Impossible Maps Final: Mapping as Autobiography

For this final, I wanted to visualize my Google Maps location history. I’ve been using different android devices since I acquired my first in December 2012, less than three months after I moved to NYC. Being a creature of habit/obsessive compulsions, I figured my location history had captured the passing fancies and preoccupations that shaped my development into an independent adult, and my (reluctantly assumed) identity as a New Yorker.

So I downloaded this history from as a 325MB json file (lol):

(Shoutout to emacs for being the only text editor on my computer capable of opening it.)

Since I wanted to practice my newfound mapbox gl skills, my second obstacle was simply using this file at all, as it wasn’t in geojson format. What I ended up doing was using the d3 library to load the json for some reason (other than being a creature of habit), looping through the data, and pushing the relevant and correctly formatted data into a javascript array.

Here’s what I got when I logged the data in the console:

So that wasn’t going to happen. To make it easier on the browser, I ended up filtering out coordinates outside of (approximately calculated) NY bounds. I also divided the data into six arrays, one for each year:

Apparently, the period from 2013-present accounts for 982,154 locations out of a total of 1,170,453— which means 188,299 locations (16% of the total) were filtered out for being beyond NYC. The reason why array[2], array[3] and array[4] contain less than half of what array[0] and array[1] do is precisely that—I spent the majority of those years traveling. Array[5] is even smaller because it contains 2018 data.

Okay, so the next challenge was injecting this data into a mapbox source layer. Since mapbox expects geojson formatting, I had to hack it a little (ie, steal someone else’s hack from stackoverflow):

Then, I adapted the filtering legend from this mapbox demo to my page. Here’s what I ended up with:

Here’s the breakdown by year:

Impossible Maps W4 HW

Feminist data viz notes:

  • Feminist standpoint theory: all knowledge is socially situated; the perspectives of oppressed groups (women, minorities, etc) are systematically excluded from “general” knowledge
  • Feminist data viz could:
    1. invent new ways to represent uncertainty, outsides, missing data, and flawed methods
      • can we collect and represent data that was never collected?
      • can we find the population that was excluded?
      • can we critically examine the methods of study rather than accepting the JSON as is?
    2. invent new ways to reference the material economy behind the data
      • what are the conditions that make data viz possible?
      • who are the funders?
      • who collected the data?
      • interested/stakeholders behind the data?
    3. make dissent possible
      • data viz = stable images/facts
      • re-situate data viz by destabilizing, ie making dissent possible
        • how can we talk back to the data?
        • how can we question the facts?
        • how can we present alternative views and realities?

Representation and the Necessity of Interpretation notes:

  • satellite imagery were only until recently military secrets
  • in 2000, the nyt for the first time used the newly available Ikonos satellite “as a sort of alternative investigative journalist in Chechnya” but “failed to arouse public sympathy or outrage”; however, before/after images have still become commonplace in reporting from zones of conflict
  • Sept 1999: Space Imaging launched Ikonos, the first satellite to make hires image data publicly available
  • We need to be alert to what is being highlighted and pointed toward, to the ways in which satellite evidence is used in making assertions and arguments; for every image, we should be able to inquire about its technology, location data, ownership, legibility, and source



  • I never realized that satellite imagery was born from the agenda of the US military, yet it’s not surprising. What struck me most from the latter reading was learning that Colin Powell used satellite images as incontrovertible proof that there were weapons of mass destruction in Iraq—I don’t think you can get a much better example of “interpreted data”.
  • One year later, in 2003, Ross McNutt’s team put a 44 mega-pixel camera on a small plane to watch over Fallujah, Iraq. Its images were high-def enough to track the sources of roadside bombs, and it was on all day, every day. After the war, Ross did a piloted this technology in Dayton, Ohio, as a way for the local police to identify criminals and gang members.
  • When I first heard this story, I didn’t feel too conflicted about it—bad guys were being caught and brought to justice, what’s the problem here? However, after reading Laura Kurgan’s chapter on representation and interpretation, now it feels like Ross was just thinking locally about persecuting colored people. Especially considering that a program like his would only be implemented in larger urban areas, ie where most minorities live.


Final project idea:

  • I’d like to download my location history from Google, and visualize it to get a sense of my navigational habits/biases and identify opportunities for breaking out of my comfort zone
  • I thought this was a nice use of satellite imagery; this view shows the dramatic urbanization of Shanghai over 30 years, particularly the waterfront along the Huangpu River. Also fascinating is the expanding, presumably manmade coastline


Impossible Maps W1 HW

So I have some comments.

I may be a bit biased, being a near-daily Google Maps user myself, but I quite like Google Maps better than Apple Maps. For Part 1, the author points out that far more cities are labeled in Apple Maps, particularly in zoom 8. Labelling 44 cities in such a small space completely clutters up the tile and renders it all but illegible. You don’t need that much detail at a higher-level view. Also, at a higher-level view, chances are you’re driving and not walking—which is probably why Google prioritized “shields” over cities.

However, at a lower level, Apple has these interesting high-fidelity, individual landmark markers rather than using a generic marker for each type of POIs. As a person who navigates by landmark and gets confused by street names, I actually do appreciate this detail.

Because of this, and because Google Maps tends to label far more roads and “shields” than Apple, I want to hypothesize that perhaps Apple Maps is prioritizing the pedestrian, while Google Maps is prioritizing the driver. But Apple Maps seems to give you more information at higher level zooms, then dissolves into minimalism as you zoom in and expect to get more information. As a Manhattanite, I need those subway station markers!

I would also like to express my horror at this “Frakenstein map”:

What good is that much information when you can’t read it? And when do you ever need that much information?

If users do indeed crave “the whole picture”, perhaps there should be two map modes: one for navigation, which emphasizes roads and their labels; and the other for general exploration, which emphasizes cities and POI’s. As a chronic pedestrian and global traveller, I honestly have no need for the former information—I’m either walking to a building or subway station and therefore only need street names at a low level zoom, or I’m zoomed all the way out and planning my next vacation, and therefore only need political borders and major city names.