The Snake River Plain (SRP), Idaho, hosts potential geothermal resources due to elevated groundwater temperatures associated with the thermal anomaly Yellowstone-Snake River hotspot. Project HOTSPOT has coordinated international institutions and organizations to understand subsurface stratigraphy and assess geothermal potential. Over 5.9km of core were drilled from three boreholes within the SRP in an attempt to acquire continuous core documenting the volcanic and sedimentary record of the hotspot: (1) Kimama, (2) Kimberly, and (3) Mountain Home. The Mountain Home drill hole is located along the western plain and documents older basalts overlain by sediment. Data submitted by project collaborator Doug Schmitt, University of Alberta

The Snake River Plain (SRP), Idaho, hosts potential geothermal resources due to elevated groundwater temperatures associated with the thermal anomaly Yellowstone-Snake River hotspot. Project HOTSPOT has coordinated international institutions and organizations to understand subsurface stratigraphy and assess geothermal potential. Over 5.9km of core were drilled from three boreholes within the SRP in an attempt to acquire continuous core documenting the volcanic and sedimentary record of the hotspot: (1) Kimama, (2) Kimberly, and (3) Mountain Home. The Kimama drill site was set up to acquire a continuous record of basaltic volcanism along the central volcanic axis and to test the extent of geothermal resources beneath the Snake River aquifer.

Data submitted by project collaborator Doug Schmitt, University of Alberta

Hot spots are energetically important residues at protein interfaces and they are not randomly distributed across the interface but rather clustered. These clustered hot spots form hot regions. Hot regions are important for the stability of protein complexes, as well as providing specificity to binding sites. HotRegion provides the hot region information of the interfaces by using predicted hot spot residues, and structural properties of these interface residues such as pair potentials of interface residues, accessible surface area (ASA) and relative ASA values of interface residues of both monomer and complex forms of proteins. Also, the 3D visualization of the interface and interactions among hot spot residues are provided. The number of interfaces in the database is 147909 and still growing.

The Food Insecurity Hotspots Data Set consists of grids at 250 meter (~7.2 arc-seconds) resolution that identify the level of intensity and frequency of food insecurity over the 10 years between 2009 and 2019, as well as hotspot areas that have experienced consecutive food insecurity events. The gridded data are based on subnational food security analysis provided by FEWS NET (Famine Early Warning Systems Network) in five (5) regions, including Central America and the Caribbean, Central Asia, East Africa, Southern Africa, and West Africa. Based on the Integrated Food Security Phase Classification (IPC), food insecurity is defined as Minimal, Stressed, Crisis, Emergency, and Famine.

This layer presents detectable thermal activity from MODIS satellites for the last 7 days. MODIS Global Fires is a product of NASA’s Earth Observing System Data and Information System (EOSDIS), part of NASA's Earth Science Data. EOSDIS integrates remote sensing and GIS technologies to deliver global MODIS hotspot/fire locations to natural resource managers and other stakeholders around the World.

Area Covered: World

What can I do with this layer?
The MODIS thermal activity layer can be used to visualize and assess wildfires worldwide. However, it should be noted that this dataset contains many “false positives” (e.g., oil/natural gas wells or volcanoes) since the satellite will detect any large thermal signal.


It takes approximately 2 – 4 hours after satellite overpass for MODIS Rapid Response to process the data, and for the Fire Information for Resource Management System (FIRMS) to update the website. Occasionally, hardware errors can result in processing delays beyond the 2-4 hour range. Additional information on the MODIS system status can be found at MODIS Rapid Response.

Attribute Information

• Latitude and Longitude: The center point location of the 1km (approx.) pixel flagged as containing one or more fires/hotspots (fire size is not 1km, but variable). Stored by Point Geometry. See What does a hotspot/fire detection mean on the ground?
• Brightness: The brightness temperature measured (in Kelvin) using the MODIS channels 21/22 and channel 31.
• Scan and Track: The actual spatial resolution of the scanned pixel. Although the algorithm works at 1km resolution, the MODIS pixels get bigger toward the edge of the scan. See What does scan and track mean?
• Date and Time: Acquisition date of the hotspot/active fire pixel and time of satellite overpass in UTC (client presentation in local time). Stored by Acquisition Date.
  
