Dataset used for validation of information mining process for spatial association discovery.

This is a spatial database that delineates mining-related features in areas of historic and active phosphate mining in the core of the southeastern Idaho phosphate resource area. The data has varying degrees of accuracy and attribution detail. The breakdown of areas by type of activity at active mines is detailed; however, the disturbed areas at many of the closed or inactive mines are not subdivided into specific categories detailing the type of activity that occurred.

Please see the individual layers below to access the detailed metadata.This feature layer contains three datasets:The Mining Boreholes dataset contains GIS points depicting mining boreholes digitized from the U.S. Bureau of Mines (USBM) Illinois Mineral Development Atlas (IMDA) for Jo Daviess County, Illinois. Each point includes a link to a corresponding log (if available). This is one of several datasets complied for the Karst Feature Database of Jo Daviess County, IL and hosted by the U.S. Fish and Wildlife Service.The named mines dataset contains GIS polygons depicting surveyed outlines of known (named) mine diggings from the U.S. Bureau of Mines (USBM) Illinois Mineral Department Atlas (IMDA) for Jo Daviess County, Illinois. This is one of several datasets complied for the Karst Feature Database of Jo Daviess County, IL and hosted by the U.S. Fish and Wildlife Service.The unnamed mines dataset contains GIS polygons depicting unsurveyed inferred outlines of unknown (unnamed) mine diggings from the U.S. Bureau of Mines (USBM) Illinois Mineral Development Atlas (IMDA) for Jo Daviess County, Illinois. This is one of several datasets complied for the Karst Feature Database of Jo Daviess County, IL and hosted by the U.S. Fish and Wildlife Service.

MMD uses a Geographic Information System (GIS) to locate and track its mining activities in the state. This is a computer system that can capture, store, analyze and display geographically referenced (location) information. The power of this system is its ability to draw conclusions about relationships between data that have a spatial component. GIS provides a method of displaying accurate mapping and database information to the staff and public.

The global Geographic Information System (GIS) market is booming, projected to reach $17.5 billion by 2033 with a 5.8% CAGR. Discover key trends, drivers, and regional insights in this comprehensive market analysis, covering major players and applications.

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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, gover

This geodatabase reflects the U.S. Geological Survey’s (USGS) ongoing commitment to its mission of understanding the nature and distribution of global mineral commodity supply chains by updating and publishing the georeferenced locations of mineral commodity production and processing facilities, mineral exploration and development sites, and mineral commodity exporting ports in Africa. The geodatabase and geospatial data layers serve to create a new geographic information product in the form of a geospatial portable document format (PDF) map. The geodatabase contains data layers from USGS, foreign governmental, and open-source sources as follows: (1) mineral production and processing facilities, (2) mineral exploration and development sites, (3) mineral occurrence sites and deposits, (4) undiscovered mineral resource tracts for Gabon and Mauritania, (5) undiscovered mineral resource tracts for potash, platinum-group elements, and copper, (6) coal occurrence areas, (7) electric power generating facilities, (8) electric power transmission lines, (9) liquefied natural gas terminals, (10) oil and gas pipelines, (11) undiscovered, technically recoverable conventional and continuous hydrocarbon resources (by USGS geologic/petroleum province), (12) cumulative production, and recoverable conventional resources (by oil- and gas-producing nation), (13) major mineral exporting maritime ports, (14) railroads, (15) major roads, (16) major cities, (17) major lakes, (18) major river systems, (19) first-level administrative division (ADM1) boundaries for all countries in Africa, and (20) international boundaries for all countries in Africa.

