In this course, you will explore a variety of open-source technologies for working with geosptial data, performing spatial analysis, and undertaking general data science. The first component of the class focuses on the use of QGIS and associated technologies (GDAL, PROJ, GRASS, SAGA, and Orfeo Toolbox). The second component of the class introduces Python and associated open-source libraries and modules (NumPy, Pandas, Matplotlib, Seaborn, GeoPandas, Rasterio, WhiteboxTools, and Scikit-Learn) used by geospatial scientists and data scientists. We also provide an introduction to Structured Query Language (SQL) for performing table and spatial queries. This course is designed for individuals that have a background in GIS, such as working in the ArcGIS environment, but no prior experience using open-source software and/or coding. You will be asked to work through a series of lecture modules and videos broken into several topic areas, as outlined below. Fourteen assignments and the required data have been provided as hands-on opportunites to work with data and the discussed technologies and methods. If you have any questions or suggestions, feel free to contact us. We hope to continue to update and improve this course. This course was produced by West Virginia View (http://www.wvview.org/) with support from AmericaView (https://americaview.org/). This material is based upon work supported by the U.S. Geological Survey under Grant/Cooperative Agreement No. G18AP00077. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey. After completing this course you will be able to: apply QGIS to visualize, query, and analyze vector and raster spatial data. use available resources to further expand your knowledge of open-source technologies. describe and use a variety of open data formats. code in Python at an intermediate-level. read, summarize, visualize, and analyze data using open Python libraries. create spatial predictive models using Python and associated libraries. use SQL to perform table and spatial queries at an intermediate-level.

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.

LANDISVIEW is a tool, developed at the Knowledge Engineering Laboratory at Texas A&M University, to visualize and animate 8-bit/16-bit ERDAS GIS format (e.g., LANDIS and LANDIS-II output maps). It can also convert 8-bit/16-bit ERDAS GIS format into ASCII and batch files. LANDISVIEW provides two major functions: 1) File Viewer: Files can be viewed sequentially and an output can be generated as a movie file or as an image file. 2) File converter: It will convert the loaded files for compatibility with 3rd party software, such as Fragstats, a widely used spatial analysis tool. Some available features of LANDISVIEW include: 1) Display cell coordinates and values. 2) Apply user-defined color palette to visualize files. 3) Save maps as pictures and animations as video files (*.avi). 4) Convert ERDAS files into ASCII grids for compatibility with Fragstats. (Source: http://kelab.tamu.edu/)

Microsoft recently released a free set of deep learning generated building footprints covering the United States of America. As part of that project Microsoft shared 8 million digitized building footprints with height information used for training the Deep Learning Algorithm. This map layer includes all buildings with height information for the original training set that can be used in scene viewer and ArcGIS pro to create simple 3D representations of buildings. Learn more about the Microsoft Project at the Announcement Blog or the raw data is available at Github.Click see Microsoft Building Layers in ArcGIS Online.Digitized building footprint by State and City

Alabama Greater Phoenix City, Mobile, and Montgomery

Arizona Tucson

Arkansas Little Rock with 5 buildings just across the river from Memphis

California Bakersfield, Fresno, Modesto, Santa Barbara, Sacramento, Stockton, Calaveras County, San Fran & bay area south to San Jose and north to Cloverdale

Colorado Interior of Denver

Connecticut Enfield and Windsor Locks

Delaware Dover

Florida Tampa, Clearwater, St. Petersburg, Orlando, Daytona Beach, Jacksonville and Gainesville

Georgia Columbus, Atlanta, and Augusta

Illinois East St. Louis, downtown area, Springfield, Champaign and Urbana

Indiana Indianapolis downtown and Jeffersonville downtown

Iowa Des Moines

Kansas Topeka

Kentucky Louisville downtown, Covington and Newport

Louisiana Shreveport, Baton Rouge and center of New Orleans

Maine Augusta and Portland

Maryland Baltimore

Massachusetts Boston, South Attleboro, commercial area in Seekonk, and Springfield

