Through the Department of the Interior-Bureau of Indian Affairs Enterprise License Agreement (DOI-BIA ELA) program, BIA employees and employees of federally-recognized Tribes may access a variety of geographic information systems (GIS) online courses and instructor-led training events throughout the year at no cost to them. These online GIS courses and instructor-led training events are hosted by the Branch of Geospatial Support (BOGS) or offered by BOGS in partnership with other organizations and federal agencies. Online courses are self-paced and available year-round, while instructor-led training events have limited capacity and require registration and attendance on specific dates. This dataset does not any training where the course was not completed by the participant or where training was cancelled or otherwise not able to be completed. Point locations depict BIA Office locations or Tribal Office Headquarters. For completed trainings where a participant location was not provided a point locations may not be available. For more information on the Branch of Geospatial Support Geospatial training program, please visit:https://www.bia.gov/service/geospatial-training.

Geographic Information System (GIS) Software Market Outlook

According to our latest research, the global Geographic Information System (GIS) Software market size reached USD 11.6 billion in 2024, reflecting a robust demand for spatial data analytics and location-based services across various industries. The market is experiencing a significant growth trajectory, driven by a CAGR of 12.4% from 2025 to 2033. By the end of 2033, the GIS Software market is forecasted to attain a value of USD 33.5 billion. This remarkable expansion is primarily attributed to the integration of advanced technologies such as artificial intelligence, IoT, and cloud computing, which are enhancing the capabilities and accessibility of GIS platforms.

One of the major growth factors propelling the GIS Software market is the increasing adoption of location-based services across urban planning, transportation, and utilities management. Governments and private organizations are leveraging GIS solutions to optimize infrastructure development, streamline resource allocation, and improve emergency response times. The proliferation of smart city initiatives worldwide has further fueled the demand for GIS tools, as urban planners and municipal authorities require accurate spatial data for effective decision-making. Additionally, the evolution of 3D GIS and real-time mapping technologies is enabling more sophisticated modeling and simulation, expanding the scope of GIS applications beyond traditional mapping to include predictive analytics and scenario planning.

Another significant driver for the GIS Software market is the rapid digitization of industries such as agriculture, mining, and oil & gas. Precision agriculture, for example, relies heavily on GIS platforms to monitor crop health, manage irrigation, and enhance yield forecasting. Similarly, the mining sector uses GIS for exploration, environmental impact assessment, and asset management. The integration of remote sensing data with GIS software is providing stakeholders with actionable insights, leading to higher efficiency and reduced operational risks. Furthermore, the growing emphasis on environmental sustainability and regulatory compliance is prompting organizations to invest in advanced GIS solutions for monitoring land use, tracking deforestation, and managing natural resources.

The evolution of 3D GIS is revolutionizing the way spatial data is visualized and analyzed, offering a more immersive and detailed perspective of geographic information. This technology allows for the creation of three-dimensional models that provide a realistic representation of urban landscapes, infrastructure, and natural environments. By integrating 3D GIS with real-time data feeds, organizations can enhance their spatial analysis capabilities, enabling more accurate simulations and predictions. This advancement is particularly beneficial for urban planners and architects who require detailed visualizations to assess the impact of new developments and infrastructure projects. Moreover, 3D GIS is facilitating better communication and collaboration among stakeholders by providing a common platform for visualizing complex spatial data.

The expanding use of cloud-based GIS solutions is also a key factor driving market growth. Cloud deployment offers scalability, cost-effectiveness, and remote accessibility, making GIS tools more accessible to small and medium enterprises as well as large organizations. The cloud model supports real-time data sharing and collaboration, which is particularly valuable for disaster management and emergency response teams. As organizations increasingly prioritize digital transformation, the demand for cloud-native GIS platforms is expected to rise, supported by advancements in data security, interoperability, and integration with other enterprise systems.

Regionally, North America remains the largest market for GIS Software, accounting for a significant share of global revenues. This leadership is underpinned by substantial investments in smart infrastructure, advanced transportation systems, and environmental monitoring programs. The Asia Pacific region, however, is witnessing the fastest growth, driven by rapid urbanization, government-led digital initiatives, and the expansion of the utility and agriculture sectors. Europe continues to demonstrate steady adoption, particularly in environmental manage

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

The Air, Water, and Aquatic Environments (AWAE) research program is one of eight Science Program areas within the Rocky Mountain Research Station (RMRS). Our science develops core knowledge, methods, and technologies that enable effective watershed management in forests and grasslands, sustain biodiversity, and maintain healthy watershed conditions. We conduct basic and applied research on the effects of natural processes and human activities on watershed resources, including interactions between aquatic and terrestrial ecosystems. The knowledge we develop supports management, conservation, and restoration of terrestrial, riparian and aquatic ecosystems and provides for sustainable clean air and water quality in the Interior West. With capabilities in atmospheric sciences, soils, forest engineering, biogeochemistry, hydrology, plant physiology, aquatic ecology and limnology, conservation biology and fisheries, our scientists focus on two key research problems: Core watershed research quantifies the dynamics of hydrologic, geomorphic and biogeochemical processes in forests and rangelands at multiple scales and defines the biological processes and patterns that affect the distribution, resilience, and persistence of native aquatic, riparian and terrestrial species. Integrated, interdisciplinary research explores the effects of climate variability and climate change on forest, grassland and aquatic ecosystems. Resources in this dataset:Resource Title: Projects, Tools, and Data. File Name: Web Page, url: https://www.fs.fed.us/rm/boise/AWAE/projects.html Projects include Air Temperature Monitoring and Modeling, Biogeochemistry Lab in Colorado, Rangewide Bull Trout eDNA Project, Climate Shield Cold-Water Refuge Streams for Native Trout, Cutthroat trout-rainbow trout hybridization - data downloads and maps, Fire and Aquatic Ecosystems science, Fish and Cattle Grazing reports, Geomophic Road Analysis and Inventory Package (GRAIP) tool for erosion and sediment delivery to streams, GRAIP_Lite - Geomophic Road Analysis and Inventory Package (GRAIP) tool for erosion and sediment delivery to streams, IF3: Integrating Forests, Fish, and Fire, National forest climate change maps: Your guide to the future, National forest contributions to streamflow, The National Stream Internet network, people, data, GIS, analysis, techniques, NorWeST Stream Temperature Regional Database and Model, River Bathymetry Toolkit (RBT), Sediment Transport Data for Idaho, Nevada, Wyoming, Colorado, SnowEx, Stream Temperature Modeling and Monitoring, Spatial Statistical Modeling on Stream netowrks - tools and GIS downloads, Understanding Sculpin DNA - environmental DNA and morphological species differences, Understanding the diversity of Cottusin western North America, Valley Bottom Confinement GIS tools, Water Erosion Prediction Project (WEPP), Great Lakes WEPP Watershed Online GIS Interface, Western Division AFS - 2008 Bull Trout Symposium - Bull Trout and Climate Change, Western US Stream Flow Metric Dataset

