of rural territories mainly through fiscal and social measures. Specific measures for economic development shall apply. The aim is to concentrate state aid measures for job-creating enterprises in the less populated rural areas most affected by demographic and economic decline. The ZRRs were created by the Law of Orientation for the Planning and Development of the Territory (LOADT) of 4 February 1995. The Interministerial Committee on Territorial Planning and Development (CIADT) on 3 September 2003 set out new guidelines for adapting this tool to current needs. The corresponding provisions are contained in the Law on the Development of Rural Territories of 23 February 2005 and Decree No. 2005-1435 of 21 November 2005. The list establishing the classification of municipalities in ZRR is drawn up and revised each year by order of the Prime Minister in the light of the creations, deletions and modifications of the scope of the EPCI with own taxation established on 31 December of the previous year This national sheet corresponds to part of the metadata of the COVADIS ZRR standard. This standard has been developed in a generic and non-milled manner (its name and definitions do not mention any date as in previous records) to apply to different versions of the ZRR Order. The local GeoBase layers should be named N_ZRR_ZSUP_ddd_aaaa, aaaa corresponding to the data vintage. The 1996 and 2005 RRAs remain described in National Fact Sheets #311 and #752.

Rural Revitalisation Areas (RZs) are designed to assist the development of rural territories mainly through fiscal and social measures. Specific measures for economic development shall apply. The aim is to concentrate state aid measures for job-creating enterprises in the less populated rural areas and those most affected by demographic and economic decline.The RRAs were created by the Law of 4 February 1995 of Guidance for the Development and Development of the Territory (LOADT). The Interministerial Committee on Territorial Planning and Development (CIADT) on 3 September 2003 set out new guidelines for adapting this tool to current needs. The corresponding provisions are laid down in the Law on the Development of Rural Territories of 23 February 2005 and Decree No. 2005-1435 of 21 November 2005.The list establishing the classification of municipalities as ZRR is drawn up and revised each year by order of the Prime Minister in the light of the creations, abolitions and modifications of the scope of EPCIs with own taxation established on 31 December of the previous year

Rapid and accurate extraction of road infrastructure from high-resolution remote sensing satellite imagery is important for traffic planning, construction, and management. In recent years, road extraction methods have developed rapidly, benefiting from the application of data-driven deep learning based models and the various urban road datasets. However, there are still application bottlenecks when directly transferring the current research from urban to rural areas. Specifically, most road datasets are designed for urban areas, and only a small number of rural scenes are included, without complex rural scenes. Due to the huge style differences between urban and rural roads in different geographical areas, it is difficult to apply the current datasets to rural road extraction. In this article, a large-scale high-resolution remote sensing road dataset, termed WHU-RRoad, is introduced for rural road extraction, which contains 27770 pairs of 1024 × 1024 satellite images with resolution of 0.3m and corresponding road annotations, covering a 2620.71 km2 rural area in central China. In addition, a comprehensive analysis of the performance of the current state-of-the-art deep learning based road extraction methods on the WHU-RRoad dataset is provided, where the experimental results illustrate that the proposed WHU-RRoad dataset is a challenging dataset for large-scale rural road extraction. At the same time, the WHU-RRoad dataset can meet the application requirements of rural road construction and has great application potential.

Abstract (en): The China Multi-Generational Panel Dataset - Liaoning (CMGPD-LN) is drawn from the population registers compiled by the Imperial Household Agency (neiwufu) in Shengjing, currently the northeast Chinese province of Liaoning, between 1749 and 1909. It provides 1.5 million triennial observations of more than 260,000 residents from 698 communities. The population mainly consists of immigrants from North China who settled in rural Liaoning during the early eighteenth century, and their descendants. The data provide socioeconomic, demographic, and other characteristics for individuals, households, and communities, and record demographic outcomes such as marriage, fertility, and mortality. The data also record specific disabilities for a subset of adult males. Additionally, the collection includes monthly and annual grain price data, custom records for the city of Yingkou, as well as information regarding natural disasters, such as floods, droughts, and earthquakes. This dataset is unique among publicly available population databases because of its time span, volume, detail, and completeness of recording, and because it provides longitudinal data not just on individuals, but on their households, descent groups, and communities. Possible applications of the dataset include the study of relationships between demographic behavior, family organization, and socioeconomic status across the life course and across generations, the influence of region and community on demographic outcomes, and development and assessment of quantitative methods for the analysis of complex longitudinal datasets. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.; Standardized missing values.; Created online analysis version with question text.; Checked for undocumented or out-of-range codes.. Smallest Geographic Unit: Chinese banners (8) The data are from 725 surviving triennial registers from 29 distinct populations. Each of the 29 register series corresponded to a specific rural population concentrated in a small number of neighboring villages. These populations were affiliated with the Eight Banner civil and military administration that the Qing state used to govern northeast China as well as some other parts of the country. 16 of the 29 populations are regular bannermen. In these populations adult males had generous allocations of land from the state, and in return paid an annual fixed tax to the Imperial Household Agency, and provided to the Imperial Household Agency such home products as homespun fabric and preserved meat, and/or such forest products as mushrooms. In addition, as regular bannermen they were liable for military service as artisans and soldiers which, while in theory an obligation, was actually an important source of personal revenue and therefore a political privilege. 8 of the 29 populations are special duty banner populations. As in the regular banner population, the adult males in the special duty banner populations also enjoyed state allocated land free of rent. These adult males were also assigned to provide special services, including collecting honey, raising bees, fishing, picking cotton, and tanning and dyeing. The remaining populations were a diverse mixture of estate banner and servile populations. The populations covered by the registers, like much of the population of rural Liaoning in the eighteenth and nineteenth centuries, were mostly descendants of Han Chinese settlers who came from Shandong and other nearby provinces in the late seventeenth and early eighteenth centuries in response to an effort by the Chinese state to repopulate the region. 2016-09-06 2016-09-06 The Training Guide has been updated to version 3.60. Additionally, the Principal Investigator affiliation has been corrected, and cover sheets for all PDF documents have been revised.2014-07-10 Releasing new study level documentation that contains the tables found in the appendix of the Analytic dataset codebook.2014-06-10 The data and documentation have been updated following re-evaluation.2014-01-29 Fixing variable format issues. Some variables that were supposed to be s...