Michigan Downtown Detroit

Minnesota Downtown Minneapolis

Mississippi Biloxi and Gulfport

Missouri Downtown St. Louis, Jefferson City and Springfield

Nebraska Lincoln

Nevada Carson City, Reno and Los Vegas

New Hampshire Concord

New Jersey Camden and downtown Jersey City

New Mexico Albuquerque and Santa Fe

New York Syracuse and Manhattan

North Carolina Greensboro, Durham, and Raleigh

North Dakota Bismarck

Ohio Downtown Cleveland, downtown Cincinnati, and downtown Columbus

Oklahoma Downtown Tulsa and downtown Oklahoma City

Oregon Portland

Pennsylvania Downtown Pittsburgh, Harrisburg, and Philadelphia

Rhode Island The greater Providence area

South Carolina Greensville, downtown Augsta, greater Columbia area and greater Charleston area

South Dakota greater Pierre area

Tennessee Memphis and Nashville

Texas Lubbock, Longview, part of Fort Worth, Austin, downtown Houston, and Corpus Christi

Utah Salt Lake City downtown

Virginia Richmond

Washington Greater Seattle area to Tacoma to the south and Marysville to the north

Wisconsin Green Bay, downtown Milwaukee and Madison

Wyoming Cheyenne

This resource contains data inputs and a Jupyter Notebook that is used to introduce Hydrologic Analysis using Terrain Analysis Using Digital Elevation Models (TauDEM) and Python. TauDEM is a free and open-source set of Digital Elevation Model (DEM) tools developed at Utah State University for the extraction and analysis of hydrologic information from topography. This resource is part of a HydroLearn Physical Hydrology learning module available at https://edx.hydrolearn.org/courses/course-v1:Utah_State_University+CEE6400+2019_Fall/about

In this activity, the student learns how to (1) derive hydrologically useful information from Digital Elevation Models (DEMs); (2) describe the sequence of steps involved in mapping stream networks, catchments, and watersheds; and (3) compute an approximate water balance for a watershed-based on publicly available data.

Please note that this exercise is designed for the Logan River watershed, which drains to USGS streamflow gauge 10109000 located just east of Logan, Utah. However, this Jupyter Notebook and the analysis can readily be applied to other locations of interest. If running the terrain analysis for other study sites, you need to prepare a DEM TIF file, an outlet shapefile for the area of interest, and the average annual streamflow and precipitation data. - There are several sources to obtain DEM data. In the U.S., the DEM data (with different spatial resolutions) can be obtained from the National Elevation Dataset available from the national map (http://viewer.nationalmap.gov/viewer/). Another DEM data source is the Shuttle Radar Topography Mission (https://www2.jpl.nasa.gov/srtm/), an international research effort that obtained digital elevation models on a near-global scale (search for Digital Elevation at https://www.usgs.gov/centers/eros/science/usgs-eros-archive-products-overview?qt-science_center_objects=0#qt-science_center_objects). - If not already available, you can generate the outlet shapefile by applying basic terrain analysis steps in geospatial information system models such as ArcGIS or QGIS. - You also need to obtain average annual streamflow and precipitation data for the watershed of interest to assess the annual water balance and calculate the runoff ratio in this exercise. In the U.S., the streamflow data can be obtained from the USGS NWIS website (https://waterdata.usgs.gov/nwis) and the precipitation from PRISM (https://prism.oregonstate.edu/normals/). Note that using other datasets may require preprocessing steps to make data ready to use for this exercise.

GIS Online Moisture Sensor Market Outlook

According to our latest research, the global GIS online moisture sensor market size reached USD 1.12 billion in 2024, reflecting robust adoption across key sectors such as agriculture, environmental monitoring, and industrial process control. The market is projected to grow at a CAGR of 8.7% from 2025 to 2033, reaching an estimated value of USD 2.43 billion by 2033. This impressive growth is primarily driven by the increasing demand for precision agriculture, advancements in sensor technologies, and the growing need for real-time environmental data to support sustainable resource management.