Abstract Aims: This paper sought to evaluate the infrastructure of public swimming pools in a countryside municipality of the state of São Paulo and to present the Geographic Information System (GIS) as a tool capable of assisting in the management of sports facilities and programs. Methods: This is a descriptive study since it intends to expose the characteristics of a certain context. First, documentary research was performed to map the facilities and their respective projects. After that, a field survey was conducted seeking to evaluate the infrastructure of public pools and their surroundings through observation. Lastly, using georeferencing software, the population, and socioeconomic data around these pools were obtained and analyzed. Results: It was identified ten public swimming pools, and in seven the offer of swimming projects was foreseen. The infrastructure of the pools is mainly unsatisfactory, making necessary the improvement of the installation itself and in its surroundings. According to the results of the GIS, each pool has its specific public target concerning the characteristics of the profile of the residents surrounding these facilities. Conclusion: Information regarding the public profile around sports facilities generated from a tool such as GIS showed it is possible to determine which sports projects should be prioritized in each facility, leading to improvement in the management of sports-related public policies.

Needing to answer the question of “where” sat at the forefront of everyone’s mind, and using a geographic information system (GIS) for real-time surveillance transformed possibly overwhelming data into location intelligence that provided agencies and civic leaders with valuable insights.This book highlights best practices, key GIS capabilities, and lessons learned during the COVID-19 response that can help communities prepare for the next crisis.GIS has empowered:Organizations to use human mobility data to estimate the adherence to social distancing guidelinesCommunities to monitor their health care systems’ capacity through spatially enabled surge toolsGovernments to use location-allocation methods to site new resources (i.e., testing sites and augmented care sites) in ways that account for at-risk and vulnerable populationsCommunities to use maps and spatial analysis to review case trends at local levels to support reopening of economiesOrganizations to think spatially as they consider “back-to-the-workplace” plans that account for physical distancing and employee safety needsLearning from COVID-19 also includes a “next steps” section that provides ideas, strategies, tools, and actions to help jump-start your own use of GIS, either as a citizen scientist or a health professional. A collection of online resources, including additional stories, videos, new ideas and concepts, and downloadable tools and content, complements this book.Now is the time to use science and data to make informed decisions for our future, and this book shows us how we can do it.Dr. Este GeraghtyDr. Este Geraghty is the chief medical officer and health solutions director at Esri where she leads business development for the Health and Human Services sector.Matt ArtzMatt Artz is a content strategist for Esri Press. He brings a wide breadth of experience in environmental science, technology, and marketing.

The US Census TIGER/Line Geodatabases are spatial extracts from the Census Bureau's Master Address File/Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) System, designed for use with Geographic Information Systems (GIS) software. They allow users to visualize and analyze geographic features.These geodatabases are derived from the MAF/TIGER system, a comprehensive geographic database for the United States. They contain data on geographic boundaries and features, including roads, railroads, rivers, lakes, and political boundaries (state, county, city). They also include Census statistical boundaries like census blocks, block groups, and census tracts.While TIGER/Line Geodatabases do not contain demographic data, they have geographic entity codes that can be linked to demographic data available on data.census.gov. This allows users to combine geographic features with demographic statistics for analysis and mapping.Further details of the geographic boundaries and features can be found in the TIGER/Line Shapefiles technical documentation.TIGER/Line Geodatabaseshttps://www.census.gov/geographies/mapping-files/time-series/geo/tiger-geodatabase-file.htmlTIGER/Line Geodatabase Documentationhttps://www.census.gov/programs-surveys/geography/technical-documentation/complete-technical-documentation/tiger-geodatabase-file.htmlTIGER/Line Shapefiles and TIGER/Line Files Technical Documentationhttps://www.census.gov/programs-surveys/geography/technical-documentation/complete-technical-documentation/tiger-geo-line.htmlTIGERweb REST Serviceshttps://tigerweb.geo.census.gov/tigerwebmain/TIGERweb_restmapservice.htmlTIGERweb WMS Serviceshttps://tigerweb.geo.census.gov/tigerwebmain/TIGERweb_wms.html

In this course, you will explore a variety of open-source technologies for working with geosptial data, performing spatial analysis, and undertaking general data science. The first component of the class focuses on the use of QGIS and associated technologies (GDAL, PROJ, GRASS, SAGA, and Orfeo Toolbox). The second component of the class introduces Python and associated open-source libraries and modules (NumPy, Pandas, Matplotlib, Seaborn, GeoPandas, Rasterio, WhiteboxTools, and Scikit-Learn) used by geospatial scientists and data scientists. We also provide an introduction to Structured Query Language (SQL) for performing table and spatial queries. This course is designed for individuals that have a background in GIS, such as working in the ArcGIS environment, but no prior experience using open-source software and/or coding. You will be asked to work through a series of lecture modules and videos broken into several topic areas, as outlined below. Fourteen assignments and the required data have been provided as hands-on opportunites to work with data and the discussed technologies and methods. If you have any questions or suggestions, feel free to contact us. We hope to continue to update and improve this course. This course was produced by West Virginia View (http://www.wvview.org/) with support from AmericaView (https://americaview.org/). This material is based upon work supported by the U.S. Geological Survey under Grant/Cooperative Agreement No. G18AP00077. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey. After completing this course you will be able to: apply QGIS to visualize, query, and analyze vector and raster spatial data. use available resources to further expand your knowledge of open-source technologies. describe and use a variety of open data formats. code in Python at an intermediate-level. read, summarize, visualize, and analyze data using open Python libraries. create spatial predictive models using Python and associated libraries. use SQL to perform table and spatial queries at an intermediate-level.

