Exploring Intra-Urban Travel Mobility using Large-Scale Taxi Global Positioning System Trajectories

2018 ◽  
Vol 10 (2) ◽  
Author(s):  
Haixiao Wang ◽  
Fang Liu ◽  
Jinjun Tang
Keyword(s):  
Large Scale ◽  
Human Mobility ◽  
Central Business District ◽  
Operation Efficiency ◽  
Temporal Properties ◽  
Harbin City ◽  
Residential District ◽  
Spatio Temporal ◽  
Gps Devices ◽  
Business District

Using taxi GPS trajectories data is of very importance to explore Spatio-temporal features of human mobility in transportation designing and planning. The data were collected from taxi GPS devices in Harbin city during a week. The taxi trips are extracted from GPS data, and travel distance and time in occupied and vacant states are firstly used to investigate the human mobility. Then, the urban area is divided into 400 grids. Furthermore, travelling network corresponding to taxi trips are designed to further examine the dynamics of mobility, in which the grid are considered as nodes and edge weights are defined as total number of trips among nodes. We observe some basic statistical features of network: degree, edge weights, clustering coefficients and network structure entropy. We also use the correlation between strength and degree to analyze the significance of nodes. Based on network analysis, we select two grids, a central business district and a residential district with high degree and strength, to study the spatial and temporal properties of trips that start from and end at these two grids. Finally, the correlation between trip volume and operation efficiency is explored and we find that hourly trip volume express negative correlation with operation efficiency.

scholarly journals Spatio-temporal dynamics of dengue in Brazil: seasonal travelling waves and determinants of regional synchrony

2018 ◽  
Author(s):  
Mikhail Churakov ◽  
Christian J. Villabona-Arenas ◽  
Moritz U.G. Kraemer ◽  
Henrik Salje ◽  
Simon Cauchemez
Keyword(s):  
Large Scale ◽  
Temporal Dynamics ◽  
Mosquito Control ◽  
Human Mobility ◽  
Travelling Waves ◽  
Seasonal Pattern ◽  
Temporal Analysis ◽  
Vector Ecology ◽  
Spatio Temporal ◽  
Dengue Epidemics

AbstractDengue continues to be the most important vector-borne viral disease globally and in Brazil, where more than 1.4 million cases and over 500 deaths were reported in 2016. Mosquito control programmes and other interventions have not stopped the alarming trend of increasingly large epidemics in the past few years.Here, we analyzed monthly dengue cases reported in Brazil between 2001 and 2016 to better characterize the key drivers of dengue epidemics. Spatio-temporal analysis revealed recurring travelling waves of disease occurrence. Using wavelet methods, we characterised the average seasonal pattern of dengue in Brazil, which starts in the western states of Acre and Rondônia, then travels eastward to the coast before reaching the northeast of the country. Only two states in the north of Brazil (Roraima and Amapá) did not follow the countrywide pattern and had inconsistent timing of dengue epidemics throughout the study period.We also explored epidemic synchrony and timing of annual dengue cycles in Brazilian regions. Using gravity style models combined with climate factors, we showed that both human mobility and vector ecology contribute to spatial patterns of dengue occurrence.This study offers a characterization of the spatial dynamics of dengue in Brazil and its drivers, which could inform intervention strategies against dengue and other arboviruses.Author summaryIn this paper we studied the synchronization of dengue epidemics in Brazilian regions. We found that a typical dengue season in Brazil can be described as a wave travelling from the western part of the country towards the east, with the exception of the two most northern equatorial states that experienced inconsistent seasonality of dengue epidemics.We found that the spatial structure of dengue cases is driven by both climate and human mobility patterns. In particular, precipitation was the most important factor for the seasonality of dengue at finer spatial resolutions.Our findings increase our understanding of large scale dengue patterns and could be used to enhance national control programs against dengue and other arboviruses.

scholarly journals COVID-GAN+: Estimating Human Mobility Responses to COVID-19 through Spatio-temporal Generative Adversarial Networks with Enhanced Features

2022 ◽  
Vol 13 (2) ◽  
pp. 1-23
Author(s):  
Han Bao ◽  
Xun Zhou ◽  
Yiqun Xie ◽  
Yingxue Zhang ◽  
Yanhua Li
Keyword(s):  
Spatial Heterogeneity ◽  
Real World ◽  
Large Scale ◽  
Census Data ◽  
Human Mobility ◽  
Training Data ◽  
Estimation Accuracy ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Spatio Temporal

