1
|
Lyu M, Liu K, Hall RW. Spatial Interaction Analysis of Infectious Disease Import and Export between Regions. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2024; 21:643. [PMID: 38791857 PMCID: PMC11120745 DOI: 10.3390/ijerph21050643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 05/07/2024] [Accepted: 05/12/2024] [Indexed: 05/26/2024]
Abstract
Human travel plays a crucial role in the spread of infectious disease between regions. Travel of infected individuals from one region to another can transport a virus to places that were previously unaffected or may accelerate the spread of disease in places where the disease is not yet well established. We develop and apply models and metrics to analyze the role of inter-regional travel relative to the spread of disease, drawing from data on COVID-19 in the United States. To better understand how transportation affects disease transmission, we established a multi-regional time-varying compartmental disease model with spatial interaction. The compartmental model was integrated with statistical estimates of travel between regions. From the integrated model, we derived a transmission import index to assess the risk of COVID-19 transmission between states. Based on the index, we determined states with high risk for disease spreading to other states at the scale of months, and we analyzed how the index changed over time during 2020. Our model provides a tool for policymakers to evaluate the influence of travel between regions on disease transmission in support of strategies for epidemic control.
Collapse
Affiliation(s)
- Mingdong Lyu
- National Renewable Energy Laboratory, Mobility, Behavior, and Advanced Powertrains Department, Denver, CO 80401, USA
| | - Kuofu Liu
- Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA 90089, USA; (K.L.); (R.W.H.)
| | - Randolph W. Hall
- Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA 90089, USA; (K.L.); (R.W.H.)
| |
Collapse
|
2
|
Klamser PP, Zachariae A, Maier BF, Baranov O, Jongen C, Schlosser F, Brockmann D. Inferring country-specific import risk of diseases from the world air transportation network. PLoS Comput Biol 2024; 20:e1011775. [PMID: 38266041 PMCID: PMC10843136 DOI: 10.1371/journal.pcbi.1011775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 02/05/2024] [Accepted: 12/21/2023] [Indexed: 01/26/2024] Open
Abstract
Disease propagation between countries strongly depends on their effective distance, a measure derived from the world air transportation network (WAN). It reduces the complex spreading patterns of a pandemic to a wave-like propagation from the outbreak country, establishing a linear relationship to the arrival time of the unmitigated spread of a disease. However, in the early stages of an outbreak, what concerns decision-makers in countries is understanding the relative risk of active cases arriving in their country-essentially, the likelihood that an active case boarding an airplane at the outbreak location will reach them. While there are data-fitted models available to estimate these risks, accurate mechanistic, parameter-free models are still lacking. Therefore, we introduce the 'import risk' model in this study, which defines import probabilities using the effective-distance framework. The model assumes that airline passengers are distributed along the shortest path tree that starts at the outbreak's origin. In combination with a random walk, we account for all possible paths, thus inferring predominant connecting flights. Our model outperforms other mobility models, such as the radiation and gravity model with varying distance types, and it improves further if additional geographic information is included. The import risk model's precision increases for countries with stronger connections within the WAN, and it reveals a geographic distance dependence that implies a pull- rather than a push-dynamic in the distribution process.
Collapse
Affiliation(s)
- Pascal P. Klamser
- Department of Biology, Institute for Theoretical Biology, Humboldt‐Universität zu Berlin, Berlin, Germany
- Robert Koch Institute, Berlin, Germany
| | - Adrian Zachariae
- Department of Biology, Institute for Theoretical Biology, Humboldt‐Universität zu Berlin, Berlin, Germany
- Robert Koch Institute, Berlin, Germany
| | - Benjamin F. Maier
- Department of Biology, Institute for Theoretical Biology, Humboldt‐Universität zu Berlin, Berlin, Germany
- Robert Koch Institute, Berlin, Germany
- DTU Compute, Technical University of Denmark, Kongens Lyngby, Denmark
- Copenhagen Center for Social Data Science, University of Copenhagen, Copenhagen, Denmark
| | - Olga Baranov
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, Munich, Germany
- German Center for Infection Research (DZIF), Partner Site Munich, Munich, Germany
| | - Clara Jongen
- Department of Biology, Institute for Theoretical Biology, Humboldt‐Universität zu Berlin, Berlin, Germany
- Robert Koch Institute, Berlin, Germany
| | - Frank Schlosser
- Department of Biology, Institute for Theoretical Biology, Humboldt‐Universität zu Berlin, Berlin, Germany
- Robert Koch Institute, Berlin, Germany
| | - Dirk Brockmann
- Department of Biology, Institute for Theoretical Biology, Humboldt‐Universität zu Berlin, Berlin, Germany
- Robert Koch Institute, Berlin, Germany
- Center Synergy of Systems (SynoSys), Center for Interdisciplinary Digital Sciences, Technische Universität Dresden, Dresden, Germany
| |
Collapse
|
3
|
Castro-Gonzalez L. Understanding the illicit drug distribution in England: a data-centric approach to the County Lines Model. ROYAL SOCIETY OPEN SCIENCE 2023; 10:221297. [PMID: 37153368 PMCID: PMC10154935 DOI: 10.1098/rsos.221297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 04/05/2023] [Indexed: 05/09/2023]
Abstract
The County Lines Model (CLM) is a relatively new illicit drugs distribution method found in Great Britain. The CLM has brought modern slavery and public health issues, while challenging the law-enforcement capacity to act, as coordination between different local police forces is necessary. Our objective is to understand the territorial logic behind the line operators when establishing a connection between two places. We use three different spatial models (gravity, radiation and retail models), as each one of them understands flow from place i to j in a different way. Using public data from the Metropolitan Police of London, we train and cross-validate the models to understand which of the different physical and socio-demographic variables are considered when establishing a connection. We analyse hospital admissions by drugs, disposable household income, police presence and knife crime events, in addition to the population of a particular place and the distance and travel times between two different locations. Our results show that knife crime events and hospital admissions by misuse of drugs are the most important variables. We also find that London operators distribute to the territory known as the 'south' of England, as negligible presence of them is observed outside of it.
Collapse
Affiliation(s)
- Leonardo Castro-Gonzalez
- Department of Economics, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
- Public Policy Programme, The Alan Turing Institute, British Library, 96 Euston Road, London NW1 2DB, UK
| |
Collapse
|
4
|
Iyer S, Karrer B, Citron DT, Kooti F, Maas P, Wang Z, Giraudy E, Medhat A, Dow PA, Pompe A. Large-scale measurement of aggregate human colocation patterns for epidemiological modeling. Epidemics 2023; 42:100663. [PMID: 36724622 DOI: 10.1016/j.epidem.2022.100663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 12/06/2022] [Accepted: 12/20/2022] [Indexed: 01/12/2023] Open
Abstract
To understand and model public health emergencies, epidemiologists need data that describes how humans are moving and interacting across physical space. Such data has traditionally been difficult for researchers to obtain with the temporal resolution and geographic breadth that is needed to study, for example, a global pandemic. This paper describes Colocation Maps, which are spatial network datasets that have been developed within Meta's Data For Good program. These Maps estimate how often people from different regions are colocated: in particular, for a pair of geographic regions x and y, these Maps estimate the rate at which a randomly chosen person from x and a randomly chosen person from y are simultaneously located in the same place during a randomly chosen minute in a given week. These datasets are well suited to parametrize metapopulation models of disease spread or to measure temporal changes in interactions between people from different regions; indeed, they have already been used for both of these purposes during the COVID-19 pandemic. In this paper, we show how Colocation Maps differ from existing data sources, describe how the datasets are built, provide examples of their use in compartmental modeling, and summarize ideas for further development of these and related datasets. Among the findings of this study, we observe that a pair of regions can exhibit high colocation despite few people moving between those regions. Additionally, for the purposes of clarifying how to interpret and utilize Colocation Maps, we scrutinize the Maps' built-in assumptions about representativeness and contact heterogeneity.
Collapse
Affiliation(s)
- Shankar Iyer
- Meta, 1 Hacker Way, Menlo Park, CA 94025, United States.
| | - Brian Karrer
- Meta, 1 Hacker Way, Menlo Park, CA 94025, United States
| | | | - Farshad Kooti
- Meta, 1 Hacker Way, Menlo Park, CA 94025, United States
| | - Paige Maas
- Meta, 1 Hacker Way, Menlo Park, CA 94025, United States
| | - Zeyu Wang
- Department of Economics, Stanford University, 579 Jane Stanford Way, Stanford, CA 94305, United States
| | | | - Ahmed Medhat
- Meta, 1 Hacker Way, Menlo Park, CA 94025, United States
| | - P Alex Dow
- Meta, 1 Hacker Way, Menlo Park, CA 94025, United States
| | - Alex Pompe
- Meta, 1 Hacker Way, Menlo Park, CA 94025, United States
| |
Collapse
|
5
|
Mauro G, Luca M, Longa A, Lepri B, Pappalardo L. Generating mobility networks with generative adversarial networks. EPJ DATA SCIENCE 2022; 11:58. [PMID: 36530793 PMCID: PMC9734834 DOI: 10.1140/epjds/s13688-022-00372-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 11/16/2022] [Indexed: 06/17/2023]
Abstract
The increasingly crucial role of human displacements in complex societal phenomena, such as traffic congestion, segregation, and the diffusion of epidemics, is attracting the interest of scientists from several disciplines. In this article, we address mobility network generation, i.e., generating a city's entire mobility network, a weighted directed graph in which nodes are geographic locations and weighted edges represent people's movements between those locations, thus describing the entire mobility set flows within a city. Our solution is MoGAN, a model based on Generative Adversarial Networks (GANs) to generate realistic mobility networks. We conduct extensive experiments on public datasets of bike and taxi rides to show that MoGAN outperforms the classical Gravity and Radiation models regarding the realism of the generated networks. Our model can be used for data augmentation and performing simulations and what-if analysis.
Collapse
Affiliation(s)
- Giovanni Mauro
- Institute of Information Science and Technologies, National Research Council (ISTI-CNR), Pisa, Italy
- IMT School for Advanced Studies, Lucca, Italy
- University of Pisa, Pisa, Italy
| | - Massimiliano Luca
- Free University of Bolzano, Bolzano, Italy
- Fondazione Bruno Kessler, Trento, Italy
| | - Antonio Longa
- University of Trento, Trento, Italy
- Fondazione Bruno Kessler, Trento, Italy
| | | | - Luca Pappalardo
- Institute of Information Science and Technologies, National Research Council (ISTI-CNR), Pisa, Italy
| |
Collapse
|
6
|
Kluge L, Levermann A, Schewe J. Radiation model for migration with directional preferences. Phys Rev E 2022; 106:064138. [PMID: 36671094 DOI: 10.1103/physreve.106.064138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 12/13/2022] [Indexed: 06/17/2023]
Abstract
The radiation model is a parameter-free model of human mobility that has been applied primarily for short-distance moves, such as commuting. When applied to migration, it underestimates the number of long-range moves, such as between different US states. Here we show that it additionally suffers from a conceptual inconsistency that can have substantial numerical effects on long-distance moves. We propose a modification of the radiation model that introduces a dependence on the angle between any two alternative potential destinations, accounting for the possibility that migrants may have preferences about the approximate direction of their move. We demonstrate that this modification mitigates the conceptual inconsistency and improves the model fit to observational migration data, without introducing any fitting parameters.
