In this dataset we present two maps that estimate the location and population served by domestic wells in the contiguous United States. The first methodology, called the “Block Group Method” or BGM, builds upon the original block-group data from the 1990 census (the last time the U.S. Census queried the population regarding their source of water) by incorporating higher resolution census block data. The second methodology, called the “Road-Enhanced Method” or REM, refines the locations by using a buffer expansion and shrinkage technique along roadways to define areas where domestic wells exist. The fundamental assumption with this method is that houses (and therefore domestic wells) are located near a named road. The results are presented as two nationally consistent domestic-well population datasets. While both methods can be considered valid, the REM map is more precise in locating domestic wells; the REM map had a smaller amount of spatial bias (nearly equal vs biased in type 1 error), total error (10.9% vs 23.7%,), and distance error (2.0 km vs 2.7 km), when comparing the REM and BGM maps to a California calibration map. However, the BGM map is more inclusive of all potential locations for domestic wells. The primary difference in the BGM and the REM is the mapping of low density areas. The REM has a 57% reduction in areas mapped as low density (populations greater than 0 but less than 1 person per km), concentrating populations into denser regions. Therefore, if one is trying to capture all of the potential areas of domestic-well usage, then the BGM map may be more applicable. If location is more imperative, then the REM map is better at identifying areas of the landscape with the highest probability of finding a domestic well. Depending on the purpose of a study, a combination of both maps can be used. For space concerns, the datasets have been divided into two separate geodatabases. The BGM map geodatabase and the REM map database.

Homeless and domestic violence shelters in the northeast Texas region. Counties include Bowie, Cass, Delta, Franklin, Hopkins, Lamar, Morris, Red River, and Titus.If you are in an emergency or life-threatening situation, call 9-1-1Domestic Violence ResourcesTo contact the National Domestic Violence Hotline, call 1-800-799-7233 or text "START" to 88788 using your mobile device.To visit the National Domestic Violence Hotline website, go to thehotline.orgTexas Social ServicesTo find housing, healthcare information, food, and other social services, call 2-1-1 or 1-877-541-7905To visit the Texas Health and Human Services and 2-1-1 website, go to 211texas.orgArkansas Social ServicesTo find housing, healthcare information, food, and other social services in Arkansas, call 2-1-1To visit the Arkansas 2-1-1 website, go to arkansas211.orgATCOG Housing ProgramTo contact the ATCOG Housing Program specializing in the Section 8 Rental Assistance Program, call 903-832-8636 or visit atcog.org/housingReference in this site to any specific commercial product, process, service, or the use of any trade, firm, or corporation name is for the information and convenience of the public, and does not constitute endorsement, recommendation, or favoring by the Ark-Tex Council of Governments.For questions, problems, or more information, contact gis@atcog.orghttps://atcog.org/

Migration Summary (2011-2020) Infographic to be embedded in 2022 BBTN Migration Story Map. Data for maps and tables was retrieved from: Internal Revenue Service, Statistics of Income Division Migration Data, 2011 - 2020.

The Robot-at-Home dataset (Robot@Home, paper here) is a collection of raw and processed data from five domestic settings compiled by a mobile robot equipped with 4 RGB-D cameras and a 2D laser scanner. Its main purpose is to serve as a testbed for semantic mapping algorithms through the categorization of objects and/or rooms.

This dataset is unique in three aspects:

• The provided data were captured with a rig of 4 RGB-D sensors with an overall field of view of 180°H. and 58°V., and with a 2D laser scanner.
• It comprises diverse and numerous data: sequences of RGB-D images and laser scans from the rooms of five apartments (87,000+ observations were collected), topological information about the connectivity of these rooms, and 3D reconstructions and 2D geometric maps of the visited rooms.
• The provided ground truth is dense, including per-point annotations of the categories of the objects and rooms appearing in the reconstructed scenarios, and per-pixel annotations of each RGB-D image within the recorded sequences

During the data collection, a total of 36 rooms were completely inspected, so the dataset is rich in contextual information of objects and rooms. This is a valuable feature, missing in most of the state-of-the-art datasets, which can be exploited by, for instance, semantic mapping systems that leverage relationships like pillows are usually on beds or ovens are not in bathrooms.

