Census Year 1930 Census Tracts. The dataset contains polygons representing CY 1930 census tracts, created as part of the D.C. Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO) and participating D.C. government agencies. Census tracts were identified from maps provided by the U.S. Census Bureau and the D.C. Office of Planning. The tract polygons were created by selecting street arcs from the WGIS planimetric street centerlines. Where necessary, polygons were also heads-up digitized from 1995/1999 orthophotographs. METADATA CONTENT IS IN PROCESS OF VALIDATION AND SUBJECT TO CHANGE.

This map was produced for the Murfreesboro, Tennessee Urban Tree Canopy Assessment project. Data were derived from USDA's National Agriculture Imagery Program (NAIP) 2021 imagery. Results were aggregated to various target geographies. Basemap provided by ESRI. June 10, 2022 04_UTCChangebyCensusTracts map.Note: Percentages are based on land area only.UTC Change Metrics by Census Tracts: Murfreesboro Census Tract Total Area (Acres) Land Area (Acres) UTC 2012 (Acres) UTC 2012 (%) UTC 2021 (Acres) UTC 2021 (%) UTC Change 2012-2021 (Acres) Raw Change (%) Relative Change (%)

47-149-040309 459 452 260 57% 221 49% -39 -9% -15%

47-149-040310 49 49 3 6% 3 5% 0 -1% -16%

47-149-040702 144 144 75 52% 45 31% -30 -21% -40%

47-149-040703 1,048 1,037 572 55% 460 44% -113 -11% -20%

47-149-040807 605 604 66 11% 64 11% -3 0% -4%

47-149-040808 2,321 2,319 512 22% 449 19% -63 -3% -12%

47-149-040809 798 784 270 34% 271 35% 1 0% 0%

47-149-040901 2,552 2,507 488 19% 469 19% -19 -1% -4%

47-149-040904 1,193 1,179 384 33% 312 26% -72 -6% -19%

47-149-040906 870 867 151 17% 171 20% 19 2% 13%

47-149-040907 1,530 1,525 331 22% 239 16% -93 -6% -28%

47-149-040908 811 810 93 11% 131 16% 38 5% 41%

47-149-040909 601 601 62 10% 70 12% 8 1% 13%

47-149-040910 635 623 80 13% 110 18% 30 5% 37%

47-149-040911 1,468 1,460 213 15% 168 12% -45 -3% -21%

47-149-041000 1,946 1,934 336 17% 350 18% 14 1% 4%

47-149-041102 29 29 7 23% 5 19% -1 -5% -20%

47-149-041103 1,045 1,043 228 22% 209 20% -19 -2% -8%

47-149-041104 1,007 1,004 155 15% 175 17% 20 2% 13%

47-149-041201 2,490 2,462 657 27% 540 22% -117 -5% -18%

47-149-041202 1,341 1,340 354 26% 308 23% -46 -3% -13%

47-149-041301 1,910 1,901 556 29% 579 30% 22 1% 4%

47-149-041302 1,781 1,772 519 29% 532 30% 13 1% 2%

47-149-041401 1,403 1,403 355 25% 346 25% -9 -1% -3%

47-149-041404 447 446 142 32% 139 31% -3 -1% -2%

47-149-041405 182 182 59 33% 57 32% -2 -1% -3%

47-149-041406 568 567 122 22% 133 23% 10 2% 9%

47-149-041407 572 571 103 18% 120 21% 17 3% 16%

47-149-041500 450 447 47 10% 59 13% 12 3% 26%

47-149-041601 535 535 172 32% 159 30% -13 -3% -8%

47-149-041602 504 504 156 31% 137 27% -19 -4% -12%

47-149-041700 1,101 1,098 333 30% 306 28% -27 -2% -8%

47-149-041800 4,012 3,952 1,069 27% 990 25% -80 -2% -7%

47-149-041900 528 525 164 31% 152 29% -12 -2% -8%

47-149-042000 1,256 1,216 391 32% 366 30% -26 -2% -7%

47-149-042101 1,195 1,194 388 33% 370 31% -18 -2% -5%

47-149-042102 727 727 250 34% 231 32% -19 -3% -8%

47-149-042302 1,135 1,106 277 25% 220 20% -57 -5% -21%

Totals 41,248 40,919 10,402 25% 9,661 24% -741 -2% -7%

