Category Archives: Geoweb

Papers from “Where’s the Value? Emerging Digital Economies of Geolocation” session

The written texts from the AAG panel session I co-organized with Agnieszka Lesczczynski entitled “Where’s the Value? Emerging Digital Economies of Geolocation” are now available. The panelists were Elvin Wyly (UBC), Rob Kitchin (National University of Ireland at Maynooth), Agnieszka Leszczynksi (University of Birmingham) and Julie Cupples (University of Edinburgh).

Two were posted to blogs (linked below) and two are reproduced below. Although I posted links to a couple of these previously, this blog entry collects them all. (Two panelists, Sam Kinsley and David Murakami Wood, were regrettably unable to attend.)

Thanks again to all!

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Elvin Wyly: “Capitalizing the Records of Life” (see below)

Rob Kitchin “Towards geographies of and produced by data brokers

Agnieszka Leszczynski “What makes location valuable? Geolocation as evidence, meaning, & identity” (see below)

Julie Cupples “Coloniality, masculinity and big data economies

And here again is the audio from the session.

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“Capitalizing the Records of Life”
Elvin Wyly, UBC

Let me begin with a confession.  I did some homework reading the cv’s of my colleagues on this panel, and this is where I found the answer to our central question, “Where’s the value in the emerging digital economies of geolocation?”  I.  Am.  In.  Awe.  It’s here, right here, right now, in the intersecting life-paths of extraordinary human geographers coming together to share the results of labor, creativity, critical insight and commitment.  Julie Cupples’ work on decolonizing education and geographies of media convergence intersects with Agnieszka Lesczynski’s inquiry into the gendered dimensions of the erosion of locational privacy and the “new digital spatial mediations of everyday life,” and Sam Kinsley’s ‘Contagion’ project on the movement of ideas through technologically mediated assemblages of people, devices, and algorithms.  David Murakami Wood’s Smart Cities project and editorial assemblage in the journal Surveillance and Society respond directly to the challenges and opportunities in Rob Kitchin’s (2014) call in The Data Revolution for “a more critical and philosophical framing” of the ontology, epistemology, ideology, and methodology” of the “assemblage surrounding” the production and deployment of geolocational data.  And many of these connections have been the subject of wise anticipatory reflections on Jeremy Crampton’s Open Geography, where the adjective and the verb of ‘open’ in the New Mappings Collaborative give us a dynamic critical cartography of the overwhelming political and knowledge economies of spatialized information.

As I read the cv’s of my panelists, it became obvious that the value of a record of a life — that’s the Latin curriculum vitae — is the new frontier of what James Blaut (1993) once called The Colonizer’s Model of the World, and what Kinsley (2014) has diagnosed as “the industrial retention of collective life.”  Smart cities, the social graph, the Internet of Things, the Quantified Self, the Zettabyte (270 bytes) Age analyzed by Kitchin:  all of this signifies a new quantitative revolution defined by the paradox of life in the age of post-humanist human geography.  In the closing lines of Explanation in Geography, David Harvey announced “by our models they shall know us” — a new generation of human geographers bearing the models and data of modern science; today, it’s the algorithms, models, and corporations that arrive bearing humans — millions and billions of them — whose curricula vitae can be measured, mapped, and monetized at scales that are simulteneously perrsonalized and planetary.  Facebook alone curates more than 64 thousand years of human social relations every day (four-fifths of it on mobile devices and four-fifths of it outside the U.S. and Canada) and LinkedIn CEO Jeffrey Weiner (quoted in MarketWatch, 2015) recently declared, “We want to digitally map the global economy, identifying the connections between people, companies, jobs, skills, higher educational organizations and professional knowledge and allow all forms of capital, intellectual captial, financial capital, and human capital to flow to where [they] can best be leveraged.”

