{"id":168685,"date":"2015-11-01T00:00:00","date_gmt":"2015-11-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/detecting-collective-anomalies-from-multiple-spatio-temporal-datasets-across-different-domains\/"},"modified":"2018-10-16T20:45:13","modified_gmt":"2018-10-17T03:45:13","slug":"detecting-collective-anomalies-from-multiple-spatio-temporal-datasets-across-different-domains","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/detecting-collective-anomalies-from-multiple-spatio-temporal-datasets-across-different-domains\/","title":{"rendered":"Detecting Collective Anomalies from Multiple Spatio- Temporal Datasets across Different Domains"},"content":{"rendered":"<div class=\"asset-content\">\n<p>The collective anomaly denotes a collection of nearby locations that are anomalous during a few consecutive time intervals in terms of phenomena collectively witnessed by multiple datasets. The collective anomalies suggest there are underlying problems that may not be identified based on a single data source or in a single location. It also associates individual locations and time intervals, formulating a panoramic view of an event. To detect a collective anomaly is very challenging, however, as different datasets have different densities, distributions, and scales. Additionally, to find the spatio-temporal scope of a collective anomaly is very time consuming as there are many ways to combine regions and time slots. Our method consists of three components: <em>Multiple-Source Latent-Topic (MSLT)<\/em> model, <em>Spatio-Temporal Likelihood Ratio Test (ST_LRT)<\/em> model, and a candidate generation algorithm. <em>MSLT<\/em> combines multiple datasets to infer the latent functions of a geographic region in the framework of a topic model. In turn, a region\u2019s latent functions help estimate the underlying distribution of a sparse dataset generated in the region. <em>ST_LRT<\/em> learns a proper underlying distribution for different datasets, and calculates an anomalous degree for each dataset based on a likelihood ratio test (<em>LRT<\/em>). It then aggregates the anomalous degrees of different datasets, using a skyline detection algorithm. We evaluate our method using five datasets related to New York City (NYC): 311 complaints, taxicab data, bike rental data, points of interest, and road network data, finding the anomalies that cannot be identified (or earlier than those detected) by a single dataset. Results show the advantages beyond six baseline methods.<\/p>\n<\/div>\n<p>(<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2016\/02\/release_data.zip\">Data<\/a>) (<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2016\/02\/release_code.zip\">Code<\/a>)(<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2016\/02\/Detecting20Collective20Anomalies-GIS2015-zheng.pptx\">PPT<\/a>)<\/p>\n<p><span id=\"8e2fd584-3efe-41b0-b119-2c364847a627\" class=\"ImageBlock fn\"><img decoding=\"async\" id=\"Image8e2fd584-3efe-41b0-b119-2c364847a627\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2016\/02\/\/urbancomputing-collective_anomaly_flyer.png\" alt=\"\" \/><span id=\"ImageCaption8e2fd584-3efe-41b0-b119-2c364847a627\" class=\"ImageCaptionCoreCss ImageCaption\"><\/span><\/span><\/p>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The collective anomaly denotes a collection of nearby locations that are anomalous during a few consecutive time intervals in terms of phenomena collectively witnessed by multiple datasets. The collective anomalies suggest there are underlying problems that may not be identified based on a single data source or in a single location. It also associates individual [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556,13563],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-168685","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-data-platform-analytics","msr-locale-en_us"],"msr_publishername":"ACM SIGSPATIAL 2015","msr_edition":"Proceedings of the 23rd ACM International Conference on Advances in Geographical Information Systems","msr_affiliation":"","msr_published_date":"2015-11-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"204124","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"Collective%20anomalies-GIS2015_yu.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/Collective20anomalies-GIS2015_yu.pdf","id":204124,"label_id":0},{"type":"file","title":"region_center","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2015\/11\/region_center.txt","id":446016,"label_id":0},{"type":"file","title":"Detecting%20Collective%20Anomalies-GIS2015-zheng.pptx","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/Detecting20Collective20Anomalies-GIS2015-zheng.pptx","id":204127,"label_id":0},{"type":"file","title":"release_code.zip","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/release_code.zip","id":204126,"label_id":0},{"type":"file","title":"release_data.zip","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/release_data.zip","id":204125,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":446016,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2015\/11\/region_center.txt"},{"id":204127,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/Detecting20Collective20Anomalies-GIS2015-zheng.pptx"},{"id":204126,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/release_code.zip"},{"id":204125,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/release_data.zip"},{"id":204124,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/Collective20anomalies-GIS2015_yu.pdf"}],"msr-author-ordering":[{"type":"user_nicename","value":"yuzheng","user_id":35088,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=yuzheng"},{"type":"text","value":"Huichu Zhang","user_id":0,"rest_url":false},{"type":"text","value":"Yong Yu","user_id":0,"rest_url":false}],"msr_impact_theme":[],"msr_research_lab":[199560],"msr_event":[],"msr_group":[],"msr_project":[170824],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/168685"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-research-item"}],"version-history":[{"count":3,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/168685\/revisions"}],"predecessor-version":[{"id":530099,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/168685\/revisions\/530099"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=168685"}],"wp:term":[{"taxonomy":"msr-content-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-content-type?post=168685"},{"taxonomy":"msr-research-highlight","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-highlight?post=168685"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=168685"},{"taxonomy":"msr-publication-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publication-type?post=168685"},{"taxonomy":"msr-product-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-product-type?post=168685"},{"taxonomy":"msr-focus-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-focus-area?post=168685"},{"taxonomy":"msr-platform","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-platform?post=168685"},{"taxonomy":"msr-download-source","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-download-source?post=168685"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=168685"},{"taxonomy":"msr-field-of-study","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-field-of-study?post=168685"},{"taxonomy":"msr-conference","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-conference?post=168685"},{"taxonomy":"msr-journal","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-journal?post=168685"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=168685"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=168685"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}