The Robot-at-Home dataset (Robot@Home, paper here) is a collection of raw and processed data from five domestic settings compiled by a mobile robot equipped with 4 RGB-D cameras and a 2D laser scanner. Its main purpose is to serve as a testbed for semantic mapping algorithms through the categorization of objects and/or rooms.

This dataset is unique in three aspects:

The provided data were captured with a rig of 4 RGB-D sensors with an overall field of view of 180°H. and 58°V., and with a 2D laser scanner.

It comprises diverse and numerous data: sequences of RGB-D images and laser scans from the rooms of five apartments (87,000+ observations were collected), topological information about the connectivity of these rooms, and 3D reconstructions and 2D geometric maps of the visited rooms.

The provided ground truth is dense, including per-point annotations of the categories of the objects and rooms appearing in the reconstructed scenarios, and per-pixel annotations of each RGB-D image within the recorded sequences

During the data collection, a total of 36 rooms were completely inspected, so the dataset is rich in contextual information of objects and rooms. This is a valuable feature, missing in most of the state-of-the-art datasets, which can be exploited by, for instance, semantic mapping systems that leverage relationships like pillows are usually on beds or ovens are not in bathrooms.

Robot@Home2

Robot@Home2, is an enhanced version aimed at improving usability and functionality for developing and testing mobile robotics and computer vision algorithms. It consists of three main components. Firstly, a relational database that states the contextual information and data links, compatible with Standard Query Language. Secondly,a Python package for managing the database, including downloading, querying, and interfacing functions. Finally, learning resources in the form of Jupyter notebooks, runnable locally or on the Google Colab platform, enabling users to explore the dataset without local installations. These freely available tools are expected to enhance the ease of exploiting the Robot@Home dataset and accelerate research in computer vision and robotics.

If you use Robot@Home2, please cite the following paper:

Gregorio Ambrosio-Cestero, Jose-Raul Ruiz-Sarmiento, Javier Gonzalez-Jimenez, The Robot@Home2 dataset: A new release with improved usability tools, in SoftwareX, Volume 23, 2023, 101490, ISSN 2352-7110, https://doi.org/10.1016/j.softx.2023.101490.

@article{ambrosio2023robotathome2,title = {The Robot@Home2 dataset: A new release with improved usability tools},author = {Gregorio Ambrosio-Cestero and Jose-Raul Ruiz-Sarmiento and Javier Gonzalez-Jimenez},journal = {SoftwareX},volume = {23},pages = {101490},year = {2023},issn = {2352-7110},doi = {https://doi.org/10.1016/j.softx.2023.101490},url = {https://www.sciencedirect.com/science/article/pii/S2352711023001863},keywords = {Dataset, Mobile robotics, Relational database, Python, Jupyter, Google Colab}}

Version historyv1.0.1 Fixed minor bugs.v1.0.2 Fixed some inconsistencies in some directory names. Fixes were necessary to automate the generation of the next version.v2.0.0 SQL based dataset. Robot@Home v1.0.2 has been packed into a sqlite database along with RGB-D and scene files which have been assembled into a hierarchical structured directory free of redundancies. Path tables are also provided to reference files in both v1.0.2 and v2.0.0 directory hierarchies. This version has been automatically generated from version 1.0.2 through the toolbox.v2.0.1 A forgotten foreign key pair have been added.v.2.0.2 The views have been consolidated as tables which allows a considerable improvement in access time.v.2.0.3 The previous version does not include the database. In this version the database has been uploaded.v.2.1.0 Depth images have been updated to 16-bit. Additionally, both the RGB images and the depth images are oriented in the original camera format, i.e. landscape.

The documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updatesTitle: Atlas of Gross Domestic Product, 2019Item Type: Web Mapping Application URLSummary: Atlas of 17 maps showing different aspects of Gross Domestic Product (GDP). GDP is the value of goods and services produced within a country: consumption, investment, government spending, and net exports.Notes: Prepared by: Uploaded by EMcRae_NMCDCSource: Bureau of Economic Analysis (BEA)'s GDP by county, Table CAGDP2, 2019. https://nmcdc.maps.arcgis.com/home/item.html?id=da81ea710b194166bb02ef4b1a03783bFeature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=84e53e2ddae449ca9f3bda4d8e4b759cUID: 26Data Requested: Ag CensusMethod of Acquisition: Esri Living AtlasDate Acquired: 6/16/22Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 8Tags: PENDINGMaps included:Percent of U.S. Gross Domestic Product (GDP)Percent of Each State's Gross Domestic Product (GDP)What is the manufacturing Gross Domestic Product (GDP) in the US?Durable Goods Manufacturing in the USNondurable Goods Manufacturing in the USReal Estate, Rentals, and Leasing GDP in the USAgricultural, Forestry, Fishing, and Hunting GDP in the USInformation Industry GDP in the USUtilities GDP in the USConstruction in the USAre goods or services driving Private-Sector contributions to GDP?Natural Resources and Oil GDP in the USMining, Quarrying, Oil, and Extraction GDP in the USPercent from Health Care and Social AssistanceGDP from Government SpendingDoes retail or wholesale trade contribute more to GDP?Overall GDPFeature layer created from Table CAGDP2, downloaded ‎February ‎2, ‎2021.https://www.bea.gov/data/gdp/gdp-county-metro-and-other-areas

The DesHCA project aimed to identify supportive home designs that older people would find acceptable. To contribute to this, the team aimed to find out how older people currently live in their homes and what they find positive and negative about them. The home mapping data collection exercise in DesHCA focused on learning about older people’s experiences of living in their homes as they age. The goal was to gather insights from older people to create a clear picture of what people wanted, needed, and worried about in regards to adapting their home. A creative mapping method was used to explore how older people thought about, felt about, and used their homes. The Participants were re-contacted six months later in Wave 2 of data collection and asked about any changes to their home or health since the first interview.

Participants were asked to create a map of their home (which could include taking photographs, filming, or drawing) and we also interviewed them about their home. Most participants made their creative map during the interview, allowing researchers to ask questions about specific areas and items that might otherwise have gone unnoticed. This approach allowed the creative mapping interviews to capture a lot of data on the physical aspects of people’s homes, including what they liked and disliked about their home, what worked well for them, and what they would like to change in the future if they could. They also delved further, looking beyond the building itself to learn about how participants liked to use the different areas in their home, what kind of activities they liked to do there, and how their home had changed over time.

The data consist of: -16 home maps drawn by 19 participants, -46 Wave 1 interview transcripts (11 of which involve two people) -an overview table summarising changes reported since Wave 1 interviews, and -4 interview transcripts from full Wave 2 interviews.

Most of the text in this description originally appeared on the Mapping Inequality Website. Robert K. Nelson, LaDale Winling, Richard Marciano, Nathan Connolly, et al., “Mapping Inequality,” American Panorama, ed. Robert K. Nelson and Edward L. Ayers,

"HOLC staff members, using data and evaluations organized by local real estate professionals--lenders, developers, and real estate appraisers--in each city, assigned grades to residential neighborhoods that reflected their "mortgage security" that would then be visualized on color-coded maps. Neighborhoods receiving the highest grade of "A"--colored green on the maps--were deemed minimal risks for banks and other mortgage lenders when they were determining who should received loans and which areas in the city were safe investments. Those receiving the lowest grade of "D," colored red, were considered "hazardous."

Conservative, responsible lenders, in HOLC judgment, would "refuse to make loans in these areas [or] only on a conservative basis." HOLC created area descriptions to help to organize the data they used to assign the grades. Among that information was the neighborhood's quality of housing, the recent history of sale and rent values, and, crucially, the racial and ethnic identity and class of residents that served as the basis of the neighborhood's grade. These maps and their accompanying documentation helped set the rules for nearly a century of real estate practice. "

HOLC agents grading cities through this program largely "adopted a consistently white, elite standpoint or perspective. HOLC assumed and insisted that the residency of African Americans and immigrants, as well as working-class whites, compromised the values of homes and the security of mortgages. In this they followed the guidelines set forth by Frederick Babcock, the central figure in early twentieth-century real estate appraisal standards, in his Underwriting Manual: "The infiltration of inharmonious racial groups ... tend to lower the levels of land values and to lessen the desirability of residential areas."

