Abundant Topological Outliers in Social Media Data and Their Effect on Spatial Analysis

Abstract

Twitter and related social media feeds have become valuable data sources to many fields of research. Numerous researchers have thereby used social media posts for spatial analysis, since many of them contain explicit geographic locations. However, despite its widespread use within applied research, a thorough understanding of the underlying spatial characteristics of these data is still lacking. In this paper, we investigate how topological outliers influence the outcomes of spatial analyses of social media data. These outliers appear when different users contribute heterogeneous information about different phenomena simultaneously from similar locations. As a consequence, various messages representing different spatial phenomena are captured closely to each other, and are at risk to be falsely related in a spatial analysis. Our results reveal indications for corresponding spurious effects when analyzing Twitter data. Further, we show how the outliers distort the range of outcomes of spatial analysis methods. This has significant influence on the power of spatial inferential techniques, and, more generally, on the validity and interpretability of spatial analysis results. We further investigate how the issues caused by topological outliers are composed in detail. We unveil that multiple disturbing effects are acting simultaneously and that these are related to the geographic scales of the involved overlapping patterns. Our results show that at some scale configurations, the disturbances added through overlap are more severe than at others. Further, their behavior turns into a volatile and almost chaotic fluctuation when the scales of the involved patterns become too different. Overall, our results highlight the critical importance of thoroughly considering the specific characteristics of social media data when analyzing them spatially.

New framework of Getis-Ord’s indexes associating spatial autocorrelation with interaction

“…Suppose that the spatial proximity function (SPF) is v ij = 1/r ij and x i and x j are replaced by y i and y j . Unitizing the spatial contiguity matrix, we can convert Eq (44) into Eq (10), and transform Eq (43) into Eq (11). This suggests that Getis-Ord's index is actually normalized potential energy, and spatial autocorrelation analysis and spatial interaction modeling reach the same goal by different routes.…”

“…By using one of the four approaches displayed above, we can compute the local Getis-Ord's indexes. On the other, using the formula of potential energy index and mutual energy index (K = 1, q = 1), Eqs (43) and 44, we can compute the potential energy indexes and mutual energy indexes (See S1 File and S1 Code). If K = 1 and q = 1 as given, then the potential energy indexes equal the corresponding the local Getis-Ord's indexes, and the mutual energy indexes are just the product of unitized size variable and the local Getis-Ord's indexes.…”

“…The methods of spatial autocorrelation have been developing [4,[13][14][15][16][17][18][19][20][21][22][23]. The statistics of spatial autocorrelation such as Moran's I and Ripley's K has been applied to spatial association processes in various fields, for example, man-land relationships [24], human diseases [25][26][27][28][29], animal disease transmission [30], human fertility and mortality [31,32], human genome [33], spatial pattern of urbanization [34], ecological patterns [35][36][37], maritime anomaly detection [38], and spatial sampling and data analysis [39][40][41][42][43]. In contrast, spatial interaction analysis is mainly confined to geographical research.…”

An improved algorithm for dynamic nearest-neighbour models

“…Tobler's laws have been widely acknowledged as being fundamental to geographical research (Barnes 2004, Goodchild 2004, Miller 2004, Phillips 2004, Smith 2004, Sui 2004, Tobler 2004, Westlund 2013. Also, the often local nature of geographical influence, complemented by the high complexity of geographical systems, has been widely discussed in publications (Anselin 1995, Hecht and Moxley 2009, Westerholt et al 2016, Mocnik 2018c.…”

Emphasising spatial structure in geosocial media data using spatial amplifier filtering

“…This lack of knowledge about scales leads to an overrepresentation of granular analytical scales (Westerholt et al., 2015), as urban topographies from which Twitter users send their messages induce this geometric pattern in the data sets. As different phenomena captured on possibly differing scales are reflected simultaneously in social media data sets, this has profound implications for the assumptions of stationarity and the inference mechanisms of spatial analysis techniques applied to such data (Westerholt, 2019; Westerholt et al., 2018, 2016a), as well as with respect to a general mismatch between analysis and phenomenon scales (Zhang et al., 2014). Many spatial analyses of geosocial media data are carried out in aggregated form (e.g.…”