This layer displays change in US land cover between 2001 and 2011. Pixels that changed during this period display the land cover value that they changed to. Pixels with no change are transparent.The National Land Cover Database 2011 (NLCD 2011) is the most recent national data product created by the United States Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC is a group of federal agencies who coordinate and generate consistent and relevant land cover information at the national scale for a wide variety of environmental, land management, and modeling applications. NLCD 2011 provides - for the first time - the capability to assess wall-to-wall, spatially explicit, national land cover changes and trends across the United States from 2001 to 2011. As with two previous NLCD land cover products NLCD 2011 keeps the same 16-class land cover classification scheme that has been applied consistently across the United States at a spatial resolution of 30 meters. NLCD 2011 is based primarily on a decision-tree classification of circa 2011 Landsat satellite data.The 2001/2011 land cover change layer is one of five primary data products produced as part of the NLCD 2011: 1) NLCD 2011 Land Cover 2) NLCD 2006/2011 Land Cover Change Pixels labeled with the 2011 land cover class 3) NLCD 2011 Percent Developed Imperviousness 4) NLCD 2006/2011 Percent Developed Imperviousness Change Pixels 5) NLCD 2011 Tree Canopy Cover.Land cover class categories include forest, planted/cultivated lands, wetland, grassland, water, developed areas and barren land. Land cover information is critical for local, state, and federal managers and officials to assist them with issues such as assessing ecosystem status and health, modeling nutrient and pesticide runoff, understanding spatial patterns of biodiversity, land use planning, deriving landscape pattern metrics, and developing land management policies

The dataset contains the statistical indicators relating to the structure and demographic changes calculated on the basis of the 2011, 2001 and 1991 Census data. The indicators are calculated at three levels of detail: municipality of Milan; 9 municipalities; 69 NIL with population over 3,000 inhabitants. For a limited number of indicators, the 1991 and 2001 data are disseminated only at the municipal level. The indicators are defined as follows: * 1) Population density (ratio between the resident population in the area and the surface of the area in square kilometres); * 2) Decennial percentage variation of the population (Percentage ratio between the variation of the legal population between two censuses and the legal population at the previous census); * 3) Average age of the population (Average of the ages weighted by the amount of the population of each age); * 4) Population over 65 per child (ratio between the population aged 65 and over and children under 6); * 5) Very elderly per 100 residents (Percentage ratio between the population aged 85 and over and the total population); * 6) Population of inactive age per 100 residents of working age (Percentage ratio between the population of non-working age, 0-14 years and 65 years and over, and the population of working age, 15-64 years).

The Census was held on 27 March 2011, and is a key source of information on the Welsh language. The Office for National Statistics (ONS) is responsible for the Census in Wales and England. The 2011 Census question asked “Can you understand, speak, read or write Welsh” - answered by ticking one or more of five boxes (one for each category and one for “None of these”) in any combination. This question was only asked in Wales, and results are presented for those aged 3 and over. The Census did not collect information on fluency levels or on frequency of use.

The purpose of this study is to analyze the nine grade 8 literature anthologies (adopted in Texas in 2001 and 2011) for cultural representativeness. Research Question: What are the trends in eighth-grade literature anthologies regarding the inclusion and treatment of Hispanic-authored/themed selections? To answer this question, analysis of Hispanic texts in the following areas was necessary: a. percentage of authors/texts b. commonly anthologized authors/texts c. literature genres d. placement/arrangement e. editing

The document explains the differences between the different boundary sets that ONS Geography provides. Valid for datasets between 2001 and 2011. (File Size - 393 KB)

This report includes Graduation Outcomes as calculated by the New York State Education Department.

The New York State calculation method was first adopted for the Cohort of 2001 (Class of 2005). The cohort consists of all students who first entered 9th grade in a given school year (e.g., the Cohort of 2006 entered 9th grade in the 2006-2007 school year). Graduates are defined as those students earning either a Local or Regents diploma.

This layer displays change in US land cover between 2001 and 2011. Pixels that changed during this period display the land cover value that they changed to. Pixels with no change are transparent.

The National Land Cover Database 2011 (NLCD 2011) is the most recent national data product created by the United States Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC is a group of federal agencies who coordinate and generate consistent and relevant land cover information at the national scale for a wide variety of environmental, land management, and modeling applications. NLCD 2011 provides - for the first time - the capability to assess wall-to-wall, spatially explicit, national land cover changes and trends across the United States from 2001 to 2011. As with two previous NLCD land cover products NLCD 2011 keeps the same 16-class land cover classification scheme that has been applied consistently across the United States at a spatial resolution of 30 meters. NLCD 2011 is based primarily on a decision-tree classification of circa 2011 Landsat satellite data.

