The Google Maps Platform (GMP) consulting services market is experiencing robust growth, driven by the increasing adoption of location-based services across various sectors. The market, estimated at $2 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $6 billion by 2033. This expansion is fueled by several key factors. Firstly, the rising demand for location intelligence and data-driven decision-making across large enterprises and SMEs is pushing companies to leverage GMP's capabilities. Secondly, the shift towards online services, facilitated by the increasing accessibility and affordability of high-speed internet, is bolstering the adoption of GMP consulting services for efficient mapping, navigation, and location-based marketing. Furthermore, advancements in augmented reality (AR) and virtual reality (VR) technologies integrated with GMP are creating new avenues for innovative applications, driving market growth. However, factors like the high cost of implementation and the need for specialized expertise can restrain market expansion. The market is segmented by application (large enterprises and SMEs) and service type (online and offline), with large enterprises currently dominating due to their greater resources and need for complex location-based solutions. Geographically, North America and Europe currently hold significant market shares, but the Asia-Pacific region is anticipated to exhibit the fastest growth rate due to rapid digitalization and increasing smartphone penetration. The competitive landscape is fragmented, with a mix of global consulting giants like Deloitte, Accenture, and WPP, alongside specialized GMP consulting firms such as MapsPeople and Applied Geographics. These companies are engaged in fierce competition, offering a range of services including integration, customization, application development, and ongoing support. The success of these firms is contingent on their ability to provide tailored solutions that cater to the unique needs of diverse industries and clients, and to continuously adapt to the ever-evolving features and functionalities of the GMP. A critical factor for future growth will be the ability to integrate GMP with other platforms and technologies to create holistic and effective solutions for clients, generating a compelling return on investment. This necessitates significant investment in R&D and upskilling of the workforce.

National Library of Scotland Historic Maps APIHistorical Maps of Great Britain for use in mashups and ArcGIS Onlinehttps://nls.tileserver.com/https://maps.nls.uk/projects/api/index.htmlThis seamless historic map can be:embedded in your own websiteused for research purposesused as a backdrop for your own markers or geographic dataused to create derivative work (such as OpenStreetMap) from it.The mapping is based on out-of-copyright Ordnance Survey maps, dating from the 1920s to the 1940s.The map can be directly opened in a web browser by opening the Internet address: https://nls.tileserver.com/The map is ready for natural zooming and panning with finger pinching and dragging.How to embed the historic map in your websiteThe easiest way of embedding the historical map in your website is to copy < paste this HTML code into your website page. Simple embedding (try: hello.html):You can automatically position the historic map to open at a particular place or postal address by appending the name as a "q" parameter - for example: ?q=edinburgh Embedding with a zoom to a place (try: placename.html):You can automatically position the historic map to open at particular latitude and longitude coordinates: ?lat=51.5&lng=0&zoom=11. There are many ways of obtaining geographic coordinates. Embedding with a zoom to coordinates (try: coordinates.html):The map can also automatically detect the geographic location of the visitor to display the place where you are right now, with ?q=auto Embedding with a zoom to coordinates (try: auto.html):How to use the map in a mashupThe historic map can be used as a background map for your own data. You can place markers on top of it, or implement any functionality you want. We have prepared a simple to use JavaScript API to access to map from the popular APIs like Google Maps API, Microsoft Bing SDK or open-source OpenLayers or KHTML. To use our map in your mashups based on these tools you should include our API in your webpage: ... ...

BestPlace is an innovative retail data and analytics tool created explicitly for medium and enterprise-level CPG/FMCG companies. It's designed to revolutionize your retail data analysis approach by adding a strategic location-based perspective to your existing database. This perspective enriches your data landscape and allows your business to understand better and cater to shopping behavior. An In-Depth Approach to Retail Analytics Unlike conventional analytics tools, BestPlace delves deep into each store location details, providing a comprehensive analysis of your retail database. We leverage unique tools and methodologies to extract, analyze, and compile data. Our processes have been accurately designed to provide a holistic view of your business, equipping you with the information you need to make data-driven data-backed decisions. Amplifying Your Database with BestPlace At BestPlace, we understand the importance of a robust and informative retail database design. We don't just add new stores to your database; we enrich each store with vital characteristics and factors. These enhancements come from open cartographic sources such as Google Maps and our proprietary GIS database, all carefully collected and curated by our experienced data analysts. Store Features We enrich your retail database with an array of store features, which include but are not limited to: Number of reviews Average ratings Operational hours Categories relevant to each point Our attention to detail ensures your retail database becomes a powerful tool for understanding customer interactions and preferences. Geo-Analytical Factors Each store in your database is further enhanced with geo-analytical data. We analyze: Maximum pedestrian and vehicle traffic within a defined radius Number of households and average income within the catchment area vicinity Number of schools, hospitals, universities, competitors, stores, bars, clubs, and restaurants in the surrounding area Point attendance based on mobile device location data (ensuring GDPR compliance) Our refined retail data collection and analysis provides detailed shopping behavior insights, leading to in-depth shopper analytics and retail foot traffic data that support strategic planning and execution. The Power of Points of Interest (POI) Data At BestPlace, we harness the power of Point of Interest (POI) data (to bring you the most complete retail data set.) to bring your retail data to life. Our POI data collection process involves analyzing and categorizing foot traffic data, providing a comprehensive foot traffic dataset as a result. This data allows you to understand the ebb and flow of individuals around your store locations, suggesting invaluable insights for strategic planning and operational efficiency. Leveraging GIS Data Our GIS data collection process is meticulous and comprehensive. We tap into multiple GIS data sources, providing a wealth of data to enhance your retail analytics. This process allows us to equip your database with a broad range of geospatial features, including demographic and socioeconomic information from various census data for GIS applications. By including GIS data in your analysis, you gain a multi-dimensional perspective of your retail landscape, allowing for more strategic decision-making. The Advantages of Census Data BestPlace grants you direct access to a wealth of census data sets. This transforms your retail database into a more potent tool for decision-making, providing a deeper understanding of the demographics and socioeconomic factors surrounding your store locations. With the ability to download census data directly, you can enrich your retail data analysis with valuable insights about potential customers, giving you the upper hand in your strategic planning. Extensive Use Cases BestPlace's capabilities stretch across various applications, offering value in areas such as: Competition Analysis: Identify your competitors, analyze their performance, and understand your standing in the market with our extensive POI database and retail data analytics capabilities. New Location Search: Use our rich retail store database to identify ideal locations for store expansions based on foot traffic data, proximity to key points, and potential customer demographics. Location Comparison: Compare multiple store locations based on numerous factors and make informed decisions about where to focus your resources. Distribution Optimization: Leverage our FMCG data analytics and retail traffic analytics to optimize your distribution strategy and maximize ROI. Building Machine Learning Models: Integrate our all-purpose machine learning models into your business decision processes to enable more efficient and effective decision-making. (Integrate our all-purpose machine learning models to build your own in-house solutions with the help of our data.) Comprehensive Deliverables As a BestPlace client, you receive a comprehensive produc...