• Acquisition Date: Derived Date/Time field combining Date and Time attributes.
• Satellite: Whether the detection was picked up by the Terra or Aqua satellite.
• Confidence: The detection confidence is a quality flag of the individual hotspot/active fire pixel.
• Version: Version refers to the processing collection and source of data. The number before the decimal refers to the collection (e.g. MODIS Collection 6). The number after the decimal indicates the source of Level 1B data; data processed in near-real time by MODIS Rapid Response will have the source code “CollectionNumber.0”. Data sourced from MODAPS (with a 2-month lag) and processed by FIRMS using the standard MOD14/MYD14 Thermal Anomalies algorithm will have a source code “CollectionNumber.x”. For example, data with the version listed as 5.0 is collection 5, processed by MRR, data with the version listed as 5.1 is collection 5 data processed by FIRMS using Level 1B data from MODAPS.
• Bright.T31: Channel 31 brightness temperature (in Kelvins) of the hotspot/active fire pixel.
• FRP: Fire Radiative Power. Depicts the pixel-integrated fire radiative power in MW (MegaWatts). FRP provides information on the measured radiant heat output of detected fires. The amount of radiant heat energy liberated per unit time (the Fire Radiative Power) is thought to be related to the rate at which fuel is being consumed (Wooster et. al. (2005)).
• DayNight: The standard processing algorithm uses the solar zenith angle (SZA) to threshold the day/night value; if the SZA exceeds 85 degrees it is assigned a night value. SZA values less than 85 degrees are assigned a day time value. For the NRT algorithm the day/night flag is assigned by ascending (day) vs descending (night) observation. It is expected that the NRT assignment of the day/night flag will be amended to be consistent with the standard processing.
• Hours Old: Derived field that provides age of record in hours between Acquisition date/time and latest update date/time. 0 = less than 1 hour ago, 1 = less than 2 hours ago, 2 = less than 3 hours ago, and so on.

This map is provided for informational purposes and is not monitored 24/7 for accuracy and

Dataset of villages under surveillance which were able to be scored as within or outside of a spatial hotspot. These data are included independently of S16 and S17 to allow replication of hotspot analyses both with and without employing the SatScan software. Village data include the five year case count, presence over the five years, presence in a hotspot,and all covariates detailed in S1 Data are split between S17 and S18 to include the training/testing split for BRT modeling. (CSV)

🗂️ Overview This dataset contains detailed records of fire hotspots captured by NASA's MODIS (Moderate Resolution Imaging Spectroradiometer) sensors onboard Aqua and Terra satellites during August–September 2019. The dataset focuses on the geospatial, thermal, and temporal attributes of active fires across the Australian region.

This data can be used for:

• Forest fire detection and prediction
• Hotspot intensity analysis
• Temporal and spatial fire pattern modeling
• Environmental monitoring projects

📁 Features

Column	Description
latitude	Latitude coordinate of the hotspot
longitude	Longitude coordinate of the hotspot
brightness	Brightness value measured by MODIS
scan	Scan size (km) of the satellite sensor
track	Track size (km) of the satellite sensor
acq_date	Acquisition date of the observation
acq_time	Acquisition time in HHMM (UTC)
satellite	Satellite name (Terra or Aqua)
instrument	Instrument used (MODIS sensor)
confidence	Confidence level (0–100) indicating likelihood of fire presence

📊 Distribution Highlights 🔥 Fire Density by Latitude * Most fire hotspots were recorded between latitudes -14.98° and -11.71°, with peak counts exceeding 7,500. * High concentration observed in northern Australia.

