The digital map market, currently valued at $25.55 billion in 2025, is experiencing robust growth, projected to expand at a compound annual growth rate (CAGR) of 13.39% from 2025 to 2033. This expansion is fueled by several key factors. The increasing adoption of location-based services (LBS) across various sectors, including transportation, logistics, and e-commerce, is a primary driver. Furthermore, the proliferation of smartphones and connected devices, coupled with advancements in GPS technology and mapping software, continues to fuel market growth. The rising demand for high-resolution, real-time mapping data for autonomous vehicles and smart city initiatives also significantly contributes to market expansion. Competition among established players like Google, TomTom, and ESRI, alongside emerging innovative companies, is fostering continuous improvement in map accuracy, functionality, and data accessibility. This competitive landscape drives innovation and lowers costs, making digital maps increasingly accessible to a broader range of users and applications. However, market growth is not without its challenges. Data security and privacy concerns surrounding the collection and use of location data represent a significant restraint. Ensuring data accuracy and maintaining up-to-date map information in rapidly changing environments also pose operational hurdles. Regulatory compliance with differing data privacy laws across various jurisdictions adds another layer of complexity. Despite these challenges, the long-term outlook for the digital map market remains positive, driven by the relentless integration of location intelligence into nearly every facet of modern life, from personal navigation to complex enterprise logistics solutions. The market's segmentation (although not explicitly provided) likely includes various map types (e.g., road maps, satellite imagery, 3D maps), pricing models (subscriptions, one-time purchases), and industry verticals served. This diversified market structure further underscores its resilience and potential for sustained growth. Recent developments include: December 2022 - The Linux Foundation has partnered with some of the biggest technology companies in the world to build interoperable and open map data in what is an apparent move t. The Overture Maps Foundation, as the new effort is called, is officially hosted by the Linux Foundation. The ultimate aim of the Overture Maps Foundation is to power new map products through openly available datasets that can be used and reused across applications and businesses, with each member throwing their data and resources into the mix., July 27, 2022 - Google declared the launch of its Street View experience in India in collaboration with Genesys International, an advanced mapping solutions company, and Tech Mahindra, a provider of digital transformation, consulting, and business re-engineering solutions and services. Google, Tech Mahindra, and Genesys International also plan to extend this to more than around 50 cities by the end of the year 2022.. Key drivers for this market are: Growth in Application for Advanced Navigation System in Automotive Industry, Surge in Demand for Geographic Information System (GIS); Increased Adoption of Connected Devices and Internet. Potential restraints include: Growth in Application for Advanced Navigation System in Automotive Industry, Surge in Demand for Geographic Information System (GIS); Increased Adoption of Connected Devices and Internet. Notable trends are: Surge in Demand for GIS and GNSS to Influence the Adoption of Digital Map Technology.

The linked to page on GPS.gov was created to help individuals fix mapping issues that are a common problem for users of GPS devices and online web-maps like Google Maps and MapQuest. Wrong or missing addresses on the device or map, difficulty with navigating to your location, unnecessary public navigation through your land, or repetitive wrong deliveries to your location are all problems that affect many GPS devices and online web-maps and may be able to be fixed through the resources on this website.

The global map navigation service market is experiencing robust growth, driven by the increasing penetration of smartphones, the proliferation of connected cars, and the rising demand for location-based services (LBS). The market's expansion is fueled by advancements in mapping technologies, such as high-definition (HD) maps and real-time traffic updates, which enhance user experience and safety. Furthermore, the integration of map navigation with other services, like ride-hailing apps and delivery platforms, is creating new avenues for growth. While challenges exist, such as data privacy concerns and the need for accurate map data in remote areas, the overall market outlook remains positive. We project a Compound Annual Growth Rate (CAGR) of approximately 15% from 2025 to 2033, with significant regional variations driven by factors such as infrastructure development, smartphone adoption rates, and government regulations. The market is segmented by service type (in-car, mobile, etc.), application (consumer, commercial), and technology (GPS, satellite, etc.), each exhibiting unique growth trajectories. Key players are strategically investing in research and development, mergers and acquisitions, and partnerships to strengthen their market positions and meet the evolving needs of consumers and businesses. The competitive landscape is highly fragmented, with numerous established players and emerging startups vying for market share. Companies like Google, TomTom, Garmin, and others are continually innovating to enhance their map data, user interfaces, and overall service offerings. The focus on providing personalized experiences, incorporating augmented reality (AR) features, and leveraging artificial intelligence (AI) for route optimization and traffic prediction is transforming the map navigation service market. The integration of autonomous driving technology presents a significant long-term growth opportunity, as accurate and reliable map data is crucial for the safe and efficient operation of self-driving vehicles. However, maintaining data accuracy, addressing cybersecurity threats, and ensuring compliance with evolving regulations will be critical for sustained success in this dynamic market.