The Digital Geologic-GIS Map of Sagamore Hill National Historic Site and Vicinity, New York 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 (sahi_geology.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 (sahi_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (sahi_geology.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 (sahi_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (sahi_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (sahi_geology_metadata_faq.pdf). Please read the sahi_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 (sahi_geology_metadata.txt or sahi_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:62,500 and United States National Map Accuracy Standards features are within (horizontally) 31.8 meters or 104.2 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).

The Digital Geologic-GIS Map of Mammoth Cave National Park and Vicinity, Kentucky is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (maca_geology.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 (maca_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (maca_geology.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 readme file (maca_abli_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (maca_abli_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (maca_geology_metadata_faq.pdf). Please read the maca_abli_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. 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 and Kentucky 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 (maca_geology_metadata.txt or maca_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in 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).

In 2023, Google Maps was the most downloaded map and navigation app in the United States, despite being a standard pre-installed app on Android smartphones. Waze followed, with 9.89 million downloads in the examined period. The app, which comes with maps and the possibility to access information on traffic via users reports, was developed in 2006 by the homonymous Waze company, acquired by Google in 2013.

Usage of navigation apps in the U.S. As of 2021, less than two in 10 U.S. adults were using a voice assistant in their cars, in order to place voice calls or follow voice directions to a destination. Navigation apps generally offer the possibility for users to download maps to access when offline. Native iOS app Apple Maps, which does not offer this possibility, was by far the navigation app with the highest data consumption, while Google-owned Waze used only 0.23 MB per 20 minutes.

Usage of navigation apps worldwide In July 2022, Google Maps was the second most popular Google-owned mobile app, with 13.35 million downloads from global users during the examined month. In China, the Gaode Map app, which is operated along with other navigation services by the Alibaba owned AutoNavi, had approximately 730 million monthly active users as of September 2022.

Below is a quick rundown of the tools available in the web map!The first new thing you may notice is the ability to search from in the splash window that appears. This hopefully reduces the number of clicks people will need to get to their information. There's the same search bar in the upper left once you click out of the splash screen.The Query tool has existed in this form on the sub-maps, but now it is here with all the layers. I want to highlight "Search by Legal Description" as a nifty way to find parcels associated with a specific subdivision. I also want to highlight the "find tax parcels/addresses within specified distance" queries. Those let you select every tax parcel or address within a feature you draw (a point, line, or polygon). This is good for finding what properties within a distance need to be notified of something. That can then be exported as an Excel table (csv). This can also help you identify whether something falls within certain setbacks.The Basemaps is the same as it was before. I haven't gotten the Virginia Geographic Information Network imagery from 2017 and 2021 to successfully appear here, but you can find that in the map layers at the bottom.We have a lot of data layers! I currently have the default as every group expanded out, so you can scroll and see all the layers, but you can go through and click to collapse any groups you don't want expanded. Okay, the select tool is super cool, and lets you really dive into some fun GIS attribute querying! As an example, you can select all the FEMA Flood Zones that are AO, then select all the tax parcels that are affected by (intersect) those AO zones! These results can also be exported into an Excel table. A great deal of GIS analysis is possible just using Select by Attributes and Select by Location, so this tool really ramps up the power of the web map so it can do some of what the desktop GIS software can do!Continuing our tour of the tools, we come to the coordinates tool. This one also existed already in the sub-maps, but is now with all the layers. Unfortunately, the tool is a little annoying, and won't retain my defaults. You have to click the little plus sign target thing, then you can click on the map to get the coordinates. The coordinate system defaults to WGS 1984 Web Mercator (the same thing Google Maps uses), but much of our data uses NAD 1983 State Plane Virginia South, so you can click the dropdown arrow to the right to select either one. Exciting news related to this: in 2026 they are releasing the new coordinate system on which they've been working! It should make the data in GIS more closely align with features in reality, but you will not need to change any of the ways you interact with the data.The next tool is the Elevation Profile tool. It's very nifty! You can draw a profile to see how the elevation changes, and as you move your cursor along the graph, it shows where along your transect you are! It helps explain some of the floodplain and sea level rise boundaries.You know the measure tool well, but this one retains the defaults in feet and acres, which is very exciting! No more having to change the units every time you want to measure (unless you want other than feet and acres).The draw tool is our penultimate stop on the tour! It is largely the same as what currently exists on the public web map, so I shan't delve into it here. When you draw a feature now though, it appears in the layers tab (until you close the map), which can let you toggle the drawing on and off to work with what is beneath it. It can help as you plan in where you might want to put new constructions.The print tool is also largely the same, but I've been finding the tool in this new Experience Builder format is less buggy than the one in the retiring Web App Builder that made the current Public Web Map.

