The table Firm survey wave 1 and 2 (GIS) is part of the dataset SEDRI Ethiopia firm survey (GIS), available at https://stanford.redivis.com/datasets/rxq3-9x047we25. It contains 1585 rows across 3377 variables.

The United States Public Land Survey (PLS) divided land into one square mile units, termed sections. Surveyors used trees to locate section corners and other locations of interest (witness trees). As a result, a systematic ecological dataset was produced with regular sampling over a large region of the United States, beginning in Ohio in 1786 and continuing westward.
We digitized and georeferenced archival hand drawn maps of these witness trees for 27 counties in Ohio. This dataset consists of a GIS point shapefile with 11,925 points located at section corners, recording 26,028 trees (up to four trees could be recorded at each corner). We retain species names given on each archival map key, resulting in 70 unique species common names. PLS records were obtained from hand-drawn archival maps of original witness trees produced by researchers at The Ohio State University in the 1960’s. Scans of these maps are archived as “The Edgar Nelson Transeau Ohio Vegetation Survey” at The Ohio State University: http://hdl.handle.net/1811/64106.
The 27 counties are: Adams, Allen, Auglaize, Belmont, Brown, Darke, Defiance, Gallia, Guernsey, Hancock, Lawrence, Lucas, Mercer, Miami, Monroe, Montgomery, Morgan, Noble, Ottawa, Paulding, Pike, Putnam, Scioto, Seneca, Shelby, Williams, Wyandot. Coordinate Reference System: North American Datum 1983 (NAD83). This material is based upon work supported by the National Science Foundation under grants #DEB-1241874, 1241868, 1241870, 1241851, 1241891, 1241846, 1241856, 1241930.

Usage

The prefixes f1_ and f2_ indicate that variables correspond to either wave 1 or wave 2, respectively.

Public Land Survey SystemThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the Bureau of Land Management data, displays the Public Land Survey System (PLSS) in the United States. Per BLM, "The BLM is required to perform cadastral surveys on all federal interest and Indian lands. As part of survey work, the BLM maintains an essential land grid, known as the rectangular survey system or Public Land Survey System (PLSS), which is the basis for identifying legal descriptions of land parcels."PLSS Township 7N 22EData downloaded: October 17, 2023Data source: BLM National Public Land Survey System PolygonsNGDAID: 10 (BLM National PLSS Public Land Survey System Polygons)OGC API Features Link: (Public_Land_Survey_System - OGC Features) copy this link to embed it in OGC Compliant viewersFor more information: About the Public Land Survey SystemSupport documentation: BLM National PLSS Public Land Survey System PolygonsFor feedback please contact: ArcGIScomNationalMaps@esri.comNGDA Data SetThis data set is part of the NGDA Cadastre Theme Community. Per the Federal Geospatial Data Committee (FGDC), Cadastre is defined as the "past, current, and future rights and interests in real property including the spatial information necessary to describe geographic extents. Rights and interests are benefits or enjoyment in real property that can be conveyed, transferred, or otherwise allocated to another for economic remuneration. Rights and interests are recorded in land record documents. The spatial information necessary to describe geographic extents includes surveys and legal description frameworks such as the Public Land Survey System, as well as parcel-by-parcel surveys and descriptions. Does not include federal government or military facilities."For other NGDA Content: Esri Federal Datasets

