scholarly journals The Scale Effect on Spatial Interaction Patterns: An Empirical Study Using Taxi O-D data of Beijing and Shanghai

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 51994-52003 ◽  
Author(s):  
Shiliang Zhang ◽  
Di Zhu ◽  
Xin Yao ◽  
Ximeng Cheng ◽  
Huagui He ◽  
...  
Keyword(s):  
Empirical Study ◽  
Scale Effect ◽  
Spatial Interaction ◽  
Interaction Patterns

scholarly journals Multiscale dynamic human mobility flow dataset in the U.S. during the COVID-19 epidemic

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Yuhao Kang ◽  
Song Gao ◽  
Yunlei Liang ◽  
Mingxiao Li ◽  
Jinmeng Rao ◽  
...  
Keyword(s):  
Public Health ◽  
Spatial Interaction ◽  
Human Mobility ◽  
Open Data ◽  
The United States ◽  
Interaction Patterns ◽  
Spatiotemporal Resolution ◽  
Health Crisis ◽  
Spreading Dynamics ◽  
Behaviour Changes

AbstractUnderstanding dynamic human mobility changes and spatial interaction patterns at different geographic scales is crucial for assessing the impacts of non-pharmaceutical interventions (such as stay-at-home orders) during the COVID-19 pandemic. In this data descriptor, we introduce a regularly-updated multiscale dynamic human mobility flow dataset across the United States, with data starting from March 1st, 2020. By analysing millions of anonymous mobile phone users’ visits to various places provided by SafeGraph, the daily and weekly dynamic origin-to-destination (O-D) population flows are computed, aggregated, and inferred at three geographic scales: census tract, county, and state. There is high correlation between our mobility flow dataset and openly available data sources, which shows the reliability of the produced data. Such a high spatiotemporal resolution human mobility flow dataset at different geographic scales over time may help monitor epidemic spreading dynamics, inform public health policy, and deepen our understanding of human behaviour changes under the unprecedented public health crisis. This up-to-date O-D flow open data can support many other social sensing and transportation applications.

scholarly journals A Framework for Spatial Interaction Analysis Based on Large-Scale Mobile Phone Data

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Weifeng Li ◽  
Xiaoyun Cheng ◽  
Zhengyu Duan ◽  
Dongyuan Yang ◽  
Gaohua Guo
Keyword(s):  
Mobile Phone ◽  
Large Scale ◽  
Pattern Mining ◽  
Interaction Analysis ◽  
Spatial Interaction ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Mobile Phone Data ◽  
Interaction Patterns ◽  
Critical Activity

The overall understanding of spatial interaction and the exact knowledge of its dynamic evolution are required in the urban planning and transportation planning. This study aimed to analyze the spatial interaction based on the large-scale mobile phone data. The newly arisen mass dataset required a new methodology which was compatible with its peculiar characteristics. A three-stage framework was proposed in this paper, including data preprocessing, critical activity identification, and spatial interaction measurement. The proposed framework introduced the frequent pattern mining and measured the spatial interaction by the obtained association. A case study of three communities in Shanghai was carried out as verification of proposed method and demonstration of its practical application. The spatial interaction patterns and the representative features proved the rationality of the proposed framework.

scholarly journals Communicational ties between the Republic of Bashkortostan and other Russian regions based on voice cell phone data

2020 ◽  
Vol 13 (4) ◽  
pp. 133-139
Author(s):  
Valeriy V. Yumaguzin ◽  
Maria V. Vinnik
Keyword(s):  
Cell Phone ◽  
Construction Projects ◽  
Social Ties ◽  
Spatial Interaction ◽  
Federal District ◽  
Interaction Patterns ◽  
Economic Relations ◽  
Republic Of Bashkortostan ◽  
Potential Applications ◽  
The Republic

