Ungefiltert und voller Mist, aber man kann z. B. toll sehen, wie einladend die Brückem vorm Kuhmühlenteich für Fotos sind.
Kostenlose Internetpunkteabsahnidee: Dasselbe mit dem Eiffelturm, dem Londoner Dildo u. ä. machen, Konkave Hülle außen drum, schick aufbereiten, als “Nur wo man $Wahrzeichen sieht, ist $Stadt” vermarkten, €€€€.
Je weniger transparent eine Fläche dargestellt ist, desto mehr Dokumente sind mit ihr verknüpft (ja, es ist ein Feature je Dokument D;). Eigentlich war die Seite anders aufgebaut, mit einem PDF-Viewer auf der rechten Seite. Aber da daten.transparenz.hamburg.de kein HTTPS kann (seid ihr auch so gespannt auf die UMTS-Auktion nächste Woche?), geht das aus Sicherheitsgründen nicht ohne ein Spiegeln der Daten oder einen Proxy.
Die Daten kommen größtenteils aus dem Transparenzportal. Für das Matching der angebenen Flurstücks-“IDs” zu den tatsächlichen Flurstücken war aber ein erheblicher Aufwand nötig. Das Drama ging bis hin zum Parsen aus PDFs, die mal so, mal so formatiert waren und natürlich auch voller Eingabefehler auf Behördenseite. Vielleicht schreibe ich da noch beizeiten mal einen Rant. TL;DR: Ohne die zugehörige Gemarkung ist mit einer Flurstücks-“ID” wie in den Daten angegeben keine räumliche Zuordnung möglich. In den veröffentlichten Daten stecken nur die Nenner der Flurstücksnummern, nicht aber die Gemarkungsnummern. Ziemlich absurd.
Das ganze ist nur ein Prototyp, vermutlich voller Fehler und fehlender Daten. Aber interessant und spaßig ist es, viel Freude also!
Es wäre noch eine MENGE zu tun, um das ganze rund zu machen. Falls du Lust hast, melde dich gerne. Es geht vom wilden Parsen, über Sonderregeln für kaputte Dokumente, zu Kartenstyling bis zur UI. Schön wäre es auch alles in einer anständigen Datenbank zu halten und nicht nur nach der räumlichen Dimension durchsuchen zu können.
Did this for an ex-colleague some months ago and forgot to share the how-to publically. We needed a visual representation of the current time in a layout that showed both a raster map (different layer per timeslice) and a timeseries plot of an aspect of the data (this was created outside QGIS).
Have lots of raster layers you want to iterate through. I have:
Create a new layer for your map extent. Draw your extent as geometry. Duplicate that geometry as many times as you have days. Alternatively you could of course have different geometries per day. Whatever you do, you need a layer with one feature per timeslice for the Atlas to iterate though. I have 30 days to visualise so I duplicated my extent 30 times.
Open the Field Calculator. Add a new field called date as string type (not as date type until some bug is fixed (sorry, did not make a note here, maybe sorting is/was broken?)) with an expression that represents time and orders chronologically if sorted by QGIS. For example: '2019-01-' || lpad(@row_number,2,0) (assuming your records are in the correct order if you have different geometries…)
Have your raster layers named the same way as the date attribute values.
Make a new layout.
For your Layout map check “Lock layers” and use date as expression for the “Lock layers” override. This will now select the appropriate raster layer, based on the attribute value <-> layer name, to display for each Atlas page.
Cool, if you preview the Atlas now you got a nice animation through your raster layers. Let’s do part 2:
In your layout add your timeseries graph. Give it a unique ID, e. g. “plot box”. Set its width and height via new variables (until you can get those via an expression this is needed for calculations below).
Create a box to visualise the timeslice. Set its width to map_get(item_variables('plot box'), 'plot_width') / @atlas_totalfeatures. For the height and y use/adjust this expression: map_get(item_variables('plot box'), 'plot_height'). For x comes the magic:
with_variable( 'days_total', day(to_date(maximum("date"))-to_date(minimum("date")))+1, -- number of days in timespan -- +1 because we need the number of days in total -- not the inbetween, day() to just get the number of days with_variable( 'mm_per_day', map_get(item_variables('plot box'), 'plot_width') / @days_total, with_variable( 'days', day(to_date(attribute(@atlas_feature, 'date'))-to_date(minimum("date"))), -- number of days the current feature is from the first day -- to_date because BUG attribute() returns datetime for date field @mm_per_day * @days + map_get(item_variables('plot box'), 'plot_x') ) ) )
This will move the box along the x axis accordingly.