Map SummaryAbout this map:This web map shows the 2020 census boundaries that lie within the jurisdiction of the city of Rochester, NY, based on the 2020 boundaries established by the U.S. Census Bureau. Census tracts are small, relatively permanent statistical subdivisions of a county that are uniquely numbered with a numeric code. In this feature layer, you can identify the tracts by their FIPS (Federal Information Processing Standards) code. Nationally, census tracts are drawn to average about 4,000 inhabitants living within their boundaries. The U.S. Census Bureau reviews the census tract boundaries every 10 years (in conjunction with the decennial census) and may split or merge them, depending on population change: when the Census finds that a tract has grown to have more than 8,000 inhabitants, that tract is split into two or more tracts; tracts that have shrunk in population to less than 1,200 people are merged within a neighboring tract. This review and revision process also may make adjustments of boundaries due to changes in boundaries of governmental jurisdictions, changes to more accurately place boundaries relative to visible features, or decisions by courts.Census tracts are subdivided into block groups that contain between 600 and 3,000 inhabitants. For more information on census tracts and block groups, please see the U.S. Census Bureau's website.To view the data dictionary, select the desired layer of the map in the "Layers" section below for more information.

This layer presents the 2020 U.S. Census Tract boundaries of the United States in the 50 states and the District of Columbia. This layer is updated annually. The geography is sourced from U.S. Census Bureau 2020 TIGER FGDB (National Sub-State) and edited using TIGER Hydrography to add a detailed coastline for cartographic purposes. Attribute fields include 2020 total population from the U.S. Census Public Law 94 data.This ready-to-use layer can be used in ArcGIS Pro and in ArcGIS Online and its configurable apps, dashboards, StoryMaps, custom apps, and mobile apps. The data can also be exported for offline workflows. Cite the 'U.S. Census Bureau' when using this data.

This application allows members of the Department of Code Compliance to track their progress and the survey the City of Dallas. Users are able to edit a copy of the census tract data and record if they have completed their work in that tract. The map also includes data on Code Compliance service areas and subdistricts. This application uses the map: https://dallasgis.maps.arcgis.com/home/item.html?id=f1de9a683ab44344b08ead11acbfa480It Includes the following layers:https://services2.arcgis.com/rwnOSbfKSwyTBcwN/arcgis/rest/services/CensusTract2010Review/FeatureServerhttps://services2.arcgis.com/rwnOSbfKSwyTBcwN/arcgis/rest/services/CityLimits/FeatureServerhttps://services2.arcgis.com/rwnOSbfKSwyTBcwN/arcgis/rest/services/CRMHostedLayers/FeatureServer/20

Net change in housing units arising from new buildings, demolitions, or alterations for NYC Census Tracts since 2010. The NYC Department of City Planning's (DCP) Housing Database provide the 2010 census count of housing units, the net change in Class A housing units since the census, and the count of units pending completion for commonly used political and statistical boundaries. These tables are aggregated from the DCP Housing Database, which is derived from Department of Buildings (DOB)-approved housing construction and demolition jobs filed or completed in NYC since January 1, 2010. Net housing unit change is calculated as the sum of all three construction job types that add or remove residential units: new buildings, major alterations, and demolitions, and can be used to determine the change in legal housing units across time and space. All previously released versions of this data are available at BYTES of the BIG APPLE - Archive.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The 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 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

