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.

USGS YouTube Channel Playlist for all hydrography related education webinars

This resource was created by Esri Canada Education and Research. To browse our full collection of higher-education learning resources, please visit https://hed.esri.ca/resourcefinder/.This document lists existing resources from Esri Inc. that can smoothly transition ArcMap users to using ArcGIS Pro. Resources are divided into 7 categories, ranked in order by level of expertise.To download the document (Microsoft Word format), click the Download button to the top right.To view the companion video for this document, please visit https://www.youtube.com/watch?v=Gu8NyWmvWaM.File Size: 84 KBDate Created: November 7, 2019Last Updated: November 7, 2019

Youtube Channel that has various Videos using ArcGIS to teach Geography.

Build a health resources inventory using Web AppBuilder for ArcGIS (YouTube video).This application is used by public health and human services agencies to inventory homeless, drug treatment, and other health and human service providers in a community. _Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

Disasters Resilience Pilot. Click https://www.youtube.com/watch?v=77fkGxPeX68 to watch!

Mapping incident locations from a CSV file in a web map (YouTube video).

Public Information is a configurable app template that highlights areas through authoritative data feeds and social content, allowing the public to contribute to your map. Use CasesEnhance your map by overlaying social media feeds on your operational layers. Displays geotagged social media contributions to understand what is trending through these networks centered on your theme and location. This is a good choice when you want to assess local sentiment on current events.Use a swipe tool to hide and reveal a layer within your map. This is a good choice for inspecting the difference between two scenarios. For example, you could show the difference between current sea level and a projected rise in sea level, or visualize an area before and after a tornado where the map view may want to closely inspect the difference between the scenarios at a large scale.Configurable OptionsPublic Information present content from a web map with social media feeds and can be configured using the following options:Provide a title and description, as well as configure a custom splash screen that displays when the app is first loaded.Set up an interactive layer for taking notes. This is a map notes layer contained in the web map.Enable a swipe layer and choose between vertical or horizontal orientation.Determine a default and alternate basemap to be offered in an on screen basemap widget.Enable layers to be generated via content from Instagram, Flickr, Twitter, Webcams.travel, and YouTube.Configure the ability for feature and location search.Enable or disable many UI and mapping configurable options such as overview map, bookmarks, share dialog, legend, summary information, views count, modified date, etc.Supported DevicesThis application is responsively designed to support use in browsers on desktops, mobile phones, and tablets.Data RequirementsThis application has no data requirements.Get Started This application can be created in the following ways:Click the Create a Web App button on this pageShare a map and choose to Create a Web AppOn the Content page, click Create - App - From Template Click the Download button to access the source code. Do this if you want to host the app on your own server and optionally customize it to add features or change styling.

Where to Fish For Application

New to the state? New to fishing? Want to find a new place to fish? Have you always wanted to catch a Black Crappie but don’t know where to go? Well then, check out CT DEEP Fisheries Division’s “'https://experience.arcgis.com/experience/e02d8026889042c7aa2d71be67a72b9c' rel='nofollow ugc'>Where to fish for…” application.

This simple interactive map, co-developed by CT DEEP GIS and the Fisheries Division is intended to augment our “Fish with CARE” learn to fish events. At the events, the most common question by far was, “Where can I fish near me…”. This map provides the ideal solution for both novice and experienced anglers alike. The data powering the map was collected over the past decade through routine monitoring by the Fisheries Division of public lakes, ponds and the CT River. This new map joins two previously developed interactive maps, one for Rivers/Streams (Interactive Trout Stocking Map) and one for our shoreline access, charter boats, and tackle shops (Saltwater Resources map) also help to get people onto the fish.

This video provides a quick tutorial on some of the key features.

Gridded National Soil Survey Geographic Database (gNATSGO)gNATSGO for AllRaster Soil Surveys (RSS)Web Soil SurveyGridded Soil Survey Geographic (gSSURGO) DatabasegNATSGO now uses the GeoPackage version of the SQLite SSURGO Template database instead of an ESRI File Geodatabase.

