Smart Cities: The Built Environment as the Interface to Personal Data

In another in our series of articles focusing on Smart Cities, we are reminded that buildings are, rather obviously, a central ingredient of smart cities. Dr Holger Schnädelbach explains the less obvious: how data and technology are linked to the built environment

Buildings are becoming adaptive. They adapt to their environment with the aim of being more sustainable and providing more comfortable conditions for inhabitants. They adapt to occupants to make spaces more convenient, information rich and more useful. This adaptivity is achieved by combining the building fabric with ubiquitous computing in mainstream and experimental architecture. The emerging adaptivity frequently draws on personal data supplied by and captured from inhabitants of a particular space. The emerging feedback loops between people and buildings, as well as the role of the built environment in turning personal data into big data, remain invisible to inhabitants and require further exploration.

Background

Our built environment has been instrumented with sensors, actuators and computation for more than half a century (Banham 1969), inspired by Weiser's vision of Ubiquitous Computing for around half of that time (Weiser 1991). UbiComp and now the concept of the Internet of Things propose that computation can be embedded into our surroundings and the objects we interact with in various forms. Built environment research draws on the emerging possibilities in ubiquitous computing, resulting in the still developing field of Adaptive Architecture (Schnädelbach 2010). This domain is concerned with how we specifically design buildings that respond to their environments (Roaf, Fuentes et al. 2007) and their inhabitants (Chan, Estève et al. 2008), some of which is mainstream while other work is experimental.

Adaptive Architecture is able to draw on a long history of designs for manual adaptivity, as for example demonstrated in the modernist Schröder house by Rietveld, which allows changes to the room layout through a flexible set of partitions. There, adaptations are non-technical and crucially unrecorded, apart from through the change affected in the building, ie the various physical configurations that can be created. Now, everyday adaptivity in buildings is typically shaped by computations in some form. Most people will have come into contact with automatic sliding doors and lighting triggered through motion sensors, key card door access points and perhaps also automated ventilation triggered by temperature and occupation sensors. Once the domain of the 'eco home' and the 'smart home', these technologies are increasingly becoming mainstream and appearing all around us. In stark contrast to the manual adaptivity described earlier, our actions and interactions with the building and others can now be recorded for later inspection and analysis. This can be for whatever purpose the data retention policy of a particular organisation allows, a mostly hidden side-effect of mainstream Adaptive Architecture. 

We are also seeing a rapid expansion of experimental work in this space, as for example presented in (Bullivant 2005) and (Sheil 2008). This work challenges the common definitions of what architecture even is. For example, the muscle projects emerging from Delft University create kinetic architecture, which physically re-configures in response to inhabitants (Hubers), drawing on personal data such as people's location and movements. In a similar vein to the more common work mentioned above, recording of personal data is clearly technically possible and often it is even a fundamental part of the interaction. Even though the latest prototypes begin to realise buildings not as passive places but as interaction partners, the interactional and ethical consequences of this work remain underexplored.

The Challenge of Personal Data in the Built Environment

In this context, it seems very timely to focus on the role that the built environment can play in our interaction with personal data. Personal data (eg physiological, identity, locative, activity and social networking data) and the protection of privacy is a continuing challenge (Fung, Wang et al. 2010). This challenge in part results from people willingly sharing personal data, via social media platforms such as Facebook, Twitter and Foursquare, which take personal data as 'payment' for their services and via corporate (eg e-commerce) and government sites. More recently, trends such as the 'Quantified Self' (Wolf and Kelly 2014), promising to support self-improvement through the analysis of continuously recorded personal behaviours, and 'Personal Branding' (Schawbel 2014), offering career advances through better packaging of the self, build on people's readiness to trade personal data for a perceived benefit. Technology responses have included making personal data artificially ephemeral as in the messaging service Snapchat and giving control over who accesses data back to people (McAuley, Mortier et al. 2011).

In Adaptive Architecture, the role of personal data has continuously increased. It is used to drive actuations, (eg to the lighting infrastructure, HVAC, information density, the form of buildings). This is framed by what Nigel Thrift calls 'qualculation', where a large proportion of society's processes today are underpinned and paralleled by data (Thrift 2006), and buildings and cities are at the centre of this, as the sites of the infrastructure necessary to acquire, store and use personal data. For example, Building Management Systems (BMS) have allowed operators to balance efficiency with occupant comfort and home automation directly provides occupants with control over many aspects of their homes (Harper, Rodden et al. 2008), both dealing with measuring and actuating the built environment concerned. The following briefly illustrates two particular challenges (this is clearly not extensive) that emerge by personal data playing this greater role in Adaptive Architecture.

The Personal Data Feedback Loop

Experimental work conducted at the University of Nottingham's Mixed Reality Lab has demonstrated the feedback loops that emerge when buildings are driven by personal data. This can usefully be illustrated with the ExoBuilding prototype. ExoBuilding is an adaptive environment (Schnädelbach, Glover et al. 2010) capable of measuring respiration via a belt worn around the abdomen. The respiration data is wirelessly transmitted to a computer which controls the up and down motion of ExoBuilding. We found that this simple interaction can trigger deep relaxation in people, but also found that mapping physiological data in an inappropriate way is at least very much distracting (Schnädelbach, Irune et al. 2012). In the described way, ExoBuilding and related experimental architecture create environmental conditions that directly feedback personal data on our behaviour.

