This API returns a search for the demographic information for a particular geography type and geography ID

(by Joseph Kerski)This map is for use in the "What is the spatial pattern of demographic variables around the world?" activity in Section 1 of the Going Places with Spatial Analysiscourse. The map contains population characteristics by country for 2013.These data come from the Population Reference Bureau's 2014 World Population Data Sheet.The Population Reference Bureau (PRB) informs people around the world about population, health, and the environment, empowering them to use that information to advance the well-being of current and future generations.PRB analyzes complex demographic data and research to provide the most objective, accurate, and up-to-date population information in a format that is easily understood by advocates, journalists, and decision makers alike.The 2014 year's data sheet has detailed information on 16 population, health, and environment indicators for more than 200 countries. For infant mortality, total fertility rate, and life expectancy, we have included data from 1970 and 2013 to show change over time. This year's special data column is on carbon emissions.For more information about how PRB compiles its data, see: https://www.prb.org/

This dataset provides population estimate trends from 1998 to the current year for each of California’s 58 counties, further disaggregated by Detailed Analysis Units (DAUs) - the smallest geographic units historically used by the California Department of Water Resources for water planning as part of the California Water Plan. DAUs are subdivisions of Planning Areas and often align with county boundaries, although a single DAU may span multiple counties. They have traditionally supported water demand estimates based on crop and land use types.

The population estimates were developed using U.S. Bureau Census 2000, 2010 and 2020 data. Throughout the estimation process, intermediate results were reviewed and adjusted as needed, with professional judgment applied to smooth trends where appropriate.

Since the California Water Plan is retiring DAUs as its planning and analysis framework, future updates to this dataset will transition away from DAU based geography. Instead, population estimates will be provided based on other geographic units, such as the 8-digit Hydrologic Units (HUC8) defined by the U.S. Geological Survey’s Watershed Boundary Dataset.

A dashboard is available for visualizing historical population trends by county and DAU.

This map is for human geography classrooms and tied to the AP benchmarks. Learn more about GeoInquiries at www.esri.com/geoinquiries

