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Integrating Remote Sensing and Social Sensing to Examine Socioeconomic Dynamics
A Case Study of Twitter and Nighttime Light Imagery
In the past decade, the landscape of geospatial sciences and technologies has been shifting dramatically. New sources of geospatial data on human activities and socioeconomic development become increasingly available, marked by the rise of social sensing (e.g. location‐based social media) and a growing variety of remote sensors (e.g. satellite imagery of nighttime lights). The remote sensing and social sensing provide a complementary set of information sources to examine complex socioeconomic dynamics across different spatial and temporal scales. In this chapter, we highlight the potential of integrating these two information sources in studying socioeconomic dynamics and illustrate the potential with two specific types of remote sensing and social sensing, satellite imagery of nighttime light brightness and the geo‐tagged Twitter posts. Specifically, we first explore the potentials and problems of geo‐tagged Twitter posts in representing socioeconomic factors and compare it with the commonly used NTL imagery. We then describe a practical approach to integrate the two heterogeneous data sources to improve the mapping of socioeconomic dynamics. The advantages of the integration as a reliable indicator of socioeconomic factors are then showcased using case studies .
Integrating Remote Sensing and Social Sensing to Examine Socioeconomic Dynamics
A Case Study of Twitter and Nighttime Light Imagery
In the past decade, the landscape of geospatial sciences and technologies has been shifting dramatically. New sources of geospatial data on human activities and socioeconomic development become increasingly available, marked by the rise of social sensing (e.g. location‐based social media) and a growing variety of remote sensors (e.g. satellite imagery of nighttime lights). The remote sensing and social sensing provide a complementary set of information sources to examine complex socioeconomic dynamics across different spatial and temporal scales. In this chapter, we highlight the potential of integrating these two information sources in studying socioeconomic dynamics and illustrate the potential with two specific types of remote sensing and social sensing, satellite imagery of nighttime light brightness and the geo‐tagged Twitter posts. Specifically, we first explore the potentials and problems of geo‐tagged Twitter posts in representing socioeconomic factors and compare it with the commonly used NTL imagery. We then describe a practical approach to integrate the two heterogeneous data sources to improve the mapping of socioeconomic dynamics. The advantages of the integration as a reliable indicator of socioeconomic factors are then showcased using case studies .
Integrating Remote Sensing and Social Sensing to Examine Socioeconomic Dynamics
A Case Study of Twitter and Nighttime Light Imagery
Yang, Xiaojun (editor) / Cao, Guofeng (author) / Zhao, Naizhuo (author)
Urban Remote Sensing ; 131-150
2021-09-30
20 pages
Article/Chapter (Book)
Electronic Resource
English