The Unpublished Digital Geologic-GIS Map of Organ Pipe Cactus National Monument and Vicinity, Arizona is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (orpi_geology.gdb), a 10.1 ArcMap (.mxd) map document (orpi_geology.mxd), individual 10.1 layer (.lyr) files for each GIS data layer, an ancillary map information document (orpi_geology.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.txt) and FAQ (.pdf) formats, and a GIS readme file (orpi_geology_gis_readme.pdf). Please read the orpi_geology_gis_readme.pdf for information pertaining to the proper extraction of the file geodatabase and other map files. To request GIS data in ESRI 10.1 shapefile format contact Stephanie O'Meara (stephanie.omeara@colostate.edu; see contact information below). The data is also available as a 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. Google Earth software is available for free at: http://www.google.com/earth/index.html. 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). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Northern Arizona University. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (orpi_geology_metadata.txt or orpi_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS 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). The GIS data projection is NAD83, UTM Zone 12N, however, for the KML/KMZ format the data is projected upon export to WGS84 Geographic, the native coordinate system used by Google Earth. The data is within the area of interest of Organ Pipe Cactus National Monument.

Note: This description is taken from a draft report entitled "Creation of a Database of Lakes in the St. Johns River Water Management District of Northeast Florida" by Palmer Kinser. Introduction“Lakes are among the District’s most valued resources. Their aesthetic appeal adds substantially to waterfront property values, which in turn generate tax revenues for local governments. Fish camps and other businesses, that provide lake visitors with supplies and services, benefit local economies directly. Commercial fishing on the District’s larger lakes produces some income, , but far greater economic benefits are produced from sport fishing. Some of the best bass fishing lakes in the world occur in the District. Trophy fishing, guide services and high-stakes fishing tournaments, which they support, also generate substantial revenues for local economies. In addition, the high quality of District lakes has allowed swimming, fishing, and boating to become among the most popular outdoor activities for many District residents and attracts many visitors. Others frequently take advantage of the abundant opportunities afforded for duck hunting, bird watching, photography, and other nature related activities.”(from likelihood of harm to lakes report).ObjectiveThe objective of this work was to create a consistent database of natural lake polygon features for the St. Johns River Water Management District. Other databases examined contained point features only, polygons representing a wide range of dates, water bodies not separated or coded adequately by feature type (i.e. no distinctions were made between lakes, rivers, excavations, etc.), or were incomplete. This new database will allow users to better characterize and measure the lakes resource of the District, allowing comparisons to be made and trends detected; thereby facilitating better protection and management of the resource.BackgroundPrior to creation of this database, the District had 2 waterbody databases. The first of these, the 2002 FDEP Primary Lake Location database, contained 3859 lake point features, state-wide, 1418 of which were in SJRWMD. Only named lakes were included. Data sources were the Geographic Names Information System (GNIS), USGS 1:24000 hydrography data, 1994 Digital orthophoto quarter quadrangles (DOQQs), and USGS digital raster graphics (DRGs). The second was the SJRWMD Hydrologic Network (Lake / Pond and Reservoir classes). This data base contained 42,002 lake / pond and reservoir features for the SJRWMD. Lakes with multiple pools of open water were often mapped as multiple features and many man-made features (borrow pits, reservoirs, etc.) were included. This dataset was developed from USGS map data of varying dates.MethodsPolygons in this new lakes dataset were derived from a "wet period" landcover map (SJRWMD, 1999), in which most lake levels were relatively high. Polygons from other dates, mostly 2009, were used for lakes in regionally dry locations or for lakes that were uncharacteristically wet in 1999, e.g. Alachua Sink. Our intension was to capture lakes in a basin-full condition; neither unusually high nor low. To build the data set, a selection was made of polygons coded as lakes (5200), marshy lakes (5250, enclosed saltwater ponds in salt marsh (5430), slough waters (5600), and emergent aquatic vegetation (6440). Some large, regionally significant or named man-made reservoirs were also included, as well as a small number of named excavations. All polygons were inspected and edited, where appropriate, to correct lake shores and merge adjacent lake basin features. Water polygons separated by marshes or other low-ground features were grouped and merged to form multipart features when clearly associated within a single lake basin. The initial set of lake names were captured from the Florida Primary Lake Location database. Labels were then moved where needed to insure that they fell within the water bodies referenced. Additional