This data release reformats data fields from the STATSGO soil characteristics dataset (Schwarz and Alexander, 1995) as cloud-optimized GeoTIFFs (COGs). The COG format allows standard software tools to efficiently access the datasets over an internet connection. Please refer to the documentation of the source archive (Schwarz and Alexander, 1995) for additional details on the underlying dataset. This data release includes COGs for the KF-factor (KFFACT) and soil thickness (THICK) data fields. KF-factors are defined as the saturated hydraulic conductivity of the fine soil (< 2mm) fraction in units of inches per hour. The soil thickness dataset has units of inches. Each COG raster spans the continental US at a nominal 30 meter resolution. The spatial reference is EPSG:5069. Each COG uses a float32 precision, and the NoData value (NaN) indicates raster pixels not covered by the original STATSGO dataset. The COGs also include non-physical values of -0.1, which were used by the source STATSGO archive to mark large water bodies. The COG format uses compression internally to reduce file size. As such, reading large portions of a COG into memory can require much more RAM than the nominal file size. The COGs in this dataset will require ~60GB of memory to read in full. This dataset can be reproduced by running the included rasterize_statsgo.py Python script. Please refer to the script for documentation and usage instructions. Disclaimer: Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. References: Schwarz, G.E. and Alexander, R.B., 1995, Soils data for the Conterminous United States Derived from the NRCS State Soil Geographic (STATSGO) Data Base. [Original title: State Soil Geographic (STATSGO) Data Base for the Conterminous United States.]: U.S. Geological Survey data release, https://doi.org/10.5066/P94JAULO.

This map layer portrays major dams of the United States, including Puerto Rico and the U.S. Virgin Islands. The map layer was created by extracting dams 50 feet or more in height, or with a normal storage capacity of 5,000 acre-feet or more, or with a maximum storage capacity of 25,000 acre-feet or more, from the 79,777 dams in the U.S. Army Corps of Engineers National Inventory of Dams. This is a replacement for the April 1994 map layer.

Important Note: This item is in mature support as of April 2023 and will be retired in December 2025. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. The High Resolution National Hydrography Dataset (NHD) provides a hydrologic framework of the United States’ surface water drainage network. This layer displays the key features of the High Resolution NHD including streams and rivers, waterbodies and other area features and points features such as sinks, waterfalls, gauging stations, wells, and springs.Dataset SummaryThe High Resolution NHD was created at a scale of 1:24,000 (one inch on the map equals 2,000 feet). It covers the United States including Alaska, Hawaii, and Puerto Rico. The NHD is created and maintained by the US Geological Survey. Link to source metadata NHD flowlines, areas, waterbodies and points are displayed with the following: If the label in the table of contents is gray, zoom in to see the layer. The label will be black when the features are at a visible scale on the map. Springs and Wells draw at scales of 1:20,000 and larger while the other symbols draw at scales of 1:150,000 larger.The National Hydrography Dataset is constantly being updated by stewards of the NHD. This snapshot was taken in March of 2015.What can you do with this layer?The vector features for this layer can be used for visualization and analysis in ArcGIS. The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.

No description found

The main stock market index of United States, the US500, rose to 6211 points on July 1, 2025, gaining 0.10% from the previous session. Over the past month, the index has climbed 4.64% and is up 12.75% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on July of 2025.

Residential Street Width and Value

Insight into US County Land Values and Subdivision Regulations

By [source]

About this dataset

This dataset serves as a comprehensive view into land value distributions and development regulations of residential street widths in some of the largest counties in the United States. Drawing from OpenStreetMap and county tax assessor parcel data, it provides an intricate look at the correlation between property values and streetscape configurations across 20 major cities. This information is crucial for policy makers, urban planners, developers, and homeowners alike for understanding how street design can positively affect land values - manifesting in well-planned subdivisions and healthier neighborhoods. With detailed insight into these elements available for inspection through this dataset, informed decisions can be made that can dramatically improve communities throughout America

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How to use the dataset

• Subdivision Name: This column shows the name of the subdivision that houses each street included in the dataset. Use this information to identify which streets are located in a specific suburb or area.
• Street Name: This is the official street name as it appears on OpenStreetMap records, providing information about all residential streets included in this dataset.
• Parcel Side length: This is length (in feet) of one side of each parcel associated with a specific street from OpenStreetMap records. Note that multi-parcel lots will have multiple entries for each parcel side length and should be analyzed separately for accuracy when using this representation of land availability for each residence along a video row order within a subdivision..
• Land Value Rating: A rating out of 5 stars representing the total Monetary Value, as determined by tax assessments, for all parcels within a subdivision, including those not included on OpenStreetMap records due to non-residential uses or development restrictions (ROW). Values may vary based on urban/suburban location and year assessed (if later than 2019), so consider applying these ratings with research into other datasets such as Zillow home values over time to see if there are changes in Urban Renewal projects or appreciations over time across different locations studied here.. 5 Number Of Parcels Per Street: The number of individual parcels associated with each record selected from OpenStreetMap records within this dataset, giving an indication of how much space is available per house/lot within that particular area..
  6 Right Of Way Widths: Representing width (in feet) along certain roads given to public access through city ordinances, such Right-Of-Ways extending beyond any single existing lot line up to sometimes hundreds and thousands feet into urban or residential areas serving communities' need like ambulance access etc…