The Navigation Electronic Map market is experiencing robust growth, projected to reach a market size of $3021 million in 2025, exhibiting a remarkable Compound Annual Growth Rate (CAGR) of 25.4% from 2019 to 2033. This expansion is fueled by several key factors. The increasing adoption of smartphones and connected vehicles significantly drives demand for accurate and detailed navigation maps, particularly in the rapidly expanding personal use segment. Furthermore, advancements in mapping technologies, including the transition from 2D to higher-resolution 3D mapping, and the integration of advanced features like augmented reality overlays and real-time traffic updates, are enhancing user experience and driving market growth. The commercial and military sectors also contribute significantly, with logistics companies, delivery services, and defense organizations relying heavily on sophisticated navigation systems for efficient operations and strategic planning. Growth is also expected to be fueled by increasing investment in infrastructure development and smart city initiatives, creating a demand for high-precision navigation solutions. However, certain challenges exist. Data privacy concerns and the increasing complexity of map data management could pose challenges. Competition among established players like Google, TomTom, and ESRI, along with the emergence of new entrants, creates a dynamic and competitive landscape. Despite these restraints, the long-term outlook for the Navigation Electronic Map market remains exceptionally positive. The continued integration of navigation systems into various applications, coupled with ongoing technological innovations, will likely sustain high growth rates throughout the forecast period (2025-2033). The market segmentation, encompassing 2D and 3D maps across personal, commercial, and military applications, indicates diverse avenues for growth and caters to specific user requirements. Geographic expansion, particularly in developing regions with rapidly expanding infrastructure projects, also offers significant potential for market expansion.

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.

India Location-based Services Market size was valued at USD 460 Million in 2024 and is projected to reach USD 1563 Million by 2032, growing at a CAGR of 16.7% from 2026 to 2032.India Location-based Services Market: Definition/ OverviewLocation-based services (LBS) are applications or services that use a user's geographic location to provide personalized content, services, or information. These services typically rely on technologies such as GPS, Wi-Fi, or cellular data to determine the user's position and tailor experiences based on that location. LBS can be offered through mobile apps, websites, or IoT devices, providing users with relevant information or guidance wherever they are.The application of location-based services spans across various industries, from navigation and travel to retail and marketing. For instance, apps like Google Maps or Uber use LBS to offer real-time route guidance, ride-hailing services, and traffic updates. Retailers use LBS for targeted advertising, sending promotional offers to customers when they are near a store. Additionally, LBS are used in healthcare for monitoring patient movement, in logistics for fleet management, and even in social networking apps where users can share their locations with friends.

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Digital Map Market Size 2025-2029

The digital map market size is forecast to increase by USD 31.95 billion at a CAGR of 31.3% between 2024 and 2029.

The market is driven by the increasing adoption of intelligent Personal Digital Assistants (PDAs) and the availability of location-based services. PDAs, such as smartphones and smartwatches, are becoming increasingly integrated with digital map technologies, enabling users to navigate and access real-time information on-the-go. The integration of Internet of Things (IoT) enables remote monitoring of cars and theft recovery. Location-based services, including mapping and navigation apps, are a crucial component of this trend, offering users personalized and convenient solutions for travel and exploration. However, the market also faces significant challenges.
Ensuring the protection of sensitive user information is essential for companies operating in this market, as trust and data security are key factors in driving user adoption and retention. Additionally, the competition in the market is intense, with numerous players vying for market share. Companies must differentiate themselves through innovative features, user experience, and strong branding to stand out in this competitive landscape. Security and privacy concerns continue to be a major obstacle, as the collection and use of location data raises valid concerns among consumers.

What will be the Size of the Digital Map Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the market, cartographic generalization and thematic mapping techniques are utilized to convey complex spatial information, transforming raw data into insightful visualizations. Choropleth maps and dot density maps illustrate distribution patterns of environmental data, economic data, and demographic data, while spatial interpolation and predictive modeling enable the estimation of hydrographic data and terrain data in areas with limited information. Urban planning and land use planning benefit from these tools, facilitating network modeling and location intelligence for public safety and emergency management.

Spatial regression and spatial autocorrelation analyses provide valuable insights into urban development trends and patterns. Network analysis and shortest path algorithms optimize transportation planning and logistics management, enhancing marketing analytics and sales territory optimization. Decision support systems and fleet management incorporate 3D building models and real-time data from street view imagery, enabling effective resource management and disaster response. The market in the US is experiencing robust growth, driven by the integration of Geographic Information Systems (GIS), Global Positioning Systems (GPS), and advanced computer technology into various industries.

