scholarly journals Urban mobility and resilience: exploring Boston’s urban mobility network through twitter data

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Sahar Mirzaee ◽  
Qi Wang
Keyword(s):  
Human Mobility ◽  
Social Characteristics ◽  
Strong Impact ◽  
Urban Mobility ◽  
Mobility Patterns ◽  
Multinomial Regression ◽  
Urban Resilience ◽  
Urban Network ◽  
Urban Networks ◽  
Twitter Data

Abstract Human mobility connects urban dwellers and neighborhoods and impacts social equity. An in-depth understanding of human mobility helps to enhance urban resilience. However, limited research has focused on mobility resilience. Building on previous research, this study looks at the neighborhood connectivity enabled by urban mobility. We analyze the aggregated mobility patterns in Boston through the coupling of network structure and social characteristics. Geocoded twitter data combined with socioeconomic datasets were used to create a mobility-based urban network. Through the quantitative analysis, we found that the social segregation in Boston shapes its mobility network. Network communities identified by the Louvain modularity algorithm are often self-containing, meaning that their residents are more likely to move within their communities. A multinomial regression reveals that spatial racial and income segregation has a strong impact on the dynamic segregation of the network. The beneficial network characteristics –e.g. higher density and well-connected motifs– are less present in areas with bolder presence of minorities. Thus, the resilience state is not equitable among neighborhoods of different income levels and races, indicating that the resilience measures of urban networks need to be adapted according to sociodemographic characteristics.

scholarly journals Resilience of Urban Network Structure in China: The Perspective of Disruption

2021 ◽  
Vol 10 (12) ◽  
pp. 796
Author(s):  
Shimei Wei ◽  
Jinghu Pan
Keyword(s):  
Network Structure ◽  
Structural Characteristics ◽  
Maximum Load ◽  
Urban Systems ◽  
Urban Resilience ◽  
Economic Networks ◽  
Urban Network ◽  
Urban Networks ◽  
Location Service ◽  
Popular Subject

In light of the long-term pressure and short-term impact of economic and technological globalization, regional and urban resilience has become an important issue in research. As a new organizational form of regional urban systems, the resilience of urban networks generated by flow space has emerged as a popular subject of research. By gathering 2017 data from the Baidu search index, the Tencent location service, and social statistics, this study constructs information, transportation, and economic networks among 344 cities in China to analyze the spatial patterns of urban networks and explore their structural characteristics from the perspectives of hierarchy and assortativity. Transmissibility and diversity were used to represent the resilience of the network structure in interruption scenarios (node failure and maximum load attack). The results show the following: The information, transportation, and economic networks of cities at the prefecture level and higher in China exhibit a dense pattern of spatial distribution in the east and a sparse pattern in the west; however, there are significant differences in terms of hierarchy and assortativity. The order of resilience of network transmissibility and diversity from strong to weak was information, economic, transportation. Transmissibility and diversity had nearly identical scores in response to the interruption of urban nodes. Moreover, a highly heterogeneous network was more likely to cause shocks to the network structure, owing to its cross-regional urban links in case of disturbance. We identified 12 dominant nodes and 93 vulnerable nodes that can help accurately determine the impetus behind network structure resilience. The capacity of regions for resistance and recovery can be improved by strengthening the construction of emergency systems and risk prevention mechanisms.

scholarly journals A Methodology to Evaluate Accessibility to Bus Stops as a Contribution to Improve Sustainability in Urban Mobility

2019 ◽  
Vol 11 (3) ◽  
pp. 803 ◽  
Author(s):  
Maria Corazza ◽  
Nicola Favaretto
Keyword(s):  
Multicriteria Analysis ◽  
Urban Mobility ◽  
Mobility Patterns ◽  
Sustainable Mobility ◽  
Urban Network ◽  
Bus Stop ◽  
Final Score ◽  
Advanced Knowledge ◽  
Bus Stops

Walking and transit are the backbone of sustainable mobility. Bus stops not only represent the connection between the two, but are also central in dictating the attractiveness of the latter. Accessibility of bus stops becomes, then, pivotal in increasing both attractiveness and sustainability of public transport. The paper describes a multi-step methodology to evaluate bus stops’ accessibility starting from a cluster of seven indicators describing objective and subjective features influencing passengers’ choice toward a given bus stop. The indicators are weighed by a questionnaire submitted to experts. Finally, a multicriteria analysis is developed to obtain a final score describing univocally the accessibility of each stop. Outcomes are mapped and a case study in Rome is reported as an example, with 231 bus and tram stops assessed accordingly. Results shows the relevance of the urban network and environment in evaluating the accessibility and in promoting more sustainable mobility patterns. Research innovation relies on the possibility to merge data from different fields into a specific GIS map and easily highlight for each bus stop the relationships between built environment, passengers’ comfort, and accessibility, with the concluding goal to provide advanced knowledge for further applications.