The 3D Land Surveying System market is experiencing robust growth, projected to reach a market size of $1752.7 million in 2025. While the provided CAGR is missing, considering the technological advancements driving automation in surveying and the increasing demand for precise data in infrastructure development and construction, a conservative estimate of a 7% CAGR from 2025 to 2033 is reasonable. This would indicate a significant expansion of the market, driven by factors such as the increasing adoption of advanced technologies like LiDAR and photogrammetry, rising infrastructure investments globally, and the need for efficient and accurate land data for urban planning and environmental monitoring. The market segmentation, encompassing fixed and mobile surveying systems and applications across surveying & mapping, construction, and other sectors, reveals diverse growth opportunities. The preference for mobile systems is likely to increase due to their portability and ease of use, while the construction sector is expected to be a major driver of market growth due to the rising number of construction projects globally. The regional distribution shows substantial potential across North America, Europe, and Asia-Pacific, reflecting the concentration of developed economies and significant infrastructure investments. However, developing regions in the Middle East & Africa and South America are also showing promising growth potential as infrastructure development and urbanization accelerate. Competitive dynamics involve a mix of established surveying firms and emerging technology providers, emphasizing both service-based and technology-driven solutions. The continued integration of AI and machine learning into surveying systems is likely to further enhance the efficiency and accuracy of land surveying, fueling market expansion in the coming years. This combination of technological innovation and growing infrastructural needs ensures a sustained upward trajectory for the 3D Land Surveying System market.

In support of new permitting workflows associated with anticipated WellSTAR needs, the CalGEM GIS unit extended the existing BLM PLSS Township & Range grid to cover offshore areas with the 3-mile limit of California jurisdiction. The PLSS grid as currently used by CalGEM is a composite of a BLM download (the majority of the data), additions by the DPR, and polygons created by CalGEM to fill in missing areas (the Ranchos, and Offshore areas within the 3-mile limit of California jurisdiction).CalGEM is the Geologic Energy Management Division of the California Department of Conservation, formerly the Division of Oil, Gas, and Geothermal Resources (as of January 1, 2020).Update Frequency: As Needed

This data set represents a GIS Version of the Public Land Survey System (PLSS) including both rectangular and non-rectangular surveys. These are the cadastral reference features that provide the basis and framework for mapping. This feature data set contains PLSS and Other Survey System data. The other survey systems include subdivision plats and those types of survey reference systems. This PLSS dataset was compiled by IDWR in 2016/2017 showing Public Land Survey System (PLSS) data from a variety of sources, including BLM's CadNSDI, IDL's edits to the CadNSDI alongside alignments to data from a variety of counties. Source and Edit information are provided in the QQ layer.

The Digital Geologic-GIS Map of the Rhoda Quadrangle, Kentucky is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (rhod_geology.gdb), and a 2.) Open Geospatial Consortium (OGC) geopackage. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (rhod_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (rhod_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) a readme file (maca_abli_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (maca_abli_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (rhod_geology_metadata_faq.pdf). Please read the maca_abli_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. QGIS software is available for free at: https://www.qgis.org/en/site/. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (rhod_geology_metadata.txt or rhod_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

This dataset represents the GIS Version of the Public Land Survey System including both rectangular and non-rectangular surveys. The primary source for the data is cadastral survey records housed by the BLM supplemented with local records and geographic control coordinates from states, counties as well as other federal agencies such as the USGS and USFS. The data has been converted from source documents to digital form and transferred into a GIS format that is compliant with FGDC Cadastral Data Content Standards and Guidelines for publication. This data is optimized for data publication and sharing rather than for specific 'production' or operation and maintenance. This data set includes the following: PLSS Fully Intersected (all of the PLSS feature at the atomic or smallest polygon level), PLSS Townships, First Divisions and Second Divisions (the hierarchical break down of the PLSS Rectangular surveys), and the Bureau of Census 2015 Cartographic State Boundaries. The Entity-Attribute section of this metadata describes these components in greater detail. Please note that the data on this site, although published at regular intervals, may not be the most current PLSS data that is available from the BLM. Updates to the PLSS data at the BLM State Offices may have occurred since this data was published. To ensure users have the most current data, please contact the BLM PLSS Data Set Manager.

Fayette County Ohio GIS Survey Drawings. The information provided is for reference only and subject to independent verification. User assumes all responsibility for its use.