The 2001/2011 land cover change layer is one of five primary data products produced as part of the NLCD 2011: 1) NLCD 2011 Land Cover 2) NLCD 2006/2011 Land Cover Change Pixels labeled with the 2011 land cover class 3) NLCD 2011 Percent Developed Imperviousness 4) NLCD 2006/2011 Percent Developed Imperviousness Change Pixels 5) NLCD 2011 Tree Canopy Cover.

Land cover class categories include forest, planted/cultivated lands, wetland, grassland, water, developed areas and barren land. Land cover information is critical for local, state, and federal managers and officials to assist them with issues such as assessing ecosystem status and health, modeling nutrient and pesticide runoff, understanding spatial patterns of biodiversity, land use planning, deriving landscape pattern metrics, and developing land management policies

This dataset provides physical activity and obesity prevalence estimates by county and sex in the United States from 2001 to 2011.

The dataset contains the primary census abstract of the number of villages with the population of 10,000 and above between the years 2001 2011

This dataset shows the percentage of total population change in the EU regions between 2001 and 2011.

Per thousands inhabitants (annual average)\r EU-28 = 3.64 \r HR: 2002-2011\r \r

Source: Eurostat

Analysis of ‘Natural population growth, 2001-2011’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/natural-population-growth-2001-2011 on 11 January 2022.

--- Dataset description provided by original source is as follows ---

This dataset shows the percentage of natural population growth in the EU regions between 2001 and 2011.

Total change in % . EU-28 = 0.75 HR: 2002-2011. Source: Eurostat

--- Original source retains full ownership of the source dataset ---

The dataset contains the statistical indicators relating to housing conditions calculated on the basis of 2011, 2001 and 1991 census data. The indicators are calculated at three levels of detail: municipality of Milan; 9 municipalities; 69 NIL with population exceeding 3,000 inhabitants. The indicators are defined as follows: * 1) Owned homes for every 100 homes occupied by residents (Percentage ratio between the number of owned homes occupied by residents and the total number of homes occupied by residents); * 2) Rental homes for every 100 homes occupied by residents (Percentage ratio between the number of homes occupied by rented residents and the total number of homes occupied by residents); * 3) Average size of homes (ratio between the total area (m2) of homes occupied by residents and the total number of homes occupied by residents).