🌍 Fire Density by Longitude * Longitude bands 131.83° to 133.80° and 151.52° to 153.49° recorded the most events. * Indicates hotspot clusters in central and eastern Australia.

🌡️ Brightness Distribution * Brightness ranged from 300 K to 504 K, with most hotspots observed between 320 K and 330 K.

📏 Scan & Track Stats * Most scans and tracks ranged from 1.0 to 2.0 km, with the largest group of points having scan width = 1.00 km.

📅 Temporal Spread * Timeframe: 2019-08-01 to 2019-09-30 * Fire activity peaked around mid-September, with over 3,400 detections in just 3 days.

🛰️ Satellite Usage * Aqua recorded ~57% of the data points * Terra captured ~43%

🔍 Confidence Distribution * Most hotspots have high confidence (85–100), with ~4,952 events rated between 95 and 100 confidence.

💡 Possible Use Cases * Predicting fire-prone zones using machine learning * Building interactive fire monitoring dashboards * Training models to estimate fire intensity * Time-series analysis of wildfire spread

Snapshot img

Mobile Hotspot Market Size 2024-2028

The mobile hotspot market size is forecast to increase by USD 2.63 billion at a CAGR of 9.89% between 2023 and 2028.

The market is experiencing significant growth, driven by the increasing development of smart cities and the expanding integration of Internet of Things (IoT) and smart devices. With the proliferation of connected devices, the need for reliable and portable internet connectivity solutions has become essential. Mobile hotspots, as a result, have gained immense popularity among individuals and businesses. Technological advancements continue to shape the market landscape, with improvements in battery life, network speed, and compatibility with various devices. However, challenges persist, particularly in ensuring seamless operating experiences across different mobile hotspot models and mobile network providers.
Companies seeking to capitalize on market opportunities must focus on addressing these compatibility issues and delivering user-friendly solutions. Additionally, strategic partnerships and collaborations with mobile network operators and IoT device manufacturers can provide significant competitive advantages. Overall, the market presents a promising growth trajectory, offering opportunities for innovation and strategic investments.

What will be the Size of the Mobile Hotspot Market during the forecast period?

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The market encompasses a range of portable devices and solutions that provide wireless internet connectivity, merging the capabilities of cellular data networks and Wi-Fi. These devices cater to the growing demand for reliable internet access on-the-go, particularly among remote workforces, smart devices, and travelers. The market is driven by the integration of 5G technology, which offers faster speeds and lower latency, enhancing the overall user experience. Software companies, network operators, telecom service providers, equipment manufacturers, managed service providers, and wireless hotspot controller companies contribute to the market's growth.
Mobile hotspot devices, such as pocket routers, travel routers, MIFIs, and portable Wi-Fi hotspots, are increasingly popular due to their ability to create a local Wi-Fi network using 3G/4G signals, LTE, or even VPN connections. Centralized hotspot management and advanced security features are also essential aspects of the market, ensuring secure and efficient wireless network usage.

How is this Mobile Hotspot Industry segmented?

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

Type

  Mobile hotspot router
  USB stick


End-user

  Commercial use
  Personal use


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan


  South America

    Brazil


  Middle East and Africa

    UAE


  Rest of World

By Type Insights

The mobile hotspot router segment is estimated to witness significant growth during the forecast period.

Mobile hotspot routers play a pivotal role in the expansion of the market. These devices cater to the demands of travelers, remote workers, and businesses requiring uninterrupted Internet connectivity in areas with limited or unreliable broadband options. Mobile hotspot routers offer numerous advantages, including portability, user-friendliness, and support for multiple device connections. Notable companies, such as NETGEAR and TP-Link, provide mobile hotspot routers with impressive features. For instance, NETGEAR's Nighthawk M6 offers high-performance capabilities, supporting Gigabit LTE speeds for fast and dependable Internet access. It also boasts an extended battery life and the capacity to connect up to 20 devices, making it a versatile solution for personal and professional use.

