On route to big(geo)data:

On route to big(geo)data: a socio‐technical trajectory of GIS and the questions it poses for urban governance

Christine Richter

University of Twente – ITCDepartment of Urban and Regional Planning and Geo‐information

Management

NCG Symposium, 30 November 2016, Enschede, The Netherlands

Aims of this presentation

• Highlight trends in the spatial, temporal, and organizational dimensions of geographic data collection and use based on 4 cases in India and The Netherlands (research between 2009‐2016)

• Pose three questions relevant for urban governance of and through geographic data as we move into the big data era.

GIS

Spatial Data Infrastructure

Mobile device and sensor data

City’s digital infrastructure*

Socio‐technical context of urban GIS implementation

*Datafied city ~ datafication (Mayer‐Schönberger & Cukier, K., 2013)Programmable city (http://progcity.maynoothuniversity.ie/about/)Digital skin of cities (Rabari, C. & M. Storper, 2014)Smart city (IBMs “Smarter City Challenge,” e.g. in Angelidou, 2015; Wiig, 2015)

Web 1.0….

Web 2.0….

Web 3.0/x….

Integration & efficiency...

Transparency &Participation…

Smartness (digitization + datafication)…

GIS



Case 1 – GIS under Nirmala Nagara – Cities in Karnataka, India Research between 2009 and 2012


Case 1 ‐ Background

GIS



Case 1 – GIS under Nirmala Nagara – Cities in Karnataka, IndiaCase 2 – NextDrop System – Cities in Karnataka, IndiaCase 3 – BWSSB*‐ IBM: IBM’s Big Data for Water Supply – Bangalore, IndiaResearch between November 2014 and February 2015

(*BWSSB Bangalore Water Supply and Sewerage Board)


Case 2 & 3 ‐ Background

GIS



Case 1 – GIS under Nirmala Nagara – Cities in Karnataka, IndiaCase 2 – NextDrop System – Cities in Karnataka, IndiaCase 3 – IBM’s Big Data for Water Supply – Bangalore, IndiaCase 4 – City’s digital infrastructure – Amsterdam, The NetherlandsResearch between 2015 – 2016

Case 4 ‐ Background


1. Trends in the spatial dimension of data collection and use

(Source: Richter & Georgiadou, 2014)Example: GIS under Nirmala Nagara

(Source: author’s own analysis in progress, based on project Taylor et al, 2016)Example: Amsterdam citizen perceptions about digitally generated data in Amsterdam

• Rather than being a priori defined through data collection and database design, as well as user and uses definitions, …

the socio‐spatial boundaries and categories, as well as references to movement and location emerge increasingly fast and sometimes unpredictably from recurrent and changing compositions of data as the data flows from and across different contexts of original production.

2. Trends in the temporal dimension of data collection and use

• From (data)base map to real‐time mappings and integration• From surveys at regular intervals to continuous data streaming

• From classification systems (establish/implement logic) to listing systems (continuous emergence of categorizations from labels)

From data collection to data capture

“This transformation from slow and sampled data to fast and exhaustive data has been enabled by the roll‐out of a raft of new networked, digital technologies embedded into the fabric of urban environments ...“ (Kitchin, 2016, p.2)

3. Trends in the organizational dimension of data collection and useSpecifically, the role of street level infrastructure staff* – people running both digital and physical infrastructures of the city

*in reference to ‘street level bureaucrats’ (Bovens and Zouridis, 2002; Lipsky, 1980)

• Engagement & self‐reporting as key operators of urban infrastructure (ex. NextDrop, ‘valve men team’)

Source: http://www.indianweb2.com/2013/04/17/nextdrop‐social‐startup‐case‐study/

• Real‐time monitoring and/or circumvention (ex. BWSSB‐IBM)

Real‐time data allows the BWSSB to tell if a valve has been closed or opened on time ... in order to question the

valve operator on a daily basis. This can create behavioural change in them.

