The Digital Geologic Sample Localities Map for the Clarno Unit, John Day Fossil Beds National Monument, Oregon (Plate III) is composed of GIS data layers complete with ArcMap 9.2 layer (.LYR) files, two ancillary GIS tables, a Windows Help File with ancillary map text, figures and tables, a FGDC metadata record and a 9.2 ArcMap (.MXD) Document that displays the digital map in 9.2 ArcGIS. The data were completed as a component of the Geologic Resource Evaluation (GRE) program, a National Park Service (NPS) Inventory and Monitoring (I&M) funded program that is administered by the NPS Geologic Resources Division (GRD). All GIS and ancillary tables were produced as per the NPS GRE Geology-GIS Geodatabase Data Model v. 1.4. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data is available as a 9.2 personal geodatabase (clu3_geology.mdb), and as shapefile (.SHP) and DBASEIV (.DBF) table files. The GIS data projection is NAD83, UTM Zone 11N. That data is within the area of interest of John Day Fossil Beds National Monument.

Thermal activity detected by MODIS satellites for the last 7 days.

The Digital Geologic Sample Localities Map for the Painted Hills Unit, John Day Fossil Beds National Monument, Oregon (Plate III) is composed of GIS data layers complete with ArcMap 9.2 layer (.LYR) files, two ancillary GIS tables, a Windows Help File with ancillary map text, figures and tables, a FGDC metadata record and a 9.2 ArcMap (.MXD) Document that displays the digital map in 9.2 ArcGIS. The data were completed as a component of the Geologic Resource Evaluation (GRE) program, a National Park Service (NPS) Inventory and Monitoring (I&M) funded program that is administered by the NPS Geologic Resources Division (GRD). All GIS and ancillary tables were produced as per the NPS GRE Geology-GIS Geodatabase Data Model v. 1.4. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data is available as a 9.2 personal geodatabase (phu3_geology.mdb), and as shapefile (.SHP) and DBASEIV (.DBF) table files. The GIS data projection is NAD83, UTM Zone 11N. That data is within the area of interest of John Day Fossil Beds National Monument.

This dataset is stored in MATLAB (.mat) format and contains electron density variations around the ion hole center during the 2024 Mother's Day storm.Dataset VariablesElectron Density Variation Over TimeGIS_Ne_variation.Ne: Electron density around the hole center at different times.GIS_Ne_variation.Localtime: Corresponding local time in the format [year month day hour minutes seconds].GIS_Ne_variation.Altitude: Altitude levels corresponding to Ne.Electron Density Profiles at Specific LocationsGIS_Ne_profile.Hole: Electron density at the hole center at 21:00 LT on May 10, 2024.GIS_Ne_profile.Hole_reference: Reference electron density at the hole center at 21:00 LT on May 9, 2024.GIS_Ne_profile.West_Hole: Electron density 120° west of the hole center.GIS_Ne_profile.East_Hole: Electron density 120° east of the hole center.GIS_Ne_profile.North_Hole: Electron density at 60°N along the hole longitude.GIS_Ne_profile.South_Hole: Electron density at -60°N along the hole longitude.GIS_Ne_profile.altitude: Altitude levels corresponding to the profile data.

Instead of relying on a single source or model, our Climate Data product blends decades of historical records with forward-looking projections to deliver a unified view of past, present, and future conditions. Each dataset is refined, harmonized, and tailored to match your specific region, variables, and time horizons—ensuring accuracy, continuity, and context.

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Pricing: Custom quotes available based on coverage, data volume, and deliverables. Typical engagements start at $1,000 (one-time) or $500/month for ongoing access or analytics.

The Vegetation Health Index (VHI) illustrates the severity of drought based on the vegetation health and the influence of temperature on plant conditions. The VHI is a composite index and the elementary indicator used to compute the seasonal drought indicators in ASIS: Agricultural Stress Index (ASI), Drought Intensity and Weighted Mean Vegetation Health Index (Mean VHI).If the index is below 40, different levels of vegetation stress, losses of crop and pasture production might be expected; if the index is above 60 (favorable condition) plentiful production might be expected. VHI is very useful for an advanced prediction of crop losses.

