Master's students graduate

Two of the Master's students on our project have graduated. Congratulations to Nalukui Malambo and Daniel Mutale.

Both were awarded MCom degrees by research at the graduation on the 27th March 2018.

 

Nalukui Malambo

Ms Malambo's thesis was titled: "Adoption of Smart City Agendas: Exploring the cases of Cape Town and Nairobi" and was supervised by Prof Judy Backhouse. Ms Malambo has since been appointed as a lecturer in Information Systems at the University of the Witwatersrand.

Daniel Mutale

Mr Mutale's thesis was titled: "Continued use of e-government services: An expectation confirmation theory and trust theory approach" and was supervised by Jean-Marie Bancilhon.


We are all very proud of their results.

Prof Backhouse participates in the DBSA Urban Safety Dialogue

Prof Judy Backhouse was invited to be a panelist at the Development Bank of South Africa's recent Infrastructure Dialogue on Urban Safety. This Dialogue, held on the 16th November, was subtitled: Safety in our public spaces: Can infrastructural or technological interventions save us, and in what balance?

Students Nalukui Malambo, Lizalise Myataza, Letlotlo Kothane and Bonolo Motsepe joined the discussion and took the opportunity to share information about their research during the round-table discussions. 


Below is the text of Prof Backhouse's input to the panel discussion:

Critical perspectives on the pros and cons of technology as a solution to safety in public spaces

At Wits I have been running a project titled “Information Systems for Smart Cities in Africa” for the past three years.  I've been asked to consider the questions: Can smart city projects provide a city or regional solution to address development and infrastructure problems? Or is Smart City a catchy buzzword used for corporate profit-making with limited benefits for government and the public? My answer to both these questions is a typically South African: yes, and no.

Now it is very cruel to ask an academic to speak for ten minutes. What I want to do in this very short time, is to introduce you to two analytical devices or frames for thinking about these questions. The first is helpful in trying to understand what a smart city is and the second is useful for understanding different information systems that could help in solving city problems.

So, let's start with the question: What is a Smart City? We found that no-one really agrees. But we were able to identify two different kinds of understandings and an easy way to think about them is in terms of definitions of the word smart.

Some definitions of Smart include: “polished, fashionable, indicative of wealth”, “clean, tidy and well-dressed” or “fashionable and upmarket”. So we find that for some the idea of a smart city is a city that is wealthy, successful, clean and with good infrastructure, or modern. With this understanding of a Smart City comes a focus on supporting business (often high-tech and international business), attracting talent to work in those businesses, and improving infrastructure.

Other definitions of the term Smart are: “having quick-witted intelligence” or a device that is “programmed so as to be capable of some independent action”. Such definitions of smart lead to an understanding of smart cities as places where intelligence (both human and machine) is applied to solve city problems. Projects that support research to better understand city problems and the application of technologies in collecting and analysing data to inform solutions emerge from this sense of a smart city. 

So we have these two understandings: one about appearance and wealth and the other about intelligence and understanding. Try to guess which one I favour.

One of the problems with a lot of smart city projects is that they are exclusionary. My colleague Ms Malambo spent time in Nairobi looking at the Khonza City development that is taking place there. This is an initiative to build new cities, on the outskirts of Nairobi, that are intended to be smart cities. These cities are designed with good infrastructure and services, and are promoted as places that are safe, clean and better than Nairobi itself. They clearly target highly-skilled individuals and international business. While there is some benefit for the poor and small or informal businesses in servicing these projects, their needs are not being considered directly. These projects are driven by large international construction and information technology companies and serve their interests. This kind of approach to smart cities is likely to lead to increasing inequality and divert resources away from projects with more equitable goals. 

But if we consider the second understanding of smart city as the application of intelligence to better understanding and solving city problems, we find that information technologies do offer interesting possibilities for addressing the problems of rapid urbanisation.

Now the problem we are particularly interested in today is that of urban safety.

