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St. Louis Imagery – 1990 to Now

What kind of changes to the built environment happened in your city within the past 25 years? Looking at and comparing aerial imagery can be an informative and compelling way to investigate changes. In St. Louis, many large building developments have happened over the years including:

… and much more. Luckily for the St. Louis region, the MSDIS maintains imagery services across different years. I recently found one of their imagery services from 1990 and put it into an application to compare it to more recent (I think ~2015) imagery. Check it out:

I’ve added a few preset locations to the application to some heavily changed, interesting areas. What areas of interest do you have? Let me know in the comments below.

Getting the Microsoft US Building Footprints into ArcGIS Pro

Update: there’s now an easier way to get this data into ArcGIS Pro. Please see the script that is linked in Arthur’s comment here.

Last week, Microsoft Released 125 million Building Footprints in the US as Open Data. This is a pretty exciting release of open geospatial data.

If you go to the data download page and grab one of the state json files, if you try to load this into ArcGIS Pro with the JSON to Features tool, it’s currently failing for me. I’m not totally sure why it’s failing — maybe because of the format or size of the JSON file. To get around this issue, let’s convert the GeoJSON file to Esri JSON features before importing.

To do this conversion, we’ll use the arcgis-to-geojson-utils tool.

Prerequisite: NodeJS installed.

  1. Create a new folder
  2. In that new folder, save one of the JSON files from the data source. I recommend a small file like Washington DC for your first run. (** more on that below)
  3. In that new folder, open a terminal and run: npm init … and answer all the questions
  4. Then run: npm install --save @esri/arcgis-to-geojson-utils
  5. Create a new file: index.js and put in the following script:

… replacing “FILENAME.json” on line 4 with the name of your input file that you downloaded above.

Finally, in your terminal run: node index.js. This will save a file called “out.json” that you can then input into the JSON to Features tool in ArcGIS Pro to get access to this data in Pro.

Note (**) that this script will only work for smaller sized files. The large state files that are greater than 200MB, you may need to split those up and run them separately, or write a script that loops through the JSON one line at a time. If you do this and want to share, please post in the comments below.

National Park System

If you look at all the National Parks across the US, there are about 60:

The National Park Service manages many more “park units” other than just the officially-designated “National Parks.” These include things like National Lakeshores, National Monuments, National Parkways, National Preserves, etc. In total there are about 400 units across the US:

The National Park Service has an open data portal where you can get an API of all the locations of these parks, so I took that data feed and put it into ArcGIS Web AppBuilder to create an application that allows you to filter the map by park types.

NPR, Where are the Landing Pages for Podcast Episodes?

I was just trying to share a single episode of NPR’s Podcast “How I Built This” with a friend. When I searched for the episode, no authoritative episode landing page showed up. Odd … so I go to the podcast general show page. For each recent episode you have options to:

  • Listen (on same page)
  • Add to Listen Queue (on same page)
  • Download (Direct link to MP3 via Podtrac website)
  • View a transcript

Well-run podcasts should always have an episode landing page, where you could go if you’re linking to an episode, find show notes, and also be the place for search engines to return if a user is searching for a particular episode. This last case, SEO, is particularly interesting in NPR’s case, because if you do search for this episode, you can see that many other websites are grabbing the users that should be landing on NPR’s site.

NPR, I want to share your podcast episodes, so please provide episode permalink landing pages!

Using the R-ArcGIS Bridge in Jupyter

I was sitting in a presentation a few weeks ago on the R-ArcGIS bridge and I had a question: “Can I use the R-ArcGIS bridge in my Jupyter Notebook?” When I asked one of the presenters if this would be possible, 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 ‘’. 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 using

conda create --name arcgispro-r --clone arcgispro-py3

Switching Environments

Next, I switched to the cloned environment by deactivating the arcgispro-py3 environment and activating the arcgispro-r environment.

activate arcgispro-r

Installing R-Essentials

Then, I installed r-essentials, a bundle of over 80 of the most used R packages created by the Anaconda team.

conda install -c r r-essentials

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 ‘’, 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 to read in a point feature class of seagrass data. I was also able to use 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!

~ A guest post by Gregory Brunner

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Gavin Rehkemper

JavaScript, WordPress, and GeoDev