Visuelle Datenanalyse macht Sinn

Schon wieder ist mehr als ein Jahr vergangen, in dem dieses Blog mehr oder weniger komplett brach lag. Und in der Zwischenzeit ist so viel passiert!

Im Rahmen meines Jobs beim Institut für Verkehrsforschung am Deutschen Zentrum für Luft- und Raumfahrt (DLR) in Berlin habe ich nicht nur an der theoretischen und praktischen (Weiter-)Entwicklung von großmaßstäblichen Verkehrsnachfragemodellen gearbeitet, sondern habe daneben natürlich auch die daraus und auch aus anderen Projekten resultierenden Erkenntnisse (mit-)publiziert. Bei dieser Forschung habe ich hauptächlich mit R, Shiny, PostgreSQL/PostGIS, QGIS und vereinzelt ein paar Zeilen Python gearbeitet. Und ich liebe sie alle, wann immer ich mit ihnen arbeiten darf. Aber ich fand es zunehmend schwierig und anstrengend, Daten einfach, schnell, und trotzdem optisch ansprechend zu visualisieren. Natürlich lassen sich mit R und ggplot druckreife Plots erstellen, und Shiny und Leaflet erlauben die Generierung von interaktiven Grafiken und Karten. Aber manchmal ist es einfach nicht zielführend, sich mit den Feinheiten der jeweiligen Einstellungen und dem Schreiben des notwendigen Codes zu beschäftigen. Ich empfand es insbesondere in der höchst spannenden Phase der explorativen Datenanalyse (quasi dem ersten Date mit neuen Daten im Rahmen des Analyseprozesses…) als sehr störend, dass ich mich so viel mit Code und anderen technischen Aspekten beschäftigen musste, was mich von der eigentlichen Arbeit mit den Daten abgelenkt hat, nämlich dem Verstehen der Daten. Um nochmals die Dating-Analogie zu bemühen wäre das so, als würde man sich mehr damit beschäftigen, was man zum Essen bestellt oder worüber der nächste Small-Talk gehen soll, als sich mit dem (Gesprächs-)Partner zu beschäftigen und sich nur auf ihn/sie zu fokussieren. Wahrlich kein Erfolgsrezept… Continue reading →

Why Visual Data Analysis is Great

Wow, another year has passed and so much has happened in the meantime!

During my job at the Institute for Transport Research at the German Aerospace Center (DLR) in Berlin I not only worked on the theoretical underpinnings and actual development and implementation of micro-scale traffic models but was obviously also involved in publicizing the results of said models and also other research work. I did this mostly with R, Shiny, PostgreSQL/PostGIS, QGIS and the occasional line of Python code sprinkled in-between. They’re all great. I love them with all my heart and enjoy every second I’m working with one of them. But I found it increasingly hard to visualize data easily and quickly while still being pretty. Sure R and ggplot allow for camera-ready plots, Shiny and Leaflet make it increasingly easy to put together interactive plots and maps. But sometimes fiddling with their settings and writing the necessary code is just not practical to get to the point quickly. Also, during the fascinating stage of exploratory data analysis (kind of the first date with your new data in the data analysis process…) I felt focusing too much on the code and other technical aspects which distracted me from what I was originally doing: exploring my data to get a better understanding. Going back to the dating analogy it’s like over-thinking what to order and what small-talk topic to bring up next and thereby losing the interest of your possible future partner instead of being focused exclusively on him/her. Not a recipe for success… Continue reading →

Annoying join behavior in QGIS

Today I stumbled across something I wouldn’t exactly consider a bug, but at least some rather unintuitive and annoying behavior in QGIS when performing table joins.

I did something very mundane: joining a Postgres table of spatial data to another Postgres table of attribute data. The normal way to do this (for me) is as follows:

  1. Open the spatial table using Layer > Add Layer > Add PostGIS Layers...
  2. Open the attribute table the same way (1 & 2 can be loaded in one go)
  3. Join the tables in the spatial table’s Properties dialog.

For that last step I decided to join the two tables (plr is the spatial table here, while mss has the attributes) using the field plr_id, which exists in both tables and only once on each side (hence a plain vanilla 1:1 join).

