Building an intelligent infrastructure for smarter cities

Introduction

‘Smart’ in this context means being able to predict, prevent and adapt. It also means getting the right information to the right people when and where needed. These are the co-workers responsible for the physical, intellectual, social, financial, environmental, organisational and behavioural assets that together determine quality of life.   Despite global investment in smart cities, much of this information in the UK remains bottled up in inaccessible silos, defined in different ways with inconsistent meanings. Data are being liberated, but they are not yet linked and more importantly, created for specific purposes, the data we have means different things to different people. Investment is needed in an intelligent infrastructure that connects and empowers, and we argue that geographers are uniquely placed to lead this revolution.

Globally, we will have to build the same amount of housing and infrastructure in the next 40 years that we have built over the past 4,000 years[i]. By 2015, there will be over 1.2 billion cars. Demand for renewable ecological resources will exceed two Earths well before mid-century[ii]. India alone is set to build 500 new cities over the next 20 years to house 700 million more city dwellers by 2050[iii]. Buildings and physical infrastructure provide the skeleton and circulation of these future cities but the information infrastructure provides the brain and nervous system, the ability to think collaboratively and ultimately, the ability to survive in an increasingly hostile world. To achieve this, it will need to be scalable, intelligent, open and financed.

smoggycity

Scalability

The Americans have need of the telephone, but we do not. We have plenty of messenger boys.” Sir William Preece, chief engineer of the British Post Office, 1876

Here are some astonishing facts. Think of one megabyte equating to a square metre of land. In 1920, information would have covered an area the size of Madagascar. By 2010, it covered the world. By 2020, we will need 1700 globes to represent the volume of data we will have generated[iv] and one third of all data will have passed through the cloud[v]. Examples include posts to social media sites, digital pictures and videos, surveillance cameras, SMS, tweets, audio, purchase transaction records, location-based services, identity checking, sensors on and inside buildings, RFID tags, transport networks, 350 billion annual meter readings and cell phone GPS signals. The number of devices will grow to 50 billion by 2020, and the rate is increasing[vi].  “From an individual perspective, we leave continuous trails of data, plumes of bits of information. It’s the personal exhaust from our digital interactions. These are the shadows and messy footprints of our daily lives.”[vii]  This is the background noise of a smart city and big data analysis seeks out the meaning in these trails. Despite ‘big brother’ concerns, big data could increase smartness and lower the cost of intervention across civic services, but to understand and respond to big data collaboratively, in context we also need common conceptual models of reality[viii].

Intelligence

Geographers have long maintained that location provides a common language. Location is one way of sifting through and helping make sense of big data. However, coordinates and proximity are not enough – we must understand meaning as well. The Semantic Web (SW) is the name of a long-term project started by Sir Tim Berners Lee and W3C, the World Wide Web Consortium.  The stated purpose is data on the Web, defined and linked so that it can be used by machines not just for display purposes but for automation, integration, and reuse.  Fundamentally, the SW conveys the meaning of each data item in terms of a stated fact, expressed as a relationship of some kind. These relationships will allow us to link together separate pieces of intelligence into a real-time view of a city and help create a new generation of GIS.

The SW uses a graph model that’s a natural language way of representing information, instead of the rigid models of databases. Implementing complex queries on the SW takes days or weeks, as opposed to months or years and can be a decentralised, collaborative activity. Reasoning and inference capabilities are also supported, creating a global network that could analyse the complex and fluid interactions of a city and knows no limits. Rules for actions support advanced machine reasoning capabilities and automatic response.

More complicated relationships such as the rooms in a building, buildings on a street, or any other physical, social, financial, organisational or environmental knowledge and rules can be supported by published models of reality (ontologies, built using standards such as OWL[ix]). For example, an ontology could contain the contextual rules and intelligence to determine the different spatial representations and contextual meanings associated with a simple placename[x]. However, there is not yet any concerted approach to defining and mediating the ontologies needed to support an intelligent infrastructure for cities. Having a disparate collection of ontologies referring to different aspects of the same real-world things, requires continual investment in mediation with serious risks of misinterpretation. Collaborating on well-referenced, mediated ontologies for the built environment, with appropriately structured governance will benefit the entire world.

owl

With these tools, it will become possible to expose all the information needed by citizens and service providers but also by machines including GIS, alerting and informing us on how to improve outcomes and manage problems. The SW has moved into production in leading industries such as financial services and pharmaceuticals. The Sensor Web Enablement (SWE) standard (defined by the Open Geospatial Consortium) means we can include real-time data streams about anything being monitored, whether water, electricity, GPS devices, buildings or weather.  More ontologies are being defined and used for datasets in the Linked Open Data Cloud. The Architecture, Engineering, Construction and Facilities Management (AEC/FM) standard ‘International Framework for Dictionaries’ (IFD), registered as ISO 12006-3:2007, supports the development of unified AEC/FM ontologies at the national, regional or domain levels.