As businesses undergo digital transformation, the need for reliable mobile connectivity solutions, like mobile hotspots, becomes increasingly essential for various industries, including finance, healthcare, hospitality, transportation, and communication services. With the integration of 5G technology, mobile hotspots are expected to deliver enhanced download and upload speeds, further boosting their popularity. Additionally, security features, such as VPNs and IoT technologies, are becoming increasingly important to protect against cyber threats and ensure the safety of cloud-based applications, virtual learning, and other digital services. Mobile hotspot devices and wireless hotspot gateways, along with Wi-Fi security software, cloud-based hotspot management, and professional services, are essential components of the mobile hotspot ecosystem.

Network operators, telecom service providers, equipment manufacturers, mana

The Snake River Plain (SRP), Idaho, hosts potential geothermal resources due to elevated groundwater temperatures associated with the thermal anomaly Yellowstone-Snake River hotspot. Project HOTSPOT has coordinated international institutions and organizations to understand subsurface stratigraphy and assess geothermal potential. Over 5.9km of core were drilled from three boreholes within the SRP in an attempt to acquire continuous core documenting the volcanic and sedimentary record of the hotspot: (1) Kimama, (2) Kimberly, and (3) Mountain Home. The Kimama drill site was set up to acquire a continuous record of basaltic volcanism along the central volcanic axis and to test the extent of geothermal resources beneath the Snake River aquifer.

Data submitted by project collaborator Doug Schmitt, University of Alberta

Mutational hotspots identified by HotspotFinder from 49 cancer types analysed in: Arnedo-Pac C, Muiños F, Gonzalez-Perez A, Lopez-Bigas N. Hotspot propensity across mutational processes. Mol Syst Biol. 2023:1-22. doi: doi.org/10.1038/s44320-023-00001-w Hotspots have been computed using somatic mutations in each cancer type, after excluding those overlapping cancer driver elements. HotspotFinder has been run with a threshold of 2 mutated samples per hotspot and using alternate specific mutations. For additional details, please check Materials and Methods and Appendix Note 1 in our manuscript. HotspotFinder code is available at: bitbucket.org/bbglab/hotspotfinder Source code to reproduce this data can be found at: github.com/bbglab/hotspot_propensity Cancer type identifiers are listed as follows: Acute Lymphoblastic Leukemia (ALL) Acute Myeloid Leukemia (AML) Adrenocortical Carcinoma (ACC) Anal Cancer (AN) Basal Cell Carcinoma (BCC) Biliary Tract (BILIARY_TRACT) Bladder/Urinary Tract (BLADDER_URI) Bone/Soft Tissue (BONE_SOFT_TISSUE) Bowel (BOWEL) CNS/Brain (BRAIN) Cervix (CERVIX) Colorectal Adenocarcinoma (COADREAD) Cutaneous Melanoma (SKCM) Cutaneous Squamous Cell Carcinoma (CSCC) Endometrial Carcinoma (UCEC) Ependymoma (EPM) Esophageal cancer (ES) Esophagus/Stomach cancers (ESOPHA_STOMACH) Glioblastoma Multiforme (GBM) Head and Neck (HEAD_NECK) High-Grade Glioma NOS (HGGNOS) Invasive Breast Carcinoma (BRCA) Kidney (KIDNEY) Liver (LIVER) Low-Grade Glioma NOS (LGGNOS) Lung (LUNG) Lung Neuroendocrine Tumor (LNET) Lymphoid Neoplasm (LNM) Medulloblastoma (MBL) Myeloid Neoplasm (MNM) Myeloproliferative Neoplasms (MPN) Neuroblastoma (NBL) Non-Hodgkin Lymphoma (NHL) Non-Small Cell Lung Cancer (NSCLC) Oligodendroglioma (ODG) Ovarian Cancer (OV) Pan-cancer (PANCANCER) Pancreas (PANCREAS) Pilocytic Astrocytoma (PAST) Pleural Mesothelioma (PLMESO) Prostate (PROSTATE) Retinoblastoma (RBL) Skin (SKIN) Small Bowel Cancer (SBC) Small Bowel Neuroendocrine Tumor (SBNET) Small Cell Lung Cancer (SCLC) Stomach cancer (ST) Thyroid (THYROID) Vulva (VULVA)