Research Ideas

• Utilizing this dataset, municipalities can better understand development trends across their jurisdictions and inform smart growth initiatives.
• Municipalities may benefit from analyzing the relationship between street width and land value to proactively adjust regulations to encourage further investment in their communities.
• Local homeowners could use this data set to compare the amount of street right-of-way being dedicated in different residential areas to obtain insight into potential value of real estate investments in a particular area

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: data_dictionary.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

Global Surface Summary of the Day is derived from The Integrated Surface Hourly (ISH) dataset. The ISH dataset includes global data obtained from the USAF Climatology Center, located in the Federal Climate Complex with NCDC. The latest daily summary data are normally available 1-2 days after the date-time of the observations used in the daily summaries. The online data files begin with 1929 and are at the time of this writing at the Version 8 software level. Over 9000 stations' data are typically available. The daily elements included in the dataset (as available from each station) are: Mean temperature (.1 Fahrenheit) Mean dew point (.1 Fahrenheit) Mean sea level pressure (.1 mb) Mean station pressure (.1 mb) Mean visibility (.1 miles) Mean wind speed (.1 knots) Maximum sustained wind speed (.1 knots) Maximum wind gust (.1 knots) Maximum temperature (.1 Fahrenheit) Minimum temperature (.1 Fahrenheit) Precipitation amount (.01 inches) Snow depth (.1 inches) Indicator for occurrence of: Fog, Rain or Drizzle, Snow or Ice Pellets, Hail, Thunder, Tornado/Funnel Cloud Global summary of day data for 18 surface meteorological elements are derived from the synoptic/hourly observations contained in USAF DATSAV3 Surface data and Federal Climate Complex Integrated Surface Hourly (ISH). Historical data are generally available for 1929 to the present, with data from 1973 to the present being the most complete. For some periods, one or more countries' data may not be available due to data restrictions or communications problems. In deriving the summary of day data, a minimum of 4 observations for the day must be present (allows for stations which report 4 synoptic observations/day). Since the data are converted to constant units (e.g, knots), slight rounding error from the originally reported values may occur (e.g, 9.9 instead of 10.0). The mean daily values described below are based on the hours of operation for the station. For some stations/countries, the visibility will sometimes 'cluster' around a value (such as 10 miles) due to the practice of not reporting visibilities greater than certain distances. The daily extremes and totals--maximum wind gust, precipitation amount, and snow depth--will only appear if the station reports the data sufficiently to provide a valid value. Therefore, these three elements will appear less frequently than other values. Also, these elements are derived from the stations' reports during the day, and may comprise a 24-hour period which includes a portion of the previous day. The data are reported and summarized based on Greenwich Mean Time (GMT, 0000Z - 2359Z) since the original synoptic/hourly data are reported and based on GMT.

This dataset contains the contours, in feet, of the potentiometric-surface, spring 2020, Mississippi River Valley alluvial aquifer (MRVA). The contours are referenced to the North American Vertical Datum of 1988 (NAVD 88). The contours were derived from most of the available groundwater-altitude (GWA) data from wells and surface-water-altitude (SWA) data from streamgages, measured in for spring 2020. The potentiometric contours ranged from 10 to 340 feet (3 to 104 meters) above NAVD 88. The regional direction of groundwater flow was generally towards the south-southwest, except in areas of groundwater-altitude depressions, where groundwater flows into the depressions, and near rivers, where groundwater flow generally parallels the flow in the rivers.

