A web map for the 2022 BBTN- Hispanic Population issue. This map displays the Hispanic predominance in Broward County by census tract.

Reflectance data from HyMap™ were processed using the Material Identification and Characterization Algorithm (MICA), a module of the USGS PRISM (Processing Routines in IDL for Spectroscopic Measurements) software (Kokaly, 2011), programmed in Interactive Data Language (IDL; Harris Geospatial Solutions, Broomfield, Colorado). The HyMap reflectance data are provided and described in this data release. MICA identifies the spectrally predominant mineral(s) in each pixel of imaging spectrometer data by comparing continuum-removed spectral features in the pixel’s reflectance spectrum to continuum-removed absorption features in reference spectra of minerals, vegetation, water, and other materials. Linear continuum removal is a technique to isolate an absorption feature from background spectral variations (Clark and Roush, 1984). Following continuum removal of a spectral feature in a reference spectrum and the corresponding channels in an imaging spectrometer pixel, the coefficient of determination (r2) of a linear regression of these continuum-removed values is used as the metric to judge the degree of match (or fit) between the unknown and reference spectra. MICA analysis is controlled by a command file, which lists the reference spectra for comparison to imaging spectrometer pixel spectra, the wavelength regions for continuum removal and spectral feature comparison, and other parameters (see Kokaly, 2011). For each pixel, the reference spectrum with the highest fit value identifies the predominant mineral class. The reference spectra used in this MICA analysis are available to the public in the USGS spectral library (Kokaly and others, 2017). The MICA command file used in this study was adapted from that used to process HyMap data covering Afghanistan (Kokaly and others, 2013). The MICA command file is provided in this data release and also in the digital appendix of Graham and others (2018).

Approximately 1,900 square kilometers of imagery were collected from July 14 to July 21, 2014 using a HyMap™ sensor (Cocks and others, 1998) mounted on a modified Piper Navajo aircraft. The survey area covered parts of the Wrangell and Nutzotin Mountains in the eastern Alaska Range near Nabesna, Alaska. The aircraft was flown at an altitude of approximately 5,050 meters (m) (3,480 m above the mean ground surface elevation of 1570 m) resulting in average ground spatial resolution of 6.7 m. HyMap measured reflected sunlight in 126 narrow channels that cover the wavelength region of 455 to 2,483 nanometers (nm). Data were delivered by the operators of the sensor (HyVista Corp., Australia) in units of radiance (Kokaly and others, 2017). Radiance data were converted to reflectance with procedures adapted from Kokaly and others (2013). They are described and documented in this data release. Reflectance data from HyMap were processed using the Material Identification and Characterization Algorithm (MICA), a module of the USGS PRISM (Processing Routines in IDL for Spectroscopic Measurements) software (Kokaly, 2011), programmed in Interactive Data Language (IDL; Harris Geospatial Solutions, Broomfield, Colorado). MICA identifies the spectrally predominant mineral(s) in each pixel of imaging spectrometer data by comparing continuum-removed spectral features in the pixel’s reflectance spectrum to continuum-removed absorption features in reference spectra of minerals, vegetation, water, and other materials. For each pixel, the reference spectrum with the highest fit value identifies the predominant mineral class. A map of the wavelength position of the white mica 2,200 nm Al-OH absorption feature, elsewhere referred to more concisely as white mica, was also compiled. White mica wavelength position was computed for each pixel with spectrally predominant muscovite or illite. The computation was made using a function of the USGS PRISM software (Kokaly, 2011). The white mica wavelength values were output as a classification image, with classes in 1 nm increments. Each of these three datasets (reflectance, mineral predominance, and white mica wavelength position) are documented and described as part of this U.S. Geological Survey data release.

This map answers the question "What is the most common, or predominant, education level for people in this area?" The map shows predominant educational attainment in each census tract. Darker colors indicate a greater gap between the predominant group and the next largest group.The U.S. Census Bureau asks citizens to indicate how far they went in formal education. The database includes seven different columns, each representing a count of population by that education level. A simple routine in compares the seven columns of information, and finds which one has the highest value, writing that to a string field. Each tract's transparency is set by a transparency field added to the data.Predominance maps can be created in ArcGIS Online by adding two fields, calculating their values, and setting up the renderer based on those two fields. See this blog by Jim Herries for details on how to create a predominance map in ArcGIS Online from any feature layer.See this GitHub repo by Jennifer Bell for a script you can run in ArcMap as a script tool, to calculate predominance for any columns of data you have.

