The smallest geographical unit for which the Census Bureau publishes sample data. Typically, Block Groups have a population of 600 to 3,000 people. Every Census Block Group has a unique 12-digit FIPS code.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

This layer was developed by the Research & Analytics Division of the Atlanta Regional Commission to represent the United States Census Bureau's 2000 Decennial Census data at the block geography.Attributes:FIPSSTCO = The Federal Information Processing Series (FIPS) state and county codes. FIPS codes were formerly known as Federal Information Processing Standards codes, until the National Institute of Standards and Technology (NIST) announced its decision in 2005 to remove geographic entity codes from its oversight. The Census Bureau continues to maintain and issue codes for geographic entities covered under FIPS oversight, albeit with a revised meaning for the FIPS acronym. Geographic entities covered under FIPS include states, counties, congressional districts, core based statistical areas, places, county subdivisions, subminor civil divisions, consolidated cities, and all types of American Indian, Alaska Native, and Native Hawaiian areas. FIPS codes are assigned alphabetically according to the name of the geographic entity and may change to maintain alphabetic sort when new entities are created or names change. FIPS codes for specific geographic entity types are usually unique within the next highest level of geographic entity with which a nesting relationship exists. For example, FIPS state, congressional district, and core based statistical area codes are unique within nation; FIPS county, place, county subdivision, and subminor civil division codes are unique within state. The codes for American Indian, Alaska Native, and Native Hawaiian areas also are unique within state; those areas in multiple states will have different codes for each state.TRACT2000 = Census Tract Codes and Numbers. Census tracts are identified by an up to four-digit integer number and may have an optional two-digit suffix; for example 1457.02 or 23. The census tract codes consist of six digits with an implied decimal between the fourth and fifth digit corresponding to the basic census tract number but with leading zeroes and trailing zeroes for census tracts without a suffix. The tract number examples above would have codes of 145702 and 002300, respectively.BLOCK2000= Census Block Numbers are numbered uniquely with a four-digit census block number from 0000 to 9999 within census tract, which nest within state and county. The first digit of the census block number identifies the block group. Block numbers beginning with a zero (in Block Group 0) are only associated with water-only areas.STFID = A concatenation of FIPSSTCO, TRACT2000, and BLOCK2000, which creates the entire FIPS code for this geography.WFD = Workforce Development Area (WFD) is a seven-county area created by agreement of county chief-elected officials, administered by the Atlanta Regional Commission and funded for training and employment activities under the federal Workforce Investment Act (WIA). For more information on ARC’s Workforce Development programs and services please consult www.atlantaregional.com/workforce/workforce.html.RDC_AAA = ARC Area Agency on Aging is a 10-county area funded by the Department of Human Resources and designated by the Older Americans Act to plan for the needs of the rapidly expanding group of older citizens in the Atlanta region. It is part of a statewide network of 12 AAAs and a national network of more than 670 AAAs. For more information on aging services please consult www.agewiseconnection.com.MNGWPD = The Metro North Georgia Water Planning District provides water resource plans, policies and coordination for metropolitan Atlanta. The District has developed regional plans for stormwater management, wastewater treatment and water supply and water conservation. The 15-county Water Planning District includes the ten counties in the ARC plus five additional counties (Bartow, Coweta, Forsyth, Hall, & Paulding). For more information please consult www.northgeorgiawater.org. MPO = The Metropolitan Planning Organization (MPO) is a 19-county area federally-designated for regional transportation planning to meet air quality standards and for programming projects to implement the adopted Regional Transportation Plan (RTP). The MPO planning area boundary includes the 10-county state-designated Regional Commission and nine additional counties (all of Coweta, Forsyth, & Paulding and parts of Barrow, Dawson, Newton, Pike, Spalding and Walton). This boundary takes into consideration both the current urbanized area as well as areas forecast to become urbanized in the next 20 years.MSA = the 29-County “Atlanta-Sandy Springs-Roswell, GA” Metropolitan Statistical Area (MSA) and the 39-county “Atlanta--Athens-Clarke County--Sandy Springs, GA” Combined Statistical Area (CSA), which includes the 29 counties of the Atlanta MSA along with the Athens-Clarke County and Gainesville MSAs and the micropolitan statistical areas of Calhoun, Cedartown, Jefferson, LaGrange and Thomaston, GA. The U.S. Office of Management and Budget (OMB) defines CSAs, MSAs and the smaller micropolitan statistical areas nationwide according to published standards applied to U.S. Census Bureau data. These various statistical areas describe substantial core areas of population together with adjacent communities having a high degree of economic and social integration, often illustrated in high rates of commuting from the adjacent areas to job locations in the core. For more information, please consult http://www.census.gov/population/metro/data/metrodef.htmlF1HR_NA = The Federal 1-Hour Air Quality Non-Attainment Area is a fine particulate matter standard (PM2.5). The non-attainment area under this standard includes the 15-county eight-hour ozone nonattainment area plus Barrow, Carroll, Hall, Spalding, Walton, and small parts of Heard and Putnam counties.F8HR_NA: The Federal 8-Hour Air Quality Non-Attainment Area for the 2008 eight-hour ozone standard is 15 counties.ACRES = The number of acres contained within the Block.SQ_MILES = The number of square miles contained within the Block.Source: United States Census Bureau, Atlanta Regional CommissionDate: 2000For additional information, please visit the Atlanta Regional Commission at www.atlantaregional.com