Yes, a planet file is pretty big, but extracting specific features from that is not a big data problem and you must not be scared of it. Downloading the file will probably take you magnitudes longer than extracting something from it. For me it was 45 minutes for the download, then about 8 minutes for extracting on a seriously slow (~60MB/s) spinning metal hard disk drive (no SSD).
And yes, you can load OSM PBF directly into QGIS thanks to GDAL’s support for the format.
While we are at it: Don’t use GeoJSON for anything but data transfer and maybe storage. It is not an efficient format to power your layers and GIS analyses. (OSM PDF isn’t either.)
Ever ran some GIS analysis in QGIS and it took longer than a second? Chances are that your data did not have spatial indexes for QGIS to utilise and that it could have been magnitudes faster.
I realised just today, after years of using QGIS, that it did not automatically create a spatial index when saving a Shapefile. And because of that, lots of GIS stuff I did in the past, involving saving subsets of data to quick’n’dirty Shapefiles, was slower than necessary.
An example: Running ‘Count points in polygon’ on 104 polygons with 223210 points:
Points in original GML file: 449 seconds
GML is not a format for processing but meant for data transfer, never ever think of using it for anything else
Points in ESRI Shapefile: 30 seconds
Points in GeoPackage: 3 seconds
Points in ESRI Shapefile with spatial index: 3 seconds
Same Shapefile as before but this time I had created a .qix index
So yeah, make sure you don’t only use a reasonable format for your data. And also make sure you do actually have an spatial index.
For Shapefiles, look for a .qix or .sbn side-car file somewhere in the heap of files. In QGIS you can create a spatial index for a vector layer either using the “Create spatial index” algorithm in Processing or the button of the same title in the layer properties’ source tab.
PS: GeoPackages always have a spatial index. That’s reason #143 why they are awesome.
Postgres’ WITH clauses and window functions are so awesome.
-- generate values from -90 to 90, increment by 1
WITH values AS (
SELECT generate_series AS latitude
FROM generate_series(-90, 90)
),
-- create a geographic point each
points AS (
SELECT ST_MakePoint(0, latitude)::geography AS point FROM values
)
SELECT
-- latitude values of subsequent points
format(
'%s° to %s°',
ST_Y(point::geometry),
ST_Y(lag(point::geometry) OVER ())
) AS latitudes,
-- geographic distance between subsequent points, formatted to kilometers
format(
'%s km',
to_char(ST_Distance(point, lag(point) OVER ())/1000, '999D99')
) AS distance
FROM points
OFFSET 1 -- skip the first row, no lag there
;
latitudes | distance
--------------+------------
-89° to -90° | 111.69 km
-88° to -89° | 111.69 km
-87° to -88° | 111.69 km
-86° to -87° | 111.69 km
-85° to -86° | 111.69 km
-84° to -85° | 111.68 km
-83° to -84° | 111.68 km
-82° to -83° | 111.67 km
-81° to -82° | 111.67 km
-80° to -81° | 111.66 km
-79° to -80° | 111.66 km
-78° to -79° | 111.65 km
-77° to -78° | 111.64 km
-76° to -77° | 111.63 km
-75° to -76° | 111.62 km
-74° to -75° | 111.61 km
-73° to -74° | 111.60 km
-72° to -73° | 111.59 km
-71° to -72° | 111.58 km
-70° to -71° | 111.57 km
-69° to -70° | 111.56 km
-68° to -69° | 111.54 km
-67° to -68° | 111.53 km
-66° to -67° | 111.51 km
-65° to -66° | 111.50 km
-64° to -65° | 111.49 km
-63° to -64° | 111.47 km
-62° to -63° | 111.45 km
-61° to -62° | 111.44 km
-60° to -61° | 111.42 km
-59° to -60° | 111.40 km
-58° to -59° | 111.39 km
-57° to -58° | 111.37 km
-56° to -57° | 111.35 km