lake names were hand entered using data from USGS 7.5 minute quads, Google Maps, MapQuest, Florida Department of Transportation (FDOT) county maps, and other sources. The final dataset contains 4892 polygons, many of which are multi-part.Operationally, lakes, as captured in this data base, are those features that were identified and mapped using the District’s landuse/landcover scheme in the 5200, 5250, 5430, 5600 classes referenced above; in addition to some areas mapped tin the 6440 class. Some additional features named as lakes, ponds, or reservoirs were also included, even when not currently appearing to be lakes. Some are now very marshy or even dry, but apparently held deeper pools of water in the past. A size limit of 1 acre or more was enforced, except for named features, 30 of which were smaller. The smallest lake was Fox Lake, a doline of 0.04 acres in Orange county. The largest lake, Lake George covered 43,212.8 acres.The lakes of the SJRWMD are a diverse set of features that may be classified in many ways. These include: by surrounding landforms or landcover, by successional stage (lacustrine to palustrine gradient), by hydrology (presence of inflows and/or outflows, groundwater linkages, permanence, etc.), by water quality (trophic state, water color, dissolved solids, etc.), and by origin. We chose to classify the lakes in this set by origin, based on the lake type concepts of Hutchinson (1957). These types are listed in the table below (Table 1). We added some additional types and modified the descriptions to better reflect Florida’s geological conditions (Table 2). Some types were readily identified, others are admittedly conjectural or were of mixed origins, making it difficult to pick a primary mechanism. Geological map layers, particularly total thickness of overburden above the Floridan aquifer system and thickness of the intermediate confining unit, were used to estimate the likelihood of sinkhole formation. Wind sculpting appears to be common and sometimes is a primary mechanism but can be difficult to judge from remotely sensed imagery. For these and others, the classification should be considered provisional. Many District lakes appear to have been formed by several processes, for instance, sinkholes may occur within lakes which lie between sand dunes. Here these would be classified as dune / karst. Mixtures of dunes, deflation and karst are common. Saltmarsh ponds vary in origin and were not further classified. In the northern coastal area they are generally small, circular in outline and appear to have been formed by the collapse and breakdown of a peat substrate, Hutchinson type 70. Further south along the coast additional ponds have been formed by the blockage of tidal creeks, a fluvial process, perhaps of Hutchinson’s Type 52, lateral lakes, in which sediments deposited by a main stream back up the waters of a tributary. In the area of the Cape Canaveral, many salt marsh ponds clearly occupy dune swales flooded by rising ocean levels. A complete listing of lake types and combinations is in Table 3. TypeSub-TypeSecondary TypeTectonic BasinsMarine BasinTectonic BasinsMarine BasinCompound dolineTectonic BasinsMarine BasinkarstTectonic BasinsMarine BasinPhytogenic damTectonic BasinsMarine BasinAbandoned channelTectonic BasinsMarine BasinKarstSolution LakesCompound dolineSolution LakesCompound dolineFluvialSolution LakesCompound dolinePhytogenicSolution LakesDolineSolution LakesDolineDeflationSolution LakesDolineDredgedSolution LakesDolineExcavatedSolution LakesDolineExcavationSolution LakesDolineFluvialSolution LakesKarstKarst / ExcavationSolution LakesKarstKarst / FluvialSolution LakesKarstDeflationSolution LakesKarstDeflation / excavationSolution LakesKarstExcavationSolution LakesKarstFluvialSolution LakesPoljeSolution LakesSpring poolSolution LakesSpring poolFluvialFluvialAbandoned channelFluvialFluvialFluvial Fluvial PhytogenicFluvial LeveeFluvial Oxbow lakeFluvial StrathFluvial StrathPhytogenicAeolianDeflationAeolianDeflationDuneAeolianDeflationExcavationAeolianDeflationKarstAeolianDuneAeolianDune DeflationAeolianDuneExcavationAeolianDuneAeolianDuneKarstShoreline lakesMaritime coastalKarst / ExcavationOrganic accumulationPhytogenic damSalt Marsh PondsMan madeExcavationMan madeDam

PLEASE NOTE: These data do not include data over Tasmania. Please see links relevant to that area.

GEODATA TOPO 250K Series 3 is a vector representation of the major topographic features appearing on the 1:250,000 scale NATMAPs supplied in KML format and is designed for use in a range of commercial GIS software. Data is arranged within specific themes. All data is based on the GDA94 coordinate system.

GEODATA TOPO 250K Series 3 is available as a free download product in Personal Geodatabase, ArcView Shapefile or MapInfo TAB file formats. Each package includes data arranged in ten main themes - cartography, elevation, framework, habitation, hydrography, infrastructure, terrain, transport, utility and vegetation. Data is also available as GEODATA TOPO 250K Series 3 for Google Earth in kml format for use on Google Earth TM Mapping Service.

Product Specifications

Themes: Cartography, Elevation, Framework, Habitation, Hydrography, Infrastructure, Terrain, Transport, Utility and Vegetation

Coverage: National (Powerlines not available in South Australia)

Currency: Data has a currency of less than five years for any location

Coordinates: Geographical

Datum: Geocentric Datum of Australia (GDA94)

