Measuring interaction among cities in China: A geographical awareness approach with social media data

Unlike the large body of research on investigating interactions among cities using survey data, the social media-based city interaction study has received much less exploration. Based on geographical studies of social media content in China, we develop a few indices quantifying various levels of geographical awareness among cities. (1) We find that the geographical awareness proxy by the social media-based indices can measure interactions among cities. Specifically, the geographical awareness among cities follows gravitational law and is highly correlated with mobility flows. (2) The spatial in-awareness index (SIAI) is an appropriate index indicating a city's ranking in the urban hierarchy (3) the spatial out-awareness rate (SOAR) can indicate the interactions from a focal city to other cities. Our findings also show that SOAR can predict the number of people infected during a pandemic in a city system. Once the origin city or hotspots of the outbreak and the number of infected persons within those cities are known, we can use the social media-based SOAR index to predict number of cases for other else cities in the urban system. With this information, governments can properly and efficiently deliver medical equipment and staff to cities where large populations are infected. • Develops social media-based geographical awareness indices: such as spatial out-awareness rate (SOAR) and in-awareness index (SIAI). • Using an econometric model, the study shows that geographical awareness among cities follows gravitational law with a decay function parameter of 0.308 • Use mobility flow data to verify that the social media-based indices can measure interactions among cities. • Shows that SIAI is an appropriate index for indicating a city’s ranking in the urban hierarchy • SOAR can indicate the interactions from a focal city to other cities and predict the number of people infected during a pandemic.