Combined Statistical AreasThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Census Bureau (USCB), displays Combined Statistical Areas (CSA) in the United States. Per the USCB, "CSAs are defined by the Office of Management and Budget (OMB) and consist of two or more adjacent Core Based Statistical Areas (CBSAs) that have significant employment interchanges. The CBSAs that combine to create a CSA retain separate identities within the larger CSA. Because CSAs represent groupings of CBSAs, they should not be ranked or compared with individual CBSAs."Green Bay-Shawano, WIData currency: This cached Esri federal service is checked weekly for updates from its enterprise federal source (Combined Statistical Areas) and will support mapping, analysis, data exports and OGC API – Feature access.NGDAID: 74 (Series Information for Combined Statistical Area (CSA) National TIGER/Line Shapefiles, Current)OGC API Features Link: (Combined Statistical Areas - OGC Features) copy this link to embed it in OGC Compliant viewersFor more information, please visit: Combined Statistical Areas Map (March 2020)For feedback please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Governmental Units, and Administrative and Statistical Boundaries Theme Community. Per the Federal Geospatial Data Committee (FGDC), this theme is defined as the "boundaries that delineate geographic areas for uses such as governance and the general provision of services (e.g., states, American Indian reservations, counties, cities, towns, etc.), administration and/or for a specific purpose (e.g., congressional districts, school districts, fire districts, Alaska Native Regional Corporations, etc.), and/or provision of statistical data (census tracts, census blocks, metropolitan and micropolitan statistical areas, etc.). Boundaries for these various types of geographic areas are either defined through a documented legal description or through criteria and guidelines. Other boundaries may include international limits, those of federal land ownership, the extent of administrative regions for various federal agencies, as well as the jurisdictional offshore limits of U.S. sovereignty. Boundaries associated solely with natural resources and/or cultural entities are excluded from this theme and are included in the appropriate subject themes."For other NGDA Content: Esri Federal Datasets

Combined Statistical AreasThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Census Bureau (USCB), displays Combined Statistical Areas (CSA) in the United States. Per the USCB, CSAs are defined by the Office of Management and Budget (OMB) and consist of two or more adjacent Core Based Statistical Areas (CBSAs) that have significant employment interchanges. The CBSAs that combine to create a CSA retain separate identities within the larger CSA. Because CSAs represent groupings of CBSAs, they should not be ranked or compared with individual CBSAs.Data currency: This cached Esri federal service is checked weekly for updates from its enterprise federal source (Combined Statistical Areas) and will support mapping, analysis, data exports and OGC API – Feature access.Data.gov: TIGER/Line Shapefile, 2019, nation, U.S., Current Combined Statistical Area (CSA) NationalGeoplatform: TIGER/Line Shapefile, 2019, nation, U.S., Current Combined Statistical Area (CSA) NationalFor more information, please visit: Combined Statistical Areas Map (March 2020)For feedback please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Governmental Units, and Administrative and Statistical Boundaries Theme Community. Per the Federal Geospatial Data Committee (FGDC), this theme is defined as the "boundaries that delineate geographic areas for uses such as governance and the general provision of services (e.g., states, American Indian reservations, counties, cities, towns, etc.), administration and/or for a specific purpose (e.g., congressional districts, school districts, fire districts, Alaska Native Regional Corporations, etc.), and/or provision of statistical data (census tracts, census blocks, metropolitan and micropolitan statistical areas, etc.). Boundaries for these various types of geographic areas are either defined through a documented legal description or through criteria and guidelines. Other boundaries may include international limits, those of federal land ownership, the extent of administrative regions for various federal agencies, as well as the jurisdictional offshore limits of U.S. sovereignty. Boundaries associated solely with natural resources and/or cultural entities are excluded from this theme and are included in the appropriate subject themes."For other NGDA Content: Esri Federal Datasets

The CSA mine area 1:10,000 regolith-landform map illustrates the distribution of regolith materials and the landforms on which they occur, described using the RTMAP scheme developed by Geoscience Australia