Formats: Personal Geodatabase, kml, Shapefile and MapInfo TAB

Release Date: 26 June 2006

The automotive GPS system market, valued at $35.89 billion in 2025, is projected to experience robust growth, driven by increasing vehicle production, rising consumer demand for advanced navigation features, and the integration of GPS technology into connected car ecosystems. The market's Compound Annual Growth Rate (CAGR) of 7.65% from 2025 to 2033 indicates a significant expansion, fueled by factors such as the increasing adoption of in-car entertainment and infotainment systems, and the growing popularity of safety features like advanced driver-assistance systems (ADAS) that rely heavily on GPS data. The passenger car segment dominates the market due to higher vehicle production volume compared to commercial vehicles, while the OEM channel holds a larger market share than the aftermarket due to pre-installation in new vehicles. Larger screen sizes (6-10 inches and above) are gaining traction, reflecting consumers' preference for enhanced visual experiences and intuitive interfaces. Geographic analysis reveals strong growth in Asia Pacific, driven by increasing vehicle sales and rising disposable incomes in rapidly developing economies like China and India. North America and Europe also contribute substantially, benefiting from high vehicle ownership rates and technological advancements in automotive GPS systems. Competition is fierce, with major players like Denso, Visteon, TomTom, LG Electronics, and others constantly innovating to provide feature-rich and cost-effective solutions. The market's growth trajectory is expected to be influenced by several factors including advancements in mapping technology (e.g., high-definition maps, real-time traffic updates), integration with smartphone applications, and the emergence of autonomous driving technologies that leverage precise GPS positioning. However, potential restraints include the rising cost of advanced GPS features, concerns about data privacy and security, and the potential for interference from other electronic devices. Nevertheless, the long-term outlook remains positive, with continued investment in research and development driving improvements in accuracy, reliability, and functionality of automotive GPS systems, ensuring their continued relevance in the evolving automotive landscape. The continuous integration of GPS technology within the broader context of connected car features will further propel market growth. Recent developments include: August 2023: EVgo Inc. announced its collaboration with Amazon to launch an Alexa-enabled electric vehicle charging experience for customers. The PlugShare API integration creates a seamless charging experience for Alexa-enabled EVs, including Nissan ARIYA, Ford Mustang Mach-E, and F-150 Lightning. Through simple voice requests such as, ‘Alexa, find EV charging stations near me,’ customers can locate and drive to the nearest charging station, eliminating the need to stop and search for available stations manually., January 2023: Mapbox announced its partnership with Toyota Motor Europe to offer cloud navigation powered by Mapbox Dash for Toyota's European models, including Yaris, Yaris Cross, and Aygo X. Mapbox Dash has been built on the cloud-based Mapbox platform that enables automobile manufacturers to launch complex in-car integrations in as little as three to six months, rather than the typical 12 to 36 months. Further, the company stated that customers who are willing to purchase Toyota vehicles with this feature will receive updates to optimize the user experience (UX) continuously throughout the vehicle's lifetime., May 2022: Volvo Car USA announced that its entire 2023 car lineup in the United States would consist of mild hybrid, hybrid, or electric vehicles equipped with Google built-in features, including Google Assistant for voice control, Google Maps as the vehicle’s native navigation system, and Google Play for additional apps., April 2022: HERE Technologies announced that Isuzu Trucks had deployed HERE Navigation, an off-the-shelf navigation solution for embedded in-vehicle infotainment (IVI) platforms, in its new 2022 model year F Series, FX Series, and FY Series trucks. HERE Navigation optimizes Isuzu's fleet operations with a connected in-vehicle navigation system from HERE Technologies, a leading location data and technology platform.. Key drivers for this market are: Shifting Preference of Consumers to Avail Private Medium of Transportation. Potential restraints include: High Purchase and Installation Costs. Notable trends are: Aftermarket Segment to Gain Traction during the Forecast Period.

The Digital Geologic-GIS Map of Niobrara National Scenic River and Vicinity, Nebraska is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) an ESRI file geodatabase (niob_geology.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 (niob_geology.mapx) and individual Pro layer (.lyrx) 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 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) a readme file (niob_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (niob_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (niob_geology_metadata_faq.pdf). Please read the niob_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: 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 (niob_geology_metadata.txt or niob_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:100,000 and United States National Map Accuracy Standards features are within (horizontally) 50.8 meters or 166.7 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 Pro, 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).

The Unpublished Digital Geologic-GIS Map of Fort Laramie National Historic Site and Vicinity, Wyoming is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (fola_geology.gdb), a 10.1 ArcMap (.mxd) map document (fola_geology.mxd), individual 10.1 layer (.lyr) files for each GIS data layer, an ancillary map information document (fola_geology.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.txt) and FAQ (.pdf) formats, and a GIS readme file (fola_geology_gis_readme.pdf). Please read the fola_geology_gis_readme.pdf for information pertaining to the proper extraction of the file geodatabase and other map files. To request GIS data in ESRI 10.1 shapefile format contact Stephanie O'Meara (stephanie.omeara@colostate.edu; see contact information below). The data is also available as a 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. Google Earth software is available for free at: http://www.google.com/earth/index.html. 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). 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 (fola_geology_metadata.txt or fola_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:31,680 and United States National Map Accuracy Standards features are within (horizontally) 16.1 meters or 52.8 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 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). The GIS data projection is NAD83, UTM Zone 13N, however, for the KML/KMZ format the data is projected upon export to WGS84 Geographic, the native coordinate system used by Google Earth. The data is within the area of interest of Fort Laramie National Historic Site.