Collapse
Affiliation(s)
- Lucas Kluge
- Potsdam Insitute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
- Institute of Physics and Astronomy, University of Potsdam, Karl-Liebknecht-Straße 24/25, 14476 Potsdam, Germany
| | - Anders Levermann
- Potsdam Insitute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
- Institute of Physics and Astronomy, University of Potsdam, Karl-Liebknecht-Straße 24/25, 14476 Potsdam, Germany
- Lamont-Doherty Earth Observatory, Columbia University, New York, New York 10964-1000, USA
| | - Jacob Schewe
- Potsdam Insitute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
| |
Collapse
|
7
|
Arcaute E, Ramasco JJ. Recent advances in urban system science: Models and data. PLoS One 2022; 17:e0272863. [PMID: 35976953 PMCID: PMC9384974 DOI: 10.1371/journal.pone.0272863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Cities are characterized by the presence of a dense population with a high potential for interactions between individuals of diverse backgrounds. They appear in parallel to the Neolithic revolution a few millennia ago. The advantages brought in terms of agglomeration for economy, innovation, social and cultural advancements have kept them as a major landmark in recent human history. There are many different aspects to study in urban systems from a scientific point of view, one can concentrate in demography and population evolution, mobility, economic output, land use and urban planning, home accessibility and real estate market, energy and water consumption, waste processing, health, education, integration of minorities, just to name a few. In the last decade, the introduction of communication and information technologies have enormously facilitated the collection of datasets on these and other questions, making possible a more quantitative approach to city science. All these topics have been addressed in many works in the literature, and we do not intend to offer here a systematic review. Instead, we will only provide a brief taste of some of these above-mentioned aspects, which could serve as an introduction to the collection ‘Cities as Complex Systems’. Such a non-systematic view will lead us to leave outside many relevant papers, and for this we must apologise.
Collapse
Affiliation(s)
- Elsa Arcaute
- Centre for Advanced Spatial Analysis, University College London, London, United Kingdom
- * E-mail: (EA); (JJR)
| | - José J. Ramasco
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, Palma de Mallorca, Spain
- * E-mail: (EA); (JJR)
| |
Collapse
|
8
|
The Reflection of Income Segregation and Accessibility Cleavages in Sydney’s House Prices. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2022. [DOI: 10.3390/ijgi11070413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Cities often show residential income segregation, and the price of housing is generally related to employment accessibility, but how do these factors intersect? We analyse Greater Sydney, Australia, a metropolitan area of 5 million people. Sydney is found to have reasonably even employment accessibility by car, reflecting the increasingly polycentric nature of the modern city; however, it also shows considerable income segregation and variance in property prices between different parts of the city. Entropy is used to examine diversity and mixing of different income groups. Finally, hedonic price models using ordinary-least squares and geographically-weighted regression techniques show the differing effects of employment accessibility on house prices in different parts of the city. The results show that accessibility has small to negative effects on prices in the most valuable areas, suggesting that other effects such as recreational access and employment type/quality may be more important determinants of house prices in these areas.
Collapse
|
9
|
Manlove K, Wilber M, White L, Bastille‐Rousseau G, Yang A, Gilbertson MLJ, Craft ME, Cross PC, Wittemyer G, Pepin KM. Defining an epidemiological landscape that connects movement ecology to pathogen transmission and pace‐of‐life. Ecol Lett 2022; 25:1760-1782. [DOI: 10.1111/ele.14032] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 04/21/2022] [Accepted: 05/03/2022] [Indexed: 12/20/2022]
Affiliation(s)
- Kezia Manlove
- Department of Wildland Resources and Ecology Center Utah State University Logan Utah USA
| | - Mark Wilber
- Department of Forestry, Wildlife, and Fisheries University of Tennessee Institute of Agriculture Knoxville Tennessee USA
| | - Lauren White
- National Socio‐Environmental Synthesis Center University of Maryland Annapolis Maryland USA
| | | | - Anni Yang
- Department of Fish, Wildlife, and Conservation Biology Colorado State University Fort Collins Colorado USA
- National Wildlife Research Center, United States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services National Wildlife Research Center Fort Collins Colorado USA
- Department of Geography and Environmental Sustainability University of Oklahoma Norman Oklahoma USA
| | - Marie L. J. Gilbertson
- Department of Veterinary Population Medicine University of Minnesota St. Paul Minnesota USA
- Wisconsin Cooperative Wildlife Research Unit, Department of Forest and Wildlife Ecology University of Wisconsin–Madison Madison Wisconsin USA
| | - Meggan E. Craft
- Department of Ecology, Evolution, and Behavior University of Minnesota St. Paul Minnesota USA
| | - Paul C. Cross
- U.S. Geological Survey Northern Rocky Mountain Science Center Bozeman Montana USA
| | - George Wittemyer
- Department of Fish, Wildlife, and Conservation Biology Colorado State University Fort Collins Colorado USA
| | - Kim M. Pepin
- National Wildlife Research Center, United States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services National Wildlife Research Center Fort Collins Colorado USA
| |
Collapse
|
10
|
Multiscale Accessibility—A New Perspective of Space Structuration. SUSTAINABILITY 2022. [DOI: 10.3390/su14095119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Spatial accessibility is fundamentally related to the functional, economic and social performances of cities and geographical systems and, therefore, constitutes an essential aspect for spatial planning. Despite the significant progress made in accessibility research, little attention is given to the central role of accessibility in space organization and structuration. This study aimed to fill this gap. Based on an intensive literature review, our work shows the critical role of accessibility in space organization at different scales and sizes, starting from the basic concept of accessibility and its foundations in the classical locational theories and further to the methods and theories at the forefront of research. These processes also point to a unique contribution of multiscale accessibility in space structuration. Accordingly, we offer a conceptual framework to describe the multiscale process of space structuration with respect to local-urban, regional and national scales. We believe this framework may help in studying space and, more importantly, in understanding space. We hope this perspective forms an additional tier at the conceptual and methodological levels concerning accessibility and spatial organization and will encourage empirical studies in light of the suggested view.
Collapse
|
11
|
Kiashemshaki M, Huang Z, Saramäki J. Mobility Signatures: A Tool for Characterizing Cities Using Intercity Mobility Flows. Front Big Data 2022; 5:822889. [PMID: 35284823 PMCID: PMC8908264 DOI: 10.3389/fdata.2022.822889] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 01/31/2022] [Indexed: 11/24/2022] Open
Abstract
Understanding the patterns of human mobility between cities has various applications from transport engineering to spatial modeling of the spreading of contagious diseases. We adopt a city-centric, data-driven perspective to quantify such patterns and introduce the mobility signature as a tool for understanding how a city (or a region) is embedded in the wider mobility network. We demonstrate the potential of the mobility signature approach through two applications that build on mobile-phone-based data from Finland. First, we use mobility signatures to show that the well-known radiation model is more accurate for mobility flows associated with larger Finnish cities, while the traditional gravity model appears a better fit for less populated areas. Second, we illustrate how the SARS-CoV-2 pandemic disrupted the mobility patterns in Finland in the spring of 2020. These two cases demonstrate the ability of the mobility signatures to quickly capture features of mobility flows that are harder to extract using more traditional methods.
Collapse
Affiliation(s)
| | - Zhiren Huang
- Department of Computer Science, Aalto University, Espoo, Finland
| | - Jari Saramäki
- Department of Computer Science, Aalto University, Espoo, Finland
- Helsinki Institute of Information Technology HIIT, Aalto University, Espoo, Finland
- *Correspondence: Jari Saramäki
| |
Collapse
|
12
|
Luca M, Lepri B, Frias-Martinez E, Lutu A. Modeling international mobility using roaming cell phone traces during COVID-19 pandemic. EPJ DATA SCIENCE 2022; 11:22. [PMID: 35402140 PMCID: PMC8978511 DOI: 10.1140/epjds/s13688-022-00335-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 03/21/2022] [Indexed: 05/17/2023]
Abstract
Most of the studies related to human mobility are focused on intra-country mobility. However, there are many scenarios (e.g., spreading diseases, migration) in which timely data on international commuters are vital. Mobile phones represent a unique opportunity to monitor international mobility flows in a timely manner and with proper spatial aggregation. This work proposes using roaming data generated by mobile phones to model incoming and outgoing international mobility. We use the gravity and radiation models to capture mobility flows before and during the introduction of non-pharmaceutical interventions. However, traditional models have some limitations: for instance, mobility restrictions are not explicitly captured and may play a crucial role. To overtake such limitations, we propose the COVID Gravity Model (CGM), namely an extension of the traditional gravity model that is tailored for the pandemic scenario. This proposed approach overtakes, in terms of accuracy, the traditional models by 126.9% for incoming mobility and by 63.9% when modeling outgoing mobility flows.
Collapse
Affiliation(s)
- Massimiliano Luca
- Bruno Kessler Foundation, Trento, Italy
- Free University of Bolzano, Bolzano, Italy
| | | | | | | |
Collapse
|
13
|
Kluge L, Schewe J. Evaluation and extension of the radiation model for internal migration. Phys Rev E 2021; 104:054311. [PMID: 34942836 DOI: 10.1103/physreve.104.054311] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 11/11/2021] [Indexed: 11/07/2022]
Abstract
Human migration is often studied using gravity models. These models, however, have known limitations, including analytic inconsistencies and a dependence on empirical data to calibrate multiple parameters for the region of interest. Overcoming these limitations, the radiation model has been proposed as an alternative, universal approach to predicting different forms of human mobility, but has not been adopted for studying migration. Here we show, using data on within-country migration from the USA and Mexico, that the radiation model systematically underpredicts long-range moves, while the traditional gravity model performs well for large distances. The universal opportunity model, an extension of the radiation model, shows an improved fit of long-range moves compared to the original radiation model, but at the cost of introducing two additional parameters. We propose a more parsimonious extension of the radiation model that introduces a single parameter. We demonstrate that it fits the data over the full distance spectrum and also-unlike the universal opportunity model-preserves the analytical property of the original radiation model of being equivalent to a gravity model in the limit of a uniform population distribution.
