The Spatiotemporal Big Data Store Tutorial introduces you the the capabilities of the spatiotemporal big data store in ArcGIS Data Store, available with ArcGIS Enterprise. Observation data can be moving objects, changing attributes of stationary sensors, or both. The spatiotemporal big data store enables archival of high volume observation data, sustains high velocity write throughput, and can run across multiple machines (nodes). Adding additional machines adds capacity, enabling you to store more data, implement longer retention policies of your data, and support higher data write throughput.

After completing this tutorial you will:

Understand the concepts and best practices for working with the spatiotemporal big data store available with ArcGIS Data Store. Have configured the appropriate security settings and certificates on a enterprise server, real-time server, and a data server which are necessary for working with the spatiotemporal big data store. Have learned how to process and archive large amounts of observational data in the spatiotemporal big data store. Have learned how to visualize the observational data that is stored in the spatiotemporal big data store.

Releases

Each release contains a tutorial compatible with the version of GeoEvent Server listed. The release of the component you deploy does not have to match your version of ArcGIS GeoEvent Server, so long as the release of the component is compatible with the version of GeoEvent Server you are using. For example, if the release contains a tutorial for version 10.6; this tutorial is compatible with ArcGIS GeoEvent Server 10.6 and later. Each release contains a Release History document with a compatibility table that illustrates which versions of ArcGIS GeoEvent Server the component is compatible with.

NOTE: The release strategy for ArcGIS GeoEvent Server components delivered in the ArcGIS GeoEvent Server Gallery has been updated. Going forward, a new release will only be created when

  a component has an issue,
  is being enhanced with new capabilities,
  or is not compatible with newer versions of ArcGIS GeoEvent Server.

This strategy makes upgrades of these custom
components easier since you will not have to
upgrade them for every version of ArcGIS GeoEvent Server
unless there is a new release of
the component. The documentation for the
latest release has been
updated and includes instructions for updating
your configuration to align with this strategy.

Latest

Release 4 - February 2, 2017 - Compatible with ArcGIS GeoEvent Server 10.5 and later.

Previous

Release 3 - July 7, 2016 - Compatible with ArcGIS GeoEvent Server 10.4 thru 10.8.

Release 2 - May 17, 2016 - Compatible with ArcGIS GeoEvent Server 10.4 thru 10.8.

Release 1 - March 18, 2016 - Compatible with ArcGIS GeoEvent Server 10.4 thru 10.8.

This is a collection of maps, layers, apps and dashboards that show population access to essential retail locations, such as grocery stores. Data sourcesPopulation data is from the 2010 U.S. Census blocks. Each census block has a count of stores within a 10 minute walk, and a count of stores within a ten minute drive. Census blocks known to be unpopulated are given a score of 0. The layer is available as a hosted feature layer. Grocery store locations are from SafeGraph, reflecting what was in the data as of October 2020. Access to the layer was obtained from the SafeGraph offering in ArcGIS Marketplace. For this project, ArcGIS StreetMap Premium was used for the street network in the origin-destination analysis work, because it already has the necessary attributes on each street segment to identify which streets are considered walkable, and supports a wide variety of driving parameters. The walkable access layer and drivable access layers are rasters, whose colors were chosen to allow the drivable access layer to serve as backdrop to the walkable access layer. Data PreparationArcGIS Network Analyst was used to set up a network street layer for analysis. ArcGIS StreetMap Premium was installed to a local hard drive and selected in the Origin-Destination workflow as the network data source. This allows the origins (Census block centroids) and destinations (SafeGraph grocery stores) to be connected to that network, to allow origin-destination analysis. The Census blocks layer contains the centroid of each Census block. The data allows a simple popup to be created. This layer's block figures can be summarized further, to tract, county and state levels. The SafeGraph grocery store locations were created by querying the SafeGraph source layer based on primary NAICS code. After connecting to the layer in ArcGIS Pro, a definition query was set to only show records with NAICS code 445110 as an initial screening. The layer was exported to a local disk drive for further definition query refinement, to eliminate any records that were obviously not grocery stores. The final layer used in the analysis had approximately 53,600 records. In this map, this layer is included as a vector tile layer. Methodology Every census block in the U.S. was assigned two access scores, whose numbers are simply how many grocery stores are within a 10 minute walk and a 10 minute drive of that census block. Every census block has a score of 0 (no stores), 1, 2 or more stores. The count of accessible stores was determined using Origin-Destination Analysis in ArcGIS Network Analyst, in ArcGIS Pro. A set of Tools in this ArcGIS Pro package allow a similar analysis to be conducted for any city or other area. The Tools step through the data prep and analysis steps. Download the Pro package, open it and substitute your own layers for Origins and Destinations. Parcel centroids are a suggested option for Origins, for example. Origin-Destination analysis was configured, using ArcGIS StreetMap Premium as the network data source. Census block centroids with population greater than zero were used as the Origins, and grocery store locations were used as the Destinations. A cutoff of 10 minutes was used with the Walk Time option. Only one restriction was applied to the street network: Walkable, which means Interstates and other non-walkable street segments were treated appropriately. You see the results in the map: wherever freeway overpasses and underpasses are present near a grocery store, the walkable area extends across/through that pass, but not along the freeway. A cutoff of 10 minutes was used with the Drive Time option. The default restrictions were applied to the street network, which means a typical vehicle's access to all types of roads was factored in. The results for each analysis were captured in the Lines layer, which shows which origins are within the cutoff of each destination over the street network, given the assumptions about that network (walking, or driving a vehicle). The Lines layer was then summarized by census block ID to capture the Maximum value of the Destination_Rank field. A census block within 10 minutes of 3 stores would have 3 records in the Lines layer, but only one value in the summarized table, with a MAX_Destination_Rank field value of 3. This is the number of stores accessible to that census block in the 10 minutes measured, for walking and driving. These data were joined to the block centroids layer and given unique names. At this point, all blocks with zero population or null values in the MAX_Destination_Rank fields were given a store count of 0, to help the next step. Walkable and Drivable areas are calculated into a raster layer, using Nearest Neighbor geoprocessing tool on the count of stores within a 10 minute walk, and a count of stores within a ten minute drive, respectively. This tool uses a 200 meter grid and interpolates the values between each census block. A census tracts layer containing all water polygons "erased" from the census tract boundaries was used as an environment setting, to help constrain interpolation into/across bodies of water. The same layer use used to "shoreline" the Nearest Neighbor results, to eliminate any interpolation into the ocean or Great Lakes. This helped but was not perfect. Notes and LimitationsThe map provides a baseline for discussing access to grocery stores in a city. It does not presume local population has the desire or means to walk or drive to obtain groceries. It does not take elevation gain or loss into account. It does not factor time of day nor weather, seasons, or other variables that affect a person's commute choices. Walking and driving are just two ways people get to a grocery store. Some people ride a bike, others take public transit, have groceries delivered, or rely on a friend with a vehicle. Thank you to Melinda Morang on the Network Analyst team for guidance and suggestions at key moments along the way; to Emily Meriam for reviewing the previous version of this map and creating new color palettes and marker symbols specific to this project. Additional ReadingThe methods by which access to food is measured and reported have improved in the past decade or so, as has the uses of such measurements. Some relevant papers and articles are provided below as a starting point. Measuring Food Insecurity Using the Food Abundance Index: Implications for Economic, Health and Social Well-BeingHow to Identify Food Deserts: Measuring Physical and Economic Access to Supermarkets in King County, WashingtonAccess to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their ConsequencesDifferent Measures of Food Access Inform Different SolutionsThe time cost of access to food – Distance to the grocery store as measured in minutes

Data Driven Detroit created the data by selecting locations from NETS and ESRI business data with proper NAICS codes, then adding and deleting though local knowledge and confirmation with Google Streetview. These locations are Grocery stores which primarily sell food and don't include convenience stores. Visual confirmation cues included the existence of the word "grocery" in the name, or the presence of shopping carts.

ArcGIS Tutorial seri Enterprise kali ini membahas cara mengubah Mode pada ArcGIS Data Store. Apabila ArcGIS Data Store berada pada mode READONLY kita tidak bisa melakukan publish hosted feature service sehingga kita perlu mengubahnya ke mode READWRITE.

The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making.

   BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions.


   Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself.


   For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise.


   Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. 


   This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery.


   See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt


   See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

This is a map provided by the Walmart Emergency Operations Center (EOC) to view store/club status.

As Esri’s commercial partner for parcel data, Regrid invites you to enjoy this free tile layer of parcel boundaries covering 100% of the United States. Complete parcel attributes are also available from an integrated Data Store."I think it’s fantastic that this layer exists. It's really helpful for my staff to see parcel boundaries in a quick and accessible layer."- Kate Berg, Geographic Information Systems (GIS) Manager | Department of Environment, Great Lakes, and EnergyVisit the Regrid Data Store for the ArcGIS User CommunityHassle-Free Parcel Data for Esri UsersWhen you click a parcel in the tile layer, you will see its address, size, and parcel ID number, along with a convenient link to purchase additional parcel attributes in The Regrid Data Store for the ArcGIS User Community. Once in the Data Store, you can purchase and download parcel files with attributes by the county and state for use in ArcGIS, as well as our add-on datasets like standardized zoning, matched building footprints, and matched secondary addresses.See regrid.com/esri for all of Regrid’s parcel products for the Esri ecosystem, including Feature Service delivery for ongoing parcel updates at scale.Key Features of Regrid's Parcel DataSourced & Standardized: Data combines authoritative public sources & third-party enrichments, aggregated, standardized, and matched by the Regrid team.158+ Million Parcel Records: Covering all 3,200+ US counties and territories.143+ Standardized Data Fields: Including geometry, ownership, buildings, secondary addresses, land use, and zoning.Universal Parcel ID & Placekey Location Identifier: Ensuring precise identification and integration.Detailed Attributes: Tax assessments, building counts, square footage, stacked parcels (condos), right-of-way, vacancy indicators and USPS deliverability. Comprehensive Coverage: 100% land parcel coverage across the US.Parcel Data Resources & DocumentationRegrid Data Dictionary / Parcel Data SchemaRegrid Coverage ReportParcel Data FAQsThank you to all the GIS professionals, state, county and federal officials, assessors, recorders, and public officials across the country who maintain the nation's parcel data and infrastructure.

Mapping of deicing material storage facilities in the Lake Champlain Basin was conducted during the late fall and winter of 2022-23. 126 towns were initially selected for mapping (some divisions within the GIS towns data are unincorporated “gores”). Using the list of towns, town clerk contact information was obtained from the Vermont Secretary of State’s website, which maintains a database of contact information for each town.Each town was contacted to request information about their deicing material storage locations and methods. Email and telephone scripts were developed to briefly introduce the project and ask questions about the address of any deicing material storage locations in the town, type of materials stored at each site, duration of time each site has been used, whether materials on site are covered, and the type of surface the materials are stored on, if any. Data were entered into a geospatial database application (Fulcrum). Information was gathered there and exported as ArcGIS file geodatabases and Comma Separated Values (CSV) files for use in Microsoft Excel. Data were collected for 118 towns out of the original 126 on the list (92%). Forty-three (43) towns reported that they are storing multiple materials types at their facilities. Four (4) towns have multiple sites where they store material (Dorset, Pawlet, Morristown, and Castleton). Of these, three (3) store multiple materials at one or both of their sites (Pawlet, Morristown, and Castleton). Where towns have multiple materials or locations, the record information from the overall town identifier is linked to the material stored using a unique ‘one-to-many’ identifier. Locations of deicing material facilities, as shown in the database, were based on the addresses or location descriptions provided by town staff members and was verified only using the most recent aerial imagery (typically later than 2018 for all towns). Locations have not been field verified, nor have site conditions and infrastructure or other information provided by town staff.Dataset instructions:The dataset for Deicing Material Storage Facilities contains two layers – the ‘parent’ records titled ‘salt_storage’ and the ‘child’ records titled ‘salt_storage_record’ with attributes for each salt storage site. This represents a ‘one-to-many’ data structure. To see the attributes for each salt storage site, the user needs to Relate the data. The relationship can be accomplished in GIS software. The Relate needs to be built on the following fields:‘salt_storage’: ‘fulcrum_id’‘salt_storage_record: ‘fulcrum_parent_id’This will create a one-to-many relationship between the geographic locations and the attributes for each salt storage site.

This series of products from MODIS represents the only daily global composites available and is suitable for use at global and regional levels. This True Color band composition (Bands 1 4 3 | Red, Green, Blue) most accurately shows how we see the earth’s surface with our own eyes. It is a natural looking image that is useful for land surface, oceanic and atmospheric analysis. There are four True Color products in total. For each satellite (Aqua and Terra) there is a 250 meter corrected reflectance product and a 500 meter surface reflectance product. Although the resolution is coarser than other satellites, this allows for a global collection of imagery on a daily basis, which is made available in near real-time. In contrast, Landsat needs 16 days to collect a global composite. Besides the maximum resolution difference, the surface and corrected reflectance products also differ in the algorithm used for atmospheric correction.NASA Global Imagery Browse Services (GIBS)This image layer provides access to a subset of the NASA Global Imagery Browse Services (GIBS), which are a set of standard services to deliver global, full-resolution satellite imagery. The GIBS goal is to enable interactive exploration of NASA's Earth imagery for a broad range of users. The purpose of this image layer, and the other GIBS image services hosted by Esri, is to enable convenient access to this beautiful and useful satellite imagery for users of ArcGIS. The source data used by this image layer is a finished image; it is not recommended for quantitative analysis.Several full resolution, global imagery products are built and served by GIBS in near real-time (usually within 3.5 hours of observation). These products are built from NASA Earth Observing System satellites data courtesy of LANCE data providers and other sources. The MODIS instrument aboard Terra and Aqua satellites, the AIRS instrument aboard Aqua, and the OMI instrument aboard Aura are used as sources. Several of the MODIS global products are made available on this Esri hosted service.This image layer hosted by Esri provides direct access to one of the GIBS image products. The Esri servers do not store any of this data itself. Instead, for each received data request, multiple image tiles are retrieved from GIBS, which are then processed and assembled into the proper image for the response. This processing takes place on-the-fly, for each and every request. This ensures that any update to the GIBS data is immediately available in the Esri mosaic service.Note on Time: The image service supporting this map is time enabled, but time has been disabled on this image layer so that the most recent imagery displays by default. If you would like to view imagery over time, you can update the layer properties to enable time animation and configure time settings. The results can be saved in a web map to use later or share with others.

SafeGraph Places provides baseline information for every record in the SafeGraph product suite via the Places schema and polygon information when applicable via the Geometry schema. The current scope of a place is defined as any location humans can visit with the exception of single-family homes. This definition encompasses a diverse set of places ranging from restaurants, grocery stores, and malls; to parks, hospitals, museums, offices, and industrial parks. Premium sets of Places include apartment buildings, Parking Lots, and Point POIs (such as ATMs or transit stations).

SafeGraph Places is a point of interest (POI) data offering with varying coverage depending on the country. Note that address conventions and formatting vary across countries. SafeGraph has coalesced these fields into the Places schema.

SafeGraph provides clean and accurate geospatial datasets on 51M+ physical places/points of interest (POI) globally. Hundreds of industry leaders like Mapbox, Verizon, Clear Channel, and Esri already rely on SafeGraph POI data to unlock business insights and drive innovation.

The new ArcGIS Hub Grocery Store Inventory template provides you with a framework for crowdsourcing data about resources and services in your community.

This is a layer provided by the Walmart Emergency Operations Center (EOC) to view store/club status.

Database contains information on ownership and system construction for underground storage tank facilities statewide. Database was developed in early 1990's for program management, and has been updated to more modern data systems periodically.

IMPORTANT NOTICE This item has moved to a new organization and entered Mature Support on February 3rd, 2025. This item is scheduled to be Retired and removed from ArcGIS Online on July 30th, 2025. We encourage you to switch to using the item on the new organization as soon as possible to avoid any disruptions within your workflows. If you have any questions, please feel free to leave a comment below or email our Living Atlas Curator (livingatlascurator@esri.ca) The new version of this item can be found here Stations containing prime movers, electric generators, and auxiliary equipment for converting mechanical, chemical into electric energy with an installed capacity of 1 Megawatt or more generated from renewable energy, including biomass, hydroelectric, pumped-storage hydroelectric, geothermal, solar, wind, and tidal.Mapping Resources implemented as part of the North American Cooperation on Energy Information (NACEI) between the Department of Energy of the United States of America, the Department of Natural Resources of Canada, and the Ministry of Energy of the United Mexican States.The participating Agencies and Institutions shall not be held liable for improper or incorrect use of the data described and/or contained herein. These data and related graphics, if available, are not legal documents and are not intended to be used as such. The information contained in these data is dynamic and may change over time and may differ from other official information. The Agencies and Institutions participants give no warranty, expressed or implied, as to the accuracy, reliability, or completeness of these data.Maintenance and Update Frequency: As Needed For more information visit Renewable Energy Power Plants

This web map shows the location of Oil Storage Installation Licence in Hong Kong. It is a set of data made available by the Buildings Department under the Government of Hong Kong Special Administrative Region (the "Government") at https://GEODATA.GOV.HK/ ("Hong Kong Geodata Store"). The source data is in GeoJSON format and has been processed and converted into Esri File Geodatabase format and uploaded to Esri's ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.For details about the data, source format and terms of conditions of usage, please refer to the website of Hong Kong Geodata Store at https://geodata.gov.hk.

Information Lookup is a configurable web application template that can be used to provide the general public, internal staff and other interested parties with information about a location. If no features are found at that location, a general message is displayed. Optionally, the location entered can be stored in a point layer.Configurable OptionsThe template can be configured using the following options:Lookup Layers: One or more polygon layers queried by the location specified. The pop-up defined in these layers combined into a single pop-up and displayed to the user. The layers can either be a feature service layer or a layer that is part of a dynamic map service. Use a vertical bar or pipe (|) to separate this list of layers. It is recommended that these layers visibility is turned off.Pop-up Title: The title of the pop-up when results are returned from one or more of the Lookup Layers.Pop-up Width: The width of the pop-up. pop-up Max Height: The maximum height title of the pop-up.Unavailable pop-up Title: The title of the pop-up when no results are returned from the Lookup Layers.Unavailable pop-up Message: The message to display in the pop-up when no results are returned from the Lookup Layers.Zoom Level for Location: The scale to set the map at when a location is specified.Store Location: Option to store the location specified in a point layer, if checked on, fill out the remaining parameters.Application Title: Enter a custom title for the application.Storage Layer Name: Name of the point feature service layer in the map to store the location. Editing must be enabled on this layer.Storage Layer Field: Field in the Storage Layer to store a value if a result was returned from the Lookup Layers.Yes Value: The value to store in the Storage Layer Field specified above when a result is returned from the Lookup Layers.No Value: The value to store in the Storage Layer Field specified above when no results are returned from the Lookup Layers.Display Splash Screen on Startup: Option to show a splash screen when the app loads.Splash Screen message: The message to display in the splash screen.Splash Screen Theme: The color scheme for the splash screen.Supported DevicesThis application is responsively designed to support use in browsers on desktops, mobile phones, and tablets.Data RequirementsThis web app includes the capability to edit a hosted feature service or an ArcGIS Server feature service. Creating hosted feature services requires an ArcGIS Online organizational subscription or an ArcGIS Developer account. 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.

A bathymetric survey of Nimrod Lake, Arkansas, was conducted in late April to mid-May, 2016, by the Lower Mississippi-Gulf Water Science Center (LMG WSC) of the U.S. Geological Survey (USGS) using methodologies for sonar surveys similar to those described by Wilson and Richards (2006) and Richards and Huizinga (2018). Point data from the bathymetric survey were merged with point data from an aerial LiDAR survey conducted in December, 2010, that were provided by the U.S. Army Corps of Engineers (USACE), Little Rock District. From the combined point dataset, a terrain dataset (a type of triangulated irregular network, or TIN, model) was created in Esri ArcGIS, version 10.5, for the area within the approximate extent of the flood pool of the lake as defined by the 374-ft contour of the LiDAR data. Products in this data release are stored within an Esri file geodatabase and include the following: Feature classes of point data from the bathymetric and LiDAR surveys; the terrain dataset; a feature class of bathymetric contours at 4-ft intervals; and a table of storage capacity (volume) of the lake at 1-ft increments in water surface elevation from 301-373 ft above the North American Vertical Datum of 1988 (NAVD88), seasonal conservation pool elevations 342.13, 344.63, and 345.13 NAVD88, and flood pool elevation 373.13 ft NAVD88. References: Richards, J.M. and Huizinga, R.J., 2018, Bathymetric contour map, surface area and capacity table, and bathymetric difference map for Clearwater Lake near Piedmont, Missouri, 2017: U.S. Geological Survey Scientific Investigations Map 3409: 1 sheet, https://doi.org/10.3133/sim3409; Wilson, G.L., and Richards, J.M., 2006, Procedural Documentation and Accuracy Assessment of Bathymetric Maps and Area/Capacity Tables for Small Reservoirs: U.S. Geological Survey Scientific Investigations Report 2006-5208, https://pubs.usgs.gov/sir/2006/5208/.

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.

Stave off the garbage-in-garbage-out scenario and learn how to maintain authoritative geographic data. The courses and resources below will help you build the skills needed to store, organize, update, and disseminate accurate data that supports sound decision-making.Goals Create a geodatabase to organize and manage geographic data. Deploy recommended editing workflows to update 2D and 3D data. Apply ArcGIS best practices to maintain the accuracy of geographic data over time.