This geodatabase includes spatial datasets that represent the Mississippian aquifer in the States of Alabama, Illinois, Indiana, Iowa, Kentucky, Maryland, Missouri, Ohio, Pennsylvania, Tennessee, Virginia and West Virginia. The aquifer is divided into three subareas, based on the data availability. In subarea 1 (SA1), which is the aquifer extent in Iowa, data exist of the aquifer top altitude and aquifer thickness. In subarea 2 (SA2), which is the aquifer extent in Missouri, data exist of the aquifer top and bottom aquifer surface altitudes. In subarea 3 (SA3), which is the aquifer area of the remaining States, no altitude or thickness data exist. Included in this geodatabase are: (1) a feature dataset "ds40MSSPPI_altitude_and_thickness_contours that includes aquifer altitude and thickness contours used to generate the surface rasters for SA1 and SA2, (2) a feature dataset "ds40MSSPPI_extents" that includes a polygon dataset that represents the subarea extents, a polygon dataset that represents the combined overall aquifer extent, and a polygon dataset of the Ft. Dodge Fault and Manson Anomaly, (3) raster datasets that represent the altitude of the top and the bottom of the aquifer in SA1 and SA2, and (4) georeferenced images of the figures that were digitized to create the aquifer top- and bottom-altitude contours or aquifer thickness contours for SA1 and SA2. The images and digitized contours are supplied for reference. The extent of the Mississippian aquifer for all subareas was produced from the digital version of the HA-730 Mississippian aquifer extent, (USGS HA-730). For the two Subareas with vertical-surface information, SA1 and SA2, data were retrieved from the sources as described below. 1. The aquifer-altitude contours for the top and the aquifer-thickness contours for the top-to-bottom thickness of SA1 were received in digital format from the Iowa Geologic Survey. The URL for the top was ftp://ftp.igsb.uiowa.edu/GIS_Library/IA_State/Hydrologic/Ground_Waters/ Mississippian_aquifer/mississippian_topography.zip. The URL for the thickness was ftp://ftp.igsb.uiowa.edu/GIS_Library/IA_State/Hydrologic/Ground_Waters/ Mississippian_aquifer/mississippian_isopach.zip Reference for the top map is Altitude and Configuration, in feet above mean sea level, of the Mississipian Aquifer modified from a scanned image of Map 1, Sheet 1, Miscellaneous Map Series 3, Mississippian Aquifer of Iowa by P.J. Horick and W.L. Steinhilber, Iowa Geological Survey, 1973; IGS MMS-3, Map 1, Sheet 1 Reference for the thickness map is Distribution and isopach thickness, in feet, of the Mississipian Aquifer, modified from a scanned image of Map 1, Sheet 2, Miscellaneous Map Series 3, Mississippian Aquifer of Iowa by P.J. Horick and W.L. Steinhilber, Iowa Geological Survey, 1973; IGS MMS-3, Map 1, Sheet 2 The altitude contours for the top and bottom of SA2 were digitized from georeferenced figures of altitude contours in U.S. Geological Survey Professional Paper 1305 (USGS PP1305), figure 6 (for the top surface) and figure 9 (for the bottom surface). The altitude contours for SA1 and SA2 were interpolated into surface rasters within a GIS using tools that create hydrologically correct surfaces from contour data, derive the altitude from the thickness (depth from the land surface), and merge the subareas into a single surface. The primary tool was an enhanced version of "Topo to Raster" used in ArcGIS, ArcMap, Esri 2014. ArcGIS Desktop: Release 10.2 Redlands, CA: Environmental Systems Research Institute. The raster surfaces were corrected in areas where the altitude of the top of the aquifer exceeded the land surface, and where the bottom of an aquifer exceeded the altitude of the corrected top of the aquifer.

National burn probability (BP) and conditional fire intensity level (FIL) data were generated for the conterminous United States (US) using a geospatial Fire Simulation (FSim) system developed by the US Forest Service Missoula Fire Sciences Laboratory to estimate probabilistic components of wildfire risk (Finney et al. [2011]). The FSim system includes modules for weather generation, wildfire occurrence, fire growth, and fire suppression. FSim is designed to simulate the occurrence and growth of wildfires under tens of thousands of hypothetical contemporary fire seasons in order to estimate the probability of a given area (i.e., pixel) burning under current landscape conditions and fire management practices. The data presented here represent modeled BP and FIL for the conterminous US at a 270-meter grid spatial resolution. The six FILs correspond to flame-length classes as follows: FIL1 = < 2 feet (ft); FIL2 = 2 < 4 ft.; FIL3 = 4 < 6 ft.; FIL4 = 6 < 8 ft.; FIL5 = 8 < 12 ft.; FIL6 = 12+ ft. Because they indicate conditional probabilities (i.e., representing the likelihood of burning at a certain intensity level, given that a fire occurs), the FIL*_20160830 data must be used in conjunction with the BP_20160830 data for risk assessment.

Perennial streams in Idaho have been modeled using regression equations for 7-day, 2-year low flows (7Q2) described in Wood and others (2009, U.S. Geological Survey Scientific Investigations Report 2009-5015). The model produces "synthetic" streams based on 10-meter resolution digitial elevation models that have been processed to agree closely with 1:24,000-scale National Hydrography Dataset flowlines. See Larger_Work_Citation report text for a complete description of the modeling process.

In this dataset, the synthetic stream lines have been indexed to the NHDPlus Version 01_02 (schema version 1, data version 2). Points along the synthetic streams where 7Q2 model estimates exceeded 0.1 cubic feet per second were snapped to the NHDPlus 1:100,000-scale flowlines, and then traced downstream using the NHDPlus network. The data are presented in the form of a dBase-format event table. The traced line events correspond to synthetic stream lines having PerCode values of 2 or 3.

