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(14 Posts)
Jhonatan_Garrido_Lecca
Esri Contributor

In this first installment of our blog series on Enterprise Spatial Analytics, we dive into one of the most potent yet underused features in ArcGIS Pro: Query Layers.

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HannahOlpinski
Esri Contributor

If you want to learn about how spatial data science allows analysts to extract deeper insight from data using a comprehensive set of analytical methods and spatial algorithms, this course will give you a deep dive.

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YuWang1
Esri Contributor

ArcGIS Pro 3.0 has new tools for creating feature class output from 3 types of netCDF DSG features; profiles, time series, and trajectories.

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KatieSmith
Esri Regular Contributor

Esri's no-cost Spatial Data Science massive open online course (MOOC) returns on October 5, 2022, and we invite you to register. Over six weeks, explore the latest ArcGIS capabilities for spatial analysis, predictive modeling, machine learning, and more.

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IhabHassan
Esri Contributor

Cool new features released with ArcGIS JS APIs 4.17, my first take aways:

  • View Constraints: now you can limit the user navigation to be around certain geometry, help the user to focus on specific area
  • Layer Visibility Animation: the APIs now supports fading effect while toggling layer visibility
  • OGC Support: this is emphasized with 2 new features
    • WCSLayer: new layer allows rendering raster data from OGC Web Coverage Service
    • WMSLayer time enabled: now you can configured widgets like Time Slider with time-enabled WMS layers
  • Widgets updates, now Bookmarks widgets saves the bookmark thumbnail, the user will have to set thumbnail URL 

More details can be found in this ESRI blog: What’s New in ArcGIS API for JavaScript (October 2020) 
New Code Samples for 4.17 Sample Code | ArcGIS API for JavaScript 4.17 

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IhabHassan
Esri Contributor

Ozri 2020 showcased the latest ArcGIS solutions and case studies from Australia and around the world. To access the video recordings and slides from each session, simply select a presentation from the list here

Ozri on-demand | Esri Australia 


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Jill_Saligoe-Simmel
Esri Regular Contributor

The Federal Geographic Data Committee (FGDC) is seeking public comment on the draft strategic plan for the National Spatial Data Infrastructure (NSDI). Comments are due by September 17, 2020.

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IhabHassan
Esri Contributor

For a quick and focused tech-talks on latest ESRI trends and topics, tune in to GIS Directions podcast | Esri Australia 

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GregoryBrunner
Esri Contributor

I was sitting in a presentation a few weeks ago on the R-ArcGIS bridge by Marjean Pobuda and I had a question: "Can I use the R-ArcGIS bridge in my Jupyter Notebook?"  So I asked Mark Janikas (who was also attending the presentation) if this would be possible an he said, "Yes".  So, after the presentation, I set out to get the R-ArcGIS bridge running in Jupyter.

Installing the ArcGIS-R Bridge

The first thing I did was install the R-ArcGIS bridge. I installed it using ArcGIS Pro by following the installation instructions. I am currently using R-3.4.2 and arcgisbinding '1.0.1.208'. I can verify this by going to the Geoprocessing tab in the Options section in ArcGIS Pro.

Cloning My arcgispro-py3 Environment

I did not want to break my arcgispro-py3 conda environment (the default ArcGIS Pro Python environment) so the first thing I did was clone the environment. I named the cloned environment arcgispro-r. I did this from the command line as follows:

I cloned my arcgispro-py3 environment.

Switching Environments

Next, I switched to the cloned environment by deactivating the arcgispro-py3 environment and activating the arcgispro-r environment. This can be done at the command line with two commands, deactivate and activate, as shown her:

Installing R-Essentials

Then, I installed r-essentials, a bundle of over 80 of the most used R packages created by the Anaconda team. I did that from the command line while in my arcgispro-r environment as shown here:

Running arcgisbinding in Jupyter

After I installed r-essentials, when I launched my Jupyter Notebook, I had the option to create a Python or an R notebook. I created an R notebook. To test whether the ArcGIS-R bridge is installed and accessible to my notebook, I loaded the arcgisbinding package and checked the product version number and there it was, package version '1.0.1.208', the same one I see listed in ArcGIS Pro above!

But Does It Work?

Yes, I can use the arcgisbinding package to read spatial data into R! In order to test whether I could read in data, I used arc.open to read in a point feature class of seagrass data. I was also able to use arc.select to put that feature class into a dataframe. I shared my sample notebook on GitHub at the repo arcgisbinding-in-jupyter. I am interested to know if there is anyone else out there who has tried this or is interested in using R, ArcGIS, and Jupyter. If you are, let me know!

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GregoryBrunner
Esri Contributor

What's in your workflow? A presentation by Emily Riederer (emily.riederer@capitalone.com), a Sr. Analyst for Capital One from Data Pop Up Chicago in November of 2017.

While business analysis rapidly grows more data-driven, the analyst community is slow to adapt the best practices of data science workflows. Many parallels exists between data science “top topics” (e.g. reproducibility) and business pain points, but these common needs are obscured by the different “languages” of these two communities. The opportunity cost is greatest in heavily regulated industries such as finance and insurance where documentation and compliance are paramount. 

In this talk, we will review our experience transitioning Capital One business analysts from legacy systems to open-source workflows by developing user-friendly tools. We incentivized business analysts to adopt the data science mindset by curating open-source tools and developing code packages which simplify workflows and eliminate pain points. 

Our internal R package, tidycf, reimagines cumbersome Excel cashflow statements as dataframes and uses RMarkdown templates and the RStudio IDE for an intuitive, user-friendly experience without the overhead of maintaining a custom GUI. We tackle challenges in documentation and communication while immersing new users in the R language. 

We will share best practices and lessons learned from our experience designing tools for non-technical end-users, standardizing workflows based on the RStudio IDE’s infrastructure, and evangelizing data science methods. 

Check out her slide deck for this presentation here.

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