# Archive for October, 2014

## Airspace — A deep rabbit hole

Posted by smathermather on October 25, 2014

In previous maps we looked at Class B, C, and D airspace. Let’s add in Class E0 and E5… (not yet in 3D):

(Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL)

Map showing Class B, C, D, E0, and E5 airspace

Previous posts:

https://smathermather.wordpress.com/2014/10/25/airspace-is-complicated-and-so-i-abuse-postgis-once-again/

and

https://smathermather.wordpress.com/2014/10/25/airspace-is-complicated-and-so-i-abuse-postgis-once-again-reprise/

Posted in 3D, Cartography, Database, Drone, PostGIS, PostgreSQL, SQL, UAS | Tagged: , , , , , | Leave a Comment »

## Airspace is complicated — and so I abuse PostGIS once again — Reprise…

Posted by smathermather on October 25, 2014

In the previous post: https://smathermather.wordpress.com/2014/10/25/airspace-is-complicated-and-so-i-abuse-postgis-once-again/ we explore the 3D shape and complexity of controlled airspace.

Now here’s the rest of the code. We’ll add our affine transformation ala Seth Fitsimmons:

```    SELECT
ST_Affine(
ST_Rotate(geom, -pi() / 2),
-- isometric
cos(pi() / 6), -cos(pi() / 6), 0,
sin(pi() / 6), sin(pi() / 6), 1,
0, 0, 0,
0, 0, 0
)
AS geom
```

And integrate that into our original function:

```-- Inputs are a geometry and an elevation to move the geometry to
CREATE OR REPLACE FUNCTION threed_iso(footprint geometry, elevation numeric)
RETURNS geometry AS
\$BODY\$

-- Force 3D, then translate to the input elevation
WITH floor AS
(
SELECT ST_Translate( ST_Force3DZ(footprint), 0, 0, elevation ) AS geom
),
-- Now make isometric (and begin Seth Code)
iso AS
(
SELECT
ST_Affine(
ST_Rotate(geom, -pi() / 2),
-- isometric
cos(pi() / 6), -cos(pi() / 6), 0,
sin(pi() / 6), sin(pi() / 6), 1,
0, 0, 0,
0, 0, 0
)

AS geom
FROM floor
)
-- We'll force it back to 3D so QGIS is happy
SELECT ST_Force2D(geom) FROM iso
;
\$BODY\$
LANGUAGE sql VOLATILE
COST 100;
```

And voila!

```DROP TABLE IF EXISTS class_c_isoc;

CREATE TABLE class_c_isoc AS
SELECT gid, airspace, name, lowalt, highalt, threed_iso(geom, lowalt::numeric * 5) AS geom
FROM class_c_subset;
```

Let’s take a look at Washington, DC and surrounds, another nice complicated example:

3D Figure of DC controlled airspace

And again with map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL:

3D Figure of DC controlled airspace

Posted in 3D, Cartography, Database, Drone, PostGIS, PostgreSQL, SQL, UAS | Tagged: , , , , , | 3 Comments »

## Airspace is complicated — and so I abuse PostGIS once again

Posted by smathermather on October 25, 2014

Let’s ignore for a moment the drone hobbiest / enthusiast. What is the shape of airspace for airplanes and commercial and government unmanned aircraft flying under Certificates of Authorization, and how can we visualize it?

Thanks to Anita in the last post, we have the Class B,C,D,E Airspace Shape Files which helps us define the overall shape of controlled airspace.

Map of Detroit, Cleveland, and Pittsburgh Class B Airspace

But, these are 3D things. I want to visualize them thus. Let us put some constraints on the problem. Let’s do it all in PostGIS, that way we can see it in QGIS or on the web. Let’s not use full PostGIS 3D (i.e. the SFCGAL library), not because it isn’t awesome (it truly is) but because it can be hard to install at the moment (although see https://github.com/vpicavet/docker-pggis for an easy way with docker). Finally, true 3D with unconstrained viewing angles and 100% flexibility is… is… well it usually sucks. So, we’ll stick to isometric viewing (thanks to Seth Fitzsimmons of Stamen http://stamen.com/ for his PostGIS isometric code which will be released upon his word). (Update — all the code is there…):

```-- Inputs are a geometry and an elevation to move the geometry to
CREATE OR REPLACE FUNCTION threed_iso(footprint geometry, elevation numeric)
RETURNS geometry AS
\$BODY\$

-- Force 3D, then translate to the input elevation
WITH floor AS
(
SELECT ST_Translate( ST_Force3DZ(footprint), 0, 0, elevation ) AS geom
),
-- Now make isometric (and begin Seth Code)
iso AS
(
SELECT
ST_Affine(
ST_Rotate(geom, -pi() / 2),
-- isometric
cos(pi() / 6), -cos(pi() / 6), 0,
sin(pi() / 6), sin(pi() / 6), 1,
0, 0, 0,
0, 0, 0
)

AS geom
FROM floor
)
-- We'll force it back to 3D so QGIS is happy
SELECT ST_Force2D(geom) FROM iso
;
\$BODY\$
LANGUAGE sql VOLATILE
COST 100;
```

