skip to main content


Search for: All records

Award ID contains: 1541136

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Abstract

    Seeking spatiotemporal patterns about how citizens interact with the urban space is critical for understanding how cities function. Such interactions were studied in various forms focusing on patterns of people’s presence, action, and transition in the urban environment, which are defined as human-urban interactions in this paper. Using human activity datasets that utilize mobile positioning technology for tracking the locations and movements of individuals, researchers developed stochastic models to uncover preferential return behaviors and recurrent transitional activity structures in human-urban interactions. Ad-hoc heuristics and spatial clustering methods were applied to derive meaningful activity places in those studies. However, the lack of semantic meaning in the recorded locations makes it difficult to examine the details about how people interact with different activity places. In this study, we utilized geographic context-aware Twitter data to investigate the spatiotemporal patterns of people’s interactions with their activity places in different urban settings. To test consistency of our findings, we used geo-located tweets to derive the activity places in Twitter users’ location histories over three major U.S. metropolitan areas: Greater Boston Area, Chicago, and San Diego, where the geographic context of each location was inferred from its closest land use parcel. The results showed striking spatial and temporal similarities in Twitter users’ interactions with their activity places among the three cities. By using entropy-based predictability measures, this study not only confirmed the preferential return behaviors as people tend to revisit a few highly frequented places but also revealed detailed characteristics of those activity places.

     
    more » « less
  2. Objective

    The objectives of this study were to examine (1) the linkage from airports to regional talent distribution and (2) the effect of talent on regional economic development.

    Methods

    Using the data collected in Wisconsin at the municipal level, a subcounty level, in a region of the North Central United States from 1970 to 2010 and the American Community Survey 2006–2010 five‐year estimates, and random effects models and structural equation models, we employ descriptive and inferential statistics to examine the linkage from airports to talent to regional economic development.

    Results

    We find that the farther a location is away from the airport, the lower its talent share tends to be, while greater passenger flow at the nearest airport increases a location's talent share. Given the quantity of passenger flow, a longer distance from the airport also reduces a location's talent share. The results furthermore suggest that economic development is impacted positively by passenger flow and talent share and negatively by distance to an airport.

    Conclusion

    Our results underscore the intermediate role of talent between airports and regional economic development; building the linkage from airports to talent within the context of regional economic development provides important insights for local policy making aimed at attracting talented migrants.

     
    more » « less
  3. Free, publicly-accessible full text available May 4, 2024
  4. null (Ed.)
  5. Gao, Song (Ed.)