Blocks are typically bounded by streets, roads or creeks. In cities, a census block may correspond to a city block, but in rural areas where there are fewer roads, blocks may be limited by other features. The Census Bureau established blocks covering the entire nation for the first time in 1990.There are less number of Census Blocks within Los Angeles County in 2020 Census TIGER/Line Shapefiles, compared in 2010.Updated:1. June 2023: This update includes 2022 November Santa Clarita City annexation and the addition of "Kinneloa Mesa" community (was a part of unincorporated East Pasadena). Added new data fields FIP_CURRENT to CITYCOMM_CURRENT to reflect new/updated city and communities. Updated city/community names and FIP codes of census blocks that are in 2022 November Santa Clarita City annexation and new Kinneloa Mesa community (look for FIP_Current, City_Current, Comm_Current field values)2. February 2023: Updated few Census Block CSA values based on Demographic Consultant inquiry/suggestions3. April 2022: Updated Census Block data attribute values based on Supervisorial District 2021, Service Planning Area 2022, Health District 2022 and ZIP Code Tabulation Area 2020Created: March 2021How This Data is Created? This census geographic file was downloaded from Census Bureau website: https://www2.census.gov/geo/tiger/TIGER2020PL/STATE/06_CALIFORNIA/06037/ on February 2021 and customized for LA County. New data fields are added in the census blocks 2020 data and populated with city/community names, LA County FIPS, 2021 Supervisorial Districts, 2020 Census Zip Code Tabulation Area (ZCTA) and some administrative boundary information such as 2022 Health Districts and 2022 Service Planning Areas (SPS) are also added. "Housing20" field value and "Pop20" field value is populated with PL 94-171 Redistricting Data Summary File: Decennial Census P.L. 94-171 Redistricting Data Summary Files. Similarly, "Feat_Type" field is added and populated with water, ocean and land values. Five new data fields (FIP_CURRENT to CITYCOMM_CURRENT) are added in June 2023 updates to accommodate 2022 Santa Clarita city annexation. City/community names and FIP codes of census blocks affected by 2022 November Santa Clarita City annexation are assigned based on the location of block centroids. In June 2023 update, total of 36 blocks assigned to the City of Santa Clarita that were in Unincorporated Valencia and Castaic. Note: This data includes 3 NM ocean (FEAT_TYPE field). However, user can use a definition query to remove those. Data Fields: 1. STATE (STATEFP20): State FIP, "06" for California, 2. COUNTY (COUNTYFP20): County FIP "037" for Los Angeles County, 3. CT20: (TRACTCE20): 6-digit census tract number, 4. BG20: 7-digit block group number, 5. CB20 (BLOCKCE20): 4-digit census block number, 6. CTCB20: Combination of CT20 and CB20, 7. FEAT_TYPE: Land use types such as water bodies, ocean (3 NM ocean) or land, 8. FIP20: Los Angeles County FIP code, 9. BGFIP20: Combination of BG20 and FIP20, 10. CITY: Incorporated city name, 11. COMM: Unincorporated area community name and LA City neighborhood, also known as "CSA", 12. CITYCOMM: City/Community name label, 13. ZCTA20: Parcel specific zip codes, 14. HD12: 2012 Health District number, 15. HD_NAME: Health District name, 16. SPA22: 2022 Service Planning Area number, 17. SPA_NAME: Service Planning Area name, 18. SUP21: 2021 Supervisorial District number, 19. SUP_LABEL: Supervisorial District label, 20. POP20: 2020 Population (PL 94-171 Redistricting Data Summary File - Total Population), 21. HOUSING20: 2020 housing (PL 94-171 Redistricting Data Summary File - Total Housing),22. FIP_CURRENT: Los Angeles County 2023 FIP code, as of June 2023,23. BG20FIP_CURRENT: Combination of BG20 and 2023 FIP, as of June 2023,24. CITY_CURRENT: 2023 Incorporated city name, as of June 2023,25. COMM_CURRENT: 2023 Unincorporated area community name and LA City neighborhood, also known as "CSA", as of June 2023,26. CITYCOMM_CURRENT: 2023 City/Community name label, as of June 2023.

