I’ve been working at cleaning up all the GeoJSON-Ballparks records this past month. While the MLB stadiums and many of the AAA minor league teams have been updated, the international and small market teams have not. Some were out of data by almost 5 years. Long tail baseball stadiums are what they are and I’m working on automating much of this moving forward. The last two leagues that I’m updating are the Italian Baseball League and the German Bundesliga League. I hope to finish Germany tonight but I did post Italy yesterday.
While Italy can’t go out and enjoy baseball just yet, at least their top tier baseball league has been mapped. If you’re looking for some live baseball, check out streaming Korean KBO League league (I’ve been watching the Giants of course). The next live stream will be on March 25th at approximately 7:40pm PDT.
GeoJSON-Ballparks is my favorite data project I’ve been part of. Probably because not only is it the best sport ever, but it is great keeping track of all the changes at ballparks through the years. MLB teams have mostly stopped building new ball parks so the changes are generally just updates to their names. This year the only new name was Truist Park. Oakland Coliseum reverted back from RingCentral which it never was able to become because of shenanigans. We do bring on a new ballpark in Arlington which is named almost the same as the old ballpark (Globe Life Field vs the old Globe Life Park in Arlington). Apparently the old stadium has been renovated to XFL standards so we should probably not call it a ballpark anymore. I just removed the old one since it is no longer a baseball stadium. I did the same thing with Turner Field.
I plan to review all the Spring Training Facilities of the Cactus League and the Grapefruit League and then review the AAA stadiums. We’ll have to see what happens with the MLB/MiLB negotiations. While it doesn’t affect the actual stadium points (at least in the short term, some of the fields could go away because of lack of support), the alignment of teams in leagues could be changed. So stay tuned and if you want to help out with the AAA stadiums, just create a pull request, would be greatly appreciated!
Oddly enough the biggest news this week from the iPhone 11 introduction by Apple barely got any play. In fact, on the iPhone 11 Pro website, you have to scroll past Dog Portrait mode to get any information about it. Apple describes the U1 chip thusly:
The new Apple‑designed U1 chip uses Ultra Wideband technology for spatial awareness — allowing iPhone 11 Pro to understand its precise location relative to other nearby U1‑equipped Apple devices.4 It’s like adding another sense to iPhone, and it’s going to lead to amazing new capabilities. With U1 and iOS 13, you can point your iPhone toward someone else’s, and AirDrop will prioritize that device so you can share files faster.4 And that’s just the beginning.
Makes sense right? A better way to AirDrop. But there is so much more there, “precise location relative to other nearby equipped Apple Devices“. But what is UWB and why does it matter? The UWB Alliancesays:
UWB is a unique radio technology that can use extremely low energy levels for short-range, high-bandwidth communications over a large portion of the radio spectrum. Devices powered by a coin cell can operate for a period of years without recharge or replacement. UWB technology enables a broad range of applications, from real-time locating and tracking, to sensing and radar, to secure wireless access, and short message communication. The flexibility, precision and low-power characteristics of UWB give it a unique set of capabilities unlike any other wireless technology.
From raw data alone, UWB devices can detect locations within 10 centimeters (4 inches), but depending on implementation that accuracy can be lowered to as much as 5 millimeters, according to Mickael Viot, VP of marketing at UWB chipmaker Decawave.
That’s pretty amazing. Basically it takes what makes Bluetooth LE great for discover, secures it and then makes it faster and more accurate. So we can see the consumer use cases for UWB, sharing files and finding those tiles we’ve heard so much about. But where this gets very interesting for our space is for data collection and working inside digital twins. You can already see the augmented reality use case here. A sensor has gone bad in a building, I can find it now with millimeter accuracy. But it’s not just what direction it’s how far. UWB uses “time of flight” to pinpoint location (measuring the time of signal to gauge distance), enabling it to know how far away it is. Just knowing a sensor is ahead of you is one thing, but knowing it is 20 feet away, that’s really a game changer.