There are many ways that we can apply intelligence (both human and machine) to improve urban safety. Technology enables us to collect information about crime, about how people behave. We can observe what is happening using a range of different kinds of data – visual, audio, and indirect (for example, what phone calls someone makes or the tracking data that results from someone carrying a cellphone or wearing a bracelet). We can collect enormous quantities of data and store it, have special analytical tools that enable us to delve into this data and find patterns in it that increase our understanding. Note that these technology solutions have to be used in conjunction with human intelligence to design, operate and interpret the information that results and to assign meaning and decide on actions that result. 

At this point I want to introduce the second analytical device for our discussion. Recall that the first was the distinction between two ways of looking at smart cities. This second is about two kinds of technology solutions. We have central, top-down technology solutions that are centrally implemented and controlled and we have diffuse, bottom-up technology solutions that are devised and implemented by a range of different stakeholders.

So, for example, we know that safety in public places depends on there being other people around to observe activities. Technology offers us new kinds of “eyes” in the form of  surveillance technologies that have been deployed to increase safety. One example is CCTV cameras that are installed in public spaces. These may be a good idea, but at the moment research into whether these technologies actually reduce crime is inconclusive. Some studies show that crime decreases, in some specific locations like parking lots, but not in city centres (Welsh and Farrington, 2009) others show no change and some even report increases in crime as people feel more secure and take fewer precautions or because crime is displaced to areas that are not monitored (Cerezo, 2013). But these technology solutions depend on people to be effective, so for example one study shows that surveillance systems reduced crime only when there were also effective enforcement activities (Piza, Caplan and Kennedy, 2014). In addition, it is often difficult to conclusively attribute changes in crime levels to the surveillance tools. 

Surveillance cameras are an example of what researchers call a top-down or centralised information system. That is an information system that is designed and run by a central authority, for the benefits of others. But technology also provides bottom-up or decentralised solutions, in which more people participate and shape what the information system is and does.

On this side of the spectrum are apps that help individuals take care of their safety by allowing their friends and family to track their whereabouts and receive emergency signals should the individual feel in danger. The Android apps Personal Safety Panic Alarm and bSafe are examples. The first has been downloaded 50 000 times and the latter 500 000 times and research shows that they give people a greater sense of safety. Such apps are examples of bottom-up approaches to security, where the "eyes" are friends and family members, although in one study of mobile safety apps (in Ireland), people said they would be happy to have police monitor their safety apps, despite privacy concerns (McCarthy, Caulfield and O'Mahoney, 2016). 

Even without apps, people use their cellphones to increase their safety by telling a friend where they are going and asking for a call if they have not checked in by an agreed time. These individual uses of information technology are informal information systems and are also important features of a Smart City.

Bottom-up solutions are designed by a wide range of stakeholders, including residents and small businesses, and so they bring more brains (and other resources) to bear on the problem; they may also make people feel empowered, be more effective and cheaper to implement than top-down solutions, but research in these areas is lacking, so we don't know for sure.

A smart city that wants to make use of bottom-up smart solutions would enable it's residents to be smart by enabling their use of technology. People own cellphones, but they need the skills to use them, and they need to have access to networks in order to be able to use safety solutions or to invent their own. Smart cities in Africa face the problem of getting people connected before they can make use of bottom-up solutions.   

So, I have given you two analytical devices for thinking about smart cities and their possible contribution to urban safety. First to distinguish between a smart city as wealthy and posh or as intelligently seeking understanding. There I unashamedly favour the latter. Second to think about solutions in terms of top-down and bottom-up. Here I favour both, since both have their uses. 

I want to end with three questions for discussion:

1.  How do we ensure that whatever technology solutions we introduce, the interests that are served are inclusive and not elite?
2. What are the challenges in deploying effective central, top-down technology solutions for urban safety?
3.  Can we make better use of distributed, bottom-up systems designed by more stakeholders?

References

Cerezo A. (2013). CCTV and Crime Displacement: A Quasi-experimental Evaluation. European Journal of Criminology, 10(2), 222–236.

McCarthy O.T., Caulfield B. and O'Mahoney M. (2016). How transport users perceive personal safety apps. Transportation research part F: Traffic psychology and behaviour, 43, 166–182.

Piza E.L., Caplan J.M. and Kennedy L.W. (2014). Analyzing the Influence of Micro-level Factors on CCTV Camera Effect. Journal of Quantitative Criminology, 30(2), 237-264.

