Annual Resident Population Estimates, Estimated Components of Resident Population Change, and Rates of the Components of Resident Population Change; for the United States, States, Metropolitan Statistical Areas, Micropolitan Statistical Areas, Counties, and Puerto Rico: April 1, 2010 to July 1, 2019 // Source: U.S. Census Bureau, Population Division // The contents of this file are released on a rolling basis from December through March. // Note: Total population change includes a residual. This residual represents the change in population that cannot be attributed to any specific demographic component. // Note: The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. // The Office of Management and Budget's statistical area delineations for metropolitan, micropolitan, and combined statistical areas, as well as metropolitan divisions, are those issued by that agency in September 2018. // Current data on births, deaths, and migration are used to calculate population change since the 2010 Census. An annual time series of estimates is produced, beginning with the census and extending to the vintage year. The vintage year (e.g., Vintage 2019) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the entire estimates series is revised. Additional information, including historical and intercensal estimates, evaluation estimates, demographic analysis, research papers, and methodology is available on website: https://www.census.gov/programs-surveys/popest.html.

This dataset represent 2010 Census block groups that displays 2022 age 0 to 18 population per square mile in Arizona. Using the 2010 Census block groups layer it was joined to the 2022 1-Year Population Estimate dataset from Clarita's. Then, we calculated geometry for each block groups to obtain the area per square mile. Finally, we were able to calculate population density by the total population of 18 years old and younger to the block group area (sq. mile). Last Updated: May 2022Update Frequency: Annual

Datasets archived here consist of all data analyzed in Duan et al. 2015 from Journal of Applied Ecology. Specifically, these data were collected from annual sampling of emerald ash borer (Agrilus planipennis) immature stages and associated parasitoids on infested ash trees (Fraxinus) in Southern Michigan, where three introduced biological control agents had been released between 2007 - 2010. Detailed data collection procedures can be found in Duan et al. 2012, 2013, and 2015. Resources in this dataset:Resource Title: Duan J Data on EAB larval density-bird predation and unknown factor from Journal of Applied Ecology. File Name: Duan J Data on EAB larval density-bird predation and unknown factor from Journal of Applied Ecology.xlsxResource Description: This data set is used to calculate mean EAB density (per m2 of ash phloem area), bird predation rate and mortality rate caused by unknown factors and analyzed with JMP (10.2) scripts for mixed effect linear models in Duan et al. 2015 (Journal of Applied Ecology).Resource Title: DUAN J Data on Parasitism L1-L2 Excluded from Journal of Applied Ecology. File Name: DUAN J Data on Parasitism L1-L2 Excluded from Journal of Applied Ecology.xlsxResource Description: This data set is used to construct life tables and calculation of net population growth rate of emerald ash borer for each site. The net population growth rates were then analyzed with JMP (10.2) scripts for mixed effect linear models in Duan et al. 2015 (Journal of Applied Ecology).Resource Title: DUAN J Data on EAB Life Tables Calculation from Journal of Applied Ecology. File Name: DUAN J Data on EAB Life Tables Calculation from Journal of Applied Ecology.xlsxResource Description: This data set is used to calculate parasitism rate of EAB larvae for each tree and then analyzed with JMP (10.2) scripts for mixed effect linear models on in Duan et al. 2015 (Journal of Applied Ecology).Resource Title: READ ME for Emerald Ash Borer Biocontrol Study from Journal of Applied Ecology. File Name: READ_ME_for_Emerald_Ash_Borer_Biocontrol_Study_from_Journal_of_Applied_Ecology.docxResource Description: Additional information and definitions for the variables/content in the three Emerald Ash Borer Biocontrol Study tables: Data on EAB Life Tables Calculation Data on EAB larval density-bird predation and unknown factor Data on Parasitism L1-L2 Excluded from Journal of Applied Ecology Resource Title: Data Dictionary for Emerald Ash Borer Biocontrol Study from Journal of Applied Ecology. File Name: AshBorerAnd Parasitoids_DataDictionary.csvResource Description: CSV data dictionary for the variables/content in the three Emerald Ash Borer Biocontrol Study tables: Data on EAB Life Tables Calculation Data on EAB larval density-bird predation and unknown factor Data on Parasitism L1-L2 Excluded from Journal of Applied Ecology Fore more information see the related READ ME file.