PROBLEM AND OPPORTUNITY In the United States, voting is largely a private matter. A registered voter is given a randomized ballot form or machine to prevent linkage between their voting choices and their identity. This disconnect supports confidence in the election process, but it provides obstacles to an election's analysis. A common solution is to field exit polls, interviewing voters immediately after leaving their polling location. This method is rife with bias, however, and functionally limited in direct demographics data collected. For the 2020 general election, though, most states published their election results for each voting location. These publications were additionally supported by the geographical areas assigned to each location, the voting precincts. As a result, geographic processing can now be applied to project precinct election results onto Census block groups. While precinct have few demographic traits directly, their geographies have characteristics that make them projectable onto U.S. Census geographies. Both state voting precincts and U.S. Census block groups: are exclusive, and do not overlap are adjacent, fully covering their corresponding state and potentially county have roughly the same size in area, population and voter presence Analytically, a projection of local demographics does not allow conclusions about voters themselves. However, the dataset does allow statements related to the geographies that yield voting behavior. One could say, for example, that an area dominated by a particular voting pattern would have mean traits of age, race, income or household structure. The dataset that results from this programming provides voting results allocated by Census block groups. The block group identifier can be joined to Census Decennial and American Community Survey demographic estimates. DATA SOURCES The state election results and geographies have been compiled by Voting and Election Science team on Harvard's dataverse. State voting precincts lie within state and county boundaries. The Census Bureau, on the other hand, publishes its estimates across a variety of geographic definitions including a hierarchy of states, counties, census tracts and block groups. Their definitions can be found here. The geometric shapefiles for each block group are available here. The lowest level of this geography changes often and can obsolesce before the next census survey (Decennial or American Community Survey programs). The second to lowest census level, block groups, have the benefit of both granularity and stability however. The 2020 Decennial survey details US demographics into 217,740 block groups with between a few hundred and a few thousand people. Dataset Structure The dataset's columns include: Column Definition BLOCKGROUP_GEOID 12 digit primary key. Census GEOID of the block group row. This code concatenates: 2 digit state 3 digit county within state 6 digit Census Tract identifier 1 digit Census Block Group identifier within tract STATE State abbreviation, redundent with 2 digit state FIPS code above REP Votes for Republican party candidate for president DEM Votes for Democratic party candidate for president LIB Votes for Libertarian party candidate for president OTH Votes for presidential candidates other than Republican, Democratic or Libertarian AREA square kilometers of area associated with this block group GAP total area of the block group, net of area attributed to voting precincts PRECINCTS Number of voting precincts that intersect this block group ASSUMPTIONS, NOTES AND CONCERNS: Votes are attributed based upon the proportion of the precinct's area that intersects the corresponding block group. Alternative methods are left to the analyst's initiative. 50 states and the District of Columbia are in scope as those U.S. possessions voting in the general election for the U.S. Presidency. Three states did not report their results at the precinct level: South Dakota, Kentucky and West Virginia. A dummy block group is added for each of these states to maintain national totals. These states represent 2.1% of all votes cast. Counties are commonly coded using FIPS codes. However, each election result file may have the county field named differently. Also, three states do not share county definitions - Delaware, Massachusetts, Alaska and the District of Columbia. Block groups may be used to capture geographies that do not have population like bodies of water. As a result, block groups without intersection voting precincts are not uncommon. In the U.S., elections are administered at a state level with the Federal Elections Commission compiling state totals against the Electoral College weights. The states have liberty, though, to define and change their own voting precincts https://en.wikipedia.org/wiki/Electoral_precinct. The Census Bureau... Visit https://dataone.org/datasets/sha256%3A05707c1dc04a814129f751937a6ea56b08413546b18b351a85bc96da16a7f8b5 for complete metadata about this dataset.