In 2025, only a single large gNATSGO database was created for all areas of the United States and Island Jurisdictions. State tiles were not produced.

This database is designed to be used with the new SSURGO Portal application Soil Data Viewer (SDV) tool, which has the same ratings as Web Soil Survey.
Access SSURGO Portal at https://www.nrcs.usda.gov/resources/data-and-reports/ssurgo-portal. Refer to the Quick Start Guide for SSURGO Portal installation instructions.
You can install SSURGO Portal and then use SDV to make thematic maps for the entire United States. Refer to the SSURGO Portal User Guide for SDV instructions. You can also refer to the 6 minute mark of this youtube video. https://www.youtube.com/watch?v=4FGuxqxbCG0 SDV is a replacement for the old Soil Data Viewer ArcMap tool and the old GSSURGO Create Soil Map ArcMap tool.

DB Browser is a free application for viewing and querying SQLite files. GeoPackages are SQLite files and can be opened in DB Browser.

Rasters of soil map units are delivered as 30m cell size tif files, with dbf attribute tables, statistics, and pyramids pre built.

The source.shp file shows the original source of data, with the options being Raster Soil Survey (RSS), SSURGO, and STATSGO.

The mupolygon, mupoint, muline, featpoint, featline, featdesc, and sapolygon feature classes with the database are empty but were retained due to database schema requirements.

Contact soilshotline@usda.gov for assistance.

Communication and Image - Establish a business spotlight: interviews filmed in the Clayton TV studios and posted on YouTube channels.

Author: Miriam Miriam, Crash CourseGrade/Audience: ap human geographyResource type: warm_upSubject topic(s): environmental, historyRegion: worldStandards: Objectives: See APHG course description/CEDSummary: Short video from Crash Course that reviews the concept of Environmental Determinism. Developed for AP Human Geography classes.

https://www.youtube.com/watch?v=9mGvZXvRNLs

This data set is a harmonized collection of existing data from GBIF, the EU-Forest project and the LUCAS survey. It has about 3 million observations and is supplemented by variables (e.g. location accuracy, land cover type, canopy height, etc.) which enable precise filtering for specific user applications.

The RDS file is created from an sf-object and suitable for fast reading in the R-programming environment. The CSV.GZ file contains records as a table with Easting and Northing in Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035) and can be fed in a GIS after being unzipped.

The code producing this data set is publicly available on GitLab.

Variables:

• id = unique point identifier
• easting = x coordinate
• northing = y coordinate
• country = ISO country code
• species = Latin species name
• genus = genus name
• scientific_name = long species name
• gbif_taxon_key = taxon key from GBIF
• gbif_genus_key = genus key from GBIF
• taxon_rank = species or genus
• year = year of observation
• accessed_through = database through which data was accessed (GBIF, LUCAS, EU-Forest)
• dataset_info = data set name (individual sub-data-set)
• citation = DOI citation of the individual data set
• license = distribution license
• location_accuracy = spatial accuracy of observation (meters)
• flag_location_issue = known location issues present
• flag_date_issue = known date issues present
• eoo = Extent of occurrence (applying the concept of natural geographical range used for the EU-Forest data set (Mauri et al., 2017) to all other data points. 1 = point inside species range; 0 = point outside; NA = EOO polygon not available for this species)
• dbh = Diameter Breast Height (only recorded for observations from the EU-Forest data set (Mauri et al., 2017))
• lc1 = LUCAS land cover type 1 (only recorded for observations from LUCAS data)
• lc2 = LUCAS land cover type 2 (only recorded for observations from LUCAS data)
• landmask_country = land mask overlay 30 meters (NA = not on land)
• corine = CORINE 2018 land cover type (extracted from the 100 meter raster data set)
• nightlights = light pollution observed by VIIRS (proxy for remoteness / distance to human structures)
• canopy_height = canopy height derived from GEDI waveform LiDAR point data
• natura_2000 = Natura 2000 site code (if a point falls inside a protected area (GIS-layer) this variable contains the site identification code; all sites can be explored on an interactive map)
• freq_location = number of points with identical location (in some cases one location has multiple observation, differing in species and/or year. This may lead to difficulties in certain modeling tasks)
• geometry = point geometry in ETRS89 / LAEA Europe

See this detailed documentation for more insights into each variable.

