ArcGIS Desktop 10.8.2. This is just a software. If you need a license, please send a request to Software Central (softwarecentral@ucla.edu).

The arrival of ArcGIS Pro has brought a challenge to ArcMap users. The new software is sufficiently different in architecture and layout that switching from the old to the new is not a simple process. In some ways, Pro is harder to learn for ArcMap users than for new GIS users, because some workflows have to be unlearned, or at least heavily modified. Current ArcMap users are pressed for time, trying to learn the new software while still completing their daily tasks, so a book that teaches Pro from the start is not an efficient method.Switching to ArcGIS Pro from ArcMap aims to quickly transition ArcMap users to ArcGIS Pro. Rather than teaching Pro from the start, as for a novice user, this book focuses on how Pro is different from ArcMap. Covering the most common and important workflows required for most GIS work, it leverages the user’s prior experience to enable a more rapid adjustment to Pro.AUDIENCEProfessional and scholarly; College/higher education; General/trade.AUTHOR BIOMaribeth H. Price, PhD, South Dakota School of Mines and Technology, has been using Esri products since 1991, teaching college GIS since 1995 and writing textbooks utilizing Esri’s software since 2001. She has extensive familiarity with both ArcMap/ArcCatalog and Pro, both as a user and in the classroom, as well as long experience writing about GIS concepts and developing software tutorials. She teaches GIS workshops, having offered more than 100 workshops to over 1,200 participants since 2000.Pub Date: Print: 2/14/2019 Digital: 1/28/2019 Format: PaperbackISBN: Print: 9781589485440 Digital: 9781589485457 Trim: 8 x 10 in.Price: Print: $49.99 USD Digital: $49.99 USD Pages: 172Table of ContentsPreface1 Contemplating the switch to ArcGIS ProBackgroundSystem requirementsLicensingCapabilities of ArcGIS ProWhen should I switch?Time to exploreObjective 1.1: Downloading the data for these exercisesObjective 1.2: Starting ArcGIS Pro, signing in, creating a project, and exploring the interfaceObjective 1.3: Accessing maps and data from ArcGIS OnlineObjective 1.4: Arranging the windows and panesObjective 1.5: Accessing the helpObjective 1.6: Importing a map document2 Unpacking the GUIBackgroundThe ribbon and tabsPanesViewsTime to exploreObjective 2.1: Getting familiar with the Contents paneObjective 2.2: Learning to work with objects and tabsObjective 2.3: Exploring the Catalog pane3 The projectBackgroundWhat is a project?Items stored in a projectPaths in projectsRenaming projectsTime to exploreObjective 3.1: Exploring different elements of a projectObjective 3.2: Accessing properties of projects, maps, and other items4 Navigating and exploring mapsBackgroundExploring maps2D and 3D navigationTime to exploreObjective 4.1: Learning to use the Map toolsObjective 4.2: Exploring 3D scenes and linking views5 Symbolizing mapsBackgroundAccessing the symbol settings for layersAccessing the labeling propertiesSymbolizing rastersTime to exploreObjective 5.1: Modifying single symbolsObjective 5.2: Creating maps from attributesObjective 5.3: Creating labelsObjective 5.4: Managing labelsObjective 5.5: Symbolizing rasters6 GeoprocessingBackgroundWhat’s differentAnalysis buttons and toolsTool licensingTime to exploreObjective 6.1: Getting familiar with the geoprocessing interfaceObjective 6.2: Performing interactive selectionsObjective 6.3: Performing selections based on attributesObjective 6.4: Performing selections based on locationObjective 6.5: Practicing geoprocessing7 TablesBackgroundGeneral table characteristicsJoining and relating tablesMaking chartsTime to exploreObjective 7.1: Managing table viewsObjective 7.2: Creating and managing properties of a chartObjective 7.3: Calculating statistics for tablesObjective 7.4: Calculating and editing in tables8 LayoutsBackgroundLayouts and map framesLayout editing proceduresImporting map documents and templatesTime to exploreObjective 8.1: Creating the maps for the layoutObjective 8.2: Setting up a layout page with map framesObjective 8.3: Setting map frame extent and scaleObjective 8.4: Formatting the map frameObjective 8.5: Creating and formatting map elementsObjective 8.6: Fine-tuning the legendObjective 8.7: Accessing and copying layouts9 Managing dataBackgroundData modelsManaging the geodatabase schemaCreating domainsManaging data from diverse sourcesProject longevityManaging shared data for work groupsTime to exploreObjective 9.1: Creating a project and exporting data to itObjective 9.2: Creating feature classesObjective 9.3: Creating and managing metadataObjective 9.4: Creating fields and domainsObjective 9.5: Modifying the table schemaObjective 9.6: Sharing data using ArcGIS Online10 EditingBackgroundBasic editing functionsCreating featuresModifying existing featuresCreating and editing annotationTime to exploreObjective 10.1: Understanding the editing tools in ArcGIS ProObjective 10.2: Creating pointsObjective 10.3: Creating linesObjective 10.4: Creating polygonsObjective 10.5: Modifying existing featuresObjective 10.6: Creating an annotation feature classObjective 10.7: Editing annotationObjective 10.8: Creating annotation features11 Moving forwardData sourcesIndex

