Finding Schools is now easier than ever with the College Map, the first geographic search tool published by IPEDS (Integrated Postsecondary Education Data System) providing access to over 7,000 certificate, undergraduate and graduate-level schools. This all-in-one tool enables students, parents and counselors to filter potential programs for location, major, tuition and more. Including both certificate-level programs and advanced degrees, this public application makes the often overwhelming process of school searching simple, and it’s available on mobile devices.Once the results are narrowed down, users can share their lists on social media or download in excel format. Additionally, the College Map integrates with the College Navigator, a research based search tool providing data from the complete list of IPEDS Survey indicators.All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

Table contains count and percent of county residents ages 25 years and older with less than bachelors' education attainment. The measure is summarized at county, city, zip code and census tract. Data are presented for zip codes (ZCTAs) fully within the county. Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-year estimates, Table B15002; data accessed on May 17, 2022 from https://api.census.gov. The 2020 Decennial geographies are used for data summarization.METADATA:notes (String): Lists table title, notes, sourcesgeolevel (String): Level of geographyGEOID (Numeric): Geography IDNAME (String): Name of geographypop (Numeric): Population ages 25 and olderpct_lt_bach (Numeric): Number of people ages 25 and older with less than bachelors' educationlt_bachelor (Numeric): Percent of people ages 25 and older with less than bachelors' education

This pie chart illustrates the distribution of degrees—Bachelor’s, Master’s, and Doctoral—among PERM graduates from Gis. It shows the educational composition of students who have pursued and successfully obtained permanent residency through their qualifications in Gis. This visualization helps to understand the diversity of educational backgrounds that contribute to successful PERM applications, reflecting the major’s role in fostering students’ career paths towards permanent residency in the U.S.

Raw 1/10th Degree Wind Force Probability data for all wind speeds.

This pie chart illustrates the distribution of degrees—Bachelor’s, Master’s, and Doctoral—among PERM graduates from Geographic Information Systems (Gis). It shows the educational composition of students who have pursued and successfully obtained permanent residency through their qualifications in Geographic Information Systems (Gis). This visualization helps to understand the diversity of educational backgrounds that contribute to successful PERM applications, reflecting the major’s role in fostering students’ career paths towards permanent residency in the U.S.

Evaluating multiple signals of climate change across the conterminous United States during three 30-year periods (2010�2039, 2040�2069, 2070�2099) during this century to a baseline period (1980�2009) emphasizes potential changes for growing degree days (GDD), plant hardiness zones (PHZ), and heat zones. These indices were derived using the CCSM4 and GFDL CM3 models under the representative concentration pathways 4.5 and 8.5, respectively, and included in Matthews et al. (2018). Daily temperature was downscaled by Maurer et al.�(https://doi.org/10.1029/2007EO470006 at a 1/8 degree grid scale and used to obtain growing degree days, plant hardiness zones, and heat zones.�Each of these indices provides unique information about plant health related to changes in climatic conditions that influence establishment, growth, and survival. These data and the calculated changes are provided as 14 individual IMG files for each index to assist with management planning and decision making into the future. For each of the four indices the following are included: two baseline files (1980�2009), three files representing 30-year periods for the scenario CCSM4 under RCP 4.5 along with three files of changes, and three files representing 30-year periods for the scenario GFDL CM3 under RCP 8.5 along with three files of changes. Growing degree days address an important component to general patterns of plant growth by accumulating the degree days across the growing season. This metric provides a level of detail related to defining the growing season potential. Here, we evaluate the accumulation of growing degree days at or above 5 �C (41 �F), assuming that limited growth occurs below 5 �C.�Specifically, we calculate growing degree days by first calculating the average daily temperature, based on the maximum and minimum projected daily temperature. We then subtract 5 �C from each mean value and then accumulate the positive difference values for all days within each year. The mean GDD values for the conterminous United States during the baseline period ranged from less than 100 to over 7,000 degree days, increasing from north to south with highest values in the Florida panhandle, southern Texas, southwestern Arizona, and southeastern California. GDD projections throughout the century suggest a ubiquitous increase across the United States with slightly less change in the Northeast and much greater increases throughout the southern United States under the high scenario. Original data and associated metadata can be downloaded from this website:�https://www.fs.usda.gov/rds/archive/Product/RDS-2019-0001

This data set represents a 5-meter resolution LiDAR-derived degree slope layer for New Hampshire. It was generated from a statewide Esri Mosaic Dataset which comprised 8 separate LiDAR collections that covered the state as of January, 2020. The Mosaic Dataset was used as input to the ArcGIS Spatial Analyst "Slope" geoprocessing tool which calculates the degree slope for each cell of the input raster, in this case, the statewide mosaic dataset.

