More than a week ago I went to Colorado. The purpose of this trip wasn’t hiking (okay, I went hiking as well) but to attend the State of the Map US conference held at the University of Colorado Boulder. My talk was about how we’re trying to build a geospatial community in South Florida and I presented some results of our OSM building import. Recordings came online recently so here’s a video of my talk. Enjoy.
P.S. Hiking was also great. You’ve got to visit Colorado during fall! It’s amazing.
Since this post is still getting a lot of views, you might be interested in the
outcomes of my experiments with the cross K-function. I used the function in 2 recent
papers. Links to the articles are found on the Publications page.
Juhász, L. and Hochmair, H. H. (2017). Where to catch ‘em all? – a geographic analysis
of Pokémon Go locations. Geo-spatial Information Science. 20 (3): pp. 241-251
Hochmair, H.H., Juhász, L., and Cvetojevic, S. (2018). Data Quality of Points of
Interest in Selected Mapping and Social Media Platforms. Kiefer P., Huang H., Van de
Weghe N., Raubal M. (Eds.) Progress in Location Based Services 2018. LBS 2018.
Lecture Notes in Geoinformation and Cartography (pp. 293-313) Berlin: Springer.
One of the research papers I’ve submitted recently (yes, about Pokémons!) dealt with spatial point pattern analysis. Visually it seemed that two of my point sets prefer to cluster around each other, in other words I suspected that Pokéstops have a preference of being close to Pokémon Gyms. Check the map below to see what I mean. Pokémon locations (cyan dots) are all over the place as opposed to Pokéstops (orange) that almost exclusively appear to be in the proximity of gyms (red).
To confirm what’s obvious from the map, I used the bivariate version or Ripley’s K-function (a.k.a. the cross-K function) that can help us characterize two point patterns. As it turns out, it’s not as easy to interpret as I though it would be (at least with real world data) and I was trying to get my head around it for quite some time. As a result, I came up with a simple interactive visualization of this function to illustrate what it really means. If you’re anything like me and try to understand your stats instead of just reporting the results, you might want to read on more for some musings about the cross K-function.
If you like playing with data, chances are you’ve come across D3.js, Data-Driven Documents. Here are two of the first visualizations I made with it. I’m going to write up a more detailed blog post about it later. In the meantime – without commentary -just enjoy.
Oops, I forgot to share my “new” videos I made this June. Better later then never, I guess. Anyways, it’s part of my research that aims to understand how regular people on the Internet use different mapping platforms. Well, not just mapping platforms but basically any platforms that you can think of including Instagram, Foursquare, Twitter, Facebook an many more. We know that many of you use multiple services during your daily routines. Previous research focused on each of these data sources separately so we have a lot of knowledge on them (not playing the Big Brother here, I’m talking about an aggregated level). However, we do not yet know how the same individual uses these services simultaneously. Do activity spaces overlap? Is there a single main service or do people use different services with the same intensity? Does the introduction of a new service affect previous usage patterns? Can the user base from a platform drained by another? How do these processes work in time and space? Well, and I have many more questions. Probably way more questions than I can realistically answer, especially when don’t just talk about simple social media photos but really high quality mapping activities (as in editing OpenStreetMap and taking Mapillary street level photos specifically for mapping).
I should really stop playing around with side projects and should get back to my research. But look at this! I recorded a few hundred thousand Pokemon encounters over the past few days. You know, just for fun, because that’s what grown ups do. I marked all Pokemon locations in Downtown Miami and Miami Beach with cyan dots and created a heatmap on top of them to see where you should wonder if you want to catch as many as you can.
Click for a high-res version.
To be honest, I don’t play Pokemon Go. Was never into this thing. But I do like maps and data. And this is really cool data. So, I’m thinking. Maybe. What if I made it my research… what if I could come up with something really interesting? Oh, well. Instead of trying to justify myself, I guess I’m just gonna catch ’em all along with some awesome spatial analysis.
This Monday I attended my very first Maptime Miami Meetup where Matthew Toro talked about a potentially great addition to Miami’s OSM… buildings! What makes a map detailed and fancy looking? I think it’s buildings. And landuse. And POIs. Oh well, I could continue adding items to this list for days without even starting to talk about it, really. But in any case, buildings are without a doubt the very foundation of what we can call a detailed map. Sadly, Miami’s OSM is not what we can call nice and detailed in its current state. It instantly becomes clear when you look at the map that it needs some improvement. But you know what? That’s the fun part of collaborative mapping. It’s really up to us how we build a useful map database and how detailed we want it to be. It’s us, regular people who add restaurants, bike lanes, shops and many other things we care about. Long story short, the Meetup was about importing a publicly available building dataset and making it an integral part of OpenStreetMap. I’ve decided to participate in the process, and I planned to help out with some basic stuff, throwing some ideas, maybe writing some code. You know, nothing fancy. At least that’s what I imagined. But as things rarely turn out the way we want them, now I’m the tech lead on this. Big words, I know, but they’re not mine.
Red outline: current OSM buildings. Cyan spots: buildings to be imported. Now, that’s a lot of new buildings to add!
In case you haven’t made your summer vacation plans yet, I happen to be a guest lecturer for this intensive summer workshop/course. It’s mainly designed for wildlife ecologists so you might wonder what I’ll be doing there. Well, wherever spatial data is involved, I’m there!
While I surely won’t be the person providing ecological context, hopefully my participation will provide some different insights to the world of geographic data handling with spatial databases. I’ll try my best and come up with an interesting lecture about the possibilities of PostGIS in spatial data handling. You know, all the cool stuff. Buckle up, it will be an interesting course that is extremely useful for ecologists. Hoping to see you there!
How long does it take to map an area with Mapillary? Well, apparently it’s up to you. According to he video below, it shouldn’t take long. It shows how street level photo coverage evolved in Phoenix, AZ between June 2015 and April 2016. I’m always amazed to see what can be achieved in just less than a year.
A year ago, high quality aerial imagery with a 10cm ground resolution was made available to the OSM community in Szeged, Hungary. It’s a very good example of not just sitting on the data but trying to make use of it. In theory, OpenStreetMap community can absolutely benefit from having a data source like this as there are way more details to be derived from such high resolution imagery. Also, the positional accuracy of the orthophoto is worth mentioning. You know, this is the kind of aerial photograph that you can make measurements on, like if it was a true map. It’s important because you can skip playing with different offsets and dragging your base map around to make it appear in its “true” position before you can actually start mapping. So, truth’s been told. It’s cool, but what the heck is with it?
Well, It’s been a year or so. I can talk about the benefits for days but it doesn’t really matter if no one is acting accordingly, right? There are things that “should” work in theory but when it comes to online communities… well, that’ a whole different story. Anyway, let’s lurk around and see what awesome mappers of OSM think about all this (oh, did I just say awesome people of OSM? Is it a spoiler? Oh well, I guess you have to click on the link below and read more to figure it out.)
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.