Succeeds and combines earlier versions of the tools - Topography Toolbox for ArcGIS 9.x - http://arcscripts.esri.com/details.asp?dbid=15996Riparian Topography Toolbox for calculating Height Above River and Height Above Nearest Drainage - http://arcscripts.esri.com/details.asp?dbid=16792PRISM Data Helper - http://arcscripts.esri.com/details.asp?dbid=15976Tools:UplandBeer’s AspectMcCune and Keon Heat Load IndexLandform ClassifcationPRISM Data HelperSlope Position ClassificationSolar Illumination IndexTopographic Convergence/Wetness IndexTopographic Position IndexRiparianDerive Stream Raster using Cost DistanceHeight Above Nearest DrainageHeight Above RiverMiscellaneousMoving Window Correlation

NOTE: An updated Introduction to ArcGIS GeoEvent Server Tutorial is available here. It is recommended you use the new tutorial for getting started with GeoEvent Server. The old Introduction Tutorial available on this page is relevant for 10.8.x and earlier and will not be updated.The Introduction to GeoEvent Server Tutorial (10.8.x and earlier) introduces you to the Real-Time Visualization and Analytic capabilities of ArcGIS GeoEvent Server. GeoEvent Server allows you to:

Incorporate real-time data feeds in your existing GIS data and IT infrastructure. Perform continuous processing and analysis on streaming data, as it is received. Produce new streams of data that can be leveraged across the ArcGIS system.

Once you have completed the exercises in this tutorial you should be able to:

Use ArcGIS GeoEvent Manager to monitor and perform administrative tasks. Create and maintain GeoEvent Service elements such as inputs, outputs, and processors. Use GeoEvent Simulator to simulate event data into GeoEvent Server. Configure GeoEvent Services to append and update features in a published feature service. Work with processors and filters to enhance and direct GeoEvents from event data.

The knowledge gained from this tutorial will prepare you for other GeoEvent Server tutorials available in the ArcGIS GeoEvent Server Gallery.

Releases

Each release contains a tutorial compatible with the version of GeoEvent Server listed. The release of the component you deploy does not have to match your version of ArcGIS GeoEvent Server, so long as the release of the component is compatible with the version of GeoEvent Server you are using. For example, if the release contains a tutorial for version 10.6; this tutorial is compatible with ArcGIS GeoEvent Server 10.6 and later. Each release contains a Release History document with a compatibility table that illustrates which versions of ArcGIS GeoEvent Server the component is compatible with.

NOTE: The release strategy for ArcGIS GeoEvent Server components delivered in the ArcGIS GeoEvent Server Gallery has been updated. Going forward, a new release will only be created when

  a component has an issue,
  is being enhanced with new capabilities,
  or is not compatible with newer versions of ArcGIS GeoEvent Server.

This strategy makes upgrades of these custom
components easier since you will not have to
upgrade them for every version of ArcGIS GeoEvent Server
unless there is a new release of
the component. The documentation for the
latest release has been
updated and includes instructions for updating
your configuration to align with this strategy.

