This dataset is about book subjects. It has 4 rows and is filtered where the books is Spatial data mining : theory and application. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

MV_LCLQ Data.gdbdata_real.gdbTool_codeMV-LCLQ Tool box.tbxTool usage demonstration.mxd

Through the cooperation of the LouisianaView consortium members and co-sponsored with the local USGS liaison, this annual workshop is offered free to everyone interested in up-to-date information on data availability for the geospatial emergency responder. This is a 4-day virtual workshop hosts speakers from multiple Federal, State and Private Response Teams, each presenting their data, websites, links, and contacts while also fielding questions live from those in attendance, proving again and again what a cohesive and informed network of geospatial responders can mean to the inhabitants and economic base within Louisiana, the Gulf of Mexico region and the Caribbean.

Spatial association rule mining (SARM) is an important data mining task for understanding implicit and sophisticated interactions in spatial data. The usefulness of SARM results, represented as sets of rules, depends on their reliability: the abundance of rules, control over the risk of spurious rules, and accuracy of rule interestingness measure (RIM) values. This study presents crisp-fuzzy SARM, a novel SARM method that can enhance the reliability of resultant rules. The method firstly prunes dubious rules using statistically sound tests and crisp supports for the patterns involved, and then evaluates RIMs of accepted rules using fuzzy supports. For the RIM evaluation stage, the study also proposes a Gaussian-curve-based fuzzy data discretization model for SARM with improved design for spatial semantics. The proposed techniques were evaluated by both synthetic and real-world data. The synthetic data was generated with predesigned rules and RIM values, thus the reliability of SARM results could be confidently and quantitatively evaluated. The proposed techniques showed high efficacy in enhancing the reliability of SARM results in all three aspects. The abundance of resultant rules was improved by 50% or more compared with using conventional fuzzy SARM. Minimal risk of spurious rules was guaranteed by statistically sound tests. The probability that the entire result contained any spurious rules was below 1%. The RIM values also avoided large positive errors committed by crisp SARM, which typically exceeded 50% for representative RIMs. The real-world case study on New York City points of interest reconfirms the improved reliability of crisp-fuzzy SARM results, and demonstrates that such improvement is critical for practical spatial data analytics and decision support.

This is a set of POI data sets of Shenzhen, Guangzhou, Beijing, and Shanghai cities, China.

This is a set of spatial data sets which can use for discovering spatial co-location patterns.

Library of Wroclaw University of Science and Technology scientific output (DONA database)

Chytridiomycosis, caused by the fungal pathogen Batrachochytrium dendrobatidis (Bd), is a major driver of amphibian decline worldwide. The global presence of Bd is driven by a synergy of factors, such as climate, species life history, and amphibian host suscepÂtibility. Here, using a Bayesian data-mining approach, we modeled the epidemiologiÂcal landscape of Bd to evaluate how infection varies across several spatial, ecological, and phylogenetic scales. We compiled global information on Bd occurrence, climate, species ranges, and phylogenetic diversity to infer the potential distribution and prevaÂlence of Bd. By calculating the degree of co-distribution between Bd and our set of environmental and biological variables (e.g. climate and species), we identified the factors that could potentially be related to Bd presence and prevalence using a geoÂgraphic correlation metric, epsilon (ε). We fitted five ecological models based on 1) amphibian species identity, 2) phylogenetic species varia..., Usage notes

These datasets include the geographic data used to build ecological and geographical models for Batrachochytrium dendrobatidis, as well as supplementary results of the following paper: Basanta et al. Epidemiological landscape of Batrachochytrium dendrobatidis and its impact on amphibian diversity at the global scale. Missing values are denoted by NA. Details for each dataset are provided in the README file. Datasets included:

Information of Bd records. Table S1.xls contains Bd occurrence records and prevalence of infection from the Bd-Maps online database (http://www.bd-maps.net), Olson et al. 2013) accessed in 2013, and searched Google Scholar for recent papers with Bd infection reports using the keywords ‘*Batrachochytrium dendrobatidis’*. We excluded records from studies of captive individuals and those without coordinates, keeping only records in which coordinates reflected site-specific sample locations. Supplementary figures Supplementary information S1.docx cont..., , # 1. Title of Dataset: Epidemiological landscape of Batrachochytrium dendrobatidis and its impact on amphibian diversity at global scale

2. Authors Information

M. Delia Basanta Department of Biology, University of Nevada Reno. Reno, Nevada, USA.

Julián A. Velasco Instituto de Ciencias de la Atmósfera y Cambio Climático, Universidad Nacional Autónoma de México. Ciudad de México, México.

