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Arrays in GeoJSON

So my last post was very positive. I figured out how to relate the teams that share a stadium with the stadium itself. This was important because I wanted to eliminate the redundant points that were on top of each other. For those who don’t recall, I have an example in this gist:

Now I mentioned that there were issues displaying this in GIS applications and was promptly told I was doing this incorrectly:

If you click on that tweet you’ll see basically that you can’t do it the way I want and I have to go back to the way I was doing it before:

I had a conversation with Bill Dollins about it and he sums it up susinctly:

I get it, but “Do it this way because that’s what the software can handle” is an unsatisfying answer.

So I’m stuck, I honestly don’t care if QGIS can read the data, because it can. It just isn’t optimal. What I do care about is an organized dataset in GeoJSON. So my question that I can’t get a definitive answer, “is the array I have above valid GeoJSON code?”. From what I’ve seen, yes. But nobody wants to go on record as saying absolutely. I could say, hell with it I’m moving forward but I don’t want to go down a dead end road.

April 6, 2021 geojson qgis Thoughts





GeoJSON Ballparks as JSON

In a way it is good that Sean Gillies doesn’t follow me anymore. Because I can hear his voice in my head as I was trying to do something really stupid with the project. But Sheldon helps frame what I should be doing with what I was doing:

Exactly! What the hell? Why was I trying to do something so stupid when the while point of this project is baseball ballparks in GeoJSON. Here is the problem in a nutshell and how I solved it. First off, let us simply the problem down to just one ballpark. Salt River Fields at Talking Stick is the Spring Training facility for both the Arizona Diamondbacks and the Colorado Rockies. Not only that, but there are Fall League and Rookie League teams playing there. Probably even more that I still haven’t researched. Anyway, GeoJSON Ballparks looks like this today when you just want to see that one stadium.

_Let’s just say I backed myself in this corner by starting by only having MLB ballparks, none of which at the time of the project were shared between teams.

It’s a mess right? Overlapping points, so many opportunities to screw up names. So my old school thought was just create a one-to-many relationship between the GeoJSON points and some external table. Madness! Seriously, what was I thinking? Sheldon is right, I should be doing a JSON array for the teams. Look how much nicer it all looks when I do this!

Look how nice that all is? So easy to read and it keeps the focus on the ballparks.

As I said in the earlier blog post.

The problem now is so many teams, especially in spring training, minor leagues and fall ball, share stadiums, that in GeoJSON-Ballparks, you end up with multiple dots on top of each other. No one-to-many relationship that should happen.”

The project had pivoted in a way I hadn’t anticipated back in 2014 and it was a sure a mess to maintain. So now I can focus on fixing the project with the Minor League Baseball realignment that went on this year and get an updated dataset in Github very soon.

One outcome of doing this nested array is that many GIS tools don’t understand how to display the data. Take a look at geojson.io:

geojson.io compresses the array into one big JSON-formatted string. QGIS and Github do this also. It’s an issue that I’m willing to live with. Bill Dollins shared the GeoJSON spec with me to prove the way I’m doing is correct:

3.2.  Feature Object
   A Feature object represents a spatially bounded thing.  Every Feature
   object is a GeoJSON object no matter where it occurs in a GeoJSON
   text.
   o  A Feature object has a "type" member with the value
      "Feature".
   o  A Feature object has a member with the name 
      "geometry".  The value of the geometry member SHALL
      be either a Geometry object as defined above or, in 
      the case that the Feature is unlocated, a JSON 
      null value.
   o  A Feature object has a member with the name 
      "properties".  The value of the properties member is
      an object (any JSON object or a JSON null value).

ANY JSON OBJECT! So formatting the files this way is correct and the way it should be done. I’m going to push forward on cleaning up GeoJSON Ballparks and let the GIS tools try and catch up.

April 2, 2021 geojson geojson ballparks github json qgis Thoughts





GeoJSON Ballparks and MLB Minor League Realignment

UPDATE - See the plan.

Boy, where to start? First, for those who haven’t been following, this happened over the winter.

Major League Baseball announced on Friday (February 12, 2021) a new plan for affiliated baseball, with 120 Minor League clubs officially agreeing to join the new Professional Development League (PDL). A full list of Major League teams and their new affiliates, one for each level of full-season ball, along with a complex league (Gulf Coast and Arizona) team, can be found below.

Minor League BaseballMinor League Baseball

What does that mean? Well for GeoJSON Ballparks basically every minor league team is having a modification to it. At a minimum, the old minor league names have changed. Take the Pacific Coast League that existed for over 118 years is now part of Triple-A West which couldn’t be a more boring name. All up and down the minor leagues, the names now just reflect the level of minor league the teams are. And some teams have moved from AAA to Single A and all around.

I usually wait until Spring Training is just about over to update the minor league teams but this year it almost makes zero sense. I’ve sort of backed myself into a spatial problem, unintended when I started. Basically, the project initially was just MLB teams and their ballparks. The key to that is that the teams drove the dataset, not the ballparks even though the title of the project clearly said it was. As long as nobody shared a ballpark, this worked out great. The problem now is so many teams, especially in spring training, minor leagues and fall ball, share stadiums, that in GeoJSON-Ballparks, you end up with multiple dots on top of each other. No one-to-many relationship that should happen.

So, I’m going to use this minor league realignment to fix what I should have fixed years ago. There will be two files in this dataset moving forward. One GeoJSON file of the locations of a ballpark and then a CSV (or other format) file containing the teams. Then we’ll just do the old fashioned relate between the two and the world is better again.

I’m going to fork GeoJSON-Ballparks into a new project and right the wrongs I have done against good spatial data management. I’m finally ready to play centerfield!