Phenomenon Mapped: Vegetation Health Index

Units: None

Time Interval: 10-day

Time Extent: 1984-Present

Cell Size: 1 km

Pixel Type: 32-bit Signed Integer

Data Projection: WGS 1984

Mosaic Projection: WGS 1984 Web Mercator

Source: Food and Agriculture Organization of the United Nations

Update Cycle: 10-days + 5 days lagVHI combines both the Vegetation Condition Index (VCI) and the Temperature Condition Index (TCI). The TCI is calculated using a similar equation to the VCI, but relates the current temperature to the long-term maximum and minimum, as it is assumed that higher temperatures tend to cause a deterioration in vegetation conditions. A decrease in the VHI would, for example, indicate relatively poor vegetation conditions and warmer temperatures, signifying stressed vegetation conditions, and over a longer period would be indicative of drought.In ASIS, VHI is computed in two temporal granularities: dekadal and monthly. The dekadal/monthly VHI raster layer published is further updated in the following 5 dekads (improve data precision, remove cloud pixel etc.).Flags of raster file: 251=missing, 252=cloud, 253=snow, 254=sea, 255=backgroundExplore this and related data in this web applicationMore information please visit FAO GIEWS Earth Observation website.

Feature layer showing all the fires tracked by Cal OES GIS for the year to date.

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

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

Feature layer showing all the fire perimeters tracked by Cal OES GIS for year to date.

Developed by SOLARGIS and provided by the Global Solar Atlas (GSA), this data resource contains direct normal irradiation (DNI) in kWh/m² covering the globe. Data is provided in a geographic spatial reference (EPSG:4326). The resolution (pixel size) of solar resource data (GHI, DIF, GTI, DNI) is 9 arcsec (nominally 250 m), PVOUT and TEMP 30 arcsec (nominally 1 km) and OPTA 2 arcmin (nominally 4 km). The data is hyperlinked under 'resources' with the following characteristics: DNI LTAy_AvgDailyTotals (GeoTIFF) Data format: GEOTIFF File size : 343.99 MB There are two temporal representation of solar resource and PVOUT data available: • Longterm yearly/monthly average of daily totals (LTAym_AvgDailyTotals) • Longterm average of yearly/monthly totals (LTAym_YearlyMonthlyTotals) Both type of data are equivalent, you can select the summarization of your preference. The relation between datasets is described by simple equations: • LTAy_YearlyTotals = LTAy_DailyTotals * 365.25 • LTAy_MonthlyTotals = LTAy_DailyTotals * Number_of_Days_In_The_Month For individual country or regional data downloads please see: https://globalsolaratlas.info/download (use the drop-down menu to select country or region of interest) For data provided in AAIGrid please see: https://globalsolaratlas.info/download/world. For more information and terms of use, please, read metadata, provided in PDF and XML format for each data layer in a download file. For other data formats, resolution or time aggregation, please, visit Solargis website. Data can be used for visualization, further processing, and geo-analysis in all mainstream GIS software with raster data processing capabilities (such as open source QGIS, commercial ESRI ArcGIS products and others).

Developed by SOLARGIS and provided by the Global Solar Atlas (GSA), this data resource contains global horizontal irradiation (GHI) in kWh/m² covering the globe. Data is provided in a geographic spatial reference (EPSG:4326). The resolution (pixel size) of solar resource data (GHI, DIF, GTI, DNI) is 9 arcsec (nominally 250 m), PVOUT and TEMP 30 arcsec (nominally 1 km) and OPTA 2 arcmin (nominally 4 km). The data is hyperlinked under 'resources' with the following characteristics: GHI LTAy_AvgDailyTotals (GeoTIFF) Data format: GEOTIFF File size : 268.11 MB There are two temporal representation of solar resource and PVOUT data available: • Longterm yearly/monthly average of daily totals (LTAym_AvgDailyTotals) • Longterm average of yearly/monthly totals (LTAym_YearlyMonthlyTotals) Both type of data are equivalent, you can select the summarization of your preference. The relation between datasets is described by simple equations: • LTAy_YearlyTotals = LTAy_DailyTotals * 365.25 • LTAy_MonthlyTotals = LTAy_DailyTotals * Number_of_Days_In_The_Month For individual country or regional data downloads please see: https://globalsolaratlas.info/download (use the drop-down menu to select country or region of interest) For data provided in AAIGrid please see: https://globalsolaratlas.info/download/world. For more information and terms of use, please, read metadata, provided in PDF and XML format for each data layer in a download file. For other data formats, resolution or time aggregation, please, visit Solargis website. Data can be used for visualization, further processing, and geo-analysis in all mainstream GIS software with raster data processing capabilities (such as open source QGIS, commercial ESRI ArcGIS products and others).