One of the primary growth factors fueling the GIS online moisture sensor market is the surging adoption of precision agriculture techniques worldwide. Farmers and agribusinesses are increasingly leveraging advanced moisture sensing technologies integrated with GIS platforms to monitor soil conditions, optimize irrigation schedules, and enhance crop yields. The ability to access real-time moisture data remotely has transformed traditional farming practices, allowing for data-driven decisions that conserve water and reduce operational costs. This trend is further supported by government initiatives and subsidies promoting smart farming solutions, particularly in regions facing water scarcity or climate variability. As a result, the integration of GIS and online moisture sensors has become a cornerstone in the modernization of agricultural operations, driving sustained market expansion.

Another significant driver for the GIS online moisture sensor market is the escalating focus on environmental monitoring and industrial process control. Industries such as construction, mining, and manufacturing are increasingly required to adhere to stringent environmental regulations, necessitating continuous monitoring of moisture levels in soil, air, and materials. GIS-enabled online moisture sensors provide accurate, location-based data that supports compliance, risk management, and process optimization. In addition, the proliferation of smart city initiatives and the expansion of IoT infrastructure have amplified the deployment of these sensors in urban planning, flood prediction, and infrastructure maintenance. The convergence of GIS and online sensor technologies enables seamless data visualization and analysis, making them indispensable tools for both public and private sector stakeholders.

Technological advancements in sensor design and connectivity are also playing a pivotal role in the market's growth trajectory. Innovations such as wireless and cloud-connected moisture sensors, improved accuracy through advanced materials, and miniaturization have broadened the scope of applications. These advancements have resulted in more cost-effective, durable, and easy-to-deploy solutions, fostering adoption across diverse end-user segments. Furthermore, the integration of AI and machine learning algorithms with GIS platforms is enabling predictive analytics and automated decision-making, further enhancing the value proposition of online moisture sensors. As the demand for actionable insights and real-time monitoring continues to rise, the GIS online moisture sensor market is poised for sustained innovation and expansion.

Regionally, North America and Europe are leading the market, driven by early adoption of precision agriculture, robust regulatory frameworks, and substantial investments in R&D. Asia Pacific, however, is emerging as the fastest-growing region, propelled by rapid urbanization, increasing awareness of sustainable agricultural practices, and government support for smart farming initiatives. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as industries in these regions recognize the benefits of GIS-enabled moisture monitoring for resource optimization and environmental management. Overall, the global market is characterized by dynamic regional trends, with each geography contributing uniquely to the market's evolution.

Prod

Total file size: about 367M in zip format and about 600M after extracted. (To download: click the Download button at the upper right area of this page)Alternatively, you can download the data by chapters:- Go to https://go.esri.com/gtkwebgis4- Under Group Categories on the left, click each chapter, you will see the data file to download for that chapter.

THE EARTH SCIENCE GEOINQUIRY COLLECTION

http://www.esri.com/geoinquiries

To support Esri’s involvement in the White House ConnectED Initiative, GeoInquiry instructional materials using ArcGIS Online for Earth Science education are now freely available.

The Earth Science GeoInquiry collection contains 15 free, web-mapping activities that correspond and extend map-based concepts in leading middle school Earth science textbooks. The activities, developed with GISetc of Dallas, TX use a standard inquiry-based instructional model, require only 15 minutes for a teacher to deliver, and are device agnostic. The activities harmonize with the Next Generation Science Standards. Activity topics include:

Teachers, GeoMentors, and administrators can learn more at http://www.esri.com/geoinquiries

THE EARTH SCIENCE GEOINQUIRY COLLECTION

http://www.esri.com/geoinquiries

To support Esri’s involvement in the White House ConnectED Initiative, GeoInquiry instructional materials using ArcGIS Online for Earth Science education are now freely available.

