More than 54% of the global population is already living in urban areas and research by Bank of America Merrill Lynch (BofAML) forecasts that the figure will rise to about 70% over the coming 35 years. For this reason, the US group is forecasting that urbanisation and smart cities, as well as the technology developments associated with these trends, will be some of the most powerful drivers in the global economy.
BofAML thematic investment strategist Beijia Ma tells Engineering News that, during this 35-year period, the global population is expected to increase from the current 7.2- billion people to about 9.6-billion people, one-billion of which, or 178 000 every year, will be living in urban areas.
She highlights that cities are a major driver for global economic growth, accounting for about 85% of the global gross domestic product (GDP).
“Generally, the rule of thumb is the more urbanised a country is, the higher some of its GDP growth rate will be,” Ma notes.
By 2030, it is expected that there will be 41 megacities globally, which are characterised by populations of ten-million people or more. Ma points out that emerging markets, such as South Africa, will be the primary driver of urbanisation growth, contributing as much as 90% of all urbanisation between now and 2050.
She explains that developed markets’ future contribution is expected to be so low, because about 85% of current urbanisation is in these markets.
In light of the growing population figures, smart cities and the technologies that support them will become a major investment trend. Ma points out that increasing connectivity and growing numbers of connected devices, as well as the broader Internet of Things ecosystem, will characterise the technology developments and investments in the near future.
She highlights that there are an estimated 25- billion connected devices being used worldwide, which may rise to as many as 50-billion by 2020 and one-trillion by 2035.
“This is really the backbone of the smart cities story,” says Ma, adding that one-trillion connected devices will generate significant Big Data. “In a city of one-million people, you are looking at about 200-million GB of data being generated every day.”
To manage and analyse the significant amount of Big Data being generated, smart cities will have to deploy artificial intelligence (AI), particularly the deep learning subset of AI, which Ma suggests will be the most disruptive technology and will also form the “pillar” to facilitate the step to becoming a smart city.
She explains that deep learning is more intuitive than software automation technology. “Deep learning has several layers of computer nodes, each of which has software making yes-or-no decisions similar to binary code,” Ma explains, adding that the high number of analytics or decisions taking place provides an emulation of the human cortex.
This layered architecture is known as the artificial neural network, which Ma asserts will be the key technology development for processing and understanding Big Data generated in smart cities, as “it is able to train your AI or deep learning analytics over time. This also shifts the technology uptake from a linear trajectory to something that is more parabolic”.
Meanwhile, Ma notes that the number of connected devices is not the most important factor for smart cities, but rather the integration of these devices at various layers of a city that will play a more significant role.
For example, a connected device in the home can be used to manage the heating, cooling and lighting. This information can then be fed into a building management system to improve the energy management of the building. The information from the building can then be used by the city to document energy-consumption trends, which, in turn, can be used to improve overall energy consumption in the city through smart grid and energy management systems.