Mining and clustering mobility evolution patterns from social media for urban informatics

Chen, Chien‐Cheng; Chiang, Meng-Fen; Peng, Wen-Chih

doi:10.1007/s10115-015-0853-4

Cited by 15 publications

(7 citation statements)

References 28 publications

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“…On the other side of large-scale mobility, many researchers have attempted to understand the empirical patterns governing pedestrian movement and study emergency evacuation of transport systems, buildings, and public spaces, especially in cases where crowding exists (e.g., religious commemorations, sports events, and festivals) [37]. Applications in that respect may involve many different fields, such as urban planning [38] and simulation [39], public transportation planning [40], and traffic forecasting [41,42].…”

Section: Human Mobilitymentioning

confidence: 99%

Agent-Based Simulation to Measure the Effectiveness of Citizen Sensing Applications—The Case of Missing Children

2021

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Citizen sensing applications need to have a number of users defined that ensures their effectiveness. This is not a straightforward task because neither the relationship between the size of the userbase or its effectiveness is easily quantified, nor is it clear which threshold for the number of users would make the application ‘effective’. This paper presents an approach for estimating the number of users needed for location-based crowdsourcing applications to work successfully, depending on the use case, the circumstances, and the criteria of success. It circumvents various issues, ethical or practical, in performing real-world controlled experiments and tackles this challenge by developing an agent-based modelling and simulation framework. This framework is tested on a specific scenario, that of missing children and the search for them. The search is performed with the contribution of citizens being made aware of the disappearance through a mobile application. The result produces an easily reconfigurable testbed for the effectiveness of citizen sensing mobile applications, allowing the study of the marginal utility of new users of the application. The resulting framework aims to be the digital twin of a real urban scenario, and it has been designed to be easily adapted and support decisions on the feasibility, evaluation, and targeting of the deployment of spatial crowdsourcing applications.

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Section: Human Mobilitymentioning

confidence: 99%

Agent-Based Simulation to Measure the Effectiveness of Citizen Sensing Applications—The Case of Missing Children

2021

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“…Our work can be enclosed in an alternative course of action for OSN-based mobility pattern discovery following a clustering-based approach. Basically, these works cluster the locations or paths followed by OSN users and then, on top of these clusters, make up the eventual mobility patterns [ 48 , 50 , 51 , 55 ]. In that sense, several clustering solutions have been proposed.…”

Section: Related Workmentioning

confidence: 99%

“…Similarly, Ref. [ 51 ] adapts the OPTICS algorithm, a density-based clustering for trajectories, to detect mobility patterns using the spatio-temporal features of documents from two different OSN platforms, Gowalla and Brightkite. Next, the Kullback–Leibler (KL) divergence is used as the similarity measurement to mine the evolution of these patterns through time.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy Modelling for Human Dynamics Based on Online Social Networks

Cuenca-Jara

Terroso-Sáenz

Valdés-Vela

et al. 2017

Sensors

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Human mobility mining has attracted a lot of attention in the research community due to its multiple implications in the provisioning of innovative services for large metropolises. In this scope, Online Social Networks (OSN) have arisen as a promising source of location data to come up with new mobility models. However, the human nature of this data makes it rather noisy and inaccurate. In order to deal with such limitations, the present work introduces a framework for human mobility mining based on fuzzy logic. Firstly, a fuzzy clustering algorithm extracts the most active OSN areas at different time periods. Next, such clusters are the building blocks to compose mobility patterns. Furthermore, a location prediction service based on a fuzzy rule classifier has been developed on top of the framework. Finally, both the framework and the predictor has been tested with a Twitter and Flickr dataset in two large cities.

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“…Such periods represent the characteristics of personal behaviors. And they are widely used for collaborative recommendations [2], [3], data compression [4], [5], prediction [6], [7], even in city planning [8]. If John does not follow the established periodic behavior, it indicates the possibility of abnormal behavior, i.e., anomaly detection [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Periodic Pattern Detection Algorithms for Personal Trajectory Data Based on Spatiotemporal Multi-Granularity

2019

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Identifying periodic patterns in individuals' trajectories is the basis of location awareness and personalized location services. It can help us understand personal behaviors. However, fuzziness and uncertainty of trajectory data, as well as noise and period distortion, make it difficult to recognize periodic patterns. In addition, the period lengths are usually unknown, and patterns have multiple granularities. Most of the existing mining algorithms focus on the discovery of patterns with different period lengths at a specific spatial scale, and few algorithms identify periodic patterns from the perspective of spatiotemporal multigranularity. Based on the existing studies, we propose a framework for identifying periodic patterns with different spatiotemporal granularities from personal trajectory data. First, a sequence of trajectory points is transformed into a time series of locations by using trajectory abstraction. Then, a multi-granularity behavior model is defined from spatial and temporal information. Finally, the single behavior periodic patterns can be discovered without knowing the period length by using a novel algorithm. Based on the association rules between locations, we can determine the periodic patterns of multiple behaviors from single behavior patterns. To evaluate the accuracy and efficiency of the algorithms, an artificial trajectory sequence and a real-world trajectory dataset called GeoLife are used in comparative experiments. The experimental results show that the proposed algorithm has higher accuracy on the promise of efficiency.

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Mining and clustering mobility evolution patterns from social media for urban informatics

Cited by 15 publications

References 28 publications

Agent-Based Simulation to Measure the Effectiveness of Citizen Sensing Applications—The Case of Missing Children

Agent-Based Simulation to Measure the Effectiveness of Citizen Sensing Applications—The Case of Missing Children

Fuzzy Modelling for Human Dynamics Based on Online Social Networks

Periodic Pattern Detection Algorithms for Personal Trajectory Data Based on Spatiotemporal Multi-Granularity

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