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Our Digital Rights to the City || download/order our pamphlet

The pamphlet ‘Our Digital Rights to the City’ is now out on Meatspace Press. We are pleased to offer it for free download (epub, mobi, pdf), as a low-cost paperback, and as a self-print pdf.

‘Our Digital Rights to the City’ is a small collection of articles about digital technology, data and the city. It covers a range of topics relating to the political and economic power of technologies that are now almost inescapable within the urban environment. This includes discussions surrounding security, mapping, real estate, smartphone applications and the broader idea of a ‘right to the city’ in a post-digital world.

Should we feed all the data for a given problem to a computer? Why not? Because the machine only uses data based on questions that can be answered with a yes or a no. And the computer itself only responds with a yes or a no. Moreover, can anyone claim that all the data have been assembled? Who is going to legitimate this use of totality? Who is going to demonstrate that the “language of the city”, to the extent that it is a language, coincides with ALGOL, Syntol, or FORTRAN, the languages of machines, and that this translation is not a betrayal? Doesn’t the machine risk becoming an instrument in the hands of pressure groups and politicians? Isn’t it already a weapon for those in power and those who serve them?

Henri Lefebvre in the Urban Revolution (1970)

The collection is edited by Joe Shaw and Mark Graham and its contributing authors are Jathan SadowskiValentina CarraroBart WissinkDesiree FieldsKurt IvesonTaylor SheltonSophia Drakopoulou and Mark Purcell.

Free Download:

.epub (for most e-readers except Kindle ): free download at SmashWords.

.pdf (for most things): free download from Internet Archive or view hi-res in browser at Issuu.

.mobi (for Kindle): free download from Internet Archive.

Paperback:

We have a limited print run of just 50 copies available via Big Cartel for £3 each + P&P.

Details:

Title: Our Digital Rights to the City
Edited by: Joe Shaw & Mark Graham
Contributing authors (alphabetically): Valentina Carraro, Sophia Drakapoulou, Desiree Fields, Mark Graham, Kurt Iveson, Mark Purcell, Jathan Sadowski, Joe Shaw, Taylor Shelton, Bart Wissink.
Design: Irene Beltrame
Format: Paperback, .epub, .pdf and .mobi
Length: 35 pages
Language: English
Publisher: Meatspace Press (2016)
ISBN (paperback): 978-0-9955776-0-2
ISBN (e-book): 978-0-9955776-1-9
ISBN (pdf): 978-0-9955776-2-6
License: Creative Commons BY-NC-SA

America's most influential cities: the urban geography of klout scores

My colleague Devin Gaffney and I decided to dig deeper into the geography of Klout and examine the geography of some of the largest cities in the US. We found some very interesting patterns and large differences in the average influence of users in American cities.

Klout scores, for those unfamiliar with them, fall between 0 and 100 and supposedly measure influence (higher scores indicating that a person is more influential). As, I’ve noted before, this sort of quantification of a person’s influence based on online activity is inherently problematic. It defines influence rather narrowly and then ranks each person with a highly decontextualised score that is unlikely to account for the many nuanced ways that influence is perceived and enacted. However, despite the problematic nature of the service, it is nonetheless important to attempt to better understand how it is measuring and representing people.  

We therefore decided to calculate the average Klout score of 49 of the largest American cities. The map below displays each city as a circle that is shaded and sized according to its Klout score. In the interest of clarity, only the top-ten and bottom four cities are labelled. 


First, a few words on how we collected the data: From April 8th to April 29th, 2012, approximately 195 million tweets were collected via Twitter’s “spritzer” access level. Geo-coded tweets were selected using the API’s internal methods. The resulting dataset was then cross-referenced against a list of fifty bounding boxes approximating the general conurbation of every city and its suburbs (so as to capture the full scope of the metropolitan area at large). For each resultant bounded set, 1,000 random users were selected from the city and referenced against Klout’s score API. For each city, slightly less than 1,000 users are shown, as some of the tweeting users have not been detected and scored by Klout, and as a result have no score.

The city with the best average influence score (29.1) for its users is San Francisco (which perhaps unsurprisingly is also the headquarters of Klout). San Francisco’s average score is also interestingly significantly higher than the city with the second-highest average (Austin at 27.8). We then see a tighter cluster of average city scores for Seattle in third place (27.1), and two more Bay Area cities in fourth and fifth: Oakland (27.1) and San Jose (26.8).

At the bottom end of the scale we have Houston (23.3), Jacksonville (22.9), Memphis (22.8), and Virginia Beach (22.7).

Why do we see such variance in the geography of Klout scores? Are people in San Francisco and Austin really that much more influential than people in Houston or Memphis? Klout scores certainly aren’t (well, at least they don’t appear to be) randomly assigned. They are derived by combining score of number of followers, number of people you follow, number of (and spread of) retweets etc.

But does the geography of Klout actually tell us anything useful about these cities? By themselves, I think these data tell us almost nothing. They are a very blunt and fuzzy tool applied to a limited sample and we should be hesitant about reading too much into the numbers. However, when brought together with other data and research about information production and consumption, influence, and voice they potentially allow us to us to draw more rounded pictures about the sub-national geographies of the internet.

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One interesting point is the discrepancy between these city-level scores as compared to the national scores conducted in an earlier study. While no conclusive reason has been found for this discrepancy, a few possibilities may create this effect. One theory may be that the users sampled for this report were collected on twitter in April 2012 - many of them may have since decreased the usage of their accounts, and as a result the scores may have decreased. Another theory is that there may be some correlation with users located outside of population centers having higher scores. Despite this, the data being shown was exhaustively assessed in order to determine the extent to which this discrepancy could have been in error, and has found to be accurate.
Codes on places

The divides between material place and virtual information will start to shrink even more over the next few weeks as Google begins sending out about 100,000 QR codes to businesses in the US (as reported in the NYT).


The idea is that a user will see one of these codes on a building, scan it with an android, iPhone, Blackberry etc., and then be served up information that Google has stored about that particular place. This information could include descriptions, reviews, images, and coupons. The QR code is thus in essence a portal between the bricks-and-mortar material world that we inhabit and the (invisible to the naked eye) information about those same places scattered throughout the internet.

This isn’t a new idea of course, and the technology has been quite widespread in Japan for most of this decade. However, the significance of this piece of news is Google. Given the scale of investment, the fact that Google really wants to drive adoption of this technology by subsidising some of the cost for users, and the fact that almost all new smartphones are able to read QR codes, it is quite likely that we will soon see QR codes scattered throughout our urban environments.

Virtual information really will then become an important component in the palimpsests of place. As such, a host of questions emerge. Will rival companies start creating their own bridges between the material and virtual on national or global scales? Will the practice of QR tagging the material world further entrench the power of Google to determine what we see, where we go, who we interact with and where we spend money? What will this mean for the people and places unable to make their information rise to the top of opaque ranking algorithms? And are there any potentials for an open and transparent version of this technology, in which the justifications for making some information visible and some invisible are clearly documented?