Ensuring high quality public safety data in participatory crowdsourcing used as a smart city initiative

The increase in urbanisation is making the management of city resources a difficult task. Data collected through observations of the city surroundings can be used to improve decision-making in terms of manage city resources. However, the data collected must be of quality in order to ensure that effective and efficient decisions are made. This study is focused on improving emergency and non-emergency services (city resources) by using Participatory Crowdsourcing as a data collection method (collect public safety data) utilising voice technology in the form of an advanced IVR system known as the Spoken Web. The study illustrates how Participatory Crowdsourcing can be used as a Smart City initiative by illustrating what is required to contribute to the Smart City, and developing a roadmap in the form of a model to assist decision-making when selecting the optimal Crowdsourcing initiative. A Public Safety Data Quality criteria was also developed to assess and identify the problems affecting Data Quality. This study is guided by the Design Science methodology and utilises two driving theories: the characteristics of a Smart City, and Wang and Strong’s (1996) Data Quality Framework. Five Critical Success Factors were developed to ensure high quality public safety data is collected through Participatory Crowdsourcing utilising voice technologies. These Critical Success Factors include: Relevant Public Safety Data, Public Safety Reporting Instructions, Public Safety Data Interpretation and Presentation Format, Public Safety Data Integrity and Security, and Simple Participatory Crowdsourcing System Setup