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Let me get you through one paragraph of background before we get to the fun stuff:  In an earlier video I included an example of capturing a spatial constraint from the active ArcGIS Pro map or scene and sending it into an ETL workspace.  The sample happened to be working with a WFS service; these have a bounding box parameter that can constrain the features retrieved.  WFS services also support more complex spatial operators which can be used with arbitrary geometry operands supplied as GML fragments.  However, unless you know how to put all the required XML together for WFS requests then you'll be like me and terrified of attempting it.  ArcGIS Pro 2.3 itself only supports a bounding box constraint on WFS services.


Spatial constraints are a lot easier with feature services.  This blog will show you how easy.


Core geoprocessing has supported feature services as input parameters for several releases now, why bother using Spatial ETL against feature services anyway?  Well, if your feature service is heading out the door as some other format, or you are using some transformations indicating Data Interoperability, or your feature service is very large and you don't want to use selections to subset it.  I just helped one customer who needed to dynamically handle a spatial constraint mid-ETL with a FeatureReader transformer (more on that below).  There are many use cases.


Data Interoperability is all about code-free approaches, but I'll take a wee diversion into feature service REST API query parameters so you understand what goes on.  Below is a screen shot of the HTML view of a feature service Query endpoint.  Note there is an Input Geometry parameter (supplied as JSON) and you can set how it is used, in my case it is a Polygon for which I want only features satisfying the constraint Intersects.



So, the trick with applying spatial constraints to feature services is just supplying the geometry!


In the blog download (Pro 2.3+) you'll find the sample tool used, but the approach is very simple, just apply it yourself in your own models.  Click to enlarge this graphic to see the map I used, the feature set in the map and table of contents and the model run as a tool.  The feature set is driving the analysis geometry automatically.



The tool being used is the Model named SpatiallyConstrainedGP which has an input parameter of type Feature Set.  At run time you supply a value by choosing a layer or feature class or creating a feature manually by editing in the map.



SpatiallyConstrainedGP wraps the ETL tool SpatiallyConstrainedETL like this, there is a model tool Calculate Value between the input feature set and the ETL tool:



All that is happening with Calculate Value is the input feature set is turned into a JSON string with a Python snippet:



The JSON is then supplied to the published ETL tool parameter Input Geometry (remember the Query endpoint!) and...



...the ETL tool does its stuff, considering only features intersecting my feature set...



..which is to make a spreadsheet summarizing some parcel area totals per case of an attribute:



So that's it, just grab JSON from the map when you need to supply a feature service reader with an Input Geometry parameter.  if you are using a FeatureReader transformer to read a feature service the workflow is a little different, you'll need to convert the JSON into an actual FME feature with a GeometryReplacer (the geometry encoding is Esri JSON) and supply it as the initiator Spatial Filter constraint of the FeatureReader, like this:



Now you can apply map-driven spatial constraints to your ETL!

Data Interoperability extension sees Point Cloud data, such as ASPRS LAS and Esri LAS Dataset as their own feature type, just like many other formats.  Here is some on a coastline - surf's up!   (Look above the headland)


Some high denisty LiDAR on a coastline


Formats are designed to deliver specific capabilities, but all geospatial formats have something in common - a coordinate system - and your GIS needs to be able to manage it.  LAS data is a bit of an outlier here as we expect ArcGIS users to collect their data in the coordinate system they intend to use, and stick with it, but in the case where the 'ground moves' (literally, like plate drift or quakes, or if a new datum or realization is published) then ArcGIS's comprehensive core projection tools don't yet support the format.


A situation we hear about is people have LAS data in ellipsoidal heights (say WGS84) and want to generate DEMs in orthometric heights.  Orthometric heights are gravity-defined and approximate height above mean sea level, so they are important if you need to model coastal or estuarine flooding, for example.  You can always create a DEM and reproject its vertical coordinate system with the geoid grids delivered by the ArcGIS Coordinate Systems Data install, or your own local ones, but that leaves the LAS data behind .


Your LiDAR vendor would be pleased to reprocess your LAS data but you can do it yourself with ArcGIS Data Interoperability extension.  The secret is in this transformer - CsmapReprojector:


CsMapReprojector Transformer


In the blog download there is a sample specific to accommodating a new vertical datum for New Zealand, but read between the lines in the document delivered in the download and leverage the vertical grids delivered in the Coordinate Systems Data install, or geoid grids you obtain locally, and reproject your LAS data how you need.


Then when a point says its floating you can trust it (bad I.T. pun).


Floating Point that is nothing to do with a computer data type!


