A population ecumene is the area of inhabited lands or settled areas generally delimited by a minimum population density. This ecumene shows the areas of the densest and most extended population within census subdivisions. A census subdivision (CSD) is the general term for municipalities (as determined by provincial or territorial legislation) or areas treated as municipal equivalents for statistical purposes (e.g., Indigenous Peoples reserves and communities and unorganized territories). Municipal status is defined by laws in effect in each province and territory in Canada. For further information, consult the Statistics Canada’s 2016 Illustrated Glossary (see below under Data Resources). The assemblage of dissemination block population density data from the 2016 Census of Population are used to form the ecumene areas within census subdivisions. Areas included in the ecumene are dissemination blocks where the population density is greater than or equal to 0.4 persons per square kilometre or about one person per square mile. In some areas to capture more population within the ecumene the criteria was extended to 0.2 persons per square kilometre. The ecumene areas were generalized in certain areas to remove small uninhabited areas within the ecumene areas in census subdivisions. This map can be used as an “ecumene” overlay to differentiate the sparsely populated areas from the ecumene in conjunction with census subdivision data or other large-scale maps. This ecumene shows a more meaningful distribution of the population for Canada.

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.

In this dataset we present two maps that estimate the location and population served by domestic wells in the contiguous United States. The first methodology, called the “Block Group Method” or BGM, builds upon the original block-group data from the 1990 census (the last time the U.S. Census queried the population regarding their source of water) by incorporating higher resolution census block data. The second methodology, called the “Road-Enhanced Method” or REM, refines the locations by using a buffer expansion and shrinkage technique along roadways to define areas where domestic wells exist. The fundamental assumption with this method is that houses (and therefore domestic wells) are located near a named road. The results are presented as two nationally consistent domestic-well population datasets. While both methods can be considered valid, the REM map is more precise in locating domestic wells; the REM map had a smaller amount of spatial bias (nearly equal vs biased in type 1 error), total error (10.9% vs 23.7%,), and distance error (2.0 km vs 2.7 km), when comparing the REM and BGM maps to a California calibration map. However, the BGM map is more inclusive of all potential locations for domestic wells. The primary difference in the BGM and the REM is the mapping of low density areas. The REM has a 57% reduction in areas mapped as low density (populations greater than 0 but less than 1 person per km), concentrating populations into denser regions. Therefore, if one is trying to capture all of the potential areas of domestic-well usage, then the BGM map may be more applicable. If location is more imperative, then the REM map is better at identifying areas of the landscape with the highest probability of finding a domestic well. Depending on the purpose of a study, a combination of both maps can be used. For space concerns, the datasets have been divided into two separate geodatabases. The BGM map geodatabase and the REM map database.

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).

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable. For a deep dive into the data model including every specific metric, see the ACS 2017-2021 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e21Estimate from 2017-21 ACS_m21Margin of Error from 2017-21 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_21Change, 2010-21 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLine (buffer)BeltLine Study (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Planning Unit STV (3 NPUs merged to a single geographic unit within City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)City of Atlanta Neighborhood Statistical Areas E02E06 (2 NSAs merged to single geographic unit within City of Atlanta)County (statewide)Georgia House (statewide)Georgia Senate (statewide)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)SPARCC = Strong, Prosperous And Resilient Communities ChallengeState of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)WFF = Westside Future Fund (subarea of City of Atlanta)ZIP Code Tabulation Areas (statewide)The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2017-2021). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2017-2021Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://garc.maps.arcgis.com/sharing/rest/content/items/34b9adfdcc294788ba9c70bf433bd4c1/data

Final approved map by the 2020 California Citizens Redistricting Commission for California's United States Congressional Districts; the authoritative and official delineations of California's United States Congressional Districts drawn during the 2020 redistricting cycle. The Citizens Redistricting Commission for the State of California has created statewide district maps for the State Assembly, State Senate, State Board of Equalization, and United States Congress in accordance, with the provisions of Article XXI of the California Constitution. The Commission has approved the final maps and certified them to the Secretary of State.Line drawing criteria included population equality as required by the U.S. Constitution, the Federal Voting Rights Act, geographic contiguity, geographic integrity, geographic compactness, and nesting. Geography was defined by U.S. Census Block geometry.Each of the 52 Congressional districts apportioned to California have an ideal population of 760,066, and the Commission adhered to federal constitutional mandates by requiring a district population deviation of no more than +/- one person. These districts also posed some of the Commission’s biggest challenges, and, because of strict population equality requirements, resulted in many more splits of counties, cities, neighborhoods, and communities of interest compared to State Assembly or Senate plans.