Tags

   survey, environmental behaviors, lifestyle, status, PRIZM, Baltimore Ecosystem Study, LTER, BES




   Summary


   BES Research, Applications, and Education


   Description


   Geocoded for Baltimore County. The BES Household Survey 2003 is a telephone survey of metropolitan Baltimore residents consisting of 29 questions. The survey research firm, Hollander, Cohen, and McBride conducted the survey, asking respondents questions about their outdoor recreation activities, watershed knowledge, environmental behavior, neighborhood characteristics and quality of life, lawn maintenance, satisfaction with life, neighborhood, and the environment, and demographic information. The data from each respondent is also associated with a PRIZM� classification, census block group, and latitude-longitude. PRIZM� classifications categorize the American population using Census data, market research surveys, public opinion polls, and point-of-purchase receipts. The PRIZM� classification is spatially explicit allowing the survey data to be viewed and analyzed spatially and allowing specific neighborhood types to be identified and compared based on the survey data. The census block group and latitude-longitude data also allow us additional methods of presenting and analyzing the data spatially. 


   The household survey is part of the core data collection of the Baltimore Ecosystem Study to classify and characterize social and ecological dimensions of neighborhoods (patches) over time and across space. This survey is linked to other core data including US Census data, remotely-sensed data, and field data collection, including the BES DemSoc Field Observation Survey. 



   The BES 2003 telephone survey was conducted by Hollander, Cohen, and McBride from September 1-30, 2003. The sample was obtained from the professional sampling firm Claritas, in order that their "PRIZM" encoding would be appended to each piece of sample (telephone number) supplied. Mailing addresses were also obtained so that a postcard could be sent in advance of interviewers calling. The postcard briefly informed potential respondents about the survey, who was conducting it, and that they might receive a phone call in the next few weeks. A stratified sampling method was used to obtain between 50 - 150 respondents in each of the 15 main PRIZM classifications. This allows direct comparison of PRIZM classifications. Analysis of the data for the general metropolitan Baltimore area must be weighted to match the population proportions normally found in the region. They obtained a total of 9000 telephone numbers in the sample. All 9,000 numbers were dialed but contact was only made on 4,880. 1508 completed an interview, 2524 refused immediately, 147 broke off/incomplete, 84 respondents had moved and were no longer in the correct location, and a qualified respondent was not available on 617 calls. This resulted in a response rate of 36.1% compared with a response rate of 28.2% in 2000. The CATI software (Computer Assisted Terminal Interviewing) randomized the random sample supplied, and was programmed for at least 3 attempted callbacks per number, with emphasis on pulling available callback sample prior to accessing uncalled numbers. Calling was conducted only during evening and weekend hours, when most head of households are home. The use of CATI facilitated stratified sampling on PRIZM classifications, centralized data collection, standardized interviewer training, and reduced the overall cost of primary data collection. Additionally, to reduce respondent burden, the questionnaire was revised to be concise, easy to understand, minimize the use of open-ended responses, and require an average of 15 minutes to complete. 


   The household survey is part of the core data collection of the Baltimore Ecosystem Study to classify and characterize social and ecological dimensions of neighborhoods (patches) over time and across space. This survey is linked to other core data, including US Census data, remotely-sensed data, and field data collection, including the BES DemSoc Field Observation Survey. 


   Additional documentation of this database is attached to this metadata and includes 4 documents, 1) the telephone survey, 2) documentation of the telephone survey, 3) metadata for the telephone survey, and 4) a description of the attribute data in the BES survey 2003 survey.


   This database was created by joining the GDT geographic database of US Census Block Group geographies for the Baltimore Metropolitan Statisticsal Area (MSA), with the Claritas PRIZM database, 2003, of unique classifications of each Census Block Group, and the unique PRIZM code for each respondent from the BES Household Telephone Survey, 2003. The GDT database is preferred and used because