The article aims to present social ties of the Republic of Bashkortostan based on voice cell phone data, which covers 12 million calls from and to the region during the first five days of March 2020. About 96% of calls are made within the republic and only 4% of them are interregional. The people of the Republic of Bashkortostan have close connections with those who live in neighboring regions (Orenburg, Sverdlovsk oblast, the Republic of Tatarstan and especially Chelyabinsk oblast). Being a part of the Ural Economic Region, the Volga Federal District and Volga-Ural Macro Region, the republic has turned mostly towards Ural regions. We also found that the republic has close social ties with Moscow and Moscow region, St. Petersburg and Leningrad oblast, as well as Krasnodar kray, Samara oblast and two Autonomous Districts: Khanty-Mansi and Yamalo-Nenets. We estimated the number of persons who possessed Bashkir SIM-card and were outside the republic during the research period – 183 thousand; the most of them were in the abovementioned regions. While conversation between residents lasts 50 seconds, which is among the smallest values, the calls to and from republics of Altai, Tyva, Khakassia, Sakha and Magadan oblast are 5-8 times longer. Overall, the communication pattern reflects migration flows and economic relations between regions. The results of this study can be utilized by researchers and Bashkir government to explore spatial interaction patterns between regions and may help to guide transportation planning and other potential applications, e.g. infrastructure construction projects. In conclusion, we postulate that cell phone data can be exploited as a source of social ties data, however, the strengthening communication shift into Internet space is diminishing information on the directional features of the ties.

scholarly journals Interactive visual exploration and analysis of origin-destination data

2018 ◽  
Vol 1 ◽  
pp. 1-5
Author(s):  
Linfang Ding ◽  
Liqiu Meng ◽  
Jian Yang ◽  
Jukka M. Krisp
Keyword(s):  
Visual Analytics ◽  
Spatial Interaction ◽  
Time Windows ◽  
Visual Exploration ◽  
Interaction Patterns ◽  
Web Based ◽  
Distance Threshold ◽  
Interactive Environment ◽  
Input Variables ◽  
Interactive Clustering

In this paper, we propose a visual analytics approach for the exploration of spatiotemporal interaction patterns of massive origin-destination data. Firstly, we visually query the movement database for data at certain time windows. Secondly, we conduct interactive clustering to allow the users to select input variables/features (e.g., origins, destinations, distance, and duration) and to adjust clustering parameters (e.g. distance threshold). The agglomerative hierarchical clustering method is applied for the multivariate clustering of the origin-destination data. Thirdly, we design a parallel coordinates plot for visualizing the precomputed clusters and for further exploration of interesting clusters. Finally, we propose a gradient line rendering technique to show the spatial and directional distribution of origin-destination clusters on a map view. We implement the visual analytics approach in a web-based interactive environment and apply it to real-world floating car data from Shanghai. The experiment results show the origin/destination hotspots and their spatial interaction patterns. They also demonstrate the effectiveness of our proposed approach.

scholarly journals A Spatiotemporal Constraint Non-Negative Matrix Factorization Model to Discover Intra-Urban Mobility Patterns from Taxi Trips

2019 ◽  
Vol 11 (15) ◽  
pp. 4214 ◽  
Author(s):  
Yong Gao ◽  
Jiajun Liu ◽  
Yan Xu ◽  
Lan Mu ◽  
Yu Liu
Keyword(s):  
Matrix Factorization ◽  
Spatial Interaction ◽  
Human Mobility ◽  
Urban Transport ◽  
Spatiotemporal Patterns ◽  
Sustainable Urban Development ◽  
Interaction Patterns ◽  
Urban Mobility ◽  
Mobility Patterns ◽  
Non Negative Matrix Factorization