Constantino González-Salazar. Instituto de Ciencias de la Atmósfera y Cambio Climático, Universidad Nacional Autónoma de México. Ciudad de México, México.

3. Date of data collection (single date, range, approximate date): 2019-2022

4. Geographic location of data collection: Global

DATA & FILE OVERVIEW

1. File List:

1. Table S1.xls
2. Supplementary information S1.docx
3. Table S2.xlsx
4. Table S3.xlsx
5. Table S4.xlsx

DATA-SPECIFIC INFORMATION FOR: Table S1.xls

Table S1.xls contains Bd occurrence records and prevalence of infection from the Bd-Maps online da...

Air pollution directly affects human health endpoints including growth, respiratory processes, cardiovascular health, fertility, pregnancy outcomes, and cancer. Therefore, the distribution of air pollution is a topic that is relevant to all, and of direct interest to many students. Air quality varies across space and time, often disproportionally affecting minority communities and impoverished neighborhoods. Air pollution is usually higher in locations where pollution sources are concentrated, such as industrial production facilities, highways, and coal-fired power plants. The United States Environmental Protection Agency manages a national air quality-monitoring program to measure and report air-pollutant levels across the United States. These data cover multiple decades and are publicly available via a website interface. For this lesson, students learn how to mine data from this website. They work in pairs to develop their own questions about air quality or air pollution that span spatial and/or temporal scales, and then gather the data needed to answer their question. The students analyze their data and write a scientific paper describing their work. This laboratory experience requires the students to generate their own questions, gather and interpret data, and draw conclusions, allowing for creativity and instilling ownership and motivation for deeper learning gains.

High Utility Co-location Pattern Mining (HUCPM), as an important branch of spatial data mining, aims to extract patterns with utility values that meet or exceed a predefined threshold based on user-defined utility criteria (e.g., cost, profit). However, due to the non-uniformity of spatial distribution, the utility associations between spatial features exhibit significant differences across different regions. As data scale and complexity continue to increase, mining efficiency faces significant challenges. Although various pruning strategies have been proposed to enhance mining efficiency, they cannot adaptively adjust based on the characteristics of the data distribution, making them difficult to apply widely across different datasets. To address these issues, this paper introduces the AUW-CE Miner (Adaptive Utility-Weighted Cross-Entropy Miner), a heuristic algorithm built upon an enhanced cross-entropy framework. By integrating a heuristic search mechanism, the algorithm can quickly converge to potential high utility patterns and effectively reduce redundant computational processes. Moreover, in response to the limitations of conventional cross-entropy methods for HUCPM, four core optimization strategies are designed: optimization of the initial probability distribution to guide the search direction, enhancement of sample diversity to prevent local convergence, dynamic adjustment of sample size to reduce redundant calculations, and incorporation of utility weights to improve the accuracy of probability updates. Experimental results show that the AUW-CE Miner significantly outperforms other algorithms in terms of runtime efficiency, with an average efficiency improvement of up to 56.5\%, demonstrating exceptional mining efficiency and stability.