ESRI grids showing sea salinity, linearly interpolated from CARS2000 mean and seasonal fields to 0.1 degree spaced grid, at depths of 0, 150, 500, 1000 and 2000 metres. The loess filter used to …Show full descriptionESRI grids showing sea salinity, linearly interpolated from CARS2000 mean and seasonal fields to 0.1 degree spaced grid, at depths of 0, 150, 500, 1000 and 2000 metres. The loess filter used to create CARS2000 resolves at each point a mean value and a sinusoid with 1 year period (and in some cases a 6 month period sinusoid - the "semi-annual cycle".) The provided "annual amplitude" is simply the magnitude of that annual sinusoid. CARS is a set of seasonal maps of temperature, salinity, dissolved oxygen, nitrate, phosphate and silicate, generated using Loess mapping from all available oceanographic data in the region. It covers the region 100-200E, 50-0S, on a 0.5 degree grid, and on 56 standard depth levels. Higher resolution versions are also available for the Australian continental shelf. The data was obtained from the World Ocean Atlas 98 and CSIRO Marine and NIWA archives. It was designed to improve on the Levitus WOA98 Atlas, in the Australian region. CARS2000 is derived from ocean cast data, which is always measured above the sea floor. However, for properties which do not change rapidly near the sea floor, this would not lead to a significant error. All the limitations of CARS2000 also apply here.

This pie chart illustrates the distribution of degrees—Bachelor’s, Master’s, and Doctoral—among PERM graduates from Remote Sensing/Gis. It shows the educational composition of students who have pursued and successfully obtained permanent residency through their qualifications in Remote Sensing/Gis. This visualization helps to understand the diversity of educational backgrounds that contribute to successful PERM applications, reflecting the major’s role in fostering students’ career paths towards permanent residency in the U.S.

A database (NDP-068) was generated from estimates of geographically referenced carbon densities of forest vegetation in tropical Southeast Asia for 1980. A geographic information system (GIS) was used to incorporate spatial databases of climatic, edaphic, and geomorphological indices and vegetation to estimate potential (i.e., in the absence of human intervention and natural disturbance) carbon densities of forests. The resulting map was then modified to estimate actual 1980 carbon density as a function of population density and climatic zone. The database covers the following 13 countries: Bangladesh, Brunei, Cambodia (Campuchea), India, Indonesia, Laos, Malaysia, Myanmar (Burma), Nepal, the Philippines, Sri Lanka, Thailand, and Vietnam.

The data sets within this database are provided in three file formats: ARC/INFOTM exported integer grids; ASCII (American Standard Code for Information Interchange) files formatted for raster-based GIS software packages; and generic ASCII files with x, y coordinates for use with non-GIS software packages.

The database includes ten ARC/INFO exported integer grid files (five with the pixel size 3.75 km x 3.75 km and five with the pixel size 0.25 degree longitude x 0.25 degree latitude) and 27 ASCII files. The first ASCII file contains the documentation associated with this database. Twenty-four of the ASCII files were generated by means of the ARC/INFO GRIDASCII command and can be used by most raster-based GIS software packages. The 24 files can be subdivided into two groups of 12 files each.

The files contain real data values representing actual carbon and potential carbon density in Mg C/ha (1 megagram = 10^6 grams) and integer-coded values for country name, Weck's Climatic Index, ecofloristic zone, elevation, forest or non- forest designation, population density, mean annual precipitation, slope, soil texture, and vegetation classification. One set of 12 files contains these data at a spatial resolution of 3.75 km, whereas the other set of 12 files has a spatial resolution of 0.25 degree. The remaining two ASCII data files combine all of the data from the 24 ASCII data files into 2 single generic data files. The first file has a spatial resolution of 3.75 km, and the second has a resolution of 0.25 degree. Both files also provide a grid-cell identification number and the longitude and latitude of the centerpoint of each grid cell.

The 3.75-km data in this numeric data package yield an actual total carbon estimate of 42.1 Pg (1 petagram = 10^15 grams) and a potential carbon estimate of 73.6 Pg; whereas the 0.25-degree data produced an actual total carbon estimate of 41.8 Pg and a total potential carbon estimate of 73.9 Pg.

Fortran and SASTM access codes are provided to read the ASCII data files, and ARC/INFO and ARCVIEW command syntax are provided to import the ARC/INFO exported integer grid files. The data files and this documentation are available without charge on a variety of media and via the Internet from the Carbon Dioxide Information Analysis Center (CDIAC).

