Abstract
The increase in urbanisation is making the management of city resources a difficult task. Data collected through observations (utilising humans as sensors) of the city surroundings can be used to improve decision making in terms of managing these resources. However, the data collected must be of a certain quality in order to ensure that effective and efficient decisions are made. This study is focused on the improvement of emergency and nonemergency services (city resources) through the use of participatory crowdsourcing (humans as sensors) as a data collection method (collect public safety data), utilising voice technology in the form of an interactive voice response (IVR) system. This study proposes public safety data quality criteria which were developed to assess and identify the problems affecting data quality. This study is guided by design science methodology and applies three driving theories: the data information knowledge action result (DIKAR) model, the characteristics of a smart city, and a credible data quality framework. Four critical success factors were developed to ensure that high quality public safety data is collected through participatory crowdsourcing utilising voice technologies.
Highlights
Local government accepts responsibility for maintaining the city’s infrastructure, as well as for providing a safe living environment [1]
The presentation of the critical success factors will follow, and they will be linked to the identified public safety data quality problems identified
The crowdsourcing safety Initiative (CSI) participatory project aims at providing an environment in which the public may communicate safety issues to the Buffalo City Municipality, that is, data provided by the participants related to public safety issues
Summary
Local government accepts responsibility for maintaining the city’s infrastructure, as well as for providing a safe living environment [1]. City resources are unable to support all members of the public because the ratio of number of citizens to city resources (e.g., water and electricity distribution or public safety emergency and nonemergency units) is high. This is not because city resources are scarce, but because they are not managed effectively and efficiently [2]. In order to explain how one would improve the management of city resources, the data information knowledge action result (DIKAR) model can be used. The data collected must be of sufficient quality to facilitate effective decision making regarding the management of city resources
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