Social Sensing: A New Approach to Understanding Our Socioeconomic Environments

TLDR

Big data offers new opportunities to understand socioeconomic environments, with social sensing—individual‑level geospatial data analogous to remote sensing—providing a complementary perspective that treats each person as a sensor. This article aims to conceptually link social sensing with remote sensing while highlighting key challenges in applying social sensing data and analytics. Social sensing is defined as the use of individual‑level big geospatial data and its associated analysis methods. Social sensing data reveal rich spatial interactions and place semantics that extend beyond the capabilities of traditional remote sensing.

Abstract

The emergence of big data brings new opportunities for us to understand our socioeconomic environments. We use the term social sensing for such individual-level big geospatial data and the associated analysis methods. The word sensing suggests two natures of the data. First, they can be viewed as the analogue and complement of remote sensing, as big data can capture well socioeconomic features while conventional remote sensing data do not have such privilege. Second, in social sensing data, each individual plays the role of a sensor. This article conceptually bridges social sensing with remote sensing and points out the major issues when applying social sensing data and associated analytics. We also suggest that social sensing data contain rich information about spatial interactions and place semantics, which go beyond the scope of traditional remote sensing data. In the coming big data era, GIScientists should investigate theories in using social sensing data, such as data representativeness and quality, and develop new tools to deal with social sensing data.