thoughts on my project


(1) Collecting quantitative data

Hearing comments and feedbacks from Pecha-Kucha presentation was really helpful for reexamining my project in general.  Since then, I have been investigating how do NYPD make decision on installing surveillance camera.  Apparently, they say it depends on crime rate.  However, so far, I could not find any accurate crime rate number for this.  It might be helpful if there is statistics for every each street corner.  Then I can put the data on camera locations.  But NYPD open crime statistics for every precinct, which is a large area.  And there are other possible factors for making decision on installing surveillance camera.  For example, Lower Manhattan is full of CCTVs for the sake of defending economic infrastructure from terrorists’ attacks, not because crime rate is high there.  Since the process of installing surveillance camera is related to Counterterrorism and Homeland Security issue, some of the process is veiled and hard to know.  I have to make a decision on this: should I keep trying to collect such data?


(2) Collecting qualitative data

Still, I believe interviewing average people in the neighborhood will be interesting and helpful for my project. Questions will be: do you think this neighborhood is unsafe? How do you feel about the surveillance tower? Do you know when it installed? Did you know about camera presence? etc.


(3) Identifying camera locations; how can I differentiate fixed cameras and mobile cameras?

When I actually walked the streets of Corona, I saw cameras on mobile vehicles.  This gets my mapping project complicated.  How can I map the moving cameras?  Doable option for me is visiting the site as frequently as possible and taking notes what time they were there.  And I would need to use different marks (i.e. flickering mark for mobile cameras) for fixed cameras and mobile cameras.


(4) Identifying network channel

I think one of the distinctive aspect of NYPD surveillance network is, it is all connected in one system and enables realtime reactions.  Cameras are connected to wireless video transmitter.  In this case, network channel is an important infrastructure and I would love to investigate on it. I know NYPD uses I-NET as a part of their communication channel.  Would it be helpful to investigate the infrastructure of I-NET? I need more information on this.


(5) investigating camera’s capacity

This idea came out from the Pecha-Kucha presentation – visualizing the area that cameras can see.  Work on progress.


(6) news articles/video reports

I found out that news articles are helpful for showing things unreachable to me. For example, news articles or video channel of NYPD shows the inside of Real Time Crime Center.




Surveillance Cameras

Surveillance Data Center

Surveillance Data Receivers



Type of cameras: camera / camera installed on tower (skywatch)/ camera installed on vehicle, etc.

Type of data center: NYPD headquarter

Type of data receivers: regional offices, individual police officers with notebook, etc.





Crime Rates (?)

News Reports/ Video Reports


  1. Hi there. We addressed a few of these issues in-person, but since a few of your classmates are also grappling with the mapping of mobility, I think it might be worth discussing a potential solution you and I discussed: a *temporal* mapping of the mobile van as it moves about the neighborhood. By attaching time ranges to each of the van’s locations, you can show how it migrates to reflect different social patterns, the flows of different populations, throughout the day. It might, for instance, hang out by the high school at 3pm and move over to a notoriously dangerous park in the evening.

  2. thanks, shannon! i went to the neighborhood yesterday and i thought it would be interesting if i stand near the policemen who are watching over people on roosevelt avenue in the evening and record the soundscape of there. people never talk to them, not even asking the direction or anything like that. i think this silence is important and want to touch it a little bit. following the van would be interesting too.

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