Tag Archives: mapping

Mont St. Michel

Kite aerial photography gallery

Digital Photography School has a nice gallery of kite aerial photography (or KAP, as it’s called amongst insiders):

Le Mont Saint Michel (Manche-FR)

Le Mont Saint Michel, France (© François Levalet, http://www.francoislevalet.fr)

I have to date built various kites myself and actually also one of the KAP rigs shown in the gallery and have done some KAP experiments myself. KAP is lightweight, can produce affordable aerial photography and is thus an interesting acquisition method for spatial data. For these reasons it is sometimes used for archaeologic studies and exploration, see for example here or here.

Despite ever more popular quadro- and other copters and their advantages (for example, no need for wind), I think KAP may keep a niche, for example in applications where noise may be an issue or where an especially heavy payload needs to be lifted.

Flickr as a vehicle of narrative: photos contextualised in space and time

After my project proposal had been accepted, I have attended a workshop at ETH Zurich, titled “Cartography & Narratives” organised by Barbara Piatte, Sébastien Caquard and Anne-Kathrin Reuschel in last summer. The goal of the workshop was to explore “mapping as a conceptual framework to improve our understating of narratives”. Narratives are

“an expression in discourse of a distinct mode of experiencing and thinking about the world, its structures, and its processes“ (White 2010)

or

any cultural artefact that ‘tells a story’ (Bal 2009)

I decided to investigate the photo-sharing platform Flickr as vehicle of narratives (think: the slide show of pictures from a trip, be it directly on the camera’s screen or as an image projected onto your living room wall, as one of arguably the most ubiquitous types of every day narrative).

I have uploaded a preliminary result of my workshop paper on Vimeo (view it large, for good quality):

[vimeo http://vimeo.com/56999213 w=600]

 

The movie shows the temporal and spatial patterns that emerge, when we conflate 80’000+ images taken by 4’000 photographers over the course of several years in the city of Zurich, Switzerland (I only looked at georeferenced photographs). See the description of the video on Vimeo for full information.

I will post more about the workshop results and further work, shortly.

Background of Mapocalypse

The internet has been abuzz about Apple’s iPhone 5 “mapocalypse“. The Verge has new background information: Apple took the decision to ship their own mapping app “over a year before the company’s agreement to use Google Maps expired“. Apparently, the people at Apple “felt that the older Google Maps-powered Maps in iOS were falling behind Android — particularly since they didn’t have access to turn-by-turn navigation (…).” Google on the other hand has been reported to have wished for more branding and inclusion of Latitude.

Anyway, “mapocalypse” is here and is presumably bound to stay a while, until either Apple fixes its globe or Google has finished their iOS6 mapping app and it’s been given admission to the app store by Apple. While competition is always a good thing, I’m not sure if Apple has indeed the capacity to amend their data on a global scale within a short time range. Combing through and improving the consistency of geodata from heterogeneous sources is a daunting task, after all. Google Maps (started 2005) and Google Earth (released under this moniker for the first time in 2005) also took years to arrive at a level which most users are happy with most of the time. The praise at the introduction of the new Apple maps is what their progress in quality will be measured against:

(…) when iOS software VP Scott Forstall introduced the new mapping system in June, he called it “beautiful” and “gorgeous” and stressed that “we’re doing all the cartography ourselves.”

(Source The Verge)

Enclaves, Swiss-made

I’ve highlighted the NY Times’ Opinionator blog before. Back then, Franc Jacobs wrote a piece about the delimitation of the rather fuzzy geographic entity called “Europe”.

Today, there’s a new blog post about Enclave Hunting in Switzerland. After the mandatory clichées (the relevance of the “National Yodeling Festival” can probably be gleaned from the fact that it takes place only “once every three years”… – as opposed to, say, the Montreux Jazz Festival), the piece gets more interesting when it explores the many national (intercantonal) and in fact two international enclaves of Switzerland. The curious topology of the two Appenzells and Sankt Gallen are dealt with as well as the enclaves of e.g. Fribourg and Geneva.