-55° to -56° | 111.33 km
-54° to -55° | 111.31 km
-53° to -54° | 111.30 km
-52° to -53° | 111.28 km
-51° to -52° | 111.26 km
-50° to -51° | 111.24 km
-49° to -50° | 111.22 km
-48° to -49° | 111.20 km
-47° to -48° | 111.18 km
-46° to -47° | 111.16 km
-45° to -46° | 111.14 km
-44° to -45° | 111.12 km
-43° to -44° | 111.10 km
-42° to -43° | 111.08 km
-41° to -42° | 111.06 km
-40° to -41° | 111.04 km
-39° to -40° | 111.03 km
-38° to -39° | 111.01 km
-37° to -38° | 110.99 km
-36° to -37° | 110.97 km
-35° to -36° | 110.95 km
-34° to -35° | 110.93 km
-33° to -34° | 110.91 km
-32° to -33° | 110.90 km
-31° to -32° | 110.88 km
-30° to -31° | 110.86 km
-29° to -30° | 110.84 km
-28° to -29° | 110.83 km
-27° to -28° | 110.81 km
-26° to -27° | 110.80 km
-25° to -26° | 110.78 km
-24° to -25° | 110.77 km
-23° to -24° | 110.75 km
-22° to -23° | 110.74 km
-21° to -22° | 110.72 km
-20° to -21° | 110.71 km
-19° to -20° | 110.70 km
-18° to -19° | 110.69 km
-17° to -18° | 110.67 km
-16° to -17° | 110.66 km
-15° to -16° | 110.65 km
-14° to -15° | 110.64 km
-13° to -14° | 110.63 km
-12° to -13° | 110.63 km
-11° to -12° | 110.62 km
-10° to -11° | 110.61 km
-9° to -10° | 110.60 km
-8° to -9° | 110.60 km
-7° to -8° | 110.59 km
-6° to -7° | 110.59 km
-5° to -6° | 110.58 km
-4° to -5° | 110.58 km
-3° to -4° | 110.58 km
-2° to -3° | 110.58 km
-1° to -2° | 110.58 km
0° to -1° | 110.57 km
1° to 0° | 110.57 km
2° to 1° | 110.58 km
3° to 2° | 110.58 km
4° to 3° | 110.58 km
5° to 4° | 110.58 km
6° to 5° | 110.58 km
7° to 6° | 110.59 km
8° to 7° | 110.59 km
9° to 8° | 110.60 km
10° to 9° | 110.60 km
11° to 10° | 110.61 km
12° to 11° | 110.62 km
13° to 12° | 110.63 km
14° to 13° | 110.63 km
15° to 14° | 110.64 km
16° to 15° | 110.65 km
17° to 16° | 110.66 km
18° to 17° | 110.67 km
19° to 18° | 110.69 km
20° to 19° | 110.70 km
21° to 20° | 110.71 km
22° to 21° | 110.72 km
23° to 22° | 110.74 km
24° to 23° | 110.75 km
25° to 24° | 110.77 km
26° to 25° | 110.78 km
27° to 26° | 110.80 km
28° to 27° | 110.81 km
29° to 28° | 110.83 km
30° to 29° | 110.84 km
31° to 30° | 110.86 km
32° to 31° | 110.88 km
33° to 32° | 110.90 km
34° to 33° | 110.91 km
35° to 34° | 110.93 km
36° to 35° | 110.95 km
37° to 36° | 110.97 km
38° to 37° | 110.99 km
39° to 38° | 111.01 km
40° to 39° | 111.03 km
41° to 40° | 111.04 km
42° to 41° | 111.06 km
43° to 42° | 111.08 km
44° to 43° | 111.10 km
45° to 44° | 111.12 km
46° to 45° | 111.14 km
47° to 46° | 111.16 km
48° to 47° | 111.18 km
49° to 48° | 111.20 km
50° to 49° | 111.22 km
51° to 50° | 111.24 km
52° to 51° | 111.26 km
53° to 52° | 111.28 km
54° to 53° | 111.30 km
55° to 54° | 111.31 km
56° to 55° | 111.33 km
57° to 56° | 111.35 km
58° to 57° | 111.37 km
59° to 58° | 111.39 km
60° to 59° | 111.40 km
61° to 60° | 111.42 km
62° to 61° | 111.44 km
63° to 62° | 111.45 km
64° to 63° | 111.47 km
65° to 64° | 111.49 km
66° to 65° | 111.50 km
67° to 66° | 111.51 km
68° to 67° | 111.53 km
69° to 68° | 111.54 km
70° to 69° | 111.56 km
71° to 70° | 111.57 km
72° to 71° | 111.58 km
73° to 72° | 111.59 km
74° to 73° | 111.60 km
75° to 74° | 111.61 km
76° to 75° | 111.62 km
77° to 76° | 111.63 km
78° to 77° | 111.64 km
79° to 78° | 111.65 km
80° to 79° | 111.66 km
81° to 80° | 111.66 km
82° to 81° | 111.67 km
83° to 82° | 111.67 km
84° to 83° | 111.68 km
85° to 84° | 111.68 km
86° to 85° | 111.69 km
87° to 86° | 111.69 km
88° to 87° | 111.69 km
89° to 88° | 111.69 km
90° to 89° | 111.69 km
Some awful but working JavaScript code to track a website user’s interaction with a Open Layers map. You can use this to do awesome user studies and experiments.