This dataset contains polygons that represent the boundaries of statistical neighborhoods as defined by the DC Department of Health (DC Health). DC Health delineates statistical neighborhoods to facilitate small-area analyses and visualization of health, economic, social, and other indicators to display and uncover disparate outcomes among populations across the city. The neighborhoods are also used to determine eligibility for some health services programs and support research by various entities within and outside of government. DC Health Planning Neighborhood boundaries follow census tract 2010 lines defined by the US Census Bureau. Each neighborhood is a group of between one and seven different, contiguous census tracts. This allows for easier comparison to Census data and calculation of rates per population (including estimates from the American Community Survey and Annual Population Estimates). These do not reflect precise neighborhood locations and do not necessarily include all commonly-used neighborhood designations. There is no formal set of standards that describes which neighborhoods are included in this dataset. Note that the District of Columbia does not have official neighborhood boundaries. Origin of boundaries: each neighborhood is a group of between one and seven different, contiguous census tracts. They were originally determined in 2015 as part of an analytical research project with technical assistance from the Centers for Disease Control and Prevention (CDC) and the Council for State and Territorial Epidemiologists (CSTE) to define small area estimates of life expectancy. Census tracts were grouped roughly following the Office of Planning Neighborhood Cluster boundaries, where possible, and were made just large enough to achieve standard errors of less than 2 for each neighborhood's calculation of life expectancy. The resulting neighborhoods were used in the DC Health Equity Report (2018) with updated names. HPNs were modified slightly in 2019, incorporating one census tract that was consistently suppressed due to low numbers into a neighboring HPN (Lincoln Park incorporated into Capitol Hill). Demographic information were analyzed to identify the bordering group with the most similarities to the single census tract. A second change split a neighborhood (GWU/National Mall) into two to facilitate separate analysis.

Census tracts are created by the U.S. Census Bureau to be small, relatively permanent statistical subdivisions of a county. Census tracts average about 4,000 inhabitants: minimum population –1,200 and maximum population –8,000. Census tracts are split or merged every 10 years, depending on population change, with local feedback through the Participant Statistical Areas Program (PSAP).

Qualified Census Tract geometries within Baltimore City Limits. Based on data from HUD published September 2024.Change Log2022-2-15:- Added FY2022 data- Metadata added- Columns renamed to a standard format- Agency names reformatted with Workday conventions2024-8-27:update the dataset and metadata to reflect the current data and descriptionData Dictionaryfield_namedescriptiondata_typerange_of_possible_valuesexample_valuessearchableCensus Tract 2010 The ID of the US census tract from 2010 Census results. Only Qualified Census tracts are includedTextIDs of QCT's in Baltimore city range from 24510030100 to 24510280500, but not by regular intervals since only tracts designated QCT are listed. The values are not integers, they are numerical IDs.24510070200NogeometryMulti-polygon shapes for each census tractThese are shape polygons, thus don't have a single value or expected rangeNo

This dataset contains model-based census tract level estimates in GIS-friendly format. PLACES covers the entire United States—50 states and the District of Columbia—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2022 or 2021 data, Census Bureau 2010 population estimates, and American Community Survey (ACS) 2015–2019 estimates. The 2024 release uses 2022 BRFSS data for 36 measures and 2021 BRFSS data for 4 measures (high blood pressure, high cholesterol, cholesterol screening, and taking medicine for high blood pressure control among those with high blood pressure) that the survey collects data on every other year. These data can be joined with the Census tract 2022 boundary file in a GIS system to produce maps for 40 measures at the census tract level. An ArcGIS Online feature service is also available for users to make maps online or to add data to desktop GIS software. https://cdcarcgis.maps.arcgis.com/home/item.html?id=3b7221d4e47740cab9235b839fa55cd7

This interactive map is updated continually and shows more than 800 variables at the neighborhood level (using the 2010 Census Tract geography boundaries). The data comes from various sources such as the U.S. Census Bureau’s 2010 Decennial Census, the U.S. Census Bureau’s latest American Community Survey five-year product (currently 2011-2015), Georgia Department of Public Health, and the Georgia Department of Labor. The map uses the Weave interactive platform, which allows the user to select data variables and customize related data visualizations (charts/graphs).

Thematic map showing the percent population change by census tract between 2011 and 2016 for the Saint John census metropolitan area.