Ok, now let’s rock some geometries with this bad function:

```DROP TABLE IF EXISTS class_c_isoc;

CREATE TABLE class_c_isoc AS
SELECT gid, airspace, name, lowalt, highalt, threed_iso(geom, lowalt::numeric * 5) AS geom
FROM class_c_subset;
```

And what do our controlled airspaces look like?

Isometric view of Cleveland controlled airspace

Kind of conical, in this case with some “wings” that serve as approaches. It makes sense, I guess. At the center, where the airport is, controlled airspace goes from the ground up. As we get further from the airport, a set of concentric rings starting at higher elevations provide a controlled space that allows for landing and taking off.

Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL.

There are more complicated ones, naturally. We need look no further than Detroit for additional complexity:

Visualization of Detroit controlled airspace

No wonder every time I fly through Detroit, no matter where I’m going, I end up flying over Lake Erie.

If we want really complicated, we need look no further than Cincinnati:

What is going on there? Is that due to shape of the hills, or city boundaries?

Finally, what does airspace look like over Ohio, West Virginia, and Pennsylvania (etc.), overall?

And while the following map isn’t quite right, here is a figure including many of the small airports sans controlled airspace:

View of all controlled and uncontrolled airspace across Ohio and neighbors.

May an aeronautical pox be upon you!
The above line was not intended in bad taste, but just an homage to the “red dot fever” of early neo-geography (neo-geography which I’m informed definitionally doesn’t exist). Only a few minutes ago, it dawned on me that the deleted phrase could be misinterpreted these days… .

On a related note, if you want an interesting analysis of ebola, read Zeynep Tufekci’s analysis.

(for the record, all heights are exagerated by 5x, for clarity of reading).

Also, in case it wasn’t obvious: Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL.

Posted in 3D, Cartography, Database, Drone, PostGIS, PostgreSQL, SQL, UAS | Tagged: , , , , , | 4 Comments »

## Someone is wrong on the internet…

Posted by smathermather on October 23, 2014

Ok @Mapbox, I’ve given you enough grace. I pulled the grumpy-old-man thing with Kenneth Field from ESRI a couple of weeks ago, and ended up apologizing. If i only give ESRI a few hours of grace, 3 months of grace for you is more than fair.

Now, to raise my foot up. We’ll see if it ends up in my mouth again.

Ok, so Mapbox and drones. Mapbox and drones. Those folks are excited about drones. They’ve got a toolchain their working on, and integration into their existing services, and lots of posts about drones. Downright giddiness I say:

https://duckduckgo.com/?q=site%3Amapbox.com+drones&t=canonical

And then this:

Really? 3 months ago, you launch “Don’t Fly Here”, create a repo for feedback and improvements to the data, but the biggest problem that you haven’t even tried to fix is the map really under represents the restrictions on where you should fly if you are trying to stay out of controlled air space, and the major update you do is adding temporary flight restrictions. Glitz and glory over getting the fundamentals right.

Here’s the current Mapbox no fly zones:

But, we are missing a lot of smaller airports that also have controlled airspace. Let’s be considerate to our hobbiest drone friends and put them in with 3-mile buffers and refine our 5-mile buffers on medium and large airports to include the edges of the airport, just to be on the safe side:

Well, that’s a bit less inviting. But hey, still plenty of places to fly, right? Uh, no. We forgot, according to the Aeronautical Information Manual, in certain busy airspace, there is a Mode C requirement for all craft flying in the airspace. This means you need to have a transponder to fly in this space between ground and 10,000ft above mean sea level. This is a transponder that weighs several times what your drone can lift — in otherwords a “non-starter”, effectively making these zones no-fly zones as well. What does Hopkins look like with it’s 30 nautical mile Mode C requirement?:

Now, to be fair, this is murky legal territory at best and I am not a lawyer. How much applies to hobbiests given the 1981 Advisory Circular I can’t say I know. But, if we are to propose a map to clarify where we can and cannot fly as hobbiests, then we should be including as much information (in a simple and easy to use way) as we can. On these grounds, “Don’t Fly Here” fails.

