The geographic distribution of human population is key to understanding the effects of humans on the natural world and how natural events such as storms, earthquakes, and other natural phenomenon affect humans. Dataset SummaryThis layer was created with a model that combines imagery, road intersection density, populated places, and urban foot prints to create a likelihood surface. The likelihood surface is then used to create a raster of population with a cell size of 0.00221 degrees (approximately 250 meters).The population raster is created usingDasymetriccartographic methods to allocate the population values in over 1.6 million census polygons covering the world.The population of each polygon was normalized to the 2013 United Nations population estimates by country.Each cell in this layer has an integer value depicting the number of people that are likely to reside in that cell. Tabulations based on these values should result in population totals that more accurately reflect the population of areas of several square kilometers.This layer has global coverage and was published by Esri in 2014.More information about this layer is available:Building the Most Detailed Population Map in the World

The Global Population Density Grid Time Series Estimates provide a back-cast time series of population density grids based on the year 2000 population grid from SEDAC's Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) data set. The grids were created by using rates of population change between decades from the coarser resolution History Database of the Global Environment (HYDE) database to back-cast the GRUMPv1 population density grids. Mismatches between the spatial extent of the HYDE calculated rates and GRUMPv1 population data were resolved via infilling rate cells based on a focal mean of values. Finally, the grids were adjusted so that the population totals for each country equaled the UN World Population Prospects (2008 Revision) estimates for that country for the respective year (1970, 1980, 1990, and 2000). These data do not represent census observations for the years prior to 2000, and therefore can at best be thought of as estimations of the populations in given locations. The population grids are consistent internally within the time series, but are not recommended for use in creating longer time series with any other population grids, including GRUMPv1, Gridded Population of the World, Version 4 (GPWv4), or non-SEDAC developed population grids. These population grids served as an input to SEDAC's Global Estimated Net Migration Grids by Decade: 1970-2000 data set.

Throughout history, government and industries have neglected investments in some neighborhoods, especially communities of color, who are more likely to have fewer resources.

This population dataset complements 13 other datasets as part of a study that compared ancient settlement patterns with modern environmental conditions in the Jazira region of Syria. This study examined settlement distribution and density patterns over the past five millennia using archaeological survey reports and French 1930s 1:200,000 scale maps to locate and map archaeological sites. An archaeological site dataset was created and compared to and modelled with soil, geology, terrain (contour), surface and subsurface hydrology and normal and dry year precipitation pattern datasets; there are also three spreadsheet datasets providing 1963 precipitation and temperature readings collected at three locations in the region. The environmental datasets were created to account for ancient and modern population subsistence activities, which comprise barley and wheat farming and livestock grazing. These environmental datasets were subsequently modelled with the archaeological site dataset, as well as, land use and population density datasets for the Jazira region. Ancient trade routes were also mapped and factored into the model, and a comparison was made to ascertain if there was a correlation between ancient and modern settlement patterns and environmental conditions; the latter influencing subsistence activities. Creation of this population dataset, derived from a 1961 census, was created to compare modern population density patterns with the distribution of ancient settlement patterns to ascertain if patterns are shared. There is a similarity between these patterns with higher concentrations of settlements and population along the banks of rivers until reaching the northern area of the Jazira where both extend across the wider landscape and away from rivers. Derived from 1:1 million scale map produced for the following report: Food and Agriculture Organization (FAO), United Nations. Etude des Ressources en Eaux Souterraines de la Jezireh Syrienne. Rome: FAO, 1966.Population map was copied to mylar and scanned to create a polygon coverage of the soil classes, which include land-use attribute information. Each polygon was labelled and attributed with population count. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2010-07-05 and migrated to Edinburgh DataShare on 2017-02-21.

This map shows population density of the United States. Areas in darker magenta have much higher population per square mile than areas in orange or yellow. Data is from the U.S. Census Bureau’s 2020 Census Demographic and Housing Characteristics. The map's layers contain total population counts by sex, age, and race groups for Nation, State, County, Census Tract, and Block Group in the United States and Puerto Rico. From the Census:"Population density allows for broad comparison of settlement intensity across geographic areas. In the U.S., population density is typically expressed as the number of people per square mile of land area. The U.S. value is calculated by dividing the total U.S. population (316 million in 2013) by the total U.S. land area (3.5 million square miles).When comparing population density values for different geographic areas, then, it is helpful to keep in mind that the values are most useful for small areas, such as neighborhoods. For larger areas (especially at the state or country scale), overall population density values are less likely to provide a meaningful measure of the density levels at which people actually live, but can be useful for comparing settlement intensity across geographies of similar scale." 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.