This study used crime count data from the Pittsburgh, Pennsylvania, Bureau of Police offense reports and 911 computer-aided dispatch (CAD) calls to determine the best univariate forecast method for crime and to evaluate the value of leading indicator crime forecast models. The researchers used the rolling-horizon experimental design, a design that maximizes the number of forecasts for a given time series at different times and under different conditions. Under this design, several forecast models are used to make alternative forecasts in parallel. For each forecast model included in an experiment, the researchers estimated models on training data, forecasted one month ahead to new data not previously seen by the model, and calculated and saved the forecast error. Then they added the observed value of the previously forecasted data point to the next month's training data, dropped the oldest historical data point, and forecasted the following month's data point. This process continued over a number of months. A total of 15 statistical datasets and 3 geographic information systems (GIS) shapefiles resulted from this study. The statistical datasets consist of Univariate Forecast Data by Police Precinct (Dataset 1) with 3,240 cases Output Data from the Univariate Forecasting Program: Sectors and Forecast Errors (Dataset 2) with 17,892 cases Multivariate, Leading Indicator Forecast Data by Grid Cell (Dataset 3) with 5,940 cases Output Data from the 911 Drug Calls Forecast Program (Dataset 4) with 5,112 cases Output Data from the Part One Property Crimes Forecast Program (Dataset 5) with 5,112 cases Output Data from the Part One Violent Crimes Forecast Program (Dataset 6) with 5,112 cases Input Data for the Regression Forecast Program for 911 Drug Calls (Dataset 7) with 10,011 cases Input Data for the Regression Forecast Program for Part One Property Crimes (Dataset 8) with 10,011 cases Input Data for the Regression Forecast Program for Part One Violent Crimes (Dataset 9) with 10,011 cases Output Data from Regression Forecast Program for 911 Drug Calls: Estimated Coefficients for Leading Indicator Models (Dataset 10) with 36 cases Output Data from Regression Forecast Program for Part One Property Crimes: Estimated Coefficients for Leading Indicator Models (Dataset 11) with 36 cases Output Data from Regression Forecast Program for Part One Violent Crimes: Estimated Coefficients for Leading Indicator Models (Dataset 12) with 36 cases Output Data from Regression Forecast Program for 911 Drug Calls: Forecast Errors (Dataset 13) with 4,936 cases Output Data from Regression Forecast Program for Part One Property Crimes: Forecast Errors (Dataset 14) with 4,936 cases Output Data from Regression Forecast Program for Part One Violent Crimes: Forecast Errors (Dataset 15) with 4,936 cases. The GIS Shapefiles (Dataset 16) are provided with the study in a single zip file: Included are polygon data for the 4,000 foot, square, uniform grid system used for much of the Pittsburgh crime data (grid400); polygon data for the 6 police precincts, alternatively called districts or zones, of Pittsburgh(policedist); and polygon data for the 3 major rivers in Pittsburgh the Allegheny, Monongahela, and Ohio (rivers).

Source: NASA LANCE - VNP14IMG_NRT active fire detection - World
Scale/Resolution: 375-meter
Update Frequency: Hourly using the aggregated live feed methodology
Area Covered: World

What can I do with this layer?
This layer represents the most frequently updated and most detailed global remotely sensed wildfire information. Detection attributes include time, location, and intensity. It can be used to track the location of fires from the recent past, a few hours up to seven days behind real time. This layer also shows the location of wildfire over the past 7 days as a time-enabled service so that the progress of fires over that timeframe can be reproduced as an animation.