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2012-2016, to show counts and percentages for school enrollment by education level, by census tract in the Atlanta region. 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 2012-2016). 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, click here.Attributes: GEOID10 = 2010 Census tract identifier (combination of Federal Information Processing Series (FIPS) codes for state, county, and census tract) County = County identifier (combination of Federal Information Processing Series (FIPS) codes for state and county) Area_Name = 2010 Census tract name- - - - - -Total_Population = # Total Population, 2016 Total_Population_MOE_2016 = # Total population (Margin of Error), 2016- - - - - -Num_3YrsOvr_Enrolled_School = # Population 3 years and over enrolled in school, 2016 Num_3YrsOvr_Enrolled_School_MOE = # Population 3 years and over enrolled in school (Margin of Error), 2016 Num_NurserySchool_Preschool = # Enrolled in Nursery school, preschool , 2016 Num_NurserySchool_Preschool_MOE = # Enrolled in Nursery school, preschool (Margin of Error), 2016 Pct_NurserySchool_Preschool = % Enrolled in Nursery school, preschool , 2016 Pct_NurserySchool_Preschool_MOE = % Enrolled in Nursery school, preschool (Margin of Error), 2016 Num_Kindergarten = # Enrolled in Kindergarten , 2016 Num_Kindergarten_MOE = # Enrolled in Kindergarten (Margin of Error), 2016 Pct_Kindergarten = % Enrolled in Kindergarten , 2016 Pct_Kindergarten_MOE = % Enrolled in Kindergarten (Margin of Error), 2016 Num_Elem_school_grades_1_8 = # Enrolled in Elementary school (grades 1-8) , 2016 Num_Elem_school_grades_1_8_MOE = # Enrolled in Elementary school (grades 1-8) (Margin of Error), 2016 Pct_Elem_school_grades_1_8 = % Enrolled in Elementary school (grades 1-8) , 2016 Pct_Elem_school_grades_1_8_MOE = % Enrolled in Elementary school (grades 1-8) (Margin of Error), 2016 Num_High_school_grades_9_12 = # Enrolled in High school (grades 9-12) , 2016 Num_High_school_grades_9_12_MOE = # Enrolled in High school (grades 9-12) (Margin of Error), 2016 Pct_High_school_grades_9_12 = % Enrolled in High school (grades 9-12) , 2016 Pct_High_school_grades_9_12_MOE = % Enrolled in High school (grades 9-12) (Margin of Error), 2016 Num_College_or_Grad_school = # Enrolled in College or graduate school, 2016 Num_College_or_Grad_school_MOE = # Enrolled in College or graduate school (Margin of Error), 2016 Pct_College_or_Grad_school = % Enrolled in College or graduate school, 2016 Pct_College_or_Grad_school_MOE = % Enrolled in College or graduate school (Margin of Error), 2016- - - - - -Planning_Region = Planning region designation for ARC purposes AcresLand = Land area within the tract (in acres) AcresWater = Water area within the tract (in acres) AcresTotal = Total area within the tract (in acres) SqMi_Land = Land area within the tract (in square miles) SqMi_Water = Water area within the tract (in square miles) SqMi_Total = Total area within the tract (in square miles) TRACTCE10 = Census tract Federal Information Processing Series (FIPS) code. Census tracts are identified by an up to four-digit integer number and may have an optional two-digit suffix; for example 1457.02 or 23. The census tract codes consist of six digits with an implied decimal between the fourth and fifth digit corresponding to the basic census tract number but with leading zeroes and trailing zeroes for census tracts without a suffix. The tract number examples above would have codes of 145702 and 002300, respectively. CountyName = County Name last_edited_date = Last date the feature was edited by ARC Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2012-2016

For additional information, please visit the Atlanta Regional Commission at www.atlantaregional.com.

