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.

Continuing the tradition of the best-selling Getting to Know series, Getting to Know ArcGIS Pro 2.6 teaches new and existing GIS users how to get started solving problems using ArcGIS Pro. Using ArcGIS Pro for these tasks allows you to understand complex data with the leading GIS software that many businesses and organizations use every day.Getting to Know ArcGIS Pro 2.6 introduces the basic tools and capabilities of ArcGIS Pro through practical project workflows that demonstrate best practices for productivity. Explore spatial relationships, building a geodatabase, 3D GIS, project presentation, and more. Learn how to navigate ArcGIS Pro and ArcGIS Online by visualizing, querying, creating, editing, analyzing, and presenting geospatial data in both 2D and 3D environments. Using figures to show each step, Getting to Know ArcGIS Pro 2.6 demystifies complicated process like developing a geoprocessing model, using Python to write a script tool, and the creation of space-time cubes. Cartographic techniques for both web and physical maps are included.Each chapter begins with a prompt using a real-world scenario in a different industry to help you explore how ArcGIS Pro can be applied for operational efficiency, analysis, and problem solving. A summary and glossary terms at the end of every chapter help reinforce the lessons and skills learned.Ideal for students, self-learners, and seasoned professionals looking to learn a new GIS product, Getting to Know ArcGIS Pro 2.6 is a broad textbook and desk reference designed to leave users feeling confident in using ArcGIS Pro on their own.AUDIENCEProfessional and scholarly. Higher education.AUTHOR BIOMichael Law is a cartographer and GIS professional with more than a decade of experience. He was a cartographer for Esri, where he developed cartography for books, edited and tested GIS workbooks, and was the editor of the Esri Map Book. He continues to work with GIS software, writing technical documentation, teaching training courses, and designing and optimizing user interfaces.Amy Collins is a writer and editor who has worked with GIS for over 16 years. She was a technical editor for Esri, where she honed her GIS skills and cultivated an interest in designing effective instructional materials. She continues to develop books on GIS education, among other projects.Pub Date: Print: 10/6/2020 Digital: 8/18/2020 ISBN: Print: 9781589486355 Digital: 9781589486362 Price: Print: $84.99 USD Digital: $84.99 USD Pages: 420 Trim: 7.5 x 9.25 in.Table of ContentsPrefaceChapter 1 Introducing GISExercise 1a: Explore ArcGIS OnlineChapter 2 A first look at ArcGIS Pro Exercise 2a: Learn some basics Exercise 2b: Go beyond the basics Exercise 2c: Experience 3D GISChapter 3 Exploring geospatial relationshipsExercise 3a: Extract part of a dataset Exercise 3b: Incorporate tabular data Exercise 3c: Calculate data statistics Exercise 3d: Connect spatial datasetsChapter 4 Creating and editing spatial data Exercise 4a: Build a geodatabase Exercise 4b: Create features Exercise 4c: Modify featuresChapter 5 Facilitating workflows Exercise 5a: Manage a repeatable workflow using tasks Exercise 5b: Create a geoprocessing model Exercise 5c: Run a Python command and script toolChapter 6 Collaborative mapping Exercise 6a: Prepare a database for data collection Exercise 6b: Prepare a map for data collection Exercise 6c: Collect data using ArcGIS CollectorChapter 7 Geoenabling your projectExercise 7a: Prepare project data Exercise 7b: Geocode location data Exercise 7c: Use geoprocessing tools to analyze vector dataChapter 8 Analyzing spatial and temporal patternsExercise 8a: Create a kernel density map Exercise 8b: Perform a hot spot analysis Exercise 8c: Explore the results in 3D Exercise 8d: Animate the dataChapter 9 Determining suitability Exercise 9a: Prepare project data Exercise 9b: Derive new surfaces Exercise 9c: Create a weighted suitability modelChapter 10 Presenting your project Exercise 10a: Apply detailed symbology Exercise 10b: Label features Exercise 10c: Create a page layout Exercise 10d: Share your projectAppendix Image and data source credits Data license agreement GlossaryGetting to Know ArcGIS Pro 2.6 | Official Trailer | 2020-08-10 | 00:57

