Location-Based Social Network’s Data Analysis and Spatio-Temporal Modeling for the Mega City of Shanghai, China

Khan, Naimat Ullah; Wan, Wanggen; Yu, Shui

doi:10.3390/ijgi9020076

Cited by 11 publications

(9 citation statements)

References 45 publications

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“…Khan et al [6] leverage the most popular LBSN in China, namely, Weibo, to discover temporal and spatial citizens' patterns in Shanghai. By means of these patterns and applying the kernel density estimation (KDE) technique, they are able to identify the most relevant venues in each region of the city.…”

Section: Lessons Learnedmentioning

confidence: 99%

“…These works are based on the assumption that the human activity within a region defines its current usage in a great manner. While a plethora of approaches has been successfully applied in this context, [5][6][7][8] two important limitations can be observed though.…”

Section: Introductionmentioning

confidence: 99%

“…As a matter of fact, a common approach has focused on using Call Detail Records (CDRs) or Location-Based Social Networks (LBSNs) as a primary source of mobility data, whether or not combined with other sources, that just provide static information about the target areas, like, their distribution of Points of Interest (POIs). [5][6][7][8]12 In that sense, CDRs and LBSNs are reliable sources to extract human mobility patterns at different granularity levels. However, it is hard to extract the actual means of transport utilized by the users for their displacements from these data sets.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Land‐use dynamic discovery based on heterogeneous mobility sources

2020

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Nowadays, cities are the most relevant type of human settlement and their population has been endlessly growing for decades. At the same time, we are witnessing an explosion of digital data that capture many different aspects and details of city life. This allows detecting human mobility patterns in urban areas with more detail than ever before. In this context, based on the fusion of mobility data from different and heterogeneous sources, such as public transport, transportnetwork connectivity and Online Social Networks, this study puts forward a novel approach to uncover the actual land use of a city. Unlike previous solutions, our work avoids a time-invariant approach and it considers the temporal factor based on the assumption that urban areas are not used by citizens all the time in the same manner. We have tested our solution in two different cities showing high accuracy rates.

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Section: Lessons Learnedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Land‐use dynamic discovery based on heterogeneous mobility sources

2020

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“…Ship encounter is essentially a stochastic process comprising the motion behaviors of the encountering ships. Many studies [37] point out that valuable information can be extracted from spatiotemporal behavior data. Following this rationale, the ship encounter process can be represented by transforming their AIS traces into a sequence of behavioral features that represent the spatial dependencies between the two ships.…”

Section: A Feature Extraction and Collision Risk Calibrationmentioning

confidence: 99%

A Data-Driven Approach for Collision Risk Early Warning in Vessel Encounter Situations Using Attention-BiLSTM

Jia

Yang

et al. 2020

IEEE Access

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Collision risk early warning is critical to sailing safety in vessel encounter situations because it provides ship officers with sufficient time to react to emergencies and take evasive actions in advance. In this study, we take spatiotemporal motion behaviors of encountering vessels into account since vessel motion behaviors have great influences on the occurrence of a dangerous situation. For this purpose, a datadriven approach is proposed to associate the motion behaviors with the future risk and early prediction of risk is achieved through classifying the behaviors into corresponding risk level. Specifically, we first derive a sequence of relative motion features between encountering vessels to characterize the spatial interactions that vary over time. Then a novel deep learning architecture, which combines bidirectional long short-term memory (BiLSTM) and attention mechanism, is developed to capture the spatial-temporal dependences of behaviors as well as their impacts on future risk. In particular, the BiLSTM is able to discover correlations among behaviors and the attention mechanism can emphasize the key information relevant to the risk prediction task. Exploiting the advantages of these two mechanisms makes the risk prediction more reasonable and reliable. Extensive experiments using ship trace data from the Yangtze River Estuary demonstrate that the proposed Attention-BiLSTM approach outperforms conventional LSTM in terms of accuracy and stability. Moreover, the real-time capability of the approach gives it a significant potential for use in predicting collisions at the early stages.

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“…For example, the LBS network and mobile apps (such as map services and social media data) generate substantial amounts of big data with individual and spatio-temporal semantic information and have been widely used in urban studies. Derived from studies with different research goals, many data mining algorithms have been developed for the identification of job-housing locations [32], the category of urban functional areas [33,34], and the observation and analysis of the characteristics of residents' spatio-temporal activities in specific areas [35,36]. For example, LBS-based interactions between residents' visits and urban POI have been used to redefine urban functional areas, namely the land use functional type, from the perspective of residents' actual usage [37,38].…”

Section: Introductionmentioning

confidence: 99%

Are Electric Vehicles Reshaping the City? An Investigation of the Clustering of Electric Vehicle Owners’ Dwellings and Their Interaction with Urban Spaces

Kang

Kan

Lin

2021

IJGI

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With the rapid development of electric vehicles (EVs) around the world, debates have arisen with regard to their impacts on people’s lifestyles and urban space. Mining spatio-temporal patterns from increasingly smart city sensors and personal mobile devices have become an important approach in understanding the interaction between human activity and urban space. In this study, we used location-based service data to identify EV owners and capture the distribution of home and charging stations. The research goal was to investigate that how the urban form in regions under rapid urbanization is driven by EV use, from a geographical perspective. Using a case study of the expanding metropolis of Beijing, GIS-based spatial statistical analysis was conducted to characterize the spatial-pattern of the homes of EV owners as well as their charging preferences. Our results indicate that the spatial clustering of the homes of EV owners in non-urban central areas—suburban areas—is significantly higher than that in urban central areas. According to the records of visits to charging stations, the spatial interaction distance between the dwellings of EV owners and their visits to charging stations exhibits significant distance attenuation characteristics. 88% of EV owners in this research travels within 40 km (Euclidean distance) between housing and charging stations. At the same time, there were significant differences in the spatial patterns between working days and non-working days which are affected by commuting activities. The three types of urban spatial interaction patterns were identified and categorized by visualization. This transformation to EV use in the city influences several aspects of people’s decisions and behaviors in life. Understanding the impacts will provide valuable information for the development of EVs and their implications in the electrification of transportation, smart planning, and sustainable urbanization.

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Location-Based Social Network’s Data Analysis and Spatio-Temporal Modeling for the Mega City of Shanghai, China

Cited by 11 publications

References 45 publications

Land‐use dynamic discovery based on heterogeneous mobility sources

Land‐use dynamic discovery based on heterogeneous mobility sources

A Data-Driven Approach for Collision Risk Early Warning in Vessel Encounter Situations Using Attention-BiLSTM

Are Electric Vehicles Reshaping the City? An Investigation of the Clustering of Electric Vehicle Owners’ Dwellings and Their Interaction with Urban Spaces

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