The Earth Science GeoInquiry collection contains 15 free, web-mapping activities that correspond and extend map-based concepts in leading middle school Earth science textbooks. The activities, developed with GISetc of Dallas, TX use a standard inquiry-based instructional model, require only 15 minutes for a teacher to deliver, and are device agnostic. The activities harmonize with the Next Generation Science Standards. Activity topics include:

Teachers, GeoMentors, and administrators can learn more at http://www.esri.com/geoinquiries

CanVec contains more than 60 topographic features classes organized into 8 themes: Transport Features, Administrative Features, Hydro Features, Land Features, Manmade Features, Elevation Features, Resource Management Features and Toponymic Features. This multiscale product originates from the best available geospatial data sources covering Canadian territory. It offers quality topographic information in vector format complying with international geomatics standards. CanVec can be used in Web Map Services (WMS) and geographic information systems (GIS) applications and used to produce thematic maps. Because of its many attributes, CanVec allows for extensive spatial analysis. Related Products: Constructions and Land Use in Canada - CanVec Series - Manmade Features Lakes, Rivers and Glaciers in Canada - CanVec Series - Hydrographic Features Administrative Boundaries in Canada - CanVec Series - Administrative Features Mines, Energy and Communication Networks in Canada - CanVec Series - Resources Management Features Wooded Areas, Saturated Soils and Landscape in Canada - CanVec Series - Land Features Transport Networks in Canada - CanVec Series - Transport Features Elevation in Canada - CanVec Series - Elevation Features Map Labels - CanVec Series - Toponymic Features

This data was collected by the U.S. Bureau of Land Management (BLM) in New Mexico at both the New Mexico State Office and at the various field offices. This dataset is meant to depict the surface owner or manager of the land parcels. In the vast majority of land parcels, they will be one and the same. However, there are instances where the owner and manager of the land surface are not the same. When this occurs, the manager of the land is usually indicated. BLM's Master Title Plats are the official land records of the federal government and serve as the primary data source for depiction of all federal lands. Information from State of New Mexico is the primary source for the depiction of all state lands. Auxilliary source are referenced, as well, for the depiction of all lands. Collection of this dataset began in the 1980's using the BLM's ADS software to digitize information at the 1:24,000 scale. In the mid to late 1990's the data was converted from ADS to ArcInfo software and merged into tiles of one degree of longitude by one half degree of latitude. These tiles were regularly updated. The tiles were merged into a statewide coverage. The source geodatabase for this shapefile was created by loading the merged ArcInfo coverage into a personal geodatabase. The geodatabase data were snapped to a more accurate GCDB derived land network, where available. In areas where GCDB was not available the data were snapped to digitized PLSS. In 2006, the personal geodatabase was loaded into an enterprise geodatabase (SDE). This shapefile has been created by exporting the feature class from SDE.