Runs client-side
You will get a polygon of each “view”!
You can look at them in the browser!
There are also timestamps! Hyperaccurate in milliseconds since unix epoch!
And a GeoJSON export!
This works with rotated views!
Written for Open Layers 4 using some version of JSTS, see the libs/ directory. No idea if it works with the latest versions or if Open Layers changed their API again.
Please do some funky research with it and tell me about your experiences! Apart from that, you are on your own.
There is a QGIS project with example data included. Check out the Atlas setup in the Print Layout!
Screenshot from a browser session
Resulting GeoJSON in QGIS
So if Time Manager supports the timestamp format you could interactively scroll around. I did not try, that plugin is so finicky.
Replaying what I looked at in a Open Layers web map, using QGIS Atlas
You can see fascinating patterns, e.g. “downstream” of islands in the ocean or following big rivers like the Amazon. Be wary though as snow and clouds look the same here.
Bit of EuropeIslands in the Atlantic OceanNorth AmericaParts of South-East AsiaSomewhere in RussiaAround IndiaAmazonCaribbeanAround Madagascar
It’s also fun to look at the maximum to see cloudless regions (this image is highly exaggerated and does not make too much sense anyways):
Many thanks to Joshua Stevens for motivation and infos on data availability! The initial idea was of course inspired by Charlie ‘vruba’ Lyod‘s Cloudless atlas works. I explored imagery on https://worldview.earthdata.nasa.gov/, grabbed Soumi VIIRS images via https://wiki.earthdata.nasa.gov/display/GIBS/GIBS+API+for+Developers and processed it in GDAL. Poke me repeatedly to blog about the process please. (2022: Or don’t, I didn’t document it back then and can’t remember the final specifics. I think I did use imagemagick’s -evaluate-sequence median but might have done something in vips2 instead. The 2020 approach via GDAL is much easier.)
Full resolution, georeferenced imagery as Cloud-Optimized GeoTIFF:
So I wanted to make a Star Wars hologram (you know, that “You’re my only hope” Leia one) showing real geodata. What better excuse for abusing QGIS’ Inverted Polygons and Raster Fills. So, here is what I did:
Get some geodata. I used Natural Earth’s countries, populated places and tiny countries (to have some stuff in the oceans), all in 110m.
Select a nice projection, I used “+proj=ortho +lat_0=39 +lon_0=139 +x_0=0 +y_0=0 +a=6371000 +b=6371000 +units=m +no_defs”.
I used a three layer style for the countries:
A Simple Line outline with color #4490f3, stroke width 0.3mm and the Dash Dot Line stroke style.
A Line Pattern Fill with a spacing of 0.8mm, color #46a8f3 and a stroke width of 0.4mm.
And on top of those, for some noisiness a black Line Pattern Fill rotated 45° with a spacing of 1mm and stroke width 0.1mm.
Then the Feature Blending Mode Dodge to the Layer Rendering and aha!
More special effects come from Draw Effects, I disabled Source and instead used a Blur (Gaussian, strength 2) to lose the crispness and also an Outer Glow (color #5da6ff, spread 3mm, blur radius 3) to, well, make it glow.
I used a three layer style for the populated places:
I used a Simple Marker using the “cross” symbol and a size of 1.8mm. The Dash Line stroke style gives a nice depth effect when in Draw Effects the Source is replaced with a Drop Shadow (1 Pixel, Blur radius 2, color #d6daff).
A Blending Mode of Addition for the layer itself makes it blend nicely into the globe.
I used a three layer style for the tiny countries:
I used a white Simple Marker with a size of 1.2mm and a stroke color #79c7ff, stroke width 0.4mm.
Feature Blending Mode Lighten makes sure that touching symbols blob nicely into each other.
You can now export the image at your screen resolution (I guess) using Project -> Import/Export or just make a screenshot.
Or add some more magic with random offsets and stroke widths in combination with refreshing the layers automatically at different intervals:
