Web-Enhanced GPS

Hariharan, Ramaswamy; Krumm, John; Horvitz, Eric

doi:10.1007/11426646_9

Cited by 22 publications

(12 citation statements)

References 6 publications

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“…This is one key difference between our work and previous analysis using external databases (e.g. Microsolf Map Point [8], [7]) to query for types of nearby businesses and other information. The full set of mobility features is described in Table 3.…”

Section: Extracting Location Privacy-sensitive Featuresmentioning

confidence: 96%

“…In the context of place labeling research [7], the connections between physical locations and their semantic meaning (their place category) are strong, and thus useful to infer the meaning of locations e.g., by using web data [8]. However, disclosing one's physical location is clearly sensitive from the perspective of privacy [9], [10], and it has been often argued that a generalized acceptance of many location-based services is limited by negative perceptions of the potential implications of location sharing [11].…”

Section: Introductionmentioning

confidence: 99%

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The Places of Our Lives: Visiting Patterns and Automatic Labeling from Longitudinal Smartphone Data

Tri

Gática-Pérez

2014

IEEE Trans. on Mobile Comput.

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Abstract-The location tracking functionality of modern mobile devices provides unprecedented opportunity to the understanding of individual mobility in daily life. Instead of studying raw geographic coordinates, we are interested in understanding human mobility patterns based on sequences of place visits which encode, at a coarse resolution, most daily activities. This paper presents a study on place characterization in people's everyday life based on data recorded continuously by smartphones. First, we study human mobility from sequences of place visits, including visiting patterns on different place categories. Second, we address the problem of automatic place labeling from smartphone data without using any geo-location information. Our study on a large-scale data collected from 114 smartphone users over 18 months confirms many intuitions, and also reveals findings regarding both regularly and novelty trends in visiting patterns. Considering the problem of place labeling with 10 place categories, we show that frequently visited places can be recognized reliably (over 80%) while it is much more challenging to recognize infrequent places.

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Section: Extracting Location Privacy-sensitive Featuresmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

The Places of Our Lives: Visiting Patterns and Automatic Labeling from Longitudinal Smartphone Data

Tri

Gática-Pérez

2014

IEEE Trans. on Mobile Comput.

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“…Both coarse and fine-grained location systems have been used to perform location-driven activity inference [11,12]. In work by Eagle and Pentland [13], which is the closest to ours, student type affiliations are determined by clustering location information aggregated over a period of nine months.…”

Section: Sensing Activity With Mobile Phonesmentioning

confidence: 99%

Daily Routine Classification from Mobile Phone Data

Farrahi

Gática-Pérez

2008

Machine Learning for Multimodal Interaction

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Abstract. The automatic analysis of real-life, long-term behavior and dynamics of individuals and groups from mobile sensor data constitutes an emerging and challenging domain. We present a framework to classify people's daily routines (defined by day type, and by group affiliation type) from real-life data collected with mobile phones, which include physical location information (derived from cell tower connectivity), and social context (given by person proximity information derived from Bluetooth). We propose and compare single-and multi-modal routine representations at multiple time scales, each capable of highlighting different features from the data, to determine which best characterized the underlying structure of the daily routines. Using a massive data set of 87000+ hours spanning four months of the life of 30 university students, we show that the integration of location and social context and the use of multiple time-scales used in our method is effective, producing accuracies of over 80% for the two daily routine classification tasks investigated, with significant performance differences with respect to the single-modal cues.

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“…Also approaches that map locations of GSM cell towers have been introduced (e.g., GSMLoc 1 ). Hariharan et al [2] use a commercial geocoder to map GPS information into street addresses. Li et al [3] associate semantics to individual GSM cells.…”

Section: Related Workmentioning

confidence: 99%

SerPens: A Tool for Semantically Enriched Location Information on Personal Devices

Bhattacharya

Kukkonen

Nurmi

et al. 2008

Proceedings of the 3rd International ICST Conference on Body Area Networks

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Current mobile phones provide GSM cell information and many devices also support GPS or WiFi-based location information. A problem with raw location data is that it does not provide semantic information, which makes it hard to integrate location-awareness into applications. Moreover, to understand what kind of location information is important to users, researchers currently need to perform time consuming user studies. In this paper we introduce SerPens, a tool that enables gathering semantically enriched location information on personal devices. The main novelty of SerPens is that it enables users to share and gather semantic information in a collaborative fashion. The label information is tied to a taxonomy and is accessible to applications. SerPens has been developed on top of BeTelGeuse, a Bluetooth-based data gathering tool for J2ME compatible devices.

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Web-Enhanced GPS

Cited by 22 publications

References 6 publications

The Places of Our Lives: Visiting Patterns and Automatic Labeling from Longitudinal Smartphone Data

The Places of Our Lives: Visiting Patterns and Automatic Labeling from Longitudinal Smartphone Data

Daily Routine Classification from Mobile Phone Data

SerPens: A Tool for Semantically Enriched Location Information on Personal Devices

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