Auxiliary Data.gdb: Land_use: original land use data POI_name: interests-point-data from the Amap platform (name indicates category)

New_gridded_population_dataset(.gdb): experimental result data, i.e., a gridded population map of mainland China with a resolution of 100 meters

New_minus_WorldPop_PopulationResidual(.gdb): pixel-level residuals of the new gridded population dataset with the Worldpop dataset

POI_Correlation_Coefficient: Zonal statistical output of POI kernel density values: summary of various POI kernel densities in residential areas of administrative units Summary of POI Pearson correlation coefficients: sum of Pearson's correlation coefficients for 13 types of POIs at a certain bandwidth

PopulationData_AdministrativeUnitLevel.gdb: Population_data_mainlandChina_level3: population data at the district and county level in mainland China Population_data_Name_level4_Table: township and street-level population data for provinces and municipalities

Note: Due to the storage space limitation, 3D building, nighttime light, and WorldPop datasets have not been uploaded. To access these publicly available data, please visit the official website via the "Related links" at the bottom. In addition, we are not authorized to share data for the fourth level of administrative boundaries, so we only share the corresponding population data in tabular form.

Description of Data Notes: The estimates are produced using a variety of data sources and statistical models. Therefore small estimates should not be taken to refer to particular individuals. The migration element of the components of change have been largely derived from a data source which is known to be deficient in recording young adult males and outflows from Northern Ireland. Therefore the estimates are subject to adjustment to account for this and, while deemed acceptable for their use, will not provide definitive numbers of the population in the reported groups/areas. Further information is available in the Limitations section of the statistical bulletin: NISRA 2023 Mid-year Population Estimates webpage Time Period Estimates are provided for mid-2001 to mid-2023. Methodology The cohort-component method was used to create the population estimates for 2023. This method updates the Census estimates by 'ageing on' populations and applying information on births, deaths and migration. Further information is available at: NISRA 2023 Mid-year Population Estimates webpage Geographic Referencing Population Estimates are based on a large number of secondary datasets. Where the full address was available, the Pointer Address database was used to allocate a unique property reference number (UPRN) and geo-spatial co-ordinates to each home address. These can then be used to map the address to particular geographies. Where it was not possible to assign a unique property reference number to an address using the Pointer database, or where the secondary dataset contained only postcode information, the Central Postcode Directory was used to map home address postcodes to higher geographies. A small proportion of records with unknown geography were apportioned based on the spatial characteristics of known records. Further Information NISRA Mid-year Population Estimates webpage Contact: NISRA Customer Services 02890 255156 census@nisra.gov.uk Responsible Statistician: Shauna Dunlop

This dataset features three gridded population dadasets of Germany on a 10m grid. The units are people per grid cell.

Datasets

DE_POP_VOLADJ16: This dataset was produced by disaggregating national census counts to 10m grid cells based on a weighted dasymetric mapping approach. A building density, building height and building type dataset were used as underlying covariates, with an adjusted volume for multi-family residential buildings.

DE_POP_TDBP: This dataset is considered a best product, based on a dasymetric mapping approach that disaggregated municipal census counts to 10m grid cells using the same three underyling covariate layers.

DE_POP_BU: This dataset is based on a bottom-up gridded population estimate. A building density, building height and building type layer were used to compute a living floor area dataset in a 10m grid. Using federal statistics on the average living floor are per capita, this bottom-up estimate was created.

Please refer to the related publication for details.

Temporal extent

The building density layer is based on Sentinel-2 time series data from 2018 and Sentinel-1 time series data from 2017 (doi: http://doi.org/10.1594/PANGAEA.920894)

The building height layer is representative for ca. 2015 (doi: 10.5281/zenodo.4066295)

The building types layer is based on Sentinel-2 time series data from 2018 and Sentinel-1 time series data from 2017 (doi: 10.5281/zenodo.4601219)

The underlying census data is from 2018.