The Unpublished Digital Geologic-GIS Map of Theodore Roosevelt National Park and Vicinity, North Dakota is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (thro_geology.gdb), a 10.1 ArcMap (.mxd) map document (thro_geology.mxd), individual 10.1 layer (.lyr) files for each GIS data layer, an ancillary map information document (thro_geology.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.txt) and FAQ (.pdf) formats, and a GIS readme file (thro_geology_gis_readme.pdf). Please read the thro_geology_gis_readme.pdf for information pertaining to the proper extraction of the file geodatabase and other map files. To request GIS data in ESRI 10.1 shapefile format contact Stephanie O'Meara (stephanie.omeara@colostate.edu; see contact information below). The data is also available as a 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. Google Earth software is available for free at: http://www.google.com/earth/index.html. 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). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: North Dakota 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 (thro_geology_metadata.txt or thro_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS 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: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data projection is NAD83, UTM Zone 13N, however, for the KML/KMZ format the data is projected upon export to WGS84 Geographic, the native coordinate system used by Google Earth. The data is within the area of interest of Theodore Roosevelt National Park.

The Unpublished Digital Geologic Map of Katmai National Park and Preserve, and Alagnak Wild River and Vicinity, Alaska is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (katm_geology.gdb), a 10.1 ArcMap (.MXD) map document (katm_geology.mxd), individual 10.1 layer (.LYR) files for each GIS data layer, an ancillary map information (.PDF) document (katm_geology.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.TXT) and FAQ (.HTML) formats, and a GIS readme file (katm_gis_readme.pdf). Please read the katm_gis_readme.pdf for information pertaining to the proper extraction of the file geodatabase and other map files. To request GIS data in ESRI 10.1 shapefile format contact Stephanie O’Meara (stephanie.omeara@colostate.edu; see contact information below). The data is also available as a 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. Google Earth software is available for free at: http://www.google.com/earth/index.html. 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). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey, National Park Service Geologic Resources Inventory, Smithsonian Institution and Alaska Division of Geological and Geophysical Surveys. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (katm_metadata_faq.html; available at http://nrdata.nps.gov/geology/gri_data/gis/katm/katm_metadata_faq.html). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:250,000 and United States National Map Accuracy Standards features are within (horizontally) 127 meters or 416.7 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 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: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data projection is NAD83, UTM Zone AD_1983_Alaska_AlbersN, however, for the KML/KMZ format the data is projected upon export to WGS84 Geographic, the native coordinate system used by Google Earth. The data is within the area of interest of Katmai National Park and Preserve, and Alagnak Wild River.

According to Cognitive Market Research, the global Enterprise Search Engine market size will be USD 4358.2 million in 2024. It will expand at a compound annual growth rate (CAGR) of 9.70% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 1743.28 million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.9% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 1307.46 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 1002.39 million in 2024 and will grow at a compound annual growth rate (CAGR) of 11.7% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 217.91 million in 2024 and will grow at a compound annual growth rate (CAGR) of 9.1% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 87.16 million in 2024 and will grow at a compound annual growth rate (CAGR) of 9.4% from 2024 to 2031.
The Solution category is the fastest growing segment of the Enterprise Search Engine industry

Market Dynamics of Enterprise Search Engine Market

Key Drivers for Enterprise Search Engine Market

Increasing Data Volume to Boost Market Growth

The increasing volume of data generated by organizations is a primary driver of the Enterprise Search Engine Market. As businesses accumulate vast amounts of structured and unstructured data from various sources—such as emails, documents, social media, and databases—the need for efficient retrieval and management becomes critical. Enterprise search engines enable organizations to sift through this data quickly, providing employees with timely access to information that can enhance decision-making and productivity. Additionally, the proliferation of big data technologies and cloud storage solutions contributes to data growth, necessitating robust search capabilities to ensure that valuable insights are not lost. This demand for streamlined access to comprehensive information continues to fuel the expansion of the enterprise search engine market. For instance, Google launched local search functionalities that were previewed earlier this year. These features enable users to explore their environment using their smartphone camera. Additionally, Google has added an option to search for restaurants by specific dishes and introduced new search capabilities within the Live View feature of Google Maps.

Increasing Demand for Data-Driven Decision-Making to Drive Market Growth

The rising demand for data-driven decision-making is significantly driving the Enterprise Search Engine Market. Organizations increasingly recognize the value of leveraging data analytics to inform strategic decisions, enhance operational efficiency, and improve customer experiences. As businesses strive to become more agile and responsive to market changes, they require quick access to relevant data across various departments and sources. Enterprise search engines facilitate this by enabling employees to efficiently retrieve and analyze critical information, thus supporting informed decision-making processes. Moreover, the integration of advanced analytics and artificial intelligence into enterprise search solutions further empowers organizations to derive actionable insights from their data. This trend towards a data-centric approach in business operations continues to propel the growth of the enterprise search engine market.

Restraint Factor for the Enterprise Search Engine Market

High Implementation Costs will Limit Market Growth

High implementation costs are a significant restraint on the growth of the Enterprise Search Engine Market. Deploying enterprise search solutions often involves substantial initial investments in software, hardware, and integration services. Organizations must consider expenses related to customizing the search engine to fit their unique data architectures and user needs. Additionally, ongoing maintenance, updates, and training for staff can contribute to overall costs, making it challenging for smaller businesses or those with limited budgets to adopt these systems. This financial barrier can hinder organizations from fully realizing the benefits of enterprise search engines, leading to under...

This dataset contains both large (A0) printable maps of the Torres Strait broken into six overlapping regions, based on a clear sky, clear water composite Sentinel 2 composite imagery and the imagery used to create these maps. These maps show satellite imagery of the region, overlaid with reef and island boundaries and names. Not all features are named, just the more prominent features. This also includes a vector map of Ashmore Reef and Boot Reef in Coral Sea as these were used in the same discussions that these maps were developed for. The map of Ashmore Reef includes the atoll platform, reef boundaries and depth polygons for 5 m and 10 m.