This dataset is a collection of records that communicate the locations of child care, family child care and certified child care centers in Minnesota. It was created by downloading licensing information from the Minnesota Department of Human Services (DHS), geocoding the spreadsheet records and converting them to a spatial format.

The following license types are represented:
Child Care Centers: Programs licensed to provide care to children for less than 24 hours a day in a commercial setting.
Family Child Care: Programs licensed to provide care to children for less than 24 hours a day, typically in the provider’s home or a local space, including a church.
Certified Child Care Center: Child care providers that are not required to be licensed, but are certified by DHS to be eligible to accept Child Care Assistance Program payments.

The approximately 10,000 records are combined into one layer, which can be queried to separate by license type. This version focuses on a smaller set of core attributes than can be found on DHS's Licensing Information Lookup Page; see that page for all the attributes: https://licensinglookup.dhs.state.mn.us/

Records represent licenses active as of the date shown in the Time Period of Content field. The official current status of licenses can be checked at the DHS licensing information lookup page.

A layer item maintained by a Live Feed routine that regularly computes the approximate location of the Sun, perpendicular to the planet, using the current UTC date and time. Watch as the Sun, Day/Night Terminator, and Horizon pass over your map, highlighting what areas are in dusk, darkness, and dawn. Terminator Polygons include a GeoEnriched attribute containing the approximate Total Population experiencing twilight or darkness. Originally inspired by Jim Blaney's JSAPI item, the data is maintained by a modified Python routine adapted from John Gravois' GitHub JSAPI "midnight-commander" project. Small corrections and enhancements provide the Sub-Solar Point (Sun's location on the planet), the Horizon line (mid-line of the Sun that meets the horizon), and the Twilight features showing dusk and dawn as the Sun reflects light in the atmosphere. Twilight, also known as Dawn and Dusk, is the visible reflective light in the atmosphere as the Sun dips below (Dusk) or approaches (Dawn) the Horizon. Three stages of Twilight are classified at 6 degree increments, producing various shades of darkness and vibrant colors.Civil Twilight is the first stage as the Sun moves below the Horizon (Dusk) or the last as the Sun approaches the Horizon (Dawn). Enough natural light exists for people to see without needing artificial light. Celestial objects are not yet visible. Nautical Twilight is the second stage. Artificial light is now helpful and Celestial objects can be seen with ease. Ships find it difficult to navigate by the Horizon. Astronomical Twilight is the last stage before darkness sets in. Only distant clouds are a glow on the Horizon with bright stars in full view. What can I do with this layer?A continually updated visual representation of this daily cycle provides an additional source of information for planners and decision makers that operate in an environment that is impacted by the day/night cycle. This may include logistics and fleet management, maritime, law enforcement, as well as other first responders who are engaged in search and rescue operations where daylight is critical to mission execution.Example item using Terminator to blend the Earth at Night tile image. Source:Calculation for the Position of the Sun (declination angle of the Sun)Alternate calculation for Equation of time (used to compute Sun's position)Reference details for Twilight Update Frequency:  Every 10 minutes (at 5, 15, 25, ...) using the Aggregated Live Feed Methodology Area Covered: The world Coordinate System: GCS_WGS_1984, wkid: 4326, with Latitude limited to ±87.5 degrees for maximum compatibility. Layers:Celestial Bodies: Point layer containing current location of the Sun as it is passes directly overhead, perpendicular to the planet.Horizon: Polyline layer containing the mid-line of the Sun as it sets in the horizon.Terminator: Polygon layer containing the 6 degree transitions from sunlight to civil, nautical, and astronomical Twilight (dawn and dusk) plus areas experiencing the dark of night. Also includes output from ArcGIS Online GeoEnrichment attribute field "KeyGlobalFacts.TOTPOP" containing the approximate Total Population covered by each Polygon. Sample: See animation of data mapped at midnight each day for one year. RevisionsJuly 30, 2025: Reduced Cache Control to 2 minutes down from 5 minutes. More frequent delivery for Map Layer refresh! This layer is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

