This dataset holds all materials for the Inform E-learning GIS course

Learn state-of-the-art skills to build compelling, useful, and fun Web GIS apps easily, with no programming experience required.Building on the foundation of the previous three editions, Getting to Know Web GIS, fourth edition,features the latest advances in Esri’s entire Web GIS platform, from the cloud server side to the client side.Discover and apply what’s new in ArcGIS Online, ArcGIS Enterprise, Map Viewer, Esri StoryMaps, Web AppBuilder, ArcGIS Survey123, and more.Learn about recent Web GIS products such as ArcGIS Experience Builder, ArcGIS Indoors, and ArcGIS QuickCapture. Understand updates in mobile GIS such as ArcGIS Collector and AuGeo, and then build your own web apps.Further your knowledge and skills with detailed sections and chapters on ArcGIS Dashboards, ArcGIS Analytics for the Internet of Things, online spatial analysis, image services, 3D web scenes, ArcGIS API for JavaScript, and best practices in Web GIS.Each chapter is written for immediate productivity with a good balance of principles and hands-on exercises and includes:A conceptual discussion section to give you the big picture and principles,A detailed tutorial section with step-by-step instructions,A Q/A section to answer common questions,An assignment section to reinforce your comprehension, andA list of resources with more information.Ideal for classroom lab work and on-the-job training for GIS students, instructors, GIS analysts, managers, web developers, and other professionals, Getting to Know Web GIS, fourth edition, uses a holistic approach to systematically teach the breadth of the Esri Geospatial Cloud.AUDIENCEProfessional and scholarly. College/higher education. General/trade.AUTHOR BIOPinde Fu leads the ArcGIS Platform Engineering team at Esri Professional Services and teaches at universities including Harvard University Extension School. His specialties include web and mobile GIS technologies and applications in various industries. Several of his projects have won specialachievement awards. Fu is the lead author of Web GIS: Principles and Applications (Esri Press, 2010).Pub Date: Print: 7/21/2020 Digital: 6/16/2020 Format: Trade paperISBN: Print: 9781589485921 Digital: 9781589485938 Trim: 7.5 x 9 in.Price: Print: $94.99 USD Digital: $94.99 USD Pages: 490TABLE OF CONTENTSPrefaceForeword1 Get started with Web GIS2 Hosted feature layers and storytelling with GIS3 Web AppBuilder for ArcGIS and ArcGIS Experience Builder4 Mobile GIS5 Tile layers and on-premises Web GIS6 Spatial temporal data and real-time GIS7 3D web scenes8 Spatial analysis and geoprocessing9 Image service and online raster analysis10 Web GIS programming with ArcGIS API for JavaScriptPinde Fu | Interview with Esri Press | 2020-07-10 | 15:56 | Link.

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.

This tool is a cool way to view geospatial data online! no special program necessary. This tool is a cool way to view geospatial data online! no special program necessary. This tool is a cool way to view geospatial data online! no special program necessary.

Elevation Point Cloud data is available from various sources. Visit the links below or contact Reed Burgette (reed.burgette@dogami.oregon.gov) at Department of Geology and Mineral Industries (DOGAMI) for more information. Resources: https://gis.dogami.oregon.gov/maps/lidarviewer/ ftp://lidar.engr.oregonstate.edu/ https://coast.noaa.gov/digitalcoast/ https://www.usgs.gov/programs/national-geospatial-program/national-map

This project is a component of a broader effort focused on geothermal heating and cooling (GHC) with the aim of illustrating the numerous benefits of incorporating GHC and geothermal heat exchange (GHX) into community energy planning and national decarbonization strategies. To better assist private sector investment, it is currently necessary to define and assess the potential of low-temperature geothermal resources. For shallow GHC/GHX fields, there is no formal compilation of subsurface characteristics shared among industry practitioners that can improve system design and operations. Alaska is specifically noted in this work, because heretofore, it has not received a similar focus in geothermal potential evaluations as the contiguous United States. The methodology consists of leveraging relevant data to generate a baseline geospatial dataset of low-temperature resources (less than 150 degrees C) to compare and analyze information accessible to anyone trying to understand the potential of GHC/GHX and small-scale low-temperature geothermal power in Alaska (e.g., energy modelers, communities, planners, and policymakers). Importantly, this project identifies data related to (1) the evaluation of GHC/GHX in the shallow subsurface, and (2) the evaluation of low-temperature geothermal resource availability. Additionally, data is being compiled to assess repurposing of oil and gas wells to contribute co-produced fluids toward the geothermal direct use and heating and cooling resource potential. In this work we identified new data from three different datasets of isolated geothermal systems in Alaska and bottom-hole temperature data from oil and gas wells that can be leveraged for evaluation of low-temperature geothermal resource potential. The goal of this project is to facilitate future deployment of GHC/GHX analysis and community-led programs and update the low-temperature geothermal resources assessment of Alaska. A better understanding of shallow potential for GHX will improve design and operations of highly efficient GHC systems. The deployment and impact that can be achieved for low-temperature geothermal resources will contribute to decarbonization goals and facilitate widespread electrification by shaving and shifting grid loads.

Colleges and UniversitiesThis feature layer, utilizing data from the National Center for Education Statistics (NCES), displays colleges and universities in the U.S. and its territories. NCES uses the Integrated Postsecondary Education Data System (IPEDS) as the "primary source for information on U.S. colleges, universities, and technical and vocational institutions." According to NCES, this layer "contains directory information for every institution in the 2021-22 IPEDS universe. Includes name, address, city, state, zip code and various URL links to the institution's home page, admissions, financial aid offices and the net price calculator. Identifies institutions as currently active, institutions that participate in Title IV federal financial aid programs for which IPEDS is mandatory. It also includes variables derived from the 2021-22 Institutional Characteristics survey, such as control and level of institution, highest level and highest degree offered and Carnegie classifications."Gallaudet UniversityData currency: 2021Data source: IPEDS Complete Data FilesData modification: Removed fields with coded values and replaced with descriptionsFor more information: Integrated Postsecondary Education Data SystemSupport documentation: IPEDS Complete Data Files > Directory Information > DictionaryFor feedback, please contact: ArcGIScomNationalMaps@esri.comU.S. Department of Education (ED)Per ED, "ED's mission is to promote student achievement and preparation for global competitiveness by fostering educational excellence and ensuring equal access.ED was created in 1980 by combining offices from several federal agencies." ED's employees and budget "are dedicated to:Establishing policies on federal financial aid for education, and distributing as well as monitoring those funds.Collecting data on America's schools and disseminating research.Focusing national attention on key educational issues.Prohibiting discrimination and ensuring equal access to education."

Overview of the Disaster Risk Resilience InitiativeOverview | About | 2018-2019 Activities | 2020-2021 Activities | 2022-2023 Activities | Related ProjectsThe Open Geospatial Consortium (OGC) is working with the community to improve spatial data infrastructures to provide data and services that improve community resilience for disasters and climate. Geospatial information has been proven effective in supporting both the understanding of and response to disasters. However, the ability to effectively share, use, and reuse geospatial information and applications across and between governments and nongovernmental organizations in support of disaster response and resilience is dependent upon having the required partnerships, policies, standards, architecture, and technologies already in place when disaster strikes.Geospatial Data and Technology for Preparedness, Response, and RecoveryThe OGC Spatial Data Infrastructures for Disaster Resilience initiative was initiated in 2018 to understand how to best support the development of, or combination of SDI(s) for the use in disasters, to advance the understanding of stakeholder issues, and serve stakeholders’ needs.Quality, up-to-date geospatial data and tools are central to citizen access to governmental programs and, in addition, are an important means for Federal Agencies to interact and communicate with local communities and citizens. Geospatial data and tools also have the potential to save lives, limit damage, and reduce the costs of dealing with emergencies. Disasters point out the need for integrated solutions, including on-the-ground emergency response capabilities informed by geospatial tools and technologies. Geospatial applications are critical for preparedness activities, response to events, and post-disaster management. Geospatial tools play an increasing role in disaster response by improving communication through spatial data, providing capacity for interagency and intergovernmental approaches to address disasters, and facilitating long-term strategies for recovery efforts, risk reduction, restoration, and monitoring programs.In partnership with the OGC, the FGDC, the U.S. Department of Homeland Security (DHS), and the U.S. Geological Survey (USGS) initiated a concept development study to gather information from national and global stakeholders through surveys, workshops, and interviews. The FGDC participated in national and international conferences and engaged a variety of disasters committees, executives, and stakeholders to encourage participation. The FGDC co-led two well-attended workshops with more than 200 representatives from over 80 organizations, including Federal, State, local, academic, commercial, international, and other non-governmental organizations. The resulting study identified key challenges, gaps, needs, lessons learned, best practices, and other information critical for crafting a strategy to advance the use of geospatial data and services in disaster response, including the following:The lack of an integrated policy and operational framework to facilitate rapid acceptance, qualification, ingestion, and use of relevant geospatial information from a range of government and commercial providers and citizens.The inability with existing metadata approaches to quickly discover and understand which information sources are most useful in the context of a user’s need, especially for first responders.The inability to properly fuse and synthesize multiple data sources locally to derive the knowledge necessary for rapid disaster-response decisions.The need for a persistent platform to organize and manage disaster-related geospatial information and tools necessary for collaborating organizations to address the full disaster lifecycle—preparedness, response, and recovery.Over the next 5 years, the FGDC and other collaborators will engage the disasters community to address elements of the concept development study through workshops, pilots, and other initiatives. We will demonstrate how data standards help stakeholders and decision makers gain new and beneficial perspectives into social, economic, and environmental issues related to disasters by providing access to the vast online geospatial ecosystem of resources that improve the sharing, use, and integration of information tied to locations across the globe.From 2019-2023, OGC collaborators will engage the disasters community to address elements of the concept development study through workshops, pilots, and other initiatives. We will demonstrate how data standards help stakeholders and decision makers gain new and beneficial perspectives into social, economic, and environmental issues related to disasters by providing access to the vast online geospatial ecosystem of resources that improve the sharing, use, and integration of information tied to locations across the globe.