Annual Population Estimates for the United States, States, Metropolitan Statistical Areas, Micropolitan Statistical Areas, Counties, Incorporated Places, and Minor Civil Divisions; and for Puerto Rico and Its Municipios // Source: U.S. Census Bureau, Population Division // The contents of this file are released on a rolling basis from December through May. // The estimates are developed from an April 1, 2020 base population and may incorporate changes resulting from the Count Question Resolution program and/or geographic program revisions. // The Office of Management and Budget's statistical area delineations for metropolitan, micropolitan, and combined statistical areas, as well as metropolitan divisions, are those issued by that agency. // Current data on births, deaths, and migration are used to calculate population change since April 1, 2020. An annual time series of estimates is produced, beginning with April 1, 2020 and extending to the vintage year. The vintage year refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the entire estimates series is revised. Additional information, including historical and intercensal estimates, evaluation estimates, demographic analysis, research papers, and methodology is available on website: https://www.census.gov/programs-surveys/popest.html.

This is a hybrid gridded dataset of demographic data for the world, given as 5-year population bands at a 0.5 degree grid resolution. This dataset combines the NASA SEDAC Gridded Population of the World version 4 (GPWv4) with the ISIMIP Histsoc gridded population data and the United Nations World Population Program (WPP) demographic modelling data. Demographic fractions are given for the time period covered by the UN WPP model (1950-2050) while demographic totals are given for the time period covered by the combination of GPWv4 and Histsoc (1950-2020) Method - demographic fractions Demographic breakdown of country population by grid cell is calculated by combining the GPWv4 demographic data given for 2010 with the yearly country breakdowns from the UN WPP. This combines the spatial distribution of demographics from GPWv4 with the temporal trends from the UN WPP. This makes it possible to calculate exposure trends from 1980 to the present day. To combine the UN WPP demographics with the GPWv4 demographics, we calculate for each country the proportional change in fraction of demographic in each age band relative to 2010 as: (\delta_{year,\ country,age}^{\text{wpp}} = f_{year,\ country,age}^{\text{wpp}}/f_{2010,country,age}^{\text{wpp}}) Where: - (\delta_{year,\ country,age}^{\text{wpp}}) is the ratio of change in demographic for a given age and and country from the UN WPP dataset. - (f_{year,\ country,age}^{\text{wpp}}) is the fraction of population in the UN WPP dataset for a given age band, country, and year. - (f_{2010,country,age}^{\text{wpp}}) is the fraction of population in the UN WPP dataset for a given age band, country for the year 2020. The gridded demographic fraction is then calculated relative to the 2010 demographic data given by GPWv4. For each subset of cells corresponding to a given country c, the fraction of population in a given age band is calculated as: (f_{year,c,age}^{\text{gpw}} = \delta_{year,\ country,age}^{\text{wpp}}*f_{2010,c,\text{age}}^{\text{gpw}}) Where: - (f_{year,c,age}^{\text{gpw}}) is the fraction of the population in a given age band for given year, for the grid cell c. - (f_{2010,c,age}^{\text{gpw}}) is the fraction of the population in a given age band for 2010, for the grid cell c. The matching between grid cells and country codes is performed using the GPWv4 gridded country code lookup data and country name lookup table. The final dataset is assembled by combining the cells from all countries into a single gridded time series. This time series covers the whole period from 1950-2050, corresponding to the data available in the UN WPP model. Method - demographic totals Total population data from 1950 to 1999 is drawn from ISIMIP Histsoc, while data from 2000-2020 is drawn from GPWv4. These two gridded time series are simply joined at the cut-over date to give a single dataset covering 1950-2020. The total population per age band per cell is calculated by multiplying the population fractions by the population totals per grid cell. Note that as the total population data only covers until 2020, the time span covered by the demographic population totals data is 1950-2020 (not 1950-2050). Disclaimer This dataset is a hybrid of different datasets with independent methodologies. No guarantees are made about the spatial or temporal consistency across dataset boundaries. The dataset may contain outlier points (e.g single cells with demographic fractions >1). This dataset is produced on a 'best effort' basis and has been found to be broadly consistent with other approaches, but may contain inconsistencies which not been identified. {"references": ["UN. (2019). World Population Prospects 2019: Data Booklet. Retrieved from https://population.un.org/wpp/Publications/Files/WPP2019_DataBooklet.pdf", "NASA SEDAC, & CIESIN. (2016). Gridded Population of the World, Version 4 (GPWv4): Population Count. New York, New York, USA: Columbia University. Retrieved from http://dx.doi.org/10.7927/H4X63JVC", "ISIMIP. (2018). ISIMIP Project Design and Simulation Protocol. Retrieved from https://www.isimip.org/gettingstarted/input-data-bias-correction/details/31/"]}