Collapse
Affiliation(s)
- Lucas Kluge
- Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, D-14412 Potsdam, Germany and Institute of Physics and Astronomy, University of Potsdam, Karl-Liebknecht-Strasse 24/25, 14476 Potsdam, Germany
| | - Jacob Schewe
- Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, D-14412 Potsdam, Germany
| |
Collapse
|
14
|
Tan S, Lai S, Fang F, Cao Z, Sai B, Song B, Dai B, Guo S, Liu C, Cai M, Wang T, Wang M, Li J, Chen S, Qin S, Floyd JR, Cao Z, Tan J, Sun X, Zhou T, Zhang W, Tatem AJ, Holme P, Chen X, Lu X. Mobility in China, 2020: a tale of four phases. Natl Sci Rev 2021; 8:nwab148. [PMID: 34876997 PMCID: PMC8645011 DOI: 10.1093/nsr/nwab148] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 08/05/2021] [Accepted: 08/10/2021] [Indexed: 02/05/2023] Open
Abstract
2020 was an unprecedented year, with rapid and drastic changes in human mobility due to the COVID-19 pandemic. To understand the variation in commuting patterns among the Chinese population across stable and unstable periods, we used nationwide mobility data from 318 million mobile phone users in China to examine the extreme fluctuations of population movements in 2020, ranging from the Lunar New Year travel season (chunyun), to the exceptional calm of COVID-19 lockdown, and then to the recovery period. We observed that cross-city movements, which increased substantially in chunyun and then dropped sharply during the lockdown, are primarily dependent on travel distance and the socio-economic development of cities. Following the Lunar New Year holiday, national mobility remained low until mid-February, and COVID-19 interventions delayed more than 72.89 million people returning to large cities. Mobility network analysis revealed clusters of highly connected cities, conforming to the social-economic division of urban agglomerations in China. While the mass migration back to large cities was delayed, smaller cities connected more densely to form new clusters. During the recovery period after travel restrictions were lifted, the netflows of over 55% city pairs reversed in direction compared to before the lockdown. These findings offer the most comprehensive picture of Chinese mobility at fine resolution across various scenarios in China and are of critical importance for decision making regarding future public-health-emergency response, transportation planning and regional economic development, among others.
Collapse
Affiliation(s)
- Suoyi Tan
- College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
| | - Shengjie Lai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK
| | - Fan Fang
- College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
| | - Ziqiang Cao
- College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
| | - Bin Sai
- College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
| | - Bing Song
- College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
| | - Bitao Dai
- College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
| | - Shuhui Guo
- College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
| | - Chuchu Liu
- College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
| | - Mengsi Cai
- College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
| | - Tong Wang
- College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
| | - Mengning Wang
- College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
| | - Jiaxu Li
- College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
| | - Saran Chen
- School of Mathematics and Big Data, Foshan University, Foshan 510000, China
| | - Shuo Qin
- State Key Laboratory on Blind Signal Processing, Chengdu 610041, China
| | - Jessica R Floyd
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK
| | - Zhidong Cao
- State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Jing Tan
- Chinese Evidence-Based Medicine Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Xin Sun
- Chinese Evidence-Based Medicine Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Tao Zhou
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611713, China
| | - Wei Zhang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu 610047, China
| | - Andrew J Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK
| | - Petter Holme
- Tokyo Tech World Hub Research Initiative, Institute of Innovative Research, Tokyo Institute of Technology, Tokyo 226-8503, Japan
| | - Xiaohong Chen
- School of Business, Central South University, Changsha 410083, China
| | - Xin Lu
- College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
| |
Collapse
|
15
|
Abstract
Urban scaling theory explains the increasing returns to scale of urban wealth indicators by the per capita increase of human interactions within cities. This explanation implicitly assumes urban areas as isolated entities and ignores their interactions. Here we investigate the effects of commuting networks on the gross domestic product (GDP) of urban areas in the US and Brazil. We describe the urban GDP as the output of a production process where population, incoming commuters, and interactions between these quantities are the input variables. This approach significantly refines the description of urban GDP and shows that incoming commuters contribute to wealth creation in urban areas. Our research indicates that changes in urban GDP related to proportionate changes in population and incoming commuters depend on the initial values of these quantities, such that increasing returns to scale are only possible when the product between population and incoming commuters exceeds a well-defined threshold.
Collapse
|
16
|
Alis C, Legara EF, Monterola C. Generalized radiation model for human migration. Sci Rep 2021; 11:22707. [PMID: 34811415 PMCID: PMC8609035 DOI: 10.1038/s41598-021-02109-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 11/10/2021] [Indexed: 12/03/2022] Open
Abstract
One of the main problems in the study of human migration is predicting how many people will migrate from one place to another. An important model used for this problem is the radiation model for human migration, which models locations as attractors whose attractiveness is moderated by distance as well as attractiveness of neighboring locations. In the model, the measure used for attractiveness is population which is a proxy for economic opportunities and jobs. However, this may not be valid, for example, in developing countries, and fails to take into account people migrating for non-economic reasons such as quality of life. Here, we extend the radiation model to include the number of amenities (offices, schools, leisure places, etc.) as features aside from population. We find that the generalized radiation model outperforms the radiation model by as much as 10.3% relative improvement in mean absolute percentage error based on actual census data five years apart. The best performing model does not even include population information which suggests that amenities already include the information that we get from population. The generalized radiation model provides a measure of feature importance thus presenting another avenue for investigating the effect of amenities on human migration.
Collapse
Affiliation(s)
- Christian Alis
- Analytics, Computing and Complex Systems Laboratory (ACCeSs@AIM), Asian Institute of Management, 123 Paseo De Roxas, Makati City, 1229, Philippines.
| | - Erika Fille Legara
- Analytics, Computing and Complex Systems Laboratory (ACCeSs@AIM), Asian Institute of Management, 123 Paseo De Roxas, Makati City, 1229, Philippines
| | - Christopher Monterola
- Analytics, Computing and Complex Systems Laboratory (ACCeSs@AIM), Asian Institute of Management, 123 Paseo De Roxas, Makati City, 1229, Philippines
| |
Collapse
|
17
|
Meredith HR, Giles JR, Perez-Saez J, Mande T, Rinaldo A, Mutembo S, Kabalo EN, Makungo K, Buckee CO, Tatem AJ, Metcalf CJE, Wesolowski A. Characterizing human mobility patterns in rural settings of sub-Saharan Africa. eLife 2021; 10:e68441. [PMID: 34533456 PMCID: PMC8448534 DOI: 10.7554/elife.68441] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 08/21/2021] [Indexed: 11/27/2022] Open
Abstract
Human mobility is a core component of human behavior and its quantification is critical for understanding its impact on infectious disease transmission, traffic forecasting, access to resources and care, intervention strategies, and migratory flows. When mobility data are limited, spatial interaction models have been widely used to estimate human travel, but have not been extensively validated in low- and middle-income settings. Geographic, sociodemographic, and infrastructure differences may impact the ability for models to capture these patterns, particularly in rural settings. Here, we analyzed mobility patterns inferred from mobile phone data in four Sub-Saharan African countries to investigate the ability for variants on gravity and radiation models to estimate travel. Adjusting the gravity model such that parameters were fit to different trip types, including travel between more or less populated areas and/or different regions, improved model fit in all four countries. This suggests that alternative models may be more useful in these settings and better able to capture the range of mobility patterns observed.
Collapse
Affiliation(s)
- Hannah R Meredith
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
| | - John R Giles
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
| | - Javier Perez-Saez
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
| | - Théophile Mande
- Bureau d'Etudes Scientifiques et Techniques - Eau, Energie, Environnement (BEST-3E), Ouagadougou, Burkina Faso
| | - Andrea Rinaldo
- Dipartimento di Ingegneria Civile Edile ed Ambientale, Università di Padova, Padova, Italy
- Laboratory of Ecohydrology, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Simon Mutembo
- Department of International Health, International Vaccine Access Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
- Macha Research Trust, Choma, Zambia
| | - Elliot N Kabalo
- Zambia Information and Communications Technology Authority, Lusaka, Zambia
| | | | - Caroline O Buckee
- Department of Epidemiology and the Center for Communicable Disease Dynamics, Harvard TH Chan School of Public Health, Boston, United States
| | - Andrew J Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, United Kingdom
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology and the Princeton School of Public and International Affairs, Princeton University, Princeton, United States
| | - Amy Wesolowski
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
| |
Collapse
|
18
|
Spatially resolved simulations of the spread of COVID-19 in three European countries. PLoS Comput Biol 2021; 17:e1009090. [PMID: 34283832 PMCID: PMC8323901 DOI: 10.1371/journal.pcbi.1009090] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 07/30/2021] [Accepted: 05/18/2021] [Indexed: 01/16/2023] Open
Abstract
We explore the spatial and temporal spread of the novel SARS-CoV-2 virus under containment measures in three European countries based on fits to data of the early outbreak. Using data from Spain and Italy, we estimate an age dependent infection fatality ratio for SARS-CoV-2, as well as risks of hospitalization and intensive care admission. We use them in a model that simulates the dynamics of the virus using an age structured, spatially detailed agent based approach, that explicitly incorporates governmental interventions and changes in mobility and contact patterns occurred during the COVID-19 outbreak in each country. Our simulations reproduce several of the features of its spatio-temporal spread in the three countries studied. They show that containment measures combined with high density are responsible for the containment of cases within densely populated areas, and that spread to less densely populated areas occurred during the late stages of the first wave. The capability to reproduce observed features of the spatio-temporal dynamics of SARS-CoV-2 makes this model a potential candidate for forecasting the dynamics of SARS-CoV-2 in other settings, and we recommend its application in low and lower-middle income countries which remain understudied.
Collapse
|
19
|
How risky is it to visit a supermarket during the pandemic? PLoS One 2021; 16:e0253835. [PMID: 34197504 PMCID: PMC8248742 DOI: 10.1371/journal.pone.0253835] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2021] [Accepted: 06/15/2021] [Indexed: 01/22/2023] Open
Abstract
We performed large-scale numerical simulations using a composite model to investigate the infection spread in a supermarket during a pandemic. The model is composed of the social force, purchasing strategy and infection transmission models. Specifically, we quantified the infection risk for customers while in a supermarket that depended on the number of customers, the purchase strategies and the physical layout of the supermarket. The ratio of new infections compared to sales efficiency (earned profit for customer purchases) was computed as a factor of customer density and social distance. Our results indicate that the social distance between customers is the primary factor influencing infection rate. Supermarket layout and purchasing strategy do not impact social distance and hence the spread of infection. Moreover, we found only a weak dependence of sales efficiency and customer density. We believe that our study will help to establish scientifically-based safety rules that will reduce the social price of supermarket business.
Collapse
|
20
|
Abstract
Human mobility impacts many aspects of a city, from its spatial structure1-3 to its response to an epidemic4-7. It is also ultimately key to social interactions8, innovation9,10 and productivity11. However, our quantitative understanding of the aggregate movements of individuals remains incomplete. Existing models-such as the gravity law12,13 or the radiation model14-concentrate on the purely spatial dependence of mobility flows and do not capture the varying frequencies of recurrent visits to the same locations. Here we reveal a simple and robust scaling law that captures the temporal and spatial spectrum of population movement on the basis of large-scale mobility data from diverse cities around the globe. According to this law, the number of visitors to any location decreases as the inverse square of the product of their visiting frequency and travel distance. We further show that the spatio-temporal flows to different locations give rise to prominent spatial clusters with an area distribution that follows Zipf's law15. Finally, we build an individual mobility model based on exploration and preferential return to provide a mechanistic explanation for the discovered scaling law and the emerging spatial structure. Our findings corroborate long-standing conjectures in human geography (such as central place theory16 and Weber's theory of emergent optimality10) and allow for predictions of recurrent flows, providing a basis for applications in urban planning, traffic engineering and the mitigation of epidemic diseases.
