3,530 research outputs found
A Survey of Location Prediction on Twitter
Locations, e.g., countries, states, cities, and point-of-interests, are
central to news, emergency events, and people's daily lives. Automatic
identification of locations associated with or mentioned in documents has been
explored for decades. As one of the most popular online social network
platforms, Twitter has attracted a large number of users who send millions of
tweets on daily basis. Due to the world-wide coverage of its users and
real-time freshness of tweets, location prediction on Twitter has gained
significant attention in recent years. Research efforts are spent on dealing
with new challenges and opportunities brought by the noisy, short, and
context-rich nature of tweets. In this survey, we aim at offering an overall
picture of location prediction on Twitter. Specifically, we concentrate on the
prediction of user home locations, tweet locations, and mentioned locations. We
first define the three tasks and review the evaluation metrics. By summarizing
Twitter network, tweet content, and tweet context as potential inputs, we then
structurally highlight how the problems depend on these inputs. Each dependency
is illustrated by a comprehensive review of the corresponding strategies
adopted in state-of-the-art approaches. In addition, we also briefly review two
related problems, i.e., semantic location prediction and point-of-interest
recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur
Creating Full Individual-level Location Timelines from Sparse Social Media Data
In many domain applications, a continuous timeline of human locations is
critical; for example for understanding possible locations where a disease may
spread, or the flow of traffic. While data sources such as GPS trackers or Call
Data Records are temporally-rich, they are expensive, often not publicly
available or garnered only in select locations, restricting their wide use.
Conversely, geo-located social media data are publicly and freely available,
but present challenges especially for full timeline inference due to their
sparse nature. We propose a stochastic framework, Intermediate Location
Computing (ILC) which uses prior knowledge about human mobility patterns to
predict every missing location from an individual's social media timeline. We
compare ILC with a state-of-the-art RNN baseline as well as methods that are
optimized for next-location prediction only. For three major cities, ILC
predicts the top 1 location for all missing locations in a timeline, at 1 and
2-hour resolution, with up to 77.2% accuracy (up to 6% better accuracy than all
compared methods). Specifically, ILC also outperforms the RNN in settings of
low data; both cases of very small number of users (under 50), as well as
settings with more users, but with sparser timelines. In general, the RNN model
needs a higher number of users to achieve the same performance as ILC. Overall,
this work illustrates the tradeoff between prior knowledge of heuristics and
more data, for an important societal problem of filling in entire timelines
using freely available, but sparse social media data.Comment: 10 pages, 8 figures, 2 table
Enhancing Situation Awareness in Real Time Geospatial Visualization
Today’s hardware and software infrastructure enables large-scale visualization of (near) real-time data in a geospatial context, which is attractive in a variety of application areas ranging from military applications to disaster management and incident response. The drawback on the other hand is that combined visualizations might lead to an overload of human processing capacity, for instance if multiple dynamic aspects must be observed in parallel. The objective for our research is to develop a solution which enhances the situation awareness of the user by identifying the appropriate means of visualizing information based on the user’s task and a given situation. In this paper, we propose an approach and associated system architecture how this objective can be reached. The primary ingredient of our approach is the combination of a GIS system and logic-based ontology representation and reasoning system
Inferring transportation mode from smartphone sensors:Evaluating the potential of Wi-Fi and Bluetooth
Understanding which transportation modes people use is critical for smart cities and planners to better serve their citizens. We show that using information from pervasive Wi-Fi access points and Bluetooth devices can enhance GPS and geographic information to improve transportation detection on smartphones. Wi-Fi information also improves the identification of transportation mode and helps conserve battery since it is already collected by most mobile phones. Our approach uses a machine learning approach to determine the mode from pre-prepocessed data. This approach yields an overall accuracy of 89% and average F1 score of 83% for inferring the three grouped modes of self-powered, car-based, and public transportation. When broken out by individual modes, Wi-Fi features improve detection accuracy of bus trips, train travel, and driving compared to GPS features alone and can substitute for GIS features without decreasing performance. Our results suggest that Wi-Fi and Bluetooth can be useful in urban transportation research, for example by improving mobile travel surveys and urban sensing applications
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Context-awareness for mobile sensing: a survey and future directions
The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions
A stigmergy-based analysis of city hotspots to discover trends and anomalies in urban transportation usage
A key aspect of a sustainable urban transportation system is the
effectiveness of transportation policies. To be effective, a policy has to
consider a broad range of elements, such as pollution emission, traffic flow,
and human mobility. Due to the complexity and variability of these elements in
the urban area, to produce effective policies remains a very challenging task.
With the introduction of the smart city paradigm, a widely available amount of
data can be generated in the urban spaces. Such data can be a fundamental
source of knowledge to improve policies because they can reflect the
sustainability issues underlying the city. In this context, we propose an
approach to exploit urban positioning data based on stigmergy, a bio-inspired
mechanism providing scalar and temporal aggregation of samples. By employing
stigmergy, samples in proximity with each other are aggregated into a
functional structure called trail. The trail summarizes relevant dynamics in
data and allows matching them, providing a measure of their similarity.
Moreover, this mechanism can be specialized to unfold specific dynamics.
Specifically, we identify high-density urban areas (i.e hotspots), analyze
their activity over time, and unfold anomalies. Moreover, by matching activity
patterns, a continuous measure of the dissimilarity with respect to the typical
activity pattern is provided. This measure can be used by policy makers to
evaluate the effect of policies and change them dynamically. As a case study,
we analyze taxi trip data gathered in Manhattan from 2013 to 2015.Comment: Preprin
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