Relevant bibliographies by topics / Spatial outlier detection

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Spatial outlier detection'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Spatial outlier detection"

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LU, CHANG-TIEN, DECHANG CHEN, and YUFENG KOU. "MULTIVARIATE SPATIAL OUTLIER DETECTION." International Journal on Artificial Intelligence Tools 13, no. 04 (2004): 801–11. http://dx.doi.org/10.1142/s021821300400182x.

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A spatial outlier is a spatially referenced object whose non-spatial attribute values are significantly different from the values of its neighborhood. Identification of spatial outliers can lead to the discovery of unexpected, interesting, and useful spatial patterns for further analysis. Previous work in spatial outlier detection focuses on detecting spatial outliers with a single attribute. In the paper, we propose two approaches to discover spatial outliers with multiple attributes. We formulate the multi-attribute spatial outlier detection problem in a general way, provide two effective de
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Baba, Ali Mohammed, Habshah Midi, and Nur Haizum Abd Rahman. "Spatial Outlier Accommodation Using a Spatial Variance Shift Outlier Model." Mathematics 10, no. 17 (2022): 3182. http://dx.doi.org/10.3390/math10173182.

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Outlier detection has been a long-debated subject among researchers due to its effect on model fitting. Spatial outlier detection has received considerable attention in the recent past. On the other hand, outlier accommodation, particularly in spatial applications, retains vital information about the model. It is pertinent to develop a method that is capable of accommodating detected spatial outliers in a fashion that retains vital information in the spatial models. In this paper, we formulate the variance shift outlier model (SVSOM) in the spatial regression as a robust spatial model using re
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Mohd Ali, Nur Fatihah, Ibrahim Mohamed, Rossita Mohamad Yunus, and Faridah Othman. "Spatial Functional Outlier Detection in Multivariate Spatial Functional Data." Sains Malaysiana 53, no. 6 (2024): 1463–76. http://dx.doi.org/10.17576/jsm-2024-5306-18.

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Multivariate spatial functional data consists of multiple functions of time-dependent attributes observed at each spatial point. This study focuses on detecting spatial outliers in spatial functional data. Firstly, we develop a new method called Mahalanobis Distance Spatial Outlier (MDSO) to detect functional outliers in the data. The method introduces the multivariate functional Mahalanobis semi-distance and multivariate pairwise functional Mahalanobis semi-distance metrics based on the multivariate functional principal components analysis to calculate the dissimilarity between functions at e
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Janeja, Vandana P., and Vijayalakshmi Atluri. "Spatial outlier detection in heterogeneous neighborhoods." Intelligent Data Analysis 13, no. 1 (2009): 85–107. http://dx.doi.org/10.3233/ida-2009-0357.

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Singh, Alok Kumar, and S. Lalitha. "A novel spatial outlier detection technique." Communications in Statistics - Theory and Methods 47, no. 1 (2017): 247–57. http://dx.doi.org/10.1080/03610926.2017.1301477.

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Xin, Liu, Zhang Shaoliang, and Zheng Pulin. "Spatial Outlier Detection of CO2 Monitoring Data Based on Spatial Local Outlier Factor." Journal of Engineering Science and Technology Review 8, no. 5 (2015): 110–16. http://dx.doi.org/10.25103/jestr.085.15.

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Huda, Nur'ainul Miftahul, Utriweni Mukhaiyar, and Nurfitri Imro'ah. "AN ITERATIVE PROCEDURE FOR OUTLIER DETECTION IN GSTAR(1;1) MODEL." BAREKENG: Jurnal Ilmu Matematika dan Terapan 16, no. 3 (2022): 975–84. http://dx.doi.org/10.30598/barekengvol16iss3pp975-984.

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Outliers are observations that differ significantly from others that can affect the estimation results in the model and reduce the estimator's accuracy. To deal with outliers is to remove outliers from the data. However, sometimes important information is contained in the outlier, so eliminating outliers is a misinterpretation. There are two types of outliers in the time series model, Innovative Outlier (IO) and Additive Outlier (AO). In the GSTAR model, outliers and spatial and time correlations can also be detected. We introduce an iterative procedure for detecting outliers in the GSTAR mode
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Filzmoser, Peter, and Mariella Gregorich. "Multivariate Outlier Detection in Applied Data Analysis: Global, Local, Compositional and Cellwise Outliers." Mathematical Geosciences 52, no. 8 (2020): 1049–66. http://dx.doi.org/10.1007/s11004-020-09861-6.