Estimating human mobility responses to the large-scale spreading of the COVID-19 pandemic is crucial, since its significance guides policymakers to give Non-pharmaceutical Interventions, such as closure or reopening of businesses. It is challenging to model due to complex social contexts and limited training data. Recently, we proposed a conditional generative adversarial network (COVID-GAN) to estimate human mobility response under a set of social and policy conditions integrated from multiple data sources. Although COVID-GAN achieves a good average estimation accuracy under real-world conditions, it produces higher errors in certain regions due to the presence of spatial heterogeneity and outliers. To address these issues, in this article, we extend our prior work by introducing a new spatio-temporal deep generative model, namely, COVID-GAN+. COVID-GAN+ deals with the spatial heterogeneity issue by introducing a new spatial feature layer that utilizes the local Moran statistic to model the spatial heterogeneity strength in the data. In addition, we redesign the training objective to learn the estimated mobility changes from historical average levels to mitigate the effects of spatial outliers. We perform comprehensive evaluations using urban mobility data derived from cell phone records and census data. Results show that COVID-GAN+ can better approximate real-world human mobility responses than prior methods, including COVID-GAN.

scholarly journals Deriving Spatio-Temporal Geographies of Human Mobility from GPS traces

2021 ◽  
Author(s):  
Alessia Calafiore ◽  
Nombuyisielo Murage ◽  
Andrea Nasuto ◽  
Francisco Rowe
Keyword(s):  
Structural Changes ◽  
Human Mobility ◽  
Gps Data ◽  
Mobility Patterns ◽  
Individual Level ◽  
Temporal Properties ◽  
Detection Approach ◽  
Collective Movements ◽  
Spatio Temporal ◽  
City Structure

This paper leverages on the opportunities presented by individual level GPS data to study human mobility. It develops a methodology to understand the spatio-temporal properties of collective movements using network science. Through a spatially-weighted community detection approach, we derived functional neighbourhoods from human mobility patterns from GPS data and analyse the extent to which they vary across time. The results show that while the overall city structure remains stable, functional neighbourhoods tend to contract and expand over the course of the day. This work proposes a methodological framework and emphasises the importance of detecting short-term structural changes in cities based on human mobility.

scholarly journals Identifying multiscale spatio-temporal patterns in human mobility using manifold learning

2020 ◽  
Vol 6 ◽  
pp. e276 ◽  
Author(s):  
James R. Watson ◽  
Zach Gelbaum ◽  
Mathew Titus ◽  
Grant Zoch ◽  
David Wrathall
Keyword(s):  
Manifold Learning ◽  
Large Scale ◽  
Human Mobility ◽  
Mobile Network ◽  
Human Migration ◽  
Mobility Patterns ◽  
Great Opportunity ◽  
Spectral Graph ◽  
Spectral Graph Wavelets ◽  
Spatio Temporal

When, where and how people move is a fundamental part of how human societies organize around every-day needs as well as how people adapt to risks, such as economic scarcity or instability, and natural disasters. Our ability to characterize and predict the diversity of human mobility patterns has been greatly expanded by the availability of Call Detail Records (CDR) from mobile phone cellular networks. The size and richness of these datasets is at the same time a blessing and a curse: while there is great opportunity to extract useful information from these datasets, it remains a challenge to do so in a meaningful way. In particular, human mobility is multiscale, meaning a diversity of patterns of mobility occur simultaneously, which vary according to timing, magnitude and spatial extent. To identify and characterize the main spatio-temporal scales and patterns of human mobility we examined CDR data from the Orange mobile network in Senegal using a new form of spectral graph wavelets, an approach from manifold learning. This unsupervised analysis reduces the dimensionality of the data to reveal seasonal changes in human mobility, as well as mobility patterns associated with large-scale but short-term religious events. The novel insight into human mobility patterns afforded by manifold learning methods like spectral graph wavelets have clear applications for urban planning, infrastructure design as well as hazard risk management, especially as climate change alters the biophysical landscape on which people work and live, leading to new patterns of human migration around the world.

