The global workstation desktop market is booming, projected to reach $40 billion by 2033 with a 7% CAGR. Driven by AI, big data, and specialized applications, this market analysis reveals key trends, restraints, and leading companies in North America, Europe, and Asia Pacific. Discover insights into CAD/CAM, GIS, and simulation workstation demand.

The global workstation desktop market is experiencing robust growth, driven by increasing demand across diverse sectors. The market's expansion is fueled by the rising adoption of high-performance computing (HPC) in fields like CAD/CAM, GIS, and image processing, where powerful workstations are crucial for complex simulations and data analysis. The shift towards cloud-based solutions and virtualization is also influencing the market, with businesses seeking optimized workflows and reduced IT infrastructure costs. This trend is likely to lead to a surge in demand for high-end, versatile workstations capable of handling both local and cloud-based workloads. Furthermore, the burgeoning gaming industry contributes to the market's growth, as professional gamers and content creators require powerful machines for seamless performance and high-resolution graphics rendering. Competition among major players like Hewlett Packard Enterprise, Dell, and Lenovo is intensifying, leading to innovations in processing power, memory capacity, and graphics capabilities. This competitive landscape fosters continuous improvements in workstation performance and affordability, further driving market expansion. Despite these positive drivers, the market faces some challenges. The high initial cost of workstations can be a barrier for entry, particularly for smaller businesses and individual users. Furthermore, rapid technological advancements necessitate frequent upgrades, adding to the overall cost of ownership. The fluctuating prices of key components, such as GPUs and processors, also contribute to market uncertainty. However, these challenges are being mitigated by the increasing availability of flexible financing options and leasing models, making high-performance computing more accessible. Geographic segmentation reveals a strong concentration of demand in North America and Europe, with the Asia-Pacific region exhibiting significant growth potential due to rapid industrialization and technological advancements. The projected CAGR (let's assume a conservative 7% based on industry trends) suggests a consistent and healthy expansion of the market over the forecast period (2025-2033). The market segmentation by application (CAD/CAM, GIS, etc.) and type (universal vs. dedicated workstations) offers valuable insights into specific market niches and future growth opportunities.

The Computer Vision in Geospatial Imagery market is experiencing robust growth, driven by increasing demand for accurate and efficient geospatial data analysis across various sectors. Advancements in artificial intelligence (AI), deep learning, and high-resolution imaging technologies are fueling this expansion. The market's ability to extract valuable insights from aerial and satellite imagery is transforming industries such as agriculture, urban planning, environmental monitoring, and defense. Applications range from precision agriculture using drone imagery for crop health monitoring to autonomous vehicle navigation and infrastructure inspection using high-resolution satellite data. The integration of computer vision with cloud computing platforms facilitates large-scale data processing and analysis, further accelerating market growth. We estimate the 2025 market size to be approximately $2.5 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This growth is expected to continue, driven by increasing adoption of advanced analytics and the need for real-time geospatial intelligence. Several factors contribute to this positive outlook. The decreasing cost of high-resolution sensors and cloud computing resources is making computer vision solutions more accessible. Furthermore, the growing availability of large datasets for training sophisticated AI models is enhancing the accuracy and performance of computer vision algorithms in analyzing geospatial data. However, challenges remain, including data privacy concerns, the need for robust data security measures, and the complexity of integrating diverse data sources. Nevertheless, the overall market trend remains strongly upward, with significant opportunities for technology providers and users alike. The key players listed—Alteryx, Google, Keyence, and others—are actively shaping this landscape through innovative product development and strategic partnerships.

Can your desktop computer crunch the large GIS datasets that are becoming increasingly common across the geosciences? Do you have access to, or the know how to, take advantage of advanced high performance computing (HPC) capability? Web based cyberinfrastructure takes work off your desk or laptop computer and onto infrastructure or "cloud" based data and processing servers. This talk will describe the HydroShare collaborative environment and web based services being developed to support the sharing and processing of hydrologic data and models. HydroShare supports the storage and sharing of a broad class of hydrologic data including time series, geographic features and rasters, multidimensional space-time data and structured collections of data representing river geometry. Web service tools and a python client library provide researchers with access to high performance computing resources without requiring them to become HPC experts. This reduces the time and effort spent in finding and organizing the data required to prepare the inputs for hydrologic models and facilitates the management of online data and execution of models on HPC systems. This talk will illustrate web and client based use of data services that support the delineation of watersheds to define a modeling domain, then extract terrain and land use information to automatically configure the inputs required for hydrologic models. These services support the Terrain Analysis Using Digital Elevation Model (TauDEM) tools for watershed delineation and generation of hydrology-based terrain information such as wetness index and stream networks. These services also support the derivation of inputs for the Utah Energy Balance snowmelt model used to address questions such as how climate, land cover and land use change may affect snowmelt inputs to runoff generation. These cases serve as examples for how this approach can be extended to other models to enhance the use of web and data services in the geosciences.