The global GIS data collector market is experiencing robust growth, driven by increasing adoption of precision agriculture, expanding infrastructure development projects, and the rising demand for accurate geospatial data across various industries. The market, estimated at $2.5 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033, reaching approximately $4.2 billion by 2033. Key drivers include the increasing availability of affordable and high-precision GPS technology, coupled with advancements in data processing and cloud-based solutions. The integration of GIS data collectors with other technologies, such as drones and IoT sensors, is further fueling market expansion. The demand for high-precision GIS data collectors is particularly strong in sectors like surveying, mapping, and construction, where accuracy is paramount. While the market faces challenges such as high initial investment costs and the need for specialized expertise, the overall growth trajectory remains positive. The market is segmented by application (agriculture, industrial, forestry, and others) and by type (general precision and high precision). North America and Europe currently hold significant market shares, but the Asia-Pacific region is anticipated to experience rapid growth in the coming years due to substantial infrastructure development and increasing government investments in geospatial technologies. The competitive landscape is characterized by both established players like Trimble, Garmin, and Hexagon (Leica Geosystems) and emerging companies offering innovative solutions. These companies are constantly innovating, integrating advanced technologies like AI and machine learning to enhance data collection and analysis capabilities. This competition is driving down prices and improving product quality, benefiting end-users. The increasing use of mobile GIS and cloud-based data management solutions is also transforming the industry, making data collection and analysis more accessible and efficient. Future growth will be largely influenced by the advancement of 5G networks, enabling faster data transmission and real-time applications, and the increasing adoption of automation and AI in data processing workflows. Furthermore, government regulations promoting the use of accurate geospatial data for sustainable development and environmental monitoring are creating new opportunities for the market’s expansion.

The Geographic Information System (GIS) industry is experiencing robust growth, projected to maintain a Compound Annual Growth Rate (CAGR) of 10.80% from 2025 to 2033. This expansion is driven by increasing adoption across diverse sectors, including agriculture, utilities, mining, construction, transportation, and oil and gas. The rising need for precise location-based data for efficient operations, optimized resource management, and informed decision-making fuels this market growth. Advancements in hardware, such as high-resolution sensors and drones, coupled with sophisticated software capabilities like advanced spatial analytics and cloud-based GIS solutions, are key contributors. Furthermore, the proliferation of location-based services (LBS) and the growing adoption of telematics and navigation systems are expanding the applications of GIS technology. While data security concerns and the need for skilled professionals present some challenges, the overall market outlook remains positive. The segmentation of the GIS market reveals a strong demand across various components (hardware and software) and functionalities (mapping, surveying, telematics and navigation, and location-based services). North America currently holds a significant market share due to early adoption and technological advancements, but regions like Asia are exhibiting rapid growth fueled by infrastructure development and increasing digitalization. Leading companies like Bentley Systems, Esri, Trimble, and Hexagon AB are at the forefront of innovation, continuously developing and implementing advanced GIS solutions to meet the evolving needs of different industries. The forecast for the next decade points to further market consolidation, with leading players investing heavily in research and development to enhance their product offerings and expand their market reach. The continued integration of GIS with other technologies such as AI and IoT will further drive market expansion and create new opportunities for growth. Comprehensive Coverage GIS Industry Report (2019-2033) This in-depth report provides a comprehensive analysis of the Geographic Information System (GIS) industry, projecting robust growth from $XXX million in 2025 to $YYY million by 2033. The study covers the historical period (2019-2024), base year (2025), and forecast period (2025-2033), offering invaluable insights for businesses, investors, and policymakers. Keywords: GIS market, GIS software, GIS hardware, GIS solutions, geospatial technology, location intelligence, mapping software, surveying equipment, spatial analysis, geospatial analytics. Recent developments include: November 2022 : The new Geodata Portal and broadband maps for the state will be accessible starting on November 18, 2022, according to a statement from the Connecticut Office of Policy and Management (OPM). This announcement was made on GIS Day 2022, which encourages people to learn about geography and the practical uses of GIS that can improve society., November 2022 : The lt. governor of the Indian state, Jammu and Kashmir, launched a GIS-based system in the region. It highlights the significance of GIS technology in addressing new challenges and exploring new opportunities and its real-world applications, accelerating growth in business, government, and society.. Key drivers for this market are: Growing role of GIS in smart cities ecosystem, Integration of location-based mapping systems with business intelligence systems. Potential restraints include: Integration issues with traditional systems, Data quality and accuracy issues. Notable trends are: The Rising Smart Cities Development and Urban Planning to Drive the Market Growth.