scholarly journals Shrinkage in Tokyo’s Central Business District: Large-Scale Redevelopment in the Spatially Shrinking Office Market

2019 ◽  
Vol 11 (10) ◽  
pp. 2742 ◽  
Author(s):  
Kohei Kawai ◽  
Masatomo Suzuki ◽  
Chihiro Shimizu
Keyword(s):  
Large Scale ◽  
Service Sector ◽  
Geographical Area ◽  
Central Area ◽  
Central Business District ◽  
Building Performance ◽  
Long Run ◽  
Level Data ◽  
Office Market ◽  
Business District

Although metropolises continue to grow worldwide, they face the risk of shrinkage. This study seeks to capture and contextualize the “shrinkage” of the office market in Tokyo, a city that is one of the largest in the world but whose labor force has been shrinking since 1995. Employing unique property-level data on office building performance and use, this study quantifies the geographical distribution of office supply over time and shows that the geographical area of office supply is shrinking from the fringes, in line with the large-scale redevelopment of the central area since the collapse of the asset bubble in the early 1990s. As a result, analyses of changes in the vacancy rate and rent premium (from hedonic regressions) suggest that old office properties in the suburbs have recently faced more vacancies and lower rent premiums, even during the upturn peak of around 2007. This evidence suggests that (i) the concept of shrinking cities is also applicable in a spatial context, even for service sector workplaces in a nation’s central metropolis, and that (ii) allowing large-scale redevelopment in the central area while the economy remains powerful can transform the metropolis into a more compact form, which may be desirable in the long run.

HERMAS

2021 ◽  
Vol 5 (3) ◽  
pp. 1-21
Author(s):  
Yiwei Song ◽  
Dongzhe Jiang ◽  
Yunhuai Liu ◽  
Zhou Qin ◽  
Chang Tan ◽  
...  
Keyword(s):  
Large Scale ◽  
Human Mobility ◽  
Representation Learning ◽  
User Profiling ◽  
Similarity Measurement ◽  
Learning Framework ◽  
Computation Efficiency ◽  
Trajectory Similarity ◽  
Spatio Temporal ◽  
Embedding Performance

Efficient representations for spatio-temporal cellular Signaling Data (SD) are essential for many human mobility applications. Traditional representation methods are mainly designed for GPS data with high spatio-temporal continuity, and thus will suffer from poor embedding performance due to the unique Ping Pong Effect in SD. To address this issue, we explore the opportunity offered by a large number of human mobility traces and mine the inherent neighboring tower connection patterns. More specifically, we design HERMAS, a novel representation learning framework for large-scale cellular SD with three steps: (1) extract rich context information in each trajectory, adding neighboring tower information as extra knowledge in each mobility observation; (2) design a sequence encoding model to aggregate the embedding of each observation; (3) obtain the embedding for a trajectory. We evaluate the performance of HERMAS based on two human mobility applications, i.e. trajectory similarity measurement and user profiling. We conduct evaluations based on a 30-day SD dataset with 130,612 users and 2,369,267 moving trajectories. Experimental results show that (1) for the trajectory similarity measurement application, HERMAS improves the Hitting Rate (HR@10) from 15.2% to 39.2%; (2) for the user profiling application, HERMAS improves the F1-score for around 9%. More importantly, HERMAS significantly improves the computation efficiency by over 30x.

The Central Business District of Melbourne and the Dispersal and Reconcentration of Capital

1986 ◽  
Vol 18 (11) ◽  
pp. 1447-1461 ◽  
Author(s):  
S T Moser ◽  
N P Low
Keyword(s):  
State Government ◽  
Large Scale ◽  
Central Area ◽  
Central Business District ◽  
The State ◽  
Private Capital ◽  
Spatial Dynamic ◽  
State Capital ◽  
Business District

This paper is a discussion of the complex spatial dynamic at work in the second largest state capital in Australia. What is happening to the central business district, it is argued, has to be seen in the context of the interaction between the state government and private capital. The evolving sociospatial structure of Melbourne will continue to be conditioned by the changing balance between the opportunities for capital which arise in the course of suburbanisation and the need for the state government and large-scale property interests to maintain a higher rate of investment in the central area.

scholarly journals Reshaping the Gdańsk Shipyard—The Birthplace of the Solidarity Movement. The Complexity of Adaptive Reuse in the Heritage Context