[1] The Progress by Population Group analysis is a component of the Healthy People 2020 (HP2020) Final Review. The analysis included subsets of the 1,111 measurable HP2020 objectives that have data available for any of six broad population characteristics: sex, race and ethnicity, educational attainment, family income, disability status, and geographic location. Progress toward meeting HP2020 targets is presented for up to 24 population groups within these characteristics, based on objective data aggregated across HP2020 topic areas. The Progress by Population Group data are also available at the individual objective level in the downloadable data set. [2] The final value was generally based on data available on the HP2020 website as of January 2020. For objectives that are continuing into HP2030, more recent data will be included on the HP2030 website as it becomes available: https://health.gov/healthypeople. [3] For more information on the HP2020 methodology for measuring progress toward target attainment and the elimination of health disparities, see: Healthy People Statistical Notes, no 27; available from: https://www.cdc.gov/nchs/data/statnt/statnt27.pdf. [4] Status for objectives included in the HP2020 Progress by Population Group analysis was determined using the baseline, final, and target value. The progress status categories used in HP2020 were: a. Target met or exceeded—One of the following applies: (i) At baseline, the target was not met or exceeded, and the most recent value was equal to or exceeded the target (the percentage of targeted change achieved was equal to or greater than 100%); (ii) The baseline and most recent values were equal to or exceeded the target (the percentage of targeted change achieved was not assessed). b. Improved—One of the following applies: (i) Movement was toward the target, standard errors were available, and the percentage of targeted change achieved was statistically significant; (ii) Movement was toward the target, standard errors were not available, and the objective had achieved 10% or more of the targeted change. c. Little or no detectable change—One of the following applies: (i) Movement was toward the target, standard errors were available, and the percentage of targeted change achieved was not statistically significant; (ii) Movement was toward the target, standard errors were not available, and the objective had achieved less than 10% of the targeted change; (iii) Movement was away from the baseline and target, standard errors were available, and the percent change relative to the baseline was not statistically significant; (iv) Movement was away from the baseline and target, standard errors were not available, and the objective had moved less than 10% relative to the baseline; (v) No change was observed between the baseline and the final data point. d. Got worse—One of the following applies: (i) Movement was away from the baseline and target, standard errors were available, and the percent change relative to the baseline was statistically significant; (ii) Movement was away from the baseline and target, standard errors were not available, and the objective had moved 10% or more relative to the baseline. NOTE: Measurable objectives had baseline data. SOURCE: National Center for Health Statistics, Healthy People 2020 Progress by Population Group database.

A population ecumene is the area of inhabited lands or settled areas generally delimited by a minimum population density. Two population data sets from the 2016 Census of Population were used to build two specialized ecumene maps. The census division ecumene was built from dissemination area population density data and the census subdivision ecumene was built from the dissemination block population density data. For information on census divisions, census subdivisions, dissemination areas, and dissemination blocks consult the Statistics Canada’s 2016 Illustrated Glossary (see below under Data Resources). Areas included in the ecumene (for either the census division or census subdivision) are areas where the population density is greater than or equal to 0.4 persons per square kilometre or about 1 person per square mile. In some areas to capture more population within the ecumene the criteria was extended to 0.2 persons per square kilometre. The ecumene areas were generalized in certain regions either to enhance the size of some isolated ecumene areas or to remove small internal uninhabited areas within the ecumene. Either of these ecumene resources can be used as an “ecumene” map overlay to differentiate the sparsely populated areas from the ecumene in conjunction with the appropriate census geography or other small-scale and large-scale maps.