Note:  The blog download and the Geoprocessing gallery sample here are equivalent.

Agencies around the world publish their data on the web using a great variety of technologies, and while standards exist to make them accessible within ArcGIS, nothing performs within ArcGIS like our own services.  Sometimes it just makes sense to regularly synchronize data from its system of record to ArcGIS Online or Portal.  This blog is about how to do that efficiently.


To see if you should read further, download the blog attachment NZ_Street_Address.lyrx and add it to a new map in Pro, then using the Locate pane and the ArcGIS World Geocoding Service zoom to Wellington, NZL (or your favorite other New Zealand locality).  Zoom in to 1:5000 or larger scale, pan around, turn on the label classes Standard Number, Suffix Number and Range Number and inspect the address point house numbers.  Identify features.  Select features.  You are accessing a feature layer in an ArcGIS Online standard feature data store.  Here are links to the item and service.  If you have a reasonable internet connection you will have a good map exploration experience.  The layer you are looking at has over 2 million features.  You can download the data.  You can use it in geoprocessing.  The data is maintained weekly and the synchronization process averaging thousands of updates each week takes under 2 minutes.  The approach uses no coding.  If you want to do this for data accessible to you then read on (click on images to enlarge them).



Firstly, what data sources are candidates for this treatment?  Anything accessible to ArcGIS Data Interoperability extension, which is all these formats and feeds in many storage repositories.  My specific example happens to use data available by WFS service but that is not critical to the discussion, the approach is generic.


Lets dig a little deeper.  To look at the layer a little more closely, with ArcGIS Online as your active portal, Add Data from All Portal with search tags 'LDS' and 'ETL'.



You'll see the same point features (with default symbology) but also in your table of contents there is a standalone table 'Timestamps' with one row:




The value in UpdatedUTC is refreshed at each synchronization so will differ from the graphic but its the key to synchronization.  It lives within the feature service as a layer.  The UTC time of synchronization is the final step of the process that also writes feature updates.


So what are all the steps?  To follow you'll need ArcGIS Pro 2.3+ with Data Interoperability extension installed and enabled, and to have downloaded the toolbox and ETL tool source .fmw files in the blog download  Add the toolbox to your project, you'll see these ETL tools in it:



Right click each ETL tool and repair the source path to its .fmw file.


My target data is available as a bulk download, which i took as a file geodatabase.  I copied the address point feature class into my project home geodatabase.  In any event get your target data into your project home geodatabase, using ETL processes if necessary.


Next I made the Timestamp table using MakeTimestampTable, which looks like this:

See note below its not a great idea to use the table name 'Timestamps' but we'll let it go for now

Repair the destination file geodatabase path to be the same as your features of interest.  If you run MakeTimestampTable in edit mode you can pick your own initial timestamp value with a useful date picker.  I used UTC time but didn't have to get it exact, if you do and live in Greenwich UK then look at your watch and ignore any current daylight savings adjustment, otherwise use a little Python after making the table with any value:



Then calculate UpdatedUTC to equal DownloadedUTC and you'll have it:



Its at this point in blog writing you find out its a really bad idea to use a table name 'Timestamps' as it is too close to a reserved word in many database technologies including file geodatabase, but as it doesn't affect my goal here I'll leave it, but if you go into production use another name!


Now stand up a feature service.  Add your target data and the timestamp table to a map, then select both objects in the table of contents:



Then right click and choose Share as Web Layer:



Configure the service to be a feature layer in the folder you want and let it load.


Included in Synchronize.tbx is an ETL tool LoadData that creates a feature service too if you want to go that way.



Now for the synchronization stuff in the ETL tool Synchronize:



The design of your version will depend on your target data, but in broad strokes:


  • The current UTC time at the beginning of processing is captured
  • The timestamps layer (table) is read from the Esri web layer
  • Your target data is read from its system of record
  • Inserts, Updates and Deletes are derived between the target source and Esri web layer
  • Inserts, Updates and Deletes are validated by unique identifier comparison with the Esri layer
  • Deletes are committed
  • Updates are written
  • Inserts are written
  • The timestamps layer (table) is updated with the UTC time captured when processing began


For my target data the curator provides a changeset API that let me build from/to times into a WFS call which gave exact insert, update and delete sets.  If your data has timestamps for created, edited and retired values you can do this yourself.  If you have nothing to go on you can derive changesets by reading all data from both sources and doing brute force change detection with the UpdateDetector transformer, although this of course may take time.