This data publication contains two collections of raster maps of forest attributes across Canada, the first collection for year 2001, and the second for year 2011. The 2001 collection is actually an improved version of an earlier set of maps produced also for year 2001 (Beaudoin et al 2014, DOI: https://doi.org/10.1139/cjfr-2013-0401) that is itself available through the web site “http://nfi-nfis.org”. Each collection contains 93 maps of forest attributes: four land cover classes, 11 continuous stand-level structure variables such as age, volume, biomass and height, and 78 continuous values of percent composition for tree species or genus. The mapping was done at a spatial resolution of 250m along the MODIS grid. Briefly the method uses forest polygon information from the first version of photoplots database from Canada’s National Forest Inventory as reference data, and the non-parametric k-nearest neighbors procedure (kNN) to create the raster maps of forest attributes. The approach uses a set of 20 predictive variables that include MODIS spectral reflectance data, as well as topographic and climate data. Estimates are carried out on target pixels across all Canada treed landmass that are stratified as either forest or non-forest with 25% forest cover used as a threshold. Forest cover information was extracted from the global forest cover product of Hansen et al (2013) (DOI: https://doi.org/10.1126/science.1244693). The mapping methodology and resultant datasets were intended to address the discontinuities across provincial borders created by their large differences in forest inventory standards. Analysis of residuals has failed to reveal residual discontinuities across provincial boundaries in the current raster dataset, meaning that our goal of providing discontinuity-free maps has been reached. The dataset was developed specifically to address strategic issues related to phenomena that span multiple provinces such as fire risk, insect spread and drought. In addition, the use of the kNN approach results in the maintenance of a realistic covariance structure among the different variable maps, an important property when the data are extracted to be used in models of ecosystem processes. For example, within each pixel, the composition values of all tree species add to 100%. * Details on the product development and validation can be found in the following publication: Beaudoin, A., Bernier, P.Y., Villemaire, P., Guindon, L., Guo, X.-J. 2017. Tracking forest attributes across Canada between 2001 and 2011 using a kNN mapping approach applied to MODIS imagery, Canadian Journal of Forest Research 48: 85–93. DOI: https://doi.org/10.1139/cjfr-2017-0184 * Please cite this dataset as: Beaudoin A, Bernier PY, Villemaire P, Guindon L, Guo XJ. 2017. Species composition, forest properties and land cover types across Canada’s forests at 250m resolution for 2001 and 2011. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Quebec, Canada. https://doi.org/10.23687/ec9e2659-1c29-4ddb-87a2-6aced147a990 * This dataset contains these NFI forest attributes: ## LAND COVER : landbase vegetated, landbase non-vegetated, landcover treed, landcover non-treed ## TREE STRUCTURE : total above ground biomass, tree branches biomass, tree foliage biomass, stem bark biomass, stem wood biomass, total dead trees biomass, stand age, crown closure, tree stand heigth, merchantable volume, total volume ## TREE SPECIES : abies amabilis (amabilis fir), abies balsamea (balsam fir), abies lasiocarpa (subalpine fir), abies spp. (unidentified fir), acer macrophyllum (bigleaf maple), acer negundo (manitoba maple, box-elder), acer pensylvanicum (striped maple), acer rubrum (red maple), acer saccharinum (silver maple), acer saccharum (sugar maple), acer spicatum (mountain maple), acer spp. (unidentified maple), alnus rubra (red alder), alnus spp. (unidentified alder), arbutus menziesii (arbutus), betula alleghaniensis (yellow birch), betula papyrifera (white birch), betula populifolia (gray birch), betula spp. (unidentified birch), carpinus caroliniana (blue-beech), carya cordiformis (bitternut hickory), chamaecyparis nootkatensis (yellow-cedar), fagus grandifolia (american beech), fraxinus americana (white ash), fraxinus nigra (black ash), fraxinus pennsylvanica (red ash), juglans cinerea (butternut), juglans nigra (black walnut), juniperus virginiana (eastern redcedar), larix laricina (tamarack), larix lyallii (subalpine larch), larix occidentalis (western larch), larix spp. (unidentified larch), malus spp. (unidentified apple), ostrya virginiana (ironwood, hop-hornbeam), picea abies (norway spruce), picea engelmannii (engelmann spruce), picea glauca (white spruce), picea mariana (black spruce), picea rubens (red spruce), picea sitchensis (sitka spruce), picea spp. (unidentified spruce), pinus albicaulis (whitebark pine), pinus banksiana (jack pine), pinus contorta (lodgepole pine), pinus monticola (western white pine), pinus ponderosa (ponderosa pine), pinus resinosa (red pine), pinus spp. (unidentified pine), pinus strobus (eastern white pine), pinus sylvestris (scots pine), populus balsamifera (balsam poplar), populus grandidentata (largetooth aspen), populus spp. (unidentified poplar), populus tremuloides (trembling aspen), populus trichocarpa (black cottonwood), prunus pensylvanica (pin cherry), prunus serotina (black cherry), pseudotsuga menziesii (douglas-fir), quercus alba (white oak), quercus macrocarpa (bur oak), quercus rubra (red oak), quercus spp. (unidentified oak), salix spp. (unidentified willow), sorbus americana (american mountain-ash), thuja occidentalis (eastern white-cedar), thuja plicata (western redcedar), tilia americana (basswood), tsuga canadensis (eastern hemlock), tsuga heterophylla (western hemlock), tsuga mertensiana (mountain hemlock), tsuga spp. (unidentified hemlock), ulmus americana (white elm), unidentified needleaf, unidentified broadleaf, broadleaf species, needleaf species, unknown species

URL: interested users can create a NLCD 2001-2011 Level I change map and apply Equation listed in table 3 of the Open Access paper published in IJRS (https://doi.org/10.1080/01431161.2017.1410298) to replicate results. Data: excel files of the accuracy assessment results. The data can be used to replicate slope and intercepts reported in IJRS paper. This dataset is associated with the following publication: Wickham, J., S.V. Stehman, and C.G. Homer. Spatial Patterns of NLCD Land Cover Change Thematic Accuracy (2001 - 2011). INTERNATIONAL JOURNAL OF REMOTE SENSING. Taylor & Francis, Inc., Philadelphia, PA, USA, 39(6): 1729-1743, (2018).

According to India's last census details, Hindus made up the majority of the population in the country, followed by Muslims. At the same time, almost three million people did not state their religion for the census. India has historically been a religiously pluralistic and multiethnic democracy, with a substantial proportion of all major religions of the world along with several minority and tribal religions.

Excel Age-Range creator for 2001 and 2011 Census population figures.

https://s3-eu-west-1.amazonaws.com/londondatastore-upload/census-custom.png" alt="2011 Census custom age tool" />

This Excel-based tool enables users to query the single year of age raw data so that any age range can easily be calculated without having to carry out often complex, and time consuming formulas that could also be open to human error.

