scholarly journals Semantic Enhancement of Human Urban Activity Chain Construction Using Mobile Phone Signaling Data

2021 ◽  
Vol 10 (8) ◽  
pp. 545
Author(s):  
Shaojun Liu ◽  
Yi Long ◽  
Ling Zhang ◽  
Hao Liu
Keyword(s):  
Mobile Phone ◽  
Human Activity ◽  
Human Activities ◽  
Traffic Management ◽  
Activity Patterns ◽  
Temporal Distribution ◽  
Urban Dynamics ◽  
Sensing Data ◽  
Activity Chain ◽  
Spatiotemporal Behavior

Data-driven urban human activity mining has become a hot topic of urban dynamic modeling and analysis. Semantic activity chain modeling with activity purpose provides scientific methodological support for the analysis and decision-making of human behavior, urban planning, traffic management, green sustainable development, etc. However, the spatial and temporal uncertainty of the ubiquitous mobile sensing data brings a huge challenge for modeling and analyzing human activities. Existing approaches for modeling and identifying human activities based on massive social sensing data rely on a large number of valid supervised samples or limited prior knowledge. This paper proposes an effective methodology for building human activity chains based on mobile phone signaling data and labeling activity purpose semantics to analyze human activity patterns, spatiotemporal behavior, and urban dynamics. We fully verified the effectiveness and accuracy of the proposed method in human daily activity process construction and activity purpose identification through accuracy comparison and spatial-temporal distribution exploration. This study further confirms the possibility of using big data to observe urban human spatiotemporal behavior.

scholarly journals Mining Non-Zero-Rare Sequential Patterns On Activity Recognition

2019 ◽  
Vol 5 (1) ◽  
pp. 1-9
Author(s):  
Mohammad Iqbal ◽  
Chandrawati Putri Wulandari ◽  
Wawan Yunanto ◽  
Ghaluh Indah Permata Sari
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activities ◽  
Activity Patterns ◽  
Human Activity Recognition ◽  
Experimental Result ◽  
Sequential Patterns ◽  
Motion Sensor ◽  
Motion Sensors ◽  
Human Activity Patterns

Discovering rare human activity patterns—from triggered motion sensors deliver peculiar information to notify people about hazard situations. This study aims to recognize rare human activities using mining non-zero-rare sequential patterns technique. In particular, this study mines the triggered motion sensor sequences to obtain non-zero-rare human activity patterns—the patterns which most occur in the motion sensor sequences and the occurrence numbers are less than the pre-defined occurrence threshold. This study proposes an algorithm to mine non-zero-rare pattern on human activity recognition called Mining Multi-class Non-Zero-Rare Sequential Patterns (MMRSP).  The experimental result showed that non-zero-rare human activity patterns succeed to capture the unusual activity. Furthermore, the MMRSP performed well according to the precision value of rare activities.

scholarly journals Fusing Mobile Phone and Travel Survey Data to Model Urban Activity Dynamics

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Chao Yang ◽  
Yuliang Zhang ◽  
Xianyuan Zhan ◽  
Satish V. Ukkusuri ◽  
Yifan Chen
Keyword(s):  
Mobile Phone ◽  
Survey Data ◽  
Large Scale ◽  
Activity Patterns ◽  
Mobile Phone Data ◽  
Activity Data ◽  
Travel Survey ◽  
Activity Chain ◽  
Urban Activity ◽  
Activity Dynamics

A key issue to understand urban system is to characterize the activity dynamics in a city—when, where, what, and how activities happen in a city. To better understand the urban activity dynamics, city-wide and multiday activity participation sequence data, namely, activity chain as well as suitable spatiotemporal models, are needed. The commonly used household travel survey data in activity analysis suffers from limited sample size and temporal coverage. The emergence of large-scale spatiotemporal data in urban areas, such as mobile phone data, provides a new opportunity to infer urban activities and the underlying dynamics. However, the challenge is the absence of labeled activity information in mobile phone data. Consequently, how to fuse the useful information in household survey data and mobile phone data to build city-wide, multiday, and all-time activity chains becomes an important research question. Moreover, the multidimension structure of the activity data (e.g., location, start time, duration, type) makes the extraction of spatiotemporal activity patterns another difficult problem. In this study, the authors first introduce an activity chain inference model based on tensor decomposition to infer the missing activity labels in large-scale and multiday activity data, and then develop a spatiotemporal event clustering model based on DBSCAN, called STE-DBSCAN, to identify the spatiotemporal activity patterns. The proposed approaches achieved good accuracy and produced patterns with a high level of interpretability.

scholarly journals A NEW HIERARCHICAL CLUSTERING APPROACH FOR SPARSE MOBILE PHONE TRAJECTORIES

2018 ◽  
Vol XLII-4 ◽  
pp. 697-701
Author(s):  
W. Wang ◽  
Z. Luan ◽  
B. He ◽  
X. Li ◽  
D. Zhang ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Hierarchical Clustering ◽  
Human Activity ◽  
Urban Space ◽  
Activity Pattern ◽  
Activity Patterns ◽  
Social Activity ◽  
Spatial Uncertainty ◽  
Clustering Approach ◽  
Human Activity Patterns