In the Synchronize ETL tool there are some less obvious features.  The sequence of feature writing is determined by writer order in the Navigator pane, top down.  Writing the timestamp update is therefore enforced to be last, so if anything fails it will not be falsely updated.  ArcGIS Online and Portal feature writers in Delete and Update mode require the ObjectID value in the Esri service to be sent with the feature, so the values are picked up mid stream with a FeatureReader and joined on a layer unique identifier.  Similarly, the Inserts stream looks for existing unique identifiers before writing, only features known to not exist pass.


In the opening paragraph I said the approach uses no coding.  There is a math function used (floor) to calculate a batch number in modulo 20 chunks to obtain target service ObjectIDs.  That's as close to writing code you need to get, although you are free to use Python if you like.


While I mention coding, in a production environment you would want to run synchronization as a scheduled task.  This begins as a Python script.  I stub one out here that assumes things like ETL web connections are available to the process owner, which is easily done by sharing the connection file in a well known directory. 



Another approach I'll blog about separately is calling the FME engine executable directly in a scheduled task.


Do explore the ETL tools supplied and send questions to this forum.


I hope this gives you confidence to build your own synchronizations.  Enjoy!

We're going on a journey to the bottom of the sea, but the real message here is the ability of ArcGIS Data Interoperability to reach out to the web (or anywhere) and get feature and media data into a geodatabase feature class with attachments without having to code.  Well just a tiny bit, but you don't have to sweat the details like a coder.


A colleague came to me asking if ArcGIS Data Interoperability could bring together CSV position and time data of a submersible's missions and related media content and get it all into geodatabase.  In production all data sources will be on the web.  No problem.  Data Interoperability isn't just about formats and transformations, it is also about integrations, and building them without coding.



Python comes into the picture as a final step that avoids a lot of tricky ETL work.  The combination of an ETL tool and a little ArcPy is a huge productivity multiplier for all you interoperators out there.  Explore the post download for how the CSV and media sources are brought together - very simply - below is the whole 'program':



ArcGIS Data Interoperability has a great 'selling point', namely that you can avoid coded workflows and use the visual programming paradigm of Workbench to get data from A to B and in the shape you want.  I often show colleagues how to efficiently solve integration problems with Data Interoperability and its always pleasing to see them 'get it' that challenges don't have to be tackled with code.


Low level coding is the thing we're avoiding.  ArcGIS geoprocessing tools are accessible as Python functions; using geoprocessing tools this way is just a command-line invocation of what you have access to in the Geoprocessing pane and Analysis ribbon tools gallery and so on.  If this is news to you, take a look and run the Get Count tool first from the system toolbox and then use the result in the Python window.


Here is the tool experience:



Now in the Catalog pane History view, right click the result and send to the Python window:



You'll see the Python expression equivalent of the tool:



Note I haven't written any code...


Where am i going with this?  ArcGIS Data Interoperability concepts differ a little from core geoprocessing in that input and output parameters tend to be parent workspaces and not feature types or tables within them.  You frequently write to geodatabases for example, in which case the output parameter of the ETL tool is the geodatabase, not the feature classes and tables themselves, although these are configured in the ETL tool.


What if you need to do something before, during, or after your ETL process for which there is a powerful ArcGIS geoprocessing tool available but which would be really hard to do in Workbench?


You use high level ArcGIS Python functions to do this work in Workbench.


I'll give a simple, powerful example momentarily, but first some Workbench Python tips.


Workbench allows you to configure your Python environment; to avoid any clash with Pro's package management just go with the default and use these settings:


In Tools>FME Options>Translation check you prefer Pro's Python environment:



In your Workbench, check your Python Compatibility will use the preference.



Now you know the ArcGIS Python environment can be used.


For my use case I'll provide a real example (attached below) where I need to create and load geodatabase attachments.  We cannot do this entirely in Workbench (except by how I'll show you) because it cannot create the attachments relationship class.  You could do that manually ahead of loading data, but then you still have to turn images into blob data, manage linking identifiers and other things that make your head hurt, so lets use ArcPy.   Manual steps also preclude creating new geodatabases with the ETL tool, which I want to support.


The example Workbench writes a feature class destined to have attachments, and a table that can be used to load them.  You can research the processing, but the key element to inspect is the shutdown script run after completion, see Tool Parameters>Scripting>Shutdown Python Script.


Here is the embedded script:



Now this isn't a lot of code, a few imports, accessing the dictionary available in every Workbench session to get output path values used at run-time, then just two lines calling geoprocessing tools as functions to load the attachments.


