Urban data governance and policies : a comparison using case studies

Abstract

Due to increasing internet access and mobile phone usage, the collection of data has exploded globally in the past decade. At the same time, the processing power and storage capabilities have become cheap and prevalent, and sophistication and complexity of AI and machine learning algorithms have advanced and are widespread. Data is a new type of capital. Public governance has not yet caught up, and because of the specialized technical expertise required, the public sector has permitted and sometimes even invited private companies to make the rules. Caught up in these trends is a tension between cities wanting to be at the forefront of technology while needing to act in the public interest and protect the marginalized and underrepresented. This thesis utilizes a comparative analysis of two case studies, Toronto and New York, to examine the existing urban data governance models and identify some learnings from the comparison. Within each case study, the thesis employs systems architectural concepts such as stakeholder mapping and architectural decisions to illustrate differences and highlight shortcomings and potential recommendations to address in future policy proposals.