Semantic place labels are labels like \u201chome\u201d, \u201cwork\u201d, and \u201cschool\u201d given to geographic locations where a person spends time. Such labels are important both for giving understandable location information to people and for automatically inferring activities. Deployed products often compute semantic labels with heuristics, which are difficult to program reliably. In this paper, we develop Placer, an algorithm to infer semantic places labels. It uses data from two large, government diary studies to create a principled algorithm for labeling places based on machine learning. Our labeling reduces to a classification problem, where we classify locations into different label categories based on individual demographics, the timing of visits, and nearby businesses. Using these government studies gives us an unprecedented amount of training and test data. For instance, one of our experiments used training data from 87,600 place visits (from 10,372 distinct people) evaluated on 1,135,053 visits (from 124,517 distinct people). We show labeling accuracy for a number of experiments, including one that gives a 14 percentage point increase in accuracy when labeling is a function of nearby businesses in addition to demographic and time features. We also test on GPS data from 28 subjects.<\/p>\n","protected":false},"excerpt":{"rendered":"
Semantic place labels are labels like \u201chome\u201d, \u201cwork\u201d, and \u201cschool\u201d given to geographic locations where a person spends time. Such labels are important both for giving understandable location information to people and for automatically inferring activities. Deployed products often compute semantic labels with heuristics, which are difficult to program reliably. In this paper, we develop […]<\/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":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13563],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-332747","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-data-platform-analytics","msr-locale-en_us"],"msr_publishername":"","msr_edition":"2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2013) September 8-12, 2013, Zurich, 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