Annual Resident Population Estimates, Estimated Components of Resident Population Change, and Rates of the Components of Resident Population Change for States and Counties // Source: U.S. Census Bureau, Population Division // Note: Total population change includes a residual. This residual represents the change in population that cannot be attributed to any specific demographic component. See Population Estimates Terms and Definitions at http://www.census.gov/popest/about/terms.html. // Net international migration in the United States includes the international migration of both native and foreign-born populations. Specifically, it includes: (a) the net international migration of the foreign born, (b) the net migration between the United States and Puerto Rico, (c) the net migration of natives to and from the United States, and (d) the net movement of the Armed Forces population between the United States and overseas. // The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. See Geographic Terms and Definitions at http://www.census.gov/popest/about/geo/terms.html for a list of the states that are included in each region and division. // For detailed information about the methods used to create the population estimates, see http://www.census.gov/popest/methodology/index.html. // Each year, the Census Bureaus Population Estimates Program (PEP) utilizes current data on births, deaths, and migration to calculate population change since the most recent decennial census, and produces a time series of estimates of population. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. The vintage year (e.g., V2014) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the Census Bureau revises estimates for years back to the last census. As each vintage of estimates includes all years since the most recent decennial census, the latest vintage of data available supersedes all previously produced estimates for those dates. The Population Estimates Program provides additional information including historical and intercensal estimates, evaluation estimates, demographic analysis, and research papers on its website: http://www.census.gov/popest/index.html.

Annual Estimates of the Resident Population by Single Year of Age and Sex for the United States // Source: U.S. Census Bureau, Population Division // Note: The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program.// For detailed information about the methods used to create the population estimates, see http://www.census.gov/popest/methodology/index.html. // Each year, the Census Bureau's Population Estimates Program (PEP) utilizes current data on births, deaths, and migration to calculate population change since the most recent decennial census, and produces a time series of estimates of population. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. The vintage year (e.g., V2014) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the Census Bureau revises estimates for years back to the last census. As each vintage of estimates includes all years since the most recent decennial census, the latest vintage of data available supersedes all previously produced estimates for those dates. The Population Estimates Program provides additional information including historical and intercensal estimates, evaluation estimates, demographic analysis, and research papers on its website: http://www.census.gov/popest/index.html.

Annual Population Estimates for the United States; States; Metropolitan Statistical Areas, Micropolitan Statistical Areas, and Related Statistical Areas; Counties; and Subcounty Places; and for Puerto Rico and Its Municipios: April 1, 2010 to July 1, 2018 // Source: U.S. Census Bureau, Population Division // The contents of this file are released on a rolling basis from December through May. // Note: The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. // The Office of Management and Budget's statistical area delineations for metropolitan, micropolitan, and combined statistical areas, as well as metropolitan divisions, are those issued by that agency in July 2015. // The 2010 Census did not ascertain the military status of the household population. Therefore, variables for the 2010 Census civilian, civilian noninstitutionalized, and resident population plus Armed Forces overseas populations cannot be derived and are not available on this file. // For detailed information about the methods used to create the population estimates, see https://www.census.gov/programs-surveys/popest/technical-documentation/methodology.html. // Each year, the Census Bureau's Population Estimates Program (PEP) utilizes current data on births, deaths, and migration to calculate population change since the most recent decennial census, and produces a time series of estimates of population. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. The vintage year (e.g., Vintage 2017) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the Census Bureau revises estimates for years back to the last census. As each vintage of estimates includes all years since the most recent decennial census, the latest vintage of data available supersedes all previously produced estimates for those dates. The Population Estimates Program provides additional information including historical and intercensal estimates, evaluation estimates, demographic analysis, and research papers on its website: https://www.census.gov/programs-surveys/popest.html.