The two international enclaves of Switzerland: Büsingen and Campione (NY Times)

Continue reading

Where was I?

I acknowledge, it’s been rather quiet in these regions of the web. Why, you ask?

I have been rather busy with a sort-of spinoff project I pursue with two friends. After having published about the Twitter network of journalists here, here and here, I directed my interest towards politicians. With two friends, Tom Wider and Filip Zirin, I started SoMePolis.ch:

If you haven’t clicked through yet: SoMePolis aims to investigate the social media usage of members of the Swiss parliament. Swiss parliament has two chambers: the national chamber with 200 members and the chamber of states with 46 members. So, in total there are 246 potential Social Media users. On Twitter we have so far found 62 accounts which seem credibly enough to belong to Swiss MPs.

Our first few posts have found a very interested audience, one of the big newspapers picked our story up as well as a regional radio station.

If you’re interested in our results (and we will keep publishing more), this post is a good start (though, in German) or this post if you excel more at French. Or you can follow the project on Twitter.

The Twitter network of Swiss MPs

Economist’s Africa Twitter map provides some teachable insights

Mark Graham has posted a critique of a “Twitter map” that featured in the Economist at Zerogeography. The map was compiled by Portland Communications and Tweetminster and shows the number of tweets per country (original version of the map can be found in this presentation by Portland Communications):

Africa Twitter map by Portland Communications, Economist

Mark Graham raises these interesting points regarding this map:

  • 11m Tweets in Africa over a three months period is probably vastly underestimated, since the joint Portland Communications/Tweetminster analysis looked only at geocoded tweets.
  • The analysis doesn’t account for the provencance of the tweets: are many of them issued by few users or are actually many people behind the many tweets of a country? This is likely a very relevant point, since it is found with many crowdsourcing projects that a small minority of the users contributes the majority of the content. It may be the same with Twitter, the only question which remains then is: could it be that the proportion of heavy contributors varies between countries (thus harming comparability of countries)
  • The analysis doesn’t relate the number of tweets to the number of inhabitants. We have thus no way of knowing whether a big number of tweets means an extraordinarily high proportion of Twitter users in the population, or not.

Mark states that in a study conducted by him and his team using the Twitter Streaming API, it was found that only 0.7% of all tweets indeed contain geolocation information. (and thus the Africa Twitter map is based on a really small sample of the tweets which have been sent from within African countries!). That proportion was something I have wondered about since I have started to tinker with the Twitter REST API a few weeks ago. Other than the Streaming API (the so-called “firehose”), the REST API has tight query limits, so I haven’t acquired a big enough sample of tweets to actually make the judgment regarding the prevalence of location information in tweets (acquiring a random sample of tweets is also not the aim of my studies).

As Mark further points out this shortcoming on the data side makes the map potentially useless, in the worst case even misleading: Users in different countries may expose location in their tweets with different probabilities, due to for example:

  • different brand mix of end user devices (for example, different prevalence of smartphones versus dumbphones (which can use Twitter via SMS)
  • different mix of Twitter clients. Twitter clients may expose the location sharing settings in different ways and may rather encourage or discourage a user to opt into or out of location sharing
  • varying awareness of, or views on, privacy issues around location sharing
  • different societal norms towards location sharing

If the prevalence of location sharing is different in different countries, the Africa Twitter map cannot serve even as a proxy of the true numbers of Tweets sent from African countries.

Further takeaways thanks to Mark Graham:

  • Using the location information in description fields of Twitter users’ profiles is a bad substitute for actual location information attached to tweets.
  • Time zone information as another approach to rough positioning of a Twitter user isn’t a feasible alternative route either, since many users don’t bother to set it in their profile.
  • And, most importantly and generally applicable: Any analysis of data from social media or crowdsourcing initiatives has to scrutinise the data for potential confounding variables, inherent biases, flaws in data collection (sampling), data processing and analysis. No analysis is complete without these questions asked – if they’re not clarified in the analysis, it’s the end user’s duty, though unfortunately it can be difficult without access to the raw data.