Data format

The data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems.

Further information

For further information, please see the publication or contact Franz Schug (franz.schug@geo.hu-berlin.de).
A web-visualization of this dataset is available here.

Publication

Schug, F., Frantz, D., van der Linden, S., & Hostert, P. (2021). Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates. PLOS ONE. DOI: 10.1371/journal.pone.0249044

Acknowledgements

Census data were provided by the German Federal Statistical Offices.

Funding
This dataset was produced with funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).

Please note, we recommend using the new Map Viewer in ArcGIS Online. There is an issue in Map Viewer Classic with the display of grid cell values. The clickable area of each cell is shifted to the northwest. This can result in neighbouring pixel values being displayed. The underlying data is correct, and the values display correctly in the new Map Viewer and in ArcGIS Pro. The Australian population grid 2022 is a modelled 1 km x 1 km grid representation of the estimated resident population (ERP) of Australia from 30 June 2022. The population grid is created by reaggregating estimated resident population data from Statistical Areas Level 1 (SA1) to a 1 km x 1 km grid across Australia based on point data representing residential address points. The value of each grid cell represents the estimated population density (number of people per square kilometre) within each 1 km x 1 km grid cell.

SA1 boundaries are defined by the Australian Statistical Geography Standard (ASGS) Edition 3 (2021) and the 1 km x 1 km grid is based on the National Nested Grid.

Data considerations Caution must be taken when using the population grid as it presents modelled data only; it is not an exact measure of population across Australia. Contact the Australian Bureau of Statistics (ABS) If you have questions, feedback or would like to receive updates about this web service, please email geography@abs.gov.au. For information about how the ABS manages any personal information you provide view the ABS privacy policy.

Data and geography references Source data publication: Regional population, 2022 Additional data input: ABS Address Register Geographic boundary information: Australian Statistical Geography Standard (ASGS) Edition 3, National Nested Grid Further information: Regional population methodology Source: Australian Bureau of Statistics (ABS)

The population of Latin America and the Caribbean increased from 175 million in 1950 to 515 million in 2000. Where did this growth occur? What is the magnitude of change in different places? How can we visualize the geographic dimensions of population change in Latin America and the Caribbean? We compiled census and other public domain information to analyze both temporal and geographic changes in population in the region. Our database includes population totals for over 18,300 administrative districts within Latin America and the Caribbean. Tabular census data was linked to an administrative division map of the region and handled in a geographic information system. We transformed vector population maps to raster surfaces to make the digital maps comparable with other commonly available geographic information. Validation and error-checking analyses were carried out to compare the database with other sources of population information. The digital population maps created in this project have been put in the public domain and can be downloaded from our website. The Latin America and Caribbean map is part of a larger multi-institutional effort to map population in developing countries. This is the third version of the Latin American and Caribbean population database and it contains new data from the 2000 round of censuses and new and improved accessibility surfaces for creating the raster maps.

In a gold standard map, G, a pixel is in the category if it is above the threshold: x ∈ Gτ if and only if x > τ. Otherwise, x ∉ Gτ. Similarly, the categorization is applied to a candidate map, M. Pixels are classified as true positives (TP), true negatives (TN), false negatives (FN), and false positives (FP) as described in the table. Accuracy profiles are plotted in Fig 6.

This dataset contains gridded human population with a spatial resolution of 1 km x 1 km for the UK based on Census 2021 (Census 2022 for Scotland) and Land Cover Map 2021 input data. Data on population distribution for the United Kingdom is available from statistical offices in England, Wales, Northern Ireland and Scotland and provided to the public e.g. via the Office for National Statistics (ONS). Population data is typically provided in tabular form or, based on a range of different geographical units, in file types for geographical information systems (GIS), for instance as ESRI Shapefiles. The geographical units reflect administrative boundaries at different levels of detail, from Devolved Administration to Output Areas (OA), wards or intermediate geographies. While the presentation of data on the level of these geographical units is useful for statistical purposes, accounting for spatial variability for instance of environmental determinants of public health requires a more spatially homogeneous population distribution. For this purpose, the dataset presented here combines 2021/2022 UK Census population data on Output Area level with Land Cover Map 2021 land-use classes 'urban' and 'suburban' to create a consistent and comprehensive gridded population data product at 1 km x 1 km spatial resolution. The mapping product is based on British National Grid (OSGB36 datum).