Capitalized curricula vitae, however, are automating and accelerating what Anne Buttimer once called the ‘dance macabre’ of the knowledge economies of spatialized information, because the deceptively friendly concept of ‘human capital’ is in fact a deadly contradiction:  capital is dead labor, the accumulated financial and technological appropriation of surplus value created through human labor, human creativity, and human thought.  Buttimer’s remark about geospatial information being “a chilly recording by a detached observer, a hollow rattle of bones” hurt — because this is what she said in a conversation to the legendary time-geographer Torsten Hägerstrand, who in the 1940s spent years with his wife Britt in the church-register archives of a rural Swedish parish to understand “a human population in its time and space context.  Here’s what Hägerstrand (2006, p. xi) recalls:

 “[We] worked out the individual biographies of all the many thousands of individuals who had lived in the area over the last hundred years.  We followed them all from year to year, from home to home, and from position to position.  As the data accumulated, we watched the drama of life unfold before our eyes with graphic clarity.  It was something of stark poetry to see the people who lived around us, many of whom we knew, as the tips of stems, endlessly twisting themselves down in the realm of times past.”

Hägerstrand wrote that he was disturbed and alarmed by Buttimer’s words, and I am too, because Allan Pred (2005, p. 328) began his obituary for Hägerstrand by quoting Walter Benjamin, emphasizing that it is not only knowledge or wisdom, but above all real life — “the stuff stories are made of” — which “first assumes transmissible form at the moment of …death.”  But just as “every text has a life history” (Pred, 2005, p. 331) that comes to an end, now Allan Pred’s curriculum vitae has also assumed transmissible form of the market-driven, distorted sort you can track through the evolving Hägerstrandian time-space prisms of the digitized network society.  Hägerstrand is dead, but he has a Google Scholar profile that’s constantly updated by the search robots, and the valorized geolocatable knowledge of his citations put him in a dance macabre of apocalyptic quantification:  he is “worth” only 1.093 percent of the valorization of another dead curriculum vitae, that of Foucault, who’s also on Google Scholar.  The world is falling in love with geography, but we don’t need more than just a few human geographers to do geography, thanks to the self-replicating algorithms and bots of the corporate cloud of cognitive capital.

The geolocatable knowledge economy is thus a bundle of contradictions and the endgame of the organic composition of human capital.  Human researchers spending years in the archives to build databases are now put into competition with the fractal second derivatives of code:  how do I balance my respect and reverence for our new generation of geographers screen-scraping APIs and coding in R, D3, Python, and Ruby on Rails without giving up what we have learned from the slow, patient, embodied labor of previous generations working by hand?  I see the tips of stems, not just in Hägerstrand’s small Swedish parish, but right here, in this room.  Tips of stems, endlessly twisting down in the realm of times past — but in today’s times where each flower now faces unprecedented competition in every domain:  jobs, research support, academic freedom, human care, human recognition, human attention.  Tips of stems, endlessly twisting through time-spaces of a present suffused with astronomical volumes of geographical data in what the historian George Dyson (2012) calls the “universe of self-replicating code.”  Tips of stems, tracing out an entirely new ontology of socio-spatial sampling theory defined by the automated mashup analytics that now combine Hägerstrand’s time-space diagrams with Heisenberg’s observational uncertainties, Alan Turing’s (1950) ‘universal machine,’ and Foucault’s archaeology of knowledge blended with Marx’s conception of the “general intellect” and Auguste Comte’s notion of the ‘Great Being’ of all the accumulated knowledge of intergenerational human knowledge, tradition, and custom.  Tips of stems, tracing lifeworlds of a situationist social physics that treats smartphones as “brain extenders” (Kurzweil, 2014) converging into a planetary “hive mind” (Shirky, 2008) while reconfiguring the observational infrastructures and human labor relations of an empiricist hijacking of positivism:  if Chris Anderson (2008) is correct that the petabyte age of data renders the scientific method obsolete, then who needs theory?