Federation University Australia ’s Centre for eCommerce and Communications were engaged by
DIIRD to assist in researching the business case for a GIS application as part of the GippsLink Project, 2010.

The project stakeholders across six LGAs -Bass Coast Shire; Baw Baw Shire; East Gippsland Shire; Latrobe City; South Gippsland Shire, Wellington Shire participated in an online survey to gauge current usage of online GIS systems and to obtain feedback to ascertain priorities. The results of that survey form this dataset which is stored using Lime survey software. 23 responses were obtained.

BackgroundNeural tube defects (NTDs) are congenital birth defects that occur in the central nervous system, and they have the highest incidence among all birth defects. Shanxi Province in China has the world’s highest rate of NTDs. Since the 1990s, China’s government has worked on many birth defect prevention programs to reduce the occurrence of NTDs, such as pregnancy planning, health education, genetic counseling, antenatal ultrasonography and serological screening. However, the rate of NTDs in Shanxi Province is still higher than the world’s average morbidity rate after intervention. In addition, Shanxi Province has abundant coal reserves, and is the largest coal production province in China. The objectives of this study are to determine the temporal and spatial variation of the NTD rate in rural areas of Shanxi Province, China, and identify geographical environmental factors that were associated with NTDs in the risk area.MethodsIn this study, Heshun County and Yuanping County in Shanxi Province, which have high incidence of NTDs, were selected as the study areas. Two paired sample T test was used to analyze the changes in the risk of NTDs from the time dimension. Ripley’s k function and spatial filtering were combined with geographic information system (GIS) software to study the changes in the risk of NTDs from the spatial dimension. In addition, geographical detectors were used to identify the risk geographical environmental factors of NTDs in the study areas, especially the areas close to the coal sites and main roads.ResultsIn both Heshun County and Yuanping County, the incidence of NTDs was significantly (P

Free and reduced lunch data for each participating public school in Alaska. This data set includes the number of students receiving free lunches and reduced price lunches, and the percentage of the students enrolled in either of these programs. Students qualify for free and reduced meals under the National School Lunch Program.Where possible the data is mapped at the location of School that is associated with the program - however some data rows represent non-school entities. See source DEED data center https://education.alaska.gov/cnp/nslp for source dataSource: Alaska Department of Education & Early Development, School Nutrition Programs

This data has been visualized in a Geographic Information Systems (GIS) format and is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data. For more information and for questions about this data, see: Alaska Department of Education & Early Development Data Center.