Population by age groups, race and gender, and the poverty by race is attached to the split tract geography to create this split tract with population and poverty data. Split tract data is the product of 2020 census tracts split by 2023 incorporated city boundaries and unincorporated community/countywide statistical areas (CSA) boundaries as of July 1, 2023. The census tract boundaries have been altered and aligned where necessary with legal city boundaries and unincorporated areas, including shoreline/coastal areas. Census Tract:Every 10 years the Census Bureau counts the population of the United States as mandated by Constitution. The Census Bureau (https://www.census.gov/)released 2020 geographic boundaries data including census tracts for the analysis and mapping of demographic information across the United States. City Boundary:City Boundary data is the base map information for the County of Los Angeles. These City Boundaries are based on the Los Angeles County Seamless Cadastral Landbase. The Landbase is jointly maintained by the Los Angeles County Assessor and the Los Angeles County Department of Public Works (DPW). This layer represents current city boundaries within Los Angeles County. The DPW provides the most current shapefiles representing city boundaries and city annexations. True, legal boundaries are only determined on the ground by surveyors licensed in the State of California.Countywide Statistical Areas (CSA): The countywide Statistical Area (CSA) was defined to provide a common geographic boundary for reporting departmental statistics for unincorporated areas and incorporated Los Angeles city to the Board of Supervisors. The CSA boundary and CSA names are established by the CIO and the LA County Enterprise GIS group worked with the Los Angeles County Board of Supervisors Unincorporated Area and Field Deputies that reflect as best as possible the general name preferences of residents and historical names of areas. This data is primarily focused on broad statistics and reporting, not mapping of communities. This data is not designed to perfectly represent communities, nor jurisdictional boundaries such as Angeles National Forest. CSA represent board approved geographies comprised of Census block groups split by cities.Data Fields:CT20: 2020 Census tractFIP22: 2023 City FIP CodeCITY: City name for incorporated cities and “Unincorporated” for unincorporated areas (as of July 1, 2023) CSA: Countywide Statistical Area (CSA) - Unincorporated area community names and LA City neighborhood names.CT20FIP23CSA: 2020 census tract with 2023 city FIPs for incorporated cities and unincorporated areas and LA neighborhoods. SPA22: 2022 Service Planning Area (SPA) number.SPA_NAME: Service Planning Area name.HD22: 2022 Health District (HD) number: HD_NAME: Health District name.POP23_AGE_0_4: 2023 population 0 to 4 years oldPOP23_AGE_5_9: 2023 population 5 to 9 years old POP23_AGE_10_14: 2023 population 10 to 14 years old POP23_AGE_15_17: 2022 population 15 to 17 years old POP23_AGE_18_19: 2023 population 18 to 19 years old POP23_AGE_20_44: 2023 population 20 to 24 years old POP23_AGE_25_29: 2023 population 25 to 29 years old POP23_AGE_30_34: 2023 population 30 to 34 years old POP23_AGE_35_44: 2023 population 35 to 44 years old POP23_AGE_45_54: 2023 population 45 to 54 years old POP23_AGE_55_64: 2023 population 55 to 64 years old POP23_AGE_65_74: 2023 population 65 to 74 years old POP23_AGE_75_84: 2023 population 75 to 84 years old POP23_AGE_85_100: 2023 population 85 years and older POP23_WHITE: 2023 Non-Hispanic White POP23_BLACK: 2023 Non-Hispanic African AmericanPOP23_AIAN: 2023 Non-Hispanic American Indian or Alaska NativePOP23_ASIAN: 2023 Non-Hispanic Asian POP23_HNPI: 2023 Non-Hispanic Hawaiian Native or Pacific IslanderPOP23_HISPANIC: 2023 HispanicPOP23_MALE: 2023 Male POP23_FEMALE: 2023 Female POV23_WHITE: 2023 Non-Hispanic White below 100% Federal Poverty Level POV23_BLACK: 2023 Non-Hispanic African American below 100% Federal Poverty Level POV23_AIAN: 2023 Non-Hispanic American Indian or Alaska Native below 100% Federal Poverty Level POV23_ASIAN: 2023 Non-Hispanic Asian below 100% Federal Poverty Level POV23_HNPI: 2023 Non-Hispanic Hawaiian Native or Pacific Islander below 100% Federal Poverty Level POV23_HISPANIC: 2023 Hispanic below 100% Federal Poverty Level POV23_TOTAL: 2023 Total population below 100% Federal Poverty Level POP23_TOTAL: 2023 Total PopulationAREA_SQMil: Area in square mile.POP23_DENSITY: 2023 Population per square mile.POV23_PERCENT: 2023 Poverty rate/percentage.How this data created?Population by age groups, ethnic groups and gender, and the poverty by ethnic groups is attributed to the split tract geography to create this data. Split tract polygon data is created by intersecting 2020 census tract polygons, LA Country City Boundary polygons and Countywide Statistical Areas (CSA) polygon data. The resulting polygon boundary aligned and matched with the legal city boundary whenever possible. Notes:1. Population and poverty data estimated as of July 1, 2023. 2. 2010 Census tract and 2020 census tracts are not the same. Similarly, city and community boundaries are as of July 1, 2023.

