Urban planning in the fourth industrial age

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Photo by Kevin Ku from Pexels.

Much has been said about how the legacy of colonialism and apartheid continues to haunt South Africa, even more than a quarter of a century since the arrival of democracy. 

Perhaps one of the greatest markers of this legacy is our spatial planning, which leaves huge swathes of our population out of reach from the central economic hubs. The inherent separation and exclusion that defined apartheid were not just limited to pass laws and separate development but in the fundamental way that people were settled in the country. Designed to segregate and enforce policy-driven inequality, the group areas act is still ubiquitous in the urban metropolis and rural reserves.

The Fourth Industrial Revolution (4IR), the era we have entered which is fundamentally shifting every aspect of society through intelligent technologies, has been touted as the key to finding solutions to many of our deep-seated problems. As we look at redefining urban planning, various technologies can be deployed. If we intelligently use these technologies when we service remote areas of the country and densely populated areas, for example, we could undermine the negative conversation over service delivery, which is often perceived as desperate and tainted in South Africa.

In August 2020, the Presidential Commission on the Fourth Industrial Revolution (PC4IR), of which I am deputy chair, presented a set of eight recommendations. One of these is to build 4IR infrastructure which integrates with existing economic and social infrastructure. The infrastructure envisioned is data-enabled, software-based, and has cloud access. 

Digital infrastructure is expected to improve access to information and thereby promoting transparency of government activities and processes, and in turn, build interconnected empowered communities. The 4IR can help ease South Africa’s vast service delivery challenges. For example, we should investigate the generation and delivery of energy, the extension and improvement of water infrastructure and health and educational infrastructure to create a coherent and comprehensive infrastructure network.

This is imperative when you consider that according to Municipal IQ, the rapid growth of informal settlements coupled with an unwillingness on the part of metros to accept them as a permanent reality has resulted in a slow response to the service delivery needs of communities in our largest metros.

We have already seen the piloting of programmes to tackle service delivery gaps. Last year, the Minister of Co-operative Governance and Traditional Affairs (Cogta) Dr Nkosazana Dlamini-Zuma announced the piloting of a new district development model to address corruption, poverty, economic growth, unemployment, spatial planning and skills development in municipalities. 

The model, which was launched in the eThekwini municipality, will “synchronise planning by all spheres of government and involve citizens and civil society in the development of South Africa’s 44 municipal districts and 8 Metros.” The idea behind the model is to manage urbanisation, growth, and development, support local economic drivers, accelerate the land release and land development, as well as to invest in infrastructure for integrated human settlements, economic activity, and the provision of essential services. It also seeks to address service delivery problems in municipalities. At the end of July, the urgent roll-out of this model was announced, based on 52 district and metro spaces that have been participated. 

There are other avenues where we can use 4IR technology, particularly at a local government level. Artificial Intelligence (AI), for example, can improve urban planning by optimising routes for transport operators, reducing commuters’ journey times – a particularly significant move given our urban layout. 

Municipal governments could utilise traffic data for the planning of roads and the monitoring of traffic patterns. This is quite a simple concept. This has already been successfully piloted in cities in the United States. This makes use of an AI system which detects vehicles in images from traffic cameras. This information can be sent to a control centre, where algorithms analyse traffic density. If the system detects congestion, it can direct traffic lights to re-route traffic, based on real-time data.

Machine learning could also provide myriad solutions in terms of water supply, for instance. This can be in areas such as predictive analysis to manage our supply networks, data analysis to track water consumption and water end-users, as well as the management of sewage treatment plants or desalination plants. AI could be applied to predict which services, such as energy, water and sanitation, have a shortfall. If this had been employed ahead of the pandemic, we could identify which schools do not have access to water instead of the current inefficient manual audits. Much of this technology could have been deployed as solutions for the logistical nightmare of screening, testing, reaching the vulnerable and distribution of food parcels.

Of course, as one talks of spatial planning, often the forgotten segment is our vast informal settlements. This has to be at the centre of relooking at urban planning, particularly given how prevalent these settlements are but how often they are overlooked. There is already technology in place that can map existing informal settlements. Machine learning data-sets can be put in place to detect informal settlements. It is possible to detect informal settlements using freely available low-resolution (LR) data, which makes collecting this information easier. 

Researchers from several universities involved in the Frontier Development Lab Europe programme have developed two deep learning-based tools that can automatically classify informal settlements using freely available satellite and aerial imagery. While a global project, this has already been done in some South African informal settlements. 

The first tool was trained to classify what the spectrum of an informal settlement looks like, based on LR data, and can detect everything that is and is not an informal settlement. The second tool used high-resolution (HR) satellite imagery, which was analysed to locate settlements that do not contain unique spectral data on low-resolution systems.  This was a more cost-intensive process. These kind of projects, however, are still in quite a nascent stage, and they require securing and availing data to enable innovation, which is another recommendation of the PC4IR.

Another 4IR technology relevant to urban planning is blockchain, which is used to create the cryptocurrency, Bitcoin. In Estonia, blockchain technology has been used to protect data of people and create a secure digital identity. With the advent of the coronavirus, blockchain can be used to secure our transporting system by integrating Covid-19 data, movement of people and modes of transportation.    

As the coronavirus pandemic forces us to relook at policy and ways our society functions, many of the answers could lie in the technologies of the 4IR. We may be able to subvert the narrative around our spatial planning yet.

Professor Tshilidzi Marwala is the Vice-Chancellor and Principal of the University of Johannesburg. He is the Deputy Chair of the Presidential Commission on the Fourth Industrial Revolution. He is the author of the upcoming: Closing the Gap-Fourth industrial revolution in Africa.