Latest

Release 7 - March 30, 2018 - Compatible with ArcGIS GeoEvent Server 10.6 and later.

Previous

Release 6 - January 12, 2018 - Compatible with ArcGIS GeoEvent Server 10.5 thru 10.8.

Release 5 - July 30, 2016 - Compatible with ArcGIS GeoEvent Server 10.4 thru 10.8.

Release 4 - July 30, 2015 - Compatible with ArcGIS GeoEvent Server 10.3.x.

Release 3 - April 24, 2015 - Compatible with ArcGIS GeoEvent Server 10.3.x. Not available.

Release 2 - January 22, 2015 - Compatible with ArcGIS GeoEvent Server 10.3.x. Not available.

Release 1 - April 11, 2014 - Compatible with ArcGIS GeoEvent Server 10.2.x.

Use the Attachment Viewer template to provide an app for users to explore a layer's features and review attachments with the option to update attribute data. Present your images, videos, and PDF files collected using ArcGIS Field Maps or ArcGIS Survey123 workflows. Choose an attachment-focused layout to display individual images beside your map or a map-focused layout to highlight your map next to a gallery of images. Examples: Review photos collected during emergency response damage inspections. Display the results of field data collection and support downloading images for inclusion in a report. Present a map of land parcel along with associated documents stored as attachments. Data requirements The Attachment Viewer template requires a feature layer with attachments. It includes the capability to view attachments of a hosted feature service or an ArcGIS Server feature service (10.8 or later). Currently, the app can display JPEG, JPG, PNG, GIF, MP4, QuickTime (.mov), and PDF files in the viewer window. All other attachment types are displayed as a link. Key app capabilities App layout - Choose between an attachment-focused layout, which displays one attachment at a time in the main panel of the app with the map on the side, or a map-focused layout, which displays the map in the main panel of the app with a gallery of attachments. Feature selection - Allows users to select features in the map and view associated attachments. Review data - Enable tools to review and update existing records. Zoom, pan, download images - Allow users to interact with and download attachments. Language switcher - Provide translations for custom text and create a multilingual app. Home, Zoom controls, Legend, Layer List, Search Supportability This web app is designed responsively to be used in browsers on desktops, mobile phones, and tablets. We are committed to ongoing efforts towards making our apps as accessible as possible. Please feel free to leave a comment on how we can improve the accessibility of our apps for those who use assistive technologies.

The Seabed Landform Classification Toolset is a GIS toolbox designed to classify seabed landforms on continental and island shelf settings. The user is guided through a series of classification steps within an ArcGIS toolbox to classify prominent seabed features termed ‘seabed landforms’, which characterise the morphology of the seabed surface. Seabed landforms include reefs/banks, peaks, plains, scarps, channels and depressions. Plain areas can additionally be classified into high and low features at localised and broad scales to capture features within plain surfaces. Common variables for seabed classification are utilised, including slope, bathymetric position index and ruggedness, and a series of procedures are applied to identify reef outcrops and minimise noise. The classification approach applies a whole-seascape classification which is aimed to offer a flexible and user-friendly approach to extract key seabed features from high-resolution shelf bathymetry data.

This toolset was developed using ESRI ArcGIS Desktop 10.8 and requires an Advanced licence with Spatial Analyst and 3D Analyst and extensions. It utilises scripts within the Benthic Terrain Modeler toolset (Walbridge et al. 2018) and Geomorphometry and Gradients Metrics Toolbox (Evans et al., 2014).

Please read the User Guide and supporting documentation for information on how to run the toolset. A web explainer is available at: https://arcg.is/1Tqmv50

The Seabed Landform Classification Toolset is also available for download on GitHub (https://github.com/LinklaterM/Seabed-Landforms-Classification-Toolset/).

The toolset was developed by the Coastal and Marine Team, NSW Department of Climate Change, Energy, the Environment and Water (formerly NSW Department of Planning and Environment), funded by NSW Climate Change Fund through the Coastal Management Funding Package and the Marine Estate Management Authority.

Please cite this toolset as: Linklater, M, Morris, B.D. and Hanslow, D.J. (2023) Classification of seabed landforms on continental and island shelves. Frontiers of Marine Science, 10, https://doi.org/10.3389/fmars.2023.1258556.