The statistic shows the total population of India from 2019 to 2029. In 2023, the estimated total population in India amounted to approximately 1.43 billion people.

Total population in India

India currently has the second-largest population in the world and is projected to overtake top-ranking China within forty years. Its residents comprise more than one-seventh of the entire world’s population, and despite a slowly decreasing fertility rate (which still exceeds the replacement rate and keeps the median age of the population relatively low), an increasing life expectancy adds to an expanding population. In comparison with other countries whose populations are decreasing, such as Japan, India has a relatively small share of aged population, which indicates the probability of lower death rates and higher retention of the existing population.

With a land mass of less than half that of the United States and a population almost four times greater, India has recognized potential problems of its growing population. Government attempts to implement family planning programs have achieved varying degrees of success. Initiatives such as sterilization programs in the 1970s have been blamed for creating general antipathy to family planning, but the combined efforts of various family planning and contraception programs have helped halve fertility rates since the 1960s. The population growth rate has correspondingly shrunk as well, but has not yet reached less than one percent growth per year.

As home to thousands of ethnic groups, hundreds of languages, and numerous religions, a cohesive and broadly-supported effort to reduce population growth is difficult to create. Despite that, India is one country to watch in coming years. It is also a growing economic power; among other measures, its GDP per capita was expected to triple between 2003 and 2013 and was listed as the third-ranked country for its share of the global gross domestic product.

The data in the dataset come from the Estonian Statistical Database. Grid-based population data are updated once a year. Census data are georeferenced to building accuracy, which allows data to be aggregated to grid level of different resolution. The building's centroid was used as the basis for aggregating into squares. The data of several buildings within the square were linked to the square where the building's centroid was located. If the population data could not be linked to the building (the data were linked to the building by address and in some cases the address was not identifiable), the data were added in the middle of the village or census station. Counted homeless people are also associated with the village or precinct centre.

The 1 km x 1 km square map of the population covers the whole territory of Estonia, including only inhabited squares. Grid-based data serve as a basis for making competent decisions in the preparation of social plans and development plans, including regional development plans. Grid-based data are used in scientific research, in the private sector mainly to select the best location and to define the target group.

This is a comment on the preliminary Congressional Commission redistricting map. Along with providing feedback on that map, it offers a draft alternative that better meets the criteria of the Colorado Constitution. As background, I participated in redistricting initiatives in South Bend, Indiana, in the mid-1980s and for Indiana legislative seats after the 1990 census. I didn’t engage with redistricting during the rest of my 20-year military career. After retiring, and while serving as Public Trustee for El Paso County, I participated in redistricting efforts at the county and city level. I also stood for El Paso County Clerk in 2010. I have lived in Colorado since 2000. The draft alternative map is created using Dave’s Redistricting App (DRA) and can be found at https://davesredistricting.org/join/346f297c-71d1-4443-9110-b92e3362b105. I used DRA because it was more user-friendly in that it allows selection by precinct and by city or town, while the tool provided by the commission seems to allow only selection by census block (or larger clusters). The two tools also use slightly different population estimates, but this will be resolved when the 2020 data are released in August. These comments acknowledge that any map created using estimated populations will need to change to account for the actual census data.

