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October 4, 2010 | by  | in Features |
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Tourists versus locals: Deciphering data-porn

The fact that tourism is one of our largest industries means it is inevitable that we often cross paths with some of its less attractive side-effects. On a sunny day in Wellington it often feels as though we can’t move for the throngs of the Kathmandu-clad German couples who inundate the waterfront with their unremitting snap-happy attitude, intensely phallic zoom lenses and outmoded Birkenstocks. While the common conception is that the tourist masses sift from attraction to attraction, keeping to the well-trodden path and presumably avoiding contact with anything remotely quotidian, a recent project by Californian Eric Fisher may give us a new understanding of the tourist’s experience which isn’t so far removed from our own.

What the maps show

Fisher’s image-maps show the locations of geographically referenced photographs in cities around the world, taken from the internet based photo-sharing platform Flickr. The ‘geo-tagged’ data is input to a computer programme which then categorises the information provided by members into highly complex visual models, such as the one shown here for Wellington. These ‘digital footprints’ by themselves are immensely complex and impossible to decipher, but Fisher’s project to visualise vast quantities of data presents us with a new means to analyse and understand the built environment.

The geo-referenced points show the activity of photographers around the city and are a fairly good representation of the image-ability of our city, or at least a measure of the most visually stimulating places to visit. Further to the distribution of photograph locations, the data has been has been grouped and colour-coded into ‘tourists’ and ‘locals’ through a system based on the time spent in any one location. Those who have uploaded geo-tagged photographs over a period of one month or longer are broadly categorised as locals and made blue, while those who have taken photos for less than that period are classified as ‘tourists’ and coded red. In addition to the polarisation of these two categories, a third colour, yellow represents photographs of unknown origin, where their author has no registered activity on Flickr in another city and is thus unable to be classified either tourist or local due to a lack of information.

Wellington City

It’s fascinating how the data begins to reveal an image of the city from the eyes of its spectators. All the main tourist attractions are there: from Te Papa (the largest patch of red) to Parliament grounds and the Botanic Gardens—the sites that ‘make’ Wellington are clear to see. Even the Cable Car can be made out as a continuous stream of yellow extending out perpendicular from the duotone Lambton Quay.

In addition to the intensely concentrated ‘hotspots’, the aggregation of thousands of points begins to visually describe the patterns of movement and usage throughout the city. In the Wellington example, there is a strongly defined axis of movement to the south and west from the intersection of Cuba St and Courtenay Place. While this distinct pathway might not present anything particularly groundbreaking in the way we already understand the city, it is immensely useful in allowing us to visualise a particular phenomenon and compare these areas to the less-traversed paths in a quantifiable way.

Data Visualisation

The field of graphic design has recently seen a rise in popularity of the data-visualisation, particularly in both newspapers and popular magazines. With the information saturation of the so-called ‘digital flood’, a large amount of data exists in the ether that is either totally incomprehensible or at least of little consequence in the way it is presented to us. Recent development in data visualisation has made it possible to manage large amounts of digital information or traces and re-package it as a ‘super-graphic’ (such as Fisher’s maps) that organise the data into a much more intelligible tool. By using computer programmes to group, simplify and then display data within the bounds of a manageable image, we are now able to make sense of some of the vast torrents of data that seem to assault our retinas with every click of the mouse.

While this new tool is pretty fascinating to your typical viewer, a particular criticism is that such a visualisation relies on technological means that undeniably disadvantages those without access to photo-sharing networks like Flickr. This effect becomes quite noticeable when one compares a similar map of say, San Francisco (with a relatively large number of Flickr users) with a city of similar population like Johannesburg (whose percentage of Flickr users is considerably lower). The impact of such bias is that the map of the latter becomes disproportionately sketchy by comparison and less accurate for that locale.

In addition to the bias created by the use of such programmes, the maps are created from photographs and the information they present is likely to give us an understanding of the visual quality of any one space as opposed to something potentially more valuable, such as knowledge of how the space is used and experienced. That being said, there is still a lot of valuable information buried in the images that is of particular interest to architects and urban designers as a new tool to measure and understand spaces in the city.

Applications

Looking at these images, it’s easy enough to be seduced by their visual appeal. The maps are, after all, incredibly graphically striking, particularly when viewed from a distance. The real question is whether such visualisations are contributing to our understanding of the built world, or merely an example of what is known as ‘data porn.’ While the maps may appear to only reiterate what we already know about the behaviour of both tourists and locals, the immense wealth of information presented in the data allows us to analyse behavioural patterns in much more detail and beyond the scope of traditional cartographic representation.

The dynamic nature of the data and its ability to be updated adds additional interest. By further classifying the data set by aspects of time and seasonality it would be possible to analyse how different conditions (such as weather and light) impact the behaviour of both locals and tourists as well as allowing for comparisons to be made over greater periods of time. This information could then be used to make useful predictions for urban designers and city planners to anticipate the growing requirements of different user groups and to make predictions about the future form the city may take. The question is, really, what kind of relationship we wish to develop between tourists, locals and the city.

Website: http://www.flickr.com/photos/walkingsf/sets/72157624209158632/

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