Presentation at Kansas University GIS Days November 18, 2015

Local libraries and community organizations that will allow you to use computers free of charge. Some locations also offer the following resources: Wi-fi, Printing, Internet, iPad rental, Classes, and special areas for kids and teens.

This table contains details about computer ownership and internet subscription by demographic characteristic in King County. This dataset has been developed for the Determinant of Equity - Digital Equity presentation. In includes information about Access to an Internet Subscription and Computer in the Household equity indicators. Fields describe the total number of households (Denominator), number of households without a computer or without an internet subscription (Numerator), the type of equity indicator being measured (Indicator), and the value that describes this measurement (Indicator Value).

The data for this dataset was compiled from the American Community Survey (ACS) 1-year and 5-year estimates. Vintages

1-year estimates: 2017-2019, 2021-2022 5-year estimates: 2013-2017, 2014-2018, 2015-2019, 2016-2020, 2017-2021, 2018-2022

Variables

B28002 - PRESENCE AND TYPES OF INTERNET SUBSCRIPTIONS IN HOUSEHOLD B28003 - PRESENCE OF A COMPUTER AND TYPE OF INTERNET SUBSCRIPTION IN HOUSEHOLD B28004 - HOUSEHOLD INCOME IN THE LAST 12 MONTHS B28005 - AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD B28009B - PRESENCE OF A COMPUTER AND TYPE OF INTERNET SUBSCRIPTION IN HOUSEHOLD (BLACK OR AFRICAN AMERICAN ALONE HOUSEHOLDER) - B28009I - PRESENCE OF A COMPUTER AND TYPE OF INTERNET SUBSCRIPTION IN HOUSEHOLD (HISPANIC OR LATINO HOUSEHOLDER)

For more information about King County's equity efforts, please see:

Equity, Racial & Social Justice Vision Ordinance 16948 describing the determinates of equity Determinants of Equity and Data Tool

This dataset was created to be used in my Capstone Project for the Google Data Analytics Professional Certificate. Data was web scraped from the state websites to combine the GIS information like FIPS, latitude, longitude, and County Codes by both number and Mailing Number.

RStudio was used for this web scrape and join. For details on how it was done you can go to the following link for my Github repository.

Feel free to follow my Github or LinkedIn profile to see what I end up doing with this Dataset.

A parcel-based land use summary of the Denver region All files are uploaded in a zip archive file format- they will need to be extracted before opening in ArcGIS, QGIS, or similar programs. In general we recommend using the gdb source rather than shp sources for data due to some data loss and field truncation in the shape file archives. Projected Coordinate System NAD 1983 HARN StatePlane Colorado Central FIPS 0502 (US Feet) WKID: 2877 Sources Colorado Public Parcels – link Aggregated by the Governor's Office of Information Technology Geospatial Information Systems; contains all public parcels in the state, including land use codes supplied by counties and other jurisdictions. Sourced on 7/9/2025. Zoning 2023 – link Zoning shapefile sourced from DRCOG, used to supply possible ‘land use’ where land use codes were not provided in the Colorado Public Parcels file. Land Use Categorizations Below are the land use codes I categorized parcels by Residential: Any residence land use (single family, multi-unit, senior) Zoning Follow Up: Should not be in the final dataset – used to indicate there's no land use data and needs to be backfilled with zoning classifications Vacant: Parcel indicated as vacant Commercial: Any commercial use, including office, retail Exempt/Government: Exempt land use or government land use (eg: city hall, fire station) Agricultural: Agricultural, or ranch use Other/Unknown: Some other use / cannot be determined Industrial: Industrial, including meat packing School: Schools (K‑12, college, public and private) Mixed Use: A parcel specifically marked as mixed use Open Space/Parks/Recreation: Open space, park, outdoor recreation (eg: cabins, camping, etc) Medical: Hospitals, medical offices, etc Caveats Many parcels did not contain land use codes or contained land use codes that could not be discerned. In that case, zoning designations were appended to estimate the land use. Even with the above process, many parcels were missing a land use classification. Files & Feature Layers Land Use Parcel Standardization.xlsx: A spreadsheet where I standardized land use codes into the categories above. The tab Land Use Codes is where categorizations were based on the parcels’ land use codes and descriptions, while Zoning Code Follow Up used the zoning classification that had greatest geographic overlap with the parcel. public_parcel_drcog.shp: The original Colorado Public Parcels file, with the majority‑overlapping zoning code from Zoning 2023 added, and the land use categorizations from Land Use Parcel Standardization appended. parcel_land_use.shp: A final feature class, derived from public_parcel_drcog and dissolved by the zoning categorization appended from Land Use Parcel Standardization. Land Use Data.gdb: Contains all the above feature classes.