Tabular data of population by age groups, race and gender, and the poverty by race is attached to the split tract geography to create this split tract with population and poverty data. Split tract data is the product of 2010 census tracts split by 2019 incorporated city boundaries and unincorporated community/countywide statistical areas (CSA) boundaries. The census tract boundaries have been altered and aligned where necessary with legal city boundaries and unincorporated areas, including shoreline/coastal areas. Census Tract:Every 10 years the Census Bureau counts the population of the United States as mandated by Constitution. The Census Bureau (https://www.census.gov/) released 2010 geographic boundaries data including census tracts for the analysis and mapping of demographic information across the United States. City Boundary:City Boundary data is the base map information for the County of Los Angeles. These City Boundaries are based on the Los Angeles County Seamless Cadastral Landbase. The Landbase is jointly maintained by the Los Angeles County Assessor and the Los Angeles County Department of Public Works (DPW). This layer represents current city boundaries within Los Angeles County. The DPW provides the most current shapefiles representing city boundaries and city annexations. True, legal boundaries are only determined on the ground by surveyors licensed in the State of California.Countywide Statistical Areas (CSA): The countywide Statistical Area (CSA) was defined to provide a common geographic boundary for reporting departmental statistics for unincorporated areas and incorporated Los Angeles city to the Board of Supervisors. The CSA boundary and CSA names are established by the CIO and the LA County Enterprise GIS group worked with the Los Angeles County Board of Supervisors Unincorporated Area and Field Deputies that reflect as best as possible the general name preferences of residents and historical names of areas. This data is primarily focused on broad statistics and reporting, not mapping of communities. This data is not designed to perfectly represent communities, nor jurisdictional boundaries such as Angeles National Forest. CSA represent board approved geographies comprised of Census block groups split by cities.Data Field:CT10: 2010 Census tractFIP19: 2019 City FIP CodeCITY: City name for incorporated cities and “Unincorporated” for unincorporated areas (as of July 1, 2019) CSA: Countywide Statistical Area (CSA) - Unincorporated area community names and LA City neighborhood names.CT10FIP19CSA: 2010 census tract with 2019 city FIPs for incorporated cities, unincorporated areas and LA neighborhoods. SPA12: 2012 Service Planning Area (SPA) number.SPA_NAME: Service Planning Area name.HD12: 2012 Health District (HD) number: HD_NAME: Health District name.POP19_AGE_0_4: 2019 population 0 to 4 years oldPOP19_AGE_5_9: 2019 population 5 to 9 years old POP19_AGE_10_14: 2019 population 10 to 14 years old POP19_AGE_15_17: 2019 population 15 to 17 years old POP19_AGE_18_19: 2019 population 18 to 19 years old POP19_AGE_20_44: 2019 population 20 to 24 years old POP19_AGE_25_29: 2019 population 25 to 29 years old POP19_AGE_30_34: 2019 population 30 to 34 years old POP19_AGE_35_44: 2019 population 35 to 44 years old POP19_AGE_45_54: 2019 population 45 to 54 years old POP19_AGE_55_64: 2019 population 55 to 64 years old POP19_AGE_65_74: 2019 population 65 to 74 years old POP19_AGE_75_84: 2019 population 75 to 84 years old POP19_AGE_85_100: 2019 population 85 years and older POP19_WHITE: 2019 Non-Hispanic White POP19_BLACK: 2019 Non-Hispanic African AmericanPOP19_AIAN: 2019 Non-Hispanic American Indian or Alaska NativePOP19_ASIAN: 2019 Non-Hispanic Asian POP19_HNPI: 2019 Non-Hispanic Hawaiian Native or Pacific IslanderPOP19_HISPANIC: 2019 HispanicPOP19_MALE: 2019 Male POP19_FEMALE: 2019 Female POV19_WHITE: 2019 Non-Hispanic White below 100% Federal Poverty Level POV19_BLACK: 2019 Non-Hispanic African American below 100% Federal Poverty Level POV19_AIAN: 2019 Non-Hispanic American Indian or Alaska Native below 100% Federal Poverty Level POV19_ASIAN: 2019 Non-Hispanic Asian below 100% Federal Poverty Level POV19_HNPI: 2019 Non-Hispanic Hawaiian Native or Pacific Islander below 100% Federal Poverty Level POV19_HISPANIC: 2019 Hispanic below 100% Federal Poverty Level POV19_TOTAL: 2019 Total population below 100% Federal Poverty Level POP19_TOTAL: 2019 Total PopulationAREA_SQMIL: Area in square milePOP19_DENSITY: Population per square mile.POV19_PERCENT: Poverty percentage.How this data created?The tabular data of population by age groups, by ethnic groups and by gender, and the poverty by ethnic groups is attributed to the split tract geography to create this data. Split tract polygon data is created by intersecting 2010 census tract polygons, LA Country City Boundary polygons and Countywide Statistical Areas (CSA) polygon data. The resulting polygon boundary aligned and matched with the legal city boundary whenever possible. Note:1. Population and poverty data estimated as of July 1, 2019. 2. 2010 Census tract and 2020 census tracts are not the same. Similarly, city and community boundary are not the same because boundary is reviewed and updated annually.