Simply select the lower and upper age range for both males and females and the spreadsheet will return the total population for the range.

This file uses the single year of age data from the 2011 Census released on 24 September 2012, which was available for all Local Authorities.

The ward data is currently modelled data for sex, based on single year of age data from Table qs103ew. The final data will be inserted into the tool when it is released in summer 2013.

Also included are the 2001 Census figures for comparison.

This tool was created by the GLA Intelligence Unit.

A seperate Custom Age-Range Tool for Census 2011 Workday population is available below. This is for local authorities and higher geographies only.
Download data from ONS website

A map showing the distribution of various age groups in the City for 2001, 2006, 2011 and 2016 from the 2016 Statistics Canada Census Data.Size: 11" x 17"Colour: Full ColourFormat: PDF

The dataset contains the statistical indicators relating to the characteristics of families and couples calculated on the basis of 2011, 2001 and 1991 Census data. The indicators are calculated at three levels of detail: municipality of Milan; 9 municipalities; 69 NIL with higher population to the 3,000 inhabitants. For a limited number of indicators, the 1991 data are not available and the 2001 data are disseminated only at the municipal level. The indicators are defined as follows: * 1) Average number of members per family (ratio between the total number of residents in the family and the number of families); * 2) Families with only one member every 100 families (Percentage ratio between the number of one-member families and the total number of families); * 3) Families with 5 or more members every 100 families (Percentage ratio between the number of families with 5 or more members and the total number of families); * 4) Young couples with children for every 100 young couples (Percentage ratio between the number of young couples with children and the total number of young couples; both members of the couple less than 35 years old); * 5) Young people living alone for every 100 young people (Percentage ratio between the number of one-person households, without cohabitants, made up of a person aged 15-34 and the total population aged 15-34); * 6) Population over 65 years old living alone every 100 over 65 years old (Percentage ratio between the number of one-person households, without cohabitants, made up of a person aged 65+ and the population aged 65+); * 7) Mixed couples every 100 couples (Percentage ratio between the number of couples with a foreign and an Italian component and the total number of couples); * 8) Unmarried couples every 100 couples (Percentage ratio between the number of unmarried couples and the total number of couples); * 9) Single-parent households for every 100 households with children (Percentage ratio between the number of single-parent households and the total number of households with children).

This dataset shows the percentage of people that cycle to work. The data is broken douwn by Euro Region for England and Wales and is derived from teh 2001 and 2011 census data released by ONS http://www.ons.gov.uk/ons/index.html. Derived from ONS tables and combined with Euro regions that are available through the OS Opendata Boundary Line product http://www.ons.gov.uk/ons/index.html http://www.ordnancesurvey.co.uk/business-and-government/products/boundary-line.html. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2014-04-11 and migrated to Edinburgh DataShare on 2017-02-22.

This update to the Alaska National Land Cover Database (NLCD) 2016 replaces the files dated 20200213. In this update the landcover footprint was extended along the northern coast to include the islands that were missed in previous versions, and several duplicate roads (offset by 1 or 2 pixels) were removed on the Aleutian Islands. The Alaska National Land Cover Database 2016 was created using change detection between the nominal dates of 2011 and 2016 utilizing Google Earth engine composites of Landsat imagery. Traditionally, previous classifications of Alaska used path row data and spectral comparisons between path rows along with ancillary data to derive areas of change. Alaska has many challenges for land cover classification, with the largest of these being a very short “leaf on” imagery season for acceptable phenology. This is compounded by persistent cloud cover during this growing season, and increasing terrain shadow and sun angle problems outside of this season. For this reason, the timeframe of 10 years was needed for the first update to the Alaska land cover in order to have enough acceptable imagery between these 2001 and 2011 nominal dates that met these criteria in order to provide reasonable change detection. For 2016, there was not enough complete path row imagery to do this similar change detection. Google Earth engine was employed to create a composite imagery mosaic that uses much smaller pieces of Landsat imagery to create a complete Landsat imagery snapshot used to create change detection between 2011 and 2016. Although this composite technique is not as scientifically rigorous as utilizing direct Landsat imagery, it was the only method available to us during production that would allow completion of change detection in the desired five year timeframe. The original published 2001 and 2011 Alaska NLCD classifications are generally unchanged, except for slight updates in the northern part of the state to remove perennial ice and snow that were a result of lack of suitable imagery in the 2001 timeframe representing minimum snow and ice extent. See the 2001 and 2011 Land Cover products for the specific metadata process steps associated with the creation of those datasets.