The Surveillance Project:

Toronto City

PART 1/2

CLASSROOM PROJECT

OCAD University

Mentor: Isabel Meirelles
From Data to Perception

TEAM

Manisha Laroia

Sananda Dutta

Nilam Sari

Nadine Valcin

DURATION

4 weeks | 2020

PROJECT TYPE

Data mapping & Representation

/ ABOUT

The Surveillance Study is a two-part project which involved collecting data and using it to create a 'city-level intervention' for the residents to interact with the data. The second part of the project was to build a Data Story using the data research conducted and data collected.

The City Intervention was conceived to raise awareness among Torontonians about the extent of private and police surveillance as well as electronic data collection in Toronto streets, lanes and public spaces through an investigation of the Entertainment District.

Through the project, we were asking,"How many eyes are on you? "

/ RESEARCH & SCOPING

We used a variety of online map sources to gather the data points which were then manually added to a map created in Illustrator. This served as our Secondary Research going into the mapping of city data points. This was done as all the software we tested required the export of precise latitude and longitude coordinates which made the task unwieldy for our needs.

We collected 3 types of data points that yield different kinds of data
Toronto Police Department traffic cameras Radio-frequency identification points including ATMs, Bike Share, TTC subway entrance and streetcar stops CCTVs operated by private businesses and buildings.

We mapped the electronically available data points for the entire Entertainment District from Queen Street West to Lakeshore Boulevard West between Spadina and University Avenues.

We used the following resources to get the data we needed.

Toronto traffic cameras on Open File Toronto (Map) | Source
Toronto traffic cameras on Open Toronto (CSV format) | Source
ATM map on Google Maps | Source
Cirrus Network ATM Map | Source
Bike Share Map | Source
TTC Queen Streetcar | Source
King Streetcar | Source
Osgoode Subway Entrances | Source
St Andrew Subway Entrances | Source

Images above show:

Cluster-mapping used to put down information that we were reading in the papers and articles in resources.

Brainstorming ideas for the ‘City Intervention’.

/ ETHNOGRAPHIC DATA WALK

For the private CCTV cameras, we did manual data collection by walking around the neighbourhood. This also allowed us to confirm the data from the online sources.

Since the manual data collection for the private CCTVs was more laborious, we selected a smaller territory for that sample to the area bounded by Queen and Adelaide Streets West between Spadina and University Avenues in order to get a representative sample.

In mapping on paper we each adopted a style of marking the map i.e. a self-devised key to collect the data. In doing so we saw emerge a way of perception and understanding of the city-data.

As a group carrying out an ethnographic data walk, we would each notice different things, and different parts of the wide street view in front of us making us more aware and able to collect more diverse observations.

/ DATA VISUALIZATIONS

In making the data visualizations, we had three key intents:

Collating all the data points we collected for analysis and to find patterns in it.
Visuals for narrating our data story
For Projections for the city intervention.

We used the secondary research data to make the map for the Entertainment District and used a more focussed map to plot the data points in the block around the school where we manually collected data. We categorized the data into two main sets:

Camera points (CCTV, city cameras, private camera)
Tap or swipe points (RFID, payment cards, loyalty cards)

/ FINDINGS FROM CITY ETHNOGRAPHIC DATA WALK

Our team’s data walks were very informative in terms of revealing the scope of private surveillance in the neighbourhood. As we had already collected and mapped the publicly available data collection points which were substantial, we were surprised to find that the private CCTV cameras clearly outnumbered them.

The location of private surveillance cameras also surprised us. Streets like Spadina Avenue had virtually none, whereas Twilight Lane between Richmond Street West and Nelson Street has 13 cameras. Adelaide Street West between Duncan and Simcoe Streets also had a high

concentration of private CCTVs, mainly in bars and restaurants. One hypothesis is that these businesses stay open late on the weekends and attract a possibly rowdier clientele.

There have also been several shootings in the Entertainment district in recent years which may be another explanation.

/ CITY INTERVENTION: Street Projection

The City Intervention involved us projecting the visualization on the street-facing windows of the Grad School building so as to attract the viewer and intercept them in a discussion about the data they were looking at and what they knew and felt about the state of different methods of city-based data tracking.