Tabular data of population by age groups, race and gender, and the poverty by race is attached to the split tract geography to create this split tract with population and poverty data. Split tract data is the product of 2010 census tracts split by 2012 incorporated city boundaries and unincorporated community/countywide statistical areas (CSA) boundaries. The census tract boundaries have been altered and aligned where necessary with legal city boundaries and unincorporated areas, including shoreline/coastal areas. Census Tract:Every 10 years the Census Bureau counts the population of the United States as mandated by Constitution. The Census Bureau (https://www.census.gov/) released 2010 geographic boundaries data including census tracts for the analysis and mapping of demographic information across the United States. City Boundary:City Boundary data is the base map information for the County of Los Angeles. These City Boundaries are based on the Los Angeles County Seamless Cadastral Landbase. The Landbase is jointly maintained by the Los Angeles County Assessor and the Los Angeles County Department of Public Works (DPW). This layer represents current city boundaries within Los Angeles County. The DPW provides the most current shapefiles representing city boundaries and city annexations. True, legal boundaries are only determined on the ground by surveyors licensed in the State of California.Countywide Statistical Areas (CSA): The countywide Statistical Area (CSA) was defined to provide a common geographic boundary for reporting departmental statistics for unincorporated areas and incorporated Los Angeles city to the Board of Supervisors. The CSA boundary and CSA names are established by the CIO and the LA County Enterprise GIS group worked with the Los Angeles County Board of Supervisors Unincorporated Area and Field Deputies that reflect as best as possible the general name preferences of residents and historical names of areas. This data is primarily focused on broad statistics and reporting, not mapping of communities. This data is not designed to perfectly represent communities, nor jurisdictional boundaries such as Angeles National Forest. CSA represent board approved geographies comprised of Census block groups split by cities.Data Field:CT10: 2010 Census tractFIP12: 2012 City FIP CodeCITY: City name for incorporated cities and “Unincorporated” for unincorporated areas (as of July 1, 2012) CT10FIP12: 2010 census tract with 2012 city FIPs for incorporated cities and unincorporated areas. SPA12: 2012 Service Planning Area (SPA) number.SPA_NAME: Service Planning Area name.HD12: 2012 Health District (HD) number: HD_NAME: Health District name.POP12_AGE_0_4: 2012 population 0 to 4 years oldPOP12_AGE_5_9: 2012 population 5 to 9 years old POP12_AGE_10_14: 2012 population 10 to 14 years old POP12_AGE_15_17: 2012 population 15 to 17 years old POP12_AGE_18_19: 2012 population 18 to 19 years old POP12_AGE_20_44: 2012 population 20 to 24 years old POP12_AGE_25_29: 2012 population 25 to 29 years old POP12_AGE_30_34: 2012 population 30 to 34 years old POP12_AGE_35_44: 2012 population 35 to 44 years old POP12_AGE_45_54: 2012 population 45 to 54 years old POP12_AGE_55_64: 2012 population 55 to 64 years old POP12_AGE_65_74: 2012 population 65 to 74 years old POP12_AGE_75_84: 2012 population 75 to 84 years old POP12_AGE_85_100: 2012 population 85 years and older POP12_WHITE: 2012 Non-Hispanic White POP12_BLACK: 2012 Non-Hispanic African AmericanPOP12_AIAN: 2012 Non-Hispanic American Indian or Alaska NativePOP12_ASIAN: 2012 Non-Hispanic Asian POP12_HNPI: 2012 Non-Hispanic Hawaiian Native or Pacific IslanderPOP12_HISPANIC: 2012 HispanicPOP12_MALE: 2012 Male POP12_FEMALE: 2012 Female POV12_WHITE: 2012 Non-Hispanic White below 100% Federal Poverty Level POV12_BLACK: 2012 Non-Hispanic African American below 100% Federal Poverty Level POV12_AIAN: 2012 Non-Hispanic American Indian or Alaska Native below 100% Federal Poverty Level POV12_ASIAN: 2012 Non-Hispanic Asian below 100% Federal Poverty Level POV12_HNPI: 2012 Non-Hispanic Hawaiian Native or Pacific Islander below 100% Federal Poverty Level POV12_HISPANIC: 2012 Hispanic below 100% Federal Poverty Level POV12_TOTAL: 2012 Total population below 100% Federal Poverty Level POP12_TOTAL: 2012 Total PopulationAREA_SQMIL: Area in square milePOP12_DENSITY: Population per square mile.POV12_PERCENT: Poverty rate/percentage.How this data created?The tabular data of population by age groups, by ethnic groups and by gender, and the poverty by ethnic groups is attributed to the split tract geography to create this data. Split tract polygon data is created by intersecting 2010 census tract polygons, LA Country City Boundary polygons and Countywide Statistical Areas (CSA) polygon data. The resulting polygon boundary aligned and matched with the legal city boundary whenever possible. Note:1. Population and poverty data estimated as of July 1, 2012. 2. 2010 Census tract and 2020 census tracts are not the same. Similarly, city and community boundary are not the same because boundary is reviewed and updated annually.