Q: How much rain and snow fell through the month? A: Colors show monthly precipitation totals across the contiguous United States. The darker the color, the higher the total precipitation. Q: Where do these measurements come from? A: Daily measurements of rain and snow come from weather stations in the Global Historical Climatology Network (GHCN-D). Volunteer observers or automated instruments gather the data and submit them to the National Centers for Environmental Information (NCEI). After scientists check the quality of the data to omit any systematic errors, they calculate each station’s monthly total precipitation and plot it on a 5x5 km gridded map. To fill in the grid at locations without stations, a computer program interpolates (or estimates) values, accounting for the distribution of stations and various physical relationships, such as the way temperature changes with elevation. The resulting product is the NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid). Q: What do the colors mean? A: Areas shown in white received little or no measurable precipitation for the month. Areas shown in the lightest green received less than one inch of water from rain or snow. The darker the color on the map, the higher the precipitation for the month. Areas shown in dark blue received eight inches or more of precipitation that fell as either rain or snow. Note that snowfall totals are reported as the amount of liquid water they produce upon melting. Thus, a 10-inch snowfall that melts to produce one inch of liquid water would be counted as one inch of precipitation. Q: Why do these data matter? A: Farmers and gardeners who depend on rain for their plants want to know if enough precipitation has fallen to support plant growth. Similarly, forest managers and ranchers check monthly precipitation to monitor the status of the environment. Water managers who work to ensure that towns and cities have enough water for drinking, washing, and industrial uses are also interested in how much precipitation falls each month. Q: How did you produce these snapshots? A: Data Snapshots are derivatives of existing data products; to meet the needs of a broad audience, we present the source data in a simplified visual style. This set of snapshots is based on climate data (NClimGrid) produced by and available from the National Centers for Environmental Information (NCEI). To produce our images, we invoke a set of scripts that access the source data and represent them according to our selected color ramps on our base maps. Q: Data Format Description A: NetCDF (Version: 4) Additional information The data used in these snapshots can be downloaded from different places and in different formats. We used these specific data sources: NClimGrid Total Precipitation References NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid) NOAA Monthly U.S. Climate Divisional Database (NClimDiv) Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions NCEI Monthly National Analysis Climate at a Glance - Data Information NCEI Climate Monitoring - All Products Source: https://www.climate.gov/maps-data/data-snapshots/data-source/precipitation-monthly-total This upload includes two additional files:* Precipitation - Monthly Total _NOAA Climate.gov.pdf is a screenshot of the main Climate.gov site for these snapshots (https://www.climate.gov/maps-data/data-snapshots/data-source/precipitation-monthly-total )* Cimate_gov_ Data Snapshots.pdf is a screenshot of the data download page for the full-resolution files.

OverviewThe multiple hazard index for the United States Counties was designed to map natural hazard relating to exposure to multiple natural disasters. The index was created to provide communities and public health officials with an overview of the risks that are prominent in their county, and to facilitate the comparison of hazard level between counties. Most existing hazard maps focus on a single disaster type. By creating a measure that aggregates the hazard from individual disasters, the increased hazard that results from exposure to multiple natural disasters can be better understood. The multiple hazard index represents the aggregate of hazard from eleven individual disasters. Layers displaying the hazard from each individual disaster are also included.

The hazard index is displayed visually as a choropleth map, with the color blue representing areas with less hazard and red representing areas with higher hazard. Users can click on each county to view its hazard index value, and the level of hazard for each individual disaster. Layers describing the relative level of hazard from each individual disaster are also available as choropleth maps with red areas representing high, orange representing medium, and yellow representing low levels of hazard.Methodology and Data CitationsMultiple Hazard Index

The multiple hazard index was created by coding the individual hazard classifications and summing the coded values for each United States County. Each individual hazard is weighted equally in the multiple hazard index. Alaska and Hawaii were excluded from analysis because one third of individual hazard datasets only describe the coterminous United States.

Avalanche Hazard

University of South Carolina Hazards and Vulnerability Research Institute. “Spatial Hazard Events and Losses Database”. United States Counties. “Avalanches United States 2001-2009”. < http://hvri.geog.sc.edu/SHELDUS/

Downloaded 06/2016.

Classification

Avalanche hazard was classified by dividing counties based upon the number of avalanches they experienced over the nine year period in the dataset. Avalanche hazard was not normalized by total county area because it caused an over-emphasis on small counties, and because avalanches are a highly local hazard.

None = 0 AvalanchesLow = 1 AvalancheMedium = 2-5 AvalanchesHigh = 6-10 Avalanches

Earthquake Hazard

United States Geological Survey. “Earthquake Hazard Maps”. 1:2,000,000. “Peak Ground Acceleration 2% in 50 Years”. < http://earthquake.usgs.gov/hazards/products/conterminous/

. Downloaded 07/2016.

Classification

Peak ground acceleration (% gravity) with a 2% likelihood in 50 years was averaged by United States County, and the earthquake hazard of counties was classified based upon this average.

Low = 0 - 14.25 % gravity peak ground accelerationMedium = 14.26 - 47.5 % gravity peak ground accelerationHigh = 47.5+ % gravity peak ground acceleration

Flood Hazard

United States Federal Emergency Management Administration. “National Flood Hazard Layer”. 1:10,000. “0.2 Percent Annual Flood Area”. < https://data.femadata.com/FIMA/Risk_MAP/NFHL/

. Downloaded 07/2016.

Classification

The National Flood Hazard Layer 0.2 Percent Annual Flood Area was spatially intersected with the United States Counties layer, splitting flood areas by county and adding county information to flood areas. Flood area was aggregated by county, expressed as a fraction of the total county land area, and flood hazard was classified based upon percentage of land that is susceptible to flooding. National Flood Hazard Layer does not cover the entire United States; coverage is focused on populated areas. Areas not included in National Flood Hazard Layer were assigned flood risk of Low in order to include these areas in further analysis.

Low = 0-.001% area susceptibleMedium = .00101 % - .005 % area susceptibleHigh = .00501+ % area susceptible

Heat Wave Hazard

United States Center for Disease Control and Prevention. “National Climate Assessment”. Contiguous United States Counties. “Extreme Heat Events: Heat Wave Days in May - September for years 1981-2010”. Downloaded 06/2016.

