Giuseppe Arbia, Vincenzo Nardelli
arXiv 23 Oct 2024 · Statistics — Methodology
arXiv:2410.18261 · PDF · DOI · OpenAlex · Extracted main text
In the analysis of large spatial datasets, identifying and treating spatial outliers is essential for accurately interpreting geographical phenomena. While spatial correlation measures, particularly Local Indicators of Spatial Association (LISA), are widely used to detect spatial patterns, the presence of abnormal observations frequently distorts the landscape and conceals critical spatial relationships. These outliers can significantly impact analysis due to the inherent spatial dependencies present in the data. Traditional influence function (IF) methodologies, commonly used in statistical analysis to measure the impact of individual observations, are not directly applicable in the spatial context because the influence of an observation is determined not only by its own value but also by its spatial location, its connections with neighboring regions, and the values of those neighboring observations. In this paper, we introduce a local version of the influence function (LIF) that accounts for these spatial dependencies. Through the analysis of both simulated and real-world datasets, we demonstrate how the LIF provides a more nuanced and accurate detection of spatial outliers compared to traditional LISA measures and local impact assessments, improving our understanding of spatial patterns.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Nardelli, V. and G. Arbia (2024) On robust measures of spatial correlation self | 0.874 | 5 | 2 | 100% |
| 2 | Hampel, F. R (1974) The influence curve and its role in robust estimation | 0.644 | 2 | 2 | 100% |
| 3 | Hampel, F. R., E. M. Ronchetti, P. J. Rousseeuw, and W. A. Stahel (1986) Robust Statistics: The Approach Based on Influence Functions | 0.405 | 1 | 1 | 100% |
| 4 | Huber, P. J (1981) Robust Statistics | 0.405 | 1 | 1 | 100% |
| 5 | Maronna, R. A., R. D. Martin, and V. J. Yohai (2006) Robust Statistics: Theory and Methods | 0.405 | 1 | 1 | 100% |
| 6 | Anselin, L (1995) Local indicators of spatial association—lisa | 0.405 | 1 | 1 | 100% |
| 7 | Moran, P. A (1950) Notes on continuous stochastic phenomena | 0.405 | 1 | 1 | 100% |
| 8 | Pesaran, M. H. and C. F. Yang (2020) Econometric analysis of production networks with dominant units | 0.405 | 1 | 1 | 100% |
| 9 | Rousseeuw, P. J. and A. M. Leroy (1897) Robust regression and outlier detection | 0.405 | 1 | 1 | 100% |
Showing the top 9 of 9 scored citations.