Student -Vanuatu National University

DOUGLAS COUNTY SURVEY/GISGIS PARCEL MAPPING GUIDELINES FOR PARCEL DISCREPANCIESIt is the intent of the Douglas County GIS Parcel Mapping to accurately identify the areas of land parcels to be valued and taxed 1. Discrepancies in areas• The Auditor/Assessor (tax) acreage areas started with the original US General Land Office (GLO) township plat maps created from the Public Land Survey (PLS) that was done between 1858 and 1871. The recovery of the PLS corners and the accurate location of these corners with GPS obtained coordinates has allowed for accurate section subdivisions, which results in accurate areas for parcels based on legal descriptions, which may be significantly different than the original areas. (See Example 2)• Any parcel bordering a meandered lake and/or a water boundary will likely have a disparity of area between the Auditor/Assessor acreages and the GIS acreages because of the inaccuracy of the original GLO meander lines from which the original areas were determined. Water lines are not able to be drafted to the same accuracy as the normal parcel lines. The water lines are usually just sketched on a survey and their dimensions are not generally given on a land record. The water boundaries of our GIS parcels are located from aerial photography. This is a subjective determination based on the interpretation by the Survey/GIS technician of what is water. Some lakes fluctuate significantly and the areas of all parcels bordering water are subject to constant change. In these cases the ordinary high water line (OHW) is attempted to be identified. Use of 2-foot contours will be made, if available. (See Example 1)• Some land records do not accurately report the area described in the land description and the description area is ignored. (See Example 3)• The parcel mapping has made every attempt to map the parcels based on available survey information as surveyed and located on the ground. This may conflict with some record legal descriptions.Solutions• If an actual survey by a licensed Land Surveyor is available, it will be utilized for the tax acreage.• If the Auditor/Assessor finds a discrepancy between the tax and GIS areas, they will request a review by the County Survey/GIS department.• As a starting guideline, the County Survey/GIS department will identify all parcels that differ in tax area versus GIS parcel area of 10 % or more and a difference of at least 5 acres. (This could be expanded later after the initial review.)• Each of these identified parcels will be reviewed individually by the County Survey/GIS department to determine the reason for the discrepancy and a recommendation will be made by the County Survey/GIS department to the Auditor/Assessor if the change should be made or not.• If a change is to be made to the tax area, a letter will be sent to the taxpayer informing them that their area will be changed during the next tax cycle, which could affect their property valuation. This letter will originate from the Auditor/Assessor with explanation from the County Survey/GIS department. 2. Gaps and Overlaps• Land descriptions for adjoining parcels sometimes overlap or leave a gap between them.o In these instances the Survey/GIS technician has to make a decision where to place this boundary. A number of circumstances are reviewed to facilitate this decision as these dilemmas are usually decided on a case by case basis. All effort will be made to not leave a gap, but sometimes this is not possible and the gap will be shown with “unknown” ownership. (Note: The County does not have the authority to change boundaries!)o Some of the circumstances reviewed are: Which parcel had the initial legal description? Does the physical occupation of the parcel line as shown on the air photo more closely fit one of the described parcels? Interpretation of the intent of the legal description. Is the legal description surveyable?Note: These overlaps will be shown on the GIS map with a dashed “survey line” and accompanying text for the line not used for the parcel boundary. 3. Parcel lines that do not match location of buildings Structures on parcels do not always lie within the boundaries of the parcel. This may be a circumstance of building without the benefit of a survey or of misinterpreting these boundaries. The parcel lines should be shown accurately as surveyed and/or described regardless of the location of structures on the ground. NOTE: The GIS mapping is not a survey, but is an interpretation of parcel boundaries predicated upon resources available to the County Survey/GIS department.Gary Stevenson Page 1 7/21/2017Example 1Example 2A Example 2B Example 3

Survey sections and private claims for Michigan's Upper and Lower PeninsulasMore Metadata

This PDF includes the GIS Maps for the City of Gainesville 2020 Neighborhood Survey. It segments survey results by geography and commission district. The file preview window will not display due to file size restrictions, please download the file to view.

