What are smart cities and smart places?
Understanding ‘smart’ strategies in cities and urban policy
22 September 2020
For governments and communities, ‘smart’ strategies in relation to cities and urban policy have appeal in that they offer technological solutions to urban problems such as waste management, community engagement and environmental sustainability.
What is a smart place?
At its core, a ‘smart place’ program is about acquiring appropriate data about that particular built or natural environment and using that data effectively to understand and, if appropriate, control what is happening in that place, whether it be an urban, regional or rural area.
Governments, planners and business entities have been collecting and collating data about particular areas for many years in order to manage environments or produce greater profits, but ‘smart’ projects are different in that they use large datasets, mined in bulk from modern electronic devices, that can be analysed to extract patterns of behaviour’. The electronically recorded data sets— also known as ‘big data’— can be anything from the patterns of pedestrian traffic as recorded by the changing position of mobile phones as they’re carried by people, to temperature sensors in buildings and other infrastructure, to electronic records of the prices of dwellings in a region.
With this data a government can tailor the delivery of services and infrastructure in ways that are most useful for people living in that area.
What is an innovation district?
An innovation district (or innovation precinct) is a contained geographic area in which a group of related industry, research and education organisations and businesses occupy buildings and offices in relatively close proximity to each other. The intention in creating such districts is that the physical clustering of innovation activities creates dense networks of firms and workers, and this spatial proximity in turn fosters collaboration across firms and the multiplication of financial value and employment.’
Governments are keen to encourage such districts as they believe the businesses and organisations build partnerships, launch entrepreneurial ventures, and help local businesses improve their competitiveness, productivity and innovative capacity.
What is a smart city?
A ‘smart city’ strategy may include aspects of a ‘smart places’ scheme but it often goes well beyond managing local problems or concerns. A definitional feature of smart city initiatives, and one that has made the concept popular internationally, ‘has been their potential to deliver economic benefits … and their ability to increase a city’s competiveness locally and internationally.’ Indeed, in this model of a smart city, investment, efficiency and optimisation are key features of smart city economies, driven by innovation and entrepreneurialism characterised by startup business models.
What is the role of ‘big data’?
The use of data or ‘big data’ is often central to any smart city strategy. There are of course limitations with the collection of any dataset (such as due to misunderstood observations to biases of the observer), however for any smart city or place strategy, relying on large, technologically acquired datasets does raise a number of specific issues.
Automatically acquired data can have benefits including that:
- the data can be relatively cheap to acquire
- it can provide lots of data samples so may capture the full breadth of what is happening
- data can be acquired in a timely manner
There are also a number of potential problems with technologically acquired datasets including:
- data is only acquired from what the sensors are capable of monitoring or detecting
- inability to capture data from people who can’t afford electronic devices or who choose not to use such devices
- datasets can be very large and expensive to store
- the software programs to analyse the data and to present it in ways that are meaningful can be very expensive
- the assumptions about how the data will be used (or even which bits of the data are most important) may be different between the people who designed the technology to acquire and analyse the data and the people or institutions that use the results of the data analysis
- limitations in the internal capacity of organisations to afford regular upgrades and maintain a workforce with a relevant (and constantly changing) skill set to operate analytical systems
Importantly, any analysis of data must always be aware that what is actually occurring on the ground may be different to what is recorded in the dataset (i.e. ‘the map is not the territory’).
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