It’s cool though. It’s an open source data project in an open source community. Community contributions and knowledge will fix all mistakes in time, so my pull request to get to the second map will be reviewed and rejected on solid grounds or integrated, right?

https://github.com/mapbox/drone-feedback/pull/40

Posted in Drone, UAS | Tagged: , , | 11 Comments »

## Government 2.0 is not the platform

Posted by smathermather on October 8, 2014

(In which Steve vehemently disagrees with a major thought leader on the nature of Civic Technology… .)

(TLDR: Open Data is the platform. Governance participates in, contributes to, and benefits from Open Data, but government is not the platform, Open Data is the platform.)

I’m sorry, Tim O’Reilly. You are a brilliant man, have some truly great ideas, and some successes I hear as a publisher and VC, but on Gov 2.0, you are wrong, or at least 90 degrees away from right.

I like Open Data. I hate Government as a Platform. Why? Two reasons. First (selfish reason), being a platform as your default position does not always have the best views.

To be fair, though, it’s deeper than just the views. Public servants are, after all, like all good persons, servants, so we can’t whine too much about platform / infrastructure services. This is part of what we signed up for.

Counterpoint: historically, government is not just about infrastructure (roads, bridges, sewers, data), but also has a role to play in services. Great Open Data is served not just in raw form, but with great APIs, great interfaces, great front ends. I think this is particularly true (or should be) in the Parks and Recreation sector. Domain expertise matters, and we should be leveraging the domain expertise of our hard working public servants where we can.

But, I can’t win this on selfish arguments alone. Perhaps that is our role in the public sector. We do the pedantic, boring work, we provide the data, and clever, brilliant people who are necessarily outside in the private sector do the value added work.

Let us start with the Classic Example — Google and Portland’s Trimet put together the GTFS standard that allows for transit agencies across the US to share their transit information and thus make it available through services like Google Maps. Thus it is that the platform is governments serving GTFS feeds, private industry (Google) does their magic in creating a common mapping interface for people to easily navigate complicated transit systems, and an angel gets it’s wings.

I would not suggest that this arrangement is bad. Is it a commodification of the commons by means of Open Data? Yes? Do we all benefit from it? Ya. Mostly (although use the transit app for Seoul, Korea for truly great transit software). It’s an acceptable trade (ignoring the commodification of all of our private lives in the trade, but that’s a separate issue from Trimet and GTFS). (The trade works in part, too, because Google derives value from being it’s own privatized Commons, but that’s an analysis for another time, and perhaps a PhD or two)

But if this is the only model we put forth as our Open Data model, we are missing some really important elements. Succinctly, it is not Government that is or should be the platform, any more than OpenStreetMap’s public domain inputs are the most valuable. Open Data is the platform. Governance participates in, contributes to, and benefits from Open Data, but within the context of data, government is not the platform, Open Data is the platform.

It is easy to loose sight of this within the context of Open Data as a new Commons. We have come to some terms with Open Source Software as a Commons. We understand, sometimes with large swaths of apocryphal sociological analysis, elements of the value of Commons within the context of Open Source Software. It is now time to understand Open Data with similar breadth and depth.

When I think of Open Data, I do not want Transit! I want transitivity. Transitivity between legal structures (private and public) and between sectors (health, education, technology, human services, etc.). I want Open Data to be a new Commons that we all contribute to from multiple sectors and structures.

Tonight, as we discussed the future of Open Data in Cleveland, I was refreshed by the interest in placing Open Data as that hub, as that Commons, within the context of the verticals and plays that we seek to engage in a “loosely coupled coalition of the willing”. Bravo to Lev Gonick and others at the meeting who articulated those interlacing pieces of the coalition as follows:

#OpenEd
#OpenGov
#OpenHealth
#OpenEcology
#OpenSustainability

And so, it bears repeating: Open Data is the platform. Governance participates in, contributes to, and benefits from Open Data, but government is not the platform, Open Data is the platform. A new Commons. These are exciting times.