This layer was deprecated on 12/31The layer will still be publicly available, but no longer update. Information and links on how to access the new updated feature service in ArcGIS Marketplace will be posted here soonSafeGraph is just a data company. That's all we do.Social Distancing MetricsDue to the COVID-19 pandemic, people are currently engaging in social distancing. In order to understand what is actually occurring at a census block group level, SafeGraph is offering a temporary Social Distancing Metrics product. This product is delivered daily (3 days delayed from actual).The data was generated using a panel of GPS pings from anonymous mobile devices. We determine the common nighttime location of each mobile device over a 6 week period to a Geohash-7 granularity (~153m x ~153m). For ease of reference, we call this common nighttime location, the device's "home". We then aggregate the devices by home census block group and provide the metrics set out below for each census block group.To preserve privacy, we apply differential privacy to all of the device count metrics other than the device_count.SchemaColumn NameDescriptionTypeExampleorigin_census_block_groupThe unique 12-digit FIPS code for the Census Block Group. Please note that some CBGs have leading zeros.String131000000000date_range_startStart time for measurement period in ISO 8601 format of YYYY-MM-DDTHH:mm:SS±hh:mm (local time with offset from GMT). The start time will be 12 a.m. of any day.String2020-03-01T00:00:00-06:00date_range_endEnd time for measurement period in ISO 8601 format of YYYY-MM-DDTHH:mm:SS±hh:mm (local time with offset from GMT). The end time will be the following 12 a.m.String2020-03-02T00:00:00-06:00device_countNumber of devices seen in our panel during the date range whose home is in this census_block_group. Home is defined as the common nighttime location for the device over a 6 week period where nighttime is 6 pm - 7 am. Note that we do not include any census_block_groups where the count <5.Integer100distance_traveled_from_homeMedian distance traveled from the geohash-7 of the home by the devices included in the device_count during the time period (excluding any distances of 0). We first find the median for each device and then find the median for all of the devices.Integer200completely_home_device_countOut of the device_count, the number of devices which did not leave the geohash-7 in which their home is located during the time period.Integer40median_home_dwell_timeMedian dwell time at home geohash-7 ("home") in minutes for all devices in the device_count during the time period. For each device, we summed the observed minutes at home across the day (whether or not these were contiguous) to get the total minutes for each device. Then we calculate the median of all these devices.Integer1200part_time_work_behavior_devicesOut of the device_count, the number of devices that spent one period of between 3 and 6 hours at one location other than their geohash-7 home during the period of 8 am - 6 pm in local time. This does not include any device that spent 6 or more hours at a location other than home.Integer10full_time_work_behavior_devicesOut of the device_count, the number of devices that spent greater than 6 hours at a location other than their home geohash-7 during the period of 8 am - 6 pm in local time.Integer0For data definitions and complete documentation visit SafeGraph Developer and Data Scientist Docs.For statistics on the dataset, see SafeGraph Summary Statistics.Data is available as a hosted Feature Service to easily integrate with all ESRI products in the ArcGIS ecosystem.Want More? Want this POI data for use outside of ArcGIS Online? Want POI data for Canada? Want POI building footprints (Geometry)?Want more detailed category information (Core Places)?Want phone numbers or operating hours (Core Places)?Want POI visitor insights & foot-traffic data (Places Patterns)?To see more, preview & download all SafeGraph Places, Patterns, & Geometry data from SafeGraph’s Data Bar.Or drop us a line! Your data needs are our data delights. Contact: support-esri@safegraph.comView Terms of Use