WEATHER_daily_2005_2009WEATHER_daily_2005_2009 contains summarized daily data from automated weather stations, processed with Hobo ware software (Onset Computer Corp., Bourne, MA, USA). Columns are as follows. | site_abbr = abbreviated site weather_id = site index no. | demography_id = index number of the site if it resided at a population where demographic data were collected | easting_UTM_NAD27 - easting in meters UTM NAD 1927, zone 11 North | northing_UTM_NAD27 - northing in meters UTM NAD 1927, zone 11 North | elevation - meters above sea level | date_time | year | month | Precip_mm – daily cumulative precipitation in mm | Tmax_C - daily maximum temperature, in degrees centigrade | Tmin_C – daily minimum temperature, in degrees centigrade | Tmean_C - daily mean temperature, in degrees centigradeWEATHER_seasonal_meansWEATHER_seasonal_means_2005-2009 contains data collected by automated weather stations and processed by Hoboware (Onset Computer Corporation, Bourne, MA, USA), summarized by segment of the growing season and by year, in columns as follows. Variables not described have names that should be self-explanatory. | site_abbr - abbreviated site name | weather_id = station index no. | demography_site_id - index no. if station resided at population where demographic data were collected | easting - in meters, UTM NAD 1927, zone 11 North | northing - in meters, UTM NAD 1927, zone 11 North | elevation - metera above sea level | NovJan2005_2009_Tmax | NovJan2005_2009_Tmin | NovJan2005_2009_Tmean | FebJun2006_2009_Tmax | FebJun2006_2009_Tmin | FebJun2006_2009_Tmean | FebOct_2006_2009_ Tmax | FebOct_2006_2009_Tmin | FebOct_2006_2009_Tmean| Nov2005Jan2006_precip | FebJun2006_precip | JulOct2006_precip | JanOct2006 precip | Nov2006Jan2007_precip | FebJun2007_precip | JulOct2007_precip | JanOct2007_precip | Nov2007Jan2008_precip | FebJun2008_precip | JulOct2008_precip | JanOct2008_precip | Nov2008Jan2009_precipDEMOGRAPHY_41pops_2006_2009The file called "DEMOGRAPHY_41pops_2006_2009" contains the following variables, based on repeated field observations. | Site = population name | demography_site_id – an index number for each population | Easting – eastward position (longitude) in meters, UTM NAD 27 zone 11 North | Northing – northward position (latitude) in meters, UTM NAD 27 zone 11 North | area(ha) – population area in hectares, as estimated in 2006 | Year - year | No_plots – number of 0.5 m-squared quadrats sampled for demography data | No fruiting plants – mean no. of plants that reached fruiting stage within quadrats | No fruits – total number of fruiting plants (1-4 per quadrat) that underlie the following per-plant estimates | mean no fruits per plant – self-explanatory | stdev fr per plant – standard deviation of the above | mean no seeds per fruit = average number of seeds per fruit in a sample of single fruits (from median positions on stems) from each of 30 individuals per population | stdev sds per fr = standard deviation of the above variable | mean fruiting plants per m2 = average density of fruiting plants per meter-squared | stdevfrplm2 = standard deviation of the above | mean total fruits per m2 = average number of fruits (on any number of plants) per meter-squared | stdevfrm2 = standard deviation of the above | mean total seeds per m2 = average number of fruits (on any number of plants) per meter-squared | stdevsdm2 = standard deviation of the above | No fruiting plants per site = estimated total population size of fruiting individuals | stdev fr pl per site = standard deviation of the above | total no fruits per site = estimate of the total number of fruits in each population | stdev no fr per site = standard deviation of the above | Total no seeds per site = estimate of the total number of fruits in each population | stdev no sds per site – standard deviation of the aboveDEMOGRAPHY_vital_rate_estimates_20pops_2005_2009The file "DEMOGRAPHY_vital_rate_estimates_20pops_2005_2009" contains the following variables, estimated from field observations and experiments, in columns as follows. | Population - abbreviated site name | demography_site_id = index no.| SiteArea(ha) - population area in ha; : s1(R1), CL_lo, CL_up, s1(R2), CL_lo, CL_up, s1(R3), CL_lo, CL_up, s2(R1), CL_lo, CL_up, s2(R2), CL_lo, CL_up, s2(R3), CL_lo, CL_up, s3(R1), CL_lo, CL_up, s3(R2), CL_lo, CL_up, s4(R1), CL_lo, CL_up, s4(R2), CL_lo, CL_up, s5(R1), CL_lo, CL_up, s6(R1), CL_lo, CL_up, g1(R1), CL_lo, CL_up, g1(R2), CL_lo, CL_up, g1(R3), CL_lo, CL_up, g2(R1), CL_lo, CL_up, g2(R2), CL_lo, CL_up, g3(R1), CL_lo, CL_up, sigma(06), CL_lo, CL_up, sigma(07), CL_lo, CL_up, sigma(08), CL_lo, CL_up, sigma(09), CL_lo, CL_up, F(06), SE, F(07), SE, F(08), SE, F(09), SE, phi(06), SE, phi(07), SE, phi(08), SE, phi(09), SE, MeanSeedlings(06), SE, MeanSeedlings(07), SE, MeanSeedlings(08), SE, MeanSeedlings(09), SE, MeanFruiting(06), SE, MeanFruiting(07), SE, MeanFruiting(08), SE, MeanFruiting(09), SE, SdlngO/E(07), SdlngO/E(08), Lambda_SSPEC...

Data associated with the paper: Who Tweets with Their Location? Understanding the Relationship Between Demographic Characteristics and the Use of Geoservices and Geotagging on Twitter Luke Sloan & Jeffrey Morgan

Author: Joseph Kerski, post_secondary_educator, Esri and University of DenverGrade/Audience: high school, ap human geography, post secondary, professional developmentResource type: lessonSubject topic(s): population, maps, citiesRegion: africa, asia, australia oceania, europe, north america, south america, united states, worldStandards: All APHG population tenets. Geography for Life cultural and population geography standards. Objectives: 1. Understand how population change and demographic characteristics are evident at a variety of scales in a variety of places around the world. 2. Understand the whys of where through analysis of change over space and time. 3. Develop skills using spatial data and interactive maps. 4. Understand how population data is communicated using 2D and 3D maps, visualizations, and symbology. Summary: Teaching and learning about demographics and population change in an effective, engaging manner is enriched and enlivened through the use of web mapping tools and spatial data. These tools, enabled by the advent of cloud-based geographic information systems (GIS) technology, bring problem solving, critical thinking, and spatial analysis to every classroom instructor and student (Kerski 2003; Jo, Hong, and Verma 2016).

