Xuebin Wei
Biographic Data
| ID | 3720660 |
|---|---|
| NAME | Xuebin Wei |
| GIVEN NAMES | Xuebin |
| FAMILY NAME | Wei |
| SIGNATURE | WEI X |
| AFFILIATIONS | James Madison University |
| ORCID | 0000-0003-2197-5184 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2021 |
| H-INDEX | 0 |
Constructing and analyzing spatial-social networks from location-based social media data
People interact with each other in space and time. Improved understanding of human interactions in spatial, temporal, and social dimensions are highly beneficial for research and practices in public health, urban planning, and other fields. Traditional methods of collecting social interaction data are time-intensive and resource-consuming, resulting in relatively small sample sizes and limited information. Furthermore, traditional methods often o…
Where we are in fighting against Covid-19
On March 11, 2020, the World Health Organization announced the novel coronavirus disease (COVID-19) as a pandemic. Despite an increasing number of international attempts using maps to present and communicate COVID-19-related information in different organizations, most map products have only used the presentation function of maps. Against this backdrop, we offer an automatically daily-updated, color-blind-friendly, Tableau-based interactive dashb…
No prominent works on this page.
Where we are in fighting against Covid-19
On March 11, 2020, the World Health Organization announced the novel coronavirus disease (COVID-19) as a pandemic. Despite an increasing number of international attempts using maps to present and communicate COVID-19-related information in different organizations, most map products have only used the presentation function of maps. Against this backdrop, we offer an automatically daily-updated, color-blind-friendly, Tableau-based interactive dashb…
Constructing and analyzing spatial-social networks from location-based social media data
People interact with each other in space and time. Improved understanding of human interactions in spatial, temporal, and social dimensions are highly beneficial for research and practices in public health, urban planning, and other fields. Traditional methods of collecting social interaction data are time-intensive and resource-consuming, resulting in relatively small sample sizes and limited information. Furthermore, traditional methods often o…
Computer Science (2 works) · Data science (2 works) · Data-Driven Disease Surveillance (2 works) · Geography (2 works) · 2019-20 coronavirus outbreak (1 work) · Biology (1 work) · Business (1 work) · COVID-19 Digital Contact Tracing (1 work) · COVID-19 epidemiological studies (1 work) · Dashboard (1 work)