Welsh B.C. and Farrington D.P. (2009) Public Area CCTV and Crime RPevention: An Updated Systematic Review and Meta-Analysis. Justice Quarterly, 26(4).

Data collection: A tale of two African cities

Intrepid master's student Nalukui Malambo has been completing her data collection and writes here about the experience...

Nairobi (Kenya)

My data collection trip in Nairobi was conducted over a period of 14 days from the 6th to 19th of April 2016. Being the first time in Kenya and not knowing anyone or where I was going, a quick realisation that bravery was on call kicked in immediately.

The data collection was more of an adventurous process than usual, the pace started out a little slower, the first few days involved orienting myself to the city, sorting out tech (buying a sim card, registering on the local network, and finding the right power adapters took a whole day), learning how to navigate the local transportation networks and routes, finding suitable accommodation, restaurants, local supermarkets and understanding the basic words in the commonly spoken local language Swahili.  Although Kenya's official language is English, local languages are still widely spoken. Navigation around the city was made easier by the assistance of a student guide (extremely important if you are there for the first time).

I conducted a total of 11 interviews within and around Nairobi with various stakeholders identified for the research.  The first set of interviews were conducted in Machakos county, specifically in an area called Konza. Konza is situated 60 kilometres outside Nairobi, earmarked to be a new Smart city by the government of Kenya to be built from scratch, locally referred to as “Konza Technology City”. 

"Getting around was interesting..."

Getting around was interesting. My trip from Nairobi to Konza started out on a Friday at 6am using local public minibuses known as matatu. It took approximately 3 hours and 3 matatus to Konza, as there is no direct route to Konza using public transport. Access into the residential area from the main road required the use of a local motor-bike (boda-boda) to the area chief’s office. Before conducting the interviews, permission from the current Chief was requested and granted. Stakeholders interviewed at Konza included: the area Chief, a former Councillor, the secondary school principal and local residents, as well as a site visit to the construction area where the city structures are to be constructed. At the time of my site visit, the Konza project team had just broken ground a week before.  On observation, Konza is a beautiful area and strategically located on Kenya’s main highway leading from Nairobi to Mombasa. The land still remains pretty much virgin land, with an abundance of all sorts of wild animals, a swampy patch and plenty of vegetation.

Interviewing the Konza area Chief

Back in Nairobi, some of the interviews were conducted at the Konza administrative offices. The Konza project office (KotDA) was established to oversee the implementation of the Konza Technology City project tasked with managing the new Smart City project under the directorate of CEO John Tanui. Overall, data collection in Nairobi took on an unstructured approach, partly because of the nature of the research being exploratory, as well as the nature of key informants that snowballed from informant to informant. In addition, I spent most of my free time (which included weekends) collecting information on the smart city agenda from pedestrians, noting construction activities and taking photographs. I also interviewed representatives of organisations that are indirectly involved in the Smart City agenda such as the United Nations, IBM, Cisco, University of Nairobi etc.

Cape Town (South Africa)

My data collection trip to Cape Town started out on the 7th to 12^th February 2016. In contrast to Nairobi, the trip was short and structured. Before leaving for Cape Town, I had diarised eight interviews and managed to secure an extra four while in Cape Town. I conducted at least three interviews per day with the various stakeholders involved in the Smart City initiatives of Cape Town.

My way around Cape Town was simple. The city has well run and organised, structured public transport. I used Uber for most of my morning rides (to avoid arriving late at my meeting destinations) as my accommodation was situated 11 kilometres out of the way of public transportation.  It was easy to find my way around the city even though this was not my first time in Cape Town. I hopped on and off public transportation for my afternoon ride (train, taxi and buses) to the hotel and any other meeting places I had to get to.

I would like to think my data collection trip to Cape Town prepared me to some extent for my Nairobi trip from a research point of view, but the experience was very different and I was not prepared for the differences in how the cities worked. 

From this, I learnt that a certain comfort with ambiguity is necessary to conducting research in an African city, which most often comes with the upside of learning creative problem solving skills. I learnt that information is available, but not necessarily in the places you expect to find it. Most importantly, I learnt that you have to be brave, patient and flexible to collect data in African cities.