Classification

Heat wave was classified by dividing counties based upon the number of heat wave days they experienced over the 30 year time period described in the dataset.

Low = 126 - 171 Heat wave DaysMedium = 172 – 187 Heat wave DaysHigh = 188 – 255 Heat wave Days

Hurricane Hazard

National Oceanic and Atmospheric Administration. Coastal Services Center. “Historical North Atlantic Tropical Cyclone Tracks, 1851-2004”. 1: 2,000,000. < https://catalog.data.gov/dataset/historical-north-atlantic-tropical-cyclone-tracks-1851-2004-direct-download

. Downloaded 06/2016.

National Oceanic and Atmospheric Administration. Coastal Services Center. “Historical North Pacific Tropical Cyclone Tracks, 1851-2004”. 1: 2,000,000. < https://catalog.data.gov/dataset/historical-north-atlantic-tropical-cyclone-tracks-1851-2004-direct-download

. Downloaded 06/2016.

Classification

Atlantic and Pacific datasets were merged. Tropical storm and disturbance tracks were filtered out leaving hurricane tracks. Each hurricane track was assigned the value of the category number that describes that event. Weighting each event by intensity ensures that areas with higher intensity events are characterized as being more hazardous. Values describing each hurricane event were aggregated by United States County, normalized by total county area, and the hurricane hazard of counties was classified based upon the normalized value.

Landslide Hazard

United States Geological Survey. “Landslide Overview Map of the United States”. 1:4,000,000. “Landslide Incidence and Susceptibility in the Conterminous United States”. < https://catalog.data.gov/dataset/landslide-incidence-and-susceptibility-in-the-conterminous-united-states-direct-download

. Downloaded 07/2016.

Classification

The classifications of High, Moderate, and Low landslide susceptibility and incidence from the study were numerically coded, the average value was computed for each county, and the landslide hazard was classified based upon the average value.

Long-Term Drought Hazard

United States Drought Monitor, Drought Mitigation Center, United States Department of Agriculture, National Oceanic and Atmospheric Administration. “Drought Monitor Summary Map”. “Long-Term Drought Impact”. < http://droughtmonitor.unl.edu/MapsAndData/GISData.aspx >. Downloaded 06/2016.

Classification

Short-term drought areas were filtered from the data; leaving only long-term drought areas. United States Counties were assigned the average U.S. Drought Monitor Classification Scheme Drought Severity Classification value that characterizes the county area. County long-term drought hazard was classified based upon average Drought Severity Classification value.

Low = 1 – 1.75 average Drought Severity Classification valueMedium = 1.76 -3.0 average Drought Severity Classification valueHigh = 3.0+ average Drought Severity Classification value

Snowfall Hazard

United States National Oceanic and Atmospheric Administration. “1981-2010 U.S. Climate Normals”. 1: 2,000,000. “Annual Snow Normal”. < http://www1.ncdc.noaa.gov/pub/data/normals/1981-2010/products/precipitation/

. Downloaded 08/2016.

Classification

Average yearly snowfall was joined with point location of weather measurement stations, and stations without valid snowfall measurements were filtered out (leaving 6233 stations). Snowfall was interpolated using least squared distance interpolation to create a .05 degree raster describing an estimate of yearly snowfall for the United States. The average yearly snowfall raster was aggregated by county to yield the average yearly snowfall per United States County. The snowfall risk of counties was classified by average snowfall.

None = 0 inchesLow = .01- 10 inchesMedium = 10.01- 50 inchesHigh = 50.01+ inches

Tornado Hazard

United States National Oceanic and Atmospheric Administration Storm Prediction Center. “Severe Thunderstorm Database and Storm Data Publication”. 1: 2,000,000. “United States Tornado Touchdown Points 1950-2004”. < https://catalog.data.gov/dataset/united-states-tornado-touchdown-points-1950-2004-direct-download

. Downloaded 07/2016.

Classification

Each tornado touchdown point was assigned the value of the Fujita Scale that describes that event. Weighting each event by intensity ensures that areas with higher intensity events are characterized as more hazardous. Values describing each tornado event were aggregated by United States County, normalized by total county area, and the tornado hazard of counties was classified based upon the normalized value.

Volcano Hazard

Smithsonian Institution National Volcanism Program. “Volcanoes of the World”. “Holocene Volcanoes”. < http://volcano.si.edu/search_volcano.cfm

. Downloaded 07/2016.

Classification

Volcano coordinate locations from spreadsheet were mapped and aggregated by United States County. Volcano count was normalized by county area, and the volcano hazard of counties was classified based upon the number of volcanoes present per unit area.

None = 0 volcanoes/100 kilometersLow = 0.000915 - 0.007611 volcanoes / 100 kilometersMedium = 0.007612 - 0.018376 volcanoes / 100 kilometersHigh = 0.018377- 0.150538 volcanoes / 100 kilometers

Wildfire Hazard

United States Department of Agriculture, Forest Service, Fire, Fuel, and Smoke Science Program. “Classified 2014 Wildfire Hazard Potential”. 270 meters. < http://www.firelab.org/document/classified-2014-whp-gis-data-and-maps

. Downloaded 06/2016.