How is this Digital Map Industry segmented?

The digital map industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Navigation
  Geocoders
  Others


Type

  Outdoor
  Indoor


Solution

  Software
  Services


Deployment

  On-premises
  Cloud


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Indonesia
    Japan
    South Korea


  Rest of World (ROW)

By Application Insights

The navigation segment is estimated to witness significant growth during the forecast period. Digital maps play a pivotal role in various industries, particularly in automotive applications for driver assistance systems. These maps encompass raster data, aerial photography, government data, and commercial data, among others. Open-source data and proprietary data are integrated to ensure map accuracy and up-to-date information. Map production involves the use of GPS technology, map projections, and GIS software, while map maintenance and quality control ensure map accuracy. Location-based services (LBS) and route optimization are integral parts of digital maps, enabling real-time navigation and traffic data.

Data validation and map tiles ensure data security. Cloud computing facilitates map distribution and map customization, allowing users to access maps on various devices, including mobile mapping and indoor mapping. Map design, map printing, and reverse geocoding further enhance the user experience. Spatial analysis and data modeling are essential for data warehousing and real-time navigation. The automotive industry's increasing adoption of connected cars and long-term evolution (LTE) technologies have fueled the demand for digital maps. These maps enable driver assistance applications,

The Location-Based Services (LBS) market is experiencing robust growth, driven by the increasing penetration of smartphones, the proliferation of mobile internet usage, and the rising adoption of location-aware applications across various sectors. The market's expansion is fueled by the increasing demand for personalized services, improved navigation solutions, and the integration of LBS into diverse industries, including transportation, retail, healthcare, and advertising. The integration of advanced technologies like AI and IoT is further enhancing LBS capabilities, providing more accurate location data and facilitating the development of innovative applications. While challenges like data privacy concerns and the need for robust infrastructure remain, the overall market outlook is highly positive, indicating substantial growth opportunities in the coming years. The competitive landscape includes established players like Google and TomTom alongside emerging companies specializing in specific LBS niches. Regional variations exist, with North America and Europe currently leading in market share due to high smartphone penetration and advanced technological infrastructure; however, significant growth potential is expected in Asia-Pacific regions, driven by rapid urbanization and increasing mobile adoption. The forecast period of 2025-2033 suggests a continued upward trajectory for the LBS market. Assuming a moderate CAGR (let's assume, for illustrative purposes, a CAGR of 12%), the market will witness significant expansion across all segments. The adoption of advanced analytics and data-driven decision-making within businesses is expected to fuel growth in enterprise applications. Furthermore, the development of more precise positioning technologies and the integration of 5G networks will further enhance the accuracy and performance of LBS, paving the way for new and innovative applications that leverage real-time location data. Competition will likely intensify as companies strive to offer superior services and enhanced features. This will lead to increased innovation, making the LBS landscape even more dynamic and responsive to evolving user needs.

The Location Intelligence Platforms (LIP) market is experiencing robust growth, projected to reach $14.15 billion in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 11.7% from 2019 to 2033. This expansion is fueled by several key drivers. The increasing adoption of big data analytics and the growing need for businesses to understand customer location data for targeted marketing and improved operational efficiency are major contributing factors. Furthermore, the rising demand for location-based services across various industries, including retail, logistics, and healthcare, is significantly boosting market growth. Technological advancements, such as the improved accuracy of GPS and GIS technologies, and the development of more sophisticated analytical tools, are also contributing to the market’s expansion. Competitive pressures among leading players like Esri, Pitney Bowes, and Google are likely driving innovation and affordability within the market, making LIP solutions more accessible to a wider range of businesses. Despite the positive outlook, market growth might face some challenges. Data privacy concerns and the complexity associated with implementing and managing LIP systems could hinder wider adoption. However, the continuous development of user-friendly interfaces and robust data security measures is likely mitigating these concerns. The market is segmented by various factors including deployment type (cloud, on-premise), solution type (analytics, mapping, visualization), and industry verticals. While specific segment data is unavailable, it is reasonable to infer that the cloud deployment model and analytics solutions will dominate due to scalability and ease of use. The forecast period of 2025-2033 suggests continued market expansion driven by the ongoing digital transformation across various sectors and the increasing reliance on location-based data for informed decision-making.

