To achieve true data interoperability is to eliminate format and data model barriers, allowing you to seamlessly access, convert, and model any data, independent of format. The ArcGIS Data Interoperability extension is based on the powerful data transformation capabilities of the Feature Manipulation Engine (FME), giving you the data you want, when and where you want it.In this course, you will learn how to leverage the ArcGIS Data Interoperability extension within ArcCatalog and ArcMap, enabling you to directly read, translate, and transform spatial data according to your independent needs. In addition to components that allow you to work openly with a multitude of formats, the extension also provides a complex data model solution with a level of control that would otherwise require custom software.After completing this course, you will be able to:Recognize when you need to use the Data Interoperability tool to view or edit your data.Choose and apply the correct method of reading data with the Data Interoperability tool in ArcCatalog and ArcMap.Choose the correct Data Interoperability tool and be able to use it to convert your data between formats.Edit a data model, or schema, using the Spatial ETL tool.Perform any desired transformations on your data's attributes and geometry using the Spatial ETL tool.Verify your data transformations before, after, and during a translation by inspecting your data.Apply best practices when creating a workflow using the Data Interoperability extension.

ArcMap'ten ArcGIS Pro'ya geçişle birlikte eski Personal Geodatabase (.mdb) verilerinizi daha yeni ve verimli olan File Geodatabase (.gdb) formatına ArcGIS Data Interoperability aracılığıyla topluca nasıl dönüştürebileceğinizi göreceksiniz.Alıştırmayı yapmak için gerekli tahmini süre: 30 DakikaYazılım gereksinimi: ArcGIS Data Interoperability

Buildings feature layer for a section in UJI campus derived from a True Orthoimage of the area with an intention of checking the accuracy and completeness of the OpenStreetMap data for the area. The True Orthoimage generated from processing some Drone imagery data of the area.

High-quality GIS land use maps for the Twin Cities Metropolitan Area for 1968 that were developed from paper maps (no GIS version existed previously).The GIS shapefiles were exported using ArcGIS Quick Import Tool from the Data Interoperability Toolbox. The coverage files was imported into a file geodatabase then exported to a .shp file for long-term use without proprietary software. An example output of the final GIS file is include as a pdf, in addition, a scan of the original 1968 map (held in the UMN Borchert Map Library) is included as a pdf. Metadata was extracted as an xml file. Finally, all associated coverage files and original map scans were zipped into one file for download and reuse. Data was uploaded to ArcGIS Online 3/9/2020. Original dataset available from the Data Repository of the University of Minnesota: http://dx.doi.org/10.13020/D63W22

A combination of stormwater system data throughout Stark County, Ohio. The data is combined using an ETL via the data interoperability extension for ArcGIS Pro. Each weekend, the ETL is automatically ran via Python/Windows Task Scheduler to update the data with any changes from the past week from each of the source datasets. The source data is stored in ArcGIS SDE databases that Stark County GIS (SCGIS) provides for departments, cities, villages, and townships within the county. SCGIS currently maintains SDE databases for Canton, Alliance, Louisville, North Canton, Beach City, Easton Canton, Minerva, Meyers Lake, Stark County Engineer (SCE), and each of the townships. In addition to those datasets (which are updated weekly), this layer also includes data from the cities of Massillon and Canal Fulton, which are not stored in databases maintained by SCGIS. Data for those two cities is updated separately as new iterations become available.As this layer encompasses the entire county, source feature classes are consolidated into 4 layers to improve performance on ArcGIS Online. Discharge points are the point at which water exits part of the stormwater system, such as the outlet of a pipe or ditch. It includes outfalls defined under NPDES Phase II. Structures includes both inlets (catch basins, yard drains, etc.) and manholes. Pipes includes storm sewers, as well as culverts (pipes in which both ends are daylit). Finally, the ditches layer includes roadside ditches, as well as off-road ditches in some areas/instances.

This is a demonstration layer implementing streamlined INSPIRE data according to the INSPIRE rules for Alternative Encoding. It is provided as a courtesy and should not be used for any purpose other than demonstration.