This dataset contains human population density for the state of California and a small portion of western Nevada for the year 2000. The population density is based on US Census Bureau data and has a cell size of 990 meters.

The purpose of the dataset is to provide a consistent statewide human density GIS layer for display, analysis and modeling purposes.

The state of California, and a very small portion of western Nevada, was divided into pixels with a cell size 0.98 km2, or 990 meters on each side. For each pixel, the US Census Bureau data was clipped, the total human population was calculated, and that population was divided by the area to get human density (people/km2) for each pixel.

Monthly Population Estimates by Universe, Age, Sex, Race, and Hispanic Origin for the United States: April 1, 2010 to December 1, 2018 // Source: U.S. Census Bureau, Population Division // The contents of this file are released on a rolling basis from December through June. // Note: 'In combination' means in combination with one or more other races. The sum of the five race-in-combination groups adds to more than the total population because individuals may report more than one race. Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. Responses of 'Some Other Race' from the 2010 Census are modified. This results in differences between the population for specific race categories shown for the 2010 Census population in this file versus those in the original 2010 Census data. For more information, see https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/modified-race-summary-file-method/mrsf2010.pdf. // The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. // Persons on active duty in the Armed Forces were not enumerated in the 2010 Census. Therefore, variables for the 2010 Census civilian, civilian noninstitutionalized, and resident population plus Armed Forces overseas populations cannot be derived and are not available on these files. // For detailed information about the methods used to create the population estimates, see https://www.census.gov/programs-surveys/popest/technical-documentation/methodology.html. // Each year, the Census Bureau's Population Estimates Program (PEP) utilizes current data on births, deaths, and migration to calculate population change since the most recent decennial census, and produces a time series of estimates of population. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. The vintage year (e.g., V2017) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the Census Bureau revises estimates for years back to the last census. As each vintage of estimates includes all years since the most recent decennial census, the latest vintage of data available supersedes all previously produced estimates for those dates. The Population Estimates Program provides additional information including historical and intercensal estimates, evaluation estimates, demographic analysis, and research papers on its website: https://www.census.gov/programs-surveys/popest.html.

The latest 8 and 10 digit HUC boundaries, along with the calculated US Census population within each subbasin and watershed for 2020, 2010, and 2000.

HUC boundaries are from the USGS National Hydrography Watershed Boundary Dataset. US Census 2020, 2010, and 2000 Block Data was acquired through NC OneMap.

Subbasin and watershed population estimates were derived from the 2020, 2010, and 2000 Block population data from the US Census. The ArcGIS Tool "Summarize Within" was used to calculate the total population within each subbasin and watershed for each census period. As census blocks and HUC boundaries do not always coincide, the calculated population is only an estimate and is not to be used as an exact figure.

Annual Resident Population Estimates by Single Year of Age, Sex, Race, and Hispanic Origin // Source: U.S. Census Bureau, Population Division // The contents of this file are released on a rolling basis from December through June. // Note: 'In combination' means in combination with one or more other races. The sum of the five race-in-combination groups adds to more than the total population because individuals may report more than one race. Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. Responses of 'Some Other Race' from the 2010 Census are modified. This results in differences between the population for specific race categories shown for the 2010 Census population in this file versus those in the original 2010 Census data. For more information, see http://www.census.gov/popest/data/historical/files/MRSF-01-US1.pdf. // The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. // For detailed information about the methods used to create the population estimates, see http://www.census.gov/popest/methodology/index.html. // Each year, the Census Bureau's Population Estimates Program (PEP) utilizes current data on births, deaths, and migration to calculate population change since the most recent decennial census, and produces a time series of estimates of population. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. The vintage year (e.g., V2015) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the Census Bureau revises estimates for years back to the last census. As each vintage of estimates includes all years since the most recent decennial census, the latest vintage of data available supersedes all previously produced estimates for those dates. The Population Estimates Program provides additional information including historical and intercensal estimates, evaluation estimates, demographic analysis, and research papers on its website: http://www.census.gov/popest/index.html.