This map answers the question "What is the most common, or predominant, education level for people in this area?" The map shows predominant educational attainment in each census tract. Darker colors indicate a greater gap between the predominant group and the next largest group.The U.S. Census Bureau asks citizens to indicate how far they went in formal education. The database includes seven different columns, each representing a count of population by that education level. A simple routine in compares the seven columns of information, and finds which one has the highest value, writing that to a string field. Each tract's transparency is set by a transparency field added to the data.Predominance maps can be created in ArcGIS Online by adding two fields, calculating their values, and setting up the renderer based on those two fields. See this blog by Jim Herries for details on how to create a predominance map in ArcGIS Online from any feature layer.See this GitHub repo by Jennifer Bell for a script you can run in ArcMap as a script tool, to calculate predominance for any columns of data you have.

Political leanings are represented in this map.

A web mapping application that displays the overall Hispanic Latino predominance map and by origin groups for Census Tracts in Broward County.

Data Driven Detroit calculated the predominant race (if any) for census tracts in the Detroit, Tri-County region. The data come from the 2010 Census PL file. The census table splits out races by hispanic and non-hispanic ethnicity. For the purposes of this feature, White, Black, Hispanic or no predominant race were used as the possible categories. If there was no race or ethnicnicity over 50% of the population, then there is no predominant race.

This map shows the predominant household income by county, tract, and block group in the US in 2018. County is symbolized using color for the predominant income range. Tract and block group use color and size to show the predominant income range and count of total households. There are 9 income ranges:Household Income less than $15,000Household Income $15,000-$24,999Household Income $25,000-$34,999Household Income $35,000-$49,999Household Income $50,000-$74,999Household Income $75,000-$99,999Household Income $100,000-$149,999Household Income $150,000-$199,999Household Income $200,000 or greaterThe source of data is Esri's 2018 Demographic estimates. For more information about Esri's demographic data, visit the Updated Demographics documentation.

Output of the 2016 EUSeaMap broad-scale predictive model, produced by EMODnet Seabed Habitats and aggregated into the Benthic Broad Habitat Types of the Marine Strategy Framework Directive (as defined in the Commission Decision 17 May 2017).

The extent of the mapped area includes the Mediterranean Sea, Black Sea, Baltic Sea, and areas of the North Eastern Atlantic extending from the Canary Islands in the south to Norway and Iceland in the North.

The map was produced using a "top-down" modelling approach using classified habitat descriptors to determine a final output habitat.

Habitat descriptors differ per region but include: Biological zone Energy class Oxygen regime Salinity regime Seabed Substrate Riverine input

Habitat descriptors (excepting Substrate) are calculated using underlying physical data and thresholds derived from statistical analyses or expert judgement on known conditions.

The model is produced in Arc Model Builder (10.1). For more information on the modelling process please read the EMODnet Seabed Habitats Technical report (See Online Resources)

The model was created using raster input layers with a cell size of 0.002dd (roughly 250 meters). The model includes the sublittoral zone only; due to the high variability of the littoral zone, a lack of detailed substrate data and the resolution of the model, it is difficult to predict littoral habitats at this scale.

For details on methodology see: Populus J. And Vasquez M. (Eds), 2017. EUSeaMap, a European broad-scale seabed habitat map. Ifremer Available from: http://archimer.ifremer.fr/doc/00388/49975/

This map shows what country naturalized US citizens were born in using the Charts & Size and Predominance mapping styles. The area with the highest amount of foreign born naturalized US citizens is shown by color. Areas are: Africa, Asia, Europe, Latin America, Northern America, and Oceania.Data are available in 5-year estimates at the state, county, and tract level for the entire US.The data in this map contains the most recent American Community Survey (ACS) data from the U.S. Census Bureau. The Living Atlas layer in this map updates annually when the Census releases their new figures. To learn more, visit this FAQ, or visit the ACS website. Web Map originally owned by Summers Cleary

This layer shows population broken down by race and Hispanic origin. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the predominant race living within an area, and the total population in that area. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B03002Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This multi-scale map shows the predominant (most numerous) race/ethnicity living within an area. Map opens at the state level, centered on the lower 48 states. Data is from U.S. Census Bureau's 2020 PL 94-171 data for state, county, tract, block group, and block.The map's colors indicate which of the eight race/ethnicity categories have the highest total count.Race and ethnicity highlights from the U.S. Census Bureau:White population remained the largest race or ethnicity group in the United States, with 204.3 million people identifying as White alone. Overall, 235.4 million people reported White alone or in combination with another group. However, the White alone population decreased by 8.6% since 2010.Two or More Races population (also referred to as the Multiracial population) has changed considerably since 2010. The Multiracial population was measured at 9 million people in 2010 and is now 33.8 million people in 2020, a 276% increase.“In combination” multiracial populations for all race groups accounted for most of the overall changes in each racial category.All of the race alone or in combination groups experienced increases. The Some Other Race alone or in combination group (49.9 million) increased 129%, surpassing the Black or African American population (46.9 million) as the second-largest race alone or in combination group.The next largest racial populations were the Asian alone or in combination group (24 million), the American Indian and Alaska Native alone or in combination group (9.7 million), and the Native Hawaiian and Other Pacific Islander alone or in combination group (1.6 million).Hispanic or Latino population, which includes people of any race, was 62.1 million in 2020. Hispanic or Latino population grew 23%, while the population that was not of Hispanic or Latino origin grew 4.3% since 2010.View more 2020 Census statistics highlights on race and ethnicity.