If you would like to know more about the creation of this data set, see

1. the R-Markdown documenting the process (GitLab repository)
2. the talk at OpenGeoHub Summer School 2020 (Youtube)

Some advice: This data set is a puzzle with pieces from many different sources. Take some time to explore before including it in your work. Use summary statistics to see which variables have NAs and how many. Choose your filtering criteria wisely. For example, some points with the highest location accuracy have no record for the year of observations. You would exclude these, if "year > 1990" was your criteria.

This work has received funding from the European Union's the Innovation and Networks Executive Agency (INEA) under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 (https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095).

Este Story Map Journal ofrece la dimensión geográfica de la Crisis Fiscal, con un énfasis en las quiebras Capítulo 11 en Puerto Rico desde 2008 hasta el presente. De igual modo, hacemos una comparación entre el Capítulo 11 y el Capítulo 7 de Guaynabo, segundo municipio con más quiebras. Finalmente, se muestra cómo se ha manifestado la Crisis Fiscal en el paisaje urbano de la Avenida F.D. Roosevelt a través de un inventario de sus negocios.Capas de informaciónGeographic Mapping Technologies, Corp. Población e ingreso 2010.Documentos en PDF US Bankruptcy Court District of Puerto Rico. (2016) Casos ante la Ley de Quiebras Capítulo 7 y Capítulo 11 01/01/2008-09/26/2016. Documento PDF, p.1-306. ImágenesBarreiro Morales, C. (2016) Universidad, la estación de tren [Fotografía]. ---. (2016) Vista a la costa sur [Fotografía]. ---. (2016) Vista a la sierra desde la carretera [Fotografía]. Barreiro Morales, C. & Rivera Vilches, A. (2016) Negocios en desuso en la Avenida FD Roosevelt [Fotografía]. Rivera Vilches, A. (2016) Los Tres Picachos, Barrio Coabey, Jayuya [Fotografía]. National Aeronautics and Space Administration. (2016). Puerto Rico Goes Dark [Fotografía]. Retirado de http://earthobservatory.nasa.gov/IOTD/view.php?id=88796&linkId=29141575.

Mapas webUFT. Fotos aéreas 1998 del CRIM [ArcGIS Online Web Map]. Retirado de https://www.satasgis.crimpr.net/crimgis/rest/services/Mapas/Imagen_1998/MapServer.

Páginas de la web

https://data.pr.gov/en/Transportaci-n/Annual-Average-Daily-Traffic-AADT-Transito-Promedi/7kaq-zyym/data

http://www.prb.uscourts.gov/

Reportajes de periódico

Díaz Román, M. (2005, 1 de marzo). En la quiebra la mitad de los municipios. El Nuevo Día, p.6.

García Pelatti, L. (2009, 31 de marzo). Se disparan las quiebras. El Vocero, p.37.

Marrero, R. (2009, 9 de noviembre). Alarmante alza en quiebras boricuas. Primera Hora, p.4.