Collapse
|
21
|
Highway Freight Transportation Diversity of Cities Based on Radiation Models. ENTROPY 2021; 23:e23050637. [PMID: 34065367 PMCID: PMC8160748 DOI: 10.3390/e23050637] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 05/09/2021] [Accepted: 05/13/2021] [Indexed: 11/16/2022]
Abstract
Using a unique data set containing about 15.06 million truck transportation records in five months, we investigate the highway freight transportation diversity of 338 Chinese cities based on the truck transportation probability pij from one city to another. The transportation probabilities are calculated from the radiation model based on the geographic distance and its cost-based version based on the driving distance as the proxy of cost. For each model, we consider both the population and the gross domestic product (GDP), and find quantitatively very similar results. We find that the transportation probabilities have nice power-law tails with the tail exponents close to 0.5 for all the models. The two transportation probabilities in each model fall around the diagonal pij=pji but are often not the same. In addition, the corresponding transportation probabilities calculated from the raw radiation model and the cost-based radiation model also fluctuate around the diagonal pijgeo=pijcost. We calculate four sets of highway truck transportation diversity according to the four sets of transportation probabilities that are found to be close to each other for each city pair. It is found that the population, the gross domestic product, the in-flux, and the out-flux scale as power laws with respect to the transportation diversity in the raw and cost-based radiation models. It implies that a more developed city usually has higher diversity in highway truck transportation, which reflects the fact that a more developed city usually has a more diverse economic structure.
Collapse
|
22
|
Li R, Gao S, Luo A, Yao Q, Chen B, Shang F, Jiang R, Stanley HE. Gravity model in dockless bike-sharing systems within cities. Phys Rev E 2021; 103:012312. [PMID: 33601646 DOI: 10.1103/physreve.103.012312] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 01/04/2021] [Indexed: 11/07/2022]
Abstract
Due to previous technical challenges with the collection of data on riding behaviors, there have only been a few studies focusing on patterns and regularities of biking traffic, which are crucial to understand to help achieve a greener and more sustainable future urban development. Recently, with the booming of the sharing economy, and the development of the Internet of Things (IoT) and mobile payment technology, dockless bike-sharing systems that record information for every trip provide us with a unique opportunity to study the patterns of biking traffic within cities. We first reveal a spatial scaling relation between the cumulative volume of riding activities and the corresponding distance to the city center, and a power law distribution on the volume of biking flows between fine-grained locations in both Beijing and Shanghai. We validate the effectiveness of the general gravity model on predicting biking traffic at fine spatial resolutions, where population-related parameters are less than unity, indicating that smaller populations are relatively more important per capita in generating biking traffic. We then further study the impacts of spatial scale on the gravity model and reveal that the distance-related parameter grows in a similar way as population-related parameters when the spatial scale of the locations increases. In addition, the flow patterns of some special locations (sources and sinks) that cannot be fully explained by the gravity model are studied.
Collapse
Affiliation(s)
- Ruiqi Li
- UrbanNet Lab, College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Shuai Gao
- UrbanNet Lab, College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Ankang Luo
- UrbanNet Lab, College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Qing Yao
- School of Systems Science, Beijing Normal University, Beijing 100875, China.,Centre for Complexity Science, Imperial College London, London SW7 2AZ, United Kingdom
| | - Bingsheng Chen
- Centre for Complexity Science, Imperial College London, London SW7 2AZ, United Kingdom
| | - Fan Shang
- UrbanNet Lab, College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Rui Jiang
- UrbanNet Lab, College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - H Eugene Stanley
- Center for Polymer Studies and Physics Department, Boston University, Boston, Massachusetts 02215, USA
| |
Collapse
|
23
|
Kotsubo M, Nakaya T. Kernel-based formulation of intervening opportunities for spatial interaction modelling. Sci Rep 2021; 11:950. [PMID: 33441794 PMCID: PMC7807028 DOI: 10.1038/s41598-020-80246-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 12/18/2020] [Indexed: 11/09/2022] Open
Abstract
Understanding spatial interactions such as human mobility has been one of the main analytical themes in geography, spatial economics, and traffic engineering for a long time. The intervening opportunities models, including the radiation model, provide a framework to elucidate spatial interactions generated by an individual's distance-ordered decision-making process. However, such classical definitions of intervening opportunities have often failed to predict realistic flow volumes, particularly for short-distance flows. To overcome this problem, we have proposed a new formulation of intervening opportunities with a kernel function to introduce a fuzziness in spatial search behaviours of destinations, to develop a new variant of the radiation model. The mobility patterns resulting from the modified radiation model that included kernel-based intervening opportunities outperformed the original radiation model when fitted to four datasets of inter-regional flows.
Collapse
Affiliation(s)
- Masaki Kotsubo
- Graduate School of Environmental Studies, Tohoku University, 468-1, Aoba, Aramaki, Aoba-ku, Sendai-city, Miyagi, 980-0845, Japan.
| | - Tomoki Nakaya
- Graduate School of Environmental Studies, Tohoku University, 468-1, Aoba, Aramaki, Aoba-ku, Sendai-city, Miyagi, 980-0845, Japan
| |
Collapse
|
24
|
Zhou WX, Wang L, Xie WJ, Yan W. Predicting highway freight transportation networks using radiation models. Phys Rev E 2020; 102:052314. [PMID: 33327199 DOI: 10.1103/physreve.102.052314] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Accepted: 11/01/2020] [Indexed: 11/07/2022]
Abstract
Highway freight transportation (HFT) plays an important role in the economic activities. Predicting HFT networks is not only scientifically significant in the understanding of the mechanism governing the formation and dynamics of these networks, but also of practical significance in highway planning and design for policymakers and truck allocation and route planning for logistic companies. In this work we apply parameter-free radiation models to predict the HFT network in mainland China and assess their predictive performance using metrics based on links and fluxes, which can be done in reference to the real directed and weighted HFT network between 338 Chinese cities constructed from about 15.06 million truck transportation records in five months. It is found that the radiation models exhibit relatively high accuracy in predicting links but low accuracy in predicting fluxes on links. Similar to gravity models, radiation models also suffer difficulty in predicting long-distance links and the fluxes on them. Nevertheless, the radiation models perform well in reproducing several scaling laws of the HFT network. The adoption of population or gross domestic product in the model has a minor impact on the results, and replacing the geographic distance by the path length taken by most truck drivers does not improve the results.
Collapse
Affiliation(s)
- Wei-Xing Zhou
- School of Business, East China University of Science and Technology, Shanghai 200237, China.,Department of Mathematics, East China University of Science and Technology, Shanghai 200237, China.,Research Center for Econophysics, East China University of Science and Technology, Shanghai 200237, China
| | - Li Wang
- School of Business, East China University of Science and Technology, Shanghai 200237, China
| | - Wen-Jie Xie
- School of Business, East China University of Science and Technology, Shanghai 200237, China.,Research Center for Econophysics, East China University of Science and Technology, Shanghai 200237, China
| | - Wanfeng Yan
- Research Center for Econophysics, East China University of Science and Technology, Shanghai 200237, China.,Zhicang Technologies, Beijing 100016, China
| |
Collapse
|
25
|
Hilton B, Sood AP, Evans TS. Predictive limitations of spatial interaction models: a non-Gaussian analysis. Sci Rep 2020; 10:17474. [PMID: 33060807 PMCID: PMC7566590 DOI: 10.1038/s41598-020-74601-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Accepted: 09/30/2020] [Indexed: 11/09/2022] Open
Abstract
We present a method to compare spatial interaction models against data based on well known statistical measures that are appropriate for such models and data. We illustrate our approach using a widely used example: commuting data, specifically from the US Census 2000. We find that the radiation model performs significantly worse than an appropriately chosen simple gravity model. Various conclusions are made regarding the development and use of spatial interaction models, including: that spatial interaction models fit badly to data in an absolute sense, that therefore the risk of over-fitting is small and adding additional fitted parameters improves the predictive power of models, and that appropriate choices of input data can improve model fit.
Collapse
Affiliation(s)
- B Hilton
- Centre for Complexity Science and Theoretical Physics Group, Physics Department, Imperial College London, London, SW7 2AZ, UK
| | - A P Sood
- Centre for Complexity Science and Theoretical Physics Group, Physics Department, Imperial College London, London, SW7 2AZ, UK
| | - T S Evans
- Centre for Complexity Science and Theoretical Physics Group, Physics Department, Imperial College London, London, SW7 2AZ, UK.
| |
Collapse
|
26
|
PATANARAPEELERT KLOT. INVESTIGATING THE ROLE OF WITHIN- AND BETWEEN-PATCH MOVEMENT IN A DYNAMIC MODEL OF DISEASE SPREAD. J BIOL SYST 2020. [DOI: 10.1142/s0218339020500187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The impact of human mobility on the spreading of disease in a metapopulation is emphasized on interconnecting between patches, whereas the current volume of movement within the local population is usually neglected. Here, the role of internal commuters is taken into account by two means, a local transmission rate and the volume of internal commuters. Dynamic model of human mobility in the metapopulation with gravity coupling is presented. In conjunction with the disease spreading, the impact on invasion threshold and epidemic final size are analyzed. For two-patch model, we show that under fixing parameters in gravity model, the existence of invasion threshold depends on the difference of local transmission rates and the proportion of internal commuters between two patches. For a fully connected network with an identical transmission rate, the difference in patch final sizes is driven by patch distribution of internal commuters. By neglecting the effect of spatial variation in a simple core–satellite model, we show that the heterogeneity of internal commuters and gravity coupling induce a complex pattern of threshold, which depend mostly on the exponent in gravity model, and are responsible for the differences among local epidemic sizes.
Collapse
Affiliation(s)
- KLOT PATANARAPEELERT
- Department of Mathematics, Faculty of Science, Silpakorn University, Rajamankha Nai Rd., Amphoe Muang, Nakorn Pathom Province 73000, Thailand
| |
Collapse
|
27
|
Zhang X, Zheng Y, Zhao Z, Ye X, Zhang P, Wang Y, Chen Z. The education-chasing labor rush in China identified by a heterogeneous migration-network game. Sci Rep 2020; 10:12917. [PMID: 32737355 PMCID: PMC7395130 DOI: 10.1038/s41598-020-68913-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2020] [Accepted: 06/26/2020] [Indexed: 11/26/2022] Open
Abstract
Despite persistent efforts in understanding the motives and patterns of human migration behaviors, little is known about the microscopic mechanism that drives migration and its association with migrant types. To fill the gap, we develop a population game model in which migrants are allowed to be heterogeneous and decide interactively on their destination, the resulting migration network emerges naturally as an Nash equilibrium and depends continuously on migrant features. We apply the model to Chinese labor migration data at the current and expected stages, aiming to quantify migration behavior and decision mode for different migrant groups and at different stages. We find the type-specific migration network differs significantly for migrants with different age, income and education level, and also differs from the aggregated network at both stages. However, a deep analysis on model performance suggests a different picture, stability exists for the decision mechanism behind the “as-if” unstable migration behavior, which also explains the relative invariance of low migration efficiency in different settings. Finally, by a classification of cities from the estimated game, we find the richness of education resources is the most critical determinant of city attractiveness for migrants, which gives hint to city managers in migration policy design.