<p><strong>Abstract.</strong> Understanding the pattern of human activities benefits both the living service providing for the public and the policy-making for urban planners. The development of location-aware technology enables us to acquire large volume individual trajectories with different spatial and temporal resolution, such as GPS trajectories, mobile phone positioning data, social media check-in data, Wifi, and Bluetooth. However, the highest population penetrated mobile phone positioning trajectories are hard to infer human activity pattern directly, because of the sparsity in both space and time. This article presents a hierarchical clustering approach by using the move and stay sequences inferred from spare mobile phone trajectories to uncover the hidden human activity pattern. Personal stays at some places and following moves are first extracted from mobile phone trajectories, considering the spatial uncertainty of position. The similarity of trajectories is measured with a new indicator defined by the area of a spatial-temporal polygon bound with normalized trajectories. Finally, a hierarchical clustering method is developed to group trajectories with similar stay-move chains from the bottom to the top. The obtained clusters are analyzed to identify human activity patterns. An experiment with mobile phone users’ one-day trajectories in Shenzhen, China was conducted to test the performance of the proposed clustering approach. The results indicate all used trajectories are classified into 10 clusters representing typical daily activity patterns from the simple home-staying mode to complex home-working-social activity daily cycles. This study not only unravels the hidden activity patterns behind massive sparse trajectories but also deepens our understanding of the interaction of human activity and urban space.</p>

scholarly journals A Multiscale Survival Process for Modeling Human Activity Patterns

PLoS ONE ◽  
2016 ◽  
Vol 11 (3) ◽  
pp. e0151473 ◽  
Author(s):  
Tianyang Zhang ◽  
Peng Cui ◽  
Chaoming Song ◽  
Wenwu Zhu ◽  
Shiqiang Yang
Keyword(s):  
Human Activity ◽  
Activity Patterns ◽  
Human Activity Patterns

scholarly journals Adaptive Modeling of Urban Dynamics During Ephemeral Event via Mobile Phone Traces

2016 ◽  
Vol 4 (2) ◽  
pp. 31-47
Author(s):  
Suhad Faisal Behadili ◽  
Cyrille Bertelle ◽  
Loay E. George
Keyword(s):  
Mobile Phone ◽  
Adaptive Modeling ◽  
Urban Dynamics

scholarly journals SEA LEVEL CHANGES ALONG THE COASTS OF KEA ISLAND AND PALEOGEOGRAPHICAL COASTAL RECONSTRUCTION OF ARCHAEOLOGICAL SITES

2017 ◽  
Vol 43 (1) ◽  
pp. 453 ◽  
Author(s):  
N.D Mourtzas
Keyword(s):  
Sea Level ◽  
Human Activity ◽  
Human Activities ◽  
Archaeological Sites ◽  
Classical Period ◽  
Sea Level Changes ◽  
Coastal Morphology ◽  
Sea Levels

Sea level changes during the Upper Holocene submerged the coasts of Kea in three different phases about 5.50m, 3.90m and 1.50m respectively below the contemporary sea level thus causing sea transgression along the shores of Kea, which varied from 8m to 78m depending on the coastal morphology. These changes caused the alteration of the earlier morphology at coastal archaeological sites of the Island, as the prehistoric settlement of Ayia Irini and Classical period port of Karthaia, as well as, submerged under the sea areas of coastal human activity during antiquity, as the ancient schist quarry at Spathi bay. The study of historical, geomorphological and sedimentological data indicative of previous sea levels allow the paleogeographical reconstruction of the coasts during the period of human activities in these areas.

scholarly journals Temporal Distribution of Arsenic and Metals in Soil From King George Island, Antarctica

2022 ◽  
Vol 8 ◽  
Author(s):  
Tailisi H. Trevizani ◽  
Rosalinda C. Montone ◽  
Rubens C. L. Figueira
Keyword(s):  
Human Activities ◽  
Temporal Distribution ◽  
Polar Regions ◽  
Control Points ◽  
King George Island ◽  
Admiralty Bay ◽  
Direct Exposure ◽  
Antarctic Continent ◽  
The Antarctic ◽  
Continue Monitoring

The polar regions are vulnerable to impacts caused by local and global pollution. The Antarctic continent has been considered an environment that has remained little affected by human activities. Direct exposure to contaminants may occur in areas continuously occupied by research stations for several decades. Admiralty Bay on the southeast coast of King George Island, has potential for being affected by human activities due research stations operating in the area, including the Brazilian Commandant Ferraz Antarctic Station (CFAS). The levels of metals and arsenic were determined in soils collected near CFAS (points 5, 6, 7, and 9), Base G and at two points distant from the CFAS: Refuge II and Hennequin. Samples were collected after the fire in CFAS occurred in February 2012, up to December 2018 to assess the environmental impacts in the area. Al and As were related with Base G. Refuge II and Hennequin can be considered as control points for this region. As a consequence of the accident, the increased levels for Cd, Cu, Pb, and Zn, especially at point 9 (inside the CFAS) and in the soil surrounding the CFAS in 2013. The results from 2016 to 2018 demonstrated a reduction in levels of all studied metals near CFAS, which may be related to the leaching of metals into Admiralty Bay; it is thus, being important the continue monitoring soil, sediments, and Antarctic biota.

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