Shaped by human movement, place connectivity is quantified by the strength of spatial interactions among locations . For decades, spatial scientists have researched place connectivity, applications, and metrics . The growing popularity of social media provides a new data stream where spatial social interaction measures are largely devoid of privacy issues, easily assessable, and harmonized . In this study, we introduced a place connectivity index (PCI) based on spatial interactions among places revealed by geotagged tweets as a multiscale, spatiotemporal-continuous, and easy-to-implement measurement . The proposed PCI, established and demonstrated at the US county level, exhibits a strong positive association with SafeGraph population movement records (10 percent penetration in the US population) and Facebook social connectedness index (SCI), a popular connectivity index based on social networks . We found that PCI has a strong state boundary effect and that it generally follows the distance decay effect, although this force is weaker in more urbanized counties with a denser population . Our investigation further suggests that PCI has great potential in addressing real-world problems that require place connectivity knowledge, exemplified with two applications : 1) modeling the spatial spread of a contagious disease (e.g., COVID-19), and 2) modeling hurricane evacuation destination choices . The methodological and contextual knowledge of PCI, together with the launched visualization platform and data sharing capability, is expected to support research fields requiring knowledge in human spatial interactions.