The Spain Location-Based Services (LBS) market is experiencing robust growth, projected to reach a market size of €880 million in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 13.75% from 2019 to 2033. This expansion is driven by several key factors. Firstly, the increasing smartphone penetration and widespread adoption of mobile internet in Spain fuels demand for location-aware applications across various sectors. Secondly, advancements in technologies such as GPS, GIS, and IoT are enhancing the accuracy and capabilities of LBS, creating new opportunities in areas like smart city initiatives, logistics optimization, and personalized advertising. The tourism sector in Spain also acts as a significant catalyst, with tourists heavily reliant on navigation apps, location-based recommendations, and other LBS features. Furthermore, government initiatives promoting digital transformation and smart infrastructure development contribute positively to market growth. However, challenges exist. Data privacy concerns and regulations surrounding the collection and utilization of location data impose constraints. Competition among established players and new entrants in the LBS market intensifies, potentially impacting profit margins. Also, the effective implementation of LBS requires reliable infrastructure and consistent internet connectivity, which may pose challenges in certain areas of Spain. Despite these restraints, the market's strong growth trajectory is expected to continue, fueled by the increasing integration of LBS into various aspects of daily life and the continuous innovation within the sector. The market segmentation by component (hardware, software, services), location (indoor, outdoor), application (mapping, analytics, advertising, etc.), and end-user (transportation, IT, healthcare, etc.) reveals diverse opportunities for market players to specialize and capitalize on specific segments' unique demands and growth potential within the Spanish context. Recent developments include: February 2023: Mercedes-Benz and Google unveiled an extensive and visionary partnership aimed at revolutionizing the automotive industry and elevating the digital luxury car experience to new heights. In an industry-first move, Mercedes-Benz is set to develop its distinct navigation system, harnessing the advanced capabilities of the Google Maps Platform to craft an unparalleled driving experience. This groundbreaking collaboration will grant Mercedes-Benz exclusive access to Google's cutting-edge geospatial technologies, providing users with an array of exceptional features. These include comprehensive location data, automatic route optimization, up-to-the-minute traffic updates, and even predictive traffic insights, among other remarkable functionalities., January 2023: Mapbox, the leading platform for mapping and location services, joined forces with Toyota Motor Europe to introduce Cloud Navigation powered by Mapbox Dash. This transformative partnership brings an unprecedented level of real-time information to Toyota's Yaris, Yaris Cross, and Aygo X models, enhancing the driving experience in terms of efficiency, convenience, and safety. Alongside precision lane-level navigation, drivers can access a wealth of features such as live parking availability, speed limit alerts, and warnings for speed cameras. Furthermore, an upcoming pilot program will enable Toyota drivers to conveniently handle parking and fuel payments directly through their infotainment systems, further streamlining the driving experience.. Key drivers for this market are: Growing Demand for Geo-based Marketing, Emerging Use-cases for LBS due to High Penetration of Social Media and Location-based App Adoption. Potential restraints include: Growing Demand for Geo-based Marketing, Emerging Use-cases for LBS due to High Penetration of Social Media and Location-based App Adoption. Notable trends are: Indoor Location Segment is Expected to Hold Significant Share of the Market.

Geospatial Services Land management within the US Forest Service and on the 900,000+ acre Monongahela National Forest (NF) is driven by a wide mix of resource and societal demands that prove a challenge in fulfilling the Forest Service’s mission of “Caring for the Land and Serving the People.” Programmatically, the 2006 Land and Resource Management Plan guide natural resource management activities on lands administered by the Monongahela National Forest. The Forest Plan describes management direction and practices, resource protection methods and monitoring, desired resource conditions, and the availability and suitability of lands for resource management. Technology enables staff to address these land management issues and Forest Plan direction by using a science-based approach to facilitate effective decisions. Monongahela NF geospatial services, using enabling-technologies, incorporate key tools such as Environmental Systems Research Institute’s ArcGIS desktop suite and Trimble’s global positioning system (GPS) units to meet program and Forest needs. Geospatial Datasets The Forest has a broad set of geospatial datasets that capture geographic features across the eastern West Virginia landscape. Many of these datasets are available to the public through our download site. Selected geospatial data that encompass the Monongahela National Forest are available for download from this page. A link to the FGDC-compliant metadata is provided for each dataset. All data are in zipped format (or available from the specified source), in one of two spatial data formats, and in the following coordinate system: Coordinate System: Universal Transverse Mercator Zone: 17 Units: Meters Datum: NAD 1983 Spheroid: GRS 1980 Map files – All map files are in pdf format. These maps illustrate the correlated geospatial data. All maps are under 1 MB unless otherwise noted. Metadata file – This FGDC-compliant metadata file contains information pertaining to the specific geospatial dataset. Shapefile – This downloadable zipped file is in ESRI’s shapefile format. KML file – This downloadable zipped file is in Google Earth’s KML format. Resources in this dataset:Resource Title: Monongahela National Forest Geospatial Data. File Name: Web Page, url: https://www.fs.usda.gov/detail/mnf/landmanagement/gis/?cid=stelprdb5108081 Selected geospatial data that encompass the Monongahela National Forest are available for download from this page.