A detailed explanation of how this dataset was put together, including data sources and methodologies, follows below.Please see the "Terms of Use" section below for the Data DictionaryDATA ACQUISITION AND CLEANING PROCESSThis dataset was built from 5 separate datasets queried during the months of April and May 2023 from the Census Microdata System (link below):https://data.census.gov/mdat/#/All datasets include information on Property Value (VALP) by: Educational Attainment (SCHL), Gender (SEX), a specified race or ethnicity (RAC or HISP), and are grouped by Public Use Microdata Areas (PUMAS). PUMAS are geographic areas created by the Census bureau; they are weighted by land area and population to facilitate data analysis. Data also Included totals for the state of New Mexico, so 19 total geographies are represented. Datasets were downloaded separately by race and ethnicity because this was the only way to obtain the VALP, SCHL, and SEX variables intersectionally with race or ethnicity data. Datasets were downloaded separately by race and ethnicity because this was the only way to obtain the VALP, SCHL, and SEX variables intersectionally with race or ethnicity data. Cleaning each dataset started with recoding the SCHL and HISP variables - details on recoding can be found below.After recoding, each dataset was transposed so that PUMAS were rows and SCHL, VALP, SEX, and Race or Ethnicity variables were the columns.Median values were calculated in every case that recoding was necessary. As a result, all Property Values in this dataset reflect median values.At times the ACS data downloaded with zeros instead of the 'null' values in initial query results. The VALP variable also included a "-1" variable to reflect N/A values (details in variable notes). Both zeros and "-1" values were removed before calculating median values, both to keep the data true to the original query and to generate accurate median values.Recoding the SCHL variable resulted in 5 rows for each PUMA, reflecting the different levels of educational attainment in each region. Columns grouped variables by race or ethnicity and gender. Cell values were property values.All 5 datasets were joined after recoding and cleaning the data. Original datasets all include 95 rows with 5 separate Educational Attainment variables for each PUMA, including New Mexico State totals.Because 1 row was needed for each PUMA in order to map this data, the data was split by Educational Attainment (SCHL), resulting in 110 columns reflecting median property values for each race or ethnicity by gender and level of educational attainment.A short, unique 2 to 5 letter alias was created for each PUMA area in anticipation of needing a unique identifier to join the data with. GIS AND MAPPING PROCESSA PUMA shapefile was downloaded from the ACS site. The Shapefile can be downloaded here: https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/PUMA_TAD_TAZ_UGA_ZCTA/MapServerThe DBF from the PUMA shapefile was exported to Excel; this shapefile data included needed geographic information for mapping such as: GEOID, PUMACE. The UIDs created for each PUMA were added to the shapefile data; the PUMA shapfile data and ACS data were then joined on UID in JMP.The data table was joined to the shapefile in ARC GiIS, based on PUMA region (specifically GEOID text).The resulting shapefile was exported as a GDB (geodatabase) in order to keep 'Null' values in the data. GDBs are capable of including a rule allowing null values where shapefiles are not. This GDB was uploaded to NMCDCs Arc Gis platform. SYSTEMS USEDMS Excel was used for data cleaning, recoding, and deriving values. Recoding was done directly in the Microdata system when possible - but because the system is was in beta at the time of use some features were not functional at times.JMP was used to transpose, join, and split data. ARC GIS Desktop was used to create the shapefile uploaded to NMCDC's online platform. VARIABLE AND RECODING NOTESTIMEFRAME: Data was queried for the 5 year period of 2015 to 2019 because ACS changed its definiton for and methods of collecting data on race and ethinicity in 2020. The change resulted in greater aggregation and les granular data on variables from 2020 onward.Note: All Race Data reflects that respondants identified as the specified race alone or in combination with one or more other races.VARIABLE:ACS VARIABLE DEFINITIONACS VARIABLE NOTESDETAILS OR URL FOR RAW DATA DOWNLOADRACBLKBlack or African American ACS Query: RACBLK, SCHL, SEX, VALP 2019 5yrRACAIANAmerican Indian and Alaska Native ACS Query: RACAIAN, SCHL, SEX, VALP 2019 5yrRACASNAsian ACS Query: RACASN, SCHL, SEX, VALP 2019 5yrRACWHTWhite ACS Query: RACWHT, SCHL, SEX, VALP 2019 5yrHISPHispanic Origin ACS Query: HISP ORG, SCHL, SEX, VALP 2019 5yrHISP RECODE: 24 original separate variablesThe Hispanic Origin (HISP) variable originally included 24 subcategories reflecting Mexican, Central American, South American, and Caribbean Latino, and Spanish identities from each Latin American counry. 7 recoded VariablesThese 24 variables were recoded (grouped) into 7 simpler categories for data analysis: Not Spanish/Hispanic/Latino, Mexican, Caribbean Latino, Central American, South American, Spaniard, All other Spanish/Hispanic/Latino Female. Not Spanish/Hispanic/Latino was not really used in the final dataset as the race datasets provided that information.SCHLEducational Attainment25 original separate variablesThe Educational Attainment (SCHL) variable originally included 25 subcategories reflecting the education levels of adults (over 18) surveyed by the ACS. These include: Kindergarten, Grades 1 through 12 separately, 12th grade with no diploma, Highschool Diploma, GED or credential, less than 1 year of college, more than 1 year of college with no degree, Associate's Degree, Bachelor's Degree, Master's Degree, Professional Degree, and Doctorate Degree.SCHL RECODE: 5 recoded variablesThese 25 variables were recoded (grouped) into 5 simpler categories for data analysis: No High School Diploma, High School Diploma or GED, Some College, Bachelor's Degree, and Advanced or Professional DegreeSEXGender2 variables1 - Male, 2 - FemaleVALPProperty Value1 variableValues were rounded and top-coded by ACS for anonymity. The "-1" variable is defined as N/A (GQ/ Vacant lots except 'for sale only' and 'sold, not occupied' / not owned or being bought.) This variable reflects the median value of property owned by individuals of each race, ethnicity, gender, and educational attainment category.PUMAPublic Use Microdata Area18 PUMAsPUMAs in New Mexico can be viewed here:https://nmcdc.maps.arcgis.com/apps/mapviewer/index.html?webmap=d9fed35f558948ea9051efe9aa529eafData includes 19 total regions: 18 Pumas and NM State TotalsNOTES AND RESOURCESThe following resources and documentation were used to navigate the ACS PUMS system and to answer questions about variables:Census Microdata API User Guide:https://www.census.gov/data/developers/guidance/microdata-api-user-guide.Additional_Concepts.html#list-tab-1433961450Accessing PUMS Data:https://www.census.gov/programs-surveys/acs/microdata/access.htmlHow to use PUMS on data.census.govhttps://www.census.gov/programs-surveys/acs/microdata/mdat.html2019 PUMS Documentation:https://www.census.gov/programs-surveys/acs/microdata/documentation.2019.html#list-tab-13709392012014 to 2018 ACS PUMS Data Dictionary:https://www2.census.gov/programs-surveys/acs/tech_docs/pums/data_dict/PUMS_Data_Dictionary_2014-2018.pdf2019 PUMS Tiger/Line Shapefileshttps://www.census.gov/cgi-bin/geo/shapefiles/index.php?year=2019&layergroup=Public+Use+Microdata+Areas Note 1: NMCDC attemepted to contact analysts with the ACS system to clarify questions about variables, but did not receive a timely response. Documentation was then consulted.Note 2: All relevant documentation was reviewed and seems to imply that all survey questions were answered by adults, age 18 or over. Youth who have inherited property could potentially be reflected in this data.Dataset and feature service created in May 2023 by Renee Haley, Data Specialist, NMCDC.

This GIS dataset offers a link to the California portion of the Nonindigenous Aquatic Species (NAS) information resource for the United States Geological Survey. The NAS program has been established as a central repository for accurate and spatially referenced biogeographic accounts of nonindigenous aquatic species. The program provides scientic reports, online/realtime queries, spatial data sets, regional contact lists, and general information. The goal of the information system is to provide timely, reliable data about the presence and distribution of nonindigenous aquatic species. The NAS database contains locality information for more than 1100 species of vertebrates, invertebrates, and vascular plants. The NAS program provides a continual national repository of distribution information for nonindigenous aquatic species that is used to gain an understanding of aquatic introductions, identify geographic gaps, and access the status of introduced aquatic species nationwide. Data are obtained from many sources including literature, museums, databases, monitoring programs, state and federal agencies, professional communications, online reporting forms, and Aquatic Nuisance Species (ANS) hotline reports. The NAS program defines a nonindigenous aquatic species as a member(s) of a species that enters a body of water of aquatic ecosystem outside of its historic or native range. This includes not only species that arrived from outside of North America but also species native to North America that have been introduced to drainages outside their ranges within the country. Please visit http://nas.er.usgs.gov for more information and to see all of the products and data available through the NAS program.

