In this course, you will explore a variety of open-source technologies for working with geosptial data, performing spatial analysis, and undertaking general data science. The first component of the class focuses on the use of QGIS and associated technologies (GDAL, PROJ, GRASS, SAGA, and Orfeo Toolbox). The second component of the class introduces Python and associated open-source libraries and modules (NumPy, Pandas, Matplotlib, Seaborn, GeoPandas, Rasterio, WhiteboxTools, and Scikit-Learn) used by geospatial scientists and data scientists. We also provide an introduction to Structured Query Language (SQL) for performing table and spatial queries. This course is designed for individuals that have a background in GIS, such as working in the ArcGIS environment, but no prior experience using open-source software and/or coding. You will be asked to work through a series of lecture modules and videos broken into several topic areas, as outlined below. Fourteen assignments and the required data have been provided as hands-on opportunites to work with data and the discussed technologies and methods. If you have any questions or suggestions, feel free to contact us. We hope to continue to update and improve this course. This course was produced by West Virginia View (http://www.wvview.org/) with support from AmericaView (https://americaview.org/). This material is based upon work supported by the U.S. Geological Survey under Grant/Cooperative Agreement No. G18AP00077. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey. After completing this course you will be able to: apply QGIS to visualize, query, and analyze vector and raster spatial data. use available resources to further expand your knowledge of open-source technologies. describe and use a variety of open data formats. code in Python at an intermediate-level. read, summarize, visualize, and analyze data using open Python libraries. create spatial predictive models using Python and associated libraries. use SQL to perform table and spatial queries at an intermediate-level.

This dataset provides the raw anonymised (quantitative) data from the EDSA demand analysis. This data has been gathered from surveys performed with those who identify as data scientists and manages of data scientists in different sectors across Europe. The coverage of the data includes level of current expertise of the individual or team (data scientist and manager respectively) in eight key areas. The dataset also includes the importance of the eight key areas as capabilities of a data scientist. Further the dataset includes a breakdown of key tools, technologies and training delivery methods required to enhance the skill set of data scientists across Europe. The EDSA dashboard provides an interactive view of this dataset and demonstrates how it is being used within the project. The dataset forms part of the European Data Science Academy (EDSA) project which received funding from the European Unions's Horizon 2020 research and innovation programme under grant agreement No 643937. This three year project ran/runs from February 2015 to January 2018. Important note on privacy: This dataset has been collected and made available in a pseudo anonymous way, as agreed by participants. This means that while each record represents a person, no sensitive identifiable information, such as name, email or affiliation is available (we don't even collect it). Pseudo anonymisation is never full proof, however the projects privacy impact assessment has concluded that the risk resulting from the de-anonymisation of the data is extremely low. It should be noted that data is not included of participants who did not explicitly agree that it could be shared pseudo anonymously (this was due to a change of terms after the survey had started gathering responses, meaning any early responses had come from people who didn't see this clause). If you have any concerns please contact the data publisher via the links below.

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Geographic Information System Analytics Market Size 2024-2028

The geographic information system analytics market size is forecast to increase by USD 12 billion at a CAGR of 12.41% between 2023 and 2028.

The GIS Analytics Market analysis is experiencing significant growth, driven by the increasing need for efficient land management and emerging methods in data collection and generation. The defense industry's reliance on geospatial technology for situational awareness and real-time location monitoring is a major factor fueling market expansion. Additionally, the oil and gas industry's adoption of GIS for resource exploration and management is a key trend. Building Information Modeling (BIM) and smart city initiatives are also contributing to market growth, as they require multiple layered maps for effective planning and implementation. The Internet of Things (IoT) and Software as a Service (SaaS) are transforming GIS analytics by enabling real-time data processing and analysis.
Augmented reality is another emerging trend, as it enhances the user experience and provides valuable insights through visual overlays. Overall, heavy investments are required for setting up GIS stations and accessing data sources, making this a promising market for technology innovators and investors alike.

What will be the Size of the GIS Analytics Market during the forecast period?