The GRASS GIS database containing the input raster layers needed to reproduce the results from the manuscript entitled:

"Mapping forests with different levels of naturalness using machine learning and landscape data mining" (under review)

Abstract:

To conserve biodiversity, it is imperative to maintain and restore sufficient amounts of functional habitat networks. Hence, locating remaining forests with natural structures and processes over landscapes and large regions is a key task. We integrated machine learning (Random Forest) and wall-to-wall open landscape data to scan all forest landscapes in Sweden with a 1 ha spatial resolution with respect to the relative likelihood of hosting High Conservation Value Forests (HCVF). Using independent spatial stand- and plot-level validation data we confirmed that our predictions (ROC AUC in the range of 0.89 - 0.90) correctly represent forests with different levels of naturalness, from deteriorated to those with high and associated biodiversity conservation values. Given ambitious national and international conservation objectives, and increasingly intensive forestry, our model and the resulting wall-to-wall mapping fills an urgent gap for assessing fulfilment of evidence-based conservation targets, spatial planning, and designing forest landscape restoration.

This database was compiled from the following sources:

1. HCVF. A database of High Conservation Value Forests in Sweden. Swedish Environmental Protection Agency.

source: https://geodata.naturvardsverket.se/nedladdning/skogliga_vardekarnor_2016.zip

1. NMD. National Land Cover Data. Swedish Environmental Protection Agency.

source: https://www.naturvardsverket.se/en/services-and-permits/maps-and-map-services/national-land-cover-database/

1. DEM. Terrain Model Download, grid 50+. Lantmateriet, Swedish Ministry of Finance.

source: https://www.lantmateriet.se/en/geodata/geodata-products/product-list/terrain-model-download-grid-50/

1. GFC. Global Forest Change. Global Land Analysis and Discovery, University of Maryland.

source: https://glad.earthengine.app

1. LIGHTS. A harmonized global nighttime light dataset 1992–2018. Land pollution with night-time lights expressed as calibrated digital numbers (DN).

source: https://doi.org/10.6084/m9.figshare.9828827.v2

1. POPULATION. Total Population in Sweden. Statistics Sweden.

source: https://www.scb.se/en/services/open-data-api/open-geodata/grid-statistics/

To learn more about the GRASS GIS database structure, see:

https://grass.osgeo.org/grass82/manuals/grass_database.html

Motivation

Maus et al created the first database of the spatial extent of mining areas by mobilizing nearly 20 years of Landsat data. This dataset is imperative for GlobES, as mining areas are specified in the IUCN habitat class scheme. Yet, this dataset is temporally static. To tackle this flaw, we mined the Landsat archive to infer the first observable year of mining.

Approach

For each mining area polygon, we collected 50 random samples within it and 50 random samples along its borders. This was meant to reflect increasing spectral differences between areas within and outside a mining exploration after its onset. Then, for each sample, we used Google Earth Engine to extract spectral profiles for every available acquisition between 1990 and 2020.

After completing the extraction, we estimate mean spectral profiles for each acquisition date, once for the samples “inside” the mining area, and another for those “outside” of it. In this process, we masked pixels afflicted by clouds and cloud shadows using Landsat's quality information.

Using the time-series of mean profiles, at each mining site and for each unique date, we normalized the “inside” and “outside” multi-spectral averages and estimated the Root Mean Square Error (RMSE) between them. The normalization step aimed at emphasizing differences in the shape of the spectral profiles rather than on specific values, which can be related to radiometric innacuracies, or simply to differences in acquisition dates. This resulted in an RMSE time-series for each mining site.

We then used these data to infer the first mining year. To achieve this, we first derived a cumulative sum of the RMSE time-series with the intent of removing noise while preserving abrupt directional changes. For example, if a mine was introduced in a forest, it would drive an increase in the RMSE due to the removal of trees, whereas the outskirts of the mine would remain forested. In this example, the accumulated values would tilt upwards. However, if a mining exploration was accompanied by the removal of vegetation along its outskirts where bare land was common, a downwards shift is RMSE values is more likely as the landscape becomes more homogenization.

To detect the date marking a shift in RMSE values, we used a knee/elbow detection algorithm implemented in the python package kneebow, which uses curve rotation to infer the inflection/deflection point of a time series. Here, downward trends correspond to the elbow and upward trends to the knee. To determine which of these metrics was the most adequate, we use the Area Under the Curve (AUC). An elbow is characterized by a convex shape of a time-series which makes the AUC greater than 50%. However, if the shape of the curve is concave, the knee is the most adequate metric. We limited the detection of shifts to time-series with at least 100 time steps. When below this threshold, we assumed the mine (or the the conditions to sustain it) were present since 1990.

