{"id":168662,"date":"2015-08-12T00:00:00","date_gmt":"2015-08-12T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/inferring-air-quality-for-station-location-recommendation-based-on-urban-big-data\/"},"modified":"2018-10-16T20:40:25","modified_gmt":"2018-10-17T03:40:25","slug":"inferring-air-quality-for-station-location-recommendation-based-on-urban-big-data","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/inferring-air-quality-for-station-location-recommendation-based-on-urban-big-data\/","title":{"rendered":"Inferring Air Quality for Station Location Recommendation Based on Urban Big Data"},"content":{"rendered":"<div class=\"asset-content\">\n<p>This paper tries to answer two questions. First, how to infer real-time air quality of any arbitrary location given environmental data and historical air quality data from very sparse monitoring locations. Second, if one needs to establish few new monitoring stations to improve the inference quality, how to determine the best locations for such purpose? The problems are challenging since for most of the locations (>99%) in a city we do not have any air quality data to train a model from. We design a semi-supervised inference model utilizing existing monitoring data together with heterogeneous city dynamics, including meteorology, human mobility, structure of road networks, and point of interests (POIs). We also pro-pose an entropy-minimization model to suggest the best locations to establish new monitoring stations. We evaluate the proposed ap-proach using Beijing air quality data, resulting in clear advantages over a series of state-of-the-art and commonly used methods.<\/p>\n<p>&nbsp;<\/p>\n<p><span id=\"1193facf-dfda-4652-8dee-7f8d09f6df91\" class=\"ImageBlock fn\"><span id=\"ImageCaption1193facf-dfda-4652-8dee-7f8d09f6df91\" class=\"ImageCaptionCoreCss ImageCaption\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-213316\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2014\/03\/urbanair_station-selection.png\" alt=\"urbanair_station-selection\" width=\"596\" height=\"203\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2014\/03\/urbanair_station-selection.png 740w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2014\/03\/urbanair_station-selection-300x102.png 300w\" sizes=\"auto, (max-width: 596px) 100vw, 596px\" \/><\/span><\/span><\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper tries to answer two questions. First, how to infer real-time air quality of any arbitrary location given environmental data and historical air quality data from very sparse monitoring locations. Second, if one needs to establish few new monitoring stations to improve the inference quality, how to determine the best locations for such purpose? [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"KDD 2015","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Proceedings of the 21th SIGKDD conference on Knowledge Discovery and Data Mining","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"\u00a9 ACM. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version can be found at http:\/\/dl.acm.org.","msr_conference_name":"Proceedings of the 21th SIGKDD conference on Knowledge Discovery and Data Mining","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"Hsun-Ping Hsieh, Shou-De Lin","msr_other_contributors":"","msr_speaker":"","msr_award":"","msr_affiliation":"","msr_institution":"","msr_host":"","msr_version":"","msr_duration":"","msr_original_fields_of_study":"","msr_release_tracker_id":"","msr_s2_match_type":"","msr_citation_count_updated":"","msr_published_date":"2015-08-12","msr_highlight_text":"","msr_notes":"","msr_longbiography":"","msr_publicationurl":"http:\/\/urbanair.msra.cn\/","msr_external_url":"","msr_secondary_video_url":"","msr_conference_url":"","msr_journal_url":"","msr_s2_pdf_url":"","msr_year":2015,"msr_citation_count":0,"msr_influential_citations":0,"msr_reference_count":0,"msr_s2_match_confidence":0,"msr_microsoftintellectualproperty":true,"msr_s2_open_access":false,"msr_s2_author_ids":[],"msr_pub_ids":[],"msr_hide_image_in_river":0,"footnotes":""},"msr-research-highlight":[],"research-area":[13556,13563],"msr-publication-type":[193716],"msr-publisher":[],"msr-focus-area":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-168662","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":"KDD 2015","msr_edition":"Proceedings of the 21th SIGKDD conference on Knowledge Discovery and Data Mining","msr_affiliation":"","msr_published_date":"2015-08-12","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":"204205","msr_publicationurl":"http:\/\/urbanair.msra.cn\/","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"airstation-kdd15-yuzheng.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/airstation-kdd15-yuzheng.pdf","id":204205,"label_id":0},{"type":"file","title":"KDD15_inferAir.pptx","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/KDD15_inferAir.pptx","id":204206,"label_id":0},{"type":"url","title":"http:\/\/urbanair.msra.cn\/","viewUrl":false,"id":false,"label_id":0}],"msr_related_uploader":"","msr_citation_count":0,"msr_citation_count_updated":"","msr_s2_paper_id":"","msr_influential_citations":0,"msr_reference_count":0,"msr_arxiv_id":"","msr_s2_author_ids":[],"msr_s2_open_access":false,"msr_s2_pdf_url":null,"msr_attachments":[{"id":0,"url":"http:\/\/urbanair.msra.cn\/"},{"id":204206,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/KDD15_inferAir.pptx"},{"id":204205,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/airstation-kdd15-yuzheng.pdf"}],"msr-author-ordering":[{"type":"text","value":"Hsun-Ping Hsieh","user_id":0,"rest_url":false},{"type":"text","value":"Shou-De Lin","user_id":0,"rest_url":false},{"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"}],"msr_impact_theme":[],"msr_research_lab":[199560],"msr_event":[],"msr_group":[],"msr_project":[171316,170824],"publication":[],"video":[],"msr-tool":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":171316,"post_title":"Urban Air","post_name":"urban-air","post_type":"msr-project","post_date":"2014-03-24 02:17:14","post_modified":"2018-04-02 19:26:10","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/urban-air\/","post_excerpt":"Using a diversity of big data to infer and predict fine-grained air quality throughout a city, and finally tackle air pollutions. \u00a0 http:\/\/urbanair.msra.cn\/\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0Install Mobile Apps Many countries are suffering from air pollutions. Many cities have built a few\u00a0air quality monitoring stations to inform people urban air quality every hour. Influenced by multiple complex factors, however,\u00a0urban air quality is\u00a0highly skewed in a city, varying by locations significantly and changing over time differently in different places.&hellip;","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/171316"}]}},{"ID":170824,"post_title":"Urban Computing","post_name":"urban-computing","post_type":"msr-project","post_date":"2016-07-03 10:26:01","post_modified":"2018-04-07 17:32:40","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/urban-computing\/","post_excerpt":"Concept\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 (\u4e2d\u6587\u4e3b\u9875) Urban computing is a process of acquisition, integration, and analysis of big and heterogeneous data generated by a diversity of sources in urban spaces, such as sensors, devices, vehicles, buildings, and human, to tackle the major issues that cities face, e.g. air pollution, increased energy consumption and traffic congestion. 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