Terms of UseData Limitations and DisclaimerThe user’s use of and/or reliance on the information contained in the Document shall be at the user’s own risk and expense. MassDEP disclaims any responsibility for any loss or harm that may result to the user of this data or to any other person due to the user’s use of the Document.This is an ongoing data development project. Attempts have been made to contact all PWS systems, but not all have responded with information on their service area. MassDEP will continue to collect and verify this information. Some PWS service areas included in this datalayer have not been verified by the PWS or the municipality involved, but since many of those areas are based on information published online by the municipality, the PWS, or in a publicly available report, they are included in the estimated PWS service area datalayer.Please note: All PWS service area delineations are estimates for broad planning purposes and should only be used as a guide. The data is not appropriate for site-specific or parcel-specific analysis. Not all properties within a PWS service area are necessarily served by the system, and some properties outside the mapped service areas could be served by the PWS – please contact the relevant PWS. Not all service areas have been confirmed by the systems.Please use the following citation to reference these data:MassDEP, Water Utility Resilience Program. 2025. Community and Non-Transient Non-Community Public Water System Service Area (PubV2025_3).IMPORTANT NOTICE: This MassDEP Estimated Water Service datalayer may not be complete, may contain errors, omissions, and other inaccuracies and the data are subject to change. This version is published through MassGIS. We want to learn about the data uses. If you use this dataset, please notify staff in the Water Utility Resilience Program (WURP@mass.gov).This GIS datalayer represents approximate service areas for Public Water Systems (PWS) in Massachusetts. In 2017, as part of its “Enhancing Resilience and Emergency Preparedness of Water Utilities through Improved Mapping” (Critical Infrastructure Mapping Project ), the MassDEP Water Utility Resilience Program (WURP) began to uniformly map drinking water service areas throughout Massachusetts using information collected from various sources. Along with confirming existing public water system (PWS) service area information, the project collected and verified estimated service area delineations for PWSs not previously delineated and will continue to update the information contained in the datalayers. As of the date of publication, WURP has delineated Community (COM) and Non-Transient Non-Community (NTNC) service areas. Transient non-community (TNCs) are not part of this mapping project.Layers and Tables:The MassDEP Estimated Public Water System Service Area data comprises two polygon feature classes and a supporting table. Some data fields are populated from the MassDEP Drinking Water Program’s Water Quality Testing System (WQTS) and Annual Statistical Reports (ASR).The Community Water Service Areas feature class (PWS_WATER_SERVICE_AREA_COMM_POLY) includes polygon features that represent the approximate service areas for PWS classified as Community systems.The NTNC Water Service Areas feature class (PWS_WATER_SERVICE_AREA_NTNC_POLY) includes polygon features that represent the approximate service areas for PWS classified as Non-Transient Non-Community systems.The Unlocated Sites List table (PWS_WATER_SERVICE_AREA_USL) contains a list of known, unmapped active Community and NTNC PWS services areas at the time of publication.ProductionData UniversePublic Water Systems in Massachusetts are permitted and regulated through the MassDEP Drinking Water Program. The WURP has mapped service areas for all active and inactive municipal and non-municipal Community PWSs in MassDEP’s Water Quality Testing Database (WQTS). Community PWS refers to a public water system that serves at least 15 service connections used by year-round residents or regularly serves at least 25 year-round residents.All active and inactive NTNC PWS were also mapped using information contained in WQTS. An NTNC or Non-transient Non-community Water System refers to a public water system that is not a community water system and that has at least 15 service connections or regularly serves at least 25 of the same persons or more approximately four or more hours per day, four or more days per week, more than six months or 180 days per year, such as a workplace providing water to its employees.These data may include declassified PWSs. Staff will work to rectify the status/water services to properties previously served by declassified PWSs and remove or incorporate these service areas as needed.Maps of service areas for these systems were collected from various online and MassDEP sources to create service areas digitally in GIS. Every PWS is assigned a unique PWSID by MassDEP that incorporates the municipal ID of the municipality it serves (or the largest municipality it serves if it serves multiple municipalities). Some municipalities contain more than one PWS, but each PWS has a unique PWSID. The Estimated PWS Service Area datalayer, therefore, contains polygons with a unique PWSID for each PWS service area.A service area for a community PWS may serve all of one municipality (e.g. Watertown Water Department), multiple municipalities (e.g. Abington-Rockland Joint Water Works), all or portions of two or more municipalities (e.g. Provincetown Water Dept which serves all of Provincetown and a portion of Truro), or a portion of a municipality (e.g. Hyannis Water System, which is one of four PWSs in the town of Barnstable).Some service areas have not been mapped but their general location is represented by a small circle which serves as a placeholder. The location of these circles are estimates based on the general location of the source wells or the general estimated location of the service area - these do not represent the actual service area.Service areas were mapped initially from 2017 to 2022 and reflect varying years for which service is implemented for that service area boundary. WURP maintains the dataset quarterly with annual data updates; however, the dataset may not include all current active PWSs. A list of unmapped PWS systems is included in the USL table PWS_WATER_SERVICE_AREA_USL available for download with the dataset. Some PWSs that are not mapped may have come online after this iteration of the mapping project; these will be reconciled and mapped during the next phase of the WURP project. PWS IDs that represent regional or joint boards with (e.g. Tri Town Water Board, Randolph/Holbrook Water Board, Upper Cape Regional Water Cooperative) will not be mapped because their individual municipal service areas are included in this datalayer.PWSs that do not have corresponding sources, may be part of consecutive systems, may have been incorporated into another PWSs, reclassified as a different type of PWS, or otherwise taken offline. PWSs that have been incorporated, reclassified, or taken offline will be reconciled during the next data update.Methodologies and Data SourcesSeveral methodologies were used to create service area boundaries using various sources, including data received from the systems in response to requests for information from the MassDEP WURP project, information on file at MassDEP, and service area maps found online at municipal and PWS websites. When provided with water line data rather than generalized areas, 300-foot buffers were created around the water lines to denote service areas and then edited to incorporate generalizations. Some municipalities submitted parcel data or address information to be used in delineating service areas.Verification ProcessSmall-scale PDF file maps with roads and other infrastructure were sent to every PWS for corrections or verifications. For small systems, such as a condominium complex or residential school, the relevant parcels were often used as the basis for the delineated service area. In towns where 97% or more of their population is served by the PWS and no other service area delineation was available, the town boundary was used as the service area boundary. Some towns responded to the request for information or verification of service areas by stating that the town boundary should be used since all or nearly all of the municipality is served by the PWS.Sources of information for estimated drinking water service areasThe following information was used to develop estimated drinking water service areas:EOEEA Water Assets Project (2005) water lines (these were buffered to create service areas)Horsely Witten Report 2008Municipal Master Plans, Open Space Plans, Facilities Plans, Water Supply System Webpages, reports and online interactive mapsGIS data received from PWSDetailed infrastructure mapping completed through the MassDEP WURP Critical Infrastructure InitiativeIn the absence of other service area information, for municipalities served by a town-wide water system serving at least 97% of the population, the municipality’s boundary was used. Determinations of which municipalities are 97% or more served by the PWS were made based on the Percent Water Service Map created in 2018 by MassDEP based on various sources of information including but not limited to:The Winter population served submitted by the PWS in the ASR submittalThe number of services from WQTS as a percent of developed parcelsTaken directly from a Master Plan, Water Department Website, Open Space Plan, etc. found onlineCalculated using information from the town on the population servedMassDEP staff estimateHorsely Witten Report 2008Calculation based on Water System Areas Mapped through MassDEP WURP Critical Infrastructure Initiative, 2017-2022Information found in publicly available PWS planning documents submitted to MassDEP or as part of infrastructure planningMaintenanceThe

