You have been assigned a new project, which you have researched, and you have identified the data that you need.The next step is to gather, organize, and potentially create the data that you need for your project analysis.In this course, you will learn how to gather and organize data using ArcGIS Pro. You will also create a file geodatabase where you will store the data that you import and create.After completing this course, you will be able to perform the following tasks:Create a geodatabase in ArcGIS Pro.Create feature classes in ArcGIS Pro by exporting and importing data.Create a new, empty feature class in ArcGIS Pro.

In this course, you will learn about some common types of data used for GIS mapping and analysis, and practice adding data to a file geodatabase to support a planned project.Goals Create a file geodatabase. Add data to a file geodatabase. Create an empty geodatabase feature class.

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.

Summary This feature class documents the fire history on CMR from 1964 - present. This is 1 of 2 feature classes, a polygon and a point. This data has a variety of different origins which leads to differing quality of data. Within the polygon feature class, this contains perimeters that were mapped using a GPS, hand digitized, on-screen digitized, and buffered circles to the estimated acreage. These 2 files should be kept together. Within the point feature class, fires with only a location of latitude/longitude, UTM coordinate, TRS and no estimated acreage were mapped using a point location. GPS started being used in 1992 when the technology became available. Records from FMIS (Fire Management Information System) were reviewed and compared to refuge records. Polygon data in FMIS only occurs from 2012 to current and many acreage estimates did not match. This dataset includes ALL fires no matter the size. This feature class documents the fire history on CMR from 1964 - present. This is 1 of 2 feature classes, a polygon and a point. This data has a variety of different origins which leads to differing quality of data. Within the polygon feature class, this contains perimeters that were mapped using a GPS, hand digitized, on-screen digitized, and buffered circles to the estimated acreage. These 2 files should be kept together. Within the point feature class, fires with only a location of latitude/longitude, UTM coordinate, TRS and no estimated acreage were mapped using a point location. GPS started being used in 1992 when the technology became available. Data origins include: Data origins include: 1) GPS Polygon-data (Best), 2) GPS Lat/Long or UTM, 3)TRS QS, 4)TRS Point, 6)Hand digitized from topo map, 7) Circle buffer, 8)Screen digitized, 9) FMIS Lat/Long. Started compiling fire history of CMR in 2007. This has been a 10 year process.FMIS doesn't include fires polygons that are less than 10 acres. This dataset has been sent to FMIS for FMIS records to be updated with correct information. The spreadsheet contains 10-15 records without spatial information and weren't included in either feature class. Fire information from 1964 - 1980 came from records Larry Eichhorn, BLM, provided to CMR staff. Mike Granger, CMR Fire Management Officer, tracked fires on an 11x17 legal pad and all this information was brought into Excel and ArcGIS. Frequently, other information about the fires were missing which made it difficult to back track and fill in missing data. Time was spent verifiying locations that were occasionally recorded incorrectly (DMS vs DD) and converting TRS into Lat/Long and/or UTM. CMR is divided into 2 different UTM zones, zone 12 and zone 13. This occasionally caused errors in projecting. Naming conventions caused confusion. Fires are frequently names by location and there are several "Soda Creek", "Rock Creek", etc fires. Fire numbers were occasionally missing or incorrect. Fires on BLM were included if they were "Assists". Also, fires on satellite refuges and the district were also included. Acreages from GIS were compared to FMIS acres. Please see documentation in ServCat (URL) to see how these were handled.

LouVelo is a docked bikeshare program owned by Louisville Metro Government and operated by Cyclehop since May of 2017. The System includes Approximately 250 bikes, 25 Docked Stations in Louisville, and an additional 3 stations owned and operated by the City of Jeffersonville in Partnership with Cyclehop. These data will be updated on a monthly basis to show monthly trends in ridership along with general patterns of use with pick up and drop off location data. These data are updated and maintained for use in the Louisville Metro Open Data Portal LouVelo Dashboard to show ridership for the entirety of the program. Some stations have been relocated since the programs founding. For up to date information on dock locations please view the system map on the LouVelo website. This dashboard is maintained by Louisville Metro Public Works.For any questions please contact:James GrahamMobility CoordinatorLouisville Metro Public WorksDivision of Transportation444 S. 5th, St, Suite 400Louisville, KY 40202(502) 574-6473james.graham@louisvilleky.govFor more information about the LouVelo bikeshare program please visit their website.

