Measuring and modeling activity space

Delineating and Modelling Activity Space Using Geotagged Social Media Data

Big data can advance the identification of activity spaces and the understanding of spatial equity. This study employs geotagged Twitter data to delineate activity spaces and investigates the association between the spatial range of activity spaces and neighborhood characteristics in the Los Angeles metropolitan area. To do so, first, we estimate two spatial measures of activity spaces: (1) the distance between users’ home location and activity locations; and (2) the area covered between home and various activity locations.

Second, we investigate the relationships between activity spaces and neighborhood-level socioeconomic and spatial characteristics. Our findings, on the one hand, confirm some existing knowledge in this field and, on the other hand, raise new questions for future exploration. The findings enrich the growing empirical literature on activity spaces and provide a proof of concept using geotagged social media data to investigate spatial inequity.

Activity spaces identified using social media data, on the one hand, confirm existing knowledge in this field and, on the other hand, raise new questions for future exploration. The results confirm the findings of larger activity spaces for young people and blacks but smaller activity spaces for residents living in places of high population density. Meanwhile, questions that have not been fully examined emerge. We observe large activity spaces for residents living in neighborhoods with high employment density. Because the literature on activity spaces is still rapidly evolving, our research contributes to the growing literature by generating findings worthy of future exploration.

Hu L., Li Z., Ye X., (2020) Delineating and Modelling Activity Space Using Geotagged Social Media DataCartography and Geographic Information Science, (in press)

Measuring Inter-city Network using Digital Footprints from Twitter Users

City connectivity is an important measurement in characterizing human dynamics from regional to international scales. World City Network has been built based on companies’ communication. The interactions between spatial and social dimensions of cities have both conceptual and practical significance. To further expand the studies of inter-city network in the big social data context, this research builds a network at the county level using digital footprints from Twitter users.

We identify the connection strength of each pair of counties based on the amounts of shared Twitter users who leave digital footprints on both counties. Using the shared user amount as the weighted link and each county as the node, we build a county-to-county user flow network. Various network structures have been detected at the state level.

Comparing NY and SC, these two states present very different network structures. NY has a single-core structure, with the three counties make up New York City. On the other hand, SC has a network structure with multiple cores: Charleston County, Richland County, and Horry County. In addition to these three counties, there are some second-tier counties and then third-tier counties. New York City is the center of not only the New York state, but also the whole nation. In SC, more than one city contributes to top tier of the network.

In addition, by creating a direct flow chain, we can identify influential counties and its hinterland. This network demonstrates how human mobility operate across various spatial settings and distances.

Jiang Y., Li Z., Ye X.,(2018), Measuring inter-city network using digital footprints from Twitter usersProceedings of the 2nd ACM SIGSPATIAL International Workshop on PredictGIS, 11/06/2018, Seattle, Washington, USA.

Huang Q., Li Z., Li J., (2016), Mining Frequent Trajectory Patterns from Online Footprints7th ACM SIGSPATIAL International Workshop on GeoStreaming (IWGS), San Francisco, California, USA.

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