Location of special curriculum schools and programs in Los Angeles CountyThis dataset is maintained through the County of Los Angeles Location Management System. The Location Management System is used by the County of Los Angeles GIS Program to maintain a single, comprehensive geographic database of locations countywide. For more information on the Location Management System, visit http://egis3.lacounty.gov/lms/.

This study is an investigation of the agrarian landscape in the western part of Ostergotland County (in Sweden). The study was published in the form of a dissertation in Human Geography written (in German) by Steffan Helmfrid, and defended in 1962. Two maps were produced as a part of the dissertation, based on historical material. These maps show the agrarian landscape as it was around 1640. Helmfrid's analogue process for producing these maps was unique for the 1960s. These two maps are made available here.

Two maps are made available here:

1) Name: No Title (Working version) scale: 1:50,000 coordinate system: no coordinates file format: tiff (.tif) file size: 198 MB file name: helmfrid_orginal_photoshop.tif

2) Name: "The Agrarian Landscape on the Vadstena plains around the year 1640" scale: 1:100,000 coordinate system: RT 90 2.5 gon V (EPSG 3021) file format: geotiff (.tif) file size: 40 MB file name: helmfrid_rectify.tif

The first map is an unpublished working version that was used as the basis for the second map which is the published version contained in Helmfrid's dissertation. The working version has a larger-scale, which means it is more detailed than the second map.

The second map was digitized and rectified in 2009 by Johan Berg, also from Stockholm University. This was done as part of a project on land relations during the Younger Iron Age in western Ostergotland. (See link to separate data , 2019-102, in SND's catalog). The working version (the first map) was digitized in 2019 when it was decided to publish Helmfrid's maps via the Swedish National Data Service.

The working version was based on a compilation of 400 smaller village and farm maps, all from the 1640s. These sources can be found in the following historical property maps (in Swedish: geometriska jordeböcker): D5, D6, D7, D8 and D10 (See https://riksarkivet.se/visa-kartsamlingar -- "D" refers to maps from Ostergotland county). All the original historical maps were signed by one surveyor -- Larson Groth.

The rectified map can be opened in most Geographic Information System (GIS) programs. The map can also be opened in image editing software, but without coordinates. The working version (the first map) is also a raster file, but because the working version was never rectified, it has been saved as a normal tiff file (.tif), not a geotiff. It can be opened in image editing programs like Photoshop or opensource Gimp.

More information on how Helmfrid produced these maps and a link and reference to his dissertation can be found (in Swedish) in the attached documentation file.

In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within California’s State Waters. The program supports a large number of coastal-zone- and ocean-management issues, including the California Marine Life Protection Act (MLPA) (California Department of Fish and Wildlife, 2008), which requires information about the distribution of ecosystems as part of the design and proposal process for the establishment of Marine Protected Areas. A focus of CSMP is to map California’s State Waters with consistent methods at a consistent scale. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology. It is emphasized that the more interpretive habitat and geology data rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. This approach and CSMP planning is based in part on recommendations of the Marine Mapping Planning Workshop (Kvitek and others, 2006), attended by coastal and marine managers and scientists from around the state. That workshop established geographic priorities for a coastal mapping project and identified the need for coverage of “lands” from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California’s State Waters. Unfortunately, surveying the zone from MHHW out to 10-m water depth is not consistently possible using ship-based surveying methods, owing to sea state (for example, waves, wind, or currents), kelp coverage, and shallow rock outcrops. Accordingly, some of the data presented in this series commonly do not cover the zone from the shore out to 10-m depth. This data is part of a series of online U.S. Geological Survey (USGS) publications, each of which includes several map sheets, some explanatory text, and a descriptive pamphlet. Each map sheet is published as a PDF file. Geographic information system (GIS) files that contain both ESRI ArcGIS raster grids (for example, bathymetry, seafloor character) and geotiffs (for example, shaded relief) are also included for each publication. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed September 20, 2013). The California Seafloor Mapping Program is a collaborative venture between numerous different federal and state agencies, academia, and the private sector. CSMP partners include the California Coastal Conservancy, the California Ocean Protection Council, the California Department of Fish and Wildlife, the California Geological Survey, California State University at Monterey Bay’s Seafloor Mapping Lab, Moss Landing Marine Laboratories Center for Habitat Studies, Fugro Pelagos, Pacific Gas and Electric Company, National Oceanic and Atmospheric Administration (NOAA, including National Ocean Service–Office of Coast Surveys, National Marine Sanctuaries, and National Marine Fisheries Service), U.S. Army Corps of Engineers, the Bureau of Ocean Energy Management, the National Park Service, and the U.S. Geological Survey. These web services for the Santa Barbara Channel map area includes data layers that are associated to GIS and map sheets available from the USGS CSMP web page at https://walrus.wr.usgs.gov/mapping/csmp/index.html. Each published CSMP map area includes a data catalog of geographic information system (GIS) files; map sheets that contain explanatory text; and an associated descriptive pamphlet. This web service represents the available data layers for this map area. Data was combined from different sonar surveys to generate a comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic including exposed bedrock outcrops, large fields of sand waves, as well as many human impacts on the seafloor. To validate geological and biological interpretations of the sonar data, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and photographic imagery; these “ground-truth” surveying data are available from the CSMP Video and Photograph Portal at https://doi.org/10.5066/F7J1015K. The “seafloor character” data layer shows classifications of the seafloor on the basis of depth, slope, rugosity (ruggedness), and backscatter intensity and which is further informed by the ground-truth-survey imagery. The “potential habitats” polygons are delineated on the basis of substrate type, geomorphology, seafloor process, or other attributes that may provide a habitat for a specific species or assemblage of organisms. Representative seismic-reflection profile data from the map area is also include and provides information on the subsurface stratigraphy and structure of the map area. The distribution and thickness of young sediment (deposited over the past about 21,000 years, during the most recent sea-level rise) is interpreted on the basis of the seismic-reflection data. The geologic polygons merge onshore geologic mapping (compiled from existing maps by the California Geological Survey) and new offshore geologic mapping that is based on integration of high-resolution bathymetry and backscatter imagery seafloor-sediment and rock samplesdigital camera and video imagery, and high-resolution seismic-reflection profiles. The information provided by the map sheets, pamphlet, and data catalog has a broad range of applications. High-resolution bathymetry, acoustic backscatter, ground-truth-surveying imagery, and habitat mapping all contribute to habitat characterization and ecosystem-based management by providing essential data for delineation of marine protected areas and ecosystem restoration. Many of the maps provide high-resolution baselines that will be critical for monitoring environmental change associated with climate change, coastal development, or other forcings. High-resolution bathymetry is a critical component for modeling coastal flooding caused by storms and tsunamis, as well as inundation associated with longer term sea-level rise. Seismic-reflection and bathymetric data help characterize earthquake and tsunami sources, critical for natural-hazard assessments of coastal zones. Information on sediment distribution and thickness is essential to the understanding of local and regional sediment transport, as well as the development of regional sediment-management plans. In addition, siting of any new offshore infrastructure (for example, pipelines, cables, or renewable-energy facilities) will depend on high-resolution mapping. Finally, this mapping will both stimulate and enable new scientific research and also raise public awareness of, and education about, coastal environments and issues. Web services were created using an ArcGIS service definition file. The ArcGIS REST service and OGC WMS service include all Santa Barbara Channel map area data layers. Data layers are symbolized as shown on the associated map sheets.

