In this post I discuss Urban Information Systems, and how they fit into wider society.
What are they?
Urban information systems centralise and mediate diverse types of information that have been extracted from urban data. In turn, these arrays of information can help people produce relevant knowledge and facilitate better decision-making in spatial planning and business.
In the absence of any authoritative definition, “urban information systems” encompass the following information management systems: Decision Support Systems (DSS), Planning Support Systems (PSS), Spatial Planning Support Systems (SPSS), and City Information Models. These often rely on spatially-explicit technologies such as Geographic Information Systems (GIS) and multifunctional 3D visualisation platforms that allow to display and overlay diverse types of data. Urban Information Systems enable to monitor and visualise resource use, flows, and processes so as to aid decision-making, with the promise to support more sustainable urban management and strategic planning .
Urban information systems can be closely connected to modelling software that can simulate floods, mobility, urban densification, physical connectivity, as well as perform complex multivariate spatial analyses (e.g. Models can be descriptive (describe current processes), predictive (e.g. simulate changes in the environment and compare land use scenarios) and prescriptive (e.g. visualise required changes in the environment or in behaviours to meet specific goals and targets, such as through backcasting). While urban models and Planning Support Systems typically have different objectives and target audiences , the increasing compatibility of data, software and hardware now allows for greater fluidity between modelling and decision-support outputs.
Data, the IoE, and smart cities
At the core of Urban Information Systems is data. More traditional forms of urban data include cadastre data, real estate, tax, incomes, crime statistics, health statistics, construction management (e.g. BIM), planning applications, energy use… Urban data also includes the fuzzy notion of “Big Data”: data collected through myriads of physical sensors to monitor environmental conditions and processes (e.g. climate, air quality, water, energy, noise, traffic, people…), data collected through diverse mobile and stationary devices (e.g. smartphones, computers, drones), social media, and consumer behaviour data. Some Big Data is Open Data, but not always. Many local authorities and research clusters are now deploying networks of sensors in the urban environment (Mone, 2015), from Chicago and Newcastle to Nairobi, leveraging affordable real-time data.
The complex assemblage of interconnected infrastructures, networks and devices that mediate the production, distribution, processing and storage of data are powered by the Internet of Things (IoT), with its heavy reliance on cloud computing, storage capacity and services; these constitute the backbone of smart cities (Jin, Gubbi, Marusic, & Palaniswami, 2014). The IoT becomes the “Internet of Everything” (IoE) as it pervades and monitors all parts of our daily lives. Such relentless data accumulation is often portrayed as enabling to improve life in cities, thanks to the work of the emerging workforce of expertly trained data scientists (Power, 2016b), as well as low-cost business- and government-led crowdsourcing and procurement initiatives, such as civic hackathons (Johnson & Robinson, 2014).
3D visualisation platforms
Visualising everything on a single platform promises to be a highly effective way of understanding and coordinating disparate trends in the urban environment. Integrated PSS technologies for collaborative project management and public engagement now exist to mediate more effective communication and decision-making across a wide variety of stakeholders. For example, a projected aim of the Virtual Newcastle Gateshead 3D city information model, developed at Northumbria University, is to become openly accessible to all local stakeholders to allow more effective communication and collaboration between local authorities, construction professionals, businesses, non-profit organisations and the public. Challenges include putting the model online, and improve the quantity and quality of data that could leverage more applications for use in the city-region.
There are also institutional and organisational hurdles to adopting 3D Urban Information Systems. Delivering a keynote at the GISRUK 2017 conference in Manchester, Andrew Hudson-Smith (Director of CASA at UCL) shared how the pioneer 3D city model of London which they developed in the early 2000s never quite caught on with Ken Livingstone, then Mayor of London. The planning times were simply not ripe. In a similar vein, my former supervisor at KTH, Stockholm, also told me how, despite the increasing availability of fancy 3D GIS visualisations, local planners in Sweden still rely on the good-old paper local plans as statutory documents. The enticing text and image descriptions of which actually bring life to such flat plans, on the other hand, are not legally binding.
Similarly, Marco te Brömmelstroet has shown on the basis of an experiment that while Planning Support Systems (PSS) can improve planning processes when they are actually taken up, they do not systematically improve the quality of planning outcomes. Furthermore, even as PSS become more user-friendly, they are not necessarily more useful to planners, because of organisational and political factors within planning administrations which PSS-developers still need to consider in product-design and deployment. In the absence of true iterative collaborative processes between industry and academics, the implementation gap for Urban Information Systems is set to persist (M. te Brömmelstroet, 2016; Marco te Brömmelstroet, 2016).