Other toolsets utilised by the Seabed Landform Classification Toolset include: Benthic Terrain Modeler: Walbridge, S., Slocum, N., Pobuda, M., and Wright, D. J. (2018). Unified geomorphological analysis workflows with Benthic Terrain Modeler. Geosciences 8, 94. Geomorphometry and Gradients Metrics Toolbox: Evans, J., Oakleaf, J., and Cushman, S. (2014). An ArcGIS Toolbox for Surface Gradient and Geomorphometric Modeling, Version 2.0-0. https://github.com/jeffreyevans/GradientMetrics.

This file is a digital geospatial Environmental Systems Research Institute (Esri) ArcGIS polygon feature class of the tile index for Cook County, Illinois. Each tile is 2,500' on a side, and covers an area of 6,250,000 square feet or 143 acres. There are a total of 5,347 tiles, and each tile represents the boundary or extent of each ortho image. This dataset includes a coordinate based tile identification number, a delivery area number, and a project tile category. The delivery area numbers and project tile attributes are a proprietary classification that are unique to this project. This dataset is stored within an ArcGIS 10.8 geodatabase. This dataset is projected using the Transverse Mercator map projection. The grid coordinate system used is the Illinois State Plane Coordinate System, East Zone (Zone Number Zone 3776, FIPS 1201), NAD83(2011) (horizontal datum), with ground coordinates expressed in U.S. Survey Feet.

Map Package containing all layers used for the mapping presented in the paper by Ferguson et al., (2023) in Earth and Planetary Science Letters for Mimas. Should there be an issue with a mosaic not loading properly, please reach out to me at sierra.ferguson @ swri . org.

This template is used to compute urban growth between two land cover datasets, that are classified into 20 classes based on the Anderson Level II classification system. This raster function template is used to generate a visual representation indicating urbanization across two different time periods. Typical datasets used for this template is the National Land Cover Database. A more detailed blog on the datasets can be found on ArcGIS Blogs. This template works in ArcGIS Pro Version 2.6 and higher. It's designed to work on Enterprise 10.8.1 and higher.References:Raster functionsWhen to use this raster function templateThe template is useful to generate an intuitive visualization of urbanization across two images.Sample Images to test this againstNLCD2006 and NLCD2011How to use this raster function templateIn ArcGIS Pro, search ArcGIS Living Atlas for raster function templates to apply them to your imagery layer. You can also download the raster function template, attach it to a mosaic dataset, and publish it as an image service. The output is a visual representation of urban sprawl across two images. Applicable geographiesThe template is designed to work globally.

Geographic Information System (GIS) Tools Market Outlook

The global Geographic Information System (GIS) tools market size was valued at approximately USD 10.8 billion in 2023, and it is projected to reach USD 21.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.9% from 2024 to 2032. The increasing demand for spatial data analytics and the rising adoption of GIS tools across various industries are significant growth factors propelling the market forward.

One of the primary growth factors for the GIS tools market is the surging demand for spatial data analytics. Spatial data plays a critical role in numerous sectors, including urban planning, environmental monitoring, disaster management, and natural resource exploration. The ability to visualize and analyze spatial data provides organizations with valuable insights, enabling them to make informed decisions. Advances in technology, such as the integration of artificial intelligence (AI) and machine learning (ML) with GIS, are enhancing the capabilities of these tools, further driving market growth.

Moreover, the increasing adoption of GIS tools in the construction and agriculture sectors is fueling market expansion. In construction, GIS tools are used for site selection, route planning, and resource management, enhancing operational efficiency and reducing costs. Similarly, in agriculture, GIS tools aid in precision farming, crop monitoring, and soil analysis, leading to improved crop yields and sustainable farming practices. The ability of GIS tools to provide real-time data and analytics is particularly beneficial in these industries, contributing to their widespread adoption.