This layer is a time series of the annual ESA CCI (Climate Change Initiative) land cover maps of the world. ESA has produced land cover maps for the years since 1992. These are available at the European Space Agency Climate Change Initiative website.Time Extent: 1992-2019Cell Size: 300 meterSource Type: ThematicPixel Type: 8 Bit UnsignedData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: ESA Climate Change InitiativeUpdate Cycle: AnnualWhat can you do with this layer?This layer may be added to ArcGIS Online maps and applications and shown in a time series to watch a "time lapse" view of land cover change since 1992 for any part of the world. The same behavior exists when the layer is added to ArcGIS Pro.In addition to displaying all layers in a series, this layer may be queried so that only one year is displayed in a map. This layer can be used in analysis. For example, the layer may be added to ArcGIS Pro with a query set to display just one year. Then, an area count of land cover types may be produced for a feature dataset using the zonal statistics tool. Statistics may be compared with the statistics from other years to show a trend.To sum up area by land cover using this service, or any other analysis, be sure to use an equal area projection, such as Albers or Equal Earth.Different Classifications Available to MapFive processing templates are included in this layer. The processing templates may be used to display a smaller set of land cover classes.Cartographic Renderer (Default Template)Displays all ESA CCI land cover classes.*Forested lands TemplateThe forested lands template shows only forested lands (classes 50-90).Urban Lands TemplateThe urban lands template shows only urban areas (class 190).Converted Lands TemplateThe converted lands template shows only urban lands and lands converted to agriculture (classes 10-40 and 190).Simplified RendererDisplays the map in ten simple classes which match the ten simplified classes used in 2050 Land Cover projections from Clark University.Any of these variables can be displayed or analyzed by selecting their processing template. In ArcGIS Online, select the Image Display Options on the layer. Then pull down the list of variables from the Renderer options. Click Apply and Close. In ArcGIS Pro, go into the Layer Properties. Select Processing Templates from the left hand menu. From the Processing Template pull down menu, select the variable to display.Using TimeBy default, the map will display as a time series animation, one year per frame. A time slider will appear when you add this layer to your map. To see the most current data, move the time slider until you see the most current year.In addition to displaying the past quarter century of land cover maps as an animation, this time series can also display just one year of data by use of a definition query. For a step by step example using ArcGIS Pro on how to display just one year of this layer, as well as to compare one year to another, see the blog called Calculating Impervious Surface Change.Hierarchical ClassificationLand cover types are defined using the land cover classification (LCCS) developed by the United Nations, FAO. It is designed to be as compatible as possible with other products, namely GLCC2000, GlobCover 2005 and 2009.This is a heirarchical classification system. For example, class 60 means "closed to open" canopy broadleaved deciduous tree cover. But in some places a more specific type of broadleaved deciduous tree cover may be available. In that case, a more specific code 61 or 62 may be used which specifies "open" (61) or "closed" (62) cover.Land Cover ProcessingTo provide consistency over time, these maps are produced from baseline land cover maps, and are revised for changes each year depending on the best available satellite data from each period in time. These revisions were made from AVHRR 1km time series from 1992 to 1999, SPOT-VGT time series between 1999 and 2013, and PROBA-V data for years 2013, 2014 and 2015. When MERIS FR or PROBA-V time series are available, changes detected at 1 km are re-mapped at 300 m. The last step consists in back- and up-dating the 10-year baseline LC map to produce the 24 annual LC maps from 1992 to 2015.Source dataThe datasets behind this layer were extracted from NetCDF files and TIFF files produced by ESA. Years 1992-2015 were acquired from ESA CCI LC version 2.0.7 in TIFF format, and years 2016-2018 were acquired from version 2.1.1 in NetCDF format. These are downloadable from ESA with an account, after agreeing to their terms of use. https://maps.elie.ucl.ac.be/CCI/viewer/download.phpCitationESA. Land Cover CCI Product User Guide Version 2. Tech. Rep. (2017). Available at: maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdfMore technical documentation on the source datasets is available here:https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=doc*Index of all classes in this layer:10 Cropland, rainfed11 Herbaceous cover12 Tree or shrub cover20 Cropland, irrigated or post-flooding30 Mosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%)40 Mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%)50 Tree cover, broadleaved, evergreen, closed to open (>15%)60 Tree cover, broadleaved, deciduous, closed to open (>15%)61 Tree cover, broadleaved, deciduous, closed (>40%)62 Tree cover, broadleaved, deciduous, open (15-40%)70 Tree cover, needleleaved, evergreen, closed to open (>15%)71 Tree cover, needleleaved, evergreen, closed (>40%)72 Tree cover, needleleaved, evergreen, open (15-40%)80 Tree cover, needleleaved, deciduous, closed to open (>15%)81 Tree cover, needleleaved, deciduous, closed (>40%)82 Tree cover, needleleaved, deciduous, open (15-40%)90 Tree cover, mixed leaf type (broadleaved and needleleaved)100 Mosaic tree and shrub (>50%) / herbaceous cover (<50%)110 Mosaic herbaceous cover (>50%) / tree and shrub (<50%)120 Shrubland121 Shrubland evergreen122 Shrubland deciduous130 Grassland140 Lichens and mosses150 Sparse vegetation (tree, shrub, herbaceous cover) (<15%)151 Sparse tree (<15%)152 Sparse shrub (<15%)153 Sparse herbaceous cover (<15%)160 Tree cover, flooded, fresh or brakish water170 Tree cover, flooded, saline water180 Shrub or herbaceous cover, flooded, fresh/saline/brakish water190 Urban areas200 Bare areas201 Consolidated bare areas202 Unconsolidated bare areas210 Water bodies

THE EARTH SCIENCE GEOINQUIRY COLLECTION

To support Esri’s involvement in the White House ConnectED Initiative, GeoInquiry instructional materials using ArcGIS Online for Earth Science education are now freely available.