Linking variation in quantitative traits to variation in the genome is an important, but challenging task in the study of life-history evolution. Linkage maps provide a valuable tool for the unravelling of such trait-gene associations. Moreover, they give insight into recombination landscapes and between- species karyotype evolution. Here we used genotype data, generated from a 10k SNP-chip, of over 2000 individuals to produce high-density linkage maps of the great tit (Parus major), a passerine bird, which serves as a model species for ecological and evolutionary questions. We created independent maps from two distinct populations: a captive F2-cross from The Netherlands (NL) and a wild population from the United Kingdom (UK). The two maps contained 6554 SNPs in 32 linkage groups, spanning 2010 cM and 1917 cM for the NL and UK populations respectively, and were similar in size and marker order. Subtle levels of heterochiasmy within and between chromosomes were remarkably consistent between the populations, suggesting that local departures from sex-equal recombination rates have evolved. This key and surprising result would have been impossible to detect if only one population was mapped. A comparison with zebra finch Taeniopygia guttata, chicken Gallus gallus and the green anole lizard Anolis carolinensis genomes provided further insight into the evolution of avian karyotypes.

Final approved map by the 2020 California Citizens Redistricting Commission for the California State Assembly; the authoritative and official delineations of the California State Assembly drawn during the 2020 redistricting cycle. The Citizens Redistricting Commission for the State of California has created statewide district maps for the State Assembly, State Senate, State Board of Equalization, and United States Congress in accordance, with the provisions of Article XXI of the California Constitution. The Commission has approved the final maps and certified them to the Secretary of State.Line drawing criteria included population equality as required by the U.S. Constitution, the Federal Voting Rights Act, geographic contiguity, geographic integrity, geographic compactness, and nesting. Geography was defined by U.S. Census Block geometry.80 Assembly districts have an ideal population of around 500,000 people each, and in consideration of population equality, the Commission chose to limit the population deviation range to as close to zero percent as practicable. With these districts, the Commission was able to respect many local communities of interest and group similar communities; however, it was more difficult to keep densely populated counties, cities, neighborhoods, and larger communities of interest whole due to the district size and correspondingly smaller number allowable in the population deviation percentage.

A population ecumene is the area of inhabited lands or settled areas generally delimited by a minimum population density. This ecumene shows the areas of the densest and most extended population within census subdivisions. A census subdivision (CSD) is the general term for municipalities (as determined by provincial or territorial legislation) or areas treated as municipal equivalents for statistical purposes (e.g., Indigenous Peoples reserves and communities and unorganized territories). Municipal status is defined by laws in effect in each province and territory in Canada. The assemblage of dissemination block population density data from the 2016 Census of Population are used to form the ecumene areas within census subdivisions. Areas included in the ecumene are dissemination blocks where the population density is greater than or equal to 0.4 persons per square kilometre or about one person per square mile. In some areas to capture more population within the ecumene the criteria was extended to 0.2 persons per square kilometre. The ecumene areas were generalized in certain areas to remove small uninhabited areas within the ecumene areas in census subdivisions. This map can be used as an “ecumene” overlay to differentiate the sparsely populated areas from the ecumene areas in conjunction with census subdivision data or other large-scale maps. This ecumene shows a more meaningful distribution of the population for Canada.

The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Land and Geographic Unit Area Grids measure land areas in square kilometers and the mean Unit size (population-weighted) in square kilometers. The land area grid permits the summation of areas (net of permanent ice and water) at the same resolution as the population density, count, and urban-rural grids. The mean Unit size grids provide a quantitative surface that indicates the size of the input Unit(s) from which population count and density grids are derived. Additional global grids are created from the 30 arc-second grid at 1/4, 1/2, and 1 degree resolutions. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT).