We all need theory — we humans.  Theory is the intergenerational inheritance of human inquiry, human thought, and human struggle.  Let me be clear:  I mean no disrespect to the extraordinary achievements of the new generation of data revolutionaries represented by my distinguished panelists, and all of you who can code circles around my pathetic do-loop rusty routines in FORTRAN, Cobol, and SAS.  Tips of stems, twisting themselves down into the realms of human history:  take a look around, at one of the last generations of human geospatial analysts, before we’re all replaced by algorithmic aggregation.  Yesterday’s revolution was humans doing quantification.  Today’s revolution is quantification doing humans.


Anderson, Chris (2008).  “The End of Theory:  The Data Deluge Makes the Scientific Method Obsolete.”  Wired, June 23.

Blaut, James (1993).  The Colonizer’s Model of the World.  New York:  Guilford Press.

Dyson, George (2012).  “A Universe of Self-Replicating Code.”  Edge, March 26, at

Hägerstrand, Torsten (2006).  “Foreword.”  In Anne Buttimer and Tom Mels, By Northern Lights:  On the Making of Geography in Sweden.  Aldershot:  Ashgate, xi-xiv.

Kitchin, Rob (2014).  The Data Revolution:  Big Data, Open Data, Data Infrastructures and Their Consequences.  London:  Sage Publications.

Kinsley, Sam (2014).  “Memory Programmes:  The Industrial Retention of Collective Life.”  Cultural Geographies, October.

Kurzweil, Ray (2014).  Comments at ‘Will Innovation Save Us?’ with Richard Florida and Ray Kurzweil.  Vancouver:  Simon Fraser University Public Square, October.

MarketWatch (2015).  “LinkedIn Wants to Map the Global Economy.”  MarketWatch, April 9.

Pred, Allan (2005).  “Hägerstrand Matters:  Life(-path) and Death Matters — Some Touching Remarks.”  Progress in Human Geography 29(3), 328-332.

Shirky, Clay (2008).  Here Comes Everybody:  The Power of Organizing Without Organizations.  New York:  Penguin.

Turing, Alan M. (1950).  “Computing Machinery and Intelligence.”  Mind 59(236), 433-460.


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“What makes location valuable? Geolocation as evidence, meaning, & identity”
Agnieszka Lesczcynski, University of Birmingham

I want to invert the question that Jeremy and myself posed to the panel when organizing this session by asking, rather than ‘where is the value’ in geolocation, what is it that makes geolocation valuable? In contending that there are particular kinds of economies emerging around location, it is because geolocation itself is somehow intrinsically valuable, and I’d like to make some preliminary propositions to this end.

Over the last few years I have been particularly interested in the ways in which emergent surveillance practices of the securities agencies, made broadly known to us through the as yet still-unfolding Snowden revelations, are crystallizing around big data – its collection, mining, interception, aggregation, and analytics. And specifically, I’m particularly interested in the ways in which locational data is figuring as central within these emergent regimes of dataveillance. Indeed at the close of 2013, Barton Gellman and Ashkan Soltani, reporting in the Washington Post, identified at least ten American signals intelligence programmes or SIGADs that explicitly sweep up locational data – i.e., where location data is the target or object of data capture, interception, and aggregation.