This dataset contains all working files used in the development of these maps. This includes all a copy of all the source datasets and all derived satellite image tiles and QGIS files used to create the maps. This includes cloud free Sentinel 2 composite imagery of the Torres Strait region with alpha blended edges to allow the creation of a smooth high resolution basemap of the region.

The base imagery is similar to the older base imagery dataset: Torres Strait clear sky, clear water Landsat 5 satellite composite (NERP TE 13.1 eAtlas, AIMS, source: NASA).

Most of the imagery in the composite imagery from 2017 - 2021.

Method: The Sentinel 2 basemap was produced by processing imagery from the World_AIMS_Marine-satellite-imagery dataset (not yet published) for the Torres Strait region. The TrueColour imagery for the scenes covering the mapped area were downloaded. Both the reference 1 imagery (R1) and reference 2 imagery (R2) was copied for processing. R1 imagery contains the lowest noise, most cloud free imagery, while R2 contains the next best set of imagery. Both R1 and R2 are typically composite images from multiple dates.

The R2 images were selectively blended using manually created masks with the R1 images. This was done to get the best combination of both images and typically resulted in a reduction in some of the cloud artefacts in the R1 images. The mask creation and previewing of the blending was performed in Photoshop. The created masks were saved in 01-data/R2-R1-masks. To help with the blending of neighbouring images a feathered alpha channel was added to the imagery. The processing of the merging (using the masks) and the creation of the feathered borders on the images was performed using a Python script (src/local/03-merge-R2-R1-images.py) using the Pillow library and GDAL. The neighbouring image blending mask was created by applying a blurring of the original hard image mask. This allowed neighbouring image tiles to merge together.

The imagery and reference datasets (reef boundaries, EEZ) were loaded into QGIS for the creation of the printable maps.

To optimise the matching of the resulting map slight brightness adjustments were applied to each scene tile to match its neighbours. This was done in the setup of each image in QGIS. This adjustment was imperfect as each tile was made from a different combinations of days (to remove clouds) resulting in each scene having a different tonal gradients across the scene then its neighbours. Additionally Sentinel 2 has slight stripes (at 13 degrees off the vertical) due to the swath of each sensor having a slight sensitivity difference. This effect was uncorrected in this imagery.

Single merged composite GeoTiff: The image tiles with alpha blended edges work well in QGIS, but not in ArcGIS Pro. To allow this imagery to be used across tools that don't support the alpha blending we merged and flattened the tiles into a single large GeoTiff with no alpha channel. This was done by rendering the map created in QGIS into a single large image. This was done in multiple steps to make the process manageable.

The rendered map was cut into twenty 1 x 1 degree georeferenced PNG images using the Atlas feature of QGIS. This process baked in the alpha blending across neighbouring Sentinel 2 scenes. The PNG images were then merged back into a large GeoTiff image using GDAL (via QGIS), removing the alpha channel. The brightness of the image was adjusted so that the darkest pixels in the image were 1, saving the value 0 for nodata masking and the boundary was clipped, using a polygon boundary, to trim off the outer feathering. The image was then optimised for performance by using internal tiling and adding overviews. A full breakdown of these steps is provided in the README.md in the 'Browse and download all data files' link.

The merged final image is available in export\TS_AIMS_Torres Strait-Sentinel-2_Composite.tif.

Change Log: 2023-03-02: Eric Lawrey Created a merged version of the satellite imagery, with no alpha blending so that it can be used in ArcGIS Pro. It is now a single large GeoTiff image. The Google Earth Engine source code for the World_AIMS_Marine-satellite-imagery was included to improve the reproducibility and provenance of the dataset, along with a calculation of the distribution of image dates that went into the final composite image. A WMS service for the imagery was also setup and linked to from the metadata. A cross reference to the older Torres Strait clear sky clear water Landsat composite imagery was also added to the record.

22 Nov 2023: Eric Lawrey Added the data and maps for close up of Mer. - 01-data/TS_DNRM_Mer-aerial-imagery/ - preview/Torres-Strait-Mer-Map-Landscape-A0.jpeg - exports/Torres-Strait-Mer-Map-Landscape-A0.pdf Updated 02-Torres-Strait-regional-maps.qgz to include the layout for the new map.

Source datasets: Complete Great Barrier Reef (GBR) Island and Reef Feature boundaries including Torres Strait Version 1b (NESP TWQ 3.13, AIMS, TSRA, GBRMPA), https://eatlas.org.au/data/uuid/d2396b2c-68d4-4f4b-aab0-52f7bc4a81f5

Geoscience Australia (2014b), Seas and Submerged Lands Act 1973 - Australian Maritime Boundaries 2014a - Geodatabase [Dataset]. Canberra, Australia: Author. https://creativecommons.org/licenses/by/4.0/ [license]. Sourced on 12 July 2017, https://dx.doi.org/10.4225/25/5539DFE87D895

Basemap/AU_GA_AMB_2014a/Exclusive_Economic_Zone_AMB2014a_Limit.shp The original data was obtained from GA (Geoscience Australia, 2014a). The Geodatabase was loaded in ArcMap. The Exclusive_Economic_Zone_AMB2014a_Limit layer was loaded and exported as a shapefile. Since this file was small no clipping was applied to the data.