The Earth Science GeoInquiry collection contains 15 free, web-mapping activities that correspond and extend map-based concepts in leading middle school Earth science textbooks. The activities, developed with GISetc of Dallas, TX use a standard inquiry-based instructional model, require only 15 minutes for a teacher to deliver, and are device agnostic. The activities harmonize with the Next Generation Science Standards. Activity topics include:

Teachers, GeoMentors, and administrators can learn more at www.esri.com/geoinquiries

THE U.S. HISTORY GEOINQUIRY COLLECTIONhttp://www.esri.com/geoinquiriesTo support Esri’s involvement in the White House ConnectED Initiative, GeoInquiry instructional materials using ArcGIS Online for Earth Science education are now freely available. The U.S. History GeoInquiry collection contains 15 free, web-mapping activities that correspond and extend map-based concepts in leading high school U.S. History textbooks. The activities use a standard inquiry-based instructional model, require only 15 minutes for a teacher to deliver, and are device agnostic. The activities harmonize with the C3 curriculum standards for social studies education. Activity topics include:· The Great Exchange· The 13 Colonies - 1700s· The War Before Independence (The American Revolution)· The War of 1812· Westward, ho! (Trails west)· The Underground Railroad· From Compromise to Conflict· A nation divided: The Civil War· Native American Lands· Steel and the birth of a city (natural resources)· World War I· Dust Bowl· A day that lived in infamy (Pearl Harbor)· Operation Overlord - D-Day· Hot spots in the Cold WarTeachers, GeoMentors, and administrators can learn more at http://www.esri.com/geoinquiries

This activity observes, compares, and contrasts the natural resources throughout the five regions of the U.S.THE GRADE 4 INTERDISCIPLINARY GEOINQUIRY COLLECTIONhttp://www.esri.com/geoinquiriesTo support Esri’s involvement in the White House ConnectED Initiative, GeoInquiry instructional materials using ArcGIS Online for elementary grade 4 education are now freely available.The Grade 4 Interdisciplinary GeoInquiry collection contains 15 free, web-mapping activities that correspond and extend map-based concepts in leading elementary textbooks. The activities use a standard inquiry-based instructional model, require only 15 minutes for a teacher to deliver, and are device/laptop agnostic. The activities harmonize with the Next Generation Science Standards, the C3 Framework, and Common Core. Activity topics include:Teachers, GeoMentors, and administrators can learn more at http://www.esri.com/geoinquiries

THE EARTH SCIENCE GEOINQUIRY COLLECTION

http://www.esri.com/geoinquiries

To support Esri’s involvement in the White House ConnectED Initiative, GeoInquiry instructional materials using ArcGIS Online for Earth Science education are now freely available.

The Earth Science GeoInquiry collection contains 15 free, web-mapping activities that correspond and extend map-based concepts in leading middle school Earth science textbooks. The activities, developed with GISetc of Dallas, TX use a standard inquiry-based instructional model, require only 15 minutes for a teacher to deliver, and are device agnostic. The activities harmonize with the Next Generation Science Standards. Activity topics include:

Teachers, GeoMentors, and administrators can learn more at http://www.esri.com/geoinquiries

Explore the route that Lewis and Clark followed and identify elevation levels throughout the regions of the United States.THE GRADE 4 INTERDISCIPLINARY GEOINQUIRY COLLECTIONhttp://www.esri.com/geoinquiriesTo support Esri’s involvement in the White House ConnectED Initiative, GeoInquiry instructional materials using ArcGIS Online for elementary grade 4 education are now freely available.The Grade 4 Interdisciplinary GeoInquiry collection contains 15 free, web-mapping activities that correspond and extend map-based concepts in leading elementary textbooks. The activities use a standard inquiry-based instructional model, require only 15 minutes for a teacher to deliver, and are device/laptop agnostic. The activities harmonize with the Next Generation Science Standards, the C3 Framework, and Common Core. Activity topics include:Teachers, GeoMentors, and administrators can learn more at http://www.esri.com/geoinquiries

THE EARTH SCIENCE GEOINQUIRY COLLECTION

http://www.esri.com/geoinquiries

To support Esri’s involvement in the White House ConnectED Initiative, GeoInquiry instructional materials using ArcGIS Online for Earth Science education are now freely available.