  • Under a SIGAD designated HAPPYFOOT, the NSA taps directly into mobile app data traffic that streams smartphone locations to location-based advertising networks organized around the delivery of proximately relevant mobile ads, often unencrypted and in the clear. This locational data, which is often determined through mobile device GPS capabilities, is far higher-resolution than network location, allowing the NSA “to map Internet addresses to physical locations more precisely than is possible with traditional Internet geolocation services”
  • Documents dating from 2010 reveal that the NSA and GCHQ exploit weaknesses in ‘leaky’ mobile social and gaming applications that veil secondary data mining operations behind primary interfaces, piggybacking off of commercial data collection by syphoning up personal information including location under a signals intelligence program code-named ‘TRACKER SMURF’ after the children’s animated classic
  • In perhaps the most widely publicized example, the NSA collects over 5 billion cell phone location registers off of cell towers worldwide, bulk processing this location data through an analytics suite code-named CO-TRAVELLER which looks to identify new targets for surveillance on the basis of parallel movement with existing targets of surveillance – i.e., individuals whose cell phones ping off of the same cell towers in the same succession at the same time as individuals already under surveillance.
  • Just a few months ago, it was leaked that the CSE, or Canada’s version of the NSA, was tracking domestic as well as foreign travelers via Wi-Fi at major Canadian airports for up to two weeks as they transited through the airports and subsequently through other ‘nodes’ including other domestic and international airports, urban Wi-Fi hotspots, transport hubs, major hotels and conference centers, and even public libraries both within Canada and beyond in a pilot project for the NSA;
  • and, most recently, under a SIGAD code-named LEVITATION, the CSE has been demonstrated to be intercepting data cable traffic to monitor up to 15 million file downloads a day. Particularly significant in the leaked CSE document detailing this programme is that the CSE explicitly states that it is looking to location data to improve LEVITATION capabilities for intercepting both GPS waypoints and “[d]evices close to places” so as to further isolate and develop surveillance targets, including those carrying and using devices within proximity of designated locations.

So the question is, why geolocation? Why is it of such great interest to the securities agencies? And here I want to argue that it is of interest because it is inherently valuable, and uniquely so amongst other forms of PII. And this value is latent in the spatio-temporal and spatial-relational nature of geolocation data.

  • the spatio-temporal nature of many spatial big data productions means that it may be enrolled as definitive evidence of our complicity or involvement in particular kinds of socially disruptive events or emergencies by virtue of our presence, or as in the case of CO-TRAVELER, co-presence and co-movement, in particular spaces at particular times
  • furthermore, longitudinal retention of highly precise, time-stamped geoloational data traces allow for the reconstruction of detailed individual spatial histories, which like the CO-TRAVELER example, similarly participate within what Kate Crawford has recently characterized as emergent truth economies of big data in which data is truth;
  • the relational nature of spatial big data productions, in which our data may be used to discern our religious, ethnic, political and other kinds of personal affiliations and identifies on the basis of the kinds of places that we visit and the ability to establish linkages with other PII across data flows;
  • and, in this vein, the ways in which locations are inherently meaningful – for example, they may be as revealing of highly sensitive information about ourselves as our DNA. For instance, on the basis of the specialty of a medical office that we visit, this information may be revelatory of the fact that we may have a degenerative genetic disease and the nature of that disease – information that socially we otherwise understand as some of the most private information about ourselves.
  • and, of course, the ways in which location is not only revealing of identity positions, but it is identity – for example, a group of researchers determined that unique individuals could be identified form the spatial metadata of only four cell phone calls at a very high confidence level.

So in asking where is the value in geolocation, my take is that it is valuable – to both the intelligence apparatuses that I have highlighted here but also corporate entities – because it is uniquely sensitive – revealing and identifying – amongst other forms of PII.

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Zombie GIS

Ask most people what is GIS and they’ll not be able to tell you. Ask most people in a GIS class (such as the Intro GIS class I am teaching this spring), in a geography or allied field, and they’ll likely tell you a bunch of things that would have heads nodding at Esri: it is software, data, visualization, mapping and analytic capabilities for spatially referenced data. (Here’s Esri’s own answer.) If I ask most of our graduate students in our department (famously grounded in social theory as it is) they’ll likely tell me it is something like ArcGIS: Big GIS, costing thousands of dollars, hard to learn, for specialists only.

All of these, in different ways, are wrong to the point of dangerousness. Some are out of date. Some are irrelevant to the realities on the ground (hem-hem, including those of our grad students). All of them, it seems to me, refer to something that may as well be considered dead, but won’t die: zombie GIS. So who killed it?