The growing importance of location-based services (LBS) in various applications is another key driver for the GIS tools market. LBS are extensively used in navigation, logistics, and transportation, providing real-time location information and route optimization. The proliferation of smartphones and the development of advanced GPS technologies have significantly increased the demand for LBS, thereby boosting the GIS tools market. Additionally, the integration of GIS with other technologies, such as the Internet of Things (IoT) and Big Data, is creating new opportunities for market growth.

Regionally, North America holds a significant share of the GIS tools market, driven by the high adoption of advanced technologies and the presence of major market players. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, owing to increasing investments in infrastructure development, smart city projects, and the growing use of GIS tools in emerging economies such as China and India. Europe, Latin America, and the Middle East & Africa are also expected to contribute to market growth, driven by various government initiatives and increasing awareness of the benefits of GIS tools.

Component Analysis

The GIS tools market can be segmented by component into software, hardware, and services. The software segment is anticipated to dominate the market due to the increasing demand for advanced GIS software solutions that offer enhanced data visualization, spatial analysis, and decision-making capabilities. GIS software encompasses a wide range of applications, including mapping, spatial data analysis, and geospatial data management, making it indispensable for various industries. The continuous development of user-friendly and feature-rich software solutions is expected to drive the growth of this segment.

Hardware components in the GIS tools market include devices such as GPS units, remote sensing devices, and plotting and digitizing tools. The hardware segment is also expected to witness substantial growth, driven by the increasing use of advanced hardware devices that provide accurate and real-time spatial data. The advancements in GPS technology and the development of sophisticated remote sensing devices are key factors contributing to the growth of the hardware segment. Additionally, the integration of hardware with IoT and AI technologies is enhancing the capabilities of GIS tools, further propelling market expansion.

The services segment includes consulting, integration, maintenance, and support services related to GIS tools. This segment is expected to grow significantly, driven by the increasing demand for specialized services that help organizations effectively implement and manage GIS solutions. Consulting services assist organizations in selecting the right GIS tools and optimizing their use, while integration services ensure seamless integr

Data for "A Preliminary Regional Geomorphologic Map in Utopia Planitia of the Tianwen-1 Zhurong Landing Region" is a collection of the files used in the associated publication for this dataset. This presents the dataset used for developing an initial geomorphologic map of the landing region of the Zhurong rover from the Tianwen-1 spacecraft.Description of each item:ArcMap Data: This folder contains two subfolders: Features and Units. Each folder contains ArcMap 10.8 shapefiles and data that must be linked with the UtopiaPlanitia_22N26N_108E112E_GeomorphologicMap.mxd map file.Concentric Graben Thickness Estimates Data.txt: This file contains all measurements used in the calculations of material cover thicknesses for concentric grabens in the associated publication for this dataset.Figures: These figures are those in the publication for this dataset, in .jpg format.List of Basemap Images.txt: This file is a list of the publicly available images used as basemap images in mapping.UtopiaPlanitia_22N26N_108E112E_GeomorphologicMap.mxd: This is the ArcMap 10.8 map file. It needs to be linked with the ArcMap Data folder to access the mapped feature classes. It also needs to be linked with image files, if any listed in "List of Basemap Images.txt" (downloaded separately).For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu

Det digitale jordartskort viser overfladegeologien i digital form. I denne version 7.0 fra 2023 er 93% af Danmarks landareal klassificeret, og kortet kompletteres løbende. Kortet er et resultat af den systematiske geologiske kortlægning af Danmark. Informationerne er indsamlet ved feltarbejde, hvor jordprøver udtages ved hjælp af et hånsspyd i ca.1 meters dybde, det vil sige lige under pløjelag og jordbundsudviklingen. Afstanden mellem jordprøverne er 100-200 meter. Jordarterne er inddelt i 82 typer. Kort- og jordartsbeskrivelsen er udgivet i GEUS rapport 2023/29, hvor yderligere oplysninger kan findes. Datapakken indeholder filer til brug i GIS systemerne ArcGIS v. 10.8.2, ArcGISPro version 3.2 og QGIS v. 3.28.4.