Data for this map was collected using metadata found in th image files, Garmin GPS point collectio, EpiCollect form and waypoint mapping using Google maps. MMap is a reference to be used by a variety of agencies to locate and treat Japanese Knotweed.

Service Area 2009 - 30 cm Aerials for ArcGIS Online/Bing Maps/Google Maps, etc.Contact Info: Name: GIS Team Email: GISteam@cityoftacoma.orgCompany: DigitalGlobePROJECT SPECIFICATIONS -- ACCURACY REPORTProject: WA Seattle-30cm-0709_4488Datum: NAD 83Projection: UTMZone: 10Units: metersGSD (pixel size): 30 cmCamera Type: Leica Geosystems ADS40-SH51Average Acquisition Altitude: 9,600 feet above ground levelDEM Source: National Elevation Dataset (NED)Reference Source: Airborne GPS/IMUPhoto Date Range: July 2 through July 22, 2009Cloud cover: Entire Market is cloud freeResolution: Entire Market acquired at 30 cm GSDSun Angle: Entire Market acquired with sun angle above 30 degreesAccuracy Points Measured: 47Accuracy: 2.01 meters CE90Accuracy report, date prepared: September 30, 2009Accuracy report prepared by: Mark MeyerOriginal ArcGIS coordinate system: Type: Projected Geographic coordinate reference: GCS_North_American_1983_HARN Projection: NAD_1983_HARN_StatePlane_Washington_South_FIPS_4602_Feet Well-known identifier: 2927Geographic extent - Bounding rectangle: West longitude: -122.655808 East longitude: -121.999192 North latitude: 47.352623 South latitude: 46.791574Extent in the item's coordinate system: West longitude: 1105996.657074 East longitude: 1264997.323324 South latitude: 539001.060567 North latitude: 740002.627067

Geologisk kart over Svalbard (1:250 000).

https://api.npolar.no/dataset/eafafbb7-b3df-4c71-a2df-316e80a7992e/_file/daf3eeae9d3aeb5bdf9a2b9f86ba8bab?key=8ee185b7c7f70470041e8801b3451517+Uyhjrqc9jddVIG52JAZO6t00BYN7eakD" alt="Mobilkart i felt">

Sammendrag (for English see below)

Dette geologiske kartet fra Norsk Polarinstitutt har blitt produsert med tanke på å brukes på smart-telefon, nettbrett eller PC uten nett-tilkobling, for eksempel til feltarbeid eller som et hendig oppslags-kart. Kartet består av 5 raster-filer i GIS-formatet JPEG2000 og er tilgjengelig som nedlasting fra datasenteret til Norsk Polarinstitutt

Informasjon om de geologiske enhetene er plassert som tekst-merkelapper direkte i kartbildet, i motsetning til en vanlig tegnforklaring. Ved å zoome inn på kartet finnes informasjon om geologiske enheter, vist med blå tekst (alder i parentes). I tillegg er hvert enhet (farge) merket med en tilsvarende 4-sifret kode i blå skrift.

I felten kan mobile dingser med GPS vise brukeren sin posisjon på kartet. Avhengig av skjermoppløsning er full detaljgrad i kartet synlig på ca. 1:30 000-skala, men kartet kan også vises på mye større skala for å se f.eks. regionale geologiske trekk.

Kartet kan vises på Android eller iOS-enheter med appen "Geoviewer" fra Extensis (tidligere Lizardtech). På datamaskin fungerer QGIS eller ArcMap bra for å vise kartet. Se forklaring på hvordan overføre kartet til din smart-telefon eller nettbrett lenger nede på sida.

Data

Kartet er laget ved å bruke data fra Norsk Polarinstitutt 1:250 000-skala geologiske kart for Svalbard, opprinnelig publisert i "Geoscience Atlas of Svalbard" av Dallmann (ed.) 2015. Dette kartet er generalisert fra 1:100 000-skala kart-data i hovedkartserien til Norsk Polarinstitutt, og er publisert i Geoscience Atlas of Svalbard (Dallmann 2015).