WARNING: This is a pre-release dataset and its fields names and data structures are subject to change. It should be considered pre-release until the end of 2024. Expected changes:Metadata is missing or incomplete for some layers at this time and will be continuously improved.We expect to update this layer roughly in line with CDTFA at some point, but will increase the update cadence over time as we are able to automate the final pieces of the process.This dataset is continuously updated as the source data from CDTFA is updated, as often as many times a month. If you require unchanging point-in-time data, export a copy for your own use rather than using the service directly in your applications.PurposeCounty and incorporated place (city) boundaries along with third party identifiers used to join in external data. Boundaries are from the authoritative source the California Department of Tax and Fee Administration (CDTFA), altered to show the counties as one polygon. This layer displays the city polygons on top of the County polygons so the area isn"t interrupted. The GEOID attribute information is added from the US Census. GEOID is based on merged State and County FIPS codes for the Counties. Abbreviations for Counties and Cities were added from Caltrans Division of Local Assistance (DLA) data. Place Type was populated with information extracted from the Census. Names and IDs from the US Board on Geographic Names (BGN), the authoritative source of place names as published in the Geographic Name Information System (GNIS), are attached as well. Finally, the coastline is used to separate coastal buffers from the land-based portions of jurisdictions. This feature layer is for public use.Related LayersThis dataset is part of a grouping of many datasets:Cities: Only the city boundaries and attributes, without any unincorporated areasWith Coastal BuffersWithout Coastal BuffersCounties: Full county boundaries and attributes, including all cities within as a single polygonWith Coastal BuffersWithout Coastal BuffersCities and Full Counties: A merge of the other two layers, so polygons overlap within city boundaries. Some customers require this behavior, so we provide it as a separate service.With Coastal Buffers (this dataset)Without Coastal BuffersPlace AbbreviationsUnincorporated Areas (Coming Soon)Census Designated Places (Coming Soon)Cartographic CoastlinePolygonLine source (Coming Soon)Working with Coastal BuffersThe dataset you are currently viewing includes the coastal buffers for cities and counties that have them in the authoritative source data from CDTFA. In the versions where they are included, they remain as a second polygon on cities or counties that have them, with all the same identifiers, and a value in the COASTAL field indicating if it"s an ocean or a bay buffer. If you wish to have a single polygon per jurisdiction that includes the coastal buffers, you can run a Dissolve on the version that has the coastal buffers on all the fields except COASTAL, Area_SqMi, Shape_Area, and Shape_Length to get a version with the correct identifiers.Point of ContactCalifornia Department of Technology, Office of Digital Services, odsdataservices@state.ca.govField and Abbreviation DefinitionsCOPRI: county number followed by the 3-digit city primary number used in the Board of Equalization"s 6-digit tax rate area numbering systemPlace Name: CDTFA incorporated (city) or county nameCounty: CDTFA county name. For counties, this will be the name of the polygon itself. For cities, it is the name of the county the city polygon is within.Legal Place Name: Board on Geographic Names authorized nomenclature for area names published in the Geographic Name Information SystemGNIS_ID: The numeric identifier from the Board on Geographic Names that can be used to join these boundaries to other datasets utilizing this identifier.GEOID: numeric geographic identifiers from the US Census Bureau Place Type: Board on Geographic Names authorized nomenclature for boundary type published in the Geographic Name Information SystemPlace Abbr: CalTrans Division of Local Assistance abbreviations of incorporated area namesCNTY Abbr: CalTrans Division of Local Assistance abbreviations of county namesArea_SqMi: The area of the administrative unit (city or county) in square miles, calculated in EPSG 3310 California Teale Albers.COASTAL: Indicates if the polygon is a coastal buffer. Null for land polygons. Additional values include "ocean" and "bay".GlobalID: While all of the layers we provide in this dataset include a GlobalID field with unique values, we do not recommend you make any use of it. The GlobalID field exists to support offline sync, but is not persistent, so data keyed to it will be orphaned at our next update. Use one of the other persistent identifiers, such as GNIS_ID or GEOID instead.AccuracyCDTFA"s source data notes the following about accuracy:City boundary changes and county boundary line adjustments filed with the Board of Equalization per Government Code 54900. This GIS layer contains the boundaries of the unincorporated county and incorporated cities within the state of California. The initial dataset was created in March of 2015 and was based on the State Board of Equalization tax rate area boundaries. As of April 1, 2024, the maintenance of this dataset is provided by the California Department of Tax and Fee Administration for the purpose of determining sales and use tax rates. The boundaries are continuously being revised to align with aerial imagery when areas of conflict are discovered between the original boundary provided by the California State Board of Equalization and the boundary made publicly available by local, state, and federal government. Some differences may occur between actual recorded boundaries and the boundaries used for sales and use tax purposes. The boundaries in this map are representations of taxing jurisdictions for the purpose of determining sales and use tax rates and should not be used to determine precise city or county boundary line locations. COUNTY = county name; CITY = city name or unincorporated territory; COPRI = county number followed by the 3-digit city primary number used in the California State Board of Equalization"s 6-digit tax rate area numbering system (for the purpose of this map, unincorporated areas are assigned 000 to indicate that the area is not within a city).Boundary ProcessingThese data make a structural change from the source data. While the full boundaries provided by CDTFA include coastal buffers of varying sizes, many users need boundaries to end at the shoreline of the ocean or a bay. As a result, after examining existing city and county boundary layers, these datasets provide a coastline cut generally along the ocean facing coastline. For county boundaries in northern California, the cut runs near the Golden Gate Bridge, while for cities, we cut along the bay shoreline and into the edge of the Delta at the boundaries of Solano, Contra Costa, and Sacramento counties.In the services linked above, the versions that include the coastal buffers contain them as a second (or third) polygon for the city or county, with the value in the COASTAL field set to whether it"s a bay or ocean polygon. These can be processed back into a single polygon by dissolving on all the fields you wish to keep, since the attributes, other than the COASTAL field and geometry attributes (like areas) remain the same between the polygons for this purpose.SliversIn cases where a city or county"s boundary ends near a coastline, our coastline data may cross back and forth many times while roughly paralleling the jurisdiction"s boundary, resulting in many polygon slivers. We post-process the data to remove these slivers using a city/county boundary priority algorithm. That is, when the data run parallel to each other, we discard the coastline cut and keep the CDTFA-provided boundary, even if it extends into the ocean a small amount. This processing supports consistent boundaries for Fort Bragg, Point Arena, San Francisco, Pacifica, Half Moon Bay, and Capitola, in addition to others. More information on this algorithm will be provided soon.Coastline CaveatsSome cities have buffers extending into water bodies that we do not cut at the shoreline. These include South Lake Tahoe and Folsom, which extend into neighboring lakes, and San Diego and surrounding cities that extend into San Diego Bay, which our shoreline encloses. If you have feedback on the exclusion of these items, or others, from the shoreline cuts, please reach out using the contact information above.Offline UseThis service is fully enabled for sync and export using Esri Field Maps or other similar tools. Importantly, the GlobalID field exists only to support that use case and should not be used for any other purpose (see note in field descriptions).Updates and Date of ProcessingConcurrent with CDTFA updates, approximately every two weeks, Last Processed: 12/17/2024 by Nick Santos using code path at https://github.com/CDT-ODS-DevSecOps/cdt-ods-gis-city-county/ at commit 0bf269d24464c14c9cf4f7dea876aa562984db63. It incorporates updates from CDTFA as of 12/12/2024. Future updates will include improvements to metadata and update frequency.

