A Brief Statistical and Geostatistical Survey of the Relationship between COVID-19 and By-Mail Balloting in the 2020 North Carolina General Election

Abstract

Adding to the already polarizing 2020 general election was the COVID-19 pandemic. One way in which this pandemic greatly affected the election was through an increased participation in by-mail, or mail-in, ballots. The state of North Carolina experienced a 316 percent increase in by-mail votes between 2016 and 2020, when approximately 977,186 votes were cast by mail. It is no surprise that this increase was due to the COVID-19 pandemic; however, these by-mail voting patterns are spatial in nature and vary across the state. This research measures to what degree COVID-19 rates affected by-mail voting rates. Using geographic information systems data developed from robust tabular files provided by the North Carolina State Board of Elections, by-mail votes were calculated and mapped at ZIP code scale and compared to COVID-19 rates measured at different dates. By-mail rates taken from final absentee tallies for the highest and lowest COVID-19 ZIP codes saw no significant differences across multiple dates (30 September 2020 and 31 October 2020) when COVID-19 data were collected. COVID-19 hot spots (high COVID-19 rates surrounded by other high COVID-19 rates) were extracted using geostatistical techniques and compared to COVID-19 cold spots (low COVID-19 rates surrounded by other low COVID-19 rates). It was found the lowest by-mail rates actually occurred in these COVID-19 hot spots across both dates, as well a metric that expressed percentage change in COVID-19 rates in the month before the 2020 election.

COVID-19使得已经两极分化的2020年美国大选, 变得更加雪上加霜。COVID-19影响选举的一种方式是邮寄选票的增加。2016年至2020年, 北卡罗来纳州的邮寄选票增加了316%, 共约977,186张。毫无疑问, COVID-19导致了邮寄选票的增加。然而, 邮寄选票在本质上是空间性的, 并且在北卡罗来纳州的各个地方具有差异性。本研究计算了COVID-19发病率对邮寄选票比例的影响程度。利用北卡罗来纳州选举委员会提供的准确的表格文件, 本文制作了地理信息系统数据, 在邮政编码尺度上对邮寄选票进行计算和制图, 并将这些邮寄选票与不同时间的COVID-19发病率进行了比较。在拥有最高和最低COVID-19发病率的邮政编码和不同时间（2020年9月30日和2020年10月31日）, 从缺失人数统计中得到的邮寄选票比例没有显著差异。利用地学统计方法提取COVID-19热点（COVID-19高发病率在空间上被其它高发病率所包围）, 并与COVID-19冷点（COVID-19低发病率在空间上被其它低发病率所包围）进行比较。结果发现, 在这两个时间内, 最低邮寄选票比例出现在COVID-19热点地区。本文还制定了一个指标, 可以表示2020年大选前一个月的COVID-19发病率百分比的变化。

Por si faltare algo a la ya polarizadora elección general del 2020, se presentó la pandemia del COVID-19. Un modo como la pandemia afectó grandemente la elección fue la participación de votantes mediante el sufragio por correo. El estado de Carolina del Norte experimentó un incremento del 316 por ciento de los votos por correo entre el 2016 y el 2020, cuando 977.186 votos fueron depositados por correo. No hay ninguna sorpresa de que este incremento se debió a la pandemia del COVID-19; sin embargo, estos patrones de votación por correo son de naturaleza espacial y varían a través del estado. Esta investigación mide el grado con el que las tasas de COVID-19 afectaron las tasas de votación por correo. Usando datos de sistemas de información geográfica desarrollados a partir de archivos tabulares robustos suministrados por el Consejo Electoral del Estado de Carolina del Norte, los votos por correo se calcularon y mapearon a la escala de los códigos ZIP y se compararon con las tasas de COVID-19 medidas en fechas diferentes. Las tasas de votación por correo tomadas del conteo final de votantes ausentes en los códigos ZIP con valores más altos y más bajos no registraron diferencias significativas a través de fechas múltiples (30 de septiembre de 2020 y 31 de octubre de 2020) cuando los datos de COVID-19 fueron recogidos. Los puntos calientes de COVID-19 (altas tasas de COVID-19 rodeadas de otras altas tasas de COVID-19) se extrajeron usando técnicas geoestadísticas, comparadas con puntos fríos de COVID-19 (tasas bajas de COVID-19 rodeadas por otras tasas bajas de COVID-19). Se encontró que las más bajas tasas de votación por correo ocurrieron realmente en estos puntos calientes de COVID-19 a través de las dos fechas, lo mismo que una métrica que expresaba el porcentaje de cambio en las tasas de COVID-19 en el mes anterior a la elección de 2020.

Key Words:

• by-mail voting
• cluster analysis
• COVID-19 mapping
• election mapping
• geographic information systems

关键词:

• 邮寄选票
• 聚类分析
• COVID-19制图
• 选举制图
• 地理信息系统。

Palabras clave:

• análisis de clúster
• cartografía electoral
• mapeo de COVID-19
• votación por correo

Additional information

Funding

This research was supported in part by the ACCORD (Advanced Center for COVID-19 Related Disparities) project. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the University of North Carolina System or North Carolina Central University.

Notes on contributors

Timothy Mulrooney

TIMOTHY MULROONEY is an Associate Professor in the Department of Environmental, Earth and Geospatial Sciences at North Carolina Central University in Durham, NC 27707. E-mail: [email protected]. The focus of his research and teaching is in GIS database development, standards, and applications, with work on the subjects of GIS metadata, the quality assurance and quality control of geospatial data, mapping of the food environment, and health phenomena such as the emerging COVID-19 pandemic.

Christopher McGinn

CHRISTOPHER McGINN is an Associate Professor in the Department of Environmental, Earth and Geospatial Sciences at North Carolina Central University in Durham, NC 27707. E-mail: [email protected]. The focus of his research and teaching is human and political geography, including voting patterns in the state of North Carolina and abroad as well as the explanatory socioeconomic and policy factors that drive these patterns at various scales.