(from interview with IBM in Bangalore, October 2014)

(Source: Merchant et al, 2014)

‘I think we are virtually invisible, at least on a digital level. I have very little touch points with the city of

Amsterdam. Nearly everything I need to do with the city is either completely automated without any touch

points or the rare following up about your driving license or passport. That's all.’(focus group of Dutch technology developers, 3.12.15, Source: Taylor et al, 2015)

• Displacement and/or deletion (ex. Amsterdam)

• New mechanisms for spatial data collection and use change the roles and practices of actors, who

maintain the physical urban infrastructure (transportation, water, land and buildings…) and who constitute the interface between user and provider of infrastructural services*.

(*in case of actually, theoretically understood, or normatively desired public services and goods: between citizens and government.)

Questions for urban governance and related research

• Are and how are (social and spatial) classification practices and decisions transferred from human interpreters of data to algorithms; and what are the effects of this on urban governance? (ex. automatic feature extraction of ‘poor people’ and ‘informal settlements’ from remotely sensed imagery)

• Do and how do continuous data streaming and real‐time availability change how urban planning is conceptualized and done in practice; and what are the effects of this on urban governance? (ex. Coletta and Kitchin, 2016 pre‐print)

• How is the digital data sphere shifting the current mechanisms that enable the maintenance and running of the physical urban infrastructure at key human personnel nodes? (valve operators, ‘line men,’ bill collectors, land surveyors)

Thank you.References:

• Angelidou, M. (2015). Smart cities: A conjuncture of four forces. CITIES 47, 95‐106.

• Bovens, M., and S. Zouridis. 2002. From Street‐level to System‐level Bureaucracies: How Information and Communication Technology Is Transforming Administrative Discretion and Constitutional Control. PUBLIC ADMINISTRATION REVIEW 62(2), 174‐84, doi:10.1111/0033‐3352.00168.

• Coletta, C. and R. Kitchin (2016). Algorhythmic governance: Regulating the ‘heartbeat’ of a city using the Internet of Things. Paper has been submitted to the Algorithms in Culture workshop to be held in University of California Berkeley, 1‐2 December 2016. Published as an open access pre‐print on SocArXiv: https://osf.io/bp7c4/.

• Kitchin, R. (2016). The ethics of smart cities and urban science. PHILOSOPHICAL TRANSACTIONS R. SOC. A 374, 1‐15, http://dx.doi.org/10.1098/rsta.2016.0115.

• Lipsky, Michael. 1980. Street‐Level Bureaucracy: Dilemmas of the Individual in Public Services. New York: Russell Sage Foundation.

• Mayer‐Schönberger, V. and Cukier, K. (2013). Big data: a revolution that will transform how we live, work, and think. Boston: Houghton Mifflin Harcourt.

• Merchant, A., MS Mohan Kumar, P. N. Ravindra, P. Vyas, and U. Manohar 2014. Analytics driven water management system for bangalore city. PROCEDIA ENGINEERING 70, 1137‐1146.

• Rabari, C. & M. Storper (2014). The digital skin of cities: urban theory and research in the age of the sensored and metered city, ubiquitous computing and big data. CAMBRIDGE JOURNAL OF REGIONS, ECONOMY AND SOCIETY 8, 27‐42.

• Richter, C. and Georgiadou, Y. (2016). Practices of Legibility making in Indian Cities: GIS property mapping and slum listing in government schemes. INFORMATION TECHNOLOGY FOR DEVELOPMENT 22, 75‐93.

• Taylor, L., Richter, C., Jameson, S., and C. Perez del Pulgar (2016). Customers, Users or Citizens? Inclusion, Spatial Data and Governance in the Smart City. June 9 2016. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2792565. Accessed on: 15 June 2016.

• Wiig, Alan (2015). IBM’s smart city as techno‐utopian policy mobility. CITY 19(2–3), 258–273, http://dx.doi.org/10.1080/13604813.2015.1016275

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