VideosBarreiro Morales, C. & Rivera Vilches, A. (2016) Time Lapse del tráfico en la Avenida FD Roosevelt. Portafolio Global CNN: La Crisis de Puerto Rico según Alejandro Garcia Padilla, Pt. 1. Retirado de: https://www.youtube.com/watch?v=ROZpPNoi0EY

Autoras

Cecilia Barreiro Morales

Andrea S. Rivera Vilches

Network screening analysis data of road lanes with speed changes in Pennsylvania completed in 2024. Data can be filtered by county, planning partner and engineering district.Notes from the PennDOT HSNS Video https://www.youtube.com/watch?v=liXTnqxZjCgNetwork screening analysis can be used for safety analysis and decision making to decrease frequency and severity of crashes in Pennsylvania.Network screening is a method from the Highway Safety Manual (HSM) that compares expected crash frequencies and crash severities to historical crash data based on Part C of HSM. It helps evaluate facilities and identify and prioritize locations that are likely to respond to safety improvement investments. FHWA states that employing traditional networking screening with systemic safety analysis can be an agency’s first step toward a comprehensive safety management program. The network screening is the first step in the Roadway Safety Management Process (Part B of the HSM) and it considers crash history, roadway factors and traffic characteristics.Roadway Safety Management Process (Part B of the HSM) Steps Network Screening Diagnosis Select Countermeasures Economic Appraisal Prioritize Projects Safety effectiveness evaluationRoadway safety management process parallels the method by which PennDOT selects and evaluates projects for Federal Highway Safety Improvement Program.SPF: safety performance functionPositive/high excess cost locations are good candidates for safety improvements.Urban: sites within urban boundaries (Census) where population is more than 5,000 people.Rural: sites outside of urban boundaries (Census) where population is less than 5,000 people.Crashes within 250 feet of an intersection are assigned to the intersection for analysis.

Notes from the PennDOT HSNS Video https://www.youtube.com/watch?v=liXTnqxZjCgNetwork screening analysis can be used for safety analysis and decision making to decrease frequency and severity of crashes in Pennsylvania.Network screening is a method from the Highway Safety Manual (HSM) that compares expected crash frequencies and crash severities to historical crash data based on Part C of HSM. It helps evaluate facilities and identify and prioritize locations that are likely to respond to safety improvement investments. FHWA states that employing traditional networking screening with systemic safety analysis can be an agency’s first step toward a comprehensive safety management program. The network screening is the first step in the Roadway Safety Management Process (Part B of the HSM) and it considers crash history, roadway factors and traffic characteristics.Roadway Safety Management Process (Part B of the HSM) Steps Network Screening Diagnosis Select Countermeasures Economic Appraisal Prioritize Projects Safety effectiveness evaluationRoadway safety management process parallels the method by which PennDOT selects and evaluates projects for Federal Highway Safety Improvement Program.SPF: safety performance functionPositive/high excess cost locations are good candidates for safety improvements.Urban: sites within urban boundaries (Census) where population is more than 5,000 people.Rural: sites outside of urban boundaries (Census) where population is less than 5,000 people.Crashes within 250 feet of an intersection are assigned to the intersection for analysis.