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AbstractOutliers are encountered in all practical situations of data analysis, regardless of the discipline of application. However, the term outlier is not uniformly defined across all these fields since the differentiation between regular and irregular behaviour is naturally embedded in the subject area under consideration. Generalized approaches for outlier identification have to be modified to allow the diligent search for potential outliers. Therefore, an overview of different techniques for multivariate outlier detection is presented within the scope of selected kinds of data frequently
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Matkan, A. A., M. Hajeb, B. Mirbagheri, S. Sadeghian, and M. Ahmadi. "SPATIAL ANALYSIS FOR OUTLIER REMOVAL FROM LIDAR DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-2/W3 (October 22, 2014): 187–90. http://dx.doi.org/10.5194/isprsarchives-xl-2-w3-187-2014.

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Outlier detection in LiDAR point clouds is a necessary process before the subsequent modelling. So far, many studies have been done in order to remove the outliers from LiDAR data. Some of the existing algorithms require ancillary data such as topographic map, multiple laser returns or intensity data which may not be available, and some deal only with the single isolated outliers. This is an attempt to present an algorithm to remove both the single and cluster types of outliers, by exclusively use of the last return data. The outliers will be removed by spatial analyzing of LiDAR point clouds
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Yixin Chen, Xin Dang, Hanxiang Peng, H. L. Bart, and H. L. Bart. "Outlier Detection with the Kernelized Spatial Depth Function." IEEE Transactions on Pattern Analysis and Machine Intelligence 31, no. 2 (2009): 288–305. http://dx.doi.org/10.1109/tpami.2008.72.

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Dissertations / Theses on the topic "Spatial outlier detection"

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Parana-Liyanage, Krishani. "Outlier detection in spatial data using the m-SNN algorithm." DigitalCommons@Robert W. Woodruff Library, Atlanta University Center, 2013. http://digitalcommons.auctr.edu/dissertations/1299.

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Outlier detection is an important topic in data analysis because of its applications to numerous domains. Its application to spatial data, and in particular spatial distribution in path distributions, has recently attracted much interest. This recent trend can be seen as a reflection of the massive amounts of spatial data being gathered through mobile devices, sensors and social networks. In this thesis we propose a nearest neighbor distance based method the Modified-Shared Nearest Neighbor outlier detection (m-SNN) developed for outlier detection in spatial domains. We modify the SNN techniqu
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Schubert, Erich. "Generalized and efficient outlier detection for spatial, temporal, and high-dimensional data mining." Diss., Ludwig-Maximilians-Universität München, 2013. http://nbn-resolving.de/urn:nbn:de:bvb:19-166938.

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Knowledge Discovery in Databases (KDD) ist der Prozess, nicht-triviale Muster aus großen Datenbanken zu extrahieren, mit dem Ziel, dass diese bisher unbekannt, potentiell nützlich, statistisch fundiert und verständlich sind. Der Prozess umfasst mehrere Schritte wie die Selektion, Vorverarbeitung, Evaluierung und den Analyseschritt, der als Data-Mining bekannt ist. Eine der zentralen Aufgabenstellungen im Data-Mining ist die Ausreißererkennung, das Identifizieren von Beobachtungen, die ungewöhnlich sind und mit der Mehrzahl der Daten inkonsistent erscheinen. Solche seltene Beobachtungen können
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Kou, Yufeng. "Abnormal Pattern Recognition in Spatial Data." Diss., Virginia Tech, 2006. http://hdl.handle.net/10919/30145.

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In the recent years, abnormal spatial pattern recognition has received a great deal of attention from both industry and academia, and has become an important branch of data mining. Abnormal spatial patterns, or spatial outliers, are those observations whose characteristics are markedly different from their spatial neighbors. The identification of spatial outliers can be used to reveal hidden but valuable knowledge in many applications. For example, it can help locate extreme meteorological events such as tornadoes and hurricanes, identify aberrant genes or tumor cells, discover highway traffic
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Schubert, Erich [Verfasser], and Hans-Peter [Akademischer Betreuer] Kriegel. "Generalized and efficient outlier detection for spatial, temporal, and high-dimensional data mining / Erich Schubert. Betreuer: Hans-Peter Kriegel." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2013. http://d-nb.info/1048522377/34.

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SZEKÉR, MÁTÉ. "Spatio-temporal outlier detection in streaming trajectory data." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-155739.

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This thesis investigates the problem of detecting spatiotemporalanomalies in streamed trajectory data using both supervised and unsupervised algorithms. Anomaly detection can be understood as an unsupervised classification problem which requires the knowledge of the normal course of events or how the anomalies manifest themselves. To this end, an algorithm is proposed to identify the normative pattern in a streamed dataset. A non-parametric algorithm based on SVM is proposed for classifying trajectories basedon the explicit geometric properties alone. A parametric algorithm based on dynamic Ma
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ALBANESE, ALESSIA. "A ROUGH SET APPROACH TO OUTLIER DETECTION IN SPATIO TEMPORAL DATA." Doctoral thesis, Università degli Studi di Milano, 2011. http://hdl.handle.net/2434/155480.