The World Wide Web has become a valuable means to display, collect, and share geospatial data and maps. This course will explore the use of web technologies for developing web map applications. Students will learn to produce audience appropriate maps in the web environment using a variety of technologies and methods. This class will specifically focus on client-side technologies and is broken into three broad components. First, you will learn to design maps and apps using the tools made available by the Environmental Systems Research Institute (ESRI) including ArcGIS Online, ArcGIS Web AppBuilder, ArcGIS ExperienceBuilder, and ArcGIS Pro. Next, you will explore client-side web programming technologies and languages. You will learn to define web content using hypertext markup language (HTML), style web content using cascading style sheets (CSS) and Bootstrap, and add functionality to a website using JavaScript and jQuery. Lastly, you will learn to produce web maps using the ESRI ArcGIS API for JavaScript and the free and open-source Leaflet JavaScript API. Through exploring these topics, you will also learn how the web works and how geospatial data are stored for use in the web environment. You will also develop some coding skills specific to HTML, CSS, and JavaScript. By the end of this course, you will be able to design a multipage website that includes web maps. After completing this course you will be able to: describe how the web works using client-server architecture. produce an audience appropriate web map using ArcGIS Online. publish data and maps for use in the web environment. describe geospatial data formats for use on the web and when each is most appropriate. solicit crowd sourced data using feature services to collect volunteered geographic information (VGI). produce and publish a web map application using Web AppBuilder for ArcGIS or ArcGIS ExperienceBuilder. develop multipage websites that are well styled and responsive using HTML, CSS, and Bootstrap. add functionality to web pages and web maps using JavaScript and jQuery. produce customized web maps using the ArcGIS API for JavaScript and the Leaflet JavaScript API.

Prior experience of GIS is variable, but a number of PGCE students and in-service teachers reported negative prior experiences with geospatial technology. Common complaints include a course focussed on data students found irrelevant, with learning exercises in the form of list-like instructions. The complexity of desktop GIS software is also often mentioned as off-putting.

In this seminar, you will learn about the spatial analysis tools built directly into the ArcGIS.com map viewer. You will learn of the spatial analysis capabilities in ArcGIS Online for Organizations, whether for analyzing your own data, data that's publicly available on ArcGIS Online, or a combination of both. You will learn the overall features and benefits of ArcGIS Online Analysis, how to get started, and how to choose the right approach in order to solve a specific spatial problem.

Seattle Parks and Recreation ARCGIS park feature map layer web services are hosted on Seattle Public Utilities' ARCGIS server. This web services URL provides a live read only data connection to the Seattle Parks and Recreations Golf Courses dataset.

This course introduces the ribbon-style interface, project-based organization, key capabilities, and ArcGIS Pro terminology.Goals Import an ArcMap map document. Create 2D and 3D features and modify their symbols. Perform analysis using geoprocessing tools and raster processing functions. Create map layouts, project packages, and web layers to share your work.

Prior experience of GIS is variable, but a number of PGCE students and in-service teachers reported negative prior experiences with geospatial technology. Common complaints include a course focussed on data students found irrelevant, with learning exercises in the form of list-like instructions. The complexity of desktop GIS software is also often mentioned as off-putting.

ArcGIS Living Atlas of the World is a rich and growing collection of valuable geographic maps and data from organizations around the globe. Access to Living Atlas content is part of your ArcGIS Online organizational subscription. In this course, you will discover and use Living Atlas maps and layers that are ready to use for instruction. You will explore ways to connect Living Atlas content to the subjects that you teach.

ArcGIS Dashboards Training Videos for COVID-19With the current COVID-19 situation across the world, there’s been a proliferation of corona virus themed dashboards emerging over the last few weeks in ArcGIS Online. Many of these were created with ArcGIS Dashboards, which enables users to convey information by presenting location-based analytics using intuitive and interactive data visualizations on a single screen._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...

