Volunteered geographic services

Savelyev, Alexander; Xu, Sen; Janowicz, Krzysztof; Mülligann, Christoph; Thatcher, Jim; Luo, Wei

doi:10.1145/2068976.2068980

Cited by 17 publications

(4 citation statements)

References 8 publications

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“…Enable hyper‐local activity services : We show that the proposed platform permits user‐generated geographic services (Savelyev et al, ) that rely on up‐to‐date information for enhancing place‐based activities. For instance, one can trigger distant sensors to detect whether a class is already in session or not using the sensors of mobile devices and machine learning.…”

Section: Research Contributionmentioning

confidence: 96%

“…moving from indoors to outdoors, or changing floors inside a building) more accurately than standard location technologies (e.g. GNSS, WiFi). Detect and differentiate place types : We show that through this platform it is possible to detect and differentiate place types on a university campus based purely on sensors readings from a mobile device. Enable hyper‐local activity services : We show that the proposed platform permits user‐generated geographic services (Savelyev et al, ) that rely on up‐to‐date information for enhancing place‐based activities. For instance, one can trigger distant sensors to detect whether a class is already in session or not using the sensors of mobile devices and machine learning.…”

Section: Research Contributionmentioning

confidence: 99%

“…Savelyev et al () coined a new concept of volunteered geographic services (VGS) in which users contribute to location‐based content while simultaneously improving crowd‐sourced service‐oriented platforms. They develop a novel architecture using the Linked Data paradigm and Semantic Web technologies to support location‐based human‐oriented services, such as ridesharing.…”

Section: Related Workmentioning

confidence: 99%

“…A second's worth of anonymized sensor readings from a limited set of distributed devices would be enough to build a rich, hyper‐local (Xia et al, ) model of an ambient field such as illuminance. Crowd‐sourced sensor readings compose the backbone of a community‐driven service‐oriented platform, a concept envisioned by Volunteered Geographic Services (Savelyev, Xu, Janowicz, Mulligann, Thatcher, & Luo, ) and other systems before.…”

Section: Introductionmentioning

confidence: 99%

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Crowdsensing smart ambient environments and services

Regalia

McKenzie

Gao

et al. 2016

Transactions in GIS

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Whether it be Smart Cities, Ambient Intelligence, or the Internet of Things, current visions for future urban spaces share a common core, namely the increasing role of distributed sensor networks and the ondemand integration of their data to power real-time services and analytics. Some of the greatest hurdles to implementing these visions include security risks, user privacy, scalability, the integration of heterogeneous data, and financial cost. In this work, we propose a crowdsensing mobile-device platform that empowers citizens to collect and share information about their surrounding environment via embedded sensor technologies. This approach allows a variety of urban areas (e.g., university campuses, shopping malls, city centers, suburbs) to become equipped with a free ad-hoc sensor network without depending on proprietary instrumentation. We present a framework, namely the GeoTracer application, as a proof-of-concept to conduct multiple experiments simulating use-case scenarios on a university campus.First, we demonstrate that ambient sensors (e.g. temperature, pressure, humidity, magnetism, illuminance, and audio) can help determine a change in environment (e.g. moving from indoors to outdoors, or floor changes inside buildings) more accurately than typical positioning technologies (e.g. global navigation satellite system, Wi-Fi, etc.).Furthermore, each of these sensors contributes a different amount of data to detecting events. for example, illuminance has the highest information gain when trying to detect changes between indoors and outdoors. Second, we show that through this platform it is possible to detect and differentiate place types on a university campus based on inferences made through ambient sensors. Lastly, we train classifiers to determine the activities that a place can afford at different times (e.g. good for studying or not, basketball courts in use or empty) based on sensor-driven semantic signatures. K E Y W O R D S crowdsensing, smart city, semantics, volunteered geographic services 382 |

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Section: Research Contributionmentioning

confidence: 96%

Section: Research Contributionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Crowdsensing smart ambient environments and services

Regalia

McKenzie

Gao

et al. 2016

Transactions in GIS

Self Cite

View full text Add to dashboard Cite

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Volunteered Geographic Information

Dietz¹,

Suh²

2018

Encyclopedia of Social Network Analysis and Mining

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use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this book are included in the book's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the book's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Citizens as Expert Sensors: One Step Up on the VGI Ladder

Karimipour

Azari

2014

Lecture Notes in Geoinformation and Cartography

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Volunteered geographic information is one the most significant advances in GIScience following the well-known elements of Web 2.0. It has been mainly looked at as a source of information, and the focus has been on improving its usage, understanding, and quality. This paper opens a discussion on considering VGI as an implicit source of users' experience, which provides general users with solutions to help them take actions like an expert. As a case study, the idea has been applied on estimating the optimum travel time path through information collected by experts. Having introduced the methodology, the results for some examples are presented and discussed.

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Volunteered geographic services

Cited by 17 publications

References 8 publications

Crowdsensing smart ambient environments and services

Crowdsensing smart ambient environments and services

Volunteered Geographic Information

Citizens as Expert Sensors: One Step Up on the VGI Ladder

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