Til å produsere dette kartet er topografiske data fra S100 (topografi, vann) og S250 (kystlinje)-datasettene fra Norsk Polarinstitutt brukt. Fjellskygge er konstruert med S0 Terrengmodell med 20 meter pr. pixel oppløsning. Bre og snøflekk-områder er vist med datasettet for 2001-2010 av König mfl. (2013), som gir et mer oppdatert bilde av blotning-situasjonen nær breer og snøflekker. Områder der geologiske polygoner ikke er justert til nye blotninger er vist i brunt. Kystlinjen er i noen tilfeller endret for å tilpasses bre-fronter som ender i sjøen.

Forbehold om datakvalitet Dette er et nytt geologisk kartprodukt, og det kan forekomme feil. Spesielt tegnforklaring, som er skrevet direkte på geologiske enheter, kan være problematisk i noen områder. Vi er interessert i tilbakemelding på mulige forbedringer av kartet. Send gjerne tilbakemeldinger på e-post til Geokart@npolar.no.

Dette er et geologisk kart ment for å formidle vitenskapelige data, og er ikke egnet for navigasjon. Noen områder av Svalbard er ennå ikke kartlagt i detalj, og en del av dataene er av eldre dato, så datakvaliteten for dette kartet er varierende. Kartet kan inneholde feil i grunnlagsdata, kartpresentasjon, kartografi og tekst-beskrivelser. For en stor del er geologien kartlagt for en mindre detaljert skala enn den det er mulig å oppnå med dette kartproduktet, så geologiske trekk og enheter vil i ulik grad fremstå feilplassert ved bruk av god GPS-posisjon og detaljert zoom-nivå. Breer og spesielt bre-fronter er i konstant forandring, og selv om ganske oppdaterte data er brukt for å lage kartet, vil det være feil i en del bre-posisjoner. Vær oppmerksom på at det topografiske grunnlaget som er brukt her i mange tilfeller er av nyere dato enn det som opprinnelig var brukt under kartleggingen i felt. Dette kan også føre til feil i kartet.

Geologiske kart-data vil kontinuerlig være gjenstand for re-tolkning og endring. For en full beskrivelse av kartleggingsprogrammet ved Norsk Polarinstitutt, geologiske kart-data presentert her og referanser, se Dallmann (ed.) 2015, eller besøk npolar.no

Hvordan overføre kartet til mobilenheter

Direkte nedlasting Kartet kan nå lastes ned direkte til mobilenheten via lenker øverst. Det er 5 linker, en for hvert område. Enten lagres filene på enheten, eller du vil få et valg om å åpne fila direkte i Geoviewer. NB: Sørg for at det er nok ledig lagringsplass på mobilenheten og vær oppmerksom på fil-størrelsen (550 MB), spesielt hvis det er et betalt internett-abonnenement.

Via PC, kabel eller Dropbox:

NP_S250_Geologi_mobilkart kan brukes direkte i GIS-systemer på PC, mens for bruk på nettbrett og mobil anbefales gratis-appen Geoviewer fra Lizardtech.

Etter å ha lastet ned til PC og pakket opp ZIP-filene, kan kartene for Android-enheter eksempelvis overføres til ønsket plassering på enheten via USB-kabel. For iOS-enheter kan en bruke f.eks. nettjenesten Dropbox som kanal fra PC til enhet. Når kartene er lagret på enheten, kan en legge til de kartrutene en ønsker fra menyen i Geoviewer.

Referanser Kartdata Svalbard 1:100 000 (S100 Kartdata) (2014). Norwegian Polar Institute (Tromsø, Norway): https://data.npolar.no/dataset/645336c7-adfe-4d5a-978d-9426fe788ee3

M König, J Kohler, C Nuth (2013). Glacier Area Outlines - Svalbard. Norwegian Polar Institute https://data.npolar.no/dataset/89f430f8-862f-11e2-8036-005056ad0004

Dallmann, W.K., (ed.) (2015). Geoscience Atlas of Svalbard, Norsk Polarinstitutt Rapportserie nr. 148

Terrengmodell Svalbard (S0 Terrengmodell) (2014). Norwegian Polar Institute (Tromsø, Norway): https://data.npolar.no/dataset/dce53a47-c726-4845-85c3-a65b46fe2fea

English

Geological map of Svalbard (1:250 000).

Abstract This geological map from the Norwegian Polar Institute has been prepared to be used offline on a smartphone, tablet or computer, for example for field work or a handy reference. It consists of 5 raster-files in the JPEG2000 GIS-format, available to download from the Norwegian Polar Institute data centre data.npolar.no via https://data.npolar.no/dataset/eafafbb7-b3df-4c71-a2df-316e80a7992e/.