This map uses an archive of Version 1.0 of the CEJST data as a fully functional GIS layer. See an archive of the latest version of the CEJST tool using Version 2.0 of the data released in December 2024 here.This map assesses and identifies communities that are disadvantaged according to updated Justice40 Initiative criteria. Census tracts in the U.S. and its territories that meet the Version 1.0 criteria are shaded in solid blue colors.Details of the assessment are provided in the popup for every census tract in the United States and its territories American Samoa, Guam, the Northern Mariana Islands, Puerto Rico, and the U.S. Virgin Islands. This map uses 2010 census tracts from Version 1.0 of the source data downloaded November 22, 2022.Use this map in your dashboards, apps, or storymaps to help plan for grant applications, to perform spatial analysis, and to create informative dashboards and web applications. See this blog post for more information.If you have been using a previous version of the Justice40 data, please know that this Version 1.0 differs in many ways. See the updated Justice40 Initiative criteria for current specifics. From the source:This data "highlights disadvantaged census tracts across all 50 states, the District of Columbia, and the U.S. territories. Communities are considered disadvantaged:If they are in census tracts that meet the thresholds for at least one of the tool’s categories of burden, orIf they are on land within the boundaries of Federally Recognized TribesCategories of BurdensThe tool uses datasets as indicators of burdens. The burdens are organized into categories. A community is highlighted as disadvantaged on the CEJST map if it is in a census tract that is (1) at or above the threshold for one or more environmental, climate, or other burdens, and (2) at or above the threshold for an associated socioeconomic burden.In addition, a census tract that is completely surrounded by disadvantaged communities and is at or above the 50% percentile for low income is also considered disadvantaged.Census tracts are small units of geography. Census tract boundaries for statistical areas are determined by the U.S. Census Bureau once every ten years. The tool utilizes the census tract boundaries from 2010. This was chosen because many of the data sources in the tool currently use the 2010 census boundaries."PurposeThe goal of the Justice40 Initiative is to provide 40 percent of the overall benefits of certain Federal investments in [eight] key areas to disadvantaged communities. These [eight] key areas are: climate change, clean energy and energy efficiency, clean transit, affordable and sustainable housing, training and workforce development, the remediation and reduction of legacy pollution, [health burdens] and the development of critical clean water infrastructure." Source: Climate and Economic Justice Screening tool"Sec. 219. Policy. To secure an equitable economic future, the United States must ensure that environmental and economic justice are key considerations in how we govern. That means investing and building a clean energy economy that creates well‑paying union jobs, turning disadvantaged communities — historically marginalized and overburdened — into healthy, thriving communities, and undertaking robust actions to mitigate climate change while preparing for the impacts of climate change across rural, urban, and Tribal areas. Agencies shall make achieving environmental justice part of their missions by developing programs, policies, and activities to address the disproportionately high and adverse human health, environmental, climate-related and other cumulative impacts on disadvantaged communities, as well as the accompanying economic challenges of such impacts. It is therefore the policy of my Administration to secure environmental justice and spur economic opportunity for disadvantaged communities that have been historically marginalized and overburdened by pollution and underinvestment in housing, transportation, water and wastewater infrastructure, and health care." Source: Executive Order on Tackling the Climate Crisis at Home and AbroadUse of this Data"The pilot identifies 21 priority programs to immediately begin enhancing benefits for disadvantaged communities. These priority programs will provide a blueprint for other agencies to help inform their work to implement the Justice40 Initiative across government." Source: The Path to Achieving Justice 40Symbology updated 2/19/2023 to show additional tracts whose overlap with tribal lands is greater than 0% but less than 1%, to be designated as "Partially Disadvantaged" alongside tracts whose overlap with tribal lands is 1% or more.

2020 census tract data for the City of Redding, California.This layer presents the USA 2020 Census Tract boundaries of the United States in the 50 states and the District of Columbia. It is updated annually as Tract boundaries change. The geography is sourced from US Census Bureau 2020 TIGER FGDB (National Sub-State). Geography last updated May 2022.Attribute fields include 2020 total population from the US Census PL94 data.