2021 ◽  
Vol 13 (13) ◽  
pp. 7183
Author(s):  
Piotr Lorens ◽  
Łukasz Bugalski
Keyword(s):  
Large Scale ◽  
Adaptive Reuse ◽  
Central Business District ◽  
City Centre ◽  
Brownfield Redevelopment ◽  
Solidarity Movement ◽  
The Subject ◽  
The Creation ◽  
Business District ◽  
The City

The Gdańsk Shipyard—the birthplace of the Solidarity movement—is host to a unique example of a multi-layered brownfield redevelopment project, an area that is burdened by a complex history, overlapping heritage, and multiple memories. These circumstances require an integrated yet differentiated approach to the site’s heritage and make the creation of one homogeneous narration of its future impossible. At the same time, the size of the area, as well as its location within Gdańsk city centre, has meant that its future has been the subject of numerous discussions and speculations conducted over the last 20 years—starting from the creation of a large-scale open-air museum and continuing to the localization of the new Central Business District of the city. Consequently, that broad discussion carried out regarding the scope of redevelopment projects has been rooted in the possible introduction of diverse models of adaptive reuse. This variety of possible approaches also includes discussion on the mode of integrating heritage in the redevelopment processes. The goal of this paper—written just before the initiation of the final stage of the conceptual part of the project—is to present the complexity of approaches to issues related to redevelopment and heritage preservation.

scholarly journals Understanding the Heterogeneity of Human Mobility Patterns: User Characteristics and Modal Preferences

2021 ◽  
Vol 13 (24) ◽  
pp. 13921
Author(s):  
Laiyun Wu ◽  
Samiul Hasan ◽  
Younshik Chung ◽  
Jee Eun Kang
Keyword(s):  
Large Scale ◽  
Human Mobility ◽  
Mobility Models ◽  
Mobility Patterns ◽  
User Characteristics ◽  
Transit System ◽  
Demographic Groups ◽  
Urban Dynamics ◽  
User Groups ◽  
Spatio Temporal

Characterizing individual mobility is critical to understand urban dynamics and to develop high-resolution mobility models. Previously, large-scale trajectory datasets have been used to characterize universal mobility patterns. However, due to the limitations of the underlying datasets, these studies could not investigate how mobility patterns differ over user characteristics among demographic groups. In this study, we analyzed a large-scale Automatic Fare Collection (AFC) dataset of the transit system of Seoul, South Korea and investigated how mobility patterns vary over user characteristics and modal preferences. We identified users’ commuting locations and estimated the statistical distributions required to characterize their spatio-temporal mobility patterns. Our findings show the heterogeneity of mobility patterns across demographic user groups. This result will significantly impact future mobility models based on trajectory datasets.

scholarly journals Early alterations of large-scale brain networks temporal dynamics in young children with autism

2020 ◽  
Author(s):  
Aurélie Bochet ◽  
Holger Franz Sperdin ◽  
Tonia Anahi Rihs ◽  
Nada Kojovic ◽  
Martina Franchini ◽  
...  
Keyword(s):  
Large Scale ◽  
Developmental Stages ◽  
Temporal Dynamics ◽  
Brain Networks ◽  
Autism Spectrum ◽  
Compensatory Mechanism ◽  
Temporal Properties ◽  
Early Developmental Stages ◽  
Spatio Temporal ◽  
Early Developmental

ABSTRACTDisruption of large-scale brain networks is associated with autism spectrum disorders (ASD). Recently, we found that directed functional connectivity alterations of social brain networks are a core component of atypical brain development at early developmental stages in ASD (Sperdin et al., 2018). Here, we investigated the spatio-temporal dynamics of whole-brain neuronal networks at a subsecond scale in 90 toddlers and preschoolers (47 with ASD) using an EEG microstate approach. Results revealed the presence of five microstate classes that best described the entire dataset (labeled as microstate classes A-E). Microstate class C related to the Default Mode Network (DMN) occurred less in children with ASD. Analysis of brain-behavioural relationships within the ASD group suggested that a compensatory mechanism from microstate C was associated with less severe symptoms and better adaptive skills. These results demonstrate that the temporal properties of some specific EEG microstates are altered in ASD at early developmental stages.

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