Information about the geological units has been placed as text labels (in blue typescript) directly on the map, as opposed to a regular legend. By zooming in, information about each geological unit on the map can be found, shown in blue text (age in parentheses). In addition, each unit is labelled with a corresponding 4-digit code also in blue typescript.

In the field, GPS-enabled devices can show the user's location on the map. Depending on screen resolution, full detail of the map (including text labels) is best viewed at ca. 1:30 000 scale, but the map can also be viewed at much larger scales to see e.g. regional geological features.

For mobile use, the app "Geoviewer" from Extensis (formerly Lizardtech) can be used. On a computer, QGIS works well to view these maps. See an explanation below on how to transfer the map to your tablet or smartphone.

Data

The map is made using data from the Norwegian Polar Institute 1:250 000-scale geological map for Svalbard, originally published in Dallmann (ed.) 2015. This geological map has been generalised from the 1:100 000-scale main map series published by the Norwegian Polar Institute, and is published in Geoscience Atlas of Svalbard (Dallmann 2015).

For the purpose of this map product, topographic data from the Norwegian Polar Institute S100 Map (topography, water) and S250 (coastline) data sets have been used. Hill shade was created using the NPI S0 Terrengmodell at 20 meters/pixel resolution. Glacier and snow patch outlines are shown using the 2001-2010 dataset of glacier area outlines for Svalbard by König et al. (2013), which gives a more up to date picture of the outcrop situation near glaciers or snow patches. Areas where geology polygons have not been re-adjusted to the new outcrops are shown in brown. The coast line-data has been adjusted in some cases to adapt to glacier fronts ending in the sea.

Disclaimer This is a new geological map product, and errors may occur. In particular the legend, which have been printed directly on the geological units, can be problematic in places. We appreciate feedback on the map that can be used to improve the map in future versions. Please email feedback to Geokart@npolar.no.

This is a geological map meant to convey scientific data, and is not suited for navigation. This map product may contain errors in base data, map presentation, cartography and text descriptions. Much of the geology was originally mapped for a less detailed scale than what is possible to obtain with this map, so geological features will to varying degrees appear out-of place when a good GPS-position and detailed zoom level is used. Glaciers and in particular glaciers fronts are dynamic features, and although using fairly up-to-date data, this map does contain errors in glacier front positions. Note that the topographic base data used here in many cases is of a newer vintage than the data originally used for geological mapping in the field. This may cause some errors in the map. Some areas of Svalbard have not yet been mapped in detail and some of the data are of older origin, so the data quality presented on this map is variable.

Geological map data will be subject to continual re-interpretation and editing. For a full description of the bedrock mapping programme at the Norwegian Polar Institute, the geological map data presented here and

Point locations for Daughters of Utah Pioneers satelite museums. Digitized March 2016. Locations were determined by a combination of geocoding street addresses and referencing NAIP 2014 aerial imagery and Google Street View.Horizontal_Positional_Accuracy: GPS = geocode or aerial imagery placement; Asserted = could not locate exact building location, placed at approximate street address

This hosted feature layer has been published in RI State Plane Feet NAD 83. Data are collected by a variety of means, primarily through the use of the Rhode Island Advisory Commission on Historical Cemeteries online cemetery database (https://rihistoriccemeteries.org), existing public records, and publications. Location data are verified against aerial photography available from RIGIS, Google Maps, and Bing Maps, and verified in the field as needed and when possible by volunteer researchers equipped with consumer grade GPS units. The purpose of this dataset is to represent both known and suspected locations of historic cemeteries in the State of Rhode Island. Attribute definitions indicate which data points have been verified. This dataset supplements the Rhode Island Advisory Commission on Historical Cemeteries online cemetery database. Users should note that this dataset is out of date and incomplete. Refer to the Rhode Island Advisory Commission on Historical Cemeteries online cemetery database (https://rihistoriccemeteries.org) for the most current information.

Landslides pose a serious threat in urban areas with steep and varied terrain, such as the Koya district in northern Iraq. This study aims to produce landslide susceptibility maps (LSMs) using remote sensing data and three models: Analytical Hierarchy Process (AHP), Fuzzy Logic SUM, and Fuzzy Gamma 0.9. A total of 280 landslides were identified through field surveys, GPS, and Google Earth, and used for model training and validation. Fifteen conditioning factors were used, including slope, geology, land cover, rainfall, and drainage density. The resulting LSMs were classified into five susceptibility levels. The Fuzzy Gamma 0.9 model identified 9.15% of the area as highly susceptible, while the Fuzzy SUM model showed 20.79%. Validation using the Area Under the Curve (AUC) revealed that the Fuzzy Gamma 0.9 model (AUC = 0.80) performed better than AHP (0.711) and Fuzzy SUM (0.716). These results provide valuable insights for land use planning and hazard mitigation.

