A statistical framework for measuring the temporal stability of human mobility patterns

Dong, Zhihang; Chen, Yen‐Chi; Dobra, Adrian

doi:10.1080/02664763.2019.1711363

Cited by 4 publications

(3 citation statements)

References 34 publications

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“…age, gender, education, etc) or with different identities (e.g. locals or visitors) can have distinct movement patterns (Dong et al, 2020; a further disaggregation of population sub-groups within such data might also provide useful insights. Second, we currently aggregate the mobility network over the entire study period.…”

Section: Discussionmentioning

confidence: 99%

Entangled footprints: Understanding urban neighbourhoods by measuring distance, diversity, and direction of flows in Singapore

Chen¹,

Chuang

Poorthuis

2021

Computers, Environment and Urban Systems

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Traditional approaches to human mobility analysis in Geography often rely on census or survey data that is resource-intensive to collect and often has a limited spatio-temporal scope. The advent of new technologies (e.g. geosocial media platforms) provides opportunities to overcome these limitations and, if properly leveraged, can yield more granular insights about human mobility. In this paper, we use an anonymized Twitter dataset collected in Singapore from 2012 to 2016 to investigate this potential to help understand the footprints of urban neighbourhoods from both a spatial and a relational perspective.We construct home-to-destination networks of individual users based on their inferred home locations. In aggregated form, these networks allow us to analyze three specific mobility indicators at the neighbourhood level, namely the distance, diversity, and direction of urban interactions. By mapping these three indicators of the spatial footprint of each neighbourhood, we can capture the nuances in the position of individual neighbourhoods within the larger urban network. An exploratory spatial regression reveals that socio-economic characteristics (e.g. share of rental housing) and the built environment (i.e. land use) only partially explain these three indicators and a residual analysis points to the need to explicitly include each neighbourhood's position within the transportation network in future work.

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Section: Discussionmentioning

confidence: 99%

Entangled footprints: Understanding urban neighbourhoods by measuring distance, diversity, and direction of flows in Singapore

Chen¹,

Chuang

Poorthuis

2021

Computers, Environment and Urban Systems

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show abstract

“…According to the model, restrictive policies which affect national mobility have a two-fold effect: first, they contribute to slow down the infection dynamics, thus increasing the stock of disposable workers (not infected); second, they also limit the number of actual workers which are allowed to produce. For the Italian case, indeed, during the lockdown phase only a subset of essential sectors was allowed to continue their economic activities, while the others were forced to close when remote work was not feasible [46][47][48][49] . We assume that L ν i (t) , the disposable labor force (from now on we will use "labor force" and "workers" interchangeably) in the ν th economic sector inside the ith district at each instant t, is a fraction of the total labor force L ν i , which depends on the ratio of infected people in that district with the respect to the total number of individuals in the population, N(t):…”

Section: A Model For Economic Lossesmentioning

confidence: 99%

After the lockdown: simulating mobility, public health and economic recovery scenarios

Spelta

Flori

Pierri

et al. 2020

Sci Rep

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The spread of SARS-COV-2 has affected many economic and social systems. This paper aims at estimating the impact on regional productive systems in Italy of the interplay between the epidemic and the mobility restriction measures put in place to contain the contagion. We focus then on the economic consequences of alternative lockdown lifting schemes. We leverage a massive dataset of human mobility which describes daily movements of over four million individuals in Italy and we model the epidemic spreading through a metapopulation SIR model, which provides the fraction of infected individuals in each Italian district. To quantify economic backslashes this information is combined with socio-economic data. We then carry out a scenario analysis to model the transition to a post-lockdown phase and analyze the economic outcomes derived from the interplay between (a) the timing and intensity of the release of mobility restrictions and (b) the corresponding scenarios on the severity of virus transmission rates. Using a simple model for the spreading disease and parsimonious assumptions on the relationship between the infection and the associated economic backlashes, we show how different policy schemes tend to induce heterogeneous distributions of losses at the regional level depending on mobility restrictions. Our work shed lights on how recovery policies need to balance the interplay between mobility flows of disposable workers and the diffusion of contagion.

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“…This calls for a better understanding of the patterns of human mobility during emergencies and in the immediate post-disaster relief. Indeed the study of mobility habits is a foundational instance for several issues ranging from traffic forecasting, up to virus spreading and urban planning [13][14][15][16]. However, a quantitative assessment of its statistical properties at different geographical scales remains elusive [17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Human Mobility in Response to COVID-19 in France, Italy and UK

Galeazzi¹,

Cinelli²,

Bonaccorsi³

et al. 2020

Preprint

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The policies implemented to hinder the COVID-19 outbreak represent one of the largest critical events in history. The understanding of this process is fundamental for crafting and tailoring post-disaster relief. In this work we perform a massive data analysis, through geolocalized data from 13M Facebook users, on how such a stress affected mobility patterns in France, Italy and UK. We find that the general reduction of the overall efficiency in the network of movements is accompanied by geographical fragmentation with a massive reduction of long-range connections. The impact, however, differs among nations according to their initial mobility structure. Indeed, we find that the mobility network after the lockdown is more concentrated in the case of France and UK and more distributed in Italy. Such a process can be approximated through percolation to quantify the substantial impact of the lockdown.

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A statistical framework for measuring the temporal stability of human mobility patterns

Cited by 4 publications

References 34 publications

Entangled footprints: Understanding urban neighbourhoods by measuring distance, diversity, and direction of flows in Singapore

Entangled footprints: Understanding urban neighbourhoods by measuring distance, diversity, and direction of flows in Singapore

After the lockdown: simulating mobility, public health and economic recovery scenarios

Human Mobility in Response to COVID-19 in France, Italy and UK

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