Database that provides access to population, housing, economic, and geographic data from several censuses and surveys about the United States, Puerto Rico and the Island Areas. Census data may be compiled into tables, maps and downloadable files, which can be viewed or printed. A large selection of pre-made tables and maps satisfies many information requests. By law, no one is permitted to reveal information from these censuses and surveys that could identify any person, household, or business. The following data are available: * American Community Survey * ACS Content Review * American Housing Survey * Annual Economic Surveys * Annual Surveys of Governments * Census of Governments * Decennial Census * Economic Census * Equal Employment Opportunity (EEO) Tabulation * Population Estimates Program * Puerto Rico Community Survey

Context

The United States Census is a decennial census mandated by Article I, Section 2 of the United States Constitution, which states: "Representatives and direct Taxes shall be apportioned among the several States ... according to their respective Numbers."
Source: https://en.wikipedia.org/wiki/United_States_Census

Content

The United States census count (also known as the Decennial Census of Population and Housing) is a count of every resident of the US. The census occurs every 10 years and is conducted by the United States Census Bureau. Census data is publicly available through the census website, but much of the data is available in summarized data and graphs. The raw data is often difficult to obtain, is typically divided by region, and it must be processed and combined to provide information about the nation as a whole.

The United States census dataset includes nationwide population counts from the 2000 and 2010 censuses. Data is broken out by gender, age and location using zip code tabular areas (ZCTAs) and GEOIDs. ZCTAs are generalized representations of zip codes, and often, though not always, are the same as the zip code for an area. GEOIDs are numeric codes that uniquely identify all administrative, legal, and statistical geographic areas for which the Census Bureau tabulates data. GEOIDs are useful for correlating census data with other censuses and surveys.

Fork this kernel to get started.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:census_bureau_usa

https://cloud.google.com/bigquery/public-data/us-census

Dataset Source: United States Census Bureau

Use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by Steve Richey from Unsplash.

Inspiration

What are the ten most populous zip codes in the US in the 2010 census?

What are the top 10 zip codes that experienced the greatest change in population between the 2000 and 2010 censuses?

https://cloud.google.com/bigquery/images/census-population-map.png" alt="https://cloud.google.com/bigquery/images/census-population-map.png"> https://cloud.google.com/bigquery/images/census-population-map.png

description: This static map image portrays legislative district boundaries of district 03 as of March 9, 2002. A Legislative district is a political subdivisions into which a state is divided for electing members to the Legislature. Each legislative district is represented by one senator and two representatives. Since Idaho's legislative districts are not further split into two House of Representative districts, both representatives, like the senator, serve the entire district. The districts are established by state law and are redrawn following a decennial census to maintain equal population in each.; abstract: This static map image portrays legislative district boundaries of district 03 as of March 9, 2002. A Legislative district is a political subdivisions into which a state is divided for electing members to the Legislature. Each legislative district is represented by one senator and two representatives. Since Idaho's legislative districts are not further split into two House of Representative districts, both representatives, like the senator, serve the entire district. The districts are established by state law and are redrawn following a decennial census to maintain equal population in each.

description: This static map image portrays legislative district boundaries of district 14 as of March 9, 2002. A Legislative district is a political subdivisions into which a state is divided for electing members to the Legislature. Each legislative district is represented by one senator and two representatives. Since Idaho's legislative districts are not further split into two House of Representative districts, both representatives, like the senator, serve the entire district. The districts are established by state law and are redrawn following a decennial census to maintain equal population in each.; abstract: This static map image portrays legislative district boundaries of district 14 as of March 9, 2002. A Legislative district is a political subdivisions into which a state is divided for electing members to the Legislature. Each legislative district is represented by one senator and two representatives. Since Idaho's legislative districts are not further split into two House of Representative districts, both representatives, like the senator, serve the entire district. The districts are established by state law and are redrawn following a decennial census to maintain equal population in each.

description: This static map image portrays legislative district boundaries of district 09 as of March 9, 2002. A Legislative district is a political subdivisions into which a state is divided for electing members to the Legislature. Each legislative district is represented by one senator and two representatives. Since Idaho's legislative districts are not further split into two House of Representative districts, both representatives, like the senator, serve the entire district. The districts are established by state law and are redrawn following a decennial census to maintain equal population in each.; abstract: This static map image portrays legislative district boundaries of district 09 as of March 9, 2002. A Legislative district is a political subdivisions into which a state is divided for electing members to the Legislature. Each legislative district is represented by one senator and two representatives. Since Idaho's legislative districts are not further split into two House of Representative districts, both representatives, like the senator, serve the entire district. The districts are established by state law and are redrawn following a decennial census to maintain equal population in each.