Welcome to the Machine / Ghost in the shell
The data being produced about urban environments and their inhabitants is now becoming so large that it outpaces the capacity to derive meaning from it. As the volume, velocity and diversity of data production and distribution increases exponentially, machine learning is becoming a core facilitator of the data-driven smart(-er?) city. The smart-cities hype builds on the Big Data hype itself, where innovation is fostered almost for its own sake, driven by fluid forms of governance, and where social inclusiveness is potentially held on the back burner (see former posts in this blog). The very act of learning to speak and think in the language of the machine (i.e. coding languages) is becoming increasingly valued in-and-of-itself, as observed by Yuval Noah Harari in Sapiens, and it certainly serves the smart city well. Digital literacy and data science are the backbone of tomorrow’s urban information systems. The process of “sensing the city” through all possible means is making the physical, digital and human make-up of cities increasingly fused or “cyborg-like” (see Gandy, 2005; Mitchell, 2003).
In this context, the ubiquitous digital technologies that facilitate life in cities also allow to monitor flows and processes with increasing minutiae and sophistication, for good or evil. Not only can human constituents be tracked physically, they can also be profiled based on the information they provide by interacting with urban as well as digital environments, such as social media. Dan Power (2016a), expert in the field of Decision Support Systems, argues that the societal push towards big data analytics and ubiquitous computing runs the risk of being usurped by totalitarian-minded power mongers, in the style of George Orwell’s 1984. Big Data invites Big Brother? Complex Urban Information Systems are also central to dystopian movies such as Brazil and novels such as Fahrenheit 451. Other researchers such as Stephen Graham and Louise Amoore also warn against the powerful surveillance uses of smart and networked technologies. Will machines take control over human decisions? The highly automated junk financial transactions did largely contribute to precipitate the global economic crunch in 2008…
Today’s breed of Urban Information Systems is relatively new, but older insights could come to the rescue. An engaging article by Edward Hearle (1968) provides some useful reflections for effective urban management information systems that can help modern cities separate the wheat from the chaff in the “smart cities” and “Big Data” agendas, and ween off data-addiction. Information derived from data is great, but how about insight and wisdom?
The post thus begs the following questions: Do urban information systems hold the promise of supporting more sustainable cities? Or are they the 21st century version of Pandora’s box? Probably both. Urban information systems can mediate an effective and participatory coordination of urban metabolisms, just as they risk being hijacked by some mustachioed Big Brother supported by a fear-stricken constituency. Urban Information Systems may be just a cog in the great wheel of neoliberal urbanisation (see David Harvey). Following Pandora’s lead, we can and must hope for the better (but must also work hard to achieve it). The types of information that technology can mediate are a mean to improve spatial planning, rather than an end. In my understanding, the information that matters most in using Urban Information Systems is what our intentions and motivations are as individuals and communities.
Gandy, M. (2005). Cyborg urbanization: Complexity and monstrosity in the contemporary city. International Journal of Urban and Regional Research, 29(1), 26-49. doi:10.1111/j.1468-2427.2005.00568.x
Hearle, E. F. R. (1968). Urban management information systems. Socio-Economic Planning Sciences, 1(3), 215-221. doi:10.1016/0038-0121(68)90010-4
Jin, J., Gubbi, J., Marusic, S., & Palaniswami, M. (2014). An Information Framework for Creating a Smart City Through Internet of Things. IEEE Internet of Things Journal, 1(2), 112-121. doi:10.1109/JIOT.2013.2296516
Johnson, P., & Robinson, P. (2014). Civic Hackathons: Innovation, Procurement, or Civic Engagement? Review of Policy Research, 31(4), 349-357. doi:10.1111/ropr.12074
Mitchell, W. J. (2003). Me[plus plus]: the cyborg self and the networked city. Cambridge, Mass: MIT.
Mone, G. (2015). The new smart cities. Commun. ACM, 58(7), 20-21. doi:10.1145/2771297
Power, D. J. (2016a). “Big Brother” can watch us. Journal of Decision Systems, 25(sup1), 578-588. doi:10.1080/12460125.2016.1187420
Power, D. J. (2016b). Data science: supporting decision-making. Journal of Decision Systems, 25(4), 345-356. doi:10.1080/12460125.2016.1171610
te Brömmelstroet, M. (2016). PSS are more user-friendly, but are they also increasingly useful? Transportation Research Part A: Policy and Practice, 91, 166-177. doi:10.1016/j.tra.2016.05.012
te Brömmelstroet, M. (2016). Towards a pragmatic research agenda for the PSS domain. Transportation Research Part A: Policy and Practice. doi:https://doi.org/10.1016/j.tra.2016.05.011