The VIIRS thermal activity layer can be used to visualize and assess wildfires worldwide. However, it should be noted that this dataset contains many “false positives” (e.g., oil/natural gas wells or volcanoes) since the satellite will detect any large thermal signal.

Fire points in this service are generally available within 3 1/4 hours after detection by a VIIRS device. LANCE estimates availability at around 3 hours after detection, and esri livefeeds updates this feature layer every 15 minutes from LANCE.

Even though these data display as point features, each point in fact represents a pixel that is >= 375 m high and wide. A point feature means somewhere in this pixel at least one "hot" spot was detected which may be a fire.

VIIRS is a scanning radiometer device aboard the Suomi NPP and NOAA-20 satellites that collects imagery and radiometric measurements of the land, atmosphere, cryosphere, and oceans in several visible and infrared bands. The VIIRS Thermal Hotspots and Fire Activity layer is a livefeed from a subset of the overall VIIRS imagery, in particular from NASA's VNP14IMG_NRT active fire detection product. The downloads are automatically downloaded from LANCE, NASA's near real time data and imagery site, every 15 minutes.

The 375-m data complements the 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Hotspots and Fire Activity layer; they both show good agreement in hotspot detection but the improved spatial resolution of the 375 m data provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters.

Attribute information

• Latitude and Longitude: The center point location of the 375 m (approximately) pixel flagged as containing one or more fires/hotspots.
• Satellite: Whether the detection was picked up by the Suomi NPP satellite (N) or NOAA-20 satellite (1). For best results, use the virtual field WhichSatellite, redefined by an arcade expression, that gives the complete satellite name.
• Confidence: The detection confidence is a quality flag of the individual hotspot/active fire pixel. This value is based on a collection of intermediate algorithm quantities used in the detection process. It is intended to help users gauge the quality of individual hotspot/fire pixels. Confidence values are set to low, nominal and high. Low confidence daytime fire pixels are typically associated with areas of sun glint and lower relative temperature anomaly (<15K) in the mid-infrared channel I4. Nominal confidence pixels are those free of potential sun glint contamination during the day and marked by strong (>15K) temperature anomaly in either day or nighttime data. High confidence fire pixels are associated with day or nighttime saturated pixels.
• Please note: Low confidence nighttime pixels occur only over the geographic area extending from 11 deg E to 110 deg W and 7 deg N to 55 deg S. This area describes the region of influence of the South Atlantic Magnetic Anomaly which can cause spurious brightness temperatures in the mid-infrared channel I4 leading to potential false positive alarms. These have been removed from the NRT data distributed by FIRMS.
• FRP: Fire Radiative Power. Depicts the pixel-integrated fire radiative power in MW (MegaWatts). FRP provides information on the measured radiant heat output of detected fires. The amount of radiant heat energy liberated per unit time (the Fire Radiative Power) is thought to be related to the rate at which fuel is being consumed (Wooster et. al. (2005)).
• DayNight: D = Daytime fire, N = Nighttime fire
• Hours Old: Derived field that provides age of record in hours between Acquisition date/time and latest update date/time. 0 = less than 1 hour ago, 1 = less than 2 hours ago, 2 = less than 3 hours ago, and so on.

This dataset contains hotspot point data, derived from satellite-born instruments that detect light in the thermal wavelengths found on the Digital Earth Australia Hotspots application. Typically, satellite data are processed with a specific algorithm that highlights areas with an unusually high temperature. Hotspot sources include the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the National Aeronautics and Space Administration (NASA) Terra and Aqua satellites, the Advanced Very High Resolution Radiometer (AVHRR) night time imagery from the National Oceanic and Atmospheric Administration (NOAA) satellites, the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi- NPP satellite. Please note: As these data are stored on a Corporate system, we are only able to supply the web services (see download links).

email earth.observation@ga.gov.au.