These are the data and code for the first column in the Spatial Demography journal's Software and Code series

Modeled vote choice of geographic, demographic, and turnout subgroups with survey and ecological data in the US. The current version models Presidential vote choice in 2016 and 2020 for each contemporaneous congressional district and each of four racial groups. The methodology is introduced and explained in Kuriwaki et al. (2023). “The Geography of Racially Polarized Voting: Calibrating Surveys at the District Level.” American Political Science Review. https://osf.io/mk9e6/

Students will explore U.S. census data to see the spatial differences in the United States’ population. The activity uses a web-based map and is tied to the AP Human Geography benchmarks. Learning outcomes:· Unit 2, A1: Geographical analysis of population (density, distribute and scale)· Unit 2, A3: Geographical analysis of population (composition: age, sex, income, education and ethnicity)· Unit 2, A4: Geographical analysis of population (patterns of fertility, mortality and health)Find more advanced human geography geoinquiries and explore all geoinquiries at http://www.esri.com/geoinquiries

Somerville at a Glance is an introduction to Somerville's population, housing, education, home value and other demographic trends over time and compared to Massachusetts as a whole. The current version uses Census and ACS data from 2010 to 2023.

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

Here is the code from my third column on the spatial demography journal. In this code, I use R to calculate commonly used measures of residential segregation.

Two datasets provide geographic, land use and population data for US Counties within the contiguous US. Land area, water area, cropland area, farmland area, pastureland area and idle cropland area are given along with latitude and longitude of the county centroid and the county population. Variables in this dataset come from the US Dept. of Agriculture (USDA) Natural Resources Conservation Service (NRCS) and the US Census Bureau.

EOS-WEBSTER provides seven datasets which provide county-level data on agricultural management, crop production, livestock, soil properties, geography and population. These datasets were assembled during the mid-1990's to provide driving variables for an assessment of greenhouse gas production from US agriculture using the DNDC agro-ecosystem model [see, for example, Li et al. (1992), J. Geophys. Res., 97:9759-9776; Li et al. (1996) Global Biogeochem. Cycles, 10:297-306]. The data (except nitrogen fertilizer use) were all derived from publicly available, national databases. Each dataset has a separate DIF.

The US County data has been divided into seven datasets.

US County Data Datasets:

1) Agricultural Management 2) Crop Data (NASS Crop data) 3) Crop Summary (NASS Crop data) 4) Geography and Population 5) Land Use 6) Livestock Populations 7) Soil Properties

The United States Census Bureau publishes geographic units used for tabulation of the 2020 Census population data in the 2020 TIGER/Line Shapefile. The geographic units, which remain constant throughout the decade, include counties, census tracts, block groups, and blocks. Fields have been added so data formatted or published by the council can be joined to the shapefile for analysis. Each Shapefile (.shp) is in a compressed file (.zip) format. Blocks.zip - Census Blocks BlockGroups.zip - Block Groups Tracts.zip - Census Tracts Counties.zip - Counties Cities.zip - Census Places (Cities) CDPs.zip - Census Designated Places Each 'Pop' file contains the 2020 Census population for the corresponding geographic level. BlocksPop.zip - Census Blocks 2020 Census Population BlockGroupPop.zip - Census Block Groups 2020 Census Population TractsPop.zip - Census Tracts 2020 Census Population CountiesPop.zip - Counties 2020 Census Population

Data from the last two demographic censuses carried out in Brazil (years 2000 and 2010). City: Maringá, State of Paraná.

Code - Variable VD1 - Inhabitants per household VD2 - Heads between 10 and 19 years old VD3 - Dependency ratio VD4 - Up to one year old VE1 - Heads Without Income VE2 - Heads with more than 20 minimum wages VE3 - Heads with up to 02 minimum wages VED1 - Illiterate people (10-14 years old) VED2 - Illiterate household heads VH1 - Household without bathroom VH2 - Households with 04 bathrooms or more VH3 - Households connected to the sewage network VH4 - Households connected to the general-water network VH5 - Rented or leased households

Data from: Google VA1 - Vegetation Coverage Index (NDVI)

Business creations and closures from the Inter-Departmental Business Register, a low-level geographic breakdown for the UK, quarterly data. These are official statistics in development

Estimates of the usual resident population for health geographies in England (including current areas and former Primary Care Organisations).

These data were 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 2018-2022 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:_e22Estimate from 2018-22 ACS_m22Margin of Error from 2018-22 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_22Change, 2010-22 (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)BeltLineStatistical (buffer)BeltLineStatisticalSub (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 Statistical Areas (City of Atlanta)County (statewide)Georgia House (statewide)Georgia Senate (statewide)HSSA = High School Statistical Area (11 county region)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)State 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)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 2018-2022). 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: 2018-2022Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://opendata.atlantaregional.com/documents/3b86ee614e614199ba66a3ff1ebfe3b5/about