Collapse
Affiliation(s)
- Xiaoqi Zhang
- National School of Development, Southeast University, Nanjing, 210000, China
| | - Yanqiao Zheng
- School of Finance, Zhejiang University of Finance and Economics, Hangzhou, 310018, China.
| | - Zhijun Zhao
- Institute of Economics, Chinese Academy of Social Science, Beijing, 100836, China
| | - Xinyue Ye
- Department of Landscape Architecture and Urban Planning, Texas A&M University, College Station, TX, 77840, USA
| | - Peng Zhang
- Institute of Economics, Chinese Academy of Social Science, Beijing, 100836, China
| | - Yougui Wang
- School of Systems Science, Beijing Normal University, Beijing, 100875, China
| | - Zhan Chen
- School of Statistics, Capital University of Economics and Business, Beijing, 100070, China
| |
Collapse
|
28
|
Chaters GL, Johnson PCD, Cleaveland S, Crispell J, de Glanville WA, Doherty T, Matthews L, Mohr S, Nyasebwa OM, Rossi G, Salvador LCM, Swai E, Kao RR. Analysing livestock network data for infectious disease control: an argument for routine data collection in emerging economies. Philos Trans R Soc Lond B Biol Sci 2020; 374:20180264. [PMID: 31104601 DOI: 10.1098/rstb.2018.0264] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Livestock movements are an important mechanism of infectious disease transmission. Where these are well recorded, network analysis tools have been used to successfully identify system properties, highlight vulnerabilities to transmission, and inform targeted surveillance and control. Here we highlight the main uses of network properties in understanding livestock disease epidemiology and discuss statistical approaches to infer network characteristics from biased or fragmented datasets. We use a 'hurdle model' approach that predicts (i) the probability of movement and (ii) the number of livestock moved to generate synthetic 'complete' networks of movements between administrative wards, exploiting routinely collected government movement permit data from northern Tanzania. We demonstrate that this model captures a significant amount of the observed variation. Combining the cattle movement network with a spatial between-ward contact layer, we create a multiplex, over which we simulated the spread of 'fast' ( R0 = 3) and 'slow' ( R0 = 1.5) pathogens, and assess the effects of random versus targeted disease control interventions (vaccination and movement ban). The targeted interventions substantially outperform those randomly implemented for both fast and slow pathogens. Our findings provide motivation to encourage routine collection and centralization of movement data to construct representative networks. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'. This theme issue is linked with the earlier issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.
Collapse
Affiliation(s)
- G L Chaters
- 1 Boyd Orr Centre for Population and Ecosystem Health, Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow , Glasgow G12 8QQ , UK
| | - P C D Johnson
- 1 Boyd Orr Centre for Population and Ecosystem Health, Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow , Glasgow G12 8QQ , UK
| | - S Cleaveland
- 1 Boyd Orr Centre for Population and Ecosystem Health, Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow , Glasgow G12 8QQ , UK
| | - J Crispell
- 2 School of Veterinary Medicine, University College Dublin , Dublin , Ireland
| | - W A de Glanville
- 1 Boyd Orr Centre for Population and Ecosystem Health, Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow , Glasgow G12 8QQ , UK
| | - T Doherty
- 3 Royal (Dick) School of Veterinary Studies and Roslin Institute, University of Edinburgh , Easter Bush Campus, Midlothian EH25 9RG , UK
| | - L Matthews
- 1 Boyd Orr Centre for Population and Ecosystem Health, Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow , Glasgow G12 8QQ , UK
| | - S Mohr
- 1 Boyd Orr Centre for Population and Ecosystem Health, Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow , Glasgow G12 8QQ , UK
| | - O M Nyasebwa
- 6 Department of Veterinary Services, Ministry of Livestock and Fisheries, Nelson Mandela Road , Dar Es Salaam , Tanzania
| | - G Rossi
- 3 Royal (Dick) School of Veterinary Studies and Roslin Institute, University of Edinburgh , Easter Bush Campus, Midlothian EH25 9RG , UK
| | - L C M Salvador
- 3 Royal (Dick) School of Veterinary Studies and Roslin Institute, University of Edinburgh , Easter Bush Campus, Midlothian EH25 9RG , UK.,4 Department of Infectious Diseases, University of Georgia , Athens, GA 30602 , USA.,5 Institute of Bioinformatics, University of Georgia , Athens, GA 30602 , USA
| | - E Swai
- 6 Department of Veterinary Services, Ministry of Livestock and Fisheries, Nelson Mandela Road , Dar Es Salaam , Tanzania
| | - R R Kao
- 3 Royal (Dick) School of Veterinary Studies and Roslin Institute, University of Edinburgh , Easter Bush Campus, Midlothian EH25 9RG , UK
| |
Collapse
|
29
|
Ying F, Wallis AOG, Beguerisse-Díaz M, Porter MA, Howison SD. Customer mobility and congestion in supermarkets. Phys Rev E 2020; 100:062304. [PMID: 31962461 DOI: 10.1103/physreve.100.062304] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Indexed: 11/07/2022]
Abstract
The analysis and characterization of human mobility using population-level mobility models is important for numerous applications, ranging from the estimation of commuter flows in cities to modeling trade flows between countries. However, almost all of these applications have focused on large spatial scales, which typically range between intracity scales and intercountry scales. In this paper, we investigate population-level human mobility models on a much smaller spatial scale by using them to estimate customer mobility flow between supermarket zones. We use anonymized, ordered customer-basket data to infer empirical mobility flow in supermarkets, and we apply variants of the gravity and intervening-opportunities models to fit this mobility flow and estimate the flow on unseen data. We find that a doubly-constrained gravity model and an extended radiation model (which is a type of intervening-opportunities model) can successfully estimate 65%-70% of the flow inside supermarkets. Using a gravity model as a case study, we then investigate how to reduce congestion in supermarkets using mobility models. We model each supermarket zone as a queue, and we use a gravity model to identify store layouts with low congestion, which we measure either by the maximum number of visits to a zone or by the total mean queue size. We then use a simulated-annealing algorithm to find store layouts with lower congestion than a supermarket's original layout. In these optimized store layouts, we find that popular zones are often in the perimeter of a store. Our research gives insight both into how customers move in supermarkets and into how retailers can arrange stores to reduce congestion. It also provides a case study of human mobility on small spatial scales.
Collapse
Affiliation(s)
- Fabian Ying
- Mathematical Institute, University of Oxford, Oxford OX2 6GG, United Kingdom
| | - Alisdair O G Wallis
- Tesco PLC, Tesco House, Shire Park, Kestrel Way, Welwyn Garden City, AL7 1GA, United Kingdom
| | | | - Mason A Porter
- Department of Mathematics, University of California, Los Angeles, California 90095, USA
| | - Sam D Howison
- Mathematical Institute, University of Oxford, Oxford OX2 6GG, United Kingdom
| |
Collapse
|
30
|
Camargo CQ, Bright J, Hale SA. Diagnosing the performance of human mobility models at small spatial scales using volunteered geographical information. ROYAL SOCIETY OPEN SCIENCE 2019; 6:191034. [PMID: 31827843 PMCID: PMC6894559 DOI: 10.1098/rsos.191034] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Accepted: 10/02/2019] [Indexed: 06/10/2023]
Abstract
Accurate modelling of local population movement patterns is a core, contemporary concern for urban policymakers, affecting both the short-term deployment of public transport resources and the longer-term planning of transport infrastructure. Yet, while macro-level population movement models (such as the gravity and radiation models) are well developed, micro-level alternatives are in much shorter supply, with most macro-models known to perform poorly at smaller geographical scales. In this paper, we take a first step to remedy this deficit, by leveraging two novel datasets to analyse where and why macro-level models of human mobility break down. We show how freely available data from OpenStreetMap concerning land use composition of different areas around the county of Oxfordshire in the UK can be used to diagnose mobility models and understand the types of trips they over- and underestimate when compared with empirical volumes derived from aggregated, anonymous smartphone location data. We argue for new modelling strategies that move beyond rough heuristics such as distance and population towards a detailed, granular understanding of the opportunities presented in different regions.
Collapse
Affiliation(s)
| | | | - Scott A. Hale
- Oxford Internet Institute, University of Oxford, Oxford, UK
- Alan Turing Institute, London, UK
| |
Collapse
|
31
|
Abstract
Understanding human mobility is crucial for applications such as forecasting epidemic spreading, planning transport infrastructure and urbanism in general. While, traditionally, mobility information has been collected via surveys, the pervasive adoption of mobile technologies has brought a wealth of (real time) data. The easy access to this information opens the door to study theoretical questions so far unexplored. In this work, we show for a series of worldwide cities that commuting daily flows can be mapped into a well behaved vector field, fulfilling the divergence theorem and which is, besides, irrotational. This property allows us to define a potential for the field that can become a major instrument to determine separate mobility basins and discern contiguous urban areas. We also show that empirical fluxes and potentials can be well reproduced and analytically characterized using the so-called gravity model, while other models based on intervening opportunities have serious difficulties. Systematic methods to characterize human mobility can lead to more accurate forecasting of epidemic spreading and better urban planning. Here the authors present a methodology to analyse daily commuting data by representing it with an irrotational vector field and a corresponding scalar potential.
Collapse
|
32
|
Spadon G, Carvalho ACPLFD, Rodrigues-Jr JF, Alves LGA. Reconstructing commuters network using machine learning and urban indicators. Sci Rep 2019; 9:11801. [PMID: 31409862 PMCID: PMC6692407 DOI: 10.1038/s41598-019-48295-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Accepted: 08/01/2019] [Indexed: 11/09/2022] Open
Abstract
Human mobility has a significant impact on several layers of society, from infrastructural planning and economics to the spread of diseases and crime. Representing the system as a complex network, in which nodes are assigned to regions (e.g., a city) and links indicate the flow of people between two of them, physics-inspired models have been proposed to quantify the number of people migrating from one city to the other. Despite the advances made by these models, our ability to predict the number of commuters and reconstruct mobility networks remains limited. Here, we propose an alternative approach using machine learning and 22 urban indicators to predict the flow of people and reconstruct the intercity commuters network. Our results reveal that predictions based on machine learning algorithms and urban indicators can reconstruct the commuters network with 90.4% of accuracy and describe 77.6% of the variance observed in the flow of people between cities. We also identify essential features to recover the network structure and the urban indicators mostly related to commuting patterns. As previously reported, distance plays a significant role in commuting, but other indicators, such as Gross Domestic Product (GDP) and unemployment rate, are also driven-forces for people to commute. We believe that our results shed new lights on the modeling of migration and reinforce the role of urban indicators on commuting patterns. Also, because link-prediction and network reconstruction are still open challenges in network science, our results have implications in other areas, like economics, social sciences, and biology, where node attributes can give us information about the existence of links connecting entities in the network.