Although miniaturized data loggers allow new insights into avian migration, incomplete knowledge of basic patterns persists, especially for nightjars. Using GPS data loggers, this study examined migration ecology of the Eastern whip-poor-will (Antrostomus vociferus), across three migration strategies: flyover, short-stay, and long-stay. We documented migration movements, conducted hotspot analyses, quantified land cover within 1-km and 5-km buffers at used and available locations, and modeled habitat selection during migration. From 2018-2020 we captured breeding whip-poor-wills from three study sites in Massachusetts and programmed GPS tags to collect data during fall and spring migration periods. Across 19 individual males (nine of them with repeated years of data), GPS tags collected 479 locations, where 30% were classified as flyover points, 33% as short-stays, and 37% as long-stay locations. We documented seasonal flexibility in migration duration, routes, and stopover locations among individuals and between years. Analyses identified hotspot clusters in fall and spring migration in the Sierra de Tamaulipas in Mexico. Land cover at used locations differed across location types at the 5-km scale, where closed forest cover increased and crop cover decreased for flyover, short-stay, and long-stay locations, and urban cover was lowest at long-stay locations. Discrete choice modeling indicated that habitat selection by migrating whip-poor-wills differs depending on the scale and migration strategy. For example, at the 5-km scale birds avoided urban cover at long-stay locations and selected closed forest cover at short-stay locations. We suggest that whip-poor-wills may use land cover cues at large spatial scales, like 5-km, to influence rush or stay tactics during migration. Methods From 2018-2020, we captured breeding whip-poor-wills from three study sites in Massachusetts and programmed GPS tags to collect data during fall and spring migration periods. Across 19 individual males (nine of them with repeated years of data), GPS tags collected 479 locations, where 30% were classified as flyover points, 33% as short-stays, and 37% as long-stay locations. Data processing We filtered and retained migration data points when loggers connected to ≥ 4 satellites and points had dilution of precision values < 5 to ensure a 3D fix of the location (Forrest et al. 2022, Bakermans et al. 2022). Using 30-m USGS DEM (digital elevation model; http://ned.usgs.gov) data, we generated the altitude of each point by converting the GPS tags’ altitude to altitude above sea level and then subtracted the local elevation (from the DEM) from the bird’s altitude (A. Korpach, pers. communication). Next, we classified migration points based on altitude and number of points at a single location as either flyover, short-stay, or long-stay. Long-stays were locations with ≥ 2 GPS points within the same vicinity (i.e., < 10 km). Short-stay and flyovers consisted of one GPS point at a single location. We differentiated short-stay versus flyover points by altitude based on the altitudes of birds at long-stay locations (mean = 17 m, range = 121 m). Short-stays were locations with elevations < 100 m (mean = 15 m), and flyover locations had an altitude ≥ 100 m above the ground (mean = 800 m). Hotspot Analyses To identify areas of high or low use during migration, we ran an optimized hotspot analysis in ArcGIS 10.8.2 to identify statistically significant spatial clusters of high (hotspot) and low values (coldspot) of migration locations using the Getis-Ord Gi* statistic (Sussman et al. 2019). This tool can “aggregate data, identify an appropriate scale of analysis, and correct for both multiple testing and spatial dependence” (ESRI 2021). Land cover classification We used ArcGIS and quantified land cover types from 2019 data using the 100-m Copernicus Global Land Service layer (Buchhorn et al. 2020). Land cover types were classified as (a) closed forest, (b) open forest, (c) shrubland, (d) herbaceous vegetation (hereafter, grassland), (e) herbaceous wetland, (f) cropland, (g) bare, (h) fresh- or saltwater, and (i) developed land (Buchhorn et al. 2020). Using the geoprocessing features of ArcMap, we quantified land cover at 5-km and 1-km circle at an actual migration location (i.e., used) and random locations (i.e., available). Habitat selection We used discrete choice modeling to determine habitat selection of Eastern whip-poor-will during migration. Discrete choice models examine the probability that an individual chooses a location based on a choice set of alternative available locations (Cooper and Millspaugh 1999). Choice sets included one used location based on the GPS fix and ten available locations. We constructed separate models for each type of migration point (i.e., flyover, short-stay, and long-stay) and spatial scale (i.e., 1 km and 5 km) with individual as a random effect. We used package jagsUI (Kellner 2021) with the software JAGS 4.3.1 (Plummer 2003).