These data were compiled for the use of training natural feature machine learning (GeoAI) detection and delineation. The natural feature classes include the Geographic Names Information System (GNIS) feature types Basins, Bays, Bends, Craters, Gaps, Guts, Islands, Lakes, Ridges and Valleys, and are an areal representation of those GNIS point features. Features were produced using heads-up digitizing from 2018 to 2019 by Dr. Sam Arundel's team at the U.S. Geological Survey, Center of Excellence for Geospatial Information Science, Rolla, Missouri, USA, and Dr. Wenwen Li's team in the School of Geographical Sciences at Arizona State University, Tempe, Arizona, USA.

The National Hydrography Dataset Plus (NHDplus) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US EPA Office of Water and the US Geological Survey, the NHDPlus provides mean annual and monthly flow estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses. For more information on the NHDPlus dataset see the NHDPlus v2 User Guide.Dataset SummaryPhenomenon Mapped: Surface waters and related features of the United States and associated territories not including Alaska.Geographic Extent: The United States not including Alaska, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaProjection: Web Mercator Auxiliary Sphere Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Source: EPA and USGSUpdate Frequency: There is new new data since this 2019 version, so no updates planned in the futurePublication Date: March 13, 2019Prior to publication, the NHDPlus network and non-network flowline feature classes were combined into a single flowline layer. Similarly, the NHDPlus Area and Waterbody feature classes were merged under a single schema.Attribute fields were added to the flowline and waterbody layers to simplify symbology and enhance the layer's pop-ups. Fields added include Pop-up Title, Pop-up Subtitle, On or Off Network (flowlines only), Esri Symbology (waterbodies only), and Feature Code Description. All other attributes are from the original NHDPlus dataset. No data values -9999 and -9998 were converted to Null values for many of the flowline fields.What can you do with this layer?Feature 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.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer is limited to scales of approximately 1:1,000,000 or larger but a vector tile layer created from the same data can be used at smaller scales to produce a webmap that displays across the full range of scales. 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. 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.Apply filters. For example you can set a filter to show larger streams and rivers using the mean annual flow attribute or the stream order attribute. Change the layer’s style and symbologyAdd labels and set their propertiesCustomize the pop-upUse as an input to the ArcGIS Online analysis tools. This layer works well as a reference layer with the trace downstream and watershed tools. The buffer tool can be used to draw protective boundaries around streams and the extract data tool can be used to create copies of portions of the data.ArcGIS ProAdd this layer to a 2d or 3d map. Use 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 ArcGIS 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.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

LouVelo is a docked bikeshare program owned by Louisville Metro Government and operated by Cyclehop since May of 2017. The System includes Approximately 250 bikes, 25 Docked Stations in Louisville, and an additional 3 stations owned and operated by the City of Jeffersonville in Partnership with Cyclehop. These data will be updated on a monthly basis to show trends in station use. These data are updated and maintained for use in the Louisville Metro Open Data Portal LouVelo Dashboard to show ridership for the entirety of the program. Some stations have been relocated since the programs founding. For up to date information on dock locations please view the system map on the LouVelo website. This dashboard is maintained by Louisville Metro Public Works.For any questions please contact:James GrahamMobility CoordinatorLouisville Metro Public WorksDivision of Transportation444 S. 5th, St, Suite 400Louisville, KY 40202(502) 574-6473james.graham@louisvilleky.govFor more information about the LouVelo bikeshare program please visit their website.