These grades were a tool for redlining: making it difficult or impossible for people in certain areas to access mortgage financing and thus become homeowners. Redlining directed both public and private capital to native-born white families and away from African American and immigrant families. As homeownership was arguably the most significant means of intergenerational wealth building in the United States in the twentieth century, these redlining practices from eight decades ago had long-term effects in creating wealth inequalities that we still see today. Mapping Inequality, we hope, will allow and encourage you to grapple with this history of government policies contributing to inequality."

Data was copied from the Mapping Inequality Website for communities in Western Pennsylvania where data was available. These communities include Altoona, Erie, Johnstown, Pittsburgh, and New Castle. Data included original and georectified images, scans of the neighborhood descriptions, and digital map layers. Data here was downloaded on June 9, 2020.

This reference contains the imagery data used in the completion of the baseline vegetation inventory project for the NPS park unit. Orthophotos, raw imagery, and scanned aerial photos are common files held here.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. In 2002, we returned to Carl Sandburg Home NHS to follow-up on the first three goals and to cooperate with the University of Georgia Center for Remote Sensing and Mapping Science on their project to map all vegetation communities in the park. We supplied the University of Georgia team with all plot data already collected and a dichotomous key to the communities of the park and walked throughout the park to help them identify unique mapping units. Since photointerpreters rely heavily on canopy and understory species composition and disturbance and ecologists rely just as heavily on the shrub and herb layer to classify types, the mapping units and the vegetation classification units do not always match up perfectly. The last step will be to work with the mappers to produce mapping units that match up well with the ecological units of the National Vegetation Classification.

What is CDC Social Vulnerability Index?ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) created the Social Vulnerability Index (SVI) to help emergency response planners and public health officials identify and map the communities that will most likely need support before, during, and after a hazardous event.SVI uses U.S Census Data to determine the social vulnerability of every county and tract. CDC SVI ranks each county and tract on 16 social factors, including poverty, lack of vehicle access, and crowded housing, and groups them into four related themes:Theme 1 - Socioeconomic StatusTheme 2 - Household CharacteristicsTheme 3 - Racial & Ethnic Minority StatusTheme 4 - Housing Type & Transportation VariablesFor a detailed description of variable uses, please refer to the full SVI 2020 Documentation.RankingsWe ranked counties and tracts for the entire United States against one another. This feature layer can be used for mapping and analysis of relative vulnerability of counties in multiple states, or across the U.S. as a whole. Rankings are based on percentiles. Percentile ranking values range from 0 to 1, with higher values indicating greater vulnerability. For each county and tract, we generated its percentile rank among all counties and tracts for 1) the sixteen individual variables, 2) the four themes, and 3) its overall position. Overall Rankings:We totaled the sums for each theme, ordered the counties, and then calculated overall percentile rankings. Please note: taking the sum of the sums for each theme is the same as summing individual variable rankings.The overall tract summary ranking variable is RPL_THEMES. Theme rankings:For each of the four themes, we summed the percentiles for the variables comprising each theme. We ordered the summed percentiles for each theme to determine theme-specific percentile rankings. The four summary theme ranking variables are: Socioeconomic Status - RPL_THEME1Household Characteristics - RPL_THEME2Racial & Ethnic Minority Status - RPL_THEME3Housing Type & Transportation - RPL_THEME4FlagsCounties and tracts in the top 10%, i.e., at the 90th percentile of values, are given a value of 1 to indicate high vulnerability. Counties and tracts below the 90th percentile are given a value of 0. For a theme, the flag value is the number of flags for variables comprising the theme. We calculated the overall flag value for each county as the total number of all variable flags. SVI Informational VideosIntroduction to CDC Social Vulnerability Index (SVI)Methods for CDC Social Vulnerability Index (SVI)More Questions?CDC SVI 2020 Full DocumentationSVI Home PageContact the SVI Coordinator