Collapse
Affiliation(s)
- Gabriel Spadon
- University of Sao Paulo, Institute of Mathematics and Computer Sciences, Sao Carlos, SP, 13566-590, Brazil.
| | - Andre C P L F de Carvalho
- University of Sao Paulo, Institute of Mathematics and Computer Sciences, Sao Carlos, SP, 13566-590, Brazil
| | - Jose F Rodrigues-Jr
- University of Sao Paulo, Institute of Mathematics and Computer Sciences, Sao Carlos, SP, 13566-590, Brazil
| | - Luiz G A Alves
- University of Sao Paulo, Institute of Mathematics and Computer Sciences, Sao Carlos, SP, 13566-590, Brazil.
- Northwestern University, Department of Chemical and Biological Engineering, Evanston, IL, 60208-3112, USA.
| |
Collapse
|
33
|
Yan XY, Zhou T. Destination choice game: A spatial interaction theory on human mobility. Sci Rep 2019; 9:9466. [PMID: 31263166 PMCID: PMC6603030 DOI: 10.1038/s41598-019-46026-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Accepted: 06/20/2019] [Indexed: 11/09/2022] Open
Abstract
With remarkable significance in migration prediction, global disease mitigation, urban planning and many others, an arresting challenge is to predict human mobility fluxes between any two locations. A number of methods have been proposed against the above challenge, including the gravity model, the intervening opportunity model, the radiation model, the population-weighted opportunity model, and so on. Despite their theoretical elegance, all models ignored an intuitive and important ingredient in individual decision about where to go, that is, the possible congestion on the way and the possible crowding in the destination. Here we propose a microscopic mechanism underlying mobility decisions, named destination choice game (DCG), which takes into account the crowding effects resulted from spatial interactions among individuals. In comparison with the state-of-the-art models, the present one shows more accurate prediction on mobility fluxes across wide scales from intracity trips to intercity travels, and further to internal migrations. The well-known gravity model is proved to be the equilibrium solution of a degenerated DCG neglecting the crowding effects in the destinations.
Collapse
Affiliation(s)
- Xiao-Yong Yan
- Institute of Transportation System Science and Engineering, Beijing Jiaotong University, Beijing, 100044, China.,Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Tao Zhou
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, 611731, China.
| |
Collapse
|
34
|
Hong I, Jung WS, Jo HH. Gravity model explained by the radiation model on a population landscape. PLoS One 2019; 14:e0218028. [PMID: 31170235 PMCID: PMC6553773 DOI: 10.1371/journal.pone.0218028] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Accepted: 05/23/2019] [Indexed: 11/18/2022] Open
Abstract
Understanding the mechanisms behind human mobility patterns is crucial to improve our ability to optimize and predict traffic flows. Two representative mobility models, i.e., radiation and gravity models, have been extensively compared to each other against various empirical data sets, while their fundamental relation is far from being fully understood. In order to study such a relation, we first model the heterogeneous population landscape by generating a fractal geometry of sites and then by assigning to each site a population independently drawn from a power-law distribution. Then the radiation model on this population landscape, which we call the radiation-on-landscape (RoL) model, is compared to the gravity model to derive the distance exponent in the gravity model in terms of the properties of the population landscape, which is confirmed by the numerical simulations. Consequently, we provide a possible explanation for the origin of the distance exponent in terms of the properties of the heterogeneous population landscape, enabling us to better understand mobility patterns constrained by the travel distance.
Collapse
Affiliation(s)
- Inho Hong
- Department of Physics, Pohang University of Science and Technology, Pohang, Republic of Korea
| | - Woo-Sung Jung
- Department of Physics, Pohang University of Science and Technology, Pohang, Republic of Korea
- Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea
- Asia Pacific Center for Theoretical Physics, Pohang, Republic of Korea
- Department of Informatics, Indiana University Bloomington, Bloomington, IN, United States of America
| | - Hang-Hyun Jo
- Department of Physics, Pohang University of Science and Technology, Pohang, Republic of Korea
- Asia Pacific Center for Theoretical Physics, Pohang, Republic of Korea
- Department of Computer Science, Aalto University, Espoo, Finland
| |
Collapse
|
35
|
Engebretsen S, Engø-Monsen K, Frigessi A, Freiesleben de Blasio B. A theoretical single-parameter model for urbanisation to study infectious disease spread and interventions. PLoS Comput Biol 2019; 15:e1006879. [PMID: 30845153 PMCID: PMC6424465 DOI: 10.1371/journal.pcbi.1006879] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Revised: 03/19/2019] [Accepted: 02/18/2019] [Indexed: 11/27/2022] Open
Abstract
The world is continuously urbanising, resulting in clusters of densely populated urban areas and more sparsely populated rural areas. We propose a method for generating spatial fields with controllable levels of clustering of the population. We build a synthetic country, and use this method to generate versions of the country with different clustering levels. Combined with a metapopulation model for infectious disease spread, this allows us to in silico explore how urbanisation affects infectious disease spread. In a baseline scenario with no interventions, the underlying population clustering seems to have little effect on the final size and timing of the epidemic. Under within-country restrictions on non-commuting travel, the final size decreases with increased population clustering. The effect of travel restrictions on reducing the final size is larger with higher clustering. The reduction is larger in the more rural areas. Within-country travel restrictions delay the epidemic, and the delay is largest for lower clustering levels. We implemented three different vaccination strategies-uniform vaccination (in space), preferentially vaccinating urban locations and preferentially vaccinating rural locations. The urban and uniform vaccination strategies were most effective in reducing the final size, while the rural vaccination strategy was clearly inferior. Visual inspection of some European countries shows that many countries already have high population clustering. In the future, they will likely become even more clustered. Hence, according to our model, within-country travel restrictions are likely to be less and less effective in delaying epidemics, while they will be more effective in decreasing final sizes. In addition, to minimise final sizes, it is important not to neglect urban locations when distributing vaccines. To our knowledge, this is the first study to systematically investigate the effect of urbanisation on infectious disease spread and in particular, to examine effectiveness of prevention measures as a function of urbanisation.
Collapse
Affiliation(s)
- Solveig Engebretsen
- Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
- Department of Infectious Disease Epidemiology and Modelling, Division for Infection Control and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | | | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
- Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
| | - Birgitte Freiesleben de Blasio
- Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
- Department of Infectious Disease Epidemiology and Modelling, Division for Infection Control and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| |
Collapse
|
36
|
Piovani D, Arcaute E, Uchoa G, Wilson A, Batty M. Measuring accessibility using gravity and radiation models. ROYAL SOCIETY OPEN SCIENCE 2018; 5:171668. [PMID: 30839729 PMCID: PMC6170557 DOI: 10.1098/rsos.171668] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2017] [Accepted: 08/20/2018] [Indexed: 05/24/2023]
Abstract
Since the presentation of the radiation model, much work has been done to compare its findings with those obtained from gravitational models. These comparisons always aim at measuring the accuracy with which the models reproduce the mobility described by origin-destination matrices. This has been done at different spatial scales using different datasets, and several versions of the models have been proposed to adjust to various spatial systems. However, the models, to our knowledge, have never been compared with respect to policy testing scenarios. For this reason, here we use the models to analyse the impact of the introduction of a new transportation network, a bus rapid transport system, in the city of Teresina in Brazil. We do this by measuring the estimated variation in the trip distribution, and formulate an accessibility to employment indicator for the different zones of the city. By comparing the results obtained with the two approaches, we are able to not only better assess the goodness of fit and the impact of this intervention, but also understand reasons for the systematic similarities and differences in their predictions.
Collapse
Affiliation(s)
- Duccio Piovani
- Centre for Advanced Spatial Analysis, University College London, 90 Tottenham Court Road, London W1T 4TJ, UK
- Nam.R, 4 rue Foucault, Paris 75116, France
| | - Elsa Arcaute
- Centre for Advanced Spatial Analysis, University College London, 90 Tottenham Court Road, London W1T 4TJ, UK
| | - Gabriela Uchoa
- Centre for Advanced Spatial Analysis, University College London, 90 Tottenham Court Road, London W1T 4TJ, UK
- Prefeitura Municipal de Teresina, Praça Marechal Deodoro da Fonseca 860, Teresina 64000-160, Brazil
| | - Alan Wilson
- Centre for Advanced Spatial Analysis, University College London, 90 Tottenham Court Road, London W1T 4TJ, UK
- The Turing Institute, 96 Euston Road, London NW1 3DB, UK
| | - Michael Batty
- Centre for Advanced Spatial Analysis, University College London, 90 Tottenham Court Road, London W1T 4TJ, UK
| |
Collapse
|
37
|
Meakin SR, Keeling MJ. Correlations between stochastic epidemics in two interacting populations. Epidemics 2018; 26:58-67. [PMID: 30213654 DOI: 10.1016/j.epidem.2018.08.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Revised: 07/07/2018] [Accepted: 08/27/2018] [Indexed: 11/25/2022] Open
Abstract
It is increasingly apparent that heterogeneity in the interaction between individuals plays an important role in the dynamics, persistence, evolution and control of infectious diseases. In epidemic modelling two main forms of heterogeneity are commonly considered: spatial heterogeneity due to the segregation of populations and heterogeneity in risk at the same location. The transition from random-mixing to heterogeneous-mixing models is made by incorporating the interaction, or coupling, within and between subpopulations. However, such couplings are difficult to measure explicitly; instead, their action through the correlations between subpopulations is often all that can be observed. Here, using moment-closure methodology supported by stochastic simulation, we investigate how the coupling and resulting correlation are related. We focus on the simplest case of interactions, two identical coupled populations, and show that for a wide range of parameters the correlation between the prevalence of infection takes a relatively simple form. In particular, the correlation can be approximated by a logistic function of the between population coupling, with the free parameter determined analytically from the epidemiological parameters. These results suggest that detailed case-reporting data alone may be sufficient to infer the strength of between population interaction and hence lead to more accurate mathematical descriptions of infectious disease behaviour.
