DOI: 10.3886/icpsr39503.v1
title: Creating Data Visualizations to Understand the Impact of Structural Racism on Anti-Asian Hate Crime During COVID-19 and to Mitigate Its Effects, United States, 2023-2025
title: Version 1

description: The COVID-19 pandemic has resulted in the scapegoating of Asians, leading to a rise in hate crimes against them and adversely affecting their mental health and well-being. This project seeks to visualize the connection between structural racism, health, and anti-Asian Hate Crime (AHC) incidents reported in both the Federal Bureau of Investigation (FBI) and news and social media collected by the the Asian American Foundation (TAAF) during the period of 2020-2021. The project will first visualize the pattern of AHC incidents reported in FBI and media data, considering variations by states, counties, and months. This project will then identify state- or county-factors associated with AHC incidents and visualize them. The project will further examine changes in mental health and coping behaviors, following AHC incidents. The project will finally explore whether community-level AAPI organizations availability can mitigate the adverse effects of AHC incidents on mental health and coping behaviors.The project's target audience includes social change networks, media outlets, academic society, federal/state legislators, and national-level professional organizations. The resulting visualizations will facilitate policy debates surrounding various aspects, such as addressing underlying risk factors related to AHC, improving data collection and reporting methods within law enforcement agencies to accurately document AHC incidences in Federal/State archives, implementing policies and interventions to mitigate adverse effects on people's health, promoting understanding of cultural differences and inclusivity.
description: To contribute to combatting stereotypes and promoting positive representations of Asian Americans to counteract the normalization of racism and hate crimes through collaboration between educational institutions, community organizations, and media.
description: The study merged data from multiple sources, including AAHI records from The Asian American Foundation (TAAF) and the Federal Bureau of Investigation (FBI), as well as contextual data at the county-level sourced from various outlets. The following sections provide detailed insights into the data compilation process:
<strong>Media Data:</strong> TAAF systematically gathered data on AAHIs reported in various news media from January 2020 to December 2021. The collected information included details such as reported media sources, dates, incident types, descriptions, locations, victim genders, and more. To identify relevant news, TAAF utilized Google News Alerts and Feedly on a weekly or biweekly basis, employing keywords associated with AAPI identities and hate incident attributes. The organization's staff members manually reviewed headlines in media articles, selecting those meeting the criteria for defining an AAHI case.
<strong>Law Enforcement Data:</strong> This study incorporated AAHI data obtained from the FBI for the years 2020 and 2021. The FBI's dataset encompassed a comprehensive range of hate crime information, including bias types and motivation, details about criminal acts, offender and victim demographics (race/ethnicity, age, gender), incident types, and locations. The AAHI data retrieved from both TAAF and the FBI were subsequently aggregated by months and counties for our data analysis.
<strong>Other Data Sources:</strong>
The data were sourced from diverse repositories, including the Johns Hopkins CSSE repository,
HeathData.gov,
MIT Election Data + Science Lab, and
County Health Rankings and Roadmaps. To conduct a comprehensive analysis, this study integrated monthly data with AAHIs based on both the month and the county of occurrence (e.g., COVID-19 death case rate). Additionally, yearly data (e.g., frequent physical distress) were merged with the monthly AAHI data, based on the year and the county of occurrence. Finally, time-invariant data (e.g., 2020 U.S. presidential election democratic vote shares) were merged with AAHI data, aligning with the county of occurrence.
description: Response Rates: When estimating a county-random effects model, the dependent variables include the counts of anti-Asian hate incidents (AAHI) reported in media data, those reported by law enforcement, and the ratio of counts between these two sources. The explanatory variables consist of county-level contextual factors. The data is structured at the county and monthly level, covering the years 2020 and 2021. In the panel regression model, the response rate is 60.42%.
description: The FBI's data collection process involved a two-step procedure, wherein a responding officer initially assessed whether an incident was suspected to be bias-motivated. If deemed so, the case underwent a secondary review by a judgment officer/unit before being reported to the FBI Uniform Crime Reporting (UCR) program (GLESS and CLESU, 2022). Although the Hate Crime Statistics Act of 1990 mandated the Attorney General to collect hate crime data from law enforcement agencies at all levels, submitting data to the FBI UCR program was voluntary (GLESS and CLESU, 2022). Consequently, bias-motivated offenses data might be incomplete if some law enforcement agencies choose not to participate in the program. Furthermore, cases lacking address information for AAHIs prevented the identification of the county of occurrence, leading to their exclusion from the analysis.
description: Datasets: 
DS1: Creating Data Visualizations to Understand the Impact of Structural Racism on Anti-Asian Hate Crime During COVID-19 and to Mitigate Its Effects, United States, 2023-2025
description: County-level statistics in the United States. Smallest Geographic Unit: County
description: web scraping