Detecting spatial flow outliers in the presence of spatial autocorrelation
Topics: Behavioral Geography
, Quantitative Methods
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Keywords: spatial data mining, spatial outlier detection, spatial flow data, spatial autocorrelation, human mobility
Session Type: Virtual Paper Abstract
Day: Friday
Session Start / End Time: 2/25/2022 03:40 PM (Eastern Time (US & Canada)) - 2/25/2022 05:00 PM (Eastern Time (US & Canada))
Room: Virtual 14
Authors:
Jiannan Cai, The Chinese University of Hong Kong
Mei-Po Kwan, The Chinese University of Hong Kong
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Abstract
This study formulates a novel spatial flow data analysis problem, namely spatial flow outlier (SFO) detection, which aims to discover spatial flows whose non-spatial attribute values are significantly different from their neighborhoods. Different from the general spatial flow patterns (e.g., flow clusters), which are the main concern in the current literature, SFOs represent unusual local instabilities and are valuable for revealing anomalous spatial interactions between regions. Detecting SFOs is challenging because the underlying distribution of the flow data is unknown a priori, and inappropriate distribution assumptions may lead to misleading decisions on SFOs. To solve this significant methodological issue, we propose a spatial-autocorrelation-aware detection method. This method detects SFOs by testing the local difference of attribute values in flow neighborhoods against the null hypothesis that neighboring flows are similar. To construct this null hypothesis, we develop a distribution-free model by reconstructing the observed spatial autocorrelation. A case study using the commuting flow data in Chicago demonstrates that the choice and modeling of the null hypothesis has a significant influence on the statistical inference of SFOs. By taking the inherent spatial autocorrelation into account, our method can more objectively assess the significance of SFOs than two baseline methods based on the normality and randomization hypotheses.
Detecting spatial flow outliers in the presence of spatial autocorrelation
Category
Virtual Paper Abstract
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