This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, provides annual maps of Antarctic ice velocity. The maps are assembled using SAR data from the Japanese Space Agency's (JAXA) ALOS PALSAR, the European Space Agency's (ESA) ENVISAT ASAR and Copernicus Sentinel-1, the Canadian Space Agency's (CSA) RADARSAT-1, RADARSAT-2, the German Aerospace Agency's (DLR) TerraSAR-X (TSX) and TanDEM –X (TDX), and the U.S. Geological Survey's (USGS) Landsat-8 optical imagery.. See Antarctic Ice Sheet Velocity and Mapping Data for related data.

For the original data source: https://data.census.gov/cedsci/table?tid=ACSDP5Y2020.DP02. Layer published for the Equity Explorer, a web experience developed by the LA County CEO Anti-Racism, Diversity, and Inclusion (ARDI) initiative in collaboration with eGIS and ISD. Visit the Equity Explorer to explore disability status and other equity related datasets and indices, including the COVID Vulnerability and Recovery Index. Disability status for census tracts in LA County from the US Census American Communities Survey (ACS), 2020. Estimates are based on 2020 census tract boundaries, and tracts are joined to 2021 Supervisorial Districts, Service Planning Areas (SPA), and Countywide Statistical Areas (CSA). For more information about this dataset, please contact egis@isd.lacounty.gov

PDF Map of UNEP Climate Security Assessment in North-Eastern Côte d'Ivoire project location.

Supermarkets are one of the most popular and convenient ways in which Americans gain access to healthy food, such as fresh meat and fish, or fresh fruits and vegetables. There are various ways in which people gain access to supermarkets. People in the suburbs drive to supermarkets and load up the car with many bags of food. People in cities depend much more on walking to the local store, or taking a bus or train.This map came about after asking a simple question: how many Americans live within a reasonable walk or drive to a supermarket?In this case, "reasonable" was defined as a 10 minute drive, or a 1 mile walk. The ArcGIS Network Analyst extension performed the calculations on streets data from StreetMap Premium, and the ArcGIS Spatial Analyst extension created a heat map of the walkable access and drivable access to supermarkets.The green dots represent populations in poverty who live within one mile of a supermarket. The red dots represent populations in poverty who live beyond a one mile walk to a supermarket, but may live within a 10 minute drive...which presumes they have access to a car or public transit. The grey dots represent the total population in a given area.This is an excellent map to use as backdrop to show how people are improving access to healthy food in their community. Open this map in ArcGIS Pro or ArcGIS Online to use it as a backdrop to your local analysis work. Or open it in ArcGIS Explorer to add your favorite farmers' market, CSA, or transit line -- then share that map via Facebook, Twitter or email. See this web map for a map with a popup layer.This map shows data for the entire U.S. The supermarkets included in the analysis have annual sales of $1 million or more.Data source: see this map package.