Environmental pollution is a persistent problem in terrestrial ecosystems, including remote mountain areas. This study investigates the extent and patterns of littering on three popular hiking trails among mountaineers and tourists in the Dolomites range located in northeastern Italy. The data was collected adopting a citizen science approach with the participation of university students surveying the trails and recording the macroscopic waste items through a GPS-based offline platform. The waste items were categorized according to their material type, usage, and geographical location, and the sorted data was applied to Esri GIS ArcMapTM 10.8.1.

The map package files (merged.mpk) were prepared and can be opened by Arc Gis 10.8.2 and above versions. The map package data files include the SAR data (RISAT-1 from ISRO-Bhoonidhi) in HH,HV- polarizations, DEM ( USGS ) and IRS LISS III (Bhuvan-NRSC) data with the 30m spatial resolution were downloaded from the respective websites. Geology data in 1:50,000 scale is downloaded from GSI Bhukosh. The resolution merged data of Optical and SAR data has been prepared using Brovey transform in ERDAS 2015 software. The output file have advantages of both optical and microwave features. Extracted the Lineaments(.shp) from the coupled data of merged SAR and improved and verified with the DEM, Optical, SAR and Geology data sets. All these data generation and Statistical calculation done with the help of ArcGIS software. ArcGIS guide will help to create shape files, Attribute table calculations of length, classification. Azumutal trend calculations of each lineaments done using Split lines and other geometric calculations giving the trend of each lineament and finally export the map (All .jpg files). Rose diagrams was prepared based on the trend of lineaments with the help of Rockworks 17 software. The generated Azimuthal trend data in lineament shape file can be import to linears - utilites - Rose diagram. I was prepared Rose diagram of different class of lineaments using frequency calculation method. Lineaments are the linear geological features can extend from few meters to hundreds of kms. Geologically lineaments are either structural or stratigraphical, typically it will comprise fault, fold axis, bedding contacts, dyke intrusions, shear zone or a straight coast line. Mapping lineaments using remote sensing is economical, faster can act as a preliminary study. Generally lineaments have been mapped using the optical remote sensing data such as Landsat, Resourcesat etc. For India, Lineaments were mapped using the LISS III and LISS IV of Resourcesat-1 & 2 at a scale of 1:50k. However in tropical region like India, limited exposure of ground due to vegetation cover, lineaments may go unnoticed in optical remote sensing data. This problem can be overcome by Synthetic Aperture Radar (SAR) data, which can penetrate ground significantly. With the launch of RISAT-1satelite, data availability of SAR data is immense for Indian region. Aim of this study to explore the SAR data and merged SAR and optical data for lineament mapping.

File "All points in the study area"is a shape file that extracts the corresponding values in the tif image from the tif image to the points of the raster in the study area at 30m*30m using the "Multi-value Extract to Point" tool in ArcGIS 10.8.

The Spatiotemporal Big Data Store Tutorial introduces you the the capabilities of the spatiotemporal big data store in ArcGIS Data Store, available with ArcGIS Enterprise. Observation data can be moving objects, changing attributes of stationary sensors, or both. The spatiotemporal big data store enables archival of high volume observation data, sustains high velocity write throughput, and can run across multiple machines (nodes). Adding additional machines adds capacity, enabling you to store more data, implement longer retention policies of your data, and support higher data write throughput.

After completing this tutorial you will:

Understand the concepts and best practices for working with the spatiotemporal big data store available with ArcGIS Data Store. Have configured the appropriate security settings and certificates on a enterprise server, real-time server, and a data server which are necessary for working with the spatiotemporal big data store. Have learned how to process and archive large amounts of observational data in the spatiotemporal big data store. Have learned how to visualize the observational data that is stored in the spatiotemporal big data store.

Releases

Each release contains a tutorial compatible with the version of GeoEvent Server listed. The release of the component you deploy does not have to match your version of ArcGIS GeoEvent Server, so long as the release of the component is compatible with the version of GeoEvent Server you are using. For example, if the release contains a tutorial for version 10.6; this tutorial is compatible with ArcGIS GeoEvent Server 10.6 and later. Each release contains a Release History document with a compatibility table that illustrates which versions of ArcGIS GeoEvent Server the component is compatible with.

