Field theory for recurrent mobility

Mazzoli, Mattia; Molas, Alex; Bassolas, Aleix; Lenormand, Maxime; Colet, Pere; Ramasco, José J.

doi:10.1038/s41467-019-11841-2

Cited by 70 publications

(75 citation statements)

References 53 publications

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“…Also, probabilistic human mobility prediction are widely performed for congestion and advertisement optimization [29][30][31][32] . Recently, the potential within the big cities has been estimated using the vector field generated from the Origin-Destination matrix, which includes the number of people traveling between all pairs of spots 33 . However, studies of collective human flow (vector field) within scale of cities, which we call here mesoscopic scales, have been rarely addressed.…”

Section: Detail Observation Of Human Locations Became Available Recenmentioning

confidence: 99%

Universal scaling laws of collective human flow patterns in urban regions

Shida

Takayasu

Havlin

et al. 2020

Sci Rep

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Detail observation of human locations became available recently by the development of information technology such as mobile phones with GPS (Global Positioning System). We analyzed temporal changes of global human flow patterns in urban regions based on mobile phones’ GPS data in 9 large cities in Japan. By applying a new concept of drainage basins in analogous to river flow patterns, we discovered several universal scaling relations. These include, the number of moving people in a drainage basin of diameter L is proportional to $$L^3$$ L 3 in the morning rush hour, which is surprisingly different from reasonable intuition of proportionality to the 2 dimensional area, $$L^2$$ L 2 . We show that this unexpected 3 dimensional feature is related to the strong attraction of the city center to become a 3 dimensional structure due skyscrapers.

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Section: Detail Observation Of Human Locations Became Available Recenmentioning

confidence: 99%

Universal scaling laws of collective human flow patterns in urban regions

Shida

Takayasu

Havlin

et al. 2020

Sci Rep

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“…In addition to traditional surveys and counting to develop OD matrices, increasing studies extracted OD matrices based on the emerging digital footprint data [23,24]. Mazzoli et al [22] extracted the OD matrices from Twitter data to map daily commuting flows in London and Paris. Lenormand et al [25] mapped the OD matrices from three datasets, including Twitter, mobile phone and census data.…”

Section: Introductionmentioning

confidence: 99%

Disparate patterns of movements and visits to points of interest located in urban hotspots across US metropolitan cities during COVID-19

Bessell

Xiao

et al. 2021

R. Soc. open sci.

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We examined the effect of social distancing on changes in visits to urban hotspot points of interest. In a pandemic situation, urban hotspots could be potential superspreader areas as visits to urban hotspots can increase the risk of contact and transmission of a disease among a population. We mapped census-block-group to point-of-interest (POI) movement networks in 16 cities in the United States. We adopted a modified coarse-grain approach to examine patterns of visits to POIs among hotspots and non-hotspots from January to May 2020. Also, we conducted chi-square tests to identify POIs with significant flux-in changes during the analysis period. The results showed disparate patterns across cities in terms of reduction in hotspot POI visitors. Sixteen cities were divided into two categories using a time series clustering method. In one category, which includes the cities of San Francisco, Seattle and Chicago, we observed a considerable decrease in hotspot POI visitors, while in another category, including the cities of Austin, Houston and San Diego, the visitors to hotspots did not greatly decrease. While all the cities exhibited overall decreased visitors to POIs, one category maintained the proportion of visitors to hotspot POIs. The proportion of visitors to some POIs (e.g. restaurants) remained stable during the social distancing period, while some POIs had an increased proportion of visitors (e.g. grocery stores). We also identified POIs with significant flux-in changes, indicating that related businesses were greatly affected by social distancing. The study was limited to 16 metropolitan cities in the United States. The proposed methodology could be applied to digital trace data in other cities and countries to study the patterns of movements to POIs during the COVID-19 pandemic.

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“…This implies that, when taking into account smaller scales, a thorough validation exercise is necessary. Although geolocated Twitter data is sparser than census, surveys and mobile phone records, the observed level of correlation allows for the interchangeability of these sources to study population density and mobility [33,37,38].…”

Section: Introductionmentioning

confidence: 99%

Migrant mobility flows characterized with digital data

Mazzoli

Diechtiareff²,

Tugores

et al. 2020

PLoS ONE

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Monitoring migration flows is crucial to respond to humanitarian crisis and to design efficient policies. This information usually comes from surveys and border controls, but timely accessibility and methodological concerns reduce its usefulness. Here, we propose a method to detect migration flows worldwide using geolocated Twitter data. We focus on the migration crisis in Venezuela and show that the calculated flows are consistent with official statistics at country level. Our method is versatile and far-reaching, as it can be used to study different features of migration as preferred routes, settlement areas, mobility through several countries, spatial integration in cities, etc. It provides finer geographical and temporal resolutions, allowing the exploration of issues not contemplated in official records. It is our hope that these new sources of information can complement official ones, helping authorities and humanitarian organizations to better assess when and where to intervene on the ground.

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Field theory for recurrent mobility

Cited by 70 publications

References 53 publications

Universal scaling laws of collective human flow patterns in urban regions

Universal scaling laws of collective human flow patterns in urban regions

Disparate patterns of movements and visits to points of interest located in urban hotspots across US metropolitan cities during COVID-19

Migrant mobility flows characterized with digital data

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