This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, provides 12 annual maps of Antarctic ice velocity from 2005-2017. The maps are assembled using SAR data from the Japanese Space Agency's (JAXA) ALOS PALSAR, the European Space Agency's (ESA) ENVISAT ASAR and Copernicus Sentinel-1, the Canadian Space Agency's (CSA) RADARSAT-1, RADARSAT-2, the German Aerospace Agency's (DLR) TerraSAR-X (TSX) and TanDEM X (TDX), and the U.S. Geological Survey's (USGS) Landsat-8 optical imagery acquired between 2005 and 2017. See Antarctic Ice Sheet Velocity and Mapping Data for related data.

U.S. Census Bureau's year 2020 block boundaries for the Portland-Vancouver-Salem, OR-WA Combined Statistical Area (CSA). Includes population, race and ethnicity, and housing characteristics. Source: Census, 2020. Date of last data update: 2024-01-26 This is official RLIS data. Contact Person: Joe Gordon joe.gordon@oregonmetro.gov 503-797-1587 RLIS Metadata Viewer: https://gis.oregonmetro.gov/rlis-metadata/#/details/3803 RLIS Terms of Use: https://rlisdiscovery.oregonmetro.gov/pages/terms-of-use

Abstract

The 2016 Ethiopia Demographic and Health Survey (EDHS) is the fourth Demographic and Health Survey conducted in Ethiopia. It was implemented by the Central Statistical Agency (CSA) at the request of the Federal Ministry of Health (FMoH). The primary objective of the 2016 EDHS is to provide up-to-date estimates of key demographic and health indicators. The EDHS provides a comprehensive overview of population, maternal, and child health issues in Ethiopia. More specifically, the 2016 EDHS: - Collected data at the national level that allowed calculation of key demographic indicators, particularly fertility and under-5 and adult mortality rates - Explored the direct and indirect factors that determine levels and trends of fertility and child mortality ? Measured levels of contraceptive knowledge and practice - Collected data on key aspects of family health, including immunisation coverage among children, prevalence and treatment of diarrhoea and other diseases among children under age 5, and maternity care indicators such as antenatal visits and assistance at delivery - Obtained data on child feeding practices, including breastfeeding - Collected anthropometric measures to assess the nutritional status of children under age 5, women age 15-49, and men age 15-59 - Conducted haemoglobin testing on eligible children age 6-59 months, women age 15-49, and men age 15-59 to provide information on the prevalence of anaemia in these groups - Collected data on knowledge and attitudes of women and men about sexually transmitted diseases and HIV/AIDS and evaluated potential exposure to the risk of HIV infection by exploring high-risk behaviours and condom use - Conducted HIV testing of dried blood spot (DBS) samples collected from women age 15-49 and men age 15-59 to provide information on the prevalence of HIV among adults of reproductive age - Collected data on the prevalence of injuries and accidents among all household members - Collected data on knowledge and prevalence of fistula and female genital mutilation or cutting (FGM/C) among women age 15-49 and their daughters age 0-14 - Obtained data on women’s experience of emotional, physical, and sexual violence.

Geographic coverage

National

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-59
• Health facility

Universe

The survey covered all de jure household members (usual residents), women age 15-49 years and men age 15-59 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2016 EDHS is the Ethiopia Population and Housing Census (PHC), which was conducted in 2007 by the Ethiopia Central Statistical Agency. The census frame is a complete list of 84,915 enumeration areas (EAs) created for the 2007 PHC. An EA is a geographic area covering on average 181 households. The sampling frame contains information about the EA location, type of residence (urban or rural), and estimated number of residential households. With the exception of EAs in six zones of the Somali region, each EA has accompanying cartographic materials. These materials delineate geographic locations, boundaries, main access, and landmarks in or outside the EA that help identify the EA. In Somali, a cartographic frame was used in three zones where sketch maps delineating the EA geographic boundaries were available for each EA; in the remaining six zones, satellite image maps were used to provide a map for each EA.

Administratively, Ethiopia is divided into nine geographical regions and two administrative cities. The sample for the 2016 EDHS was designed to provide estimates of key indicators for the country as a whole, for urban and rural areas separately, and for each of the nine regions and the two administrative cities.