Climate plays a major role in determining the distribution of plants and animals. Bioclimatology, the study of climate as it affects and is affected by living organisms, is key to understanding the patterns of forests and deserts on the landscape, where productive agricultural lands may be found, and how changes in the climate will affect rare species. This layer is part of the Ecophysiographic Project and is one of the four input layers used to create the World Ecological Land Units Map.Dataset Summary This layer provides access to a 250m cell-sized raster with a bioclimatic stratification. The source dataset was a 30-arcsecond resolution raster (equivalent to 0.86 km2 at the equator or about a 920m pixel size). The layer has the following attributes: Temperature Description - Seven classes based on the number of growing degree days (the monthly mean temperature multiplied by number of days in the month summed for all months). The 1950 to 2000 monthly average temperature was used to calculate growing degree days. Values in this field and associated number of growing degree days are:Temperature DescriptionGrowing Degree DaysVery Hot9,000 – 13,500Hot7,000 – 9,000Warm4,500 – 7,000Cool2,500 – 4,500Cold1,000 – 2,500Very Cold300 – 1,000Arctic0 - 300Aridity Description - Six classes based on an index of aridity calculated by dividing precipitation by evapotranspiration. Precipitation and evapotranspiration are average values from 1950 to 2000.Aridity DescriptionAridity IndexVery Wet1.5 – 70Wet1.0 – 1.5Moist0.6 – 1.0Semi-dry0.3 – 0.6Dry0.1 – 0.3Very Dry0.01 – 0.1Bioclimate Class - a 2-part description that combines the value of the Temperature Description field and the Aridity Description field. The alias for this field is ELU Bioclimate Reclass. This layer was created by modifying the dataset documented in the publication: Metzger and others. 2012. A high-resolution bioclimate map of the world: a unifying framework for global biodiversity research and monitoring. What can you do with this layer? This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop.This layer has query, identify, and export image services available. This layer is restricted to a maximum area of 16,000 x 16,000 pixels - an area 4,000 kilometers on a side or an area approximately the size of Europe. A service is available providing access to the data table associated with this layer. The data table services can be used by developers to quickly and efficiently query the data and to create custom applications. For more information see the World Ecophysiographic Tables.This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.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.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

Lake County, Illinois Demographic Data. Explanation of field attributes: Total Population – The entire population of Lake County. White – Individuals who are of Caucasian race. This is a percent.African American – Individuals who are of African American race. This is a percent.Asian – Individuals who are of Asian race. This is a percent. Hispanic – Individuals who are of Hispanic ethnicity. This is a percent. Does not Speak English- Individuals who speak a language other than English in their household. This is a percent. Under 5 years of age – Individuals who are under 5 years of age. This is a percent. Under 18 years of age – Individuals who are under 18 years of age. This is a percent. 18-64 years of age – Individuals who are between 18 and 64 years of age. This is a percent. 65 years of age and older – Individuals who are 65 years old or older. This is a percent. Male – Individuals who are male in gender. This is a percent. Female – Individuals who are female in gender. This is a percent. High School Degree – Individuals who have obtained a high school degree. This is a percent. Associate Degree – Individuals who have obtained an associate degree. This is a percent. Bachelor’s Degree or Higher – Individuals who have obtained a bachelor’s degree or higher. This is a percent. Utilizes Food Stamps – Households receiving food stamps/ part of SNAP (Supplemental Nutrition Assistance Program). This is a percent. Median Household Income - A median household income refers to the income level earned by a given household where half of the homes in the area earn more and half earn less. This is a dollar amount. No High School – Individuals who have not obtained a high school degree. This is a percent. Poverty – Poverty refers to families and people whose income in the past 12 months is below the poverty level. This is a percent.

This file contains the digital vector boundaries for Parishes and Non Civil Parished Areas in England and Wales as at December 2022.The boundaries available are: (BSC) Super Generalised (200m) - clipped to the coastline (Mean High Water mark).Contains both Ordnance Survey and ONS Intellectual Property Rights.REST URL of Feature Access Service –https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Parishes_and_Non_Civil_Parished_Areas_December_2022_EW_BSC_V3/FeatureServerREST URL of WFS Server –https://dservices1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/services/Parishes_and_Non_Civil_Parished_Areas_December_2022_EW_BSC_V3/WFSServer?service=wfs&request=getcapabilitiesREST URL of Map Server –https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Parishes_and_Non_Civil_Parished_Areas_December_2022_EW_BSC_V3/MapServer

This file contains the digital vector boundaries for European Electoral Regions in Great Britain, as at December 2016. The boundaries are super generalised (200m) - clipped to the coastline (Mean High Water mark). Contains both Ordnance Survey and ONS Intellectual Property Rights.REST URL of ArcGIS for INSPIRE View Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/EER_(Dec_2016)_SGCB_GB/MapServerREST URL of ArcGIS for INSPIRE Feature DownloadService – https://dservices1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/services/EER_Dec_2016_Super_Generalised_Clipped_Boundaries_GB/WFSServer?service=wfs&request=getcapabilitiesREST URL of Feature Access Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/EER_Dec_2016_SGCB_GB_2022/FeatureServer