RTB Maps is a cloud-based electronic Atlas. We used ArGIS 10 for Desktop with Spatial Analysis Extension, ArcGIS 10 for Server on-premise, ArcGIS API for Javascript, IIS web services based on .NET, and ArcGIS Online combining data on the cloud with data and applications on our local server to develop an Atlas that brings together many of the map themes related to development of roots, tubers and banana crops. The Atlas is structured to allow our participating scientists to understand the distribution of the crops and observe the spatial distribution of many of the obstacles to production of these crops. The Atlas also includes an application to allow our partners to evaluate the importance of different factors when setting priorities for research and development. The application uses weighted overlay analysis within a multi-criteria decision analysis framework to rate the importance of factors when establishing geographic priorities for research and development.Datasets of crop distribution maps, agroecology maps, biotic and abiotic constraints to crop production, poverty maps and other demographic indicators are used as a key inputs to multi-objective criteria analysis.Further metadata/references can be found here: http://gisweb.ciat.cgiar.org/RTBmaps/DataAvailability_RTBMaps.htmlDISCLAIMER, ACKNOWLEDGMENTS AND PERMISSIONS:This service is provided by Roots, Tubers and Bananas CGIAR Research Program as a public service. Use of this service to retrieve information constitutes your awareness and agreement to the following conditions of use.This online resource displays GIS data and query tools subject to continuous updates and adjustments. The GIS data has been taken from various, mostly public, sources and is supplied in good faith.RTBMaps GIS Data Disclaimer• The data used to show the Base Maps is supplied by ESRI.• The data used to show the photos over the map is supplied by Flickr.• The data used to show the videos over the map is supplied by Youtube.• The population map is supplied to us by CIESIN, Columbia University and CIAT.• The Accessibility map is provided by Global Environment Monitoring Unit - Joint Research Centre of the European Commission. Accessibility maps are made for a specific purpose and they cannot be used as a generic dataset to represent "the accessibility" for a given study area.• Harvested area and yield for banana, cassava, potato, sweet potato and yam for the year 200, is provided by EarthSat (University of Minnesota’s Institute on the Environment-Global Landscapes initiative and McGill University’s Land Use and the Global Environment lab). Dataset from Monfreda C., Ramankutty N., and Foley J.A. 2008.• Agroecology dataset: global edapho-climatic zones for cassava based on mean growing season, temperature, number of dry season months, daily temperature range and seasonality. Dataset from CIAT (Carter et al. 1992)• Demography indicators: Total and Rural Population from Center for International Earth Science Information Network (CIESIN) and CIAT 2004.• The FGGD prevalence of stunting map is a global raster datalayer with a resolution of 5 arc-minutes. The percentage of stunted children under five years old is reported according to the lowest available sub-national administrative units: all pixels within the unit boundaries will have the same value. Data have been compiled by FAO from different sources: Demographic and Health Surveys (DHS), UNICEF MICS, WHO Global Database on Child Growth and Malnutrition, and national surveys. Data provided by FAO – GIS Unit 2007.• Poverty dataset: Global poverty headcount and absolute number of poor. Number of people living on less than $1.25 or $2.00 per day. Dataset from IFPRI and CIATTHE RTBMAPS GROUP MAKES NO WARRANTIES OR GUARANTEES, EITHER EXPRESSED OR IMPLIED AS TO THE COMPLETENESS, ACCURACY, OR CORRECTNESS OF THE DATA PORTRAYED IN THIS PRODUCT NOR ACCEPTS ANY LIABILITY, ARISING FROM ANY INCORRECT, INCOMPLETE OR MISLEADING INFORMATION CONTAINED THEREIN. ALL INFORMATION, DATA AND DATABASES ARE PROVIDED "AS IS" WITH NO WARRANTY, EXPRESSED OR IMPLIED, INCLUDING BUT NOT LIMITED TO, FITNESS FOR A PARTICULAR PURPOSE. By accessing this website and/or data contained within the databases, you hereby release the RTB group and CGCenters, its employees, agents, contractors, sponsors and suppliers from any and all responsibility and liability associated with its use. In no event shall the RTB Group or its officers or employees be liable for any damages arising in any way out of the use of the website, or use of the information contained in the databases herein including, but not limited to the RTBMaps online Atlas product.APPLICATION DEVELOPMENT:• Desktop and web development - Ernesto Giron E. (GeoSpatial Consultant) e.giron.e@gmail.com• GIS Analyst - Elizabeth Barona. (Independent Consultant) barona.elizabeth@gmail.comCollaborators:Glenn Hyman, Bernardo Creamer, Jesus David Hoyos, Diana Carolina Giraldo Soroush Parsa, Jagath Shanthalal, Herlin Rodolfo Espinosa, Carlos Navarro, Jorge Cardona and Beatriz Vanessa Herrera at CIAT, Tunrayo Alabi and Joseph Rusike from IITA, Guy Hareau, Reinhard Simon, Henry Juarez, Ulrich Kleinwechter, Greg Forbes, Adam Sparks from CIP, and David Brown and Charles Staver from Bioversity International.Please note these services may be unavailable at times due to maintenance work.Please feel free to contact us with any questions or problems you may be having with RTBMaps.