Data source is the Office of Tax and Revenue’s Computer-Assisted Mass Appraisal (CAMA) system. The CAMA system is used by the Assessment Division (AD) within the Real Property Tax Administration to value real estate for ad valorem real property tax purposes.The intent of this data is to provide a sale history for active properties listed among the District of Columbia’s real property tax assessment roll. This data is updated daily. The AD constantly maintains sale data, adding new data and updating existing data. Daily updates represent a “snapshot” at the time the data was extracted from the CAMA system, and data is always subject to change.

Projects, data and layers required to complete workshops at the 2024 ODF GIS Conference. Download the zip file and extract the files to a location on your workstation.

This volume contains the papers presented at GIS Research UK 2015 (GISRUK2015) held at the School of Geography, University of Leeds, on 15-17 April 2015.

Purpose: This is an ArcGIS Pro template that GIS Specialists can use to identify vulnerable populations and special needs infrastructure most at risk to flooding events.How does it work?Determine and understand the Place Vulnerability (based on Cutter et al. 1997) and the Special Needs Infrastructure for an area of interest based on Special Flood Hazard Zones, Social Vulnerability Index, and the distribution of its Population and Housing units. The final product will be charts of the data distribution and a Hosted Feature Layer. See this Story Map example for a more detailed explanation.This uses the FEMA National Flood Hazard Layer as an input (although you can substitute your own flood hazard data), check availability for your County before beginning the Task: FEMA NFHL ViewerThe solution consists of several tasks that allow you to:Select an area of interest for your Place Vulnerability Analysis. Select a Hazard that may occur within your area of interest.Select the Social Vulnerability Index (SVI) features contained within your area of interest using the CDC’s Social Vulnerability Index (SVI) – 2016 overall SVI layer at the census tract level in the map.Determine and understand the Social Vulnerability Index for the hazard zones identified within you area of interest.Identify the Special Needs Infrastructure features located within the hazard zones identified within you area of interest.Share your data to ArcGIS Online as a Hosted Feature Layer.FIRST STEPS:Create a folder C:\GIS\ if you do not already have this folder created. (This is a suggested step as the ArcGIS Pro Tasks does not appear to keep relative paths)Download the ZIP file.Extract the ZIP file and save it to the C:\GIS\ location on your computer. Open the PlaceVulnerabilityAnalysis.aprx file.Once the Project file (.aprx) opens, we suggest the following setup to easily view the Tasks instructions, the Map and its Contents, and the Databases (.gdb) from the Catalog pane.The following public web map is included as a Template in the ArcGIS Pro solution file: Place Vulnerability Template Web MapNote 1:As this is a beta version, please take note of some pain points:Data input and output locations may need to be manually populated from the related workspaces (.gdb) or the tools may fail to run. Make sure to unzip/extract the file to the C:\GIS\ location on your computer to avoid issues.Switching from one step to the next may not be totally seamless yet.If you are experiencing any issues with the Flood Hazard Zones service provided, or if the data is not available for your area of interest, you can also download your Flood Hazard Zones data from the FEMA Flood Map Service Center. In the search, use the FEMA ID. Once downloaded, save the data in your project folder and use it as an input.Note 2:In this task, the default hazard being used are the National Flood Hazard Zones. If you would like to use a different hazard, you will need to add the new hazard layer to the map and update all query expressions accordingly.For questions, bug reports, or new requirements contact pdoherty@publicsafetygis.org

