Using computational social science to improve public safety: Zhuofei Lu shares research with policymakers
Dr Zhuofei Lu, Postdoctoral Researcher in Sociology, was recently invited to present his work to the Government of Alberta's Public Safety and Emergency Services, demonstrating how quantitative social research can support evidence-based practice and policy evaluation.
In his presentation, Dr Lu drew on two case studies. The first examined historical wildfire records from 2006 to 2024, where he applied multi-channel sequence modelling across more than 300 geographic grids to reveal patterns that could inform more targeted preparedness and response strategies.
The second focused on more than 2,000 childcare centre inspection histories between 2022 and 2025. By tracing routine and enforcement inspections, along with the remedies that followed, he was able to show how compliance trajectories develop over time.
Together, these studies highlight the potential of computational and quantitative approaches to generate insights that can strengthen policy and practice in the fields of public safety and emergency services.
Dr Lu compared descriptive statistics, regression, and survival analysis with sequence analysis, highlighting the contexts in which each method is most suitable and the kinds of questions they can address.
He then provided a detailed demonstration of sequence analysis, showing how to prepare longitudinal data as state-time sequences (including multi-channel structures), compute pairwise distances (e.g. Optimal Matching and Hamming), cluster trajectories to identify typologies, and interpret transition patterns with policy-relevant insights.
Reflecting on the event, Kayla Jiachen Liu, Integrated Analytics Lead at Public Safety and Emergency Services, noted:
Our branch was pleased to host Dr Zhuofei Lu, who delivered a presentation on sequence analysis and its application to our open-access provincial datasets.
His presentation contributed to our strategic analytics in support of government policy evaluation and demonstrated potential for further applications in police services and natural disaster data analysis.
Dr Lu's engagement underlines the Sociology Department's commitment to promoting computational social science methods in public service and strengthening the societal impact of sociological research.