Collapse
Affiliation(s)
- Sophie R Meakin
- EPSRC & MRC Centre for Doctoral Training in Mathematics for Real-World Systems, University of Warwick, United Kingdom.
| | - Matt J Keeling
- Zeeman Institute: SBIDER, Mathematics Institute and School of Life Sciences, University of Warwick, United Kingdom
| |
Collapse
|
38
|
Mathematical models of human mobility of relevance to malaria transmission in Africa. Sci Rep 2018; 8:7713. [PMID: 29769582 PMCID: PMC5955928 DOI: 10.1038/s41598-018-26023-1] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2017] [Accepted: 05/03/2018] [Indexed: 12/04/2022] Open
Abstract
As Africa-wide malaria prevalence declines, an understanding of human movement patterns is essential to inform how best to target interventions. We fitted movement models to trip data from surveys conducted at 3–5 sites throughout each of Mali, Burkina Faso, Zambia and Tanzania. Two models were compared in terms of their ability to predict the observed movement patterns – a gravity model, in which movement rates between pairs of locations increase with population size and decrease with distance, and a radiation model, in which travelers are cumulatively “absorbed” as they move outwards from their origin of travel. The gravity model provided a better fit to the data overall and for travel to large populations, while the radiation model provided a better fit for nearby populations. One strength of the data set was that trips could be categorized according to traveler group – namely, women traveling with children in all survey countries and youth workers in Mali. For gravity models fitted to data specific to these groups, youth workers were found to have a higher travel frequency to large population centers, and women traveling with children a lower frequency. These models may help predict the spatial transmission of malaria parasites and inform strategies to control their spread.
Collapse
|
39
|
Sekiguchi T, Tamura K, Masuda N. Population changes in residential clusters in Japan. PLoS One 2018; 13:e0197144. [PMID: 29742156 PMCID: PMC5942835 DOI: 10.1371/journal.pone.0197144] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2017] [Accepted: 04/28/2018] [Indexed: 11/19/2022] Open
Abstract
Population dynamics in urban and rural areas are different. Understanding factors that contribute to local population changes has various socioeconomic and political implications. In the present study, we use population census data in Japan to examine contributors to the population growth of residential clusters between years 2005 and 2010. The data set covers the entirety of Japan and has a high spatial resolution of 500 × 500 m2, enabling us to examine population dynamics in various parts of the country (urban and rural) using statistical analysis. We found that, in addition to the area, population density, and age, the shape of the cluster and the spatial distribution of inhabitants within the cluster are significantly related to the population growth rate of a residential cluster. Specifically, the population tends to grow if the cluster is "round" shaped (given the area) and the population is concentrated near the center rather than periphery of the cluster. Combination of the present results and analysis framework with other factors that have been omitted in the present study, such as migration, terrain, and transportation infrastructure, will be fruitful.
Collapse
Affiliation(s)
- Takuya Sekiguchi
- National Institute of Informatics, Chiyoda-ku, Tokyo, Japan
- JST, ERATO, Kawarabayashi Large Graph Project, c/o Global Research Center for Big Data Mathematics, NII, Chiyoda-ku, Tokyo, Japan
| | - Kohei Tamura
- Frontier Research Institute for Interdisciplinary Sciences, Tohoku University, Sendai, Miyagi, Japan
| | - Naoki Masuda
- Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom
- * E-mail:
| |
Collapse
|
40
|
Kim J, Park J, Lee W. Why do people move? Enhancing human mobility prediction using local functions based on public records and SNS data. PLoS One 2018; 13:e0192698. [PMID: 29432440 PMCID: PMC5809051 DOI: 10.1371/journal.pone.0192698] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2017] [Accepted: 01/29/2018] [Indexed: 11/18/2022] Open
Abstract
The quality of life for people in urban regions can be improved by predicting urban human mobility and adjusting urban planning accordingly. In this study, we compared several possible variables to verify whether a gravity model (a human mobility prediction model borrowed from Newtonian mechanics) worked as well in inner-city regions as it did in intra-city regions. We reviewed the resident population, the number of employees, and the number of SNS posts as variables for generating mass values for an urban traffic gravity model. We also compared the straight-line distance, travel distance, and the impact of time as possible distance values. We defined the functions of urban regions on the basis of public records and SNS data to reflect the diverse social factors in urban regions. In this process, we conducted a dimension reduction method for the public record data and used a machine learning-based clustering algorithm for the SNS data. In doing so, we found that functional distance could be defined as the Euclidean distance between social function vectors in urban regions. Finally, we examined whether the functional distance was a variable that had a significant impact on urban human mobility.
Collapse
Affiliation(s)
- Jungmin Kim
- Graduate School of Culture Technology, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Juyong Park
- Graduate School of Culture Technology, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Wonjae Lee
- Graduate School of Culture Technology, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| |
Collapse
|
41
|
Sallah K, Giorgi R, Bengtsson L, Lu X, Wetter E, Adrien P, Rebaudet S, Piarroux R, Gaudart J. Mathematical models for predicting human mobility in the context of infectious disease spread: introducing the impedance model. Int J Health Geogr 2017; 16:42. [PMID: 29166908 PMCID: PMC5700689 DOI: 10.1186/s12942-017-0115-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2017] [Accepted: 11/14/2017] [Indexed: 11/30/2022] Open
Abstract
Background Mathematical models of human mobility have demonstrated a great potential for infectious disease epidemiology in contexts of data scarcity. While the commonly used gravity model involves parameter tuning and is thus difficult to implement without reference data, the more recent radiation model based on population densities is parameter-free, but biased. In this study we introduce the new impedance model, by analogy with electricity. Previous research has compared models on the basis of a few specific available spatial patterns. In this study, we use a systematic simulation-based approach to assess the performances. Methods Five hundred spatial patterns were generated using various area sizes and location coordinates. Model performances were evaluated based on these patterns. For simulated data, comparison measures were average root mean square error (aRMSE) and bias criteria. Modeling of the 2010 Haiti cholera epidemic with a basic susceptible–infected–recovered (SIR) framework allowed an empirical evaluation through assessing the goodness-of-fit of the observed epidemic curve. Results The new, parameter-free impedance model outperformed previous models on simulated data according to average aRMSE and bias criteria. The impedance model achieved better performances with heterogeneous population densities and small destination populations. As a proof of concept, the basic compartmental SIR framework was used to confirm the results obtained with the impedance model in predicting the spread of cholera in Haiti in 2010. Conclusions The proposed new impedance model provides accurate estimations of human mobility, especially when the population distribution is highly heterogeneous. This model can therefore help to achieve more accurate predictions of disease spread in the context of an epidemic. Electronic supplementary material The online version of this article (10.1186/s12942-017-0115-7) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Kankoé Sallah
- INSERM, IRD, SESSTIM, Sciences Economiques & Sociales de la Santé & Traitement de l'Information Médicale, Aix Marseille Univ, Marseille, France. .,Prospective et Coopération, Laboratoire d'Idées, Bureau d'Etudes Recherche, Marseille, France.
| | - Roch Giorgi
- INSERM, IRD, SESSTIM, Sciences Economiques & Sociales de la Santé & Traitement de l'Information Médicale, Aix Marseille Univ, Marseille, France.,Service Biostatistique et Technologies de l'Information et de la Communication, APHM, Hôpital de la Timone, Marseille, France
| | - Linus Bengtsson
- Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden.,Flowminder Foundation, Stockholm, Sweden
| | - Xin Lu
- Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden.,Flowminder Foundation, Stockholm, Sweden.,College of Information System and Management, National University of Defense Technology, Changsha, China
| | - Erik Wetter
- Flowminder Foundation, Stockholm, Sweden.,Stockholm School of Economics, Stockholm, Sweden
| | - Paul Adrien
- DELR, Ministère de la Santé Publique et de la Population, Port-au-Prince, Haiti
| | | | - Renaud Piarroux
- UMR S 1136 INSERM, UPMC, Institut Pierre Louis d'Epidémiologie et de Santé Publique, Paris, France
| | - Jean Gaudart
- INSERM, IRD, SESSTIM, Sciences Economiques & Sociales de la Santé & Traitement de l'Information Médicale, Aix Marseille Univ, Marseille, France.,Service Biostatistique et Technologies de l'Information et de la Communication, APHM, Hôpital de la Timone, Marseille, France
| |
Collapse
|
42
|
Belyi A, Bojic I, Sobolevsky S, Sitko I, Hawelka B, Rudikova L, Kurbatski A, Ratti C. Global multi-layer network of human mobility. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE : IJGIS 2017; 31:1381-1402. [PMID: 28553155 PMCID: PMC5426086 DOI: 10.1080/13658816.2017.1301455] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2016] [Accepted: 02/28/2017] [Indexed: 05/26/2023]
Abstract
Recent availability of geo-localized data capturing individual human activity together with the statistical data on international migration opened up unprecedented opportunities for a study on global mobility. In this paper, we consider it from the perspective of a multi-layer complex network, built using a combination of three datasets: Twitter, Flickr and official migration data. Those datasets provide different, but equally important insights on the global mobility - while the first two highlight short-term visits of people from one country to another, the last one - migration - shows the long-term mobility perspective, when people relocate for good. The main purpose of the paper is to emphasize importance of this multi-layer approach capturing both aspects of human mobility at the same time. On the one hand, we show that although the general properties of different layers of the global mobility network are similar, there are important quantitative differences among them. On the other hand, we demonstrate that consideration of mobility from a multi-layer perspective can reveal important global spatial patterns in a way more consistent with those observed in other available relevant sources of international connections, in comparison to the spatial structure inferred from each network layer taken separately.
Collapse
Affiliation(s)
- Alexander Belyi
- SENSEable City Laboratory, SMART Centre, Singapore, Singapore
- Faculty of Applied Mathematics and Computer Science, Belarusian State University, Minsk, Belarus
- SENSEable City Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Iva Bojic
- SENSEable City Laboratory, SMART Centre, Singapore, Singapore
- SENSEable City Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Stanislav Sobolevsky
- SENSEable City Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Center for Urban Science + Progress, New York University, Brooklyn, NY, USA
| | - Izabela Sitko
- Department of Geoinformatics – Z_GIS, GISscience Doctoral College, University of Salzburg, Salzburg, Austria
| | - Bartosz Hawelka
- Department of Geoinformatics – Z_GIS, GISscience Doctoral College, University of Salzburg, Salzburg, Austria
| | - Lada Rudikova
- Department of Intelligent Software and Computer Systems, Yanka Kupala State University of Grodno, Grodno, Belarus
| | - Alexander Kurbatski
- Faculty of Applied Mathematics and Computer Science, Belarusian State University, Minsk, Belarus
| | - Carlo Ratti
- SENSEable City Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| |
Collapse
|
43
|
Meekan MG, Duarte CM, Fernández-Gracia J, Thums M, Sequeira AMM, Harcourt R, Eguíluz VM. The Ecology of Human Mobility. Trends Ecol Evol 2017; 32:198-210. [PMID: 28162772 DOI: 10.1016/j.tree.2016.12.006] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2016] [Revised: 12/11/2016] [Accepted: 12/15/2016] [Indexed: 10/20/2022]
Abstract
Mobile phones and other geolocated devices have produced unprecedented volumes of data on human movement. Analysis of pooled individual human trajectories using big data approaches has revealed a wealth of emergent features that have ecological parallels in animals across a diverse array of phenomena including commuting, epidemics, the spread of innovations and culture, and collective behaviour. Movement ecology, which explores how animals cope with and optimize variability in resources, has the potential to provide a theoretical framework to aid an understanding of human mobility and its impacts on ecosystems. In turn, big data on human movement can be explored in the context of animal movement ecology to provide solutions for urgent conservation problems and management challenges.
Collapse
Affiliation(s)
- Mark G Meekan
- Australian Institute of Marine Science, Indian Ocean Marine Research Centre (IOMRC), University of Western Australia (M470), 35 Stirling Highway, Crawley, WA 6009, Australia
| | - Carlos M Duarte
- King Abdullah University of Science and Technology (KAUST), Red Sea Research Center (RSRC), Biological and Environmental Sciences and Engineering (BESE), Thuwal 23955-6900, Saudi Arabia
| | - Juan Fernández-Gracia
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Michele Thums
- Australian Institute of Marine Science, Indian Ocean Marine Research Centre (IOMRC), University of Western Australia (M470), 35 Stirling Highway, Crawley, WA 6009, Australia.
| | - Ana M M Sequeira
- IOMRC and UWA Oceans Institute, The University of Western Australia, School of Animal Biology, M470, 35 Stirling Highway, Crawley, WA 6009, Australia
| | - Rob Harcourt
- Department of Biological Sciences, Macquarie University, Sydney, NSW 2109, Australia
| | - Víctor M Eguíluz
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), E07122 Palma de Mallorca, Spain
| |
Collapse
|
44
|
Li F, Feng Z, Li P, You Z. Measuring directional urban spatial interaction in China: A migration perspective. PLoS One 2017; 12:e0171107. [PMID: 28141853 PMCID: PMC5283794 DOI: 10.1371/journal.pone.0171107] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2016] [Accepted: 01/16/2017] [Indexed: 11/19/2022] Open
Abstract
The study of urban spatial interaction is closely linked to that of economic geography, urban planning, regional development, and so on. Currently, this topic is generating a great deal of interest among researchers who are striving to find accurate ways to measure urban spatial interaction. Classical spatial interaction models lack theoretical guidance and require complicated parameter-adjusting processes. The radiation model, however, as proposed by Simini et al. with rigorous formula derivation, can simulate directional urban spatial interaction. We applied the radiation model in China to simulate the directional migration number among 337 nationwide research units, comprising 4 municipalities and 333 prefecture-level cities. We then analyzed the overall situation in Chinese cities, the interaction intensity hierarchy, and the prime urban agglomerations from the perspective of migration. This was done to ascertain China's urban spatial interaction and regional development from 2000 to 2010 to reveal ground realities.
Collapse
Affiliation(s)
- Fangzhou Li
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zhiming Feng
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
| | - Peng Li
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
| | - Zhen You
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
| |
Collapse
|
45
|
Kang C, Liu Y, Guo D, Qin K. A Generalized Radiation Model for Human Mobility: Spatial Scale, Searching Direction and Trip Constraint. PLoS One 2015; 10:e0143500. [PMID: 26600153 PMCID: PMC4657960 DOI: 10.1371/journal.pone.0143500] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2015] [Accepted: 11/05/2015] [Indexed: 11/18/2022] Open
Abstract
We generalized the recently introduced “radiation model”, as an analog to the generalization of the classic “gravity model”, to consolidate its nature of universality for modeling diverse mobility systems. By imposing the appropriate scaling exponent λ, normalization factor κ and system constraints including searching direction and trip OD constraint, the generalized radiation model accurately captures real human movements in various scenarios and spatial scales, including two different countries and four different cities. Our analytical results also indicated that the generalized radiation model outperformed alternative mobility models in various empirical analyses.
Collapse
Affiliation(s)
- Chaogui Kang
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, Hubei, China
- * E-mail:
| | - Yu Liu
- Institute of Remote Sensing and Geographical Information Systems, Peking University, Beijing, China
| | - Diansheng Guo
- Department of Geography, University of South Carolina, Columbia, South Carolina, United States of America
| | - Kun Qin
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, Hubei, China
- Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan, Hubei, China
| |
Collapse
|
46
|
Abstract
Understanding human mobility patterns—how people move in their everyday lives—is an interdisciplinary research field. It is a question with roots back to the 19th century that has been dramatically revitalized with the recent increase in data availability. Models of human mobility often take the population distribution as a starting point. Another, sometimes more accurate, data source is land-use maps. In this paper, we discuss how the intra-city movement patterns, and consequently population distribution, can be predicted from such data sources. As a link between land use and mobility, we show that the purposes of people's trips are strongly correlated with the land use of the trip's origin and destination. We calibrate, validate and discuss our model using survey data.
Collapse
Affiliation(s)
- Minjin Lee
- Department of Energy Science, Sungkyunkwan University, Suwon, Korea
| | - Petter Holme
- Department of Energy Science, Sungkyunkwan University, Suwon, Korea
| |
Collapse
|
47
|
Chin WCB, Wen TH. Geographically Modified PageRank Algorithms: Identifying the Spatial Concentration of Human Movement in a Geospatial Network. PLoS One 2015; 10:e0139509. [PMID: 26437000 PMCID: PMC4593571 DOI: 10.1371/journal.pone.0139509] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2015] [Accepted: 09/12/2015] [Indexed: 11/19/2022] Open
Abstract
A network approach, which simplifies geographic settings as a form of nodes and links, emphasizes the connectivity and relationships of spatial features. Topological networks of spatial features are used to explore geographical connectivity and structures. The PageRank algorithm, a network metric, is often used to help identify important locations where people or automobiles concentrate in the geographical literature. However, geographic considerations, including proximity and location attractiveness, are ignored in most network metrics. The objective of the present study is to propose two geographically modified PageRank algorithms—Distance-Decay PageRank (DDPR) and Geographical PageRank (GPR)—that incorporate geographic considerations into PageRank algorithms to identify the spatial concentration of human movement in a geospatial network. Our findings indicate that in both intercity and within-city settings the proposed algorithms more effectively capture the spatial locations where people reside than traditional commonly-used network metrics. In comparing location attractiveness and distance decay, we conclude that the concentration of human movement is largely determined by the distance decay. This implies that geographic proximity remains a key factor in human mobility.
Collapse
Affiliation(s)
| | - Tzai-Hung Wen
- Department of Geography, National Taiwan University, Taipei, Taiwan
- * E-mail:
| |
Collapse
|
48
|
Serok N, Blumenfeld-Lieberthal E. A Simulation Model for Intra-Urban Movements. PLoS One 2015; 10:e0132576. [PMID: 26161640 PMCID: PMC4498912 DOI: 10.1371/journal.pone.0132576] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2014] [Accepted: 06/16/2015] [Indexed: 11/26/2022] Open
Abstract
Human mobility patterns (HMP) have become of interest to a variety of disciplines. The increasing availability of empirical data enables researchers to analyze patterns of people’s movements. Recent work suggested that HMP follow a Levy-flight distribution and present regularity. Here, we present an innovative agent-based model that simulates HMP for various purposes. It is based on the combination of regular movements with spatial considerations, represented by an expanded gravitation model. The agents in this model have different attributes that affect their choice of destination and the duration they stay in each location. Thus, their movement mimics real-life situations. This is a stochastic, bottom-up model, yet it yields HMP that qualitatively fit HMP empirical data in terms of individuals, as well as the entire population. Our results also correspond to real-life phenomena in terms of urban spatial dynamics, that is, the emergence of popular locations in the city due to bottom-up behavior of people. Our model is novel in being based on the assumption that HMP are space-dependent as well as follow high regularity. To our knowledge, we are the first to succeed in simulating HMP not only at the inter-city scale but also at the intra-urban one.
Collapse
Affiliation(s)
- Nimrod Serok
- The David Azrieli School of Architecture, Yolanda and David Katz Faculty of the Arts, Tel Aviv University, Ramat Aviv, Tel-Aviv, 69978, Israel
| | - Efrat Blumenfeld-Lieberthal
- The David Azrieli School of Architecture, Yolanda and David Katz Faculty of the Arts, Tel Aviv University, Ramat Aviv, Tel-Aviv, 69978, Israel
- * E-mail:
| |
Collapse
|
49
|
Yan XY, Zhao C, Fan Y, Di Z, Wang WX. Universal predictability of mobility patterns in cities. J R Soc Interface 2015; 11:20140834. [PMID: 25232053 DOI: 10.1098/rsif.2014.0834] [Citation(s) in RCA: 109] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Despite the long history of modelling human mobility, we continue to lack a highly accurate approach with low data requirements for predicting mobility patterns in cities. Here, we present a population-weighted opportunities model without any adjustable parameters to capture the underlying driving force accounting for human mobility patterns at the city scale. We use various mobility data collected from a number of cities with different characteristics to demonstrate the predictive power of our model. We find that insofar as the spatial distribution of population is available, our model offers universal prediction of mobility patterns in good agreement with real observations, including distance distribution, destination travel constraints and flux. By contrast, the models that succeed in modelling mobility patterns in countries are not applicable in cities, which suggests that there is a diversity of human mobility at different spatial scales. Our model has potential applications in many fields relevant to mobility behaviour in cities, without relying on previous mobility measurements.
Collapse
Affiliation(s)
- Xiao-Yong Yan
- School of Systems Science, Beijing Normal University, Beijing 100875, People's Republic of China Department of Transportation Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, People's Republic of China
| | - Chen Zhao
- School of Systems Science, Beijing Normal University, Beijing 100875, People's Republic of China
| | - Ying Fan
- School of Systems Science, Beijing Normal University, Beijing 100875, People's Republic of China
| | - Zengru Di
- School of Systems Science, Beijing Normal University, Beijing 100875, People's Republic of China
| | - Wen-Xu Wang
- School of Systems Science, Beijing Normal University, Beijing 100875, People's Republic of China School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ 85287, USA
| |
Collapse
|
50
|
Limits of predictability in commuting flows in the absence of data for calibration. Sci Rep 2014; 4:5662. [PMID: 25012599 PMCID: PMC4092333 DOI: 10.1038/srep05662] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2014] [Accepted: 06/20/2014] [Indexed: 11/23/2022] Open
Abstract
The estimation of commuting flows at different spatial scales is a fundamental problem for different areas of study. Many current methods rely on parameters requiring calibration from empirical trip volumes. Their values are often not generalizable to cases without calibration data. To solve this problem we develop a statistical expression to calculate commuting trips with a quantitative functional form to estimate the model parameter when empirical trip data is not available. We calculate commuting trip volumes at scales from within a city to an entire country, introducing a scaling parameter α to the recently proposed parameter free radiation model. The model requires only widely available population and facility density distributions. The parameter can be interpreted as the influence of the region scale and the degree of heterogeneity in the facility distribution. We explore in detail the scaling limitations of this problem, namely under which conditions the proposed model can be applied without trip data for calibration. On the other hand, when empirical trip data is available, we show that the proposed model's estimation accuracy is as good as other existing models. We validated the model in different regions in the U.S., then successfully applied it in three different countries.
Collapse
|