This dataset represents cellular tower locations as recorded by the Federal Communications Commission This feature class serves as base information for use in GIS systems for general planning, analytical, and research purposes. It is not intended for engineering work or to legally define FCC licensee data or FCC market boundaries. The material in these data and text files are provided as-is. The FCC disclaims all warranties with regard to the contents of these files, including their fitness. In no event shall the FCC be liable for any special, indirect, or consequential damages whatsoever resulting from loss or use, data or profits, whether in connection with the use or performance of the contents of these files, action of contract, negligence, or other action arising out of, or in connection with the use of the contents of these files. It is known that there are some errors in the licensing information - Latitude, Longitude and Ground Elevation data as well as frequency assignment data from which these MapInfo files were generated.

The Digital Shoreline Analysis System (DSAS) is a freely available software application that works within the Environmental Systems Research Institute (ESRI) Geographic Information System (ArcGIS) software. DSAS computes rate-of-change statistics for a time series of shoreline vector data. Additionally, the DSAS application is useful for computing rates of change for any boundary-change problem that incorporates a clearly-identified feature position at discrete times, such as glacier limits, river banks, or land use/cover boundaries. The "bias feature" is a shapefile representation the proxy-datum bias (PDB) data previously published in tabular format (Himmelstoss and others 2010, Himmelstoss and others 2018). These PDB data are used to shift older proxy-based shorelines so that they can be directly compared to newer datum-based shorelines and accurate rates can be calculated.

This ArcGIS model inserts a file name into a feature class attribute table. The tool allows an user to identify features by a field that reference the name of the original file. It is useful when an user have to merge multiple feature classes and needs to identify which layer the features come from.

This dataset contains the recreation opportunity information that the Forest Service collects through the Recreation Portal and shares with the public on https://www.recreation.gov, the Forest Service World Wide Web pages (https://www.fs.usda.gov/) and the Interactive Visitor Map. This recreation data contains detailed descriptions of recreational sites, areas, activities & facilities. This published dataset consists of one point feature class for recreational areas, one spatial view and three related tables such as activities, facilities & rec area advisories. The purpose of each related table is described belowRECAREAACTIVITIES: This related table contains information about the activities that are associated with the rec area.RECAREAFACILITIES: This related table contains information about the amenities that are associated with the rec area.RECAREAADVISORIES: This table contains events, news, alerts and warnings that are associated with the rec area.RECAREAACTIVITIES_V: This spatial view/feature class is generated by joining the RECAREAACTIVITIES table to the RECREATION OPPORTUNITIES Feature Class. Please note that the RECAREAID is the unique identifier present in point feature class and in the related tables as well. The RECAREAID is used as foreign key to access relate records.This published data is updated nightly from an XML feed maintained by the CIO Rec Portal team. This data is intended for public use and distribution. MetadataThis record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService OGC WMS CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.

A line feature class derived from our main Miami-Dade County STREETS Feature Class. This FeatureClass is one of a set of FeatureClasses derived from our main FeatureClass (STREETS), which were created to display information related to our County's streets. This set of derivative product include the following FeatureClasses: GeoStreets - An Arc FeatureClass of Miami-Dade County streets with seamless address ranges. GeoActualRanges - An Arc FeatureClass of Miami-Dade County treets with actual address ranges. St_Dade - An Arc FeatureClass of Miami-Dade County treets in a Coverage Structure. CartoStreets - A Polygon FreatureClass Miami-Dade County street created from the street base layer using a process called 'buffer'. It was designed for cartographic display purposes and does not have attributes.Updated: Weekly-Sat The data was created using: Projected Coordinate System: WGS_1984_Web_Mercator_Auxiliary_SphereProjection: Mercator_Auxiliary_Sphere

SLIDO-4.4 is an Esri ArcGIS version 10.7.1 file geodatabase. The geodatabase contains two feature datasets (a group of datasets within the geodatabase) containing six feature classes total and five individual feature classes. SLIDO-4.4 includes 55,736 landslide polygons in the Deposits feature class dataset and 14,985 historic landslide points locations compiled from 365 studies. The original studies vary widely in scale, scope and focus which is reflected in the wide range of accuracy, detail, and completeness with which landslides are mapped. In the future, we propose a continuous update of SLIDO. These updates should take place: 1) each time DOGAMI publishes a new GIS dataset that contains landslide inventory or susceptibility data or 2) at the end of each winter season, a common time for landslide occurrences in Oregon, which will include recent historic landslide point data. In order to keep track of the updates, we will use a primary release number such as Release 4.0 along with a decimal number identifying the update such as 4.1.

The BIS is a geodatabase containing datasets for bicycle facilities in the Chicago metropolitan area. The BIS contains separate feature classes for each identified bikeway plan (or plan with a bikeway component) within the seven-county area.This file contains a list of all bikeway plan feature classes within Bikeway Inventory System (BIS) geodatabase

Consumptive use (CU) of water is an important factor for determining water availability and groundwater storage. Many regional stakeholders and water-supply managers in the Upper Rio Grande Basin have indicated CU is of primary concern in their water-management strategies, yet CU data is sparse for this area. This polygon feature class, which represents irrigated acres for 2015, is a geospatial component of the U.S. Geological Survey National Water Census Upper Rio Grande Basin (URGB) focus area study's effort to improve quantification of CU in parts of New Mexico, west Texas, and northern Chihuahua. These digital data accompany Ivahnenko, T.I., Flickinger, A.K., Galanter, A.E., Douglas-Mankin, K.R., Pedraza, D.E., and Senay, G.B., 2021, Estimates of public-supply, domestic, and irrigation water withdrawal, use, and trends in the Upper Rio Grande Basin, 1985 to 2015: U.S. Geological Survey Scientific Investigations Report 2021–5036, 31 p., https://doi.org/10.3133/sir20215036.

Personal Geodatabase Esri format, Arcgis v 10. 4, corresponding to the geological map of Figure 1.2. The personal geodatabase contains one feature dataset 'Geology', which in turn contains a polygon feature class 'Surface' and a polyline feature class 'Line'.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. We converted the photointerpreted data into a format usable in a geographic information system (GIS) by employing three fundamental processes: (1) orthorectify, (2) digitize, and (3) develop the geodatabase. All digital map automation was projected in Universal Transverse Mercator (UTM), Zone 16, using the North American Datum of 1983 (NAD83). Orthorectify: We orthorectified the interpreted overlays by using OrthoMapper, a softcopy photogrammetric software for GIS. One function of OrthoMapper is to create orthorectified imagery from scanned and unrectified imagery (Image Processing Software, Inc., 2002). The software features a method of visual orientation involving a point-and-click operation that uses existing orthorectified horizontal and vertical base maps. Of primary importance to us, OrthoMapper also has the capability to orthorectify the photointerpreted overlays of each photograph based on the reference information provided. Digitize: To produce a polygon vector layer for use in ArcGIS (Environmental Systems Research Institute [ESRI], Redlands, California), we converted each raster-based image mosaic of orthorectified overlays containing the photointerpreted data into a grid format by using ArcGIS. In ArcGIS, we used the ArcScan extension to trace the raster data and produce ESRI shapefiles. We digitally assigned map-attribute codes (both map-class codes and physiognomic modifier codes) to the polygons and checked the digital data against the photointerpreted overlays for line and attribute consistency. Ultimately, we merged the individual layers into a seamless layer. Geodatabase: At this stage, the map layer has only map-attribute codes assigned to each polygon. To assign meaningful information to each polygon (e.g., map-class names, physiognomic definitions, links to NVCS types), we produced a feature-class table, along with other supportive tables and subsequently related them together via an ArcGIS Geodatabase. This geodatabase also links the map to other feature-class layers produced from this project, including vegetation sample plots, accuracy assessment (AA) sites, aerial photo locations, and project boundary extent. A geodatabase provides access to a variety of interlocking data sets, is expandable, and equips resource managers and researchers with a powerful GIS tool.

The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.

This Quarter Section feature class depicts PLSS Second Divisions . PLSS townships are subdivided in a spatial hierarchy of first, second, and third division. These divisions are typically aliquot parts ranging in size from 640 acres to 160 to 40 acres, and subsequently all the way down to 2.5 acres. The data in this feature class was translated from the PLSSSecondDiv feature class in the original production data model, which defined the second division for a specific parcel of land. Metadata