Measure and Map Access to Grocery StoresFrom the perspective of the people living in each neighborhood How do people in your city get to the grocery store? The answer to that question depends on the person and where they live. This collection of layers, maps and apps help answer the question.Some live in cities and stop by a grocery store within a short walk or bike ride of home or work. Others live in areas where car ownership is more prevalent, and so they drive to a store. Some do not own a vehicle, and rely on a friend or public transit. Others rely on grocery delivery for their needs. And, many live in rural areas far from town, so a trip to a grocery store is an infrequent event involving a long drive.This map from Esri shows which areas are within a ten minute walk (in green) or ten minute drive (in blue) of a grocery store in the United States and Puerto Rico. Darker color indicates access to more stores. Summarizing this data shows that 20% of U.S. population live within a 10 minute walk of a grocery store, and 90% of the population live within a 10 minute drive of a grocery store. Click on the map to see a summary for each state.Every census block is scored with a count of walkable and drivable stores nearby, making this a map suitable for a dashboard for any city, or any of the 50 states, DC and Puerto Rico. Two colorful layers visualize this definition of access, one for walkable access (suitable for looking at a city neighborhood by neighborhood) and one for drivable access (suitable for looking across a city, county, region or state).On the walkable layer, shades of green define areas within a ten minute walk of one or more grocery stores. The colors become more intense and trend to a blue-green color for the busiest neighborhoods, such as downtown San Francisco. As you zoom in, a layer of Census block points visualizes the local population with or without walkable access. As you zoom out to see the entire city, the map adds a light blue - to dark blue layer, showing which parts of the region fall within ten minutes' drive of one or more grocery stores. As a result, the map is useful at all scales, from national to regional, state and local levels. It becomes easier to spot grocery stores that sit within a highly populated area, and grocery stores that sit in a shopping center far away from populated areas. This view of a city begins to hint at the question: how many people have each type of access to grocery stores? And, what if they are unable to walk a mile regularly, or don't own a car? How to Use This MapUse this map to introduce the concepts of access to grocery stores in your city or town. This is the kind of map where people will want to look up their home or work address to validate what the map is saying against their own experiences. The map was built with that use in mind. Many maps of access use straight-line, as-the-crow-flies distance, which ignores real-world barriers to walkability like rivers, lakes, interstates and other characteristics of the built environment. Block analysis using a network data set and Origin-Destination analysis factors these barriers in, resulting in a more realistic depiction of access. There is data behind the map, which can be summarized to show how many people have walkable access to local grocery stores. The map includes a feature layer of population in Census block points, which are visible when you zoom in far enough. This feature layer of Census block centroids can be plugged into an app like this one that summarizes the population with/without walkable or drivable access. Lastly, this map can serve as backdrop to other community resources, like food banks, farmers markets (example), and transit (example). Add a transit layer to immediately gauge its impact on the population's grocery access. You can also use this map to see how it relates to communities of concern. Add a layer of any block group or tract demographics, such as Percent Senior Population (examples), or Percent of Households with Access to 0 Vehicles (examples). The map is a useful visual and analytic resource for helping community leaders, business and government leaders see their town from the perspective of its residents, and begin asking questions about how their community could be improved. Data sourcesPopulation data is from the 2020 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 September 2024. For this project, ArcGIS StreetMap Premium was used for the street network in the origin-destination analysis work, because it 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 provided by SafeGraph. The source data included NAICS code 445110 and 452311 as an initial screening. The CSV file was imported using the Data Interoperability geoprocessing tools in ArcGIS Pro, where a definition query was applied to the layer to exclude any records that were not grocery stores. The final layer used in the analysis had approximately 63,000 records. In this map, this layer is included as a vector tile layer. MethodologyEvery 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 a Lines layer, which shows which origins are within the 10 minute cutoff of each destination over the street network, given the assumptions about that network (walking, or driving a vehicle). The Lines layer is not published but is used to count how many stores each origin has access to over the road network. 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 used a 100 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.

Statewide Download (FGDB) (SHP)Users can also download smaller geographic areas of this feature service in ArcGIS Pro using the Copy Features geoprocessing tool. The New Jersey Office of Information Technology, Office of GIS (NJOGIS), in partnership with several local GIS and public safety agencies, as well as the NJ Department of Transportation, has built a comprehensive statewide NG9-1-1 database meeting and exceeding the requirements of the National Emergency Number Association (NENA) 2018 NG9-1-1 GIS Data Standard (NENA-STA-006.1-2018). The previous New Jersey statewide road segment data (Tran_road_centerline_NJ), which included the road name alias information, has been transformed into the NENA data model to create the street name alias table.The existing road centerlines were loaded into New Jersey's version of the NENA NG9-1-1 data model using Extract, Transform and Load (ETL) procedures created with Esri's Data Interoperability Extension. The data subsequently have been updated and corrected.The road centerlines no longer contain any linear referencing information. The linear referencing will only be maintained by the NJ Department of Transportation as part of the NJ Roadway Network.***NOTE*** For users who incorporate NJOGIS services into web maps and/or web applications, please sign up for the NJ Geospatial Forum discussion listserv for early notification of service changes. Visit https://nj.gov/njgf/about/listserv/ for more information.