The work on Smart Cities by Liviu-Gabriel Cretu whilst at Alexandru Loan Cuza University in Romania, has shown how intelligent inferences can be made from linked data to improve decision-making and intervention. However, there has been little progress in an agreed event-driven information architecture to support publishing and subscribing to all the various services that will be needed[xi] and there is a danger of proprietary solutions ‘locking up’ cities that are early adopters.

Openness

The ant is a collectively intelligent and individually stupid animal; man is the opposite, wrote Karl von Frisch.

ants

Collective stupidity will not solve the daunting problems that our growing cities face but collective intelligence requires access to the right information at the right time by the right people. Currently, most city information in the UK is not open. Widely reusable data about social and other assets is fragmented. Some is split between two tiers of local government, or collected by government agencies with one purpose in mind and locked away. The private sector holds much data about the built environment e.g. facilities management and extracting this needs specific legislation, e.g. carbon reporting.

The European Commission believes that giving away data created using public money stimulates new products and services. However, access to information in the UK is very inhibited compared to the US due to data protection attitudes, the narrowness of statutory gateways and commercial imperatives limiting use. This impedes innovation. An inability to connect data causes hardship and contributes to social deprivation. For example, investing in the energy efficiency of buildings or tackling social isolation requires information about those in most need as well as investing in provision. This needs value-chain thinking, identifying all the steps between investment and return even when different stakeholders are involved. Initiatives such as the London Data Store and Data.gov remain focused on static, historic data sets whereas the full potential of the intelligent infrastructure goes beyond planning and performance management to operational support and full-scale automation.

Finance

Technology giants such as Fujitsu, Toshiba, IBM and Deustche Telekom are investing heavily in what promises to be a boom market, but the role of UK local authorities is much less clear.  Tackling the issues described above in the UK will require public-private partnerships at a time when local authorities are preoccupied with spending less, and government funding remains focused largely on smart city R&D. In contrast, the State Information Centre of China has reportedly announced that 154 cities have already proposed smart-city plans driving first-round investments of £120 billion whilst the smart cities market in India is estimated to be worth $1,200 billion in the next 20 years[xii] with seven new cities under development through Japanese funding.  With its legacy of old building stock and infrastructure, the UK should be thinking about an intelligent infrastructure that will drive down the costs of becoming smarter, or risk being left behind in the global race to build smart green efficient and effective city environments.

Conclusion

While the amount of information flowing through our intelligent infrastructure is exploding, our ability to listen, assess, predict and act upon it has hardly moved forward. The perceived up-front costs of standardisation continue to exceed the discounted value of downstream sharing, further hindered by a combination of institutional resistance and disengagement.

Collaboration and the harnessing of collective intelligence will require an intelligent infrastructure that can cross silo boundaries and support new citizen-centric services, creating a new marketplace for information technology but that requires significant upfront investment, a step-change in information sharing and openness, a unified language and a common, powerful vision of the future. Custodians of our national digital assets such as Great Britain’s Ordnance Survey, will need to play a central role in realising the opportunities of an intelligent infrastructure.

Acknowledgements

Ian Bush, Bob Barr, Liviu-Gabriel Cretu

[i] World Economic Forum

[ii] Global Footprint Network

[iii] Booz & Company

[iv] Mike Sanderson. Linked data for executives: building the business case. BCS, November 2011.

[v] http://www.csc.com/insights/flxwd/78931-big_data_growth_just_beginning_to_explode

[vi] Eddie Townsend. UK Future Internet Strategy Group: Future Internet Report. s1: Technology Strategy Board, 2011.

[vii] Alan Moore. No Straight Lines

[viii] Fujitsu. Linked data: connecting and exploiting big data. White Paper, March 2012

[ix] Web Ontology Language

[x] Tim Wood. Joining up the BIM Roadmap, GIS Professional 52, June 2013

[xi] Liviu-Gabriel CRETU. Smart Cities Design using Event-driven Paradigm and Semantic Web, Informatica Economică vol. 16, no. 4/2012

[xii] IBM and McKinsey

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