Major cities are associated with comparatively few people per physician; every province has one or more census divisions in which the ratio is less than or equal to 550:1. None of the territories has a ratio this low. At the same time, each province has a significant number of areas with ratios higher than 550:1 and these tend to be located in the more rural and/or northern areas.

As of 2025, Barbados was the most densely populated country in Latin America and the Caribbean, with approximately 657.16 people per square kilometer. In that same year, Argentina's population density was estimated at approximately 16.75 people per square kilometer.

In a time of global change, having an understanding of the nature of biotic and abiotic factors that drive a species’ range may be the sharpest tool in the arsenal of conservation and management of threatened species. However, such information is lacking for most tropical and epiphytic species due to the complexity of life history, the roles of stochastic events, and the diversity of habitat across the span of a distribution. In this study, we conducted repeated censuses across the core and peripheral range of Trichocentrum undulatum, a threatened orchid that is found throughout the island of Cuba (species core range) and southern Florida (the northern peripheral range). We used demographic matrix modeling as well as stochastic simulations to investigate the impacts of herbivory, hurricanes, and logging (in Cuba) on projected population growth rates (? and ?s) among sites. Methods Field methods Censuses took place between 2013 and 2021. The longest census period was that of the Peripheral population with a total of nine years (2013–2021). All four populations in Cuba used in demographic modeling that were censused more than once: Core 1 site (2016–2019, four years), Core 2 site (2018–2019, two years), Core 3 (2016 and 2018 two years), and Core 4 (2018–2019, two years) (Appendix S1: Table S1). In November 2017, Hurricane Irma hit parts of Cuba and southern Florida, impacting the Peripheral population. The Core 5 population (censused on 2016 and 2018) was small (N=17) with low survival on the second census due to logging. Three additional populations in Cuba were visited only once, Core 6, Core 7, and Core 8 (Table 1). Sites with one census or with a small sample size (Core 5) were not included in the life history and matrix model analyses of this paper due to the lack of population transition information, but they were included in the analysis on the correlation between herbivory and fruit rate, as well as the use of mortality observations from logging for modeling. All Cuban sites were located between Western and Central Cuba, spanning four provinces: Mayabeque (Core 1), Pinar del Rio (Core 2 and Core 6), Matanzas (Core 3 and Core 5), and Sancti Spiritus (Core 4, Core 7, Core 8). At each population of T. undulatum presented in this study, individuals were studied within ~1-km strips where T. undulatum occurrence was deemed representative of the site, mostly occurring along informal forest trails. Once an individual of T. undulatum was located, all trees within a 5-m radius were searched for additional individuals. Since tagging was not permitted, we used a combination of information to track individual plants for the repeated censuses. These include the host species, height of the orchid, DBH of the host tree, and hand-drawn maps. Individual plants were also marked by GPS at the Everglades Peripheral site. If a host tree was found bearing more than one T. undulatum, then we systematically recorded the orchids in order from the lowest to highest as well as used the previous years’ observations in future censuses for individualized notes and size records. We recorded plant size and reproductive variables during each census including: the number of leaves, length of the longest leaf (cm), number of inflorescence stalks, number of flowers, and the number of mature fruits. We also noted any presence of herbivory, such as signs of being bored by M. miamensis, and whether an inflorescence was partially or completely affected by the fly, and whether there was other herbivory, such as D. boisduvalii on leaves. We used logistic regression analysis to examine the effects of year (at the Peripheral site) and sites (all sites) on the presence or absence of inflorescence herbivory at all the sites. Cross tabulation and chi-square analysis were done to examine the associations between whether a plant was able to fruit and the presence of floral herbivory by M. miamensis. The herbivory was scored as either complete or partial. During the orchid population scouting expeditions, we came across a small population in the Matanzas province (Core 5, within 10 km of the Core 3 site) and recorded the demographic information. Although the sampled population was small (N = 17), we were able to observe logging impacts at the site and recorded logging-associated mortality on the subsequent return to the site. Matrix modeling Definition of size-stage classes To assess the life stage transitions and population structures for each plant for each population’s census period we first defined the stage classes for the species. The categorization for each plant’s stage class depended on both its size