Classification

The classifications of Very High, High, Moderate, Low, Very Low, and Non-Burnable/Water wildfire hazard from the study were numerically coded, the average value was computed for each county, and the wildfire hazard was classified based upon the average value.

Monthly U.S. reported precipitation amounts in hundredths of inches (ex 100 is 1.00 inches) generated from the GTS metar(hourly) and synoptic(6-hourly)observations for selected cities based on the Weekly Weather and Crop Bulletin station list

Summary:

Estimated stand-off distance between ADS-B equipped aircraft and obstacles. Obstacle information was sourced from the FAA Digital Obstacle File and the FHWA National Bridge Inventory. Aircraft tracks were sourced from processed data curated from the OpenSky Network. Results are presented as histograms organized by aircraft type and distance away from runways.

Description:

For many aviation safety studies, aircraft behavior is represented using encounter models, which are statistical models of how aircraft behave during close encounters. They are used to provide a realistic representation of the range of encounter flight dynamics where an aircraft collision avoidance system would be likely to alert. These models currently and have historically have been limited to interactions between aircraft; they have not represented the specific interactions between obstacles and aircraft equipped transponders. In response, we calculated the standoff distance between obstacles and ADS-B equipped manned aircraft.

For robustness, this assessment considered two different datasets of manned aircraft tracks and two datasets of obstacles. For robustness, MIT LL calculated the standoff distance using two different datasets of aircraft tracks and two datasets of obstacles. This approach aligned with the foundational research used to support the ASTM F3442/F3442M-20 well clear criteria of 2000 feet laterally and 250 feet AGL vertically.

The two datasets of processed tracks of ADS-B equipped aircraft curated from the OpenSky Network. It is likely that rotorcraft were underrepresented in these datasets. There were also no considerations for aircraft equipped only with Mode C or not equipped with any transponders. The first dataset was used to train the v1.3 uncorrelated encounter models and referred to as the “Monday” dataset. The second dataset is referred to as the “aerodrome” dataset and was used to train the v2.0 and v3.x terminal encounter model. The Monday dataset consisted of 104 Mondays across North America. The other dataset was based on observations at least 8 nautical miles within Class B, C, D aerodromes in the United States for the first 14 days of each month from January 2019 through February 2020. Prior to any processing, the datasets required 714 and 847 Gigabytes of storage. For more details on these datasets, please refer to "Correlated Bayesian Model of Aircraft Encounters in the Terminal Area Given a Straight Takeoff or Landing" and “Benchmarking the Processing of Aircraft Tracks with Triples Mode and Self-Scheduling.”

Two different datasets of obstacles were also considered. First was point obstacles defined by the FAA digital obstacle file (DOF) and consisted of point obstacle structures of antenna, lighthouse, meteorological tower (met), monument, sign, silo, spire (steeple), stack (chimney; industrial smokestack), transmission line tower (t-l tower), tank (water; fuel), tramway, utility pole (telephone pole, or pole of similar height, supporting wires), windmill (wind turbine), and windsock. Each obstacle was represented by a cylinder with the height reported by the DOF and a radius based on the report horizontal accuracy. We did not consider the actual width and height of the structure itself. Additionally, we only considered obstacles at least 50 feet tall and marked as verified in the DOF.

The other obstacle dataset, termed as “bridges,” was based on the identified bridges in the FAA DOF and additional information provided by the National Bridge Inventory. Due to the potential size and extent of bridges, it would not be appropriate to model them as point obstacles; however, the FAA DOF only provides a point location and no information about the size of the bridge. In response, we correlated the FAA DOF with the National Bridge Inventory, which provides information about the length of many bridges. Instead of sizing the simulated bridge based on horizontal accuracy, like with the point obstacles, the bridges were represented as circles with a radius of the longest, nearest bridge from the NBI. A circle representation was required because neither the FAA DOF or NBI provided sufficient information about orientation to represent bridges as rectangular cuboid. Similar to the point obstacles, the height of the obstacle was based on the height reported by the FAA DOF. Accordingly, the analysis using the bridge dataset should be viewed as risk averse and conservative. It is possible that a manned aircraft was hundreds of feet away from an obstacle in actuality but the estimated standoff distance could be significantly less. Additionally, all obstacles are represented with a fixed height, the potentially flat and low level entrances of the bridge are assumed to have the same height as the tall bridge towers. The attached figure illustrates an example simulated bridge.

It would had been extremely computational inefficient to calculate the standoff distance for all possible track points. Instead, we define an encounter between an aircraft and obstacle as when an aircraft flying 3069 feet AGL or less comes within 3000 feet laterally of any obstacle in a 60 second time interval. If the criteria were satisfied, then for that 60 second track segment we calculate the standoff distance to all nearby obstacles. Vertical separation was based on the MSL altitude of the track and the maximum MSL height of an obstacle.

For each combination of aircraft track and obstacle datasets, the results were organized seven different ways. Filtering criteria were based on aircraft type and distance away from runways. Runway data was sourced from the FAA runways of the United States, Puerto Rico, and Virgin Islands open dataset. Aircraft type was identified as part of the em-processing-opensky workflow.