A Township is a land subdivision in Ontario.This information was captured through the Ontario Base Mapping Program, is maintained by the Ministry of Natural Resources and distributed through Land Information Ontario (LIO) Warehouse.The spatial accuracy for some of the townships was improved through the Ontario Parcel, Township Realignment and Township Improvement projects.Improvements may include:road allowance widthsspatial changes to better represent where the township boundaries are locatedmore consistent concession namesMaterials used to improve the location of township lines may include:township Improvement plotssurvey information i.e. retracementsoriginal township planssurveys notesthe physical features e.g. fence lines in the corporate editing environmentwater bodiesGPS Ontario Road Network geometryPlease see the full metadata record for more information.Additional DocumentationGeographic Township, Improved - Data Description (PDF)Geographic Township, Improved - Documentation (Word)Geographic Township, Improved - FAQ (PDF)StatusOn going: Data is continually being updatedMaintenance and Update FrequencyAnnually: Data is updated every yearOffice of the Surveyor General, landtenuremapping@ontario.ca

This dataset includes Tempe’s tree inventory data and benefits of the trees as calculated by i-Tree Eco in October 2021. The dataset was put together by West Coast Arborists, Inc. (WCA) in 2021.About Tempe's Tree Inventory and i-Tree EcoThis dataset contains the point locations and attributes of trees within City of Tempe and Facilities. The point dataset was originally collected by WCA, Inc. in 2017 and is routinely updated by WCA and the City of Tempe. The attributes used included TreeID, Exact DBH, Height Range, Exact Height, Condition, Botanical Name, Common Name, Latitude, and Longitude. Updates to the Tempe's point layer was made using the results from i-Tree Eco. An i-Tree Eco Analysis was run in September 2021 using i-Tree Eco v6.0.22 and the results were joined based on unique tree ID to Tempe's Tree inventory. The results from i-Tree Eco were added as attributes to the Tempe's Tree inventory. Attributes added were: Structural Value ($), Carbon Storage (lb), Carbon Storage ($), Gross Carbon Sequestration (lb/yr), Gross Carbon Sequestration ($/yr), Avoided Runoff (cubicFT/yr), Avoided Runoff ($/yr), Pollution Removal (oz/yr), Pollution Removal ($/yr) , Total Annual Benefits ($/yr), Height (ft), Canopy Cover (sqft), Tree Condition, Leaf Area (sqft), Leaf Biomass (lb), Leaf Area Index Basal Area (sqft), Cond, i-Tree_ID_BotName, i-Tree_ID_ComName and i-Tree_ID Genus. The exact definitions, meanings, calculations, etc. for the i-Tree Values can be found on i-Tree’s website via the i-Tree Eco User Manual.i-Tree Eco. i-Tree Software Suite v6.x. Web. Fall 2021. https://www.itreetools.orgi-Tree Eco Manual:https://www.itreetools.org/documents/275/EcoV6_UsersManual.2021.09.22.pdfTempe Tree and Shade Coverage (data hub site):https://urbanforestry.tempe.gov/Additional InformationSource: West Coast Arborists, Inc. (WCA) 2021; i-Tree Eco v6 2021Contact: Richard AdkinsContact E-Mail: richard_adkins@tempe.govData Source Type: GPS and Google map data; tables in CVS and Excel formatPreparation Method: Field observations and records of individual trees; value calculations based on i-Tree Eco v6 found at https://www.itreetools.org/support/resources-overview/i-tree-manuals-workbooksPublish Frequency: Every 5 years or as data becomes availablePublish Method: ManualData Dictionary

As mentioned in the project proposal, we wanted to use our skills learned in this course to predict the estimated time to travel on I-5 from a start location and a final destination (both in California) at a given time. We originally planned for this project to be similar to a scaled version of Google Map. As we worked on our project, we decided to focus our goal on seeing if our model estimation could be as accurate as google’s estimations, where google has an advantage with crowdsourced live information from GPS signals. We originally believed the application of this model could be a map app like Google Map if we develop towards navigation part, as well as possibly an Uber-like or a Lyft-like app, if we tried to integrate this model with a on-demand transportation service system, however we focused more on building at testing the model instead. As the end result, the current intended audience is people primarily drive on the I-5, as well as businesses looking into navigational time estimations that wish to use already completed research to build a foundation for their own efforts.Notable Modules Used: Python: pandas, numpy, matplotlib, sklearn, seaborn, arcgis