This is the 2022 version of the Aquifer Risk Map. The 2021 version of the Aquifer Risk Map is available here.This aquifer risk map is developed to fulfill requirements of SB-200 and is intended to help prioritize areas where domestic wells and state small water systems may be accessing raw source groundwater that does not meet primary drinking water standards (maximum contaminant level or MCL). In accordance with SB-200, the risk map is to be made available to the public and is to be updated annually starting January 1, 2021. The Fund Expenditure Plan states the risk map will be used by Water Boards staff to help prioritize areas for available SAFER funding. This is the final 2022 map based upon feedback received from the 2021 map. A summary of methodology updates to the 2022 map can be found here.This map displays raw source groundwater quality risk per square mile section. The water quality data is based on depth-filtered, declustered water quality results from public and domestic supply wells. The process used to create this map is described in the 2022 Aquifer Risk Map Methodology document. Data processing scripts are available on GitHub. Download/export links are provided in this app under the Data Download widget.This draft version was last updated December 1, 2021. Water quality risk: This layer contains summarized water quality risk per square mile section and well point. The section water quality risk is determined by analyzing the long-tern (20-year) section average and the maximum recent (within 5 years) result for all sampled contaminants. These values are compared to the MCL and sections with values above the MCL are “high risk”, sections with values within 80%-100% of the MCL are “medium risk” and sections with values below 80% of the MCL are “low risk”. The specific contaminants above or close to the MCL are listed as well. The water quality data is based on depth-filtered, de-clustered water quality results from public and domestic supply wells.Individual contaminants: This layer shows de-clustered water quality data for arsenic, nitrate, 1,2,3-trichloropropane, uranium, and hexavalent chromium per square mile section. Domestic Well Density: This layer shows the count of domestic well records per square mile. The domestic well density per square mile is based on well completion report data from the Department of Water Resources Online System for Well Completion Reports, with records drilled prior to 1970 removed and records of “destruction” removed.State Small Water Systems: This layer displays point locations for state small water systems based on location data from the Division of Drinking Water.Public Water System Boundaries: This layer displays the approximate service boundaries for public water systems based on location data from the Division of Drinking Water.Reference layers: This layer contains several reference boundaries, including boundaries of CV-SALTS basins with their priority status, Groundwater Sustainability Agency boundaries, census block group boundaries, county boundaries, and groundwater unit boundaries. ArcGIS Web Application

The aquifer risk map is being developed to fulfill requirements of SB-200 and is intended to help prioritize areas where domestic wells and state small water systems may be accessing groundwater that does not meet primary drinking water standards (maximum contaminant level or MCL). In accordance with SB-200, the risk map is to be made available to the public and is to be updated annually starting January 1, 2021. The Fund Expenditure Plan states the risk map will be used by Water Boards staff to help prioritize areas for available SAFER funding.Methodology for the draft aquifer risk map available for download.Water quality risk: This layer contains summarized water quality risk per census block group, square mile section, and well point. The overall census block group water quality risk is based on four risk factors (the count of chemicals with a long-term average (20 year) or recent result (within 2 years) above the MCL, the count of chemicals with a long-term average (20 year) or recent result (within 2 years) within 80% of the MCL, the average magnitude or results above the MCL, and the percent area with chemicals above the MCL or within 80% of the MCL). The specific chemicals that contribute to these risk factors are listed as well. Higher values for each individual risk factor contribute to a higher overall score. The scores are converted to percentiles to normalize the results. The water quality data is based on depth-filtered, declustered water quality results from public and domestic supply wells, collected following a similar methodology as the Domestic Well Needs Assessment White Paper. This layer also displays the total estimated count of domestic wells per census block group, based on the Department of Water Resources Online System for Well Completion Reports, and the total estimated count of domestic well user population, based on the United States Geological Survey Road-Enhanced Methodology (Johnson and Belitz, 2019). To provide comments or feedback on this map, please email SAFER@waterboards.ca.gov or Emily.Houlihan@Waterboards.ca.gov. Individual chemicals: This layer shows declustered water quality data for arsenic, nitrate, 1,2,3-trichloropropane, uranium, and hexavalent chromium per square mile section. The intent of the aquifer risk map is to help prioritize areas where domestic well users and state small water systems may be accessing groundwater that does not meet primary drinking water standards (maximum contaminant level or MCL) and will be updated annually starting January 1, 2021. The section water quality data is based on depth-filtered water quality results from public and domestic supply wells, collected following a similar methodology as the Domestic Well Needs Assessment White Paper. This layer contains the long-term average (20 years) as well as the count of recent results (within 2 years) above the MCL, between 80% - 100% of the MCL, and below 80% of the MCL for each square mile section. Drinking water users: This layer shows the locations of state small water systems and domestic well density. The state small water system locations were collected by the Rural Community Assistance Corporation. The locations are approximate and may not exactly represent well locations or service boundaries. The domestic well density per square mile is based on well completion report data from the Department of Water Resources Online System for Well Completion Reports. This layer also contains the public water system boundaries (available on the State Water Board REST endpoint) for reference.Reference layers: This layer contains several reference boundaries, including boundaries of CV-SALTS basins with their priority status, Groundwater Sustainability Agency boundaries, census block group boundaries, county boundaries, and groundwater unit boundaries.