The 2016 EDHS sample was stratified and selected in two stages. Each region was stratified into urban and rural areas, yielding 21 sampling strata. Samples of EAs were selected independently in each stratum in two stages. Implicit stratification and proportional allocation were achieved at each of the lower administrative levels by sorting the sampling frame within each sampling stratum before sample selection, according to administrative units in different levels, and by using a probability proportional to size selection at the first stage of sampling.

For further details on sample design, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Five questionnaires were used for the 2016 EDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, the Biomarker Questionnaire, and the Health Facility Questionnaire. These questionnaires, based on the DHS Program’s standard Demographic and Health Survey questionnaires, were adapted to reflect the population and health issues relevant to Ethiopia. Input was solicited from various stakeholders representing government ministries and agencies, nongovernmental organisations, and international donors. After all questionnaires were finalised in English, they were translated into Amarigna, Tigrigna, and Oromiffa.

Cleaning operations

All electronic data files for the 2016 EDHS were transferred via IFSS to the CSA central office in Addis Ababa, where they were stored on a password-protected computer. The data processing operation included secondary editing, which required resolution of computer-identified inconsistencies and coding of openended questions; it also required generating a file for the list of children for whom a vaccination card was not seen by the interviewers and whose vaccination records had to be checked at health facilities. The data were processed by two individuals who took part in the main fieldwork training; they were supervised by two senior staff from CSA. Data editing was accomplished using CSPro software. During the duration of fieldwork, tables were generated to check various data quality parameters and specific feedback was given to the teams to improve performance. Secondary editing and data processing were initiated in January 2016 and completed in August 2016.

Response rate

A total of 18,008 households were selected for the sample, of which 17,067 were occupied. Of the occupied households, 16,650 were successfully interviewed, yielding a response rate of 98%.

In the interviewed households, 16,583 eligible women were identified for individual interviews. Interviews were completed with 15,683 women, yielding a response rate of 95%. A total of 14,795 eligible men were identified in the sampled households and 12,688 were successfully interviewed, yielding a response rate of 86%. Although overall there was little variation in response rates according to residence, response rates among men were higher in rural than in urban areas.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: non-sampling errors and sampling errors. Non-sampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding the questions by either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2016 Ethiopia DHS (EDHS) to minimise this type of error, non-sampling errors are impossible to avoid and are difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2016 EDHS is only one of many samples that could have been selected from the same population, by using the same design and the expected size. Each of those samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error for a particular statistic (such as mean or percentage), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2016 EDHS sample is the result of a multi-stage stratified design and, consequently, it was necessary to use more complex formulae. Sampling errors are computed in either ISSA or SAS, with programs developed by ICF International. These programs use the Taylor linearisation method of variance estimation for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in Appendix B of the survey final report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar

VariableDescriptionTime FrameChildren in foster carePoint in time counts of children placed in Foster Care.Point in time data as of 12/31/2021If an estimated child population for a CSA for any of the above categories (denominator) was 50 or less, the number of referrals or cases (numerator) for that ethnicity was reduced to zero. CSAs range in size and population, from the City of Long Beach to small unincorporated enclaves encompassing a few city blocks. Communities that had low overall populations might have a “Gossip Factor”, in which counts for all ethnicities were reduced to zero.

This data set, part of the National Aeronautics and Space Administration (NASA) Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, contains seasonal (winter) ice sheet-wide velocity maps for Greenland. The maps are derived from Interferometric Synthetic Aperture Radar (InSAR) data obtained by the Canadian Space Agency's (CSA) RADARSAT-1, the Japan Aerospace Exploration Agency's (JAXA) Advanced Land Observation Satellite (ALOS), and the German Aerospace Center's (DLR) TerraSAR-X/TanDEM-X (TSX/TDX) satellites, as well as from the European Space Agency's (ESA) C-band Synthetic Aperture Radar data from Copernicus Sentinel-1A and -1B. This data set contains eleven winter Greenland ice sheet-wide mosaicked velocity maps derived from Synthetic Aperture Radar (SAR) data. Depending on the year, different platforms and sensors were used to produce these data (see Table 3). For each winter, a shapefile is included to indicate the source satellite image pairs that were processed to produce the mosaic. Since speckle tracking may fail to produce results at some points within a SAR image pair, the swaths listed in the shapefile only indicate which data could have contributed to a particular point (i.e., some data from that swath were used in the mosaic, but at any particular point, there may not have been a valid result from that swath). Joughin, I., B. Smith, I. Howat, and T. Scambos. 2015, updated 2018. MEaSUREs Greenland Ice Sheet Velocity Map from InSAR Data, Version 2. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/OC7B04ZM9G6Q. 13 Nov 2020.