March 7, 2021 geojson geojson ballparks github json Thoughts





I’m Here at HERE

Last Tuesday I started at HERE Technologies with the Professional Services group in the Americas. I’ve probably used HERE and their legacy companies data and services for most of my career so this is a really cool opportunity to work with a mobile data company.

I’m really excited about working with some of their latest data products including Premier 3D Cities (I can’t escape Digital Twins).

Digital Twins at HERE

February 22, 2021 here Professional Services Thoughts





Digital Twins and Unreal Engine

I’ve had a ton of experience with Unity and Digital Twins but I have been paying attention to Unreal Engine. I think the open nature of Unity is probably more suited for the current Digital Twin market, but competition is so important for innovation. This project where Unreal Engine was used to create a digital clone of Adelaide is striking but the article just leaves me wanting for so much more.

A huge city environment results in a hefty 3D model. Having strategies in place to ease the load on your workstation is essential. “Twinmotion does not currently support dynamic loading of the level of detail, so in the case of Adelaide, we used high-resolution 3D model tiles over the CBD and merged them together,” says Marre. “We then merged a ring of low-resolution tiles around the CBD and used the lower level of detail tiles the further away we are from the CBD.”

Well, that’s how we did it at Cityzenith. Tiles are the only way to have the detail one needs in these 3D worlds and one that geospatial practitioners are very used to dealing with their slippy maps. The eye-candy that one sees in that Adelaide project is amazing. Of course, scaling one city out is hard enough but doing so across a country or the globe is another. Still, this is an amazing start.

Seeing Epic take Twinmotion and scale it out this way is very exciting because as you can see from that video above, it really does look photorealistic.

But this gets at the core of where Digital Twins have failed. It is so very easy to do the above, crate an amazing looking model of a city, and drape imagery across it. It is a very different beast to actually create a Digital Twin where these buildings are not only linked up to external IoT devices and services but they should import BIM models and generalize as needed. They do so some rudimentary analysis of shadows which is somewhat interesting, but this kind of stuff is so easy to do and there are so many tools to do it that all this effort to create a photorealistic city seems wasted.

I think users would trade photorealistic cities for detailed IoT services integration but I will watch Aerometrex continue to develop this out. Digital Twins are still stuck in sharing videos on Vimeo and YouTube, trying to create some amazing realistic city when all people want is visualization and analysis of IoT data. That said, Aerometrex has done an amazing job building this view.

November 17, 2020 digital twin photorealistic Thoughts Twinmotion unreal engine





Moving Towards a Digital Twin Ecosystem

Smart Cities really start to become valuable when they integrate with Digital Twins. Smart Cities do really well with transportation networks and adjusting when things happen. Take, for example, construction on an important Interstate highway that connects the city core with the suburbs causes backups and a smart city can adjust traffic lights, rail, and other modes of transportation to help adjudicate the problems. This works really well because the transportation system talk to each other and decisions can be made to refocus commutes toward other modes of transportation or other routes. But unfortunately, Digital Twins don’t do a great job talking to Smart Cities.

Photo by Victor Garcia on Unsplash

A few months ago I talked about Digital Twins and messaging. The idea that:

Digital twins require connectivity to work. A digital twin without messaging is just a hollow shell, it might as well be a PDF or a JPG. But connecting all the infrastructure of the real world up to a digital twin replicates the real world in a virtual environment. Networks collect data and store it in databases all over the place, sometimes these are SQL-based such as Postgres or Oracle, and other times they are simple as SQLite or flat-file text files. But data should be treated as messages back and forth between clients.

This was in the context of a Digital Twin talking to services that might not be hardware-based, but the idea stands up for how and why a Digital Twin should be messaging the Smart City at large. Whatever benefits a Digital Twin gains from an ecosystem that collects and analyzes data for decision-making those benefits become multiplied when those systems connect to other Digital Twins. But think outside a group of Digital Twins and the benefit of the Smart City when all these buildings are talking to each other and the city to make better decisions about energy use, transportation, and other shared infrastructure across the city or even the region (where multiple Smart Cities talk to each other).

Photo by Jesse Collins on Unsplash

When all these buildings talk to each other, they can help a city plan, grow and evolve into a clean city.

What we don’t have is a common data environment (CDE) that cities can use. We have seen data sharing on a small scale in developments but not on a city-wide or regional scale. To do this we need to agree on model standards that allow not only Digital Twins to talk to each other (Something open like Bentley’s iTwin.js) and share ontologies. Then we need that Smart City CDE where data is shared, stored, and analyzed at a large scale.

One great outcome of this CDE is all this data can be combined with City ordinances to give tools like Delve from Sidewalk Labs even more data to create their generative design options. Buildings are not a bubble in a city and their impacts on the city extend out beyond the boundaries of the parcel they are built on. That’s what so exciting about this opportunity, manage assets in a Digital Twin on a micro-scale, but share generalized data about those decisions to the city at large which then can share them with other Digital Twins.

Graphic showing chart of change over timeGraphic showing chart of change over time

And lastly, individual Smart Cities aren’t bubbles either. They have huge impacts on the region or even the country that they are in. If we can figure out how to create a national CDE, one that covers a country as diverse as the United States, we can have something that can even benefit the world at large. Clean cities are the future and thinking about them on a small scale will only result in the gentrification of affluent areas and leave less well areas behind. I don’t want my children to grow up in a world like that and we have the processes in place to ensure that they have a better place than use to grow up in.

November 10, 2020 clean cities common data environment digital twin generative design iot iTwin.js smart buildings smart cities Thoughts