Contained within the 3rd Edition (1957) of the Atlas of Canada is a map that shows a map of six condensed maps of employment and related patterns for the leading service sectors as compiled from the 1951 Census. There are two maps referring to wholesale trade. One of them shows the distribution of the labour force engaged in wholesale trade. This is shown by a dot pattern using one dot for every 200 people of this labour force, and using proportional symbols for all places employing 2 000 or more. The other wholesale trade map shows percentage of net value of sales from wholesale trade in each census division. There are two similar maps of retail trade. One, showing the distribution of labour force, uses the same mapping procedure as that of wholesale trade. The second map shows retail trade as a percentage of net value of sales for each census division. The fifth map shows the distribution of the construction labour force, using the same mapping concepts as for the wholesale trade map. There is an associated pie chart showing the types of construction this labour force engages in. The sixth map shows the distribution of labour force in the fire, insurance and real estate industries, again using the mapping concepts used for the wholesale trade map. This map is accompanied by a pie chart showing employment in the various industries of this group (such as in banking).

This is the Domestic Water Supply (DWS) Intakes map that is used in the Understanding Designated Uses Story Map. The story map is part of the 2018-2020 Integrated Report Hub Site and is found on the Designated Uses Page. The data are hosted in EEC's ArcGIS Online as a hosted feature layer and represent the Drinking Water Intakes in Kentucky. Data used in the map were curated by the Water Quality Branch of the Kentucky Division of Water in an Excel spreadsheet. The Excel file was uploaded into AGOL and the service was published in AGOL from those data. Melissa Miracle in the Office of Administrative Service, IT Division of the Kentucky Energy and Environment Cabinet published in the service.

The domestic dog is becoming an increasingly valuable model species in medical genetics, showing particular promise to advance our understanding of cancer and orthopaedic disease. Here we undertake the largest canine genome-wide association study to date, with a panel of over 4,200 dogs genotyped at 180,000 markers, to accelerate mapping efforts. For complex diseases, we identify loci significantly associated with hip dysplasia, elbow dysplasia, idiopathic epilepsy, lymphoma, mast cell tumour and granulomatous colitis; for morphological traits, we report three novel quantitative trait loci that influence body size and one that influences fur length and shedding. Using simulation studies, we show that modestly larger sample sizes and denser marker sets will be sufficient to identify most moderate- to large-effect complex disease loci. This proposed design will enable efficient mapping of canine complex diseases, most of which have human homologues, using far fewer samples than required in human studies.