Supermarkets are one of the most popular and convenient ways in which Americans gain access to healthy food, such as fresh meat and fish, or fresh fruits and vegetables. There are various ways in which people gain access to supermarkets. People in the suburbs drive to supermarkets and load up the car with many bags of food. People in cities depend much more on walking to the local store, or taking a bus or train.This map came about after asking a simple question: how many Americans live within a reasonable walk or drive to a supermarket?In this case, "reasonable" was defined as a 10 minute drive, or a 1 mile walk. The ArcGIS Network Analyst extension performed the calculations on streets data from StreetMap Premium, and the ArcGIS Spatial Analyst extension created a heat map of the walkable access and drivable access to supermarkets.The green dots represent populations in poverty who live within one mile of a supermarket. The red dots represent populations in poverty who live beyond a one mile walk to a supermarket, but may live within a 10 minute drive...which presumes they have access to a car or public transit. The grey dots represent the total population in a given area.This is an excellent map to use as backdrop to show how people are improving access to healthy food in their community. Open this map in ArcGIS Pro or ArcGIS Online to use it as a backdrop to your local analysis work. Or open it in ArcGIS Explorer to add your favorite farmers' market, CSA, or transit line -- then share that map via Facebook, Twitter or email. See this web map for a map with a popup layer.This map shows data for the entire U.S. The supermarkets included in the analysis have annual sales of $1 million or more.Data source: see this map package.

Tabular data of population by age groups, race and gender, and the poverty by race is attached to the split tract geography to create this split tract with population and poverty data. Split tract data is the product of 2020 census tracts split by 2021 incorporated city boundaries and unincorporated community/countywide statistical areas (CSA) boundaries. The census tract boundaries have been altered and aligned where necessary with legal city boundaries and unincorporated areas, including shoreline/coastal areas. Census Tract:Every 10 years the Census Bureau counts the population of the United States as mandated by Constitution. The Census Bureau (https://www.census.gov/) released 2020 geographic boundaries data including census tracts for the analysis and mapping of demographic information across the United States. City Boundary:City Boundary data is the base map information for the County of Los Angeles. These City Boundaries are based on the Los Angeles County Seamless Cadastral Landbase. The Landbase is jointly maintained by the Los Angeles County Assessor and the Los Angeles County Department of Public Works (DPW). This layer represents current city boundaries within Los Angeles County. The DPW provides the most current shapefiles representing city boundaries and city annexations. True, legal boundaries are only determined on the ground by surveyors licensed in the State of California.Countywide Statistical Areas (CSA): The countywide Statistical Area (CSA) was defined to provide a common geographic boundary for reporting departmental statistics for unincorporated areas and incorporated Los Angeles city to the Board of Supervisors. The CSA boundary and CSA names are established by the CIO and the LA County Enterprise GIS group worked with the Los Angeles County Board of Supervisors Unincorporated Area and Field Deputies that reflect as best as possible the general name preferences of residents and historical names of areas. This data is primarily focused on broad statistics and reporting, not mapping of communities. This data is not designed to perfectly represent communities, nor jurisdictional boundaries such as Angeles National Forest. CSA represent board approved geographies comprised of Census block groups split by cities.Data Field:CT20: 2020 Census tractFIP21: 2021 City FIP CodeCITY: City name for incorporated cities and “Unincorporated” for unincorporated areas (as of July 1, 2021) CSA: Countywide Statistical Area (CSA) - Unincorporated area community names and LA City neighborhood names.CT20FIP21CSA: 2020 census tract with 2021 city FIPs for incorporated cities, unincorporated areas and LA neighborhoods. SPA22: 2022 Service Planning Area (SPA) number.SPA_NAME: Service Planning Area name.HD22: 2022 Health District (HD) number: HD_NAME: Health District name.POP21_AGE_0_4: 2021 population 0 to 4 years oldPOP21_AGE_5_9: 2021 population 5 to 9 years old POP21_AGE_10_14: 2021 population 10 to 14 years old POP21_AGE_15_17: 2021 population 15 to 17 years old POP21_AGE_18_19: 2021 population 18 to 19 years old POP21_AGE_20_44: 2021 population 20 to 24 years old POP21_AGE_25_29: 2021 population 25 to 29 years old POP21_AGE_30_34: 2021 population 30 to 34 years old POP21_AGE_35_44: 2021 population 35 to 44 years old POP21_AGE_45_54: 2021 population 45 to 54 years old POP21_AGE_55_64: 2021 population 55 to 64 years old POP21_AGE_65_74: 2021 population 65 to 74 years old POP21_AGE_75_84: 2021 population 75 to 84 years old POP21_AGE_85_100: 2021 population 85 years and older POP21_WHITE: 2021 Non-Hispanic White POP21_BLACK: 2021 Non-Hispanic African AmericanPOP21_AIAN: 2021 Non-Hispanic American Indian or Alaska NativePOP21_ASIAN: 2021 Non-Hispanic Asian POP21_HNPI: 2021 Non-Hispanic Hawaiian Native or Pacific IslanderPOP21_HISPANIC: 2021 HispanicPOP21_MALE: 2021 Male POP21_FEMALE: 2021 Female POV21_WHITE: 2021 Non-Hispanic White below 100% Federal Poverty Level POV21_BLACK: 2021 Non-Hispanic African American below 100% Federal Poverty Level POV21_AIAN: 2021 Non-Hispanic American Indian or Alaska Native below 100% Federal Poverty Level POV21_ASIAN: 2021 Non-Hispanic Asian below 100% Federal Poverty Level POV21_HNPI: 2021 Non-Hispanic Hawaiian Native or Pacific Islander below 100% Federal Poverty Level POV21_HISPANIC: 2021 Hispanic below 100% Federal Poverty Level POV21_TOTAL: 2021 Total population below 100% Federal Poverty Level POP21_TOTAL: 2021 Total PopulationAREA_SQMIL: Area in square milePOP21_DENSITY: Population per square mile.POV21_PERCENT: Poverty percentage.How this data created?The tabular data of population by age groups, by ethnic groups and by gender, and the poverty by ethnic groups is attributed to the split tract geography to create this data. Split tract polygon data is created by intersecting 2020 census tract polygons, LA Country City Boundary polygons and Countywide Statistical Areas (CSA) polygon data. The resulting polygon boundary aligned and matched with the legal city boundary whenever possible. Note:1. Population and poverty data estimated as of July 1, 2021. 2. 2010 Census tract and 2020 census tracts are not the same. Similarly, city and community boundary are not the same because boundary is reviewed and updated annually.

