Quantifying, Comparing Human Mobility Perturbation during Hurricane Sandy, Typhoon Wipha, Typhoon Haiyan

AbstractClimate change has intensified tropical cyclones, resulting in several recent catastrophic hurricanes and typhoons. Such disasters impose threats on populous coastal urban areas, and therefore, understanding and predicting human movements plays a critical role in disaster evacuation, response and relief. Despite its critical roles, limited research has focused on tropical cyclones and their influence on human mobility. Here, we studied how severe tropical storms could influence human mobility patterns in coastal urban populations using individuals’ movement data collected from Twitter. We selected three significant tropical storms, including Hurricane Sandy, Typhoon Wipha, and Typhoon Haiyan. We analyzed the human movement data before, during, and after each event, comparing the perturbed movement data to movement data from steady states. We also used different statistical analysis approaches to quantify the strength and duration of human mobility perturbation. The results suggest that tropical cyclones can significantly perturb human movements by changing travel frequencies and displacement probability distributions; however, the nature-derived Lévy Walk model still predominantly governs human mobility. Also, human mobility exhibits a surprisingly mild and brief perturbation for Hurricane Sandy and Typhoon Wipha, while the duration of disturbance was much longer for Typhoon Haiyan. Our finding that the Lévy Walk model can still predict human mobility suggests that bio-inspired examinations of human mobility patterns may uncover solutions to improve disaster evacuation, response and relief plans

Mengukur Perilaku Manusia dalam Skala Besar dan Secara Real-time: Studi Kasus Pola Mobilitas Penduduk dan Fase Awal Pandemi COVID-19 di Indonesia

Background: Good decisions in policy-making rely on acquiring the best possible understanding at the fast pace of what is happening and what might happen next in the population. Immediate measurements and predictions of disease spread would help authorities take necessary action to mitigate the rapid geographical spread of potential emerging infectious diseases. Unfortunately, measuring human behavior in nearly real-time, specifically at a large scale, has been labor-intensive, time-consuming, and expensive. Consequently, measurements are often unfeasible or delayed in developing in-time policy decisions. The increasing use of online services such as Twitter generates vast volumes and varieties of data, often available at high speed. These datasets might provide the opportunity to obtain immediate measurements of human behavior. Here we describe how the patterns of population mobility can be associated with the number of COVID-19 cases and, subsequently, could be used to simulate the potential path of disease spreading.Methods: Our analysis of country-scale population mobility networks is based on a proxy network from geotagged Twitter data, which we incorporated into a model to reproduce the spatial spread of the early phase COVID-19 pandemic in Indonesia. We used aggregated province-level mobility data from January through December 2019 for the baseline mobility patterns from DKI Jakarta as the origin of the 33 provinces' destinations in Indonesia.Result: We found that population mobility patterns explain 62 percent of the variation in the occurrence of COVID-19 cases in the early phases of the pandemic. In addition, we confirm that online services have the potential to measure human behavior in nearly real time.Conclusion: We believe that our work contributes to previous research by developing a scalable early warning system for public health decision-makers in charge of developing mitigation policies for the potential spread of emerging infectious diseases

Depicting urban boundaries from a mobility network of spatial interactions: A case study of Great Britain with geo-located Twitter data

Existing urban boundaries are usually defined by government agencies for administrative, economic, and political purposes. Defining urban boundaries that consider socio-economic relationships and citizen commute patterns is important for many aspects of urban and regional planning. In this paper, we describe a method to delineate urban boundaries based upon human interactions with physical space inferred from social media. Specifically, we depicted the urban boundaries of Great Britain using a mobility network of Twitter user spatial interactions, which was inferred from over 69 million geo-located tweets. We define the non-administrative anthropographic boundaries in a hierarchical fashion based on different physical movement ranges of users derived from the collective mobility patterns of Twitter users in Great Britain. The results of strongly connected urban regions in the form of communities in the network space yield geographically cohesive, non-overlapping urban areas, which provide a clear delineation of the non-administrative anthropographic urban boundaries of Great Britain. The method was applied to both national (Great Britain) and municipal scales (the London metropolis). While our results corresponded well with the administrative boundaries, many unexpected and interesting boundaries were identified. Importantly, as the depicted urban boundaries exhibited a strong instance of spatial proximity, we employed a gravity model to understand the distance decay effects in shaping the delineated urban boundaries. The model explains how geographical distances found in the mobility patterns affect the interaction intensity among different non-administrative anthropographic urban