This course demonstrates how to select, modify, create, and share web applications using ArcGIS Online. ArcGIS Online offers many different options for creating web applications that share web maps, web scenes, and spatial functions. But how do you decide which web application best meets your requirements? Each web application option implements different functions and showcases a specific look and feel. You can choose a web application that meets your organization's functional requirements, apply your organization's look and feel, and share your web map without writing any code.Two workflows will be introduced for creating web applications using ArcGIS Online:Applying your web map to an existing template applicationCreating your own web application using Web AppBuilder for ArcGISAfter completing this course, you will be able to do the following:Identify the components of a web application.Create a web application from an existing configurable app template.Create a web application using Web AppBuilder for ArcGIS.Use ArcGIS Online to deploy a web application.

Soil map units are the basic geographic unit of the Soil Survey Geographic Database (SSURGO). The SSURGO dataset is a compilation of soils information collected over the last century by the Natural Resources Conservation Service (NRCS). Map units delineate the extent of different soils. Data for each map unit contains descriptions of the soil’s components, productivity, unique properties, and suitability interpretations.Each soil type has a unique combination of physical, chemical, nutrient and moisture properties. Soil type has ramifications for engineering and construction activities, natural hazards such as landslides, agricultural productivity, the distribution of native plant and animal life and hydrologic and other physical processes. Soil types in the context of climate and terrain can be used as a general indicator of engineering constraints, agriculture suitability, biological productivity and the natural distribution of plants and animals. Data from the gSSURGO databasewas used to create this layer. To download ready-to-use project packages of useful soil data derived from the SSURGO dataset, please visit the USA SSURGO Downloader app. Dataset SummaryPhenomenon Mapped: Soils of the United States and associated territoriesGeographic Extent: The 50 United States, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaCoordinate System: Web Mercator Auxiliary SphereVisible Scale: 1:144,000 to 1:1,000Source: USDA Natural Resources Conservation ServiceUpdate Frequency: AnnualPublication Date: December 2024 What can you do with this layer?ArcGIS OnlineFeature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro.Below are just a few of the things you can do with a feature service in Online and Pro.Add this layer to a map in the map viewer. The layer is limited to scales of approximately 1:144,000 or larger but avector tile layercreated from the same data can be used at smaller scales to produce awebmapthat displays across the full scale range. The layer or a map containing it can be used in an application.Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections and apply filters. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Change the layer’s style and filter the data. For example, you could set a filter forFarmland Class= "All areas are prime farmland" to create a map of only prime farmland.Add labels and set their propertiesCustomize the pop-up ArcGIS ProAdd this layer to a 2d or 3d map. The same scale limit as Online applies in ProUse as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics. Data DictionaryAttributesKey fields from nine commonly used SSURGO tables were compiled to create the 173 attribute fields in this layer. Some fields were joined directly to the SSURGO Map Unit polygon feature class while others required summarization and other processing to create a 1:1 relationship between the attributes and polygons prior to joining the tables. Attributes of this layer are listed below in their order of occurrence in the attribute table and are organized by the SSURGO table they originated from and the processing methods used on them. Map Unit Polygon Feature Class Attribute TableThe fields in this table are from the attribute table of the Map Unit polygon feature class which provides the geographic extent of the map units.Area SymbolSpatial VersionMap Unit Symbol Map Unit TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the table using the Map Unit Key field.Map Unit NameMap Unit KindFarmland ClassInterpretive FocusIntensity of MappingIowa Corn Suitability Rating Legend TableThis table has 1:1 relationship with the Map Unit table and was joined using the Legend Key field.Project Scale Survey Area Catalog TableThe fields in this table have a 1:1 relationship with the polygons and were joined to the Map Unit table using the Survey Area Catalog Key and Legend Key fields.Survey Area VersionTabular Version Map Unit Aggregated Attribute TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the