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"Cities After COVID-19" Academic Research

Data Transparency in Anti-Asian Hate Crimes under COVID-19

In the backdrop of the Stop Asian Hate protests that swept across North America in 2021, myself and three classmates were intrigued by the methodologies employed by scholars, community leaders, and media in gathering statistics on Anti-Asian Hate Crimes. We questioned whether the authorities were effectively utilizing this data to inform their strategies.

Under the guidance of Dr. Beth Coleman, we secured a grant from both the University of Toronto and University College London to undertake formal research on this issue. Our approach involved interviewing experts from academia and community organizations to delve into existing data policies and to formulate a roadmap for enhancing collaboration between stakeholders.

Image by Jason Leung
Image by Jason Leung

Our study sheds light on the escalating anti-Asian sentiment in Canada during the pandemic, highlighting a lack of transparency in reported hate crime statistics.

As we delved into our research paper, the ongoing COVID-19 pandemic continued to evolve, along with its societal impacts. Our study aimed to provide a snapshot of the escalating anti-Asian sentiment that paralleled the spread of the disease, with a specific focus on the Canadian context.

With a population exceeding 37 million, Canada is home to approximately 6 million Asians, constituting 16% of the population. Since the outset of the pandemic, there has been a concerning surge in hate crimes targeting the Asian community, as reported by major news outlets:

  • Vancouver: 717%

  • Montreal: 633%

  • Ottawa: 600%

  • Toronto: 400%

While these figures are alarming, the lack of transparency in the data collection and interpretation processes has hindered a comprehensive understanding of the extent of Anti-Asian Hate Crimes.

Our research methodologies involved interviews and analyzed data from governmental reports, academic articles, and media sources.


We conducted two interviews with key individuals:

  • An Assistant Professor from the Department of Sociology at York University, who provided insights on data collection methods and collaboration between academia and community organizations.

  • A Project Lead at a prominent community organization supporting Chinese Canadian communities, who discussed existing data policies and strategies for addressing online racism.

Literature Review

Our research report incorporates key insights from:

  • Official governmental reports and studies

  • Academic articles

  • Media articles from reputable news outlets

Our research identifies a lack of trust in authorities among the Asian community, leading to underreporting of hate crimes.

Our research highlights a lack of trust within the Asian community towards authorities, citing complex legal procedures that hinder effective reporting of hate crimes. A significant number of unreported incidents further obscure the true extent of anti-Asian hate crimes. Additionally, police departments' data collection practices, which do not record the race variables of victims and perpetrators, present inherent limitations.


Despite intentions to address these issues, a collaboration gap exists among authorities, community organizations, and academic researchers, as each entity manages its data independently. This lack of communication hampers efforts to fully understand and address the problem of anti-Asian hate crimes in Canada.

We recommend a collaborative data approach for anti-Asian hate crimes and a review of online hate policies by social media companies.

Based on our findings, we proposed two solution frameworks:

  • Collaborative Roadmap: Stakeholders such as authorities, community organizations, and academic researchers should collaborate to collect, clean, and analyze anti-Asian hate crime data centrally. This approach ensures a consistent data management strategy and enhances public confidence.

  • Online Hate Policies: Social media companies should review their current reporting and content removal protocols for hateful content, or develop algorithms to manage such content automatically."

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