Locations of environmental programs in Los Angeles CountyThis dataset is maintained through the County of Los Angeles Location Management System. The Location Management System is used by the County of Los Angeles GIS Program to maintain a single, comprehensive geographic database of locations countywide. For more information on the Location Management System, visit http://egis3.lacounty.gov/lms/.

Terms of Use:

Data Limitations Disclaimer

The MassDEP Estimated Sewer System Service Area Boundaries datalayer may not be complete, may contain errors, omissions, and other inaccuracies, and the data are subject to change. The user’s use of and/or reliance on the information contained in the Document (e.g. data) shall be at the user’s own risk and expense. MassDEP disclaims any responsibility for any loss or harm that may result to the user of this data or to any other person due to the user’s use of the Document.

All sewer service area delineations are estimates for broad planning purposes and should only be used as a guide. The data is not appropriate for site-specific or parcel-specific analysis. Not all properties within a sewer service area are necessarily served by the system, and some properties outside the mapped service areas could be served by the wastewater utility – please contact the relevant wastewater system. Not all service areas have been confirmed by the sewer system authorities.

This is an ongoing data development project. Attempts have been made to contact all sewer/wastewater systems, but not all have responded with information on their service area. MassDEP will continue to collect and verify this information. Some sewer service areas included in this datalayer have not been verified by the POTWs, privately-owned treatment works, GWDPs, or the municipality involved, but since many of those areas are based on information published online by the municipality, the utility, or in a publicly available report, they are included in the estimated sewer service area datalayer.

Please use the following citation to reference these data

MassDEP. Water Utility Resilience Program. 2025. Publicly-Owned Treatment Work and Non-Publicly-Owned Sewer Service Areas (PubV2024_12).

We want to learn about the data uses. If you use this dataset, please notify staff in the Water Resilience program (WURP@mass.gov).

Layers and Tables:

The MassDEP Estimated Sewer System Service Area data layer comprises two feature classes and a supporting table:

Publicly-Owned Treatment Works (POTW) Sewer Service Areas feature class SEWER_SERVICE_AREA_POTW_POLY includes polygon features for sewer service areas systems operated by publicly owned treatment works (POTWs)Non-Publicly Owned Treatment Works (NON-POTW) Sewer Service Areas feature class SEWER_SERVICE_AREA_NONPOTW_POLY includes polygon features for sewer service areas for operated by NON publicly owned treatment works (NON-POTWs)The Sewer Service Areas Unlocated Sites table SEWER_SERVICE_AREA_USL contains a list of known, unmapped active POTW and NON-POTW services areas at the time of publication.

ProductionData Universe

Effluent wastewater treatment plants in Massachusetts are permitted either through the Environmental Protection Agency’s (EPA) National Pollutant Discharge Elimination System (NPDES) surface water discharge permit program or the MassDEP Groundwater Discharge Permit Program. The WURP has delineated active service areas served by publicly and privately-owned effluent treatment works with a NPDES permit or a groundwater discharge permit.

National Pollutant Discharge Elimination System (NPDES) Permits

In the Commonwealth of Massachusetts, the EPA is the permitting authority for regulating point sources that discharge pollutants to surface waters. NPDES permits regulate wastewater discharge by limiting the quantities of pollutants to be discharged and imposing monitoring requirements and other conditions. NPDES permits are typically co-issued by EPA and the MassDEP. The limits and/or requirements in the permit ensure compliance with the Massachusetts Surface Water Quality Standards and Federal Regulations to protect public health and the aquatic environment. Areas served by effluent treatment plants with an active NPDES permit are included in this datalayer based on a master list developed by MassDEP using information sourced from the EPA’s Integrated Compliance Information System (ICIS).

Groundwater Discharge (GWD) Permits

In addition to surface water permittees, the WURP has delineated all active systems served by publicly and privately owned effluent treatment works with groundwater discharge (GWD) permits, and some inactive service areas. Groundwater discharge permits are required for systems discharging over 10,000 GPD sanitary wastewater – these include effluent treatment systems for public, district, or privately owned effluent treatment systems. Areas served by an effluent treatment plant with an active GWD permit are included in this datalayer based on lists received from MassDEP Wastewater staff.

