Issues facing cities could also be addressed using big data
Big data is defined both by its volume and its timeliness, with vast amounts of data generated every second. The term covers data collected from a wide range of sources including CCTV cameras, sensors, mobile phones and search engine activity.
Cities generate a lot of big data. However, it is typically not made publicly available or shared between organizations within the city. As such, UK cities are missing an opportunity to improve their performance.
Big data has been used by cities around the world to deliver more efficient and responsive services with fewer resources
To date, the majority of projects have concentrated on solving transport issues, from congestion to road maintenance issues. Dublin, for example, is working with IBM to identify the cause of and solution to transport delays through sharing data collected from bus timetables, inductive-loop traffic detectors, closed-circuit television cameras and GPS updates from the city’s buses. This data will be used to reduce congestion and so pollution in the city (case study 5).
Case study 5: Dublin, Ireland
In 2010, in order to increase the efficiency of its public transport network without the need to invest in any major re-development, Dublin city council began sharing data generated by its city services with IBM. Data is collected from bus timetables, inductive-loop traffic detectors, closed-circuit television cameras and GPS updates from each of the city’s buses to identify the cause of delays and the most effective measures to put into place to improve traffic flow. From linking these datasets together, a digital map of the city has been built up and overlaid with the real-time positions of Dublin’s buses. This allows traffic controllers to see the current status of the entire bus network at a glance. The data is being used to identify where additional bus lanes and bus-only traffic systems would be beneficial and if bus line start times are optimal. Meteorological data is being added to the model to allow operators to analyse the effect on the transport system of extreme weather conditions and to work to reduce the impact this has on delays. This will benefit the city through reducing congestion and pollution, as well as the time taken to complete a journey.20
Boston has developed an app that uses the motion sensor in mobile phones to identify when a car hits a pothole. This has allowed the city to identify and solve road maintenance issues much more quickly than through the use of traditional road inspections (case study 6).
Case study 6: Boston, USA
The city has developed Street Bump,21 an app that uses mobile phones to map potholes in the city. This uses the motion sensor built into phones to recognise when a car hits a bump in the road. This data is then transmitted along with a location reference taken from the phone’s GPS. Existing speed bumps are already mapped so they don’t get mistaken for defects. If a number of people hit a bump in the same spot, the system recognises it as a pothole and the city inspects the site to determine if repairs are needed. The app is free to download and aims to create a real-time map of road conditions to catch problems earlier than traditional inspections.
But as the Santander case study shows (case study 7), the use of sensors to collect data has led to city benefits in areas beyond transportation. The 12,000 sensors placed all across Santander have led to a range of improvements, including a reduction in the city’s utility bills, as the sensors allow street light to automatically dim when no-one is around as well as alerting refuse collectors which bins need emptying.
Case study 7: Santander, Spain
The city has received funding from the EU to become a prototype for smart cities across Europe. Due to its small size, the city can be monitored by 12,000 sensors, providing an opportunity to assess the benefits smart cities can deliver. The sensors collect measures of air pollution, noise levels, identify available parking spaces, inform refuse collectors which bins are full and automatically dim street lights when no one is around. As well as sending this information to a control centre, residents receive real time information via their smartphones on issues such as road closures, parking availability and bus delays. Residents are also able to report issues (such as broken streetlights) directly to city hall. The city has seen a reduction in response times to addressing problems and a 25 per cent fall in electricity bills. The city’s rubbish collection costs have also fallen by 20 percent. Due to the savings seen, utility companies are happy to pay for the sensors’ upkeep.22
It is not just cities in developed countries that are looking towards big data, cities in countries such as the Ivory Coast and Brazil are also utilising big data.
IBM analysed the mobile phone data of Orange customers in Abidjan to develop a solution to the city’s congestion problem that reduced average journey times by 10 per cent (case study 8).
