Smart city is a phrase often thrown around but seldomly has any real meaning. It can mean anything from having a new cycle path to providing fast internet, but in reality the definition is not something easy to find. Despite this, reports have emerged saying that solution providers for these new cities will be worth over $400 billion by 2020.
These figures do not really mean anything, most cities are still in construction and trying to predict what is going to be required in six years time is almost impossible. It was only seven years ago that the iPhone originally came out and the need to increase bandwidths to accommodate all the new data it produces and requires. This is something that could not have been predicted.
The truth about ‘Smart Cities’ is that there is only going to be one way that they can become truly ‘smart’: through data and analytics.
So how is this the case? We have looked at the 7 best ways in which analytics could have a profound effect on ‘Smart Cities’.
Predictive Analytics have been used in several cities across the world to help predict where crimes are likely to take place through historical data and geographical data. These have seen significant success in cities like London, Los Angeles and Chicago. Through data, it is often not even necessary to make arrests, having police officers appearing in certain areas at specific times has seen crime rates drop.
Through data use like this, we could see a significantly safer cities, with police who can stop crime without needing to put themselves at risk of harm.
When looking at the city planning function, analytics and data are not often considered. People still see their use in web traffic and marketing, rather than in the physical creation of buildings and spaces.
This does a great disservice to the power than data can have on anything from building zoning to amenity creation. It allows models to be built to maximise the accessibility of certain areas or services whilst minimising the risk of overloading important elements of the city’s infrastructure. In Short, it create efficiency.
Too often we see buildings being built in areas that seem suitable but can have a considerable effect on another area, without this being taken into consideration during the planning process. Using data and modelling it is possible to map the infrastructure outcomes of any use of space with a high degree of accuracy.
During the London 2012 Olympics the network needed to deal with 18 million journeys made by spectators throughout London. It was no fluke that the network coped.
The TFL and train operators utilised data and analytics to make sure that the vast majority of journeys ran smoothly. It allowed them to input data from events to predict the numbers who would be travelling and make sure that transport was running effectively to make sure that spectators and athletes could be effectively transported to and from the stadiums.
Through using data like this throughout a transport network, it will create effective and flexible public transport, decreasing delays and increasing efficiency. Using data to not only predict when peak times will be for upcoming events, but to help monitor equipment will mean that reliability will improve and accidents will decrease.
Often when new areas are created or become popular, the infrastructure in place is not good enough to sustain continued growth, which can hinder further improvements in the area. Even basic amenities like water and electricity can be effected by a sudden influx of businesses or residents. Through the use of modelling and predictive analytics, it becomes possible for city planners to see where these areas of growth are likely to be and how large this increase will be. Amenities can then be upgraded to accommodate this.
In this way growth in certain areas can continue without the need for services to catch up.
The general gripe that many have with the idea of ‘Smart Cities’ is that governments or companies introduce fast internet speeds and then declare that because companies have the opportunity to access it, it is now officially smart. A smart city is not instantly made because people can get onto Facebook quicker or can instantly watch Cat videos.
Providing fast web access is one thing, but it needs to be in the correct areas and for the correct people. The ability to shift bandwidth within a city will be a key component to this. Knowing when and where bandwidth should be prioritised is a key part of this and data is the compass to help steer it in the right direction.
The basic premise is that bandwidth should be highest in commercial and financial areas from Monday to Friday and in more residential areas on Saturday and Sunday. But there is more complexity than this and the opportunity to maximise bandwidth down to much smaller scales, an area where data and analytics can play a key role.
For instance, if an area wants to attract more high-tech industries and web development companies allowing bandwidth to be higher in those areas is going to be important and data modelling will allow this to be done most effectively.
One of the keys to sustainability is monitoring and having effective controls in place to quickly make changes in order to keep output at a certain level. Data is the most decisive factor here, it allows for governments and companies to see how their outputs are having a positive or negative result on the city as a whole. Being able to check and control levels of pollutants can help with zoning, placing pollutants in areas of the city where they can do the least harm or helping them to reduce their harmful output.
This monitoring also creates the opportunity to see which technologies work best in reducing pollution and what new innovations could be used in particular areas in order to prevent further environmental damage.
The biggest issue that ‘Smart Cities’ have is that there are vast amounts of money spent on relatively benign work. Remodelling of landmarks or small changes that could be classified as ‘vanity projects’ use public money. Using analytics and data ‘Smart Cities’ can target where the public money would have the most impact and what work would be most adequate for that. Through targeting what where money would be best spent, the entire infrastructure of the city can be improved and wastage can be minimised.