Add vector join dialog window in QGIS 2.8

Add vector join dialog window in QGIS 2.8

That works perfectly fine, except that somehow the order of the joined fields appears to get messed up:

QGIS attirbute table with erroneously shifted field contents

QGIS attirbute table with erroneously shifted field contents

Some research revealed that this seems to be a problem caused by identical field names in the two joined tables other than the join field itself. In my case the aforementioned plr_id was used to join the two tables, but in addition both tables also had a field gid, as can be seen in the following screenshot on the left:

Table design in pgAdmin: original table including field gid on the left, fixed table without (unnecessesary) field gid on the right

Table design in pgAdmin: original table including field gid on the left, fixed table without (unnecessesary) field gid on the right

Removing this field gid from the attribute table mss was no problem, since the 1:1 relation to the spatial data uses the key plr_id anyways. As can be seen in the screenshot above on the right, the new table mss2 is identical to mss, only without the field gid. And lo-and-behold – joining this attribute table to the spatial table plr in QGIS works flawlessly now:

QGIS attirbute table with correct field contents

QGIS attirbute table with correct field contents

This problem had already been identified in QGIS 2.0 in late 2013, and has been marked as fixed in the meantime. Removing fields with identical names in the two tables is one – admittedly rather radical way – to solve circumvent the issue. Another, more intuitive way would be to choose a meaningful table prefix in the Add vector join dialog which can be seen in the first image above. As you can see I checked the Custom field name prefix checkbox but left the field empty. I prefer this, since it keeps my field names nice and tidy, but in cases where homonymous fields exist in the two tables you will run into trouble – hence entering a prefix here would be a nice and easy fix for this issue.

Everything described above was performed on QGIS 2.8.1-Wien (64bit) on a Windows 7 machine and PostgreSQL 9.1.16 on a 64bit Ubuntu 4.6.3 server (PostGIS 1.5.3).

Setting up QGIS 2.8 on MacOS X 10.10 Yosemite

The wait is finally over: the new QGIS 2.8 “Wien” has finally been released for MacOS as well! Following the (kind of) tradition of my articles showing how to install QGIS 2.6 2.4, and 2.0 on MacOS, I now sat down to write a brief walkthrough for the latest version as well.

Continue reading →

Setting up QGIS 2.6 on MacOS X 10.10 Yosemite

It’s been awfully quiet here on the blog recently. This is owed to some major changes in my life, including the successful end of my PhD program, a successful job hunt, a move from Japan to Germany, and an interesting yet challenging start in my new job at a major German research institute.

But the recent release of MacOS 10.10 “Yosemite” together with the even more recent release of the new QGIS 2.6 “Brighton” was a brilliant opportunity to not only bring back some life here, but also to continue my mini-series of articles about installing and running QGIS and other rather scientific software packages on the latest versions of MacOS (see here, here, and here for example).

So I sat down on my freshly delivered sofa between unpacked boxes to try my luck. To make a long story short, in my case the installation ran smoothly and was done in about half an hour – downloading the necessary disk images took most of time. But before I updated my QGIS 2.4 to the new version 2.6 I first tried if 2.4 still runs on my freshly upgraded MacOS Yosemite. And there was a small surprise waiting for me here, as MacOS asked me to update my Java SE 6 runtime!

Updated Java SE 6 runtime necessary

Updated Java SE 6 runtime necessary

Luckily this was no big deal, since the error message provided a link to the download page at Apple.

Apple provides the Java update

Apple provides the Java update

Easy installation of the Java update

Easy installation of the Java update

After running this update QGIS 2.4 worked fine like before.

For the download of QGIS itself I decided once again for the packages provided by William Kyngesburye a.k.a KyngChaos – not only did I never have any problems with these, but to my best knowledge they are the only available pre-compiled QGIS packages for MacOS… The installation process follows the steps known from earlier releases:

First is the new GDAL 1.11. The installation is as easy as downloading the DMG and installing GDAL from the respective PKG therein. Please ignore the NumPy package also contained in the GDAL disk image, since it’s an outdated version. Oh ya, and then there’s this thing that’s still annoying me:

That's still annoying

That’s still annoying

Gatekeeper refuses to open applications and packages from “unidentified developers” (that is, developers that can’t afford a certificate by Apple) by double-clicking. Hence you need to right-click it and select Open.

2. matplotlib and NumPy
Before we can install matplotlib we need to install NumPy. There you can find the most recent version 1.8.0-1. As is stated on the website NumPy is “included on the GDAL Framework disk image, though it may not be up to date”. And indeed the GDAL image mentioned above includes NumPy 1.6.2-1 from mid-2012…
Now that that’s out of the way we can install matplotlib 1.3.1-2.

And finally QGIS 2.6.0-1 itself. As in the other cases we open the DMG file and install from the PKG file therein. That’s it!

QGIS 2.6 "Brighton" splash screen

QGIS 2.6 “Brighton” splash screen

QGIS 2.6 "Brighton" UI on Yosemite

QGIS 2.6 “Brighton” UI on Yosemite

Now that everything was installed it was time to fire it up for the first time. And lo and behold, it works! Just like that. You can’t ask for more. Now it’s time to discover all the great new features QGIS 2.6 brings!