Geoscience Australia (2014a), Treaties - Australian Maritime Boundaries (AMB) 2014a [Dataset]. Canberra, Australia: Author. https://creativecommons.org/licenses/by/4.0/ [license]. Sourced on 12 July 2017, http://dx.doi.org/10.4225/25/5539E01878302 Basemap/AU_GA_Treaties-AMB_2014a/Papua_New_Guinea_TSPZ_AMB2014a_Limit.shp The original data was obtained from GA (Geoscience Australia, 2014b). The Geodatabase was loaded in ArcMap. The Papua_New_Guinea_TSPZ_AMB2014a_Limit layer was loaded and exported as a shapefile. Since this file was small no clipping was applied to the data.

AIMS Coral Sea Features (2022) - DRAFT This is a draft version of this dataset. The region for Ashmore and Boot reef was checked. The attributes in these datasets haven't been cleaned up. Note these files should not be considered finalised and are only suitable for maps around Ashmore Reef. Please source an updated version of this dataset for any other purpose. CS_AIMS_Coral-Sea-Features/CS_Names/Names.shp CS_AIMS_Coral-Sea-Features/CS_Platform_adj/CS_Platform.shp CS_AIMS_Coral-Sea-Features/CS_Reef_Boundaries_adj/CS_Reef_Boundaries.shp CS_AIMS_Coral-Sea-Features/CS_Depth/CS_AIMS_Coral-Sea-Features_Img_S2_R1_Depth5m_Coral-Sea.shp CS_AIMS_Coral-Sea-Features/CS_Depth/CS_AIMS_Coral-Sea-Features_Img_S2_R1_Depth10m_Coral-Sea.shp

Murray Island 20 Sept 2011 15cm SISP aerial imagery, Queensland Spatial Imagery Services Program, Department of Resources, Queensland This is the high resolution imagery used to create the map of Mer.

Marine satellite imagery (Sentinel 2 and Landsat 8) (AIMS), https://eatlas.org.au/data/uuid/5d67aa4d-a983-45d0-8cc1-187596fa9c0c - World_AIMS_Marine-satellite-imagery

Data Location: This dataset is filed in the eAtlas enduring data repository at: data\custodian\2020-2029-AIMS\TS_AIMS_Torres-Strait-Sentinel-2-regional-maps. On the eAtlas server it is stored at eAtlas GeoServer\data\2020-2029-AIMS.

The Unpublished Digital Geologic Map of Klondike Gold Rush National Historical Park and Vicinity, Alaska is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (klgo_geology.gdb), a 10.1 ArcMap (.MXD) map document (klgo_geology.mxd), individual 10.1 layer (.LYR) files for each GIS data layer, an ancillary map information (.PDF) document (klgo_geology.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.TXT) and FAQ (.HTML) formats, and a GIS readme file (klgo_gis_readme.pdf). Please read the klgo_gis_readme.pdf for information pertaining to the proper extraction of the file geodatabase and other map files. To request GIS data in ESRI 10.1 shapefile format contact Stephanie O Meara (stephanie.omeara@colostate.edu; see contact information below). The data is also available as a 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. Google Earth software is available for free at: http://www.google.com/earth/index.html. 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). 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 (klgo_metadata_faq.html; available at http://nrdata.nps.gov/geology/gri_data/gis/klgo/klgo_metadata_faq.html). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:500,000 and United States National Map Accuracy Standards features are within (horizontally) 254 meters or 833.3 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 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. 2.2. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data projection is NAD83, UTM Zone 8N, however, for the KML/KMZ format the data is projected upon export to WGS84 Geographic, the native coordinate system used by Google Earth. The data is within the area of interest of Klondike Gold Rush National Historical Park.

The Digital Geomorphic-GIS Map of Cape Lookout National Seashore, North Carolina (1:24,000 scale 2008 mapping) is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) an ESRI file geodatabase (calo_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 3.X map file (.mapx) file (calo_geomorphology.mapx) and individual Pro 3.X layer (.lyrx) 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 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) a readme file (calo_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (calo_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 (calo_geomorphology_metadata_faq.pdf). Please read the calo_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: 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: North Carolina 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 (calo_geomorphology_metadata.txt or calo_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 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual _location as presented by this dataset. Users of this data should thus not assume the _location of features is exactly where they are portrayed in Google Earth, ArcGIS Pro, 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).

The Unpublished Digital Geomorphic Map of the Shackleford Banks, North Carolina is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (shkb_geology.gdb), a 10.1 ArcMap (.MXD) map document (shkb_geology.mxd), individual 10.1 layer (.LYR) files for each GIS data layer, an ancillary map information (.PDF) document (shkb_geology.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.TXT) and FAQ (.HTML) formats, and a GIS readme file (calo_gis_readme.pdf). Please read the calo_gis_readme.pdf for information pertaining to the proper extraction of the file geodatabase and other map files. To request GIS data in ESRI 10.1 shapefile format contact Stephanie O Meara (stephanie.o meara@colostate.edu; see contact information below). The data is also available as a 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. Google Earth software is available for free at: http://www.google.com/earth/index.html. 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). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: East Carolina University. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (shkb_metadata_faq.html; available at http://nrdata.nps.gov/geology/gri_data/gis/calo/shkb_metadata_faq.html). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:10,000 and United States National Map Accuracy Standards features are within (horizontally) 5.1 meters or 16.7 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 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. 2.2. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data projection is NAD83, UTM Zone 18N, however, for the KML/KMZ format the data is projected upon export to WGS84 Geographic, the native coordinate system used by Google Earth. The data is within the area of interest of Cape Lookout National Seashore.