Creation of Unique IDs for Each Service Area

The Sewer Service Area datalayer contains polygons that represent the service area of a particular wastewater system within a particular municipality. Every discharge permittee is assigned a unique NPDES permit number by EPA or a unique GWD permit identifier by MassDEP. MassDEP WURP creates a unique Sewer_ID for each service area by combining the municipal name of the municipality served with the permit number (NPDES or GWD) ascribed to the sewer that is serving that area. Some municipalities contain more than one sewer system, but each sewer system has a unique Sewer_ID. Occasionally the area served by a sewer system will overlap another town by a small amount – these small areas are generally not given a unique ID. The Estimated sewer Service Area datalayer, therefore, contains polygons with a unique Sewer_ID for each sewer service area. In addition, some municipalities will have multiple service areas being served by the same treatment plant – the Sewer_ID for these will contain additional identification, such as the name of the system, to uniquely identify each system.

Classifying System Service Areas

WURP staff reviewed the service areas for each system and, based on OWNER_TYPE, classified as either a publicly-owned treatment work (POTW) or a NON-POTW (see FAC_TYPE field). Each service area is further classified based on the population type served (see SECTOR field).

Methodologies and Data Sources

Several methodologies were used to create service area boundaries using various sources, including data received from the sewer system in response to requests for information from the MassDEP WURP project, information on file at MassDEP, and service area maps found online at municipal and wastewater system websites. When MassDEP received sewer line data rather than generalized areas, 300-foot buffers were created around the sewer lines to denote service areas and then edited to incorporate generalizations. Some municipalities submitted parcel data or address information to be used in delineating service areas. Many of the smaller GWD permitted sewer service areas were delineated using parcel boundaries related to the address on file.

Verification Process

Small-scale pdf file maps with roads and other infrastructure were sent to systems for corrections or verifications. If the system were small, such as a condominium complex or residential school, the relevant parcels were often used as the basis for the delineated service area. In towns where 97% or more of their population is served by the wastewater system and no other service area delineation was available, the town boundary was used as the service area boundary. Some towns responded to the request for information or verification of service areas by stating that the town boundary should be used since all, or nearly all, of the municipality is served by one wastewater system.

To ensure active systems are mapped, WURP staff developed two work flows. For NPDES-permitted systems, WURP staff reviewed available information on EPA’s ICIS database and created a master list of these systems. Staff will work to routinely update this master list by reviewing the ICIS database for new NPDES permits. The master list will serve as a method for identifying active systems, inactive systems, and unmapped systems. For GWD permittees, GIS staff established a direct linkage to the groundwater database, which allows for populating information into data fields and identifying active systems, inactive systems, and unmapped systems.

All unmapped systems are added to the Sewer Service Area Unlocated List (SEWER_SERVICE_AREAS_USL) for future mapping. Some service areas have not been mapped but their general location is represented by a small circle which serves as a placeholder - the location of these circles are estimated based on the general location of the treatment plant or the general estimated location of the service area - these do not represent the actual service area.

Percent Served Statistics The attribute table for the POTW sewer service areas (SEWER_SERVICE_AREA_POTW_POLY) has several fields relating to the percent of the town served by the particular system and one field describing the percent of town served by all systems in the town. The field ‘Percent AREA Served by System’ is strictly a calculation done dividing the area of the system by the total area of the town and multiplying by 100. In contrast, the field ‘Percent Served by System’, is not based on a particular calculation or source – it is an estimate based on various sources – these estimates are for planning purposes only. Data includes information from municipal websites and associated plans, the 1990 Municipal Priority list from CMR 310 14.17, the 2004 Pioneer Institute for Public Policy Research “percent on sewer” document, information contained on NPDES Permits and MassDEP Wastewater program staff input. Not all POTW systems have percent served statistics. Percentage may reflect the percentage of parcels served, the percent of area within a community served or the population served and should not be used for legal boundary definition or regulatory interpretation.

Sources of information for estimated wastewater service areas:

EEOA Water Assets

A County Geologic Atlas (CGA) project is a study of a county's geology, and its mineral and ground-water resources. The information collected during the project is used to develop maps, data-base files, and reports. This same information is also produced as digital files. The map information is formatted as geographic information system (GIS) files with associated data bases. The maps and reports are also reproduced as portable document files (PDFs) that can be opened on virtually any computer using the free Acrobat Reader from Adobe.com. All of the digital files for the CGA's can be downloaded from the University of Minnesota Digital Conservancy. The majority of the files can also be viewed and queried through the use of this Story Map.Atlas information is commonly used in planning and environmental protection programs, as an educational resource, and by industries involved in water and mineral resources. It represents a comprehensive, detailed compilation of geologic data and interpretations within a county. The distribution and character of geologic materials determine how and where water enters the earth, and where it is stored in aquifers that can supply our needs. Geologic maps are a key element in delineating those flow paths and in relating land use to water quality. The atlas also provides a framework and terminology to support more detailed, site-specific studies. The records of water wells drilled in the area are an important source of data for constructing the maps and for understanding the distribution and use of ground water in the county. A data base of the information from those wells is one of the atlas products, and it can be queried with the GIS files to yield valuable insights for managing the ground-water resource.The atlas is also useful to non-professionals who simply wish to learn more about the geology of the county. It is a one-stop, comprehensive collection of information in a variety of forms and styles that should be useful to anyone with an interest in earth science or the county.The geologic data and maps are produced and distributed by the Minnesota Geological Survey (MGS) as Part A of an Atlas. The Minnesota Department of Natural Resources follows with an investigation of the quantity, quality, and pollution sensitivity of ground water. Their products are distributed as Part B of the atlas, at a later date. If necessary, a report with additional information that was not possible to include on the limited space of the printed maps is produced by MGS as Part C of, or included as a supplement to, an atlas. The Atlas CD or DVD, which is available online at the Digital Conservancy, includes all the atlas products developed by the Minnesota Geological Survey.