The U.S. has merely 4% of the world population, but contains 25% of the world’s COVID-19 cases. Since the COVID-19 outbreak in the U.S., Massachusetts has been leading other states in the total number of COVID-19 cases. Racial residential segregation is a fundamental cause of racial disparities in health. Moreover, disparities of access to health care have a large impact on COVID-19 cases. Thus, this study estimates racial segregation and disparities in testing site access and employs economic, demographic, and transportation variables at the city/town level in Massachusetts. Spatial regression models are applied to evaluate the relationships between COVID-19 incidence rate and related variables. This is the first study to apply spatial analysis methods across neighborhoods in the U.S. to examine the COVID-19 incidence rate. The findings are: (1) Residential segregations of Hispanic and Non-Hispanic Black/African Americans have a significantly positive association with COVID-19 incidence rate, indicating the higher susceptibility of COVID-19 infections among minority groups. (2) Non-Hispanic Black/African Americans have the shortest drive time to testing sites, followed by Hispanic, Non-Hispanic Asians, and Non-Hispanic Whites. The drive time to testing sites is significantly negatively associated with the COVID-19 incidence rate, implying the importance of the accessibility of testing sites by all populations. (3) Poverty rate and road density are significant explanatory variables. Importantly, overcrowding represented by more than one person per room is a significant variable found to be positively associated with COVID-19 incidence rate, suggesting the effectiveness of social distancing for reducing infection. (4) Different from the findings of previous studies, the elderly population rate is not statistically significantly correlated with the incidence rate because the elderly population in Massachusetts is less distributed in the hotspot regions of COVID-19 infections. The findings in this study provide useful insights for policymakers to propose new strategies to contain the COVID-19 transmissions in Massachusetts.

WorldMap is a web-based, map-centric data exploration system built on open-source geospatial technology at Harvard University. It is designed to serve collaborative research and teaching, but is also accessible to the general public. This article explains WorldMap's basic functions through several historical research projects, demonstrating its flexible scale (from neighborhood to continent) and diverse research themes (social, political, economic, cultural, infrastructural, etc.). Also shared in this article are our experiences in handling technical and institutional challenges during system development, such as synchronization of software components being developed by multiple organizations; juggling competing priorities for serving individual requests and developing a system that will enable users to support themselves; balancing promotion of the system usage with constraints on infrastructure investment; harnessing volunteered geographic information while managing data quality; as well as protecting copyrights, preserving permanent links and citations, and providing long-term archiving.

Understanding the space-time dynamics of human activities is essential in studying human security issues such as climate change impacts, pandemic spreading, or urban sustainability. Geotagged social media posts provide an open and space-time continuous data source with user locations which is convenient for studying human movement. However, the reliability of Chinese geotagged social media data for representing human mobility remains unclear. This study compares human movement data derived from the posts of Sina Weibo, one of the largest social media software in China, and that of Baidu Qianxi, a high-resolution human movement dataset from ‘Baidu Map’, a popular location-based service in China with 1.3 billion users. Correlation analysis was conducted from multiple dimensions of time periods (weekly and monthly), geographic scales (cities and provinces), and flow directions (inflow and outflow), and a case study on COVID-19 transmission was further explored with such data. The result shows that Sina Weibo data can reveal similar patterns as that of Baidu Qianxi, and that the correlation is higher at the provincial level than at the city level and higher at the monthly scale than at the weekly scale. The study also revealed spatial variations in the degree of similarity between the two sources. Findings from this study reveal the values and properties and spatiotemporal heterogeneity of human mobility data extracted from Weibo tweets, providing a reference for the proper use of social media posts as the data sources for human mobility studies.

Founded in 1818, the Harvard Map Collection (HMC) is the oldest map collection in America, holding 400,000 maps, more than 6,000 atlases, and thousands of reference books. HMC has a strong commitment to digital resources, and it manages the Harvard Geospatial Library, a foundation for geospatial data service at Harvard. The Center for Geographic Analysis (CGA) at Harvard University was founded in 2006, independent of the library system, to serve the entire university. This article presents the history, organizational structure, and operational model of CGA and HMC, reviews achievements, lessons learned, suggests future improvements, and reviews GIS-related medical research at Harvard.

The Center for Geographic Analysis (CGA) at Harvard University supports research and teaching that relies on geographic information. This includes supporting geographic analysis for public health research at Harvard. This article reviews geographic concepts that apply to public health, pertinent data available in geographic format, and GIS analytical techniques. The work-flow methodology the CGA has developed for conducting research with geographic data will be presented, highlighting successful practices to follow and pitfalls to avoid. Applications of this work flow are illustrated through an in-depth discussion of specific case studies in public health research at the university.