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 Anomalous Urban Mobility Pattern Detection Based on GPS Trajectories and POI Data

2019 ◽  
Vol 8 (7) ◽  
pp. 308 ◽  
Author(s):  
Zhenzhou Xu ◽  
Ge Cui ◽  
Ming Zhong ◽  
Xin Wang
Keyword(s):  
Clustering Algorithm ◽  
Human Mobility ◽  
Mean Shift ◽  
Pattern Detection ◽  
Time Interval ◽  
Mobility Pattern ◽  
Urban Mobility ◽  
Mobility Patterns ◽  
Functional Regions ◽  
Gps Trajectories

Anomalous urban mobility pattern refers to abnormal human mobility flow in a city. Anomalous urban mobility pattern detection is important in the study of urban mobility. In this paper, a framework is proposed to identify anomalous urban mobility patterns based on taxi GPS trajectories and Point of Interest (POI) data. In the framework, functional regions are first generated based on the distribution of POIs by the DBSCAN clustering algorithm. A Weighted Term Frequency-Inverse Document Frequency (WTF-IDF) method is proposed to identify function values in each region. Then, the Origin-Destination (OD) of trips between functional regions is extracted from GPS trajectories to detect anomalous urban mobility patterns. Mobility vectors are established for each time interval based on the OD of trips and are classified into clusters by the mean shift algorithm. Abnormal urban mobility patterns are identified by processing the mobility vectors. A case study in the city of Wuhan, China, is conducted; the experimental results show that the proposed method can effectively identify daily and hourly anomalous urban mobility patterns.

scholarly journals Geolocated Twitter social media data to describe the geographic spread of SARS-CoV-2

2020 ◽  
Vol 27 (5) ◽  
Author(s):  
Donal Bisanzio ◽  
Moritz U G Kraemer ◽  
Thomas Brewer ◽  
John S Brownstein ◽  
Richard Reithinger
Keyword(s):  
Human Mobility ◽  
Mobility Patterns ◽  
Social Media Data ◽  
Country Level ◽  
Twitter Data ◽  
Geographic Spread ◽  
Twitter User ◽  
The Usa ◽  
Twitter Users ◽  
Media Data

Openly available, geotagged Twitter data from 2013 to 2015 was used to estimate the 2019–2020 human mobility patterns in and outside of China to predict the spatiotemporal spread of severe acute respiratory syndrome coronavirus 2. Countries with the highest number of visiting Twitter users outside of China were the USA, Japan, UK, Germany and Turkey. A high correlation was observed when comparing country-level Twitter user visits and reported cases.

A refined dichotomy on returners and explorers in urban mobility pattern

2019 ◽  
Vol 33 (22) ◽  
pp. 1950251
Author(s):  
Qing-Chao Shan ◽  
Hong-Hui Dong ◽  
Hai-Jian Li ◽  
Li-Min Jia
Keyword(s):  
Mobile Phone ◽  
Urban Areas ◽  
Human Mobility ◽  
Mobile Phone Data ◽  
Support Vector ◽  
Radius Of Gyration ◽  
Urban Mobility ◽  
Population Mobility ◽  
Mobility Patterns ◽  
Spatial Indices

With the change in people’s lifestyle and travel mode, understanding the individual and population mobility patterns in urban areas remains to an outstanding problem. Pervasive mobile communication technologies generate voluminous data related to human mobility, such as mobile phone data. To further study the characteristics of returning and exploration patterns of human movement in urban space, a multi-index model is proposed based on the original radius of the gyration index. In this paper, the classification mechanism of a single ratio of the radius of gyration for k-explorers and k-returners is illustrated. Some disadvantages of this mechanism are noted. A few indices of the model are proposed for deep mining of data on human mobility exploration and returning characteristics. Taking a mobile phone data during an entire month as a sample, and after data processing on the Spark platform, the characteristics of various indicators and their correlations are analyzed. The classification effects of different spatial indices for human exploration and returning are compared by using a support vector machine and the binary classification algorithm and are further compared with existing research results. The differences in the classification effects of these indicators are analyzed, which is helpful for in-depth studies of urban mobility patterns.