Since 2005, Fisheries and Oceans Canada has been collecting monitoring data for aquatic invasive species (e.g. https://open.canada.ca/data/en/dataset/8d87f574-0661-40a0-822f-e9eabc35780d, https://open.canada.ca/data/en/dataset/503a957e-7d6b-11e9-aef3-f48c505b2a29, https://open.canada.ca/data/en/dataset/8661edcf-f525-4758-a051-cb3fc8c74423). This monitoring data, as well additional occurrence information from online databases and the scientific literature, have been paired with high resolution environmental data and oceanographic models in species distribution models that predict the present-day and future potential distributions of 12 moderate to high risk invasive species on Canada’s east and west coasts. Future distributions were predicted for 2075, under Representative Concentration Pathway 8.5 from the Intergovernmental Panel on Climate Change’s fifth Assessment Report. Present-day and future richness of these species (i.e., hotspots) has also been estimated by summing their occurrence probabilities. This data set includes the occurrence locations of each species, the present-day and future species distribution modeling results for each species, and the estimated species richness. This research has been published in the scientific literature(Lyons et al. 2020). Lyons DA, Lowen JB, Therriault TW, Brickman D, Guo L, Moore AM, Peña MA, Wang Z, DiBacco C. (In Press) Identifying Marine Invasion Hotspots Using Stacked Species Distribution Models. Biological Invasions Cite this data as: Lyons DA., Lowen JB, Therriault TW., Brickman D., Guo L., Moore AM., Peña MA., Wang Z., DiBacco C. Data of: Species distribution models and occurrence data for marine invasive species hotspot identification. Published: November 2020. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/1bbd5131-8b34-4245-b999-3b4c4259d74f

Comprehensive dataset containing 73 verified Hot Spot locations in United States with complete contact information, ratings, reviews, and location data.

Indonesia Number of Hotspot: MODIS Satelite: Java: Central Java data was reported at 10.000 Unit in 20 Aug 2019. This records an increase from the previous number of 1.000 Unit for 19 Aug 2019. Indonesia Number of Hotspot: MODIS Satelite: Java: Central Java data is updated daily, averaging 2.000 Unit from Feb 2015 (Median) to 20 Aug 2019, with 404 observations. The data reached an all-time high of 19.000 Unit in 07 Aug 2019 and a record low of 1.000 Unit in 19 Aug 2019. Indonesia Number of Hotspot: MODIS Satelite: Java: Central Java data remains active status in CEIC and is reported by Meteorology, Climatology, and Geophysics Agency. The data is categorized under Indonesia Premium Database’s Agriculture Sector – Table ID.RIH001: Number of Hotspot: by Province.

Hotspot Detection

## Overview

Hotspot Detection is a dataset for object detection tasks - it contains Hotspots annotations for 1,000 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).

