Radius of gyration is a metric to quantify distributions around a center location. Its applications range from structural engineering to molecular physics. Since it incorporates the idea of dealing with locations, it can be applied for geographic data, as well. I recently came across with it in some global mobility studies where the goal was to characterize the travel patterns of individuals. In those papers, the metric indicates whether a person is more likely to travel long distances or not. In my research, where I am interested in geographic data contributions of volunteer mappers, I found it to be extremely useful to decide if the overall contribution shows local or global patterns. For many years, local knowledge was considered to be the main advantage of this so-called user generated geographic information. Local guys know the place, let them draw maps, let them take photos and the product will be accurate. While this is most probably true, it also seems that some of these guys like to do the same thing in distant places so there might be other factors than localness that can make these data sources accurate, therefore extremely valuable. Everything is up to the people who contribute, so the ultimate goal is still to understand their behavior. Now, enough of the crazy talk. Click on “read more” to do some fancy math and coding.
In my spare time, I teamed up with my old colleagues, the lovely faculty members at the University of Szeged, Department of Physical Geography and Geoinformatics to ask them for data donation. I couldn’t be happier to announce that after months of procrastination, some other delays and some more months of procrastination on my side, finally I am able to release a high resolution aerial imagery to the OpenStreetMap community for the solely purpose of mapping.
Végre sikerült összehozni egy régi tervemet. Nagy örömömre szolgál bejelenteni, hogy a Szegedi Tudományegyetem Természeti Földrajzi és Geoinformatikai Tanszéke jóvoltából az OpenStreetMap közösség szabadon felhasználhatja a Szeged körtöltésen belüli részéről 2011-ben készült, nagy felbontású ortofotó állományt térképszerkesztés céljából.
I wanted to get rid of long line segments in my data. Since I don’t know any software tools off the top of my head that would do the job, I decided to code it myself. I have already stored everything in a spatially-enabled PostgreSQL table, and to be honest, recently I am more interested in manipulating data at the database level to save some time. So, instead of writing a python script and looping through a cursor, I created a function which I am calling in SQL queries. The picture below gives an overview of what the following function does. Basically, it breaks input geometries at line segments that exceed a certain threshold in length. Red means “no bueno”, green means “yaay!”.
Today, a new project appeared on Kickstarter from the founder of OpenStreetMap, Steve Coast. I highly encourage everyone (in case you can) to stand for it so we can enjoy some insights about OSM. It’s pretty interesting how more than 1/5 of the goal have been pledged in less then 1 day.
In addition, enjoy a recent talk about the project.
As the last part of the previous post-series about MongoDB and Twitter I’m about to show some plots about an initial speed comparison of the two DBs. As a result, these plots show how MongoDB can perform better than traditional SQL solutions if it comes to speed. Of course the overall picture is more sophisticated. In these cases I focused on the simplest approach possible – retrieving documents from Mongo and rows from Postgre.
This video was presented at the closing plenary of the FOSS4G 2014. I have no words… pretty awesome! I wasn’t even born yet. Wow, seriously, no words… Enjoy.
Originally I wanted to write about visualization in the 2nd post (and after that in the 3rd) but that post would have been too long to read. I always loose myself if it comes to writing but nevermind, finally it’s here. So, we know how to access to the DB and we can query for interesting subsets – even in a geographic way. All we have to do is to interpret our results. I’m presenting two ways, a gif animation and a wordcloud. It’s not about reinventing the wheel but still, I believe that these are useful approaches to complement each other.
In the previous posts I have introduced the topic and did some simple coding to explore the data. That’s not bad at all but usually the goal is to create something new or at least to understand what is going on. In this simple example we’re interested in the weather. We want to see what people tweeted about the weather during the data collection period. Unfortunately, a dataset of 200.000 tweets is not big enough to recreate the weather conditions for that time. Why? Simply because after getting rid of the unrelated tweets we have almost nothing to deal with. If you’re here because you’re interested in the past weather of the UK, I think you should better visit this site :). For the others, I promise I’m going to tell you how I created some maps.
image by Havadurumu
I’ve recently found Mapillary which is a great project that aims to cover the world with street level photos, just like Google’s StreetView. The big difference is that they use the crowdsourcing approach and collect images from volunteers, mostly equipped with smartphones or action cameras. All photos are available under CC BY-SA 4.0. They process all uploaded photos using computer vision on their servers. They have a nice API so everything is given. They’re open, they’re geospatial and they’re nice. You can talk with them via Twitter or email. They’ll respond. Currently, you can find them in Malmö, Sweden and in West Hollywood, Los Angeles. The project pretty soon has gone worldwide. The service was initially released in the last week of February 2014 at the Launch Festival and since then they cover 101658370 meters with 3541820 uploaded photos until September 21. Check out their site and see what they are doing. From my sight, it’s pretty impressive. I’ve shot this panorama view in Key West, FL.