Content

This repository contains the infrastructure used to infer the start of a mining operation, which is organized as following:

00_data - Contains the base data required for the operation, including a SHP file with the mining area outlines, and validation samples.

01_analysis - Contains several outputs of our analysis:

xy.tar.gz - Sample locations for each mining site.

sr.tar.gz - Spectral profiles for each sample location.

mine_start.csv - First year when we detected the start of mining.

02_code - Includes all code used in our analysis.

requirements.txt - Python module requirements that can be fed to pip to replicate our study.

config.yml - Configuration file, including information on the Landsat products used.

Input datasets and model results for associated PLOS ONE publication.

Residential Completion data is provided to MAG by member agencies on a quarterly basis, then processed and geocoded by MAG staff. The Residential Completion database contains a record for every residential completion in Maricopa County from April 1, 1990 (1990 Census date) through December 31, 2002.

Explore the expanding Geographic Information System (GIS) market, projected at USD 10,880 million in 2025 with a 5.8% CAGR. Discover key drivers, industry applications, hardware & software trends, and regional growth opportunities.

Data from the article "Unraveling spatial, structural, and social country-level conditions for the emergence of the foreign fighter phenomenon: an exploratory data mining approach to the case of ISIS", by Agustin Pájaro, Ignacio J. Duran and Pablo Rodrigo, published in Revista DADOS, v. 65, n. 3, 2022.

The U.S. Geological Survey (USGS) has compiled a geodatabase containing mineral-related geospatial data for 10 countries of interest in Southwest Asia (area of study): Afghanistan, Cambodia, Laos, India, Indonesia, Iran, Nepal, North Korea, Pakistan, and Thailand. The data can be used in analyses of the extractive fuel and nonfuel mineral industries and related economic and physical infrastructure integral for the successful operation of the mineral industries within the area of study as well as the movement of mineral products across domestic and global markets. This geodatabase reflects the USGS ongoing commitment to its mission of understanding the nature and distribution of global mineral commodity supply chains by updating and publishing the georeferenced locations of mineral commodity production and processing facilities, mineral exploration and development sites, and mineral commodity exporting ports for the countries in the area of study. The geodatabase contains data feat ...

20 minute lightning talk presentation by Claudia Wolff, from the Christian-Albrechts-University Kiel, at the Better Science through Better Data 2018 event. The video recording, slides and scribe are included.

As part of the “Arabic Corpus and Entities Dealing with Animal Disease Surveillance Extracted with PADI-web” dataset (https://doi.org/10.18167/DVN1/2B4WLR), we built a new dataset containing 284 spatial entities in Arabic, their translation into English (manually validated) and their automatic translation by three automatic tools (DeepL, Microsoft Azure, and Reverso). The dataset was updated with two new columns on September 3, 2025: GeoNames ID and GeoNames Feature Class, enabling the matching of spatial entities to the GeoNames gazetteer. The dataset is organised as a table with twelve columns : ID: The unique identifier of each article (from PADI-web database) Arabic Location: The spatial entities in Arabic, manually extracted from 53 articles collected via PADI-web English Location: The manual translation of spatial entities into English, based on existing field sources such as Google Maps and the GeoNames database GeoNames ID: The unique ID from the GeoNames database (2022 version of GeoNames: https://www.geonames.org/) corresponding to each spatial entity (empty if no match in GeoNames) GeoNames Feature Class : The feature class corresponding to the GeoNames ID (empty if no match in GeoNames) Type: A manually assigned type of spatial entity (country, city, region, village, etc.). Category: The classification of spatial entities into two categories: absolute spatial entities (ASE) and relative spatial entities (RSE). Arabic Phrases: The sentence, in Arabic, from which the spatial entity was extracted. Translation DeepL: The translation of the location by DeepL. Translation Microsoft Azure: The translation of the location by Microsoft Azure. Translation Reverso: The translation of the location by Reverso. English Sentences Translated by DeepL: The translation of the sentence by DeepL. English Sentences Translated by Microsoft Azure: The translation of the sentence by Microsoft Azure. English Sentences Translated by Reverso: The translation of the sentence by Reverso. Absolute spatial entities are direct references to precise, locatable geographic spaces, i.e. entities that can be located on a map or in a geographic database (e.g. cities such as Safi, countries such as Morocco, Egypt, etc.). Relative spatial entities are entities defined in relation to at least one other spatial entity, using spatial indicators of a topological nature (for example, “الطود شرق” (El-Tod East), “ناحية تلات” (Talat district), etc.).