Case study 8: Abidjan, Ivory Coast
In 2012, Orange released 2.5 billion call records from five million mobile phone users in Abidjan, the Ivory Coast. Using location data from 500,000 of these phones IBM analysed congestion in the city. The result was a model that predicted that the addition of two bus routes and enhancement of a third would improve the city’s transport system, resulting in time savings of 10 per cent for users. Before release, Orange removed any personal information from the data that could be used to identify individuals.23
Rio de Janeiro is using data from sensors to connect the whole city, with the information fed back into the city’s control centre. This is enabling more co-ordinated and quicker responses to crises, such as building collapses (case study 9).
Case study 9: Rio de Janeiro, Brazil
In 2010, the city asked IBM to create a city operation centre using data from cameras and sensors located throughout the city as well as connecting all of the city’s 30 agencies, from transport to the emergency services. The centre is enabling officials from across the city to collaborate to manage the movement of traffic, while also ensuring that power and water supplies work more efficiently. The city is also now better placed to deal with a crisis, such as a collapsing building, as the system makes it easier to roll out a coordinated response – transport systems can be shut down, emergency services mobilised and gas supplies cut off, while individuals can be informed of alternative routes via Twitter.24
These case studies provide an indication of how UK cities could apply big data to improve their own performance.
To date, UK cities have lagged behind in their use of big data to address city specific issues.
More recently progress has been made. In 2012 the Technology Strategy Board (TSB) ran a competition asking cities to submit proposals setting out how existing technology could be used to integrate city systems. Following the competition £24 million was awarded by TSB to Glasgow in 2013 to be a Future Cities Demonstrator. The use of big data features heavily in Glasgow’s plans. The city will create an open data platform that will provide real time information on transport disruptions, hospital A&E waiting times and energy use throughout the city, potentially identifying ways to lower energy use and reduce fuel poverty (case study 10).
Case study 10: Glasgow, UK
In January 2013, Glasgow City Council was awarded £24million from the UK Government’s Technology Strategy Board (TSB) as part of its City Demonstrator project. The funding will be used to develop a City Management system that will provide an open data platform, improving the functioning of the city in a number of ways. In particular, the council aims to reduce overcrowding on public transport and congestion on roads, thereby lowering pollution, by providing people with real time information on traffic levels on roads, bus and train delays and the location of nearby empty parking spaces. They also hope to reduce A&E waiting times by providing real-time information on waiting lists in hospitals around the city, allowing individuals to identify which hospitals would be able to see them first. Energy levels across the city will be monitored, including the new Combined Heat and Power (CHP) systems which will allow the city to store energy when demand is low and then use it during times when demand is higher. This has the potential to cut people’s fuel bills and so help reduce fuel poverty. Through monitoring footfall and retail demand the specific areas of the city attracting visitors will be identified. Combined, all this information could be used to generate a quality of life index for the city.25
TSB also provided Peterborough, London and Bristol with £3million each to develop certain aspects of their proposals. Bristol is using its funding to link data from universities, SMEs, and residents to discover how it could be used to improve city performance. Those that have supplied data will be able to trial any new products and services developed using the data (case study 11).
Case study 11: Bristol, UK
In April this year, Bristol City Council received funding from the TSB to develop its Citywide Living Lab. This will combine data provided from a range of sources including universities, SMEs, and citizens who have agreed to share their own data. Individuals and organisations will be engaged in using the data to develop new products and services through the staging of hack events and will be able to trial any new products and services.26
The case studies also show that, whilst the open data agenda is leaning towards releasing data for which demand is known, some big data projects have taken a more speculative approach. Rather than determining the demand and use for the data before it is released, organisations are releasing the data they collect as part of their day-to-day operations, after removing any personal details. Organisations are also working together to collect new data, interlinking it and then exploring what it shows about the city’s performance and future development needs, such as the pollution level data collected in Santander.
Despite the benefits that big data can bring, the use of such data at a city level is still very much in the early stages of development in the UK and it is only those cities that have received funding from a third party such as TSB that are launching projects to utilise big data.
This suggests that activities that emphasise the city level benefits from using big data and the savings it can generate in the long-run, for example through improving existing systems without the need to invest in any major re-developments, are needed.