The College and University Campuses feature class/shapefile is composed of all Post Secondary Education facilities as defined by the Homeland Infrastructure Foundation-Level Data (HIFLD) Colleges and Universities and Supplemental Colleges point feature classes/shapefiles with a POPULATION value greater than or equal to 500. Also included is a subset of campuses with a POPULATION value under 500 or equal to -999. Included are Doctoral/Research Universities, Masters Colleges and Universities, Baccalaureate Colleges, Associates Colleges, Theological seminaries, Medical Schools and other health care professions, Schools of engineering and technology, business and management, art, music, design, Law schools, Teachers colleges, Tribal colleges, and other specialized institutions. Excluded are online institutions and administrative records as well as colleges and universities that do not have a verifiable campus map. Overall, this data layer covers all 50 states, as well as Puerto Rico and other assorted U.S. territories. This feature class/shapefile contains all MEDS/MEDS+ as approved by the National Geospatial-Intelligence Agency (NGA) Homeland Security Infrastructure Program (HSIP) Team. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the "Place Keyword" section of the metadata. This feature class does not have a relationship class but is related to Supplemental Colleges and Colleges and Universities. Note that attribution is derived from the Colleges and Universities and Supplemental Colleges feature classes/shapefiles. Refer to the metadata of those feature classes/shapefiles for further information regarding attribution. This release includes 21 new records and the removal of 88 records that are no longer applicable based on the sourced datasets.

The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making.

   BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions.


   Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself.


   For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise.


   Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. 


   This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery.


   See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt


   See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

Integrated geospatial infrastructure is the modern pattern for connecting organizations across borders, jurisdictions, and sectors to address shared challenges. Implementation starts with a strategy, followed by the pillars of collaborative governance, data and technology, capacity building, and engagement. It is inherently multi-organizational.Whether you call your initiative a Spatial Data Infrastructure (SDI), Open Data, Digital Twin, Knowledge Infrastructure, Digital Ecosystem, or otherwise, collaboration is key.This guide shares good practices for new and existing ArcGIS Administrators to help you optimize ArcGIS Online for multi-organization collaboration.

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.

The Digital Geomorphic-GIS Map of Gulf Islands National Seashore (5-meter accuracy and 1-foot resolution 2006-2007 mapping), Mississippi and Florida 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 (guis_geomorphology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (guis_geomorphology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (guis_geomorphology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. 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 GIS readme file (guis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (guis_geomorphology.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 (guis_geomorphology_metadata_faq.pdf). Please read the guis_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. 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 (guis_geomorphology_metadata.txt or guis_geomorphology_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 1:26,000 and United States National Map Accuracy Standards features are within (horizontally) 13.2 meters or 43.3 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 Google Earth, 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 is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