Map Unit attribute table using the Map Unit Key field. Slope Gradient - Dominant ComponentSlope Gradient - Weighted AverageBedrock Depth - MinimumWater Table Depth - Annual MinimumWater Table Depth - April to June MinimumFlooding Frequency - Dominant ConditionFlooding Frequency - MaximumPonding Frequency - PresenceAvailable Water Storage 0-25 cm - Weighted AverageAvailable Water Storage 0-50 cm - Weighted AverageAvailable Water Storage 0-100 cm - Weighted AverageAvailable Water Storage 0-150 cm - Weighted AverageDrainage Class - Dominant ConditionDrainage Class - WettestHydrologic Group - Dominant ConditionIrrigated Capability Class - Dominant ConditionIrrigated Capability Class - Proportion of Mapunit with Dominant ConditionNon-Irrigated Capability Class - Dominant ConditionNon-Irrigated Capability Class - Proportion of Mapunit with Dominant ConditionRating for Buildings without Basements - Dominant ConditionRating for Buildings with Basements - Dominant ConditionRating for Buildings with Basements - Least LimitingRating for Buildings with Basements - Most LimitingRating for Septic Tank Absorption Fields - Dominant ConditionRating for Septic Tank Absorption Fields - Least LimitingRating for Septic Tank Absorption Fields - Most LimitingRating for Sewage Lagoons - Dominant ConditionRating for Sewage Lagoons - Dominant ComponentRating for Roads and Streets - Dominant ConditionRating for Sand Source - Dominant ConditionRating for Sand Source - Most ProbableRating for Paths and Trails - Dominant ConditionRating for Paths and Trails - Weighted AverageErosion Hazard of Forest Roads and Trails - Dominant ComponentHydric Classification - PresenceRating for Manure and Food Processing Waste - Weighted Average Component Table – Dominant ComponentMap units have one or more components. To create a 1:1 join component data must be summarized by map unit. For these fields a custom script was used to select the component with the highest value for the Component Percentage Representative Value field (comppct_r). Ties were broken with the Slope Representative Value field (slope_r). Components with lower average slope were selected as dominant. If both soil order and slope were tied, the first value in the table was selected. Component Percentage - Low ValueComponent Percentage - Representative ValueComponent Percentage - High ValueComponent NameComponent KindOther Criteria Used to Identify ComponentsCriteria Used to Identify Components at the Local LevelRunoff ClassSoil loss tolerance factorWind Erodibility IndexWind Erodibility GroupErosion ClassEarth Cover 1Earth Cover 2Hydric ConditionHydric RatingAspect Range - Counter Clockwise LimitAspect - Representative ValueAspect Range - Clockwise LimitGeomorphic DescriptionNon-Irrigated Capability SubclassNon-Irrigated Unit Capability ClassIrrigated Capability SubclassIrrigated Unit Capability ClassConservation Tree Shrub GroupGrain Wildlife HabitatGrass Wildlife HabitatHerbaceous Wildlife HabitatShrub Wildlife HabitatConifer Wildlife HabitatHardwood Wildlife HabitatWetland Wildlife HabitatShallow Water Wildlife HabitatRangeland Wildlife HabitatOpenland Wildlife HabitatWoodland Wildlife HabitatWetland Wildlife HabitatSoil Slip PotentialSusceptibility to Frost HeavingConcrete CorrosionSteel CorrosionTaxonomic ClassTaxonomic OrderTaxonomic SuborderGreat GroupSubgroupParticle SizeParticle Size ModCation Exchange Activity ClassCarbonate ReactionTemperature ClassMoist SubclassSoil Temperature RegimeEdition of Keys to Soil Taxonomy Used to Classify SoilCalifornia Storie IndexComponent Key Component Table – Weighted AverageMap units may have one or more soil components. To create a 1:1 join, data from the Component table must be summarized by map unit. For these fields a custom script was used to calculate an average value for each map unit weighted by the Component Percentage Representative Value field (comppct_r).Slope Gradient - Low ValueSlope Gradient - Representative ValueSlope Gradient - High ValueSlope Length USLE - Low ValueSlope Length USLE - Representative ValueSlope Length USLE - High ValueElevation - Low ValueElevation - Representative ValueElevation - High ValueAlbedo - Low ValueAlbedo - Representative ValueAlbedo - High ValueMean Annual Air Temperature - Low ValueMean Annual Air Temperature - Representative ValueMean Annual Air Temperature - High ValueMean Annual Precipitation - Low ValueMean Annual Precipitation - Representative ValueMean Annual Precipitation - High ValueRelative Effective Annual Precipitation - Low ValueRelative Effective Annual Precipitation - Representative ValueRelative Effective Annual Precipitation - High ValueDays between Last and First Frost - Low ValueDays between Last and First Frost - Representative ValueDays between Last and First Frost - High ValueRange Forage Annual Potential Production - Low ValueRange Forage Annual Potential Production - Representative ValueRange Forage Annual Potential Production - High ValueInitial Subsidence - Low ValueInitial Subsidence - Representative ValueInitial Subsidence -