Feature layer showing all the fires tracked by Cal OES GIS for the year to date.Fire information is taken from the National Fire and Aviation Management (FAMWEB) in the form of ICS-209 reports. During normal operations these 209 reports are downloaded once a day at 0630, or soon after. During a SOC activation these 209 reports will be downloaded twice a day at 0630 and 1800. Cal OES GIS considers the FAMWEB ICS-209s as the authoritative fire information source.Fire perimeter data is downloaded from the National Interagency Fire Center (NIFC). The data for map is updated daily, M-F, by 0800, the map text by 0900, when there are active fires being tracked by Cal OES.If the SOC is activated the fire map will be updated daily, Sun-Sat, by 0800, the map text by 0900, until the SOC stands down.For a fire to be tracked by Cal OES GIS it needs to be:>100 acres in sizeHave an ICS-209 form.CalOES GIS indicates a fire as contained when it has reached >=90% containment.

Adiabat’s Heat Wave Model blends operational-grade meteorological data with historical context to quantify human-relevant heat stress over multi-day periods. Integrated across extended heat exposure windows, it delivers consistent, high-resolution insights for climate risk assessment, resilience planning, and operational decision-making.

Temporal Consistency - Daily update frequency - near real-time insights - Continuous 15+ year record (2010-present) - No data gaps or inconsistencies that plague other datasets

Flexible Data Delivery Options - Geospatial Layers - Ready for GIS integration - Parquet & CSV Formats - Optimized for analytics workflows - Custom Precipitation Maps - Tailored visualizations - Advanced Analytics Solutions - Decision-ready insights

Perfect for... - Agriculture: Livestock heat stress monitoring, worker safety planning - Insurance: Heat-related risk modeling, claims validation, catastrophe planning - Energy: Grid demand forecasting, worker safety planning, infrastructure resilience - Public Health: Heat advisories, vulnerability mapping, community resilience programs - Government: Emergency management, urban heat mitigation, infrastructure planning - Financial Services: Climate risk assessment, ESG reporting, portfolio exposure analysis

Why Choose Adiabat's Heat Wave Model? - Operational-Grade Reliability - Built on globally trusted meteorological analyses - Consistent Quality - No temporal gaps or methodology changes - Immediate Implementation - Multiple format options for instant integration - Scalable Solutions - From raw data to custom analytics - Expert Support - Technical assistance and custom mapping services

Pricing: Custom quotes available based on coverage, data volume, and deliverables. Typical engagements start at $1,000 (one-time) or $500/month for ongoing access or analytics.

This public GIS dataset comes from the Alaska GAP project, and it is part of the final project report (Gotthard, Pyare, Huettmann et al. 2013). Here we present a copy of the original data set as a value-added product for basic use and training purposes. It consists of 53 environmental layers for all of Alaska in an ArcGIS 10 format and usually with a pixel size of 60m. These layers were compiled from various sources, and authorships should be fully honoured as stated in the details of this metadata. Output maps were clipped using a state of Alaska coastline in the Alaska Albers NAD83 projection; very small islands are excluded.The data layers were initially compiled for ecological niche models of Alaska's terrestrial biodiversity using Maxent and other Machine Learning algorithms. However, they can also be used for many other purposes, e.g. strategic conservation planning and individual information and assessments. The datasets are a snapshot in space and time (2012) but likely remain valid for years to come. It is appreciated that these data layers are 'living products', and it is hoped that this public data publication here will progress and trigger many updates and data quality improvements for Alaska and its public high-quality data over time. The following variables are included in this dataset: Boundaries Coastline, Climate Precipitation January til December Average monthly precipitation (mm), Climate Precipitation Average annual precipitation (mm), Climate Temperature January til December Average monthly temperature (deg C), Climate Temperature annual temperature (dec C), Climate First day of thaw (Julian date), Climate First day of freeze (Julian date), Climate Length of growing season Number of days, Disturbance Insect history (Year), Distance to Disturbance Insect location (m), Disturbance Fire history Year of fire (1942 til 2007), distance to Disturbance Fire location (m), Soils Grid (category), Surfacial Geology Grid values, Glacial Distance (m), Distance(m) to lotic water, Distance (m) to permafrost boundary, Distance(m) to lentic water, Saltwater Presence, Distance (m) to Sea Ice Extent 2003-2007 December, Distance (m) to Sea Ice Extent 2003-2007 July, Distance to Development Infrastructure, Landcover Vegetation (Landfire), Landcover nlcd60, Elevation (m), Slope (%), Aspect (Degrees from due south), Terrain Ruggedness index, Extent nullgrid 9999, Coast raster.