This data layer references data from a high-resolution tree canopy change-detection layer for Seattle, Washington. Tree canopy change was mapped by using remotely sensed data from two time periods (2016 and 2021). Tree canopy was assigned to three classes: 1) no change, 2) gain, and 3) loss. No change represents tree canopy that remained the same from one time period to the next. Gain represents tree canopy that increased or was newly added, from one time period to the next. Loss represents the tree canopy that was removed from one time period to the next. Mapping was carried out using an approach that integrated automated feature extraction with manual edits. Care was taken to ensure that changes to the tree canopy were due to actual change in the land cover as opposed to differences in the remotely sensed data stemming from lighting conditions or image parallax. Direct comparison was possible because land-cover maps from both time periods were created using object-based image analysis (OBIA) and included similar source datasets (LiDAR-derived surface models, multispectral imagery, and thematic GIS inputs). OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to ensure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subjected to manual review and correction.University of Vermont Spatial Analysis LaboratoryThe dataset covers the following tree canopy categories:Environmental Justice Priority AreasCensus tracts composite / quintileExisting tree canopy percentage & environmental justice priority levelExisting tree canopyPossible tree canopyRelative percentage changeFor more information, please see the 2021 Tree Canopy Assessment.

This layer was developed by the Research & Analytics Division of the Atlanta Regional Commission using data from the U.S. Census Bureau.

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 2014-2018). 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 a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

s

Significance flag for change: 1 = statistically significant with a 90% Confidence Interval, 0 = not statistically significant, blank = cannot be computed

Suffixes:

_e18

Estimate from 2014-18 ACS

_m18

Margin of Error from 2014-18 ACS

_00_v18

Decennial 2000 in 2018 geography boundary

_00_18

Change, 2000-18

_e10_v18

Estimate from 2006-10 ACS in 2018 geography boundary

_m10_v18

Margin of Error from 2006-10 ACS in 2018 geography boundary

_e10_18

Change, 2010-18

This web map indicates the annual compound rate of total population change in the United States from 2000 to 2010. Total Population is the total number of residents in an area. Residence refers to the "usual place" where a person lives. Total Population for 2000 is from the U.S. Census 2000. The 2010 Total Population variable is estimated by Esri's proven annual demographic update methodology that blends GIS with statistical technology and a unique combination of data sources.The map is symbolized so that you can easily distinguish areas of population growth (i.e. shades of green) from areas of population decline (i.e. shades of red). It uses a 3 D effect to further emphasize those trends. The map reveals interesting patterns of recent population change in various regions and communities across the United States.The map shows population change at the County and Census Tract levels. The geography depicts Counties at 25m to 750k scale, Census Tracts at 750k to 100k scale.Esri's Updated Demographics (2010/2015) – Population, age, income, sex, race, marital status and other variables are among the variables included in the database. Each year, Esri's data development team employs its proven methodologies to update more than 2,000 demographic variables for a variety of geographies. See Updated Demographics for more information.

description: This thematic map indicates the annual compound rate of total population change in the United States from 1990 to 2000. Total Population is the total number of residents in an area. Residence refers to the usual place where a person lives. Total Population for 2000 is from the U.S. Census 2000. Total Population for 1990 is from the 1990 U.S. Census adjusted to 2000 boundaries. The geography depicts states at greater than 25m scale, counties at 1m to 25m scale, Census Tracts at 250k to 1m scale, and Census Block Groups at less than 250k scale. The map has been designed to be displayed with semi-transparency of about 50% for overlay on other base maps, which is reflected in the legend for the map. For more information on this map, including the terms of use, visit us online.; abstract: This thematic map indicates the annual compound rate of total population change in the United States from 1990 to 2000. Total Population is the total number of residents in an area. Residence refers to the usual place where a person lives. Total Population for 2000 is from the U.S. Census 2000. Total Population for 1990 is from the 1990 U.S. Census adjusted to 2000 boundaries. The geography depicts states at greater than 25m scale, counties at 1m to 25m scale, Census Tracts at 250k to 1m scale, and Census Block Groups at less than 250k scale. The map has been designed to be displayed with semi-transparency of about 50% for overlay on other base maps, which is reflected in the legend for the map. For more information on this map, visit us online at http://goto.arcgisonline.com/maps/Demographics/USA_1990-2000_Population_ChangedemographicsUSA 1990-2000 Population ChangeBlock GroupsTractsCountiesStates

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The 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 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data