Network screening analysis data of ramps in Pennsylvania completed in 2024. Data can be filtered by county, planning partner and engineering district.Notes from the PennDOT HSNS Video https://www.youtube.com/watch?v=liXTnqxZjCgNetwork screening analysis can be used for safety analysis and decision making to decrease frequency and severity of crashes in Pennsylvania.Network screening is a method from the Highway Safety Manual (HSM) that compares expected crash frequencies and crash severities to historical crash data based on Part C of HSM. It helps evaluate facilities and identify and prioritize locations that are likely to respond to safety improvement investments. FHWA states that employing traditional networking screening with systemic safety analysis can be an agency’s first step toward a comprehensive safety management program. The network screening is the first step in the Roadway Safety Management Process (Part B of the HSM) and it considers crash history, roadway factors and traffic characteristics.Roadway Safety Management Process (Part B of the HSM) Steps Network Screening Diagnosis Select Countermeasures Economic Appraisal Prioritize Projects Safety effectiveness evaluationRoadway safety management process parallels the method by which PennDOT selects and evaluates projects for Federal Highway Safety Improvement Program.SPF: safety performance functionPositive/high excess cost locations are good candidates for safety improvements.Urban: sites within urban boundaries (Census) where population is more than 5,000 people.Rural: sites outside of urban boundaries (Census) where population is less than 5,000 people.Crashes within 250 feet of an intersection are assigned to the intersection for analysis.

This is a bathymetry-only view of the World Topographic Basemap. Sometimes you just need the bathymetry and don't want the not-thymetry.Where can you use it? Pretty much anywhere you might want a bathymetric layer. This vector tile layer can be used in an ArcGIS Online web map or scene, 2D or 3D ArcGIS Pro map, or any application using the ArcGIS API for Javascript. It's just like, a layer.If you are interested in learning how to isolate layers in vector basemaps yourself, here is a YouTube video with all the glorious details. The underlying data is untouched and unmoved, but his creates a custom "view" of the source basemap.Here it is all by itself in ArcGIS Online......and with an imagery basemap.Here it is in an ArcGIS Pro 3D map...Happy mapping, basemap surgeons! John Nelson

Network screening analysis data of urban road segments in Pennsylvania completed in 2024; includes roadways inside of urban area boundaries with a population of more than 5,000 people. Data can be filtered by county, planning partner and engineering district.Notes from the PennDOT HSNS Video https://www.youtube.com/watch?v=liXTnqxZjCgNetwork screening analysis can be used for safety analysis and decision making to decrease frequency and severity of crashes in Pennsylvania.Network screening is a method from the Highway Safety Manual (HSM) that compares expected crash frequencies and crash severities to historical crash data based on Part C of HSM. It helps evaluate facilities and identify and prioritize locations that are likely to respond to safety improvement investments. FHWA states that employing traditional networking screening with systemic safety analysis can be an agency’s first step toward a comprehensive safety management program. The network screening is the first step in the Roadway Safety Management Process (Part B of the HSM) and it considers crash history, roadway factors and traffic characteristics.Roadway Safety Management Process (Part B of the HSM) Steps Network Screening Diagnosis Select Countermeasures Economic Appraisal Prioritize Projects Safety effectiveness evaluationRoadway safety management process parallels the method by which PennDOT selects and evaluates projects for Federal Highway Safety Improvement Program.SPF: safety performance functionPositive/high excess cost locations are good candidates for safety improvements.Urban: sites within urban boundaries (Census) where population is more than 5,000 people.Rural: sites outside of urban boundaries (Census) where population is less than 5,000 people.Crashes within 250 feet of an intersection are assigned to the intersection for analysis.