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Spatio-temporal data mining is a growing research area dedicated to the development of algorithms and computational techniques for the analysis of large spatio-temporal databases and the disclosure of interesting and hidden knowledge in these data, mainly in terms of periodic hidden patterns and outlier detection. In this thesis, the attention has been focalized on outlier detection in spatio-temporal data. Indeed, detecting outliers which are grossly different from or inconsistent with remaining data is a major challenge in real-world knowledge discovery and data mining applications. Nowaday
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Chen, Feng. "Efficient Algorithms for Mining Large Spatio-Temporal Data." Diss., Virginia Tech, 2013. http://hdl.handle.net/10919/19220.

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Knowledge discovery on spatio-temporal datasets has attracted<br />growing interests. Recent advances on remote sensing technology mean<br />that massive amounts of spatio-temporal data are being collected,<br />and its volume keeps increasing at an ever faster pace. It becomes<br />critical to design efficient algorithms for identifying novel and<br />meaningful patterns from massive spatio-temporal datasets. Different<br />from the other data sources, this data exhibits significant<br />space-time statistical dependence, and the assumption of i.i.d. is<br />no longer valid. The exact modelin
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Vallot, Dorothée. "Modelling calving and sliding of Svalbard outlet glaciers : Spatio-temporal changes and interactions." Doctoral thesis, Uppsala universitet, Luft-, vatten och landskapslära, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-334787.

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Future sea level rise associated to global warming is one of the greatest societal and environmental challenges of tomorrow. A large part of the contribution comes from glaciers and ice sheets discharging ice and meltwater into the ocean and the recent worldwide increase is worrying. Future predictions of sea level rise try to encompass the complex processes of ice dynamics through glacier modelling but there are still large uncertainties due to the lack of observations or too coarse parameterisation, particularly for processes occurring at the glacier interfaces with the bed (sliding) and wit
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Pešek, Martin. "Získávání znalostí z časoprostorových dat." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2011. http://www.nusl.cz/ntk/nusl-237048.

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This thesis deals with knowledge discovery in spatio-temporal data, which is currently a rapidly evolving area of research in information technology. First, it describes the general principles of knowledge discovery, then, after a brief introduction to mining in the temporal and spatial data, it focuses on the overview and description of existing methods for mining in spatio-temporal data. It focuses, in particular, on moving objects data in the form of trajectories with an emphasis on the methods for trajectory outlier detection. The next part of the thesis deals with the process of implement
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Chang, Chia-ming, and 張佳銘. "Outlier Detection for Spatial Correlated Data." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/pe5c4t.

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碩士<br>國立臺灣科技大學<br>電子工程系<br>95<br>Individual sensor nodes in a large-scale wireless sensor network are subject to physically security compromises. Adversary destroys wireless sensor networks through these compromised sensors, because these compromised sensors can be used to inject false sensing data. If these compromised Sensors are not detected, these false data reports are forwarded to sink (data collection point) It will result in not only false alarms but also consumption of the battery energy in each sensor. For these cases, it is necessary to develop a mechanism to detect them, because co
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Book chapters on the topic "Spatial outlier detection"

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Aggarwal, Charu C. "Spatial Outlier Detection." In Outlier Analysis. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47578-3_11.

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Aggarwal, Charu C. "Spatial Outlier Detection." In Outlier Analysis. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-6396-2_10.

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Kou, Yufeng, and Chang-Tien Lu. "Outlier Detection, Spatial." In Encyclopedia of GIS. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_945.

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Kou, Yufeng, and Chang-Tien Lu. "Outlier Detection, Spatial." In Encyclopedia of GIS. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23519-6_945-2.

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Kou, Yufeng, and Chang-Tien Lu. "Outlier Detection, Spatial." In Encyclopedia of GIS. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-17885-1_945.

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Papadimitriou, Spiros, and Christos Faloutsos. "Cross-Outlier Detection." In Advances in Spatial and Temporal Databases. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45072-6_12.

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Achtert, Elke, Ahmed Hettab, Hans-Peter Kriegel, Erich Schubert, and Arthur Zimek. "Spatial Outlier Detection: Data, Algorithms, Visualizations." In Advances in Spatial and Temporal Databases. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22922-0_41.

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García-Pérez, Alfonso, and Yolanda Cabrero-Ortega. "Spatial Outlier Detection Using GAMs and Geographical Information Systems." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42972-4_31.

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Wu, Junjie, Senpeng Chen, Songjian Huang, and Miao Zhang. "Spatial Attribute Outlier Detection Method Based on Artificial Intelligence." In Learning and Analytics in Intelligent Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-94266-2_3.

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Kamboj, Robin, and Vrinda Gupta. "Spatial Correlation Based Outlier Detection in Clustered Wireless Sensor Network." In International Conference on Intelligent Computing and Smart Communication 2019. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0633-8_13.

