Tuesday, 29 January 2013

Big Data and beauty: Hong Kong

Social Media map of Hong Kong Island, showing a lot of popular Flickr sites and Wikipedia attesting to the cultural significance and beauty of Hong Kong Island.
The Spirit of Cities by tinorose

I have been reading The Spirit of Cities: Why the Identity of a City Matters in a Global Age Daniel A. Bell & Avner de-Shalit; which is an amazing book full of ideas about what makes cities operate. Bell and de-Shalit avoid much dull analysis of statistics rather presenting stories, history and insight that bring the cities alive for the readers.

Image Flickr 
One of the things I keep thinking is if Social Media mapping can confirm some of the ideas in this book.  Using my Happan.in tool I think I have confirmed their observations on Tel Aviv and Jerusalem.
Image from Flickr
In their chapter on Hong Kong Daniel Bell makes what seems like a utterly subject assessment about Hong Kong, claiming that for "urbanites... the best view in the world is Hong Kong's skyline."

An interesting observation, and one that in the recent passed could only be taken as a matter of opinion.  But one of the implications of Open Big Data, especially data that can be mapped from social networks like Wikipedia, Flickr, Twitter and Foursquare, is that you can actually drill in to the opinions of people about places in a more scientific and objective fashion.  You can get counts, and more importantly relatives counts of the words people use, you can see the images, count the checkins, count the Wikipedia edits and see the tweets that are coming form a place or are about a place.

Image Flickr
So can I have a subject assertion like 'people who like cities love Hong Kong's skyline' and make it in to something I can test with social media Big Data?

Yes I think I can. I am not going to try and define 'urbanties', and just assume that urbanites are human beings many of who author web content or post photos to the Internet.  Also I am not going to try define 'the world's best view' and rather take the statement to mean:
The view of Hong Kong island from Kowloon is held to beautiful and significant by a large community of people.
Further operationalising this statement into something that can be measured:
  • The areas of Hong Kong Island seen from Kowloon will have a lot written about it on the web.
  • Images of this part of Hong Kong will be posted on the Internet.
Converting these to Social Media assertions, that can be tested:
  • There will be a large number of Wikipedia entires with a lot of edits about Hong Kong island around Central where there is a large skyline.  That is people are motivated to write about it.
  • There will be a lot of images posted on Flickr and many of these will be popular with users. That is people are motivate to post images about it.
Using my happan.in tool I can actually test both of these assertions.  Happan.in maps social media activity on a Google map, including geo-tagged information from Wikipedia and Flickr.  As you can see from this image there are in fact a lot of Flickr and Wikipedia images linked to this site.



Happan.in does not just map all Wikipedia article or Flickr image with a geo-location, it only post articles that have had a significant number of edits in Wikipedia or articles that have a significant number of views in Flickr.  Therefore it is not just a map of content, it is a map of popular and significant content.  

Image from Flickr
Using this mapping we can see that what Daniel Bell writes, minus some of the hyperbole, is accurate.  The view obviously is popular with a lot of people.  People are motivated to write and edit Wikipedia about the location and people post images which are often popular on Flickr about the area.

Okay granted that this exercise is a bit academic, I doubt many people will question that city lovers like the view of Hong Kong, and the implication that it can be confirmed with Big Data seems almost pointless.  But imagine other assessment that can potentially be tested with Big Data like this.  Some statements about places that might someday be measured by sociologists using social media are far from purely academic:
  • The best place to get a good education is in the city X.
  • People who live in region Y are at a major disadvantage in finding jobs.
  • You don't see any women on the streets at night in Z.
  • This city is getting run down despite the official economic reports.
  • The government is not that popular in this north of the city, more popular in the south.
Big Open Social Data provides the opportunity to test more common sense assertions about spaces that normally would have fallen outside of the strict measures of social geography.  They also provide potential sources of sociological data without the need for large budgets or official sanction.

Certainly as of today this work tells you more about the popularity of smart phones in an area than anything else, but as more and more people post more and more of their life narratives online to share with the global community the reach and agility of sociology is about to explode. 

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