and reproductive capabilities, a method deemed appropriate for plants (Lefkovitch 1965, Cochran and Ellner 1992). A size index score was calculated for each plant by taking the total number of observed leaves and adding the length of the longest leaf, an indication of accumulated biomass (Borrero et al. 2016). The smallest plant size that attempted to produce an inflorescence is considered the minimum size for an adult plant. Plants were classified by stage based on their size index and flowering capacity as the following: (1) seedlings (or new recruits), i.e., new and small plants with a size index score of less than 6, (2) juveniles, i.e., plants with a size index score of less than 15 with no observed history of flowering, (3) adults, plants with size index scores of 15 or greater. Adult plants of this size or larger are capable of flowering but may not produce an inflorescence in a given year. The orchid’s population matrix models were constructed based on these stages. In general, orchid seedlings are notoriously difficult to observe and easily overlooked in the field due to the small size of protocorms. A newly found juvenile on a subsequent site visit (not the first year) may therefore be considered having previously been a seedling in the preceding year. In this study, we use the discovered “seedlings” as indicatory of recruitment for the populations. Adult plants are able to shrink or transition into the smaller juvenile stage class, but a juvenile cannot shrink to the seedling stage. Matrix elements and population vital rates calculations Annual transition probabilities for every stage class were calculated. A total of 16 site- and year-specific matrices were constructed. When seedling or juvenile sample sizes were < 9, the transitions were estimated using the nearest year or site matrix elements as a proxy. Due to the length of the study and variety of vegetation types with a generally large population size at each site, transition substitutions were made with the average stage transition from all years at the site as priors. If the sample size of the averaged stage was still too small, the averaged transition from a different population located at the same vegetation type was used. We avoided using transition values from populations found in different vegetation types to conserve potential environmental differences. A total of 20% (27/135) of the matrix elements were estimated in this fashion, the majority being seedling stage transitions (19/27) and noted in the Appendices alongside population size (Appendix S1: Table S1). The fertility element transitions from reproductive adults to seedlings were calculated as the number of seedlings produced (and that survived to the census) per adult plant. Deterministic modeling analysis We used integral projection models (IPM) to project the long-term population growth rates for each time period and population. The finite population growth rate (?), stochastic long-term growth rate (?s), and the elasticity were projected for each matrices using R Popbio Package 2.4.4 (Stubben and Milligan 2007, Caswell 2001). The elasticity matrices were summarized by placing each element into one of three categories: fecundity (transition from reproductive adults to seedling stage), growth (all transitions to new and more advanced stage, excluding the fecundity), and stasis (plants that transitioned into the same or a less advanced stage on subsequent census) (Liu et al. 2005). Life table response experiments (LTREs) were conducted to identify the stage transitions that had the greatest effects on observed differences in population growth between select sites and years (i.e., pre-post hurricane impact and site comparisons of same vegetation type). Due to the frequent disturbances that epiphytes in general experience as well as our species’ distribution in hurricane-prone areas, we ran transient dynamic models that assume that the populations censused were not at stable stage distributions (Stott et al. 2011). We calculated three indices for short-term transient dynamics to capture the variation during a 15-year transition period: reactivity, maximum amplification, and amplified inertia. Reactivity measures a population’s growth in a single measured timestep relative to the stable-stage growth, during the simulated transition period. Maximum amplification and amplified inertia are the maximum of future population density and the maximum long-term population density, respectively, relative to a stable-stage population that began at the same initial density (Stott et al. 2011). For these analyses, we used a mean matrix for Core 1, Core 2 Core 3, and Core 4 sites and the population structure of their last census. For the Peripheral site, we averaged the last three matrices post-hurricane disturbance and used the most-recent population structure. We standardized the indices across sites with the assumption of initial population density equal to 1 (Stott et al. 2011). Analysis was done using R Popdemo version 1.3-0 (Stott et al. 2012b). Stochastic simulation We created matrices to simulate the effects of episodic recruitment, hurricane impacts, herbivory, and logging (Appendix S1: Table S2). The Peripheral population is the longest-running site with nine years of censuses (eight