Northern Italy Land Surface Temperature 1km daily Celsius gap-filled dataset, LST daily average, 2014 - 2018.

The dataset is stored as a GRASS GIS project/mapset, in ZIP compressed format.

• Spatial resolution: 1 km
• Temporal resolution: 1 day
• Temporal extent: 2014-2018
• Units: Celsius
• Aggregation method: average
• Format: stored as a GRASS GIS 8+ project
• Software used: GRASS GIS 8.4.0

Reference:

Metz, M.; Andreo, V.; Neteler, M. A New Fully Gap-Free Time Series of Land Surface Temperature from MODIS LST Data. Remote Sens. 2017, 9, 1333. https://doi.org/10.3390/rs9121333

Original dataset license:
All data products distributed by NASA's Land Processes Distributed Active Archive Center (LP DAAC) are available at no charge. The LP DAAC requests that any author using NASA data products in their work provide credit for the data, and any assistance provided by the LP DAAC, in the data section of the paper, the acknowledgement section, and/or as a reference. The recommended citation for each data product is available on its Digital Object Identifier (DOI) Landing page, which can be accessed through the Search Data Catalog interface. For more information see: https://lpdaac.usgs.gov/products/mod09a1v006/

Data provided by:

mundialis GmbH & Co. KG
Koelnstrasse 99
53111 Bonn, Germany
https://www.mundialis.de

Annual average daily traffic is the total volume for the year divided by 365 days. The traffic count year is from October 1st through September 30th. Very few locations in California are actually counted continuously. Traffic Counting is generally performed by electronic counting instruments moved from location throughout the State in a program of continuous traffic count sampling. The resulting counts are adjusted to an estimate of annual average daily traffic by compensating for seasonal influence, weekly variation and other variables which may be present. Annual ADT is necessary for presenting a statewide picture of traffic flow, evaluating traffic trends, computing accident rates. planning and designing highways and other purposes.Traffic Census Program Page

Adiabat’s Cold Wave Model blends operational-grade meteorological data with historical context to quantify human-relevant cold stress over multi-day periods. Integrated across extended cold exposure windows, it delivers consistent, high-resolution insights for climate risk assessment, resilience planning, and operational decision-making.

Temporal Consistency - Daily update frequency - near real-time insights - Continuous 15+ year record (2010-present) - No data gaps or inconsistencies that plague other datasets

Flexible Data Delivery Options - Geospatial Layers - Ready for GIS integration - Parquet & CSV Formats - Optimized for analytics workflows - Custom Precipitation Maps - Tailored visualizations - Advanced Analytics Solutions - Decision-ready insights

Perfect for... - Agriculture: Livestock cold stress monitoring, worker safety planning - Insurance: Cold-related risk modeling, claims validation, catastrophe planning - Energy: Grid demand forecasting, worker safety planning, infrastructure resilience - Public Health: Cold advisories, vulnerability mapping, community resilience programs - Government: Emergency management, urban cold mitigation, infrastructure planning - Financial Services: Climate risk assessment, ESG reporting, portfolio exposure analysis

Why Choose Adiabat's Cold Wave Model? - Operational-Grade Reliability - Built on globally trusted meteorological analyses - Consistent Quality - No temporal gaps or methodology changes - Immediate Implementation - Multiple format options for instant integration - Scalable Solutions - From raw data to custom analytics - Expert Support - Technical assistance and custom mapping services

Pricing: Custom quotes available based on coverage, data volume, and deliverables. Typical engagements start at $1,000 (one-time) or $500/month for ongoing access or analytics.