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The geographic information system analytics market encompasses various industries, including government sectors, agriculture, and infrastructure development. Smart city projects, building information modeling, and infrastructure development are key areas driving market growth. Spatial data plays a crucial role in sectors such as transportation, mining, and oil and gas. Cloud technology is transforming GIS analytics by enabling real-time data access and analysis. Startups are disrupting traditional GIS markets with innovative location-based services and smart city planning solutions. Infrastructure development in sectors like construction and green buildings relies on modern GIS solutions for efficient planning and management. Smart utilities and telematics navigation are also leveraging GIS analytics for improved operational efficiency.
GIS technology is essential for zoning and land use management, enabling data-driven decision-making. Smart public works and urban planning projects utilize mapping and geospatial technology for effective implementation. Surveying is another sector that benefits from advanced GIS solutions. Overall, the GIS analytics market is evolving, with a focus on providing actionable insights to businesses and organizations.

How is this Geographic Information System Analytics Industry segmented?

The geographic information system analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Retail and Real Estate
  Government
  Utilities
  Telecom
  Manufacturing and Automotive
  Agriculture
  Construction
  Mining
  Transportation
  Healthcare
  Defense and Intelligence
  Energy
  Education and Research
  BFSI


Components

  Software
  Services


Deployment Modes

  On-Premises
  Cloud-Based


Applications

  Urban and Regional Planning
  Disaster Management
  Environmental Monitoring Asset Management
  Surveying and Mapping
  Location-Based Services
  Geospatial Business Intelligence
  Natural Resource Management


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    South Korea


  Middle East and Africa

    UAE


  South America

    Brazil


  Rest of World

By End-user Insights

The retail and real estate segment is estimated to witness significant growth during the forecast period.

The GIS analytics market analysis is witnessing significant growth due to the increasing demand for advanced technologies in various industries. In the retail sector, for instance, retailers are utilizing GIS analytics to gain a competitive edge by analyzing customer demographics and buying patterns through real-time location monitoring and multiple layered maps. The retail industry's success relies heavily on these insights for effective marketing strategies. Moreover, the defense industries are integrating GIS analytics into their operations for infrastructure development, permitting, and public safety. Building Information Modeling (BIM) and 4D GIS software are increasingly being adopted for construction project workflows, while urban planning and designing require geospatial data for smart city planning and site selection.

The oil and gas industry is leveraging satellite imaging and IoT devices for land acquisition and mining operations. In the public sector,

Analysis of ‘Urban planning — GIS ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/61783a6a-3ace-4691-b66b-413fafbc9e65 on 17 January 2022.

--- Dataset description provided by original source is as follows ---

List of planning certificates and building permits issued between January and April 2021, issued by Braila County Council.

--- Original source retains full ownership of the source dataset ---

This resource contains data inputs and a Jupyter Notebook that is used to introduce Hydrologic Analysis using Terrain Analysis Using Digital Elevation Models (TauDEM) and Python. TauDEM is a free and open-source set of Digital Elevation Model (DEM) tools developed at Utah State University for the extraction and analysis of hydrologic information from topography. This resource is part of a HydroLearn Physical Hydrology learning module available at https://edx.hydrolearn.org/courses/course-v1:Utah_State_University+CEE6400+2019_Fall/about

In this activity, the student learns how to (1) derive hydrologically useful information from Digital Elevation Models (DEMs); (2) describe the sequence of steps involved in mapping stream networks, catchments, and watersheds; and (3) compute an approximate water balance for a watershed-based on publicly available data.

Please note that this exercise is designed for the Logan River watershed, which drains to USGS streamflow gauge 10109000 located just east of Logan, Utah. However, this Jupyter Notebook and the analysis can readily be applied to other locations of interest. If running the terrain analysis for other study sites, you need to prepare a DEM TIF file, an outlet shapefile for the area of interest, and the average annual streamflow and precipitation data. - There are several sources to obtain DEM data. In the U.S., the DEM data (with different spatial resolutions) can be obtained from the National Elevation Dataset available from the national map (http://viewer.nationalmap.gov/viewer/). Another DEM data source is the Shuttle Radar Topography Mission (https://www2.jpl.nasa.gov/srtm/), an international research effort that obtained digital elevation models on a near-global scale (search for Digital Elevation at https://www.usgs.gov/centers/eros/science/usgs-eros-archive-products-overview?qt-science_center_objects=0#qt-science_center_objects). - If not already available, you can generate the outlet shapefile by applying basic terrain analysis steps in geospatial information system models such as ArcGIS or QGIS. - You also need to obtain average annual streamflow and precipitation data for the watershed of interest to assess the annual water balance and calculate the runoff ratio in this exercise. In the U.S., the streamflow data can be obtained from the USGS NWIS website (https://waterdata.usgs.gov/nwis) and the precipitation from PRISM (https://prism.oregonstate.edu/normals/). Note that using other datasets may require preprocessing steps to make data ready to use for this exercise.

Parcel boundary lines in this dataset are published once a year, after the boundary adjustments have been approved by Planning and Zoning and certified through the Assessor's Office. Attribute data is published at different times throughout the year, as detailed below.

*Attribute data excludes ownership and address data in this dataset. If you wish to have these data, please fill out the Public Information request form found in the Download Datasets page of the GIS Portal and email to lfrederick@co.valley.id.us.

ATTRIBUTE DATA - MONTHLY UPDATES

These fields are updated in the dataset monthly. After the public table updates are run by the Assessor's Office, Valley County GIS analyst exports the tables to append/update the new data values.

ATTRIBUTE DATA - ANNUAL UPDATES

These fields are updated annually after certification of parcel boundaries and valuation have been completed.

Terroir Analytics Platform Market Outlook

According to our latest research, the global Terroir Analytics Platform market size reached USD 1.12 billion in 2024, with a robust year-on-year growth reflecting the sector’s increasing adoption across various industries. The market is expected to expand at a CAGR of 13.7% from 2025 to 2033, projecting a value of USD 3.68 billion by 2033. This substantial growth is primarily driven by the rising demand for precision agriculture, advanced environmental monitoring, and the integration of data-driven decision-making across the food and beverage industry. As per our latest research, the market’s upward trajectory is supported by rapid technological advancements and the growing recognition of terroir analytics’ value in optimizing yield quality and sustainability.

One of the key growth factors propelling the Terroir Analytics Platform market is the increasing adoption of precision agriculture technologies. With the global agricultural sector facing mounting pressure to enhance productivity while minimizing environmental impact, stakeholders are turning to advanced analytics for actionable insights. Terroir analytics platforms utilize geospatial data, climate modeling, and soil health metrics to help farmers and producers optimize crop selection, irrigation, and fertilization strategies. This approach not only improves yield quality but also supports sustainable farming practices by reducing resource wastage. The integration of IoT sensors and AI-driven analytics further enhances the granularity and accuracy of terroir assessments, making these platforms indispensable tools for modern agricultural operations.

Another significant driver is the growing emphasis on quality differentiation and traceability within the food and beverage industry, especially among wineries and specialty food producers. Terroir analytics platforms enable producers to capture and analyze environmental variables—such as soil composition, microclimate, and topography—that directly influence the sensory attributes of agricultural products. By leveraging these insights, producers can create unique, high-value offerings that cater to discerning consumers seeking authenticity and provenance. This trend is particularly pronounced in the wine industry, where terroir-driven branding has become a critical competitive differentiator. Moreover, regulatory agencies and certification bodies are increasingly mandating transparent documentation of production conditions, further fueling demand for terroir analytics solutions.

The rapid evolution of cloud computing and the proliferation of scalable, user-friendly analytics platforms have also contributed significantly to market growth. Cloud-based deployment models offer flexibility, cost-efficiency, and ease of integration with existing enterprise systems, making terroir analytics accessible to a broader range of users, including small and medium-sized enterprises. Furthermore, the expansion of digital infrastructure in emerging economies is facilitating the adoption of advanced analytics across new geographies. Strategic partnerships between technology vendors, agribusinesses, and research institutes are fostering innovation and accelerating the development of tailored solutions that address region-specific challenges. Collectively, these factors are expected to sustain the market’s momentum over the forecast period.