Computer sessions are counted using software. EnvisionWare Computer Access and Reservation System allows library patrons to schedule time to use library computers. In addition, the Envisionware Database tracks the number of print jobs by user and printer. This functionality allows DCPL to track the number of sessions, hours of usage at each DCPL computer workstation, and the number of print jobs and pages at each printer. The output of this EnvisionWare system is a dataset that tracks the number of unique computer sessions, total hours of computer usage, printer sessions, and printer usage at each DCPL workstation by user account. The dataset includes personally identifiable information (PII) of users.WiFi sessions are also counted using software. Cisco-Meraki Wi-Fi system allows library patrons to connect to DCPL Wi-Fi. This functionality allows DCPL to track the number of wireless devices connected to DCPL Wi-Fi. The output of this Cisco-Meraki Wi-Fi system is a dataset that tracks the number Wi-Fi connections at each DCPL Branch.Gate counts are tracked Vea Web is a database system that tracks the number of entries into each library facility using heat sensors on the door of each facility. This functionality allows DCPL to track the volume of library entries at each DCPL branch every hour. The output of this Vea Web database system is a dataset that is used to report branch visits.

Hi, I'm Patrick,I initially pursued an undergraduate degree in Computer Science because I wanted to make video games; however, after taking an Environmental Science course, I wanted to see if there was a way I could study both. This led me to GIS and I made that my specialism, doing a Masters and later PhD on the subject.

CAMA_2003_BACI_1

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   MD Property View 2003 CAMA Database. For more information on the CAMA Database refer to the enclosed documentation. This layer was edited to remove spatial outliers in the CAMA Database. Spatial outliers are those points that were not geocoded and as a result fell outside of the Baltimore City Boundary. 254 spatial outliers were removed from this layer.


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   West -76.713415  East -76.526101 

   North 39.374324  South 39.200707 




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This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

Discover the booming geospatial data fusion market! This in-depth analysis reveals market size, CAGR, key trends, regional insights (North America, Europe, Asia-Pacific), leading companies (Esri, Magellium, Geo Owl), and future projections (2025-2033). Explore SaaS, PaaS solutions for earth observation, computer vision, and military applications.

This project file contains row research data and result data that have been used for the paper entitled "GIS-based multi-criteria analysis for Arabica coffee expansion in Rwanda" by Innocent Nzeyimana, Alfred E. Hartemink, Violette Geissen. http://dx.doi.org/10.6084/m9.figshare.1128594- See more at: http://figshare.com/preview/_preview/1128594#sthash.QkGK7m8Y.dpuf

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. The CURE vegetation mapping project area was divided into 11,133 polygons and 42 map classes. A total of 10,520 map polygons represent 27 natural and semi-natural vegetation map classes. Fifteen land use map classes describe 613 other polygons within the mapping area. Average polygon size across all map classes is 4.4 ha (10.8 acres). The mapping component of the CURE project used a combination of methods to interpret and delineate vegetation polygons. Initial line work was prepared by USBOR photointerpreters who delineated the most contrasting signatures, e.g., water bodies, exposed shoreline, unvegetated geology, land use types, and vegetation at the physiognomic level. The project photo interpreter used this baseline mapping and refined it by examining digital orthophotos in stereo. The stereo photography was used as needed to distinguish fine scale vegetation patterns. Ancillary datasets including plot and observation point data and classification and local descriptions of plant associations were used by the photointerpreter to assist with map class definitions and guide manual delineations. Polygons were drawn on Mylar overlays of printed orthophotos that were later scanned, or were drawn digitally on a computer screen. Heads-up digitizing consisted of delineating map class polygons on an electronic version of the digital orthophotos at a computer workstation. Digitizing was performed using vector editing in ArcGIS. The line work was refined and finalized by the SEUG GIS Specialist and the map class and other descriptive attributes for each polygon were assigned. The recreation area and the environs were interpreted and mapped to the same level of detail.

Shapefile used in the various maps in the study. Visit https://dataone.org/datasets/sha256%3A2fdaa83821076dc77d906d53f13fd8aaa6ecb2f8bf1e16082352037b5459f465 for complete metadata about this dataset.