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:
- How do we ensure that whatever technology solutions we introduce, the interests that are served are inclusive and not elite?
- What are the challenges in deploying effective central, top-down technology solutions for urban safety?
- 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).