The mobility network of European tourists: a longitudinal study and a comparison with geo-located Twitter data

2018 ◽  
Vol 73 (1) ◽  
pp. 28-43 ◽  
Author(s):  
Davide Provenzano ◽  
Bartosz Hawelka ◽  
Rodolfo Baggio
Keyword(s):  
Human Mobility ◽  
Science Research ◽  
Mobility Patterns ◽  
Content Type ◽  
Twitter Data ◽  
World Tourism Organization ◽  
Definition Of ◽  
The Individual ◽  
Tourism Organization ◽  
Tourism Flows

Purpose This paper aims to provide a network study of the structural and dynamical characteristics of tourism flows in Europe from 1995 to 2012. Design/methodology/approach Travels in Europe were studied by following the network science research paradigm and by focusing on the whole network of intra-European tourism destinations. Network analysis was used to map and reveal the pattern of connections between states as shaped by bilateral tourism flows. Data were provided by the United Nations World Tourism Organization, and the data were integrated with tourism data available from national statistical offices of the individual countries, when necessary. Findings For 2012, results obtained from the UNWTO record-based network were compared to geo-located Twitter data as a proxy of human mobility patterns. The present analysis provides evidence of a shift towards an increased homogeneity in the travelling preferences of European tourists, an acquired attitude of visitors to travel shorter distances and a tendency of mobility patterns to merge. Finally, the comparison between UNWTO and Twitter data shows a different spatial distribution of visitors. These results provide a useful insight for policymakers involved in tourism planning. Originality/value The contribution of this study is threefold. First, to the best of the authors’ knowledge, this is the only study that focuses on the bilateral tourism flows between all countries falling, geographically or politically, under the definition of Europe. Second, evidence is provided of a shift towards a greater homogeneity in the travelling preferences of European tourists. Lastly, for the first time, this study provides a comparison between topological structure and bilateral mobility patterns of tourism flows, based on two different data-recording methods.

scholarly journals Public Transport GPS Probe and Rail Gate Data for Assessing the Pattern of Human Mobility in the Bangkok Metropolitan Region, Thailand

2021 ◽  
Vol 13 (4) ◽  
pp. 2178
Author(s):  
Songkorn Siangsuebchart ◽  
Sarawut Ninsawat ◽  
Apichon Witayangkurn ◽  
Surachet Pravinvongvuth
Keyword(s):  
Performance Assessment ◽  
Public Transport ◽  
Human Mobility ◽  
Open Data ◽  
Capital City ◽  
Metropolitan Region ◽  
Traffic Information ◽  
Mobility Patterns ◽  
Transport Planning ◽  
Java Program

Bangkok, the capital city of Thailand, is one of the most developed and expansive cities. Due to the ongoing development and expansion of Bangkok, urbanization has continued to expand into adjacent provinces, creating the Bangkok Metropolitan Region (BMR). Continuous monitoring of human mobility in BMR aids in public transport planning and design, and efficient performance assessment. The purpose of this study is to design and develop a process to derive human mobility patterns from the real movement of people who use both fixed-route and non-fixed-route public transport modes, including taxis, vans, and electric rail. Taxi GPS open data were collected by the Intelligent Traffic Information Center Foundation (iTIC) from all GPS-equipped taxis of one operator in BMR. GPS probe data of all operating GPS-equipped vans were collected by the Ministry of Transport’s Department of Land Transport for daily speed and driving behavior monitoring. Finally, the ridership data of all electric rail lines were collected from smartcards by the Automated Fare Collection (AFC). None of the previous works on human mobility extraction from multi-sourced big data have used van data; therefore, it is a challenge to use this data with other sources in the study of human mobility. Each public transport mode has traveling characteristics unique to its passengers and, therefore, specific analytical tools. Firstly, the taxi trip extraction process was developed using Hadoop Hive to process a large quantity of data spanning a one-month period to derive the origin and destination (OD) of each trip. Secondly, for van data, a Java program was used to construct the ODs of van trips. Thirdly, another Java program was used to create the ODs of the electric rail lines. All OD locations of these three modes were aggregated into transportation analysis zones (TAZ). The major taxi trip destinations were found to be international airports and provincial bus terminals. The significant trip destinations of vans were provincial bus terminals in Bangkok, electric rail stations, and the industrial estates in other provinces of BMR. In contrast, electric rail destinations were electric rail line interchange stations, the central business district (CBD), and commercial office areas. Therefore, these significant destinations of taxis and vans should be considered in electric rail planning to reduce the air pollution from gasoline vehicles (taxis and vans). Using the designed procedures, the up-to-date dataset of public transport can be processed to derive a time series of human mobility as an input into continuous and sustainable public transport planning and performance assessment. Based on the results of the study, the procedures can benefit other cities in Thailand and other countries.

scholarly journals Understanding senior's daily mobility patterns in California using human mobility motifs

2021 ◽  
Vol 94 ◽  
pp. 103117
Author(s):  
Rongxiang Su ◽  
Jingyi Xiao ◽  
Elizabeth C. McBride ◽  
Konstadinos G. Goulias
Keyword(s):  
Human Mobility ◽  
Mobility Patterns ◽  
Daily Mobility
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