Statewide Download (FGDB) (SHP)Users can also download smaller geographic areas of this feature service in ArcGIS Pro using the Copy Features geoprocessing tool. The address service contains statewide address points and related landmark name alias table and street name alias table.The New Jersey Office of Information Technology, Office of GIS (NJOGIS), in partnership with several local GIS and public safety agencies, has built a comprehensive statewide NG9-1-1 database meeting and exceeding the requirements of the National Emergency Number Association (NENA) 2018 NG9-1-1 GIS Data Standard (NENA-STA-006.1-2018). The existing New Jersey Statewide Address Point data last published in 2016 has been transformed in the NENA data model to create this new address point data.The initial address points were processed from statewide parcel records joined with the statewide Tax Assessor's (MOD-IV) database in 2015. Address points supplied by Monmouth County, Sussex County, Morris County and Montgomery Township in Somerset County were incorporated into the statewide address points using customized Extract, Transform and Load (ETL) procedures.The previous version of the address points was loaded into New Jersey's version of the NENA NG9-1-1 data model using Extract, Transform and Load (ETL) procedures created with Esri's Data Interoperability Extension. Subsequent manual and bulk processing corrections and additions have been made, and are ongoing.***NOTE*** For users who incorporate NJOGIS services into web maps and/or web applications, please sign up for the NJ Geospatial Forum discussion listserv for early notification of service changes. Visit https://nj.gov/njgf/about/listserv/ for more information.

The New Jersey Office of Information Technology, Office of GIS (NJOGIS), in partnership with several local GIS and public safety agencies, as well as the NJ Department of Transportation, has built a comprehensive statewide NG9-1-1 database meeting and exceeding the requirements of the National Emergency Number Association (NENA) 2018 NG9-1-1 GIS Data Standard (NENA-STA-006.1-2018). The previous New Jersey statewide road segment data (Tran_road_centerline_NJ), which included the road name alias information, has been transformed into the NENA data model to create the street name alias table.The existing road centerlines were loaded into New Jersey's version of the NENA NG9-1-1 data model using Extract, Transform and Load (ETL) procedures created with Esri's Data Interoperability Extension. The data subsequently have been updated and corrected.The road centerlines no longer contain any linear referencing information. The linear referencing will only be maintained by the NJ Department of Transportation as part of the NJ Roadway Network.

The New Jersey Office of Information Technology, Office of GIS (NJOGIS), in partnership with several local GIS and public safety agencies, has built a comprehensive statewide NG9-1-1 database meeting and exceeding the requirements of the National Emergency Number Association (NENA) 2018 NG9-1-1 GIS Data Standard (NENA-STA-006.1-2018). The existing New Jersey Statewide Address Point data last published in 2016 has been transformed in the NENA data model to create this new address point data.The initial address points were processed from statewide parcel records joined with the statewide Tax Assessor's (MOD-IV) database in 2015. Address points supplied by Monmouth County, Sussex County, Morris County and Montgomery Township in Somerset County were incorporated into the statewide address points using customized Extract, Transform and Load (ETL) procedures.The previous version of the address points was loaded into New Jersey's version of the NENA NG9-1-1 data model using Extract, Transform and Load (ETL) procedures created with Esri's Data Interoperability Extension. Subsequent manual and bulk processing corrections and additions have been made, and are ongoing.