Description of Draft Alternative

    My process started by

identifying large-scale geographic communities of interest within Colorado: the Western Slope/mountain areas, the Eastern Plains, Colorado Springs/El Paso County, the North Front Range, and Denver Metro. Two smaller geographic communities of interest are Pueblo and the San Luis Valley—neither is nearly large enough to sustain a district and both are somewhat distinct from their neighboring communities of interest. A choice thus must be made about which other communities of interest to group them with. El Paso County is within 0.3% of the optimal population, so it is set as District 5. The true Western Slope is not large enough to sustain a district, even with the obvious addition of Jackson County. Rather than including the San Luis Valley with the Western Slope, the preliminary commission map extends the Western Slope district to include all of Fremont County (even Canon City, Florence, and Penrose), Clear Creek County, and some of northern Boulder County. The draft alternative District 3 instead adds the San Luis Valley, the Upper Arkansas Valley (Lake and Chaffee Counties, and the western part of Fremont County), Park and Teller Counties, and Custer County. The draft alternative District 4 is based on the Eastern Plains. In the south, this includes the rest of Fremont County (including Canon City), Pueblo, and the Lower Arkansas Valley. In the north, this includes all of Weld County, retaining it as an intact political subdivision. This is nearly enough population to form a complete district; it is rounded out by including the easternmost portions of Adams and Arapahoe Counties. All of Elbert County is in this district; none of Douglas County is. The draft alternative District 2 is placed in the North Front Range and includes Larimer, Boulder, Gilpin, and Clear Creek Counties. This is nearly enough population to form a complete district, so it is rounded out by adding Evergreen and the rest of Coal Creek in Jefferson County. The City and County of Denver (and the Arapahoe County enclave municipalities of Glendale and Holly Hills) forms the basis of draft alternative District 1. This is a bit too large to form a district, so small areas are shaved off into neighboring districts: DIA (mostly for compactness), Indian Creek, and part of Marston. This leaves three districts to place in suburban Denver. The draft alternative keeps Douglas County intact, as well as the city of Aurora, except for the part that extends into Douglas County. The map prioritizes the county over the city as a political subdivision. Draft alternative District 6, anchored in Douglas County, extends north into Arapahoe County to include suburbs like Centennial, Littleton, Englewood, Greenwood Village, and Cherry Hills Village. This is not enough population, so the district extends west into southern Jefferson County to include Columbine, Ken Caryl, and Dakota Ridge. The northwestern edge of this district would run along Deer Creek Road, Pleasant Park Road, and Kennedy Gulch Road. Draft alternative District 8, anchored in Aurora, includes the rest of western Arapahoe County and extends north into Adams County to include Commerce City, Brighton (except the part in Weld County), Thornton, and North Washington. In the draft alternative, this district includes a sliver of Northglenn east of Stonehocker Park. While this likely would be resolved when final population totals are released, this division of Northglenn is the most notable division of a city within a single county other than the required division of Denver. Draft alternative District 7 encompasses what is left: The City and County of Broomfield; Westminster, in both Jefferson and Adams Counties; Federal Heights, Sherrelwood, Welby, Twin Lakes, Berkley, and almost all of Northglenn in western Adams County; and Lakewood, Arvada, Golden, Wheat Ridge, Morrison, Indian Hills, Aspen Park, Genesee, and Kittredge in northern Jefferson County. The border with District 2 through the communities in the western portion of Jefferson County would likely be adjusted after final population totals are released.