This is a great way to integrate ArcGIS' powerful Python environment in ETL tools.  Sharp eyed people will notice a scripted parameter in the workspace too, it doesn't use ArcPy so I can't claim it as avoiding low level coding, it was just a way to flexibly create a folder for downloading images at run-time.  There are a number of ways to use Python in Workbench, but I would be detracting from my message here that you can start with the simple and powerful one - use ArcGIS geoprocessing where it saves work.  Enjoy!

Last week was the 2019 Esri Partner Conference followed by Developer Summit, events at which we enjoy being challenged by friends from around the world who are using ArcGIS in their work, and also other apps and formats that ArcGIS does not make or manage.


One partner from Europe asked how to use GML (Geography Markup Language) files in ArcGIS Pro.  This format is really a category of formats; the underlying XML - as a markup language intends - can be extended, usually to push a data schema down into the protocol.  He had in mind however what we know as Simple Feature GML, which makes the task - well - simpler, but that isn't critical to this discussion.


In ArcMap, Data Interoperability extension may be used to both directly read GML files (of the simple feature profile, recognized from a .gml filename extension, even without licensing Data Interoperability extension) or to make an interoperability connection to any supported GML profile, such as the complex INSPIRE themes.  This workflow is not implemented in Pro, partly because WFS services (which are usually GML "in motion") are the most common use case for GML and are natively supported in Pro, and partly because interoperability connections are being re-imagined for a future release of Pro.


In ArcGIS Pro, Data Interoperability extension can also be used to convert GML files just like in ArcMap - with the Quick Import geoprocessing tool, or with a Spatial ETL tool, but the partner thought asking everyone to license an extension would be a hurdle.


I decided to blog about an implementation pattern that does what was asked for - convert GML to geodatabase features within ArcGIS Pro - but that can also be used to convert any of the hundreds of formats and connections accessible to ArcGIS Data Interoperability.  The GML data originator has access to ArcGIS Enterprise with Data Interoperability extension so the pattern leverages that, but end users with GML files only need ArcGIS Pro and authenticated access to a geoprocessing service.  You can use this pattern to stand up any format conversion you wish at any scale - but I hasten to add it must be a free or cost-recovery-only service if you make it public.


Enough talk, how do we do this?  We're going to use ArcMap and Pro in a double act.  Why both?  At time of writing ArcGIS Pro cannot publish web tools containing Spatial ETL tools, so we'll use ArcMap for that step.


In the blog download below you'll find a 10.6.1 version toolbox which contains these tools (click any images to enlarge them):



GML2FGDB is a Spatial ETL tool that converts one or more files of any schema of simple feature GML to a file geodatabase named GML2FGDB.gdb (with overwrite!).  It looks like this if edited:

If you run it as a tool you can see it has a parameter exposed for GML geometry axis order that defaults to 1,2.  If your data is in Y,X order you can set 2,1.  3D data is supported by the 1,2,3 and 2,1,3 values.



GML2FGDBModel is a model tool that incorprates the script tool ZipGDB to compress the file geodatabase to zip file.  The compression step is necessary because geoprocessing services do not support workspaces (i.e. geodatabases) as output parameters.



GML2FGDBModel is shared as a geoprocessing service (make it synchronous) which I called GML2FGDBService:



Lastly, the model tool GML2FGDBService wraps the service and adds the script tool UnZIPGDB for the round trip from a local GML file or files, to the web service that does the translation without requiring Data Interoperability locally, then finally unzips the scratch ZIP file containing a scratch geodatabase into a user-selected destination directory.



Now GML2FGDBService can be freely used in Pro:



GML2FGDBService will always output a file geodatabase named scratch.gdb to your output directory, so be careful not to overwrite prior output!



Now anyone with access to the model tool and service can convert suitable GML files (or any other data if you refactor the Spatial ETL component) to local geodatabase using ArcGIS Pro.  Enjoy!

Hello Interoperators!


If you watched the video ArcGIS Data Interoperability In Action you might have some questions on how some of the demos were built.


I tend to create Spatial ETL tools with external sources - namely .fmw files - as I can easily drag them into Workbench after opening it from the Analysis ribbon or share them with FME users.  They are attached below to give you the pattern, but they will not all work for you without repair and replacement of credentials.  Contact me if you need help with any of them.


To head off one question - how did I create the New York county name pulldown parameter and attribute value mapping in the Simple Powerful ETL demo, see the CountyParameter.fmw workspace.  It scrapes the NY website and writes a CSV file that can be used to import parameter values for the demo tool.  You can see the pattern and recycle it elsewhere.