The New Zealand version of the Zombie Apocalypse lesson."A huge yet unknown catastrophic event has changed the world as we know it. One of the major results of this event is the spread of zombies across the globe. People all around the world are trying to survive this zombie invasion the best way they can. It will take skill, and of course BRAINS to figure out the best way to survive this catastrophe!
Do you have what it takes? Can you use what is in YOUR BRAIN to SURVIVE the ZOMBIE APOCALYPSE!?"

WSDOT template for Esri file geodatabase polygon feature class. Template has pre-defined attribute schema to help users create data that is more consistent or compliant with agency standards. Metadata has been created using the FGDC metadata style but stored in the ArcGIS format. Content presentation will change upon export to FGDC format.This service is maintained by the WSDOT Transportation Data, GIS & Modeling Office. If you are having trouble viewing the service, please contact Online Map Support at onlinemapsupport@wsdot.wa.gov.

Future Streets identified in the 2017 Transportation Strategic Plan (TSP)

This webmap is a subset of Global Landcover 1992 - 2020 Image Layer. You can access the source data from here. 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 1992-2020. These are available at the European Space Agency Climate Change Initiative website.Time Extent: 1992-2020Cell Size: 300 meterSource Type: ThematicPixel Type: 8 Bit UnsignedData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: ESA Climate Change InitiativeUpdate Cycle: Annual until 2020, no updates thereafterWhat 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 GEOINQUIRIES™ COLLECTION FOR U.S. History

http://www.esri.com/geoinquiries

The GeoInquiry™ collection for World History contains 15 free, standards-based activities that correspond and extend spatial concepts found in course textbooks frequently used in introductory world history classes. The activities use a common inquiry-based instructional model, require only 15 minutes to deliver, and are device/laptop agnostic. Each activity includes an ArcGIS Online map but requires no login or installation. The activities harmonize with the C3 Framework for social studies curriculum standards.

All World History GeoInquiries™ can be found at: http://esriurl.com/worldHistoryGeoInquiries