Comparison of Maps

Precise Population Equality
    The preliminary commission

map has exact population equality. The draft alternative map has a variation of 0.6% (4,239 persons). Given that the maps are based on population estimates, and that I left it at the precinct and municipality level, this aspect of the preliminary map is premature to pinpoint. Once final population data are released, either map would need to be adjusted. It would be simple to tweak district boundaries to achieve any desired level of equality. That said, such precision is a bit of a fallacy: errors in the census data likely exceed the 0.6% in the draft map, the census data will be a year out of date when received, and relative district populations will fluctuate over the next 10 years. Both the “good-faith effort†and “as practicable†language leave room for a bit of variance in service of other goals. The need to “justify any variance†does not mean “no variance will be allowed.†For example, it may be better to maintain unity in a community of interest or political subdivision rather than separate part of it for additional precision. The major sticking point here is likely to be El Paso County: given how close it seems to be to the optimal district size, will it be worth it to divide the county or one of its neighbors to achieve precision? The same question would be likely to apply among the municipalities in Metro Denver.

Contiguity
    The draft alternative map

meets this requirement. The preliminary commission map violates the spirit if not the actual language of this requirement. While its districts are connected by land, the only way to travel to all parts of preliminary Districts 3 and 4 without leaving the districts would be on foot. There is no road connection between the parts of Boulder County that are in District 3 and the rest of that district in Grand County without leaving the district and passing through District 2 in either Gilpin or Larimer Counties. There also is no road connection between some of the southwestern portions of Mineral County and the rest of District 4 without passing through Archuleta or Hinsdale Counties in District 3.

Voting Rights Act
    The preliminary staff

analysis assumes it would be possible to create a majority-minority district; they are correct, it can be done via a noncompact district running from the west side of Denver up to Commerce City and Brighton and down to parts of northeastern Denver and northern Aurora. Such a district would go against criteria for compactness, political subdivisions, and even other definitions of communities of interest. Staff asserts that the election of Democratic candidates in this area suffices for VRA. Appendix B is opaque regarding the actual non-White or Hispanic population in each district, but I presume that if they had created a majority-minority district they would have said so. In the draft alternative map, District 8 (Aurora, Commerce City, Brighton, and Thornton) has a 39.6% minority population and District 1 (Denver) has a 34.9% minority population. The proposals are similar in meeting this criterion.

Communities of Interest
    Staff presented a long list

of communities of interest. While keeping all of these intact would be ideal, drawing a map requires compromises based on geography and population. Many communities of interest overlap with each other, especially at their edges. This difficulty points to a reason to focus on existing subdivisions (county, city, and town boundaries): those boundaries are stable and overlap with shared public policy concerns. The preliminary commission map chooses to group the San Luis Valley, as far upstream as Del Norte and Creede, with Pueblo and the Eastern Plains rather than with the Western Slope/Mountains. To balance the population numbers, the preliminary commission map thus had to reach east in northern and central Colorado. The commission includes Canon City and Florence

Contains grid statistics on building stock and associated variables, on routes of 5 000 m x 5 000 m, as of January 1st. Historical versions 2008-2018 as annual CSV files, newer vintages in multiple formats. Building mass statistics on routes belong to the theme group “Building/Residentials” in Statistics Norway’s product group “Statistics on grids”. In the same Theme group there is also the data set of housing statistics on routes. Other themes are created grid datasets for are “Population”, “Business”, and “Earth, Forest, Hunting and Fisheries”

Research activity and published literature on the reliability and vulnerability analysis of urban areas for disaster management has grown tremendously in the recent past. Population information has played the most important role during the entire disaster management process. In this article, population information was used as the evaluation criterion, and a fuzzy multiple-attribute decision-making (MADM) approach was used to support a vulnerability analysis of the Helsinki area for disaster management. A kernel density map was produced as a result that showed the vulnerable spatial locations in the event of a disaster. Model results were first validated against the original population information kernel density maps. In the second step, the model was validated by using fuzzy set accuracy assessment and the actual domain knowledge of the rescue experts. This is a novel approach to validation, which makes it possible to see how and if computer decision-making models compare to a real decision-making process in disaster management. The validation results showed that the fuzzy model has produced a reasonably accurate result. By using fuzzy modelling, the number of vulnerable areas was reduced to a reasonable scale and compares to the actual human assessment of these areas, which allows resources to be optimised during the rescue planning and operation.

The accuracy, recall, and precision for the population classifications shown in the header and illustrated in Fig 6.