TY - GEN
T1 - Uncovering spatiotemporal biases in place-based social sensing
AU - McKenzie, Grant
AU - Janowicz, Krzysztof
AU - Keßler, Carsten
PY - 2020
Y1 - 2020
N2 - Places can be characterized by the ways that people interact with them, such as the times of day certain place types are frequented, or how place combinations contribute to urban structure. Intuitively, schools are most visited during work day mornings and afternoons, and are more likely to be near a recreation center than a nightclub. These temporal and spatial signatures are so specific that they can often be used to categorize a particular place solely by its interaction patterns. Today, numerous commercial datasets and services are used to access required information about places, social interaction, news, and so forth. As these datasets contain information about millions of the same places and the related services support tens of millions of users, one would expect that analysis performed on these datasets, e.g., to extract data signatures, would yield the same or similar results. Interestingly, this is not always the case. This has potentially far reaching consequences for researchers that use these datasets. In this work, we examine temporal and spatial signatures to explore the question of how the data acquiring cultures and interfaces employed by data providers such as Google and Foursquare, influence the final results. We approach this topic in terms of biases exhibited during service usage and data collection.
AB - Places can be characterized by the ways that people interact with them, such as the times of day certain place types are frequented, or how place combinations contribute to urban structure. Intuitively, schools are most visited during work day mornings and afternoons, and are more likely to be near a recreation center than a nightclub. These temporal and spatial signatures are so specific that they can often be used to categorize a particular place solely by its interaction patterns. Today, numerous commercial datasets and services are used to access required information about places, social interaction, news, and so forth. As these datasets contain information about millions of the same places and the related services support tens of millions of users, one would expect that analysis performed on these datasets, e.g., to extract data signatures, would yield the same or similar results. Interestingly, this is not always the case. This has potentially far reaching consequences for researchers that use these datasets. In this work, we examine temporal and spatial signatures to explore the question of how the data acquiring cultures and interfaces employed by data providers such as Google and Foursquare, influence the final results. We approach this topic in terms of biases exhibited during service usage and data collection.
U2 - 10.5194/agile-giss-1-14-2020
DO - 10.5194/agile-giss-1-14-2020
M3 - Article in proceeding
T3 - AGILE GIScience
BT - 23rd AGILE Conference on Geographic Information Science
A2 - Partsinevelos, Panagiotis
A2 - Kyriakidis, Phaedon
A2 - Kavouras, Marinos
PB - Copernicus Publications
T2 - 23rd AGILE Conference on Geographic Information Science (AFLYST)
Y2 - 16 June 2020 through 19 June 2020
ER -