This layer shows the predominant occupation by tract in 2018. Strength is shown by transparency, and number of people in labor force is shown by size.Data in map come from Esri's demographic data. The occupation fields correspond to the Bureau of Labor Statistics' Standard Occupation Classification (SOC) codes. For additional context, see the the story map that uses this feature layer, as well as the map tour.

Classified seabed substrate types (Polygon layer) for European seas. Produced by EMODnet Seabed Habitats as an input layer for the 2016 EUSeaMap broad-scale habitat model and re-used for EUSeaMap 2019, based on EMODnet Geology seabed substrate products. The extent of the mapped area includes the Mediterranean Sea, Black Sea, Baltic Sea, and areas of the North Eastern Atlantic extending from the Canary Islands in the south to Norway in the North. The layer of seabed substrate was produced using data from EMODnet geology 1:250.000 and 1:1M seabed substrate maps, and integrated with extra substrate feature relevant for habitat mapping (seagrass beds, for example). The Folk 5 classification of substrate is adopted because it is compatible with the EUNIS classification of habitats used in EUSeaMap 2016.

For details on methodology see Section 2 of: Populus J. et al 2017. EUSeaMap, a European broad-scale seabed habitat map. Ifremer. http://doi.org/10.13155/49975

Classified seabed substrate types for European seas. Produced by EMODnet Seabed Habitats as an input layer for the 2019 EUSeaMap broad-scale habitat model, based on EMODnet Geology seabed substrate products. The extent of the mapped area includes the Mediterranean Sea, Black Sea, Baltic Sea, and areas of the North Eastern Atlantic extending from the Canary Islands in the south to the Barents Sea in the north. The layer of seabed substrate was produced using data from EMODnet geology 1:100K, 1:250K and 1:1M seabed substrate maps, and integrated with extra substrate feature relevant for habitat mapping (seagrass beds, for example). The Folk 5 classification of substrate is adopted because it is compatible with the EUNIS classification of habitats used in EUSeaMap 2019.

Detailed information on the modelling process for the 2016 is found in the EMODnet Seabed Habitats technical report and its appendices (Populus et al, 2017, link in Resources). We are working on an updated report for the 2019 version.

This map shows what country naturalized US citizens were born in using the predominance mapping style. The area with the highest amount of foreign born naturalized US citizens is shown by color. Areas are: Africa, Asia, Europe, Latin America, Northern America, and Oceania.Data is available in 5-year estimates at the state, county, and tract level for the entire US.The data in this map contains the most recent American Community Survey (ACS) data from the U.S. Census Bureau. The Living Atlas layer in this map updates annually when the Census releases their new figures. To learn more, visit this FAQ, or visit the ACS website.

Energy class layer produced by EMODnet Seabed Habitats as an input layer for the 2016 EUSeaMap broad-scale habitat model. The extent of the mapped area includes the Baltic Sea, and areas of the North Eastern Atlantic and Arctic extending from the Canary Islands in the south to Norway in the North. The map of energy classes was produced using underlying wave and current data and thresholds derived from statistical analyses or expert judgement on known conditions.

Detailed information on the modelling process is found in the EMODnet Seabed Habitats technical report and its appendices (Populus et al, 2017, link in Resources).

This map shows the predominant highest level of education for the population age 25+ in the United States. This is shown by county and and census tracts throughout the US. The categories are grouped as:Less than High SchoolHigh SchoolAssociate's DegreeSome CollegeBachelor's Degree or HigherThe data shown is current-year American Community Survey (ACS) data from the US Census. The data is updated each year when the ACS releases its new 5-year estimates. For more information about this data, visit this page.To learn more about when the ACS releases data updates, click here.

Energy class layer produced by EMODnet Seabed Habitats as an input layer for the 2019 EUSeaMap broad-scale habitat model. The extent of the mapped area includes the Baltic Sea, and areas of the North Eastern Atlantic and Arctic extending from the Canary Islands in the south to Norway in the North. The map of energy classes was produced using underlying wave and current data and thresholds derived from statistical analyses or expert judgement on known conditions.

Detailed information on the modelling process for the 2016 is found in the EMODnet Seabed Habitats technical report and its appendices (Populus et al, 2017, link in Resources). We are working on an updated report for the 2019 version.