To answer that question, a clarification is in order. I am not saying that the things we used to call “GIS” are no longer relevant, critical, or worth pursuing as a career. Quite the opposite in fact: GIS has gone viral. It has been injected into the body politic and infected nearly everything from the economy, to politics, to everyday activities such as going to the shops. In doing so, however, it can no longer be called GIS. Much like the walking dead, it is not what it once was, but doesn’t seem to know it: call it “zombie GIS” a phrase I confess to having just made up. (Fans of zombies have sometimes studied them using GIS, as these great projects illustrate. However, that’s not what I’m saying–I’m saying GIS is a zombie.)

So what has it become? Putting aside whether it has evolved or simply been replaced by other developments, here are some new key terms I think we need to come to grips with:

–software-sorting of geography, or, code/space
–geolocational (privacy | tracking | surveillance)
–geopolitical digital economy
–cyber materialities
–geolocatable knowledge economy
–Big Data biographies, the “spatial self”
–Internet of Things, smart city
–“lively” Big Data
–algorithms and calculability
–governmentality of “data derivatives,” data shadow, data-image, metadata

And so on. It would be interesting to put together an “Intro GIS” course syllabus that delved into these topics; a couple of weeks on the genealogy of the algorithm, say, followed by practical exercises in tracing how value is produced, extracted and enhanced as a geopolitical assemblage, capped off by a project creating geoweb-based mappings, CartoCSS and mobile-collected data. Even if the Chair of my department wouldn’t balk at such a proposal, perhaps it would be pointed out that this isn’t GIS. Exactly. But such a notion sustains something that’s dead; hence a zombie.

Zombies are interesting to many writers because they’re neither one thing nor the other. They occupy a liminal, in-between state, and as such you don’t have to close off possibilities by choosing one thing (life) over the other (dead). In fact, the one thing may allow the other. (Think dialectics.) Thus for the Slovenian philosopher Zizek, zombies are paradoxically both creatures of habit and yet illustrative of the freedoms we can attain due to those constraints. Like when we speak by learning rules of language, this then allows us to write poetry (or blog entries). Zizek says:

What this means is that what Hegel says about habits has to be applied to zombies: at the most elementary level of our human identity, we are all zombies, and our “higher” and “free” human activities can only take place insofar as they are founded on the reliable functioning of our zombie-habits: being-a-zombie is a zero-level of humanity, the inhuman/mechanical core of humanity.

If this is true, then perhaps the “rote” and “habitual” aspects of zombie GIS are what have prompted the list of developments I give above! So perhaps we might not have CartoDB if ArcGIS had any kind of real web capabilities.

(Another reason for the attractiveness of zombies to critical theorists: it echoes the ghost-talk of the famous opening line of the Communist Manifesto: “there’s a spectre haunting Europe…” which in turn feeds off Shakespeare. Derrida made so much of this it became a subdiscipline: “hauntology.”)

So what are these things in the list doing, and in what sense do they attract our attention? One place to begin is with assemblage, a term I’m using fairly straightforwardly here to indicate a mess of intersecting interests, practices, policies, epistemologies and so on. Fortunately Rob Kitchin offers us a look at a data assemblage in his new book The Data Revolution:


As you can see, there’s quite a mix of things here. the best article I’ve found so far to guide through these issues is Jason Dittmer’s “Geopolitical Assemblages and Complexity” paper. Drawing on assemblage theory (Deleuze, DeLanda, Latour), Dittmer argues that we need a more “materialist” approach, specifically that of assemblages. These are conceived as “wholes characterised by relations of exteriority.”

There are two things which are useful about this in terms of the list above. First, it implies the necessity of not just looking at the component parts. Second, Dittmer says that what’s important is not so much object with properties (how GIS sees the world, a basically Aristotelian logic) but parts with capacities to do work in the world (“actants”). This is sometimes known as critical realism.

As Thrift pointed out in his book of collected writings on capitalism published ten years ago, the modern knowledge economy can be traced back to the late eighteenth century. This coincides with what Foucault called the rise of the “disciplines” organized sets of knowledge with rules on what counted as data, ways of explaining (whether correct by today’s standards or not) and practices of disavowel (eg of the abnormal). For Thrift, what’s interesting about capitalism today is that these knowledges have been bent back to apply to capitalism itself. (Ian Hacking has described this “looping” effect in other contexts of knowledge about people.) So we get business schools and Masters degrees in finance; topics which are not traditionally academic subjects. (GEOINT Certificates may be understood in this context as the nearest equivalent in geography.)

