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A few weeks ago, Esri released an update to the ArcGIS API for Python. The newest release includes:

Hopefully, you can tell that the new functionality in the API that I am most excited about is the spatial dataframe! The spatial dataframe extends the pandas dataframe by adding geometry, spatial reference, and other spatial components to the dataframe. In adding the spatial dataframe to the API, ArcGIS users can now read feature classes, feature services, and image services directly into a dataframe. Once in a spatial dataframe, users can perform fast statistical and spatial analysis on the dataupdate existing feature services, and convert the dataframe to a feature class or shapefile. These are just a few examples of how you can use the spatial dataframe.

What really interests me is how this can be used with an ArcGIS image service. Can I use the spatial dataframe to extract image footprints from an image service? Can I use it to perform statistical analysis image footprints over a specific part of the world?

The answer to both of these questions is Yes! In this post, I’ll walk through how to use the API for Python to extract image service footprints from the Landsat 8 Views image service, show how to use a spatial filter to extract only footprints over New Jersey, determine the mean cloud cover and most recent acquisition date of the images, and share those image footprints as a feature service. If you have ever been interested in doing any of these, check out my post on!

Esri Science pre-UC news items:


(1) You can download a separate copy of the Esri UC Q&A item: “How is Esri advancing geographic science?” on Box at .


(2) We have over 700 RSVPed for the Esri Science Symposium on Tuesday of UC. Even if you didn’t get a chance to RSVP, you can still come at any time, especially for the beer and networking at the end. There should be enough seating in SDCC Ballroom 20A. The networking reception at 5:00-6:00, will be at the SDCC Center Terrace, outside and to the LEFT of Ballroom 20, overlooking San Diego Bay.


(3) Thanks to @[Steve Kopp] for the item below:


Regarding ArcGIS on the Extreme Science and Engineering Discovery Environment (XSEDE; i.e., supercomputers): the licensing for this is figured out now and plans are moving forward with allowing research and education use of ArcGIS on the NSF XSEDE supercomputer system. Final details are getting worked through and there will be a formal press release in a month or so.


If you are someone from research or academia who wants to run something really big (geoanalytics, raster analytics, geoevent), there is now a place you can do this FOR FREE, and you can run as many ArcGIS processes as you want, again, FOR FREE.


If interested and want to know more, contact Eric Shook at Univ of Minnesota  He is the GIS domain lead for XSEDE and the primary person we are coordinating through. He can help them understand XSEDE qualification requirements and how to apply for an allocation.


Esri and XSEDE will collaborate to set up ArcGIS Enterprise with big data extensions with a Jupyter sandbox later this summer where XSEDE users can easily play and get started. For those who want to run specific or larger projects they will also be able to configure and manage their own cluster.


Background: XSEDE is the eXtreme Science and Engineering Discovery Environment, the NSF funded collection of research supercomputer centers in the US. Their hardware includes supercomputers running special OS and middleware, as well as large windows clusters for cloud configuration, including JetStream, which has over 15,000 cores and 80Tb of RAM.



Good luck at UC!

While many parts of the Esri ArcGIS Platform are able to run natively on Macs, such as ArcGIS Python API, AppStudio, Workforce for ArcGIS, and the Web GIS tools, Esri's ArcGIS Desktop products are not supported for native use on Mac OS.  Many of my sciences customers ask me how to optimize the performance of their ArcGIS inside their Parallels instances, so I wanted to publish a post with some best-practice recommendations.


  1. MacBooks and MapBook Airs are probably not going to have the power inside needed to run ArcGIS in a performant manner if you're running intensive processes.  Starting off on the right foot with a MacBook Pro will help to ensure the best performance possible.
  2. Power down your Mac, and then turn it back on (you can't do the next steps if you're already running/have run Parallels since your laptop has been turned on for some reason). 
  3. Before launching Parallels, inside your Mac OS launch your Parallels Desktop Control window. 
  4. Click on the Gear/Cog to open the settings
    1. Under Optimization:
      1. Set Performance to Faster Virtual Machine
      2. Check the box to Enable Adaptive Hypervisor
      3. Check box to Tune Windows for Speed
    2. Under Power:
      1. Set it for Better Performance (and try to always be plugged in when using Parallels)
  5. Use Parallels in Fullscreen instead of Shaddow Mode
  6. Make sure sufficient cores and RAM have been allotted to your Parallels instance
  7. Running Parallels via Bootcamp will also enhance performance when you're working heavily in ArcGIS, allowing Windows to use as many system resources as possible.  If you're gearing up for a significant amount of work in ArcGIS, rather than just quickly jumping in and doing things, consider Bootcamp.
  8. If you're using ArcMap, make sure you have the free 64-bit Background Geoprocessing Add-On installed from MyEsri to eliminate out-of-memory errors when running certain GP tools.  Remember ArcMap is native x32 bit.
  9. While ArcGIS Pro is native x64 bit, it won't necessarily run faster, it just means it can handle bigger, more complex data.  ArcGIS Pro more heavily leverages the GPU onboard, so make sure it's spec'd appropriately.  Also, if you're switching back and forth between using Pro in your Parallels and your Mac OS again and again, it can impact the acceleration, having an impact on performance (see recommendation about using in fullscreen )
  10. For those of you running ArcGIS Enterprise (previously ArcGIS Server), consider using the free Python API, which you can use natively on your Mac without Parallels to do the processing Server-side.


Are you a Mac owner who runs Parallels to leverage ArcGIS Desktop?  If so, please post any additional tips you've found for enhancing performance in the comments.  Let's turn this into a resource for the Community!

Join Esri for the Create Together Citizen Science Event


Not too late to register for the Wednesday, May 17th (9:00am – 4:00pm central) event during the CSA conference in Minnesota.  No GIS experience is necessary.  Bring ideas and mobile devices, event team will supply breakfast, lunch and coffee.  Registered participants receive extended trial versions of ArcGIS and will gain hands-on experience in creating Survey123 for ArcGIS apps for citizen science projects.

Details are here:


Look forward to seeing you in the Twin Cities.  Charmel Menzel (