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Smart cities: using data to shape our urban environments

Smart cities: using data to shape our urban environments

Organisations in Australia and overseas provide insights into how they are using information to build more intelligent cities

Rio Operations Centre: The incident commander's view provides a summary of everything happening around the city on a video wall, including surveillance cameras, maps, simulations, news updates, resources and information about incidents

Rio Operations Centre: The incident commander's view provides a summary of everything happening around the city on a video wall, including surveillance cameras, maps, simulations, news updates, resources and information about incidents

Building smart utilities

Smart grids play a key part in running a smart, efficient city. The Smart Grid, Smart City project trial, led by the Australian government and Ausgrid, recently finished, with the cost/benefit analysis currently being reviewed. Paul Budde, executive director of Smart Grid Australia, says 30 to 40 per cent of energy savings can be made across Australia using smart grid technology.

Budde says the data generated through smart grids can be combined with data coming from the weather forecast, TV networks, city cultural event planners and so on as they all affect the electricity networks.

“If you combine all of that data and analyse it then you know if there is a big event and everybody is going to be watching the television tonight, so you know what to do with your network. If you know there’s a storm coming, you know that has an effect on use of the electricity, as people stay home and use heaters to stay warm,” he says.

“Also, the mobile companies can say to the electricity companies ‘hey, there’s a big event in town so expect 100,000 people using their mobile phone at once and make sure you network in the city is able to handle that capacity.”

Smart appliances will be able to connect with the smart grid to schedule power-consuming tasks during off peak periods of the day and save on energy. It would determine the best time to automatically turn on their washing machine, for example.

The NICTA team is using machine learning and predictive analytics to anticipate when a water pipe in Sydney is about to break. Water utility companies can only afford to inspect about 1 per cent of their pipes, and there is 20,000 kilometres of underground pipes in Sydney alone. Busted water pipes overall cost the Australian economy more than $1 billion a year.

“If they break, the people who own the pipes and run them are responsible for the damage. So if you flood a basement of a building and block a road and that stops some deliveries happening, the companies affected will come and get you and will want compensation. If a really big one in the middle of a city breaks, it could cost $5 million in compensation in economic damage,” says Economou.

Some 326,571 pipes in Sydney were analysed, with 75,000 failure records. The team looked at the correlations between the age of the pipe, location, material and water pressure. In Wollongong, for example, location/geology and material had a strong correlation, showing that older pipes don't necessarily break earlier.

The team analysed the first nine years of 10 years worth of historical data to predict what would happen to the pipes in the 10th year. They then looked at the data from the 10th year to see if it matched their predicted result and it almost did completely.

Carly Perry, business manager in infrastructure, transport and logistics at NICTA, says the organisation compared its analysis with that of water utility companies and found it could predict twice the amount of pipe breakages using the same maintenance budget.

Monitoring Rio de Janeiro

Rio de Janeiro in Brazil has been developing a smart city since 2010 when it opened a 24x7 operation centre after experiencing damaging floods. The centre was first developed to do more sophisticated data analysis to help emergency services better prepare and respond to emergencies before being used for additional purposes.

The centre brings together 30 agencies into one central command centre where data from sensors, video feeds and social media is collected and analysed. Video data streaming is done in real time from 570 cameras from utility providers and the Secretariat of Public Security.

It includes an 80 square metre video wall – the biggest in Latin America – made up of 80, 46-inch screens. A smart map of the city has more than 120 layers of data. Around 30,000 metres of fibre optic cable connects the infrastructure in the centre.

“It’s not unusual for Rio to have extreme weather; it’s famous for its sharp and steep hills, mudslides and related heavy rains. Rio 'narrowcasts' weather in its operations centre.

"We’ve used our weather modelling systems and weather forecasting to 'narrowcast' down to very small areas to predict how much rain will appear, when and under what circumstances,” says Michael Dixon, general manager of smarter cities at IBM.

Using this narrowcasting, emergency response times have improved by 30 per cent, and it can predict 48 hours in advance where it is going to rain and how much rainfall will appear.

The city has also been working with the FIFA World Cup planning team on logistics to help prepare for the big event and ensure the city’s infrastructure can support the surge in population.

“The Mayor of Rio, Eduardo Paes, had a vision some years ago when he thought that there was potential for Rio to host either the World Cup and/or the Olympics.

"He started to realise a higher level of management of the city would be essential to both convince authorities that Rio was the right city to host these events. They [Rio] started off with a very clear view that they wanted to have an understanding of what was happening in the city at any point in time,” Dixon says.

The city is monitoring Twitter feeds during events to get a pulse of what’s happening in the city and respond to people’s concerns quickly.

For example, there might be a huge amount of waste in a particular area that many people are tweeting about. The team in the operations centre can identify common words in those tweets, the amount of tweets and locate where those tweets are coming from. Surveillance cameras can be used to confirm if there is a major problem, and then a waste disposal facility could act on it right away.

Smart Jakarta

The University of Wollongong’s (UOW) SMART Infrastructure Facility has developed open source software called CogniCity, which gathers and analyses data in real time on a map from Twitter users in Jakarta during the flood season. Data can be used to analyse how the city can better use resources during flood emergencies and plan long-term infrastructure transformation.

The project, PetaJakarta, was recently granted free access to Twitter’s datasets – among six successful projects to be given the grant out of 1,300 applications worldwide. The pilot project will run for one year from 1 May 2014.

“We are harvesting all the tweets; we are talking tens to hundreds of thousands during the flooding season. We do sentiment analysis on words that are related to water or flooding. All this information will be embedded in the central control room of the governor of Jakarta,” says research director Pascal Perez at the SMART Infrastructure Facility.

“The smartest sensor we’ve got and the most ubiquitous is people. Let’s use them as sensors. Not only can we use it to better understand what people think about certain kinds of infrastructure at a point in time but [we can] also use social media as a core management tool for managers of utility infrastructure and assets.”

Next page: Improving New York City's transport system

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Tags predictive analyticsNew York CityUniversity Of Wollongongsmart gridsnictadata analysissmart griddata analyticsdata modellingNational ICT Australia (NICTA)smart citiesTransport for NSWmachine learningmachine to machine (M2M)Internet of Things (IoT)FIFA World Cup 2014Rio de Janeiro Operations CentreSMART Infrastructure FacilitySmart CityRio de Janeiro Operations Centersmarter citiessmarter city

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