From the site: "An Industrial Mineral Mining Operation is a DEP primary facility type related to the Industrial Mineral Mining Program. The sub-facility types are listed below:_Deep Mine Underground mining of industrial minerals, i.e., noncoal mining. Includes, but is not limited to, industrial minerals extracted from beneath the surface by means of shafts, tunnels, adits, or other mine openings. Discharge Point Discharge of water from an area as a result of industrial mining activities, i.e., noncoal mining. Mineral Preparation Plant Facility at which industrial minerals (i.e., noncoal minerals) are cleaned and processed. Mining Stormwater GP - General permit for Stormwater discharges associated with industrial mineral mining activities in which the main pollutant is sediment. Discharge is not into a High Quality or Exceptional Value designated stream. Surface Mine Surface mining of industrial minerals (i.e., noncoal minerals) by removing material which lies above the industrial minerals. Includes, but is not limited to, strip, auger, quarry, dredging, and leaching mines."

From the site: "Coverages containing Anthracite coal seam data by quadrangle for the state of Pennsylvania. Digitized from the Harrisburg Bureau of Mining and Reclamation mylar map system each quadrangle contains multiple coverages identifying seams in that quad. Also includes coal mining refuse disposal sites, permitted sites, point coverages of deep mine entry and other surface features of deep mines and Small Operators Assistance Program (SOAP) areas."

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.

Increasing popularity of social networks made them a viable data source for many data mining applications and event detection is no exception. Researchers aim not only to find events that happen in networks but more importantly to identify and locate events occurring in the real world.In this paper, we propose an enhanced version of quadtree - convolutional quadtree (ConvTree) - and demonstrate its advantage compared to the standard quadtree. We also introduce the algorithm for searching events of different scales using geospatial data obtained from social networks. The algorithm is based on statistical analysis of historical data, generation of ConvTrees representing the normal state of the city and anomalies evaluation for events detection.Experimental study conducted on the dataset of 60 million geotagged Instagram posts in the New York City area demonstrates that the proposed approach is able to find a wide range of events from very local (indie band concert or wedding party) to city (baseball game or holiday march) and even country scale (political protest or Christmas) events. This opens up a perspective of building simple and fast yet powerful system for real-time multiscale events monitoring.

a largely commercial website with some public information large amounts of data on global mining statistics, mine locations and ownership, however payment is required to view. These pages link to a large range of different maps and spatial data, including maps of miens and geology for a range of geographic regions

Website: http://www.infomine.com/maps/linkstree.aspx#cat1404

Three semi-automated detection approaches using Sentinel-1 Synthetic Aperture Radar (SAR) have been performed to identify artisanal and small-scale mining (ASM) riverine dredges on the Madeira River in Brazil. The methods are: i) Search for Unidentified Maritime Objects (SUMO), an established method for large ocean ship detection; and two techniques specifically developed for riverine environments: ii) a local detection method; and iii) a global threshold method. The results from each method are contained on this landing page along with the visual interpretation dataset of SAR data used as the validation dataset. The pre-processed SAR data used to produce these results are found also found on this page.

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.

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 feature classes 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 copper, phosphate, and potash, (5) coal occurrence areas, (6) electric power generating facilities, (7) electric power transmission lines, (8) liquefied natural gas terminals, (9) undiscovered, technically recoverable conventional and continuous hydrocarbon resources (by USGS geologic province), (10) cumulative production and recoverable conventional resources (by oil- and gas-producing nation), and (11) major mineral exporting maritime ports.

From the site: "An Industrial Mineral Mining Operation is a DEP primary facility type related to the Industrial Mineral Mining Program. The sub-facility types are listed below:_Deep Mine Underground mining of industrial minerals, i.e., noncoal mining. Includes, but is not limited to, industrial minerals extracted from beneath the surface by means of shafts, tunnels, adits, or other mine openings. Discharge Point Discharge of water from an area as a result of industrial mining activities, i.e., noncoal mining. Mineral Preparation Plant Facility at which industrial minerals (i.e., noncoal minerals) are cleaned and processed. Mining Stormwater GP - General permit for Stormwater discharges associated with industrial mineral mining activities in which the main pollutant is sediment. Discharge is not into a High Quality or Exceptional Value designated stream. Surface Mine Surface mining of industrial minerals (i.e., noncoal minerals) by removing material which lies above the industrial minerals. Includes, but is not limited to, strip, auger, quarry, dredging, and leaching mines."