This dataset provides the components of change involved in the calculation of the population projections for local authorities in Wales. Data cover the change between each successive projection year and relate to the change from the middle of each year to the middle of the following year. The first year's data represent the change from the base year of mid-2018 to mid-2019, through the projection period to show the change for mid-2042 to mid-2043. This is the fifth set of population projections published for the 22 local authorities in Wales. Note that the projections become increasingly uncertain the further we try to look into the future.

Inputs for each of these metrics come GIS modeling of the ZOIs. Each metric is calucated for three flood events (2-year, 20-year, and 100-year) and converted to expected annual (see worksheet 'EADExample' and project report). For buidlings, roads, and critical facilities exposed, GIS modeling tallies the number/miles for each event and the worksheets here simply convert to expected annual. For expected annual damages to buildings, there are four worksheets. 'Inputs' worksheet is where we enter charateristics for each building. 'Coastal_DDF' contains a number of depth-damage functions taken from FEMA's HAZUS software and from the USACE (2015) NACCS report. 'Damage' applies characteristics of the buildings and the appropriate depth damage functions to calculate pre-project and post-project damages for each building affected at each project site and across each flood event modeled. The UID column is a unique identifier to track each combination of building and NFWF project site. Finally, the worksheet 'Metric9ByProject' sums up damages across all buildings for each project and flood scenario and converts the result to expected annual damages pre-project and post-project.

Annual State Resident Population Estimates for 6 Race Groups (5 Race Alone Groups and Two or More Races) by Age, Sex, and Hispanic Origin: April 1, 2010 to July 1, 2013 // File: 7/1/2013 State Characteristics Population Estimates // Source: U.S. Census Bureau, Population Division // Release Date: June 2014 // Note: The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. Responses of 'Some Other Race' from the 2010 Census are modified. This results in differences between the population for specific race categories shown for the 2010 Census population in this file versus those in the original 2010 Census data. For more information, see http://www.census.gov/popest/data/historical/files/MRSF-01-US1.pdf. // For detailed information about the methods used to create the population estimates, see http://www.census.gov/popest/methodology/index.html. // Each year, the Census Bureau's Population Estimates Program (PEP) utilizes current data on births, deaths, and migration to calculate population change since the most recent decennial census, and produces a time series of estimates of population. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. The vintage year (e.g., V2013) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the Census Bureau revises estimates for years back to the last census. As each vintage of estimates includes all years since the most recent decennial census, the latest vintage of data available supersedes all previously produced estimates for those dates. The Population Estimates Program provides additional information including historical and intercensal estimates, evaluation estimates, demographic analysis, and research papers on its website: http://www.census.gov/popest/index.html.

Extract from Census Population Estimate source for county and state level geographies in Virginia that can be used as population denominator to calculate a variety of rates. Counts are segmented by demographic characteristics such as sex, race, ethnicity, and age.

This dataset is updated on an annual frequency, typically by the end of summer, with data added/updated for the previous calendar year. Data Source: https://www2.census.gov/programs-surveys/popest/datasets/

This layer shows Population. This is shown by county boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.
This layer is symbolized to show the point by Population Density and size of the point by Total Population. The size of the symbol represents the total count of housing units. Population Density was calculated based on the total population and area of land fields, which both came from the U.S. Census Bureau. Formula used for Calculating the Pop Density (B01001_001E/GEO_LAND_AREA_SQ_KM). To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2015-2019ACS Table(s): B01001, B09020Data downloaded from: Census Bureau's API for American Community Survey Date of API call: February 10, 2021National Figures: data.census.gov The United States Census Bureau's American Community Survey (ACS): About the SurveyGeography & ACSTechnical Documentation News & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables. Data Processing Notes: Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes.
All of these are rendered in this dataset as null (blank) values.

A Population technical workgroup was formed in Sana’a (IOM, UNFPA, OCHA, NAMCHA and CSO ) and another one in Aden (IOM, OCHA, CSO, MOPIC including Executive Unit). IDP data flow figures (from district to district) were collected, cross checked between different sources (IOM DTM, NAMCHA, Executive unit) in cases multiple datasets for the same location were available. This resulted in a country-wide IDP movement database.