In the three windows, we displayed a back projection on white sheets with one window asking the project question, “Do you know you are being watched?” along with sharing numbers about the data tracking points in the street we are in.

The other two windows had animated projections of the Entertainment District map and a more focussed map of the block around the school.

/ FINDINGS & REFLECTIONS

After speaking to a handful of Torontonians, and asking them a very simple question ‘do you know how many CCTV cameras there on this path (Richmond street)?’ The responses we received were more on the surprising end. Most of the people weren’t aware of the high no. Of surveillance cameras on the street. They were astonished to know the fact that Richmond street itself had 33 CCTV cameras.

Majority of the people from our intervention didn’t bother about the cameras and said that they camouflaged with their environment. They didn’t go out of their way to notice them. Another astonishing revelation was that they were unaware that RFID and Interac swipe would collect data. Also, they didn’t really know where the collected data went. Most of them assumed it was for the government.

Most of them assumed it was for the government. Another revelation was that people didn’t really know the difference between data collected for the government or by private companies. The cameras that were on the streets, a majority of them were private cameras owned by the buildings, restaurants or the organizations and their collection of data wasn’t a part of the government's database.

/ CITY INTERVENTION: Experience Data Walk

An Experience Data Walk was planned for the class as a collective facilitated activity to create awareness about the intent of the project, adding to the discussion, and raising new questions.

Some of shares from after the Data walk with the class:

“I didn’t notice the cameras in the Twilight lane even though I cross it everyday”

Grocery shops don’t have cameras on the entrance but bars and restaurants have many cameras.

It is interesting how parking lots have so many more cameras than the roads.

“We have became more aware of surveillance and notice these trackers.”

There are some fake cameras also, very obvious with the way the viewer’s reflections look.

We never thought of an alley to have so many cameras, always thought it would be on the main streets; but it’s the opposite.

What happens to the data these collect?

The risk is even higher with cheaper easy to hack cameras, that are hosted on open or unsecured servers.

Crime rates compared to the density of cameras would be interesting to find

How is access to the camera footage or RFID card data managed or controlled?

Let’s

go for a

Data Walk!

/ REFERENCES

Where Even the Children Are Being Tracked (https://www.nytimes.com/interactive/2019/12/21/opinion/pasadena-smartphone-spying.html)
Dutch Artists Celebrate George Orwell's Birthday By Putting Party Hats On Surveillance Cameras (https://www.buzzfeednews.com/article/ellievhall/dutch-artists-celebrate-george-orwells-birthday-by-adorning)
Clearview app lets strangers find your name, info with snap of a photo, report says (https://www.cnet.com/news/clearview-app-lets-strangers-find-your-name-info-with-snap-of-a-photo-report-says/)
The Privacy Project, New York Times, (https://www.nytimes.com/series/new-york-times-privacy-project)

Thompson, Stuart A. and Charlie Warzel “Twelve Million Phones, One Dataset, Zero Privacy” in The New York Times, Dec. 19, 2019 (https://www.nytimes.com/interactive/2019/12/19/opinion/location-tracking-cell-phone.html)
This Data Viz Tool Explains Privacy Policies You’re Too Lazy To Read, 20 February 2018, Katharine Schwab, FastCompany https://www.fastcompany.com/90160963/this-data-viz-tool-explains-privacy-policies-youre-too-lazy-to-read
J. Becker, M. Heddier, A. Öksuz and R. Knackstedt, "The Effect of Providing Visualizations in Privacy Policies on Trust in Data Privacy and Security," 2014 47th Hawaii International Conference on System Sciences, Waikoloa, HI, 2014, pp. 3224-3233. doi: 10.1109/HICSS.2014.399 http://ieeexplore.ieee.org/stamp/stamp.jsp?p=&arnumber=6759001&isnumber=6758592

Bridle, James. Every CCTV Camera (CC). 2017.https://jamesbridle.com/works/every-cctv-camera-cc
Data Walking - http://datawalking.com/index.html