This turn to materialism could be read as a turn away from discourse, but I don’t think it’s as simple as that. Let’s say that it creates a space for a new discursive materialism. This is important because one often hears people talking about “cyber” in the same way (that’s it’s non-material). But cyberwarfare, for example, has real material effects (computers explode, freeze or send hacked info to the attackers). So, this theoretical perspective is useful for “geospatial” (as a lot of people call what used to be called GIS).

I am not an assemblage fanboy. I don’t really like Latour, I’ve only recently started reading DeLanda and I don’t particularly “get” Deleuze–although I hope to read Thousand Plateaus more thoroughly for a seminar I’ve proposed with my colleague Jeff Peters (on “Big Data Narratives”).

However, it seems to offer some useful approaches, and, if you can cut through the (relatively sparse) thickets of French social theory speak, some suggestions for research projects. The one weakness of it is avoiding what Thrift calls “slab theory” or the temptation to use assemblages to study everything, at all scales. If the geoweb (to take an example relevant here) is political, economic, legal, geopolitical, performative, embodied, etc., well–you can see the task becomes very daunting and complex (see Dittmer again on complexity).

One of the few people working in this vein is Agnieszka Leszczynski (eg., “Situating the Geoweb in Political Economy,” Progress in Human Geography 2012)Leszczynski argues that the geoweb (the collection of geospatial services and technologies online) is more than just new forms of technology (web 2.0) but actually what she calls “technoscientific capitalism” which if not an assemblage is certainly a cross-cutting point of view. Or again in her most recent article, she insists:

There is a pressing need, therefore, for theoretical, empirical, and conceptual apparatuses for apprehending and evaluating the implications of and extent to which networked location-aware devices and spatial content have assumed pervasive presences in individuals’ daily lives, and of the material effects of associated spatial big data-based productions of living.

This is the notion that reality is always the product of the myriad intersections and mutual constitution of technology, society, and space relations that are themselves the products, or effects, of mediation.

Notice the everyday level of her focus on “techno-socio-spatial-relations” (p. 4). According to her, understanding these developments as media(ted) gives:

a position from which to not only engage with these presences as inherently material, but also to divest ourselves of GIS as an epistemology for ‘reading’ emergent technologies by designating how particular software, hardware, and information artifacts are being brought together in ways that are more-than, and other-than, GIS.

I think this is right for reasons I’ve been saying above. Zombie GIS, undead as it is, cannot provide the epistemological ground for apprehending what we’re interested in. Nevertheless, out of habit, GIS lumbers on!

Harvey’s hypothesis: does it apply to Geoweb companies? @profdavidharvey

In his latest book (or one of them) David Harvey offers 17 contradictions he says lie at the heart of capital. Contradiction 8 applies to technology, and Harvey says that there are two main contradictions to do with technology; one to do with technology’s relation to nature and one its relation to labor. It is the latter one he takes up here.

Harvey argues that technology is a means to an end for capital. That end is “profitability and capital accumulation” (p. 102). How does it reach such profitability in the context of technologies?

Throughout its history, capital has invented, innovated and adopted technological forms whose dominant aim has been to enhance capital’s control over labour in both the labour process and the labour market.

There are two elements here. One is innovation or more accurately the innovation process and the need for constant innovation. Here innovation is understood not so much for what it produces (the products and services used) but for what it enables and protects, namely profits. On this view, a site such as c|net devoted to reviewing the products is irrelevant to a proper understanding of today’s society, except insofar as it fuels consumerism and the consumption of labor’s outputs.

The other element is control over labor. This control aims at “disciplining and disempowerment of the worker.” This includes a range of technologically manifested conditions; increasing automation, Taylorism and a factory system, emphasis on productivity, prevention of organized labor, etc.