The TWGs agreed to use this database to calculate the estimated population using the following formula : Estimated Population = CSO projected population( 2019) – IDPS who left the district + idps who came to the district

All members in both workgroups agreed on the final results on 3 Dec 2018. The attached dataset represents the results of the work of both groups and will be used for 2019 HNO and HRP.

A. SUMMARY This dataset includes San Francisco COVID-19 tests by race/ethnicity and by date. This dataset represents the daily count of tests collected, and the breakdown of test results (positive, negative, or indeterminate). Tests in this dataset include all those collected from persons who listed San Francisco as their home address at the time of testing. It also includes tests that were collected by San Francisco providers for persons who were missing a locating address. This dataset does not include tests for residents listing a locating address outside of San Francisco, even if they were tested in San Francisco.

The data were de-duplicated by individual and date, so if a person gets tested multiple times on different dates, all tests will be included in this dataset (on the day each test was collected). If a person tested multiple times on the same date, only one test is included from that date. When there are multiple tests on the same date, a positive result, if one exists, will always be selected as the record for the person. If a PCR and antigen test are taken on the same day, the PCR test will supersede. If a person tests multiple times on the same day and the results are all the same (e.g. all negative or all positive) then the first test done is selected as the record for the person.

The total number of positive test results is not equal to the total number of COVID-19 cases in San Francisco.

When a person gets tested for COVID-19, they may be asked to report information about themselves. One piece of information that might be requested is a person's race and ethnicity. These data are often incomplete in the laboratory and provider reports of the test results sent to the health department. The data can be missing or incomplete for several possible reasons:

• The person was not asked about their race and ethnicity.
• The person was asked, but refused to answer.
• The person answered, but the testing provider did not include the person's answers in the reports.
• The testing provider reported the person's answers in a format that could not be used by the health department.

For any of these reasons, a person's race/ethnicity will be recorded in the dataset as “Unknown.”

B. NOTE ON RACE/ETHNICITY The different values for Race/Ethnicity in this dataset are "Asian;" "Black or African American;" "Hispanic or Latino/a, all races;" "American Indian or Alaska Native;" "Native Hawaiian or Other Pacific Islander;" "White;" "Multi-racial;" "Other;" and “Unknown."

The Race/Ethnicity categorization increases data clarity by emulating the methodology used by the U.S. Census in the American Community Survey. Specifically, persons who identify as "Asian," "Black or African American," "American Indian or Alaska Native," "Native Hawaiian or Other Pacific Islander," "White," "Multi-racial," or "Other" do NOT include any person who identified as Hispanic/Latino at any time in their testing reports that either (1) identified them as SF residents or (2) as someone who tested without a locating address by an SF provider. All persons across all races who identify as Hispanic/Latino are recorded as “"Hispanic or Latino/a, all races." This categorization increases data accuracy by correcting the way “Other” persons were counted. Previously, when a person reported “Other” for Race/Ethnicity, they would be recorded “Unknown.” Under the new categorization, they are counted as “Other” and are distinct from “Unknown.”

If a person records their race/ethnicity as “Asian,” “Black or African American,” “American Indian or Alaska Native,” “Native Hawaiian or Other Pacific Islander,” “White,” or “Other” for their first COVID-19 test, then this data will not change—even if a different race/ethnicity is reported for this person for any future COVID-19 test. There are two exceptions to this rule. The first exception is if a person’s race/ethnicity value is reported as “Unknown” on their first test and then on a subsequent test they report “Asian;” "Black or African American;" "Hispanic or Latino/a, all races;" "American Indian or Alaska Native;" "Native Hawaiian or Other Pacific Islander;" or "White”, then this subsequent reported race/ethnicity will overwrite the previous recording of “Unknown”. If a person has only ever selected “Unknown” as their race/ethnicity, then it will be recorded as “Unknown.” This change provides more specific and actionable data on who is tested in San Francisco.

The second exception is if a person ever marks “Hispanic or Latino/a, all races” for race/ethnicity then this choice will always overwrite any previous or future response. This is because it is an overarching category that can include any and all other races and is mutually exclusive with the other responses.