This data release contains the GIS data supporting U.S. Geological Survey Open-File Report (OFR) 2005-1252, "The Geologic Map of Seattle—A Progress Report," published in 2005 by Kathy Goetz Troost, Derek B. Booth, Aaron P. Wisher, and Scott A. Shimel (https://doi.org/10.3133/ofr20051252). The OFR was prepared for the 2005 Washington Hydrogeology Symposium and describes the status of geologic mapping for Seattle, Washington, at the time. The map is the result of field mapping and compilation of subsurface geologic data during the years 1999–2004 and was funded by the City of Seattle and the U.S. Geological Survey. Data from more than 36,000 exploration points, geotechnical borings, monitoring wells, excavations, and outcrops were used in making the map. The northern part of the 2005 OFR and the supporting GIS data were subsequently published as two geologic maps: Booth, D.B., Troost, K.G., and Shimel, S.A., 2005, Geologic map of northwestern Seattle (part of the Seattle North 7.5’ X 15’ Quadrangle), King County, Washington: U.S. Geological Survey Scientific Investigations Map 2903, https://doi.org/10.3133/sim2903. Booth, D.B., Troost, K.G., and Shimel, S.A., 2009, Geologic map of northeastern Seattle (part of the Seattle North 7.5' x 15' quadrangle), King County, Washington: U.S. Geological Survey Scientific Investigations Map 3065, https://doi.org/10.3133/sim3065. The southern part of the 2005 OFR and the supporting GIS data were not subsequently published for various reasons. With the original authors' permission, the GIS data used to create the map shown in OFR 2005-1252 are being released here to best meet modern open-data standards and to allow for use in future studies and mapping. The data included in this data release are only those components necessary to create the map shown in OFR 2005-1252. The following map features were not available and are not included in this data release: bedding point data, faults, anticlines, and contact lines. OFR_2005-1252.gdb is an Esri geodatabase containing the following feature classes: ofr_2005_1252_geology_poly (1,068 features); ofr_2005_1252_fill_poly (424 features); ofr_2005_1252_seattle_fault_zone_poly (1 feature); ofr_2005_1252_wastage_landslide_deposits_poly (188 features); ofr_2005_1252_beds_line (6 features); and ofr_2005_1252_scarp_line (351 features). Metadata records associated with each of these elements contain more detailed descriptions of their purposes, constituent entities, and attributes. A shapefile (non-geodatabase) version of the dataset is also included, although due to character limits, some field names and text cells in the attribute tables were truncated relative to the equivalent values in the geodatabase. The authors ask that users of the geologic map data cite both the open-file report and the GIS data release: Open-File Report: Troost, K.G., Booth, D.B., Wisher, A.P., and Shimel, S.A., 2005, The geologic map of Seattle—a progress report: U.S. Geological Survey Open-File Report 2005-1252, https://doi.org/10.3133/ofr20051252. GIS data: Troost, K.G., Booth, D.B., Wisher, A.P., and Shimel, S.A., 2024, GIS data for U.S. Geological Survey OFR 2005-1252, The geologic map of Seattle—a progress report: U.S. Geological Survey data release, https://doi.org/10.5066/P93L6SPS.

This dataset contains model-based county-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. Project 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 2022 county population estimates, and American Community Survey (ACS) 2018–2022 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 2022 county boundary file in a GIS system to produce maps for 40 measures at the county 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

The U.S. Geological Survey, in collaboration with the National Oceanic and Atmospheric Administration's (NOAA) National Marine Sanctuary Program, conducted seabed mapping and related research in the Stellwagen Bank National Marine Sanctuary region from 1993 to 2004. The mapped area is approximately 3,700 square km (1,100 square nm) in size and was subdivided into 18 quadrangles. Several series of sea floor maps of the region based on multibeam sonar surveys have been published. In addition, 2,628 seabed sediment samples were collected and analyzed and approximately 10,600 still photographs of the seabed were acquired during the project. These data provide the basis for scientists, policymakers, and managers for understanding the complex ecosystem of the sanctuary region and for monitoring and managing its economic and natural resources.