Jeremy W. Crampton Matthew Wilson Matthew Zook. The New Mappings Collaboratory The University of Kentucky

Transcription

1 Jeremy W. Crampton Matthew Wilson Matthew Zook The New Mappings Collaboratory The University of Kentucky

2 Objec&ve Identify the critical factors (and metrics) of sustainable big data in spatially enabled smart campus (SESC) What is sustainable? What is means here How it helps here Three big data paradoxes (Richards & King, 2013). Richards, N.M. and King, J.H Three Paradoxes of Big Data. Stanford Law Review Online, 66(41),

3 Smart city, smart campus? 2008 as conjunction of three events (Townsend 2013) Majority living in cities More mobile subscribers than wire Internet of things (of everything) Implications of all three contested Eg., Neil Brenner The urban age in question Anthony Townsend Smart Cities (2013)

4 Cri&cal factors and metrics of big data How can big data be implemented in the smart [space]? Is it sustainable? Big data been identified both as commonly- proposed solution Eg., for enhancing national security. NSA Director: Collect it all, tag it, store it. And as creating negative outcomes Eg., geosurveillance and widening inequality

5 Sustainability Scope of sustainability science is broad: Human interactions with technology and the environment Long- term resilience (proxies?)

6 Sustainability = increasing human well- being Is big data as a concept and practice sustainable? Is it sustainable to live in a big data smart city campus? Do we know the (proxy) metrics to answer these questions?

7 Transparency paradox Big data promises to make the world more transparent, but threatens loss of privacy Identity paradox Big data threatens to deprive rights to self- identity Power paradox Big data sensors and solutions predominantly in hands of big corporate and state entities

8 If the smart campus is not sustainably designed does not lead to improved human well- being in the long term: it will fail its objectives We seek input from the workshop to develop and implement consensual proxies for big geodata

9 Transparency paradox (asymmetry): How big data works has been shrouded in secrecy But has peeled back the onion of personal and collective privacy Ball, J., B. Schneier, and G. Greenwald NSA and GCHQ Target Tor Network that Protects Anonymity of Web Users. The Guardian, 4 October

10 1. Is there a transparency asymmetry? What do we know about how big data are used on the smart campus? What is publicly known? Increased scholarly attention to both has improved knowledge of the exaflood (Sui, Elwood & Goodchild 2013) What are the oversight mechanisms of its use? Who controls the data? Less clear: 2020 or 1984? (Goodchild, 2013) Sui, D. Z., S. Elwood, and M. F. Goodchild Crowdsourcing geographic knowledge : volunteered geographic information (VGI) in theory and practice. Goodchild, M or 1984? Presentation at University of Kentucky, 4 April

11 2. Is there an iden&ty asymmetry? Big data identifies problems and potential solutions But does so by trapping people within filters, profiles, or constrained choices Eg., information you can obtain may be locationally- dependent Eg., Google ad choices, national security signature strikes

12 What knowledge is enrolled in such filters or behavioral signatures? Who stands to benefit? Is it sustainable (leading to human well- being?) University of Kentucky spent significant sum ($5 million) on 2,000 behaviorally activated cameras

13 3. Is there a power paradox? Who is controlling such knowledge (big data)? Community, state, or corporate contractor? UKY: Next Level Security Systems contracted. Who are they? How is academia enrolled? Enabling? Passive? Participatory?

14 Big data smart cities already strongly dominated by corporate sector IBM, Cisco [$40 billion/yr], Siemens, GE Smart cities $100 billion market by 2020 (Anderson, 2013) Smart grid etc. (infrastructure) Security (multi- $bn business) And by government Potentially 557,000 local governments worldwide adopting, testing, implementing smart city practices

15 Conclusions Knowledge is not just information collected and interpreted Question of who controls it Goodchild, Wainwright etc. on the Bowman Expeditions (BE) Corporate/militarization of knowledge In real world does not exist in pure form, always a context of power/knowledge BE intervenes in land tenure and land rights to render more visible to the state (Scott, 1998) Scott, J Seeing Like a State.

16 Ordinary citizens are constituted as subjects of big data The surveillant calculating state We realized we can build probabilistic maps of how your phone sees the world Foursquare lead data scientist Transparency not enough to simply add transparency Must be reversed: less secrecy for corporate/state, more privacy for citizen from ground up (privacy baked in ) Do we know the right metrics for sustainable human well- being with big data? If not, what is our path to identifying them? Reduce obstacles from state/corporate sovereignty over data Opt- in. The EU General Data Protection Regulation. Data managers