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 global defense geospatial market is anticipated to grow at a CAGR of 4.77%, from USD 20.24 billion in 2025 to USD 33.29 billion by 2033. Increasing demand for accurate and real-time geospatial data for military operations, rising adoption of advanced technologies like AI and ML for geospatial analysis, and growing need for situational awareness in complex combat scenarios are driving the market growth. Furthermore, government initiatives to modernize military capabilities and enhance defense infrastructure are expected to contribute to the market expansion. North America is projected to hold the largest market share during the forecast period. The presence of major defense contractors such as Lockheed Martin, Northrop Grumman, and Raytheon Technologies, coupled with significant defense spending by the US government, contributes to the dominance of this region. Asia-Pacific is expected to witness the fastest growth rate due to increasing investments in defense modernization programs by countries like China, India, and Japan. The Middle East & Africa region is also anticipated to exhibit strong growth owing to ongoing conflicts and the need for improved border security measures. Key market players include Maxar Technologies, Thales Group, Trimble, Garmin, Airbus, L3Harris Technologies, Hexagon, Collins Aerospace, and Esri. Recent developments include: The Defense Geospatial Market is anticipated to reach USD 30.8 billion by 2032, expanding at a CAGR of 4.77% from 2024 to 2032. Increasing geopolitical tensions and the growing need for accurate and timely geospatial intelligence are driving market growth. Advancements in technologies such as AI, machine learning, and cloud computing are further propelling market expansion.Recent developments include the U.S. Department of Defense's investment in geospatial technologies to enhance situational awareness and decision-making. Additionally, the adoption of geospatial tools for disaster management and environmental monitoring is gaining traction. Key players in the market are focusing on developing innovative solutions and expanding their geographic presence. Mergers and acquisitions are also shaping the market landscape, with companies seeking to strengthen their capabilities and expand their offerings.. Key drivers for this market are: Emerging geospatial technologies Artificial intelligence integration Growing adoption of cloud-based defense systems Increasing cross border conflicts Rise of drones and autonomous vehicles. Potential restraints include: Technology advancements Increasing defense expenditure Rise in geopolitical tensions Growing importance of geospatial intelligence Need for improved situational awareness.

This dataset contains model-based place (incorporated and census designated places) estimates in GIS-friendly format. PLACES covers the entire United States—50 states and the District of Columbia —at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2022 or 2021 data, Census Bureau 2020 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 2020 Census place boundary file in a GIS system to produce maps for 40 measures at the place 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

File List bathymetry_30s.7z (MD5: dd855211bbcdee7d6862414da23d6da2) biogeo01_07_30s.7z (MD5: 396525db0abd9de2ede3d8fdeb15e8ee) biogeo08_17_30s.7z (MD5: 96c2417eed84e85f9896536b934c53e1) Monthly_Variables_30s.7z (MD5: 89016a8d17e8d8a1dddef0a121a83f5d)

     Additional high resolution raster files:

Sea_Ice_30s.7z (MD5: 547d355294c530f63b9b0a73dedd2f3a)

     Low resolution MARSPEC data files:

MARSPEC_2o5m.7z (MD5: 923c97d185adb0c72f158a84e2981391) MARSPEC_5m.7z (MD5: 95f7c3739c4f2889c2eff18afeffa489) MARSPEC_10m.7z (MD5: d91f3127f46f7004d116f14328bf4b71) Description Ecological niche models are widely used in terrestrial studies to address critical ecological and evolutionary questions related to past and future climate change, local adaptation and speciation, the discovery of rare endemics, and biological invasions. However the application of niche models to similar questions in marine ecosystems has lagged behind, in part due to the lack of a centralized high-resolution spatial data set representing both benthic and pelagic marine environments. Here we describe the creation of MARSPEC, a high-resolution GIS database of ocean climate layers intended for marine ecological niche modeling and other applications in marine spatial ecology. MARSPEC combines information related to topographic complexity of the seafloor with bioclimatic measures of sea surface temperature and salinity for the world ocean. We derived seven geophysical variables from a high-resolution raster grid representing depth of the seafloor (bathymetry) to characterize six facets of topographic complexity (east-west and north-south components of aspect, slope, concavity of the seafloor, and plan and profile curvature) and distance from shore. We further derived 10 bioclimatic variables describing the annual mean, range, variance and extreme values for temperature and salinity from long-term monthly climatological means obtained from remotely sensed and in situ oceanographic observations. All variables were clipped to a common land mask, interpolated to a nominal 1-km (30 arc-second) grid, and converted to an ESRI raster grid file format compatible with popular GIS programs. MARSPEC is a 10-fold improvement in spatial resolution over the next-best data set (Bio-ORACLE) and is the only high-resolution global marine data set to combine variables from the benthic and pelagic environments into a single database. Additionally, we provide the monthly climatological layers used to derive the bioclimatic variables, allowing users to calculate equivalent MARSPEC variables from anomaly data for past and future climate scenarios. A detailed description of GIS processing steps required to calculate the MARSPEC variables can be found in the metadata.