From a regional perspective, North America and Europe currently lead the Terroir Analytics Platform market, accounting for a combined share of over 60% in 2024. These regions benefit from mature agricultural sectors, strong regulatory frameworks, and a high concentration of technology providers. The Asia Pacific region, however, is poised for the fastest growth, driven by rising investments in precision agriculture, expanding food and beverage industries, and increasing awareness of sustainable farming practices. Latin America and the Middle East & Africa are also emerging as promising markets, supported by government initiatives aimed at modernizing agriculture and improving food security. As the market continues to evolve, regional dynamics will play a pivotal role in shaping the adoption and development of terroir analytics platforms worldwide.

Component Analysis

The Terroir Analytics Platform market is segmented by component into Software and Services, both of which play distinct yet complementary roles in dri

EOS-WEBSTER has agreed to serve satellite image subsets for the Forest Watch ("http://www.forestwatch.sr.unh.edu") program and other educational programs which make use of satellite imagery. Forest Watch is a New England-wide environmental education activity designed to introduce teachers and students to field, laboratory, and satellite data analysis methods for assessing the state-of-health of local forest stands. One of the activities in Forest Watch involves image processing and data analysis of Landsat Thematic Mapper data (TM/ETM+) for the area around a participant's school. The image processing of local Landsat data allows the students to use their ground truth data from field-based activities to better interpret the satellite data for their own back yard. Schools use a freely available image processing software, MultiSpec ("http://dynamo.ecn.purdue.edu/%7Ebiehl/MultiSpec/"), to analyze the imagery. Value-added Landsat data, typically in a 512 x 512 pixel subset, are supplied by this collection. The Forest Watch program has supplied the data subsets in this collection based on the schools involved with their activities.

Satellite data subsets may be searched by state or other category, and by spectral type. These images may be previewed through this system, ordered, and downloaded. Some historic Landsat 5 data subsets, which were acquired for this program, are also provided through this system. Landsat 5 subsets are multispectral data with 5 bands of data (TM bands 1-5). Landsat 7 subsets contain all bands of data and each subset has three spectral file types: 1) multispectral (ETM+ bands 1-5 and 7), 2) panchromatic (ETM+ band 8), and 3) Thermal (ETM+ band 6 high and low gain channels). Each spectral type must be ordered separately; this can be accomplished by choosing more than one spectral file type in your search parameters.

These image subsets are served in the ERDAS Imagine (.img) format, which can be opened by newer versions of the MultiSpec program (versions greater than Nov. 1999). The MultiSpec program can be downloaded via the Internet at: "http://dynamo.ecn.purdue.edu/%7Ebiehl/MultiSpec/"

A header file is provided with most Landsat 7 subsets giving the specifics of the image.

Please refer to the references to learn more about Forest Watch, Landsat, and the data this satellite acquires.

In the near future we hope to release a new Satellite Interface, which would allow a user to search for satellite data from a number of platforms based on user-selected search parameters and then sub-set the data and choose an appropriate output format.

If you have any other questions regarding our Forest Watch Satellite data holdings, please contact our User Services Personnel (support@eos-webster.sr.unh.edu).

Available Data Sets:

Many New England subsets are available, based on the location of participating schools in the Forest Watch program. Additional scenes are also included based on historical use within the Forest Watch program. Other scenes may be added in the future. If you don't see a scene of the location you are interested in, and that location is within New England, then please contact User Services (support@eos-webster.sr.unh.edu) to see if we can custom-create a subset for you.

Data Format

The data are currently held in EOS-WEBSTER in ERDAS Imagine (.img) format. This format is used by new versions of the MultiSpec program, and other image processing programs. Most of the subset scenes provided through this system have been projected to a Lambert Projection so that MultiSpec can display Latitude and Longitude values for each image cell (see "http://www.forestwatch.sr.unh.edu/online/" Using Mac MultiSpec to display Lat./Lon. Coordinates).

Data can be ordered by spectral type. For Landsat 7, three spectral types are available: 1) Multispectral (bands 1-5 & 7), 2) Panchromatic (pan), and 3) Thermal (bands 6 a&b) (see Table 2). The multispectral (ms) files contain six bands of data, the panchromatic (pan) files contains one band of data, and the thermal (therm) files contain two bands of data representing a high and low sensor gain.

A header file is provided for most Landsat 7 subsets which have been projected in the Lambert projection. This header file provides the necessary information for importing the data into MultiSpec for Latitude/Longitude display.