All GeoInquiries™ can be found at: http://www.esri.com/geoinquiries

COMPLETED 2010. The data was converted from the most recent (2010) versions of the adopted plans, which can be found at https://cms3.tucsonaz.gov/planning/plans/ Supplemental Information: In March 2010, Pima Association of Governments (PAG), in cooperation with the City of Tucson (City), initiated the Planned Land Use Data Conversion Project. This 9-month effort involved evaluating mapped land use designations and selected spatially explicit policies for nearly 50 of the City's adopted neighborhood, area, and subregional plans and converting the information into a Geographic Information System (GIS) format. Further documentation for this file can be obtained from the City of Tucson Planning and Development Services Department or Pima Association of Governments Technical Services. A brief summary report was provided, as requested, to the City of Tucson which highlights some of the key issues found during the conversion process (e.g., lack of mapping and terminology consistency among plans). The feature class "Plan_boundaries" represents the boundaries of the adopted plans. The feature class "Plan_mapped_land_use" represents the land use designations as they are mapped in the adopted plans. Some information was gathered that is implicit based on the land use designation or zones (see field descriptions below). Since this information is not explicitly stated in the plans, it should only be viewed by City staff for general planning purposes. The feature class "Plan_selected_policies" represents the spatially explicit policies that were fairly straightforward to map. Since these policies are not represented in adopted maps, this feature class should only be viewed by City staff for general planning purposes only. 2010 - created by Jamison Brown, working as an independent contractor for Pima Association of Governments, created this file in 2010 by digitizing boundaries as depicted (i.e. for the mapped land use) or described in the plans (i.e. for the narrative policies). In most cases, this involved tracing based on parcel (paregion) or street center line (stnetall) feature classes. Snapping was used to provide line coincidence. For some map conversions, freehand sketches were drawn to mimick the freehand sketches in the adopted plan. Field descriptions Field descriptions for the "Plan_boundaries" feature class: Plan_Name: Plan name Plan_Type: Plan type (e.g., Neighborhood Plan) Plan_Num: Plan number ADOPT_DATE: Date of Plan adoption IMPORTANT: A disclaimer about the data as it is unofficial. URL: Uniform Resource Locator Field descriptions for the "Plan_mapped_land_use" feature class: Plan_Name: Plan name Plan_Type: Plan type (e.g., Neighborhood Plan) Plan_Num: Plan number LU_DES: Land use designation (e.g., Low density residential) LISTED_ALLOWABLE_ZONES: Allowable zones as listed in the Plan LISTED_RAC_MIN: Minimum residences per acre (if applicable), as listed in the Plan LISTED_RAC_TARGET: Target residences per acre (if applicable), as listed in the Plan LISTED_RAC_MAX: Maximum residences per acre (if applicable), as listed in the Plan LISTED_FAR_MIN: Minimum Floor Area Ratio (if applicable), as listed in the Plan LISTED_FAR_TARGET: Target Floor Area Ratio (if applicable), as listed in the Plan LISTED_FAR_MAX: Maximum Floor Area Ratio (if applicable), as listed in the Plan BUILDING_HEIGHT_MAX Building height maximum (ft.) if determined by Plan policy IMPORTANT: A disclaimer about the data as it is unofficial. URL: Uniform Resource Locator IMPLIED_ALLOWABLE_ZONES: Implied (not listed in the Plan) allowable zones IMPLIED_RAC_MIN: Implied (not listed in the Plan) minimum residences per acre (if applicable) IMPLIED_RAC_TARGET: Implied (not listed in the Plan) target residences per acre (if applicable) IMPLIED_RAC_MAX: Implied (not listed in the Plan) maximum residences per acre (if applicable) IMPLIED_FAR_MIN: Implied (not listed in the Plan) minimum Floor Area Ratio (if applicable) IMPLIED_FAR_TARGET: Implied (not listed in the Plan) target Floor Area Ratio (if applicable) IMPLIED_FAR_MAX: Implied (not listed in the Plan) maximum Floor Area Ratio (if applicable) IMPLIED_LU_CATEGORY: Implied (not listed in the Plan) general land use category. General categories used include residential, office, commercial, industrial, and other.PurposeLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Dataset ClassificationLevel 0 - OpenKnown UsesThis layer is intended to be used in the City of Tucson's Open Data portal and not for regular use in ArcGIS Online, ArcGIS Enterprise or other web applications.Known ErrorsLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Data ContactJohn BeallCity of Tucson Development Services520-791-5550John.Beall@tucsonaz.govUpdate FrequencyLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.

THE GEOINQUIRIES™ COLLECTION FOR U.S. History

http://www.esri.com/geoinquiries

The GeoInquiry™ collection for World History contains 15 free, standards-based activities that correspond and extend spatial concepts found in course textbooks frequently used in introductory world history classes. The activities use a common inquiry-based instructional model, require only 15 minutes to deliver, and are device/laptop agnostic. Each activity includes an ArcGIS Online map but requires no login or installation. The activities harmonize with the C3 Framework for social studies curriculum standards.

All World History GeoInquiries™ can be found at: http://esriurl.com/worldHistoryGeoInquiries

All GeoInquiries™ can be found at: http://www.esri.com/geoinquiries

Learn about geodatabase components and functionality as well as steps to create and add data to a file geodatabase. This course prepares you for courses that focus on advanced geodatabase components.Exercises can be completed with either ArcGIS Pro or ArcMap.