You can see this through a little known app Apple makes called Indoor Survey. Small side note, back in late 2015 I blogged about Apple’s Indoor Positioning App which ties into all this. Where you really see this use is when you go to the signup page see how data is brought into this app using a standard called Indoor Mapping Data Format. Indoor Mapping Data Format (IMDF) provides a generalized, yet comprehensive data model for any indoor location, creating a basis for orientation, navigation and discovery. IMDF is output as an archive of GeoJSON files. Going to the IMDF Sandbox really shows you what this format is about.
Basically you see a map editor that allows you to really get into how interiors are mapped and used. So Apple iPhone 11 UWB devices can help place themselves more accurately on maps and route users around building interiors. Smart buildings get smarter by the devices talking to each other. Oh and IMDF, Apple says, “For GIS and BIM specialists, there is support for IMDF in many of your favorite tools.“. I will need to spend a bit more time with IMDF but its basically GeoJSON objects so we already know how to use it.
The thing about GPS data collection is it works great outdoors, but inside it is much harder to get accuracy, especially when you need it. With Indoor Survey, devices can collect data much more accurately indoors because they know exactly where they are. If you’ve ever used Apple Maps in an airport and seen how it routes you from gate to gate, you get an idea how this works. But with UWB, you go from foot accuracy to sub centimeter. That’s a big difference.
Now we’re a long way away from UWB being ubiquitous like Bluetooth LE is. Right now as far as I can tell, only Apple has UWB chips in their devices and we don’t know how compatible this all is yet. But you can see how the roadmap is laid out here. UWB, GeoJSON and an iPhone 11. Devices help each other get better location and in turn make working with Digital Twins and data collection so much easier.
No curves. It’s a good point. GeoJSON and TopoJSON don’t support curves. But neither does Shapefiles. All three formats are meant to handle simple features. Points, polygons and line. Whereas TopoJSON handles topology, it still can’t draw true curves. But what’s the implication here? To share data that requires curves (it’s an edge case but still an important one) you have to use a proprietary format? Enter WKT. Well-known text supports much more vector types than the previous including curves. Following up on sharing data in common file formats, WKT fits the bill perfectly. Share your data as GeoJSON/TopoJSON, KML and Shapefile if needed, then use WKT for complex features. Still open completely and it is well supported with most open and proprietary software packages.
Sometimes you need to use curves and generally it does work out.
You may or may not have seen, but there is a Cuban/Tampa Bay Rays game going on today. Given the love of baseball between the two countries I’m sure we’ll see much more Cuban baseball over the next couple years. It just so happens that the GeoJSON-Ballparks project has all the professional baseball stadiums in Cuba already mapped in GeoJSON format, including Estadio Latinoamericano where the game is being played today. Enjoy!
Yesterday I posted about Chris Hogan’s walk-through of generalizing data in PostGIS to make it usable in a web app. Basically he went through the process of finding out what is the sweet spot of quality vs speed. But there are other ways to accomplish this. Mapbox happened to post about a new library called geojson-vt.
Let’s see if Mapbox GL JS can handle loading a 106 MB GeoJSON dataset of US ZIP code areas with 33,000+ features shaped by 5.4+ million points directly in the browser (without server support):
Wait, what?! A few seconds loading the data, and you can browse the whole data set smoothly and seamlessly. But how exactly does that work? Let’s find out
So that’s actually pretty amazing. We all know what GeoJSON does in the browser and how it impacts the speed of maps drawing. 100 MB+ data rendering so quickly? Impressive. Read the whole post to see how they do it and the details on how to start using it. The only limitation is that it requires mapbox-gl-js or Mapbox Mobile[footnote]which is actually a big limitation if you think about it[/footnote].UPDATE: Per Tom MacWright:
Still this comes down to using tools that make your mapping products better. Maybe Mapbox does that cheaper and quicker than you could on your own. This kind of on-the-fly simplification is what we’ve all been asking for and Mapbox is really pushing the envelope. This could be what gets people to start using their platform.