Taxi services provide an urban transport option to citizens. Massive taxi trajectories contain rich information for understanding human travel activities, which are essential to sustainable urban mobility and transportation. The origin and destination (O-D) pairs of urban taxi trips can reveal the spatiotemporal patterns of human mobility and then offer fundamental information to interpret and reform formal, functional, and perceptual regions of cities. Matrices are one of the most effective models to represent taxi trajectories and O-D trips. Among matrix representations, non-negative matrix factorization (NMF) gives meaningful interpretations of complex latent relationships. However, the independence assumption for observations is violated by spatial and temporal autocorrelation in taxi flows, which is not compensated in classical NMF models. In order to discover human intra-urban mobility patterns, a novel spatiotemporal constraint NMF (STC-NMF) model that explicitly solves spatial and temporal dependencies is proposed in this paper. It factorizes taxi flow matrices in both spatial and temporal aspects, thus revealing inherent spatiotemporal patterns. With three-month taxi trajectories harvested in Beijing, China, the STC-NMF model is employed to investigate taxi travel patterns and their spatial interaction modes. As the results, four departure patterns, three arrival patterns, and eight spatial interaction patterns during weekdays and weekends are discovered. Moreover, it is found that intensive movements within certain time windows are significantly related to region functionalities and the spatial interaction flows exhibit an obvious distance decay tendency. The outcome of the proposed model is more consistent with the inherent spatiotemporal characteristics of human intra-urban movements. The knowledge gained in this research would be useful to taxi services and transportation management for promoting sustainable urban development.

scholarly journals Using Mobility Data to Understand and Forecast COVID19 Dynamics

2020 ◽  
Author(s):  
Lijing Wang ◽  
Xue Ben ◽  
Aniruddha Adiga ◽  
Adam Sadilek ◽  
Ashish Tendulkar ◽  
...  
Keyword(s):  
Neural Network ◽  
Message Passing ◽  
Spatial Interaction ◽  
Human Mobility ◽  
State Level ◽  
Interaction Patterns ◽  
Disease Dynamics ◽  
Mobility Data ◽  
Spatio Temporal ◽  
The Impact

Disease dynamics, human mobility, and public policies co-evolve during a pandemic such as COVID-19. Understanding dynamic human mobility changes and spatial interaction patterns are crucial for understanding and forecasting COVID-19 dynamics. We introduce a novel graph-based neural network(GNN) to incorporate global aggregated mobility flows for a better understanding of the impact of human mobility on COVID-19 dynamics as well as better forecasting of disease dynamics. We propose a recurrent message passing graph neural network that embeds spatio-temporal disease dynamics and human mobility dynamics for daily state-level new confirmed cases forecasting. This work represents one of the early papers on the use of GNNs to forecast COVID-19 incidence dynamics and our methods are competitive to existing methods. We show that the spatial and temporal dynamic mobility graph leveraged by the graph neural network enables better long-term forecasting performance compared to baselines.

Discovering Spatial Interaction Patterns

2008 ◽  
pp. 95-109 ◽  
Author(s):  
Chang Sheng ◽  
Wynne Hsu ◽  
Mong Li Lee ◽  
Anthony K. H. Tung
Keyword(s):  
Spatial Interaction ◽  
Interaction Patterns

Understanding neighborhood isolation through spatial interaction network analysis using location big data

2019 ◽  
Vol 52 (6) ◽  
pp. 1027-1031 ◽  
Author(s):  
Timothy Prestby ◽  
Joseph App ◽  
Yuhao Kang ◽  
Song Gao
Keyword(s):  
Network Analysis ◽  
Large Scale ◽  
Spatial Interaction ◽  
Interaction Network ◽  
Location Based Services ◽  
Interaction Patterns ◽  
Individual Level ◽  
Mobility Data ◽  
The Usa ◽  
Hidden Biases

Hidden biases of racial and socioeconomic preferences shape residential neighborhoods throughout the USA. Thereby, these preferences shape neighborhoods composed predominantly of a particular race or income class. However, the assessment of spatial extent and the degree of isolation outside the residential neighborhoods at large scale is challenging, which requires further investigation to understand and identify the magnitude and underlying geospatial processes. With the ubiquitous availability of location-based services, large-scale individual-level location data have been widely collected using numerous mobile phone applications and enable the study of neighborhood isolation at large scale. In this research, we analyze large-scale anonymized smartphone users’ mobility data in Milwaukee, Wisconsin, to understand neighborhood-to-neighborhood spatial interaction patterns of different racial classes. Several isolated neighborhoods are successfully identified through the mobility-based spatial interaction network analysis.

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