• All: No filter, all observations that satisfied encounter conditions
• nearRunway: Aircraft within or at 2 nautical miles of a runway
• awayRunway: Observations more than 2 nautical miles from a runway
• glider: Observations when aircraft type is a glider
• fwme: Observations when aircraft type is a fixed-wing multi-engine
• fwse: Observations when aircraft type is a fixed-wing single engine
• rotorcraft: Observations when aircraft type is a rotorcraft

License

This dataset is licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International(CC BY-NC-ND 4.0).

This license requires that reusers give credit to the creator. It allows reusers to copy and distribute the material in any medium or format in unadapted form and for noncommercial purposes only. Only noncommercial use of your work is permitted. Noncommercial means not primarily intended for or directed towards commercial advantage or monetary compensation. Exceptions are given for the not for profit standards organizations of ASTM International and RTCA.

MIT is releasing this dataset in good faith to promote open and transparent research of the low altitude airspace. Given the limitations of the dataset and a need for more research, a more restrictive license was warranted. Namely it is based only on only observations of ADS-B equipped aircraft, which not all aircraft in the airspace are required to employ; and observations were source from a crowdsourced network whose surveillance coverage has not been robustly characterized.

As more research is conducted and the low altitude airspace is further characterized or regulated, it is expected that a future version of this dataset may have a more permissive license.

Distribution Statement

DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.

© 2021 Massachusetts Institute of Technology.

Delivered to the U.S. Government with Unlimited Rights, as defined in DFARS Part 252.227-7013 or 7014 (Feb 2014). Notwithstanding any copyright notice, U.S. Government rights in this work are defined by DFARS 252.227-7013 or DFARS 252.227-7014 as detailed above. Use of this work other than as specifically authorized by the U.S. Government may violate any copyrights that exist in this work.

This material is based upon work supported by the Federal Aviation Administration under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Federal Aviation Administration.

This document is derived from work done for the FAA (and possibly others); it is not the direct product of work done for the FAA. The information provided herein may include content supplied by third parties. Although the data and information contained herein has been produced or processed from sources believed to be reliable, the Federal Aviation Administration makes no warranty, expressed or implied, regarding the accuracy, adequacy, completeness, legality, reliability or usefulness of any information, conclusions or recommendations provided herein. Distribution of the information contained herein does not constitute an endorsement or warranty of the data or information provided herein by the Federal Aviation Administration or the U.S. Department of Transportation. Neither the Federal Aviation Administration nor the U.S. Department of

This data release reformats the STATSGO soil thickness (THICK) dataset (Schwarz and Alexander, 1995) as a cloud-optimized GeoTIFF (COG). The COG format allows standard software tools to efficiently access the datasets over an internet connection. The soil thickness values have units of inches. Please refer to the documentation of the source archive (Schwarz and Alexander, 1995) for additional details on the underlying dataset. The COG dataset spans the continental US at a nominal 30 meter resolution. The spatial reference is EPSG:5069. Each COG uses a float32 precision, and the NoData value (NaN) indicates raster pixels not covered by the original STATSGO dataset. The COG also includes non-physical values of -0.1, which were used by the source dataset to mark large water bodies. The COG format uses compression internally to reduce file size. As such, reading large portions of a COG into memory can require much more RAM than the nominal file size. This COG will require ~60GB of memory to read in full. This dataset can be reproduced by running the rasterize_statsgo.py Python script included in this dataset's parent folder (https://doi.org/10.5066/P13WAPYV). Please refer to the script for documentation and usage instructions. Disclaimer: Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. References: Schwarz, G.E. and Alexander, R.B., 1995, Soils data for the Conterminous United States Derived from the NRCS State Soil Geographic (STATSGO) Data Base. [Original title: State Soil Geographic (STATSGO) Data Base for the Conterminous United States.]: U.S. Geological Survey data release, https://doi.org/10.5066/P94JAULO.