Some technical infrastructure is used to acquire personal data of various types. Such data might be manipulated, interpreted, aggregated and also stored, before it is used to actuate aspects of the built environment. The resulting changes in that environment feed back on the inhabitants whom the personal data was acquired from. When physiological data is used (eg respiration data), the built environment driven by personal data can become a biofeedback environment, offering great potential for health care and potentially grave consequences for privacy, as we have started to explore through ExoBuilding.

Personal Data Turning into Big Data

The second challenge results from the broader context of each individual interactive environment, which it is framed by. The building infrastructure of Adaptive Architecture is the site of the global technical infrastructure, which turns personal data (acquired from personal devices and embedded sensors ('The Internet of Things')) into 'Big Data' (Thrift 2006, Churchill 2014), via infrastructure embedded into the built environment (eg from rooms to smart cities), stored in the cloud and mined by global IT corporations.

Some technical infrastructure sited in the built environment and in relative proximity of individuals (eg the aforementioned ID card readers, motion sensors, but also Wi-Fi, mobile networks and CCTV) is used to let people actively provide personal data and acquire personal data where individuals remain more passive (eg face recognition in CCTV). Very much invisibly, some of that data (whether this is necessary or not) is transmitted from people via the built environment to the cloud. Frequently, it is in the cloud that data will be stored, mined and used for user profiling, regulation and decision-making. Viewing this from a standpoint that possibly begins to see buildings as interaction partners, the built environment becomes 'complicit' in the invisible journey of information that is close to us to that information being stored by second and third parties for whatever illegible purposes.

Addressing the Challenge

Not fully understanding the emerging feedback loops and the role of the built environment in the big data infrastructure clearly bears risks. The resulting lack of knowledge will be detrimental to us (psychologically and economically), when adaptive architecture prototypes are implemented as mainstream in future, especially considering the size and impact of the construction sector. Missing the target, by producing adaptive buildings that are based on false assumptions will be wasteful - the effect will impact a large number of people and opportunities will be lost.

Architects and User Experience designers will now have to start considering the two questions emerging from the issues sketched out above:

·        How can adaptive buildings support people to make informed, effective, economical and ethical associations between their personal data and their environment, where those associations affect them and others?

·        How can adaptive buildings support people's informed, effective, economical and ethical interaction with personal data, as it is part of and becoming big data?

Dr Holger Schnädelbach is Nottingham Research Fellow at the Mixed Reality Lab, University of Nottingham. 

References

Banham, R. (1969). The architecture of the well-tempered environment. Chicago, University of Chicago Press.

Bullivant, L., Ed. (2005). 4dspace: Interactive Architecture. Architectural Design, Wiley-Academy.

Chan, M., D. Estève, C. Escriba and E. Campo (2008). "A review of smart homes - Present state and future challenges." Computer Methods and Programs in Biomedicine 91(1): 55-81.

Churchill, E. F. (2014). "Scrupulous, scrutable, and sumptuous: personal data futures." interactions 21(5): 20-21.

Fung, B. C. M., K. Wang, R. Chen and P. S. Yu (2010). "Privacy-preserving data publishing: A survey of recent developments." ACM Comput. Surv. 42(4): 1-53.

Harper, R., T. Rodden, Y. Rogers and A. Sellen, Eds. (2008). Being Human: Human-Computer Interaction in the Year 2020. Cambridge, UK, Microsoft Corporation.

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Schnädelbach, H. (2010). Adaptive Architecture - A Conceptual Framework. MediaCity. J. Geelhaar, F. Eckardt, B. Rudolf, S. Zierold and M. Markert. Weimar, Germany, Bauhaus-Universität Weimar: 523-555.

Schnädelbach, H. (2011). Physiological Data in Adaptive Architecture. International Adaptive Architecture Conference. F. Stacey and M. Stacey. London, UK, The Building Centre Trust and The University of Nottingham.

Schnädelbach, H., K. Glover and A. Irune (2010). ExoBuilding - Breathing Life into Architecture. NordiCHI. Reykjavik, Iceland, ACM Press.

Schnädelbach, H., A. Irune, D. Kirk, K. Glover and P. Brundell (2012). "ExoBuilding: Physiologically Driven Adaptive Architecture." ACM Transactions in Computer Human Interaction (TOCHI) 19(4): 1-22.

Sheil, B. (2008). Protoarchitecture : analogue and digital hybrids. London, John Wiley.

Thrift, N. (2006). "Movement-space: The changing domain of thinking resulting from the development of new kinds of spatial awareness." Economy and Society 33(4): 582-604.

Weiser, M. (1991). "The Computer for the Twenty-First Century." Scientific American 265(3): 94-104.

Wolf, G. and K. Kelly. (2014). "Quantified Self: Self Knowledge Through Numbers."   Retrieved 02/01, 2014, from http://quantifiedself.com.

 

Published: 2015-06-09T12:09:34

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