Page 222 - Proceedings of the 2017 ITU Kaleidoscope

S3.3 Small data and sustainable development - individuals at the center of data-driven societies
Mamello Thinyane (United Nations University, Macao SAR, China)

At the centre of data-driven societies are individuals and end-users who not only generate data,
but also benefit from the outcomes of the data-driven development. Extensive work has been
undertaken to understand and explore the challenges and potential impact of data, in particular
Big Data, for the private as well as the public sectors. Similarly work has been undertaken within
the domains of Personal Informatics and life-logging, which has investigated the role of data,
and specifically personal physical activity and health data towards improving the wellbeing of
individuals. In this research we investigate the engagement of individuals in the use of data
towards the achievement of the sustainable development imperatives as articulated in the 2030
Agenda for Sustainable Development. The paper presents: the awareness levels of the
participants with regards to the Sustainable Development Goals; their attitudes and perceptions
around monitoring of social indicators; key considerations associated with data ownership,
privacy and confidentiality of data, as well as sharing of data within the data ecosystem. The
paper subsequently discusses how these finding could inform the implementation of small data
tools to support the active engagement of individuals in data-driven societies.

Session 4: Smartening up society with data and new applications
S4.1 Fostering smart city development in developing nations: A crime series data analytics approach*
Omowunmi E. Isafiade (University of the Western Cape, South Africa) and Antoine Bigomokero
Bagula (University of the Western Cape, South Africa)

Crime remains a challenge in many parts of the world. This is compounded in low-resource
settings where police are short-staffed and there are not enough technological solutions in place
to assist security agencies with knowledge-driven decision support. While most smart city
initiatives have placed emphasis on the use of modern technology such as armed weapons for
fighting crime, this may not be sufficient to achieve a sustainable safe and smart city in resource
constrained environments, such as in Africa. In particular, crime series which is a set of crimes
considered to have been committed by the same offender is currently less explored in developing
nations despite its importance for public safety improvement. This research presents a novel
crime clustering model, CriClust, based on a dual threshold scheme for crime series pattern
(CSP) detection and mapping to derive useful knowledge from a crime dataset. Based on
analysis of 5500 (rape) crime records across 40 locations (suburbs) in Western Cape, CriClust
led to the identification of up to three series at some of the locations investigated. We present an
effective web-based system that security agencies can use for timely CSP identification to aid
strategic and viable means of combating crime in low resource settings.

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