And then there was Cartagr.am

We’re all obsessed with recording not just the hard facts of the cities we live in, but also the soft ambiance of our experience within them.  At least that’s the implication we see from the mass acceptance of geo-social tools and the content you the user create with these tools.  We’ve tried to examine these shared experiences and how they define location with Cartagr.am — a map of collective experiences through Instagram photos.

Screenshot of the Cartagr.am map

As wonderful as these collected experiences are though, we’ve been limited in the tools we can use to explore this data of personal experience.  Too often the data arrives in a one-dimensional stream designed to help us catch up with what our friends are up to or as a snapshot of what’s happening precisely at that moment — but because they are so fragmented and linearly organized, none of them tell us much about the world as a whole. Even our favorite photo-sharing sites that support geo-coded photos — like Flickr and Instagram — are heavily biased towards a time-series oriented view of the data instead of geographic or otherwise experiential, exploratory views.  Because of this, we’re forced to rely on memory if we want to understand the trends and significance of a collection of images.

Compare this to the tools available to view the hard-facts of cities — crowd-sourced street and architectural information, and so forth — and you can being to see a the large gap between traditional visualization tools and personal and expressive data visualization tools. We are lucky here at Bloom Studios that Ben and Tom, two of our co-founders, have spent years refining the theory and practice of cities, geography, and mapping for hard facts.  As such, there’s a rich toolset for discussing and presenting data — and with Cartagr.am we’ve applied this technology stack to present you with the collective experience of Instagram users.

One of Bloom’s central theses is that the experiential and personal data can be transformed into an expressive format using the same tools we’ve become experts in using for traditional factual data.  So can we use visualization tools to provide a new insight into an already rich experience?  In our current social and experiential toolkits, location is an element of context to understand the photo.  What would happen if you inverted this relationship?  What would happen if you used the photo to provide a context for a given location?  That’s the question we’ve tried to examine with Cartagr.am.

Cartagr.am attempts to provide a glimpse into the collective experience of Instagr.am users.  We’ve initially created maps that present a collective view – focusing on what’s “interesting” within a given area.  Cartagr.am is actually a cartogram — it truly measures a variable over a geographical area.  In this case we’re using the notion of “interestingness” to define what defines an area.  Using this variable we select which photos to show in a larger size than others.  We’re not restricting ourselves to a completely linear model of interestingness and size, so that we can provide users with some larger, and recognizable, photos at any zoom level.

This, we hope, gives you a glimpse into the value of Cartagr.am and examining experience geographically in a broad way.  Over time we will expand this capability, allowing you to not just view all public data, but to also restrict it to your own views of geographical experiences and those of your friends (as defined by your social network participation), making it more personally relevant — your own social (or personal) map of what matters in the world.

Technology

Cartagr.am was written using ModestMap.js for the tile mapping and SimpleGeo for the location services and the labels are the Acetate labels from FortiusOne and Stamen.  We’ve extended this stack somewhat to support richer experiences than were available to us out of the box, but have tried to keep all of these extensions as general as possible.  Tile maps are certainly common experiences now, but we did this because we’re trying to explore the possibilities available to data visualizers if they can simply swap out the data source for another – would there be sweet spots of rich experiences made available if we encourages playing with the data sources?  The tile-generation itself was bespoke, and something we’ll look into generalizing further over time and as computation restrictions are relaxed somewhat.


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