ArcGIS Image Service

Mean LIS Flash Rate Density 

Time Interval: Diurnal Climatology

Platform: TRMM

Time Extent: 1998-01-01 to 2013-12-31

Projection: GCS WGS84

Extent: (38.0°, 180.0°), (-38.0°, -180.0°)

Other Formats: OGC WMS, OGC WCS, REST


      Collection
    The LIS 0.1 Degree Very High Resolution Gridded Lightning Diurnal Climatology (VHRDC) dataset consists of gridded diurnal climatologies of total lightning flash rates seen by the Lightning Imaging Sensor (LIS) from January 1, 1998 through December 31, 2013. LIS is an instrument on the Tropical Rainfall Measurement Mission satellite (TRMM) used to detect the distribution and variability of total lightning occurring in the Earth's tropical and subtropical regions. This information can be used for severe storm detection and analysis, and also for lightning-atmosphere interaction studies. The gridded climatologies include annual mean flash rate, mean diurnal cycle of flash rate with 24 hour resolution, and mean annual cycle of flash rate with daily, monthly, or seasonal resolution. All datasets are in 0.1 degree spatial resolution. The mean annual cycle of flash rate datasets (i.e., daily, monthly or seasonal) have both 49-day and 1 degree boxcar moving averages to remove diurnal cycle and smooth regions with low flash rate, making the results more robust. (GHRC)

    Source Data: DAAC, CMR, Earthdata Search









Satellite Mapping and Analysis of Severe Hailstorms (SMASH) Project

This Hailstorm research project seeks to address knowledge gaps in the severe hail climatology using regional to global scale satellite observations and provides mechanisms to explore related datasets.

For questions/issues please contact: kristopher.m.bedka@nasa.gov

SMASH AGOL Group | NASA Applied Sciences | NASA Disasters Mapping Portal | NASA Langley Research Center Science Directorate

MGCP Cells - LINKS for download of 1 degree cell sized regions of MGCP vector data. Data is in an ESRI shape file format.Multinational Geospatial Co-production Program (MGCP) datasets covering the 1°x1° degree cells.United Kingdom and CanadaApproved for Public Release

This file contains the digital vector boundaries for Counties in England, as at 31 December 2017. The boundaries are super generalised (200m) - clipped to the coastline (Mean High Water mark). Contains both Ordnance Survey and ONS Intellectual Property Rights.REST URL of ArcGIS for INSPIRE View Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Counties_(December_2017)_SGCB_in_England/MapServerREST URL of ArcGIS for INSPIRE Feature DownloadService – https://dservices1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/services/Counties_December_2017_Super_Generalised_Clipped_Boundaries_in_England/WFSServer?service=wfs&request=getcapabilitiesREST URL of Feature Access Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Counties_December_2017_SGCB_in_England_2022/FeatureServer

Growing degree days (GDDs) are used to estimate the growth and development of plants and insects during the growing season. Growing Degree Day are computed by subtracting a base value temperature from the mean daily temperature and are assigned a value of zero if negative. Base temperatures are a point below which development does not occur for the organism in question. Growing Degree Day products are created for base 0, 5, 10 and 15 degrees Celsius. GDD values are only accumulated during the Growing Season, April 1 through October 31.

These vector contour lines are derived from the 3D Elevation Program using automated and semi-automated processes. They were created to support 1:24,000-scale CONUS and Hawaii, 1:25,000-scale Alaska, and 1:20,000-scale Puerto Rico / US Virgin Island topographic map products, but are also published in this GIS vector format. Contour intervals are assigned by 7.5-minute quadrangle, so this vector dataset is not visually seamless across quadrangle boundaries. The vector lines have elevation attributes (in feet above mean sea level on NAVD88), but this dataset does not carry line symbols or annotation.

The Catch-In-Areas database integrates catch data from the Catch Accounting System (which has the spatial resolution of a NMFS Reporting Area) into a database that resolves the GIS data into polygons of approximately 7.5 km. In unrestricted outside waters, sixty four grid IDs fit inside one state statistical area. A state statistical area is = degree in latitude and one degree in longitude block. The 7.5 km grid size was picked for two reasons 1) we were likely to pick up at least one 30 minute VMS ping for a vessel running at fishing speed; and 2) the size (.125 degree latitude) is perfectly divisible in geographic coordinates so they fit perfectly inside a state statistical area. The grid polygons are often further divided into smaller polygons by the boundary of state statistical areas, the boundary of state and federal waters, or by the boundary of Steller sea lion critical habitat (broken out at 3, 6, 10, and 20 nautical miles from each of the 154 Steller sea lion rookeries and haulouts). Where confidentiality and mapping is an issue, seven-kilometer polygon are pre-coded for grouping into (3x3) 23km polygons. Each grid-id can queried individually or by sets of pre-coded attributes, such as reporting area and distance from Steller sea lion sites.