NOTE: The release strategy for ArcGIS GeoEvent Server components delivered in the ArcGIS GeoEvent Server Gallery has been updated. Going forward, a new release will only be created when

  a component has an issue,
  is being enhanced with new capabilities,
  or is not compatible with newer versions of ArcGIS GeoEvent Server.

This strategy makes upgrades of these custom
components easier since you will not have to
upgrade them for every version of ArcGIS GeoEvent Server
unless there is a new release of
the component. The documentation for the
latest release has been
updated and includes instructions for updating
your configuration to align with this strategy.

Latest

Release 4 - February 2, 2017 - Compatible with ArcGIS GeoEvent Server 10.5 and later.

Previous

Release 3 - July 7, 2016 - Compatible with ArcGIS GeoEvent Server 10.4 thru 10.8.

Release 2 - May 17, 2016 - Compatible with ArcGIS GeoEvent Server 10.4 thru 10.8.

Release 1 - March 18, 2016 - Compatible with ArcGIS GeoEvent Server 10.4 thru 10.8.

GIS Market is Segmented by Component (Hardware and Software), by Function (Mapping, Surveying, Telematics and Navigation, Location-Based Services), by End User (Agriculture, Utilities, and Mining, Among Others), and by Geography (North America, Europe, Asia Pacific, and Rest of the World). The Report Offers Market Forecasts and Size in Value (USD) for all the Above Segments.

NRCS SSURGO Soils web map service. This is an Open GIS Consortium standard Web Map Service (WMS).Soil Data Access WMS 1.3.0 & WFS 2.0.0 Web Services HelpThe current Soil Data Access Web Map Service (WMS) supports Open Geospatial Consortium (OGC) WMS version 1.3.0 requests while the current Soil Data Access Web Feature Services (WFS) support OGC WFS version 2.0.0 (GML2/GML3) requests. GML is an acronym for Geography Markup Language, and is the XML grammar defined by the Open Geospatial Consortium (OGC) to express geographical features. GML serves as a modeling language for geographic systems as well as an open interchange format for geographic transactions on the Internet. EPSG is an acronym used throughout the Soil Data Access web pages. It stands for European Petroleum Survey Group. They publish a database of coordinate system information plus some very good related documents on map projections and datums.The current Soil Data Access WMS service is supported by the following ArcGIS Pro versions:ArcGIS PRO 3.xArcGIS PRO 2.xArcGIS PRO 1.4The current Soil Data Access WMS service is supported by the following ArcGIS Desktop versions:ArcGIS 10.8The current Soil Data Access WMS services are supported by the following ArcGIS Enterprise versions:ArcGIS Enterprise 10.6ArcGIS Enterprise 10.5The current Soil Data Access WFS services are supported by the following ArcGIS Enterprise versions:ArcGIS Enterprise 11.xArcGIS Enterprise 10.9ArcGIS Enterprise 10.8ArcGIS Enterprise 10.7ArcGIS Enterprise 10.6ArcGIS Enterprise 10.5

Market Size and Drivers: The global Geographic Information System (GIS) Tools market is projected to reach a value of USD 13.2 billion by 2033, growing at a CAGR of 10.8% from 2025. Increasing urbanization, rising awareness of spatial data analysis, and growing adoption of cloud-based GIS platforms are driving the market growth. Additionally, advancements in technology such as the Internet of Things (IoT) and mobile computing are creating new opportunities for GIS applications in sectors like transportation, emergency response, and environmental management. Competitive Landscape and Trends: The GIS Tools market is highly fragmented, with numerous vendors offering a wide range of solutions. Key players include UpKeep, Maintenance Connection, Curo, Axxerion CMMS, Asset Essentials, ServiceChannel, IBM TRIRIGA, Samsara, Infor EAM, and Avantis. The market is witnessing a trend towards consolidation and strategic partnerships to enhance product portfolios and expand geographical reach. Furthermore, the adoption of artificial intelligence (AI) and machine learning (ML) in GIS tools is expected to enhance automation and improve decision-making capabilities, driving innovation and competitiveness within the industry.