A person's race/ethnicity will be recorded as “Multi-racial” if they select two or more values among the following choices: “Asian,” “Black or African American,” “American Indian or Alaska Native,” “Native Hawaiian or Other Pacific Islander,” “White,” or “Other.” If a person selects a combination of two or more race/ethnicity answers that includes “Hispanic or Latino/a, all races” then they will still be recorded as “Hispanic or Latino/a, all races”—not as “Multi-racial.”

C. HOW THE DATASET IS CREATED COVID-19 laboratory test data is based on electronic laboratory test reports. Deduplication, quality assurance measures and other data verification processes maximize accuracy of laboratory test information.

D. UPDATE PROCESS Updates automatically at 5:00AM Pacific Time each day. Redundant runs are scheduled at 7:00AM and 9:00AM in case of pipeline failure.

E. HOW TO USE THIS DATASET San Francisco population estimates for race/ethnicity can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

Due to the high degree of variation in the time needed to complete tests by different labs there is a delay in this reporting. On March 24, 2020 the Health Officer ordered all labs in the City to report complete COVID-19 testing information to the local and state health departments.

In order to track trends over time, a user can analyze this data by sorting or filtering by the "specimen_collection_date" field.

Calculating Percent Positivity: The positivity rate is the percentage of tests that return a positive result for COVID-19 (positive tests divided by the sum of positive and negative tests). Indeterminate results, which could not conclusively determine whether COVID-19 virus was present, are not included in the calculation of percent positive. When there are fewer than 20 positives tests for a given race/ethnicity and time period, the positivity rate is not calculated for the public tracker because rates of small test counts are less reliable.

Calculating Testing Rates: To calculate the testing rate per 10,000 residents, divide the total number of tests collected (positive, negative, and indeterminate results) for the specified race/ethnicity by the total number of residents who identify as that race/ethnicity (according to the 2016-2020 American Community Survey (ACS) population estimate), then multiply by 10,000. When there are fewer than 20 total tests for a given race/ethnicity and time period, the testing rate is not calculated for the public tracker because rates of small test counts are less reliable.

Read more about how this data is updated and validated daily: https://sf.gov/information/covid-19-data-questions

F. CHANGE LOG

• 1/12/2024 - This dataset will stop updating as of 1/12/2024
• 6/21/2023 - A small number of additional COVID-19 testing records were released as part of our ongoing data cleaning efforts. An update to the race or ethnicity designation among a subset of testing records was simultaneously released.
• 1/31/2023 - updated “population_estimate” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
• 1/31/2023 - renamed column “last_updated_at” to “data_as_of”.
• 3/23/2022 - ‘Native American’ changed to ‘American Indian or Alaska Native’ to align with the census.
• 2/10/2022 - race/ethnicity categorization was changed. See section NOTE ON RACE/ETHNICITY for additional information.
• 4/16/2021 - dataset updated to refresh with a five-day data lag.

The Qinghai-Tibet Plateau has a fragile ecological environment, spanning a geographical range of 25°59′43.74″N–39°59′20.01″N and 73°29′56.26″E–104°25′20.95″E, with significant internal population regional differentiation. This study constructs a spatialized population dataset based on the Tibetan Empire period, the first era of unified governance on the plateau. First, population quantity data were obtained from historical literature and records. Second, using land use as a restrictive factor, the weights of elevation, slope, rivers, ancient settlements, post stations, and other factors were determined through the expert scoring method and Analytic Hierarchy Process to construct impact coefficients. Finally, a 1km×1km gridded population distribution model was developed using ArcGIS software to calculate population density. After verification, the error in population distribution for the Tuyuhun and Bailan regions is around 20%. There are two main reasons for the error: records of population during the Tibetan Empire period are characterized by uncertainties, with possible practices such as underreporting, undercounting, and overreporting; the number of documented ancient settlements remains incomplete, as only those ancient sites with explicit historical records have been spatially represented, thereby affecting the accuracy of population distribution. The spatialized population dataset of the Qinghai-Tibet Plateau during the Tibetan Empire period contains population density data (234 MB) and a population density distribution map (6.90 MB).