Please see the individual layers below to access the detailed metadata.This feature layer contains three datasets:The Mining Boreholes dataset contains GIS points depicting mining boreholes digitized from the U.S. Bureau of Mines (USBM) Illinois Mineral Development Atlas (IMDA) for Jo Daviess County, Illinois. Each point includes a link to a corresponding log (if available). This is one of several datasets complied for the Karst Feature Database of Jo Daviess County, IL and hosted by the U.S. Fish and Wildlife Service.The named mines dataset contains GIS polygons depicting surveyed outlines of known (named) mine diggings from the U.S. Bureau of Mines (USBM) Illinois Mineral Department Atlas (IMDA) for Jo Daviess County, Illinois. This is one of several datasets complied for the Karst Feature Database of Jo Daviess County, IL and hosted by the U.S. Fish and Wildlife Service.The unnamed mines dataset contains GIS polygons depicting unsurveyed inferred outlines of unknown (unnamed) mine diggings from the U.S. Bureau of Mines (USBM) Illinois Mineral Development Atlas (IMDA) for Jo Daviess County, Illinois. This is one of several datasets complied for the Karst Feature Database of Jo Daviess County, IL and hosted by the U.S. Fish and Wildlife Service.

The Nigeria Geospatial Analytics Market is booming, projected to reach $132.83 million by 2033 with a 5.94% CAGR. Discover key drivers, trends, and leading companies shaping this dynamic sector. Learn about the opportunities in agriculture, government, and more. Recent developments include: April 2023: Abuduganiyu Adebomehin, the Surveyor General of the Federation (SGoF), has praised Sambus Geospatial Nigeria Limited, a provider of solutions, for its dedication to the promotion of a producing high-quality, accurate, and real-time geographic data for Nigeria. The Office of the Surveyor General of the Federation (OSGoF) donated five copies of mapping software (ESRI Arc GIS Pro Advance with ten extensions), which the SGoF accepted in exchange for the praise.. Key drivers for this market are: Commercialization of spatial data, Increased smart city & infrastructure projects. Potential restraints include: Commercialization of spatial data, Increased smart city & infrastructure projects. Notable trends are: Commercialization of spatial data would drive the market in Nigeria.

This paper uses text data mining to identify long-term developments in tourism academic research from the perspectives of thematic focus, geography, and gender of tourism authorship. Abstracts of papers published in the period of 1970–2017 in high-ranking tourist journals were extracted from the Scopus database and served as data source for the analysis. Fourteen subject areas were identified using the Latent Dirichlet Allocation (LDA) text mining approach. LDA integrated with GIS information allowed to obtain geography distribution and trends of scholarly output, while probabilistic methods of gender identification based on social network data mining were used to track gender dynamics with sufficient confidence. The findings indicate that, while all 14 topics have been prominent from the inception of tourism studies to the present day, the geography of scholarship has notably expanded and the share of female authorship has increased through time and currently almost equals that of male authorship.

Explore the booming United States Geospatial Analytics Market, driven by AI, IoT, and defense needs. Get insights on market size, CAGR, and key trends shaping industries from agriculture to government. Key drivers for this market are: Increasing in Demand for Location Intelligence, Advancements of Big Data Analytics. Potential restraints include: High Costs and Operational Concerns, Concerns related to Geoprivacy and Confidential Data. Notable trends are: Network Analysis is Expected to Hold Significant Share of the Market.