US Births by County and State

1985-2015 Aggregated Data

By data.world's Admin [source]

About this dataset

This dataset contains an aggregation of birth data from the United Statesbetween 1985 and 2015. It consists of information on mothers' locations by state (including District of Columbia) and county, as well as information such as the month they gave birth, and aggregates giving the sum of births during that month. This data has been provided by both the National Bureau for Economic Research and National Center for Health Statistics, whose shared mission is to understand how life works in order to aid individuals in making decisions about their health and wellbeing. This dataset provides valuable insight into population trends across time and location - for example, which states have higher or lower birthrates than others? Which counties experience dramatic fluctuations over time? Given its scope, this dataset could be used in a number of contexts--from epidemiology research to population forecasting. Be sure to check out our other datasets related to births while you're here!

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How to use the dataset

This dataset could be used to examine local trends in birth rates over time or analyze births at different geographical locations. In order to maximize your use of this dataset, it is important that you understand what information the various columns contain.

The main columns are: State (including District of Columbia), County (coded using the FIPS county code number), Month (numbering from 1 for January through 12 for December), Year (4-digit year) countyBirths (calculated sum of births that occurred to mothers living in a county for a given month) and stateBirths (calculated sum of births that occurred to mothers living in a state for a given month). These fields should provide enough information for you analyze trends across geographic locations both at monthly and yearly levels. You could also consider combining variables such as Year with State or Year with Month or any other grouping combinations depending on your analysis goal.