This data release consists of multi-band 30-meter x 30-meter pixel rasters of estimated population and domestic self-supplied water withdrawals in Rhode Island between July 2014 and June 2021. Population raster data were generated using a national data product of 2010 population spatially distributed across land cover data and U.S. Census Bureau data of population growth estimates to adjust populations for each year 2014-2021. Estimates for changes in population between winter and summer months are also included to generate seasonal population estimates. The coefficients used to describe these variations in populations for each U.S. Census Bureau block group in Rhode Island are included in this data release. Estimated water withdrawal rasters were generated using an estimated population for each pixel and domestic per capita water use rates calculated from public-supply data. Spatial boundaries of public supplies in Rhode Island were used to classify areas of domestic water use outside of those served by public suppliers as self-supplied. Three comma-separated values (CSV) files contain the coefficients used to develop the final products. The three multi-band raster (tagged image format [TIF]) files contain the spatial representation of the estimated populations and self-supplied domestic water withdrawals in Rhode Island.

Household income is a potential predictor for a number of environmental influences, for example, application of urban pesticides. This product is a U.S. conterminous mapping of block group income derived from the 2010-2014 Census American Community Survey (ACS), adjusted by a 2013 county-level Cost-of-Living index obtained from the Council for Community and Economic Research. The resultant raster is provided at 200-m spatial resolution, in units of adjusted household income in thousands of dollars per year.

As included in this EnviroAtlas dataset, the community level domestic water use is calculated using locally available water use data per capita in gallons of water per day (GPD), distributed dasymetrically, and summarized by census block group. Domestic water use, as defined in this case, is intended to represent residential indoor and outdoor water use (e.g., cooking, hygiene, landscaping, pools, etc.) for primary residences (i.e., excluding second homes and tourism rentals). Water use in this EnviroAtlas-defined study area is estimated at 65 GPD. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

The size of the Simultaneous Localization and Mapping Industry market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 26.78% during the forecast period.Simultaneous Localization and Mapping is the process through which robots and self-driving cars map out a space they do not know. They are doing it while trying to find out where they are in this space. This process relies on sensors, such as cameras, lidar, and radar, that take pictures of the surrounding environment. SLAM algorithms perform this data processing from sensors to look for landmarks, infer distances and movements of the robot and map the environment and the localizations. SLAM applies in different fields of application. Most applications by robots rely heavily on SLAM to navigate, explore areas, and map out several areas. Self-driving cars rely on SLAM to establish real-time maps of surroundings, which allows them to navigate safely and efficiently. SLAM is also applied in other areas: warehouse automation for inventory management and order fulfillment, drone delivery for autonomous flight and package drop-off, and in medical robotics for precise surgical procedures. With the continuous development of technology, SLAM will play a much larger role so that autonomous systems can be operated safely and effectively in complex and dynamic environments. Recent developments include: November 2022 - Singapore based autonomous navigation solutions provider dConstruct introduced Ouster digital lidar to create highly accurate SLAMs and point cloud maps. Dconstruct creates these maps virtually and then studies the deployment of autonomous robots and the inspection and reconstruction of working environments. For instance - A map of a smart office building, The Galen, in Singapore was created on the cloud and was used to facilitate the deployment of autonomous robots ranging from cleaning robots to last-mile delivery robots., February 2023 - KUKA, the German manufacturer of industrial robots, launched Intralogistics Robot, with wheel sensors and laser scanners that let it safely move through its surroundings. The company claims this product is compatible to meets the highest safety requirements. It the specification such as 3D object detection, laser scanners, a payload of 1,322 pounds, and an automated guided vehicle system. The robot or the collision-free worker has been developed to work with logistics workers without the need for safety fencing. It employs eight safety zones in the front and back that can be adjusted for vehicle speeds and particular applications., July 2022 - Polymath Robotics, a start-up, developed an SDK-integrated plug-and-play software platform that enables businesses to quickly and affordably automate industrial vehicles. The start-up is developing fundamentally generalizable autonomy intending to automate the roughly 50 million industrial vehicles currently used in enclosed spaces.. Key drivers for this market are: Growing Penetration of Mapping Technologies in Domestic Robots and UAV, Advancements in Visual SLAM Algorithm; Increasing Application of SLAM in Augmented Reality. Potential restraints include: , The Risk of Interference from Other Wireless Device. Notable trends are: UAVs and Robots Will Experience Significant Growth in the Market.