      Key words: climate change; ecological niche modeling; GIS; marine spatial ecology; ocean climate; salinity; sea surface temperature; species distribution modeling.

What is NAIP?The National Agriculture Imagery Program (NAIP) acquires aerial imagery during the agricultural growing seasons in the contiguous U.S. A primary goal of the NAIP program is to make digital ortho photography available to governmental agencies and the public within a year of acquisition.NAIP is administered by the USDA's Farm Production and Conservation Business Center through the Aerial Photography Field Office in Salt Lake City. The APFO as of August 16, 2020 has transitioned to the USDA FPAC-BC's Geospatial Enterprise Operations Branch (GEO). This "leaf-on" imagery is used as a base layer for GIS programs in FSA's County Service Centers, and is used to maintain the Common Land Unit (CLU) boundaries.How can I Access NAIP?On the web GEO (APFO) public image services can be accessed through the REST endpoint here. Compressed County Mosaics (CCMs) are available to the general public through the USDA Geospatial Data Gateway. All years of available imagery may be downloaded as 1/2, 1, or 2 meter CCMs depending on the original spatial resolution. CCMs with a file size larger than 8 GB are not able to be downloaded from the Gateway. Full resolution 4 band quarter quads (DOQQs) are available for purchase from FPAC GEO. Contact the GEO Customer Service Section for information on pricing for DOQQs and how to obtain CCMs larger than 8 GB. A NAIP image service is also available on ArcGIS Online through an organizational subscription.How can NAIP be used?NAIP is used by many non-FSA public and private sector customers for a wide variety of projects. A detailed study is available in the Qualitative and Quantitative Synopsis on NAIP Usage from 2004 -2008: Click here for a list of NAIP Information and Distribution Nodes.When is NAIP acquired?NAIP projects are contracted each year based upon available funding and the FSA imagery acquisition cycle. Beginning in 2003, NAIP was acquired on a 5-year cycle. 2008 was a transition year, a three-year cycle began in 2009, NAIP was on a two-year cycle until 2016, currently NAIP is on a 3 year refresh cycle. Click here >> for an interactive PDF status map of NAIP acquisitions from 2002 - 2018. 2021 acquisition status dashboard is available here.What are NAIP Specifications?NAIP imagery is currently acquired at 60cm ground sample distance (GSD) with a horizontal accuracy that matches within four meters of photo-identifiable ground control points.The default spectral resolution beginning in 2010 is four bands: Red, Green, Blue and Near Infrared.Contractually, every attempt will be made to comply with the specification of no more than 10% cloud cover per quarter quad tile, weather conditions permitting.All imagery is inspected for horizontal accuracy and tonal quality. Make Comments/Observations about current NAIP imagery.If you use NAIP imagery and have comments or find a problem with the imagery please use the NAIP Imagery Feedback Map to let us know what you find or how you are using NAIP imagery. Click here to access the map.**The documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updates**Title: National Agriculture Imagery Program (NAIP) History 2002-2021Item Type: Web Mapping Application URL Summary: Story map depicting the highlights and changes throughout the National Agriculture Imagery Program (NAIP) from 2002-2021.Notes: Prepared by: Uploaded by EMcRae_NMCDCSource: URL referencing this original map product: https://nmcdc.maps.arcgis.com/home/item.html?id=445e3dfd16c4401f95f78ad5905a4cceFeature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=8eb6c5e7adc54ec889dd6fc9cc2c14c4UID: 26Data Requested: Ag CensusMethod of Acquisition: Living AtlasDate Acquired: May 2022Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 8Tags: PENDING

An educational institution for both instruction and examination in the higher branches of knowledge with the power to confer degrees. This point feature dataset is part of the Digital Topographic Database (DTDB). University data points are positioned within the cadastral parcel in which they are located.