This dataset provides estimated hourly dynamic line ratings for ~84,000 transmission lines across the contiguous United States from 2007-2013. The calculation methods are described in the presentation linked below, and the associated open-source Python code repository is linked in the Resources section below. Abbreviations used in filenames and descriptions are: - SLR: static line ratings - ALR: ambient-temperature-adjusted line ratings - NLR: ambient-temperature- and day/night-irradiance-adjusted line ratings - CLR: ambient-temperature- and clear-sky-irradiance-adjusted line ratings - ILR: ambient-temperature- and measured-irradiance-adjusted line ratings - DLR: full dynamic line ratings (including air temperature/pressure, wind speed/direction, and measured irradiance) Transmission lines are referenced by their ID in the Homeland Infrastructure Foundation-Level Data (HIFLD) on Transmission Lines (linked in Resources section). Time indices are in UTC. The data files contain ratios between modeled hourly ratings and modeled static ratings. Columns are indexed by HIFLD ID; rows are indexed by hourly timestamps from 2007-2013 (UTC). A data directory is also included in the Resources section. The SLR files contain modeled static ratings (the denominator of the ratios in the files described above) in amps. As described in the presentation linked in the Resources section below, SLR calculations assume an ambient air temperature of 40 C, air pressure of 101 kPa, wind speed of 2 feet per second (0.61 m/s) perpendicular to the conductor, global horizontal irradiance of 1000 W/m^2, and conductor absorptivity and emissivity of 0.8. Conductor assumptions are Linnet for ~69 kV and below, Condor for ~115 kV, Martin for ~230 kV, and Cardinal for ~345 kV and above. Caveats and Limitations Results are sensitive to the weather data used. Validation studies on the WIND Toolkit and NSRDB are available at: - King, J. et al. "Validation of Power Output for the WIND Toolkit", 2014 (https://www.nrel.gov/docs/fy14osti/61714.pdf) - Draxl, C. et al. "Overview and Meteorological Validation of the Wind Integration National Dataset Toolkit", 2015 (https://www.nrel.gov/docs/fy15osti/61740.pdf) - Sengupta, M. et al. "Validation of the National Solar Radiation Database (NSRDB) (2005-2012)", 2015 (https://www.nrel.gov/docs/fy15osti/64981.pdf) - Habte, A. et al. "Evaluation of the National Solar Radiation Database (NSRDB Version 2): 1998-2015", 2017 (https://www.nrel.gov/docs/fy17osti/67722.pdf) More work is required to determine how well ratings calculated from NSRDB and WIND Toolkit data reflect the actual ratings observed by installed sensors (such as sag or tension monitors). In general, ratings calculated from modeled weather data are not a substitute for direct sensor data. Assuming a single representative conductor type (ACSR of a single diameter) for each voltage level is an important simplification; reported line ratings at a given voltage level can vary widely. HIFLD line routes are primarily based on imagery instead of exact construction data and may have errors. We use historical weather data directly; calculated line ratings are thus more indicative of real-time ratings than forecasted ratings

Geospatial datasets were developed to estimate the depth to the top of the Dakota Sandstone in feet below land surface datum within the Ute Mountain Ute Reservation in Colorado. This study was completed by the U.S. Geological Survey (USGS) in cooperation with the Ute Mountain Ute Tribe. One dataset was created for the contours showing the altitude (in feet) of the top of the Dakota Sandstone (shapefile Kd_talt_hand), and a second dataset was created for polygons representing the outcrops of the Dakota Sandstone (shapefile Dakota_outcrop_poly). These two datasets were used in combination with USGS digital elevation models (DEM) to create a dataset for the depth of the top of the Dakota Sandstone below the land surface contoured at a 100-foot interval (shapefile kd_depth_ci100). The kd_depth_ci100 dataset was used to generate a figure showing the generalized depth to the top of the Dakota Sandstone in feet below land surface in Bauch and Arnold (2019).

The Climate Resilience Information System (CRIS) provides data and tools for developers of climate services. This layer has historical variables in decadal increments from 1950 to 2020 derived from historical observations of air temperature and precipitation. The variables included are:Annual total precipitation (inches) Annual highest precipitation total for a single day (inches) Annual highest precipitation total over a 5-day period (inches) Annual highest precipitation total over a 10-day period (inches) Annual total precipitation for all days exceeding the 90th percentile (inches) Annual total precipitation for all days exceeding the 95th percentile (inches) Annual total precipitation for all days exceeding the 99th percentile (inches) This layer uses data from the NOAA Monthly U.S. Climate Gridded Dataset (nClimGrid). Further processing by Esri is explained below.For each variable, there are mean values for the defined respective geography: counties, tribal areas, HUC-8 watersheds. The process for deriving these summaries is available from the CRIS Website’s About the Data. Other climate variables are available from the CRIS Data page. Additional geographies, including Alaska, Hawai’i and Puerto Rico will be made available in the future.GeographiesThis layer provides historic values for three geographies: county, tribal area, and HUC-8 watersheds.County: based on the U.S. Census TIGER/Line 2022 distribution. Tribal areas: based on the U.S. Census American Indian/Alaska Native/Native Hawaiian Area dataset 2022 distribution. This dataset includes federal- and state-recognized statistical areas.HUC-8 watershed: based on the USGS Washed Boundary Dataset, part of the National Hydrography Database Plus High Resolution. Time RangesHistoric climate threshold values (e.g. Days Over 90°F) were calculated for each year from 1950 to 2020. To ensure the layer displays time correctly, under 'Map properties' set Time zone to 'Universal Coordinated Time (UTC)' and under 'Time slider options' set Time intervals to '1 Decade'.Data CitationVose, Russell S., Applequist, Scott, Squires, Mike, Durre, Imke, Menne, Matthew J., Williams, Claude N. Jr., Fenimore, Chris, Gleason, Karin, and Arndt, Derek (2014): NOAA Monthly U.S. Climate Gridded Dataset (nClimGrid), Version 1. NOAA National Centers for Environmental Information. https://doi.org/10.7289/V5SX6B56.Data ExportExporting this data into shapefiles, geodatabases, GeoJSON, etc is enabled.

This dataset contains instantaneous measurements of water level in wetland units in feet above mean sea level, as measured at the U.S. Fish and Wildlife Service Ruby Lake National Wildlife Refuge. Dataset is considered provisional, preliminary, and subject to revision.