The Freight Analysis Framework (FAF5) - Regions dataset was created from 2017 base year data and was published on April 11, 2022 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The 2017 Commodity Flow Survey (CFS) contains 132 zones for U.S. domestic regions, which are directly carried over to the geography definitions for the FAF (Version 5) Regions. These geographic areas can be classified as one of the following three types: (1) Metropolitan Area (MA): The state part of a selected metropolitan statistical area (MSA) or combined statistical area (CSA). (2) The Remainder of State (ROS): The portion of a state containing the counties that are not included in the MA type CFS Areas defined above. (3) Whole State: An entire state where no MA type CFS Areas are defined within the state.

VariableDescriptionTime FrameAll children with open casesPoint in time counts of children with open cases. This includes children served while with their families and those who were placed in foster care. Point in time data as of 12/31/2021If an estimated child population for a CSA for any of the above categories (denominator) was 50 or less, the number of referrals or cases (numerator) for that ethnicity was reduced to zero. CSAs range in size and population, from the City of Long Beach to small unincorporated enclaves encompassing a few city blocks. Communities that had low overall populations might have a “Gossip Factor”, in which counts for all ethnicities were reduced to zero.

U.S. Census Bureau's year 2010 tract boundaries for the Portland-Vancouver-Salem, OR-WA Combined Statistical Area (CSA). Includes population, race and ethnicity, and housing characteristics. Source: Census, 2010. Date of last data update: 2024-01-29 This is official RLIS data. Contact Person: Joe Gordon joe.gordon@oregonmetro.gov 503-797-1587 RLIS Metadata Viewer: https://gis.oregonmetro.gov/rlis-metadata/#/details/2588 RLIS Terms of Use: https://rlisdiscovery.oregonmetro.gov/pages/terms-of-use