scholarly journals SEMANTIC LOCATION EXTRACTION FROM CROWDSOURCED DATA

2016 ◽  
Vol XLI-B2 ◽  
pp. 543-547 ◽  
Author(s):  
S. Koswatte ◽  
K. Mcdougall ◽  
X. Liu
Keyword(s):  
Disaster Management ◽  
Mobile Communication ◽  
Location Information ◽  
Information Availability ◽  
Crowdsourced Data ◽  
Event Location ◽  
Map Data ◽  
Direct Use ◽  
Data Currency ◽  
User Location

Crowdsourced Data (CSD) has recently received increased attention in many application areas including disaster management. Convenience of production and use, data currency and abundancy are some of the key reasons for attracting this high interest. Conversely, quality issues like incompleteness, credibility and relevancy prevent the direct use of such data in important applications like disaster management. Moreover, location information availability of CSD is problematic as it remains very low in many crowd sourced platforms such as Twitter. Also, this recorded location is mostly related to the mobile device or user location and often does not represent the event location. In CSD, event location is discussed descriptively in the comments in addition to the recorded location (which is generated by means of mobile device's GPS or mobile communication network). This study attempts to semantically extract the CSD location information with the help of an ontological Gazetteer and other available resources. 2011 Queensland flood tweets and Ushahidi Crowd Map data were semantically analysed to extract the location information with the support of Queensland Gazetteer which is converted to an ontological gazetteer and a global gazetteer. Some preliminary results show that the use of ontologies and semantics can improve the accuracy of place name identification of CSD and the process of location information extraction.

scholarly journals SEMANTIC LOCATION EXTRACTION FROM CROWDSOURCED DATA

2016 ◽  
Vol XLI-B2 ◽  
pp. 543-547 ◽  
Author(s):  
S. Koswatte ◽  
K. Mcdougall ◽  
X. Liu
Keyword(s):  
Disaster Management ◽  
Mobile Communication ◽  
Location Information ◽  
Information Availability ◽  
Crowdsourced Data ◽  
Event Location ◽  
Map Data ◽  
Direct Use ◽  
Data Currency ◽  
User Location

Crowdsourced Data (CSD) has recently received increased attention in many application areas including disaster management. Convenience of production and use, data currency and abundancy are some of the key reasons for attracting this high interest. Conversely, quality issues like incompleteness, credibility and relevancy prevent the direct use of such data in important applications like disaster management. Moreover, location information availability of CSD is problematic as it remains very low in many crowd sourced platforms such as Twitter. Also, this recorded location is mostly related to the mobile device or user location and often does not represent the event location. In CSD, event location is discussed descriptively in the comments in addition to the recorded location (which is generated by means of mobile device's GPS or mobile communication network). This study attempts to semantically extract the CSD location information with the help of an ontological Gazetteer and other available resources. 2011 Queensland flood tweets and Ushahidi Crowd Map data were semantically analysed to extract the location information with the support of Queensland Gazetteer which is converted to an ontological gazetteer and a global gazetteer. Some preliminary results show that the use of ontologies and semantics can improve the accuracy of place name identification of CSD and the process of location information extraction.

scholarly journals Mobility and Spatio-Temporal Exposure Control

2013 ◽  
Author(s):  
Geir M. Køien
Keyword(s):  
Risk Assessment ◽  
Risk Analysis ◽  
Location Privacy ◽  
Location Information ◽  
Exposure Control ◽  
Vm Migration ◽  
Location Data ◽  
Spatio Temporal ◽  
Mobile Subscriber ◽  
User Location

Modern risk assessment methods cover many issues and encompass both risk analysis and corresponding prevention/mitigation measures.However, there is still room for improvement and one aspect that may benefit from more work is “exposure control”.The “exposure” an asset experiences plays an important part in the risks facing the asset.Amongst the aspects that all too regularly get exposed is user identities and user location information,and in a context with mobile subscriber and mobility in the service hosting (VM migration/mobility) the problems associated with lost identity/location privacy becomes urgent.In this paper we look at “exposure control” as a way for analyzing and protecting user identity and user location data.

Benchmarking and Data Generation in Moving Objects Databases

2005 ◽  
pp. 23-28 ◽  
Author(s):  
Theodoros Tzouramanis
Keyword(s):  
Mobile Communication ◽  
Moving Objects ◽  
Query Languages ◽  
Location Information ◽  
Navigation Systems ◽  
Data Generation ◽  
Access Methods ◽  
Future Projections ◽  
Storage And Retrieval ◽  
Moving Objects Databases

Moving objects databases (MODs) provide the framework for the efficient storage and retrieval of the changing position of continuously moving objects. This includes the current and past locations of moving objects and the support of spatial queries that refer to historical location information and future projections as well. Nowadays, new spatiotemporal applications that require tracking and recording the trajectories of moving objects online are emerging. Digital battlefields, traffic supervision, mobile communication, navigation systems, and geographic information systems (GIS) are among these applications. Towards this goal, during recent years many efforts have focused on MOD formalism, data models, query languages, visualization, and access methods (Guting et al., 2000; Saltenis & Jensen, 2002; Sistla, Wolfson, Chamberlain, & Dao, 1997). However, little work has appeared on benchmarking.

Extraction of actionable information from crowdsourced disaster data

2016 ◽  
Vol 14 (6) ◽  
pp. 377 ◽  
Author(s):  
Rungsun Kiatpanont, MS ◽  
Uthai Tanlamai, PhD ◽  
Prabhas Chongstitvatana, PhD
Keyword(s):  
Machine Learning ◽  
Disaster Management ◽  
Learning Algorithm ◽  
Data Extraction ◽  
Extraction Process ◽  
Support Vector ◽  
Test Case ◽  
Crowdsourced Data ◽  
Disaster Data ◽  
Percent Accuracy

Natural disasters cause enormous damage to countries all over the world. To deal with these common problems, different activities are required for disaster management at each phase of the crisis. There are three groups of activities as follows: (1) make sense of the situation and determine how best to deal with it, (2) deploy the necessary resources, and (3) harmonize as many parties as possible, using the most effective communication channels. Current technological improvements and developments now enable people to act as real-time information sources. As a result, inundation with crowdsourced data poses a real challenge for a disaster manager. The problem is how to extract the valuable information from a gigantic data pool in the shortest possible time so that the information is still useful and actionable. This research proposed an actionable-data-extraction process to deal with the challenge. Twitter was selected as a test case because messages posted on Twitter are publicly available. Hashtag, an easy and very efficient technique, was also used to differentiate information.A quantitative approach to extract useful information from the tweets was supported and verified by interviews with disaster managers from many leading organizations in Thailand to understand their missions. The information classifications extracted from the collected tweets were first performed manually, and then the tweets were used to train a machine learning algorithm to classify future tweets. One particularly useful, significant, and primary section was the request for help category. The support vector machine algorithm was used to validate the results from the extraction process of 13,696 sample tweets, with over 74 percent accuracy. The results confirmed that the machine learning technique could significantly and practically assist with disaster management by dealing with crowdsourced data.

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