In addition, while all data were downloaded on April 5th 2017, it is worth noting that all sources used followed privacy guidelines as laid out by NCHC so individual births occurring after 2005 are not included due to geolocation concerns.
We hope you find this dataset useful and can benefit from its content! With proper understanding of what each field contains, we are confident you will gain valuable insights on birth rates across counties within the United States during this period

Research Ideas

• Establishing county-level trends in birth rates for the US over time.
• Analyzing the relationship between month of birth and health outcomes for US babies after they are born (e.g., infant mortality, neurological development, etc.).
• Comparing state/county-level differences in average numbers of twins born each year

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: allBirthData.csv | Column name | Description | |:-----------------|:-----------------------------------------------------------------------------------------------------------------| | State | The numerical order of the state where the mother lives. (Integer) | | Month | The month in which the birth took place. (Integer) | | Year | The year of the birth. (Integer) | | countyBirths | The calculated sum of births that occurred to mothers living in that county for that particular month. (Integer) | | stateBirths | The aggregate number at the level of entire states for any given month-year combination. (Integer) | | County | The county where the mother lives, coded using FIPS County Code. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit data.world's Admin.

Overview

The Daily Travel data and number of people staying home and not staying home are estimated for the Bureau of Transportation Statistics by the Maryland Transportation Institute and Center for Advanced Transportation Technology Laboratory at the University of Maryland.

The daily travel estimates are from a mobile device data panel from merged multiple data sources that address the geographic and temporal sample variation issues often observed in a single data source. The merged data panel only includes mobile devices whose anonymized location data meet a set of data quality standards, which further ensures the overall data quality and consistency. The data quality standards consider both temporal frequency and spatial accuracy of anonymized location point observations, temporal coverage and representativeness at the device level, spatial representativeness at the sample and county level, etc. A multi-level weighting method that employs both device and trip-level weights expands the sample to the underlying population at the county and state levels, before travel statistics are computed.

Notes

These data are experimental and may not meet all of our quality standards. Experimental data products are created using new data sources or methodologies that benefit data users in the absence of other relevant products. We are seeking feedback from data users and stakeholders on the quality and usefulness of these new products. Experimental data products that meet our quality standards and demonstrate sufficient user demand may enter regular production if resources permit.

Data in the charts and graphs above is updated weekly on Mondays. The data lags one week behind the current date.

Data analysis is conducted at the aggregate national, state, and county levels. To assure confidentiality and support data quality, no data are reported for a county if it has fewer than 50 devices in the sample on any given day.

Trips are defined as movements that include a stay of longer than 10 minutes at an anonymized location away from home. A movement with multiple stays of longer than 10 minutes before returning home is counted as multiple trips.

Key Features

1.Level : Indicates National, State, or County level metrics.

2.Date : The date when the data was recorded.

3.State FIPS : A two-digit code representing the FIPS state code.

4.State Postal Code : State postal code.

5.County FIPS : Five-digit FIPS county code.

6.County Name : County name.

7.Population Staying at Home : Number of residents staying at home, i.e., persons who make no trips with a trip end more than one mile away from home.

8.Population Not Staying at Home : Number of residents not staying at home.

9.Number of Trips : Number of trips made by residents, i.e., movements that include a stay of longer than 10 minutes at an anonymized location away from home.

10.Number of Trips <1 : Number of trips by residents shorter than one mile.

11.Number of Trips 1-3 : Number of trips by residents greater than one mile and shorter than 3 miles (1 ≤ trip distance < 3 miles).

12.Number of Trips 3-5 : Number of trips by residents greater than 3 miles and shorter than 5 miles (3 ≤ trip distance < 5 miles).

13.Number of Trips 5-10 : Number of trips by residents greater than 5 miles and shorter than 10 miles (5 ≤ trip distance < 10 miles).

14.Number of Trips 10-25 : Number of trips by residents greater than 10 miles and shorter than 25 miles (10 ≤ trip distance < 25 miles).

15.Number of Trips 25-50 : Number of trips by residents greater than 25 miles and shorter than 50 miles (25 ≤ trip distance < 50 miles).

16.Number of Trips 50-100 : Number of trips by residents greater than 50 miles and shorter than 100 miles (50 ≤ trip distance < 100 miles).

17.Number of Trips 100-250 : Number of trips by residents greater than 100 miles and shorter than 250 miles (100 ≤ trip distance < 250 miles).

18.Number of Trips 250-500 : Number of trips by residents greater than 250 miles and shorter than 500 miles (250 ≤ trip distance < 500 miles).

19.Number of Trips >=500 : Number of trips by residents greater than 500 miles (trip distance ≥ 500 miles).

20.Row ID : Unique row identifier.

21.Week : The week number corresponding to the recorded date.

22.Month : The month number corresponding to the recorded date.

If this was helpful, a vote is appreciated 😄!