Harvey’s contradiction then is that if docile labor force is a source of all profit, then replacing it with automation in the workplace will undermine that profit. But the evidence cited by Harvey shows that this is happening; for example increasing computer capacity and speed.

A consequence of the Harvey’s contradiction of falling profit margins is to use labor supplies that are ever-cheaper and productivity-driven. This is familiar to us as back-officing and outsourcing. For example, the iPad factories in China, or Samsung’s reported problems with child labor and suicides. Harvey says we are heading into “dangerous territory” (p. 108).

So all this raises a question of how much of this is occurring at the forefront of geographical innovation, namely the geoweb and geolocational technologies**

Harvey’s argument yields a number of testable hypotheses which could roughly be expressed as:

–are geoweb laborers experiencing increasing “control”? For example code jockeys and so-called code monkeys (see Harvey’s comment about “trained gorillas” p. 103)?
–has productivity experienced constant growth? For example, what is the life-cycle of a product (eg a GIS or web-based geoweb app)?
–have geoweb companies outsourced labor to Asia, Africa?
–on the consumer side, how are geoweb technological innovations marketed?
–are we seeing a decreasing regulatory role over labor in this sector of the economy?

I pose these hypotheses not because I know (or suspect) the answer but genuinely. I don’t know how important you find them, but I find them very interesting. It’s leading me to think that only as hands-on study like an ethnography of these companies would provide the answer, conjoined with some kind of economic overview of the geoweb sector. I don’t know if other geographers would find that interesting enough to fund, but somebody do this study!!

**By geoweb I mean the “constitutive production, governance, and technologies of the merging of georeferenced information with the web, as well as the workers and consumers of networked geographic information situated in a neoliberal economy. This includes many forms of mapping, cartography and GIS.”

Mapping all NYC’s taxi rides


Fascinating patterns are revealed by this unusual data set: all of the taxi rides captured by GPS in New York City–some 173 million trips. Mapbox has some maps and analysis.

Note also this warning: the data were “de-anonymized” fairly easily.

Mapping happiness again


Sorry to repeat this but I believe the map is even cooler now. Hover over each point to see the crowd-sourced information about happiness and greenery measures (collected by students n GEO 109, spring 2014 at the University of Kentucky). Click to see the StreetView image at that location (this is grabbed automagically from Google StreetView itself!)

Finally, if you click the link in the sub-title, it will take you to a nice Streetview portfolio of Lexington, Kentucky.

New #cartodb possibilities (and #fulcrumapp!)

Some new mapping possibilities have been added by the geoweb mapping company CartoDB.

Both offer exciting possibilities that I’ve not seen before. Most amazing to me is the capability now to use near real-time NASA imagery as your base map, and to choose any day in the last two years. The imagery is 5-9 hours old, and you can choose daytime or night-time views.

Because of the ability to choose any particular date, you can go back to a season (for eg observing glacier extension) or event (Hurricane Sandy, late October 2012).

The imagery comes from GIBS, or Global Imagery Browse Services at NASA, and includes a selection of different products. Here’s yesterday’s “dust scores” for example (click for interactive map):



You can combine from hundreds of other layers (eg sea ice + sea surface temp + chlorophyll). (At least at NASA you can, I haven’t checked this in CartoDB.)

Second, there was an interesting tidbit showing how to dynamically pull Google Streetview into your point data. So the idea is that if you have collected data at various points, and uploaded these as a table to CartoDB, you can create a new column in the table which will pull form the Google Streetview API and provide you with a picture of that location.

Like this (click for interactive map):


Personally I think that’s cool!

There is a slight bug for me–once I get the SV imagery, I can’t add other fields, but their blog shows this is possible. I’m missing something somewhere.

Finally, just as I was writing this, Fulcrumapp announced they’ve got sharing of their maps up and running! Not only does this make sharing (ie, feeding the data into a visualization capability such as Mapbox or CartoDB) but it’s live!