The Earth Science GeoInquiry collection contains 15 free, web-mapping activities that correspond and extend map-based concepts in leading middle school Earth science textbooks. The activities, developed with GISetc of Dallas, TX use a standard inquiry-based instructional model, require only 15 minutes for a teacher to deliver, and are device agnostic. The activities harmonize with the Next Generation Science Standards. Activity topics include:Teachers, GeoMentors, and administrators can learn more at http://www.esri.com/geoinquiries

This file geodatabase contains three feature classes. It includes a point feature class showing tourist attractions in Singapore, a line feature class showing rail lines in Singapore, and a polygon feature class showing rail stations in Singapore.This dataset is used in the tutorial Get started with ArcGIS Pro, but can be used for any purpose.

Notice: this is not the latest Heat Island Severity image service.This layer contains the relative heat severity for every pixel for every city in the United States, including Alaska, Hawaii, and Puerto Rico. Heat Severity is a reclassified version of Heat Anomalies raster which is also published on this site. This data is generated from 30-meter Landsat 8 imagery band 10 (ground-level thermal sensor) from the summer of 2023.To explore previous versions of the data, visit the links below:Heat Severity - USA 2022Heat Severity - USA 2021Heat Severity - USA 2020Heat Severity - USA 2019Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter than the average temperature for that same city as a whole. Severity is measured on a scale of 1 to 5, with 1 being a relatively mild heat area (slightly above the mean for the city), and 5 being a severe heat area (significantly above the mean for the city). The absolute heat above mean values are classified into these 5 classes using the Jenks Natural Breaks classification method, which seeks to reduce the variance within classes and maximize the variance between classes. Knowing where areas of high heat are located can help a city government plan for mitigation strategies.This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.What can you do with this layer?This layer has query, identify, and export image services available. Since it is served as an image service, it is not necessary to download the data; the service itself is data that can be used directly in any Esri geoprocessing tool that accepts raster data as input.In order to click on the image service and see the raw pixel values in a map viewer, you must be signed in to ArcGIS Online, then Enable Pop-Ups and Configure Pop-Ups.Using the Urban Heat Island (UHI) Image ServicesThe data is made available as an image service. There is a processing template applied that supplies the yellow-to-red or blue-to-red color ramp, but once this processing template is removed (you can do this in ArcGIS Pro or ArcGIS Desktop, or in QGIS), the actual data values come through the service and can be used directly in a geoprocessing tool (for example, to extract an area of interest). Following are instructions for doing this in Pro.In ArcGIS Pro, in a Map view, in the Catalog window, click on Portal. In the Portal window, click on the far-right icon representing Living Atlas. Search on the acronyms “tpl” and “uhi”. The results returned will be the UHI image services. Right click on a result and select “Add to current map” from the context menu. When the image service is added to the map, right-click on it in the map view, and select Properties. In the Properties window, select Processing Templates. On the drop-down menu at the top of the window, the default Processing Template is either a yellow-to-red ramp or a blue-to-red ramp. Click the drop-down, and select “None”, then “OK”. Now you will have the actual pixel values displayed in the map, and available to any geoprocessing tool that takes a raster as input. Below is a screenshot of ArcGIS Pro with a UHI image service loaded, color ramp removed, and symbology changed back to a yellow-to-red ramp (a classified renderer can also be used): A typical operation at this point is to clip out your area of interest. To do this, add your polygon shapefile or feature class to the map view, and use the Clip Raster tool to export your area of interest as a geoTIFF raster (file extension ".tif"). In the environments tab for the Clip Raster tool, click the dropdown for "Extent" and select "Same as Layer:", and select the name of your polygon. If you then need to convert the output raster to a polygon shapefile or feature class, run the Raster to Polygon tool, and select "Value" as the field.Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource CenterDr. Ladd Keith, University of ArizonaDr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAA Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and ResiliencyDisclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Dale.Watt@tpl.org with feedback.