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Conference papers on the topic "Spatial outlier detection"

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Ortega, Eduardo, Jonti Talukdar, Woohyun Paik, Fei Su, Rita Chattopadhyay, and Krishnendu Chakrabarty. "E-SCOUT: Efficient-Spatial Clustering-based Outlier Detection through Telemetry." In 2024 IEEE International Test Conference (ITC). IEEE, 2024. http://dx.doi.org/10.1109/itc51657.2024.00044.

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Souisa, Gilbert Alvaro, Setiawan, and Santi Puteri Rahayu. "Outlier Detection and Handling In Spatial Autoregressive Models With Variance Shift Outlier Models (VSOM) : (Case Study: GDRP Data of Agriculture Sector in Java Island)." In 2024 IEEE 22nd Student Conference on Research and Development (SCOReD). IEEE, 2024. https://doi.org/10.1109/scored64708.2024.10872689.

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Liu, Xutong, Chang-Tien Lu, and Feng Chen. "Spatial outlier detection." In the 18th SIGSPATIAL International Conference. ACM Press, 2010. http://dx.doi.org/10.1145/1869790.1869841.

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Liu, Xutong, Feng Chen, and Chang-Tien Lu. "Spatial categorical outlier detection." In the 19th ACM SIGSPATIAL International Conference. ACM Press, 2011. http://dx.doi.org/10.1145/2093973.2094049.

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Kou, Yufeng, Chang-Tien Lu, and Dechang Chen. "Spatial Weighted Outlier Detection." In Proceedings of the 2006 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2006. http://dx.doi.org/10.1137/1.9781611972764.71.

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Xue, Anrong, Xiqiang Duan, Handa Ma, Weihe Chen, and Shiguang Ju. "Privacy Preserving Spatial Outlier Detection." In 2008 9th International Conference for Young Computer Scientists (ICYCS). IEEE, 2008. http://dx.doi.org/10.1109/icycs.2008.345.

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Xue, Anrong, Lin Yao, Shiguang Ju, Weihe Chen, and Handa Ma. "Algorithm for Fast Spatial Outlier Detection." In 2008 9th International Conference for Young Computer Scientists (ICYCS). IEEE, 2008. http://dx.doi.org/10.1109/icycs.2008.346.

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Tang, Zhi-gang, Jun Yang, and Bing-ru Yang. "Spatial outlier detection with multiple attributes weighted." In International Conference on Photonics and Image in Agriculture Engineering (PIAGENG 2009), edited by Honghua Tan and Qi Luo. SPIE, 2009. http://dx.doi.org/10.1117/12.836759.

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Zheng, Guanjie, Susan L. Brantley, Thomas Lauvaux, and Zhenhui Li. "Contextual Spatial Outlier Detection with Metric Learning." In KDD '17: The 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2017. http://dx.doi.org/10.1145/3097983.3098143.

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Kou, Yufeng, Chang-Tien Lu, and Raimundo F. Dos Santos. "Spatial Outlier Detection: A Graph-Based Approach." In 2007 19th IEEE International Conference on Tools with Artificial Intelligence. IEEE, 2007. http://dx.doi.org/10.1109/ictai.2007.139.

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Reports on the topic "Spatial outlier detection"

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Mizrach, Amos, Michal Mazor, Amots Hetzroni, et al. Male Song as a Tool for Trapping Female Medflies. United States Department of Agriculture, 2002. http://dx.doi.org/10.32747/2002.7586535.bard.

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This interdisciplinaray work combines expertise in engineering and entomology in Israel and the US, to develop an acoustic trap for mate-seeking female medflies. Medflies are among the world's most economically harmful pests, and monitoring and control efforts cost about $800 million each year in Israel and the US. Efficient traps are vitally important tools for medfly quarantine and pest management activities; they are needed for early detection, for predicting dispersal patterns and for estimating medfly abundance within infested regions. Early detection facilitates rapid response to invasio
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Huntley, D., D. Rotheram-Clarke, R. Cocking, J. Joseph, and P. Bobrowsky. Current research on slow-moving landslides in the Thompson River valley, British Columbia (IMOU 5170 annual report). Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/331175.

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Interdepartmental Memorandum of Understanding (IMOU) 5170 between Natural Resources Canada (NRCAN), the Geological Survey of Canada (GSC) and Transport Canada Innovation Centre (TC-IC) aims to gain new insight into slow-moving landslides, and the influence of climate change, through testing conventional and emerging monitoring technologies. IMOU 5107 focuses on strategically important sections of the national railway network in the Thompson River valley, British Columbia (BC), and the Assiniboine River valley along the borders of Manitoba (MN) and Saskatchewan (SK). Results of this research ar
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