Aim: Species occurrence records are essential to understanding Earth’s biodiversity and addressing global environmental issues, but do not always reflect actual locations of occurrence. Certain geographic coordinates are assigned repeatedly to thousands of observation/collection records. This may result from imperfect data management and georeferencing practices, and can greatly bias the inferred distribution of biodiversity and associated environmental conditions. Nonetheless, these ‘biodiverse’ coordinates are often overlooked in taxon-centric studies, as they are identifiable only in aggregate across taxa and datasets, and it is difficult to determine their true circumstance without in-depth, focused investigation. Here we assess highly recurring coordinates in biodiversity data to determine artificial hotspots of occurrences. Location: Global Taxon: Land plants, birds, mammals, insects Methods: We identified highly recurring coordinates across plant, bird, insect, and mammal records in the Global Biodiversity Information Facility, the largest aggregator of biodiversity data. We determined which are likely artificial hotspots by examining metadata from over 40 million records; assessing spatial distributions of associated datasets; contacting data managers; and reviewing literature. These results were compiled into the Artificial Hotspot Occurrence Inventory (AHOI). Results: Artificial biodiversity hotspots generally comprised geopolitical and grid centroids. The associated uncertainty ranged from several square kilometers to millions. Such artificial biodiversity hotspots were most prevalent in plant records. For instance, over 100,000 plant occurrence records were assigned the centroid coordinates of Brazil, and points that have at least 1,000 associated occurrences comprised over 9 million records. In contrast, highly recurring coordinates in animal data more often reflected actual sites of observation. Main Conclusions: AHOI can be used to i) improve accuracy of biodiversity assessments; ii) estimate uncertainty associated with records from artificial hotspots and make informed decisions on whether to include them in scientific studies; and iii) identify problems in biodiversity informatics workflows and priorities for improvement. Methods Primary biodiversity records were queried from the Global Biodiversity Information Facility on January 30 and May 10, 2021 for plants (Plantae; https://doi.org/10.15468/dl.th5tn8; https://doi.org/10.15468/dl.76jc24), June 3, 2022 for birds (Aves; https://doi.org/10.15468/dl.jh3u2u), and August 23, 2021 for insects (Insecta; https://doi.org/10.15468/dl.4q2972), and mammals (Mammalia; https://doi.org/10.15468/dl.cujmgz). We then assessed the frequency of the geographic coordinates and identified the most frequently recurring sets of coordinates across each taxonomic group. Coordinates were assessed as provided in the “decimalLatitude” and “decimalLongitude” columns of the downloaded data without any rounding to be conservative. Rounding coordinates before assessing their frequency would increase the overall number of records associated with each set of coordinates and increase the risk of associating true points with georeferenced ones. Only exact matches were counted to calculate the frequency of each unique set of coordinates. We determined which of the highly-recurrent coordinates are likely artificial by examining metadata and images from datasets comprising over 40 million records to date; assessing spatial distributions of associated datasets; contacting data managers; and reviewing literature (Fig. 2). We used QGIS software to validate grid centroid coordinates by plotting the grid systems over the reported occurrence coordinates to confirm the grid centroid, grid size and the coordinate reference system. Countries represented in our dataset that utilized such grids were identified through occurrence record metadata, visual inspection of associated datasets, literature review, and data managers, and included France, the United Kingdom, Germany, the Netherlands, Belgium, Switzerland, and Spain. For each group, we started by evaluating the most recurrent set of coordinates and proceeded in order of decreasing frequency. We initially examined the top 100 recurring coordinates for plants and the top 50 recurring coordinates for each animal group. These coordinates were manually curated into the following categories when possible: grid centroid, geopolitical centroid, georeferenced location, and true observation or collection site. Some coordinates could be associated with multiple categories. It is possible that the determinations we made for highly-recurrent coordinates could also be extended to additional, less recurrent, coordinates that were assigned to other records in the datasets they belonged to (but not included in our initial survey). These data were compiled into AHOI, an inventory of highly-recurrent GBIF coordinates, with their descriptions and determinations. To validate our approach and assess whether artificial biodiversity hotspots are the result of systemic practices or errors, we additionally evaluated data from the Field Museum of Natural History, as some of the top 100 most recurring coordinates were associated with the institution. We downloaded all plant records from this dataset and evaluated all coordinates that were assigned to at least 1000 records. We found that the coordinates from this dataset represented artificial aggregates of specimens around geopolitical centroids. These verifications were also included in AHOI. Further, we listed the rationale for each individual coordinate determination and provides examples of relevant information from occurrence record metadata in the “example_description” and “reasoning” fields respectively.

This dataset contains surveys of foraging patterns of marine predators. The predators studied were black-legged kittiwake (Rissa tridactyla), thick-billed murre (Uria lomvia), the humpback whale (Megaptera novaeangliae), and the fin whale (Balaenoptera physalus). Surveys were taken once a year in 2004 and from 2006-2010 and compared the foraging patterns of these predators to pollock and euphausiid concentrations.

Thermal activity detected by MODIS satellites for the last 48 hours.