The dataset is a spatial representation of road centerlines in Somerset County, New Jersey. The New Jersey Office of Information Technology, Office of GIS (NJOGIS), in partnership with several local GIS and public safety agencies, as well as the NJ Department of Transportation, has built a comprehensive statewide NG9-1-1 database meeting and exceeding the requirements of the National Emergency Number Association (NENA) 2018 NG9-1-1 GIS Data Standard (NENA-STA-006.1-2018). The previous New Jersey statewide road segment data (Tran_road_centerline_NJ), which included the road name alias information, has been transformed into the NENA data model to create the street name alias table.The existing road centerlines were loaded into New Jersey's version of the NENA NG9-1-1 data model using Extract, Transform and Load (ETL) procedures created with Esri's Data Interoperability Extension. The data subsequently have been updated and corrected.The road centerlines no longer contain any linear referencing information. The linear referencing will only be maintained by the NJ Department of Transportation as part of the NJ Roadway Network. The data for Somerset County, New Jersey was extracted & processed from the latest NJOGIS dataset by the Somerset County Office of GIS Services (SCOGIS) on April 10, 2024

The New Jersey Office of Information Technology, Office of GIS (NJOGIS), in partnership with several local GIS and public safety agencies, as well as the NJ Department of Transportation, has built a comprehensive statewide NG9-1-1 database meeting and exceeding the requirements of the National Emergency Number Association (NENA) 2018 NG9-1-1 GIS Data Standard (NENA-STA-006.1-2018). The previous New Jersey statewide road segment data (Tran_road_centerline_NJ), which included the road name alias information, has been transformed into the NENA data model to create the street name alias table.The existing road centerlines were loaded into New Jersey's version of the NENA NG9-1-1 data model using Extract, Transform and Load (ETL) procedures created with Esri's Data Interoperability Extension. The data subsequently have been updated and corrected.The road centerlines no longer contain any linear referencing information. The linear referencing will only be maintained by the NJ Department of Transportation as part of the NJ Roadway Network.

The GIS shapefiles were exported using ArcGIS Quick Import Tool from the Data Interoperability Toolbox. The coverage files was imported into a file geodatabase then exported to a .shp file for long-term use without proprietary software. An example output of the final GIS file is include as a pdf, in addition, a scan of the original 1958 map (held in the UMN Borchert Map Library) is included as a pdf. Metadata was extracted as an xml file. Finally, all associated coverage files and original map scans were zipped into one file for download and reuse.Date completed4/28/2003Geographic coverageBounding box (W, S, E, N): -93.770810, 44.468717, -92.725647, 45.303848Persistent link to this itemhttps://dx.doi.org/10.13020/D6059Jhttps://hdl.handle.net/11299/160503ServicesFull Metadata (xml)View Usage StatisticsFunding Information:Sponsorship: MnDOT Report 2003-37Funding agency: Minnesota Department of TransportationFunding agency ID: Contract #: (c) 81655 (wo) 8Sponsorship grant: If They Come, Will You Build It? Urban Transportation Network Growth Models.Referenced byLevinson, David, and Wei Chen (2007) "Area Based Models of New Highway Route Growth." ASCE Journal of Urban Planning and Development 133(4) 250-254.https://doi.org/10.1061/(ASCE)0733-9488(2007)133:4(250)Levinson, David and Wei Chen (2005) "Paving New Ground" in Access to Destinations (ed. David Levinson and Kevin Krizek) Elsevier Publishers.

Based on the Arizona Department of Transportation’s (ADOT) previous SPR-561 ‘Needs Assessment’ study, this report continues the efforts to enhance radio interoperability between Department of Public Safety (DPS) Highway Patrol officers and ADOT field personnel. Ideally, any DPS officer should be able to communicate with any ADOT highway worker in the rural areas. The SPR 561 project recommended two pilot programs: (a) testing of secondary VHF radios to be installed in several squads of DPS patrol cars, and (b) testing of dispatch console interties, or ‘crosspatching,’ by regional DPS dispatchers and by ADOT’s Phoenix Traffic Operations Center. This report outlines the implementation and evaluation aspects of the radio interoperability pilot program, to include evaluation of user needs, suggested deployments, development of training and evaluation plans, and defining a practical evaluation period for assessment of the programs. User needs were developed through a series of stakeholder meetings and follow-up discussions and interviews to identify critical areas. Previous attempts at interoperability were reviewed and analyzed to determine successes and challenges. A train-the-trainer plan was developed to support an on-going program such that refresher courses would be available, as well as opportunities to train new employees. The evaluation plan identifies the feedback methods used for the program, which included field interviews, dispatch log sheets, and interoperability field reports. Data reduction was performed to determine response times, message latency, any follow-up meetings needed, level of confusion, frequency of use, and user perception. An evaluation period of seven months was sufficient to support the analysis of radio interoperability. Of three final recommendations from this successful pilot program, the key point is to deploy secondary car-to-car VHF radios in all highway patrol units in the rural areas of the state for direct contact with ADOT personnel. The dispatch console crosspatch resource is also in place as a tool for special events, incidents, or joint operations such as forest fires or winter storms. Ongoing training, testing, and feedback comprise a third key element.

