Revealing latent characteristics of mobility networks with coarse-graining

Hamedmoghadam, Homayoun; Ramezani, Mohsen; Saberi, Meead

doi:10.1038/s41598-019-44005-9

Cited by 29 publications

(41 citation statements)

References 33 publications

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“…The objective of this study is to examine movement patterns to urban hotspots in United States cities during the initial 2020 COVID-19 outbreak. Urban mobility and movement patterns are important characteristics of urban dynamics, reflecting the collective human behaviour and social interactions [1,2]. Urban mobility drives the spatial flux of populations, and effective epidemic control measures greatly rely on the characterization of urban mobility patterns [3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…Origin-destination (OD) network analysis provides a powerful tool to study mobility patterns under such a situation and are especially helpful for locating hotspots and studying the urban mobility patterns of visiting urban hotspots [16,17]. Louail et al and Hamedmoghadam et al used the OD matrix and a coarse-grain approach to study the mobility among hotspots and non-hotspots [1,18,19]. The OD matrices aggregate the mobility of individuals from one point to another [20,21].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

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.

View full text Add to dashboard Cite

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“…Another direction for future work would be the choice of other "hotspot" identification techniques, including that described in [42], and to study the effects of spatial resolution of the grid on their results.…”

Section: Plos Onementioning

confidence: 99%

“…After “hotspot” detection following the described approach, the authors in [ 40 ] and [ 41 ] study the coarse grained mobility patterns in a city by breaking down the urban mobility OD matrix into a 2 × 2 block matrix corresponding to combinations between “hotspot” and “non-hotspot” locations. The authors in [ 42 ] also study and discover universal coarse grained mobility patterns in cities, but instead of using a threshold-based approach, they resort to a non-parametric clustering method for identifying “hotspots”.…”

Section: Introductionmentioning

confidence: 99%

Ranking places in attributed temporal urban mobility networks

et al. 2020

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Drawing on the recent advances in complex network theory, urban mobility flow patterns, typically encoded as origin-destination (OD) matrices, can be represented as weighted directed graphs, with nodes denoting city locations and weighted edges the number of trips between them. Such a graph can further be augmented by node attributes denoting the various socioeconomic characteristics at a particular location in the city. In this paper, we study the spatio-temporal characteristics of "hotspots" of different types of socioeconomic activities as characterized by recently developed attribute-augmented network centrality measures within the urban OD network. The workflow of the proposed paper comprises the construction of temporal OD networks using two custom data sets on urban mobility in Rome and London, the addition of socioeconomic activity attributes to the OD network nodes, the computation of network centrality measures, the identification of "hotspots" and, finally, the visualization and analysis of measures of their spatio-temporal heterogeneity. Our results show structural similarities and distinctions between the spatial patterns of different types of human activity in the two cities. Our approach produces simple indicators thus opening up opportunities for practitioners to develop tools for real-time monitoring and visualization of interactions between mobility and economic activity in cities.

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“…Different from the previous methods, we model the city as a graph and exploit the influences between non-adjacent areas sufficiently by defining the edges via OD relations in the graph. Note that the concept of OD is also used by Hamedmoghadam et al [19], but they aim at predict flow between ODs, while we regard OD as an assistant to improve taxi demand prediction.…”

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confidence: 99%

Incorporating Graph Attention and Recurrent Architectures for City-Wide Taxi Demand Prediction

2019

IJGI

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Taxi demand prediction is one of the key factors in making online taxi hailing services more successful and more popular. Accurate taxi demand prediction can bring various advantages including, but not limited to, enhancing user experience, increasing taxi utilization, and optimizing traffic efficiency. However, the task is challenging because of complex spatial and temporal dependencies of taxi demand. In addition, relationships between non-adjacent regions are also critical for accurate taxi demand prediction, whereas they are largely ignored by existing approaches. To this end, we propose a novel graph and time-series learning model for city-wide taxi demand prediction in this paper. It has two main building blocks, the first one utilize a graph network with attention mechanism to effectively learn spatial dependencies of taxi demand in a broader perspective of the entire city, and the output at each time interval is then transferred to the second block. In the graph network, the edge is defined by an Origin–Destination relation to capture non-adjacent impacts. The second one uses a neural network which is adept with processing sequence data to capture the temporal correlations of city-wide taxi demand. Using a large, real-world dataset and three metrics, we conduct an extensive experimental study and find that our model outperforms state-of-the-art baselines by 9.3% in terms of the root-mean-square error.

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Revealing latent characteristics of mobility networks with coarse-graining

Cited by 29 publications

References 33 publications

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

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

Ranking places in attributed temporal urban mobility networks

Incorporating Graph Attention and Recurrent Architectures for City-Wide Taxi Demand Prediction

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