A combination of stormwater ditch data throughout Stark County, Ohio. Ditches includes roadside ditches, as well as off-road ditches in some areas/instances. The data is combined using an ETL via the data interoperability extension for ArcGIS Pro. Each weekend, the ETL is automatically ran via Python/Windows Task Scheduler to update the data with any changes from the past week from each of the source datasets. The source data is stored in ArcGIS SDE databases that Stark County GIS (SCGIS) provides for departments, cities, villages, and townships within the county. SCGIS currently maintains SDE databases for Canton, Alliance, Louisville, North Canton, Beach City, Easton Canton, Minerva, Meyers Lake, Stark County Engineer (SCE), and each of the townships. In addition to those datasets (which are updated weekly), this layer also includes data from the cities of Massillon and Canal Fulton, which are not stored in databases maintained by SCGIS. Data for those two cities is updated separately as new iterations become available.

The New Jersey Office of Information Technology, Office of GIS (NJOGIS), in partnership with several local GIS and public safety agencies, as well as the NJ Department of Transportation, has built a comprehensive statewide NG9-1-1 database meeting and exceeding the requirements of the National Emergency Number Association (NENA) 2018 NG9-1-1 GIS Data Standard (NENA-STA-006.1-2018). The previous New Jersey statewide road segment data (Tran_road_centerline_NJ), which included the road name alias information, has been transformed into the NENA data model to create the street name alias table.The existing road centerlines were loaded into New Jersey's version of the NENA NG9-1-1 data model using Extract, Transform and Load (ETL) procedures created with Esri's Data Interoperability Extension. The data subsequently have been updated and corrected.The road centerlines no longer contain any linear referencing information. The linear referencing will only be maintained by the NJ Department of Transportation as part of the NJ Roadway Network.

This is a demonstration layer implementing streamlined INSPIRE data according to the INSPIRE rules for Alternative Encoding. It is provided as a courtesy and should not be used for any purpose other than demonstration.ArcGIS INSPIRE Open Data is a lightweight solution for European public sector organizations implementing the INSPIRE and PSI-2/Open Data Directives. See the Getting to know ArcGIS INSPIRE Open Data story map to learn more.Geodatabase (GDB) templates are available on the ArcGIS INSPIRE Open Data demonstration Hub. INSPIRE Alternative Encoding documentation on GitHub is publicly available per the Implementing Rules on interoperability of spatial data sets and services (Commission Regulation (EU) No 1089/2010). These resources are provided as-is and are freely available.

The New Jersey Office of Information Technology, Office of GIS (NJOGIS), in partnership with several local GIS and public safety agencies, as well as the NJ Department of Transportation, has built a comprehensive statewide NG9-1-1 database meeting and exceeding the requirements of the National Emergency Number Association (NENA) 2018 NG9-1-1 GIS Data Standard (NENA-STA-006.1-2018). The previous New Jersey statewide road segment data (Tran_road_centerline_NJ), which included the road name alias information, has been transformed into the NENA data model to create the street name alias table.The existing road centerlines were loaded into New Jersey's version of the NENA NG9-1-1 data model using Extract, Transform and Load (ETL) procedures created with Esri's Data Interoperability Extension. The data subsequently have been updated and corrected.The road centerlines no longer contain any linear referencing information. The linear referencing will only be maintained by the NJ Department of Transportation as part of the NJ Roadway Network.

A combination of stormwater structure data throughout Stark County, Ohio. Structures includes both inlets (catch basins, yard drains, etc.) and manholes. The data is combined using an ETL via the data interoperability extension for ArcGIS Pro. Each weekend, the ETL is automatically ran via Python/Windows Task Scheduler to update the data with any changes from the past week from each of the source datasets. The source data is stored in ArcGIS SDE databases that Stark County GIS (SCGIS) provides for departments, cities, villages, and townships within the county. SCGIS currently maintains SDE databases for Canton, Alliance, Louisville, North Canton, Beach City, Easton Canton, Minerva, Meyers Lake, Stark County Engineer (SCE), and each of the townships. In addition to those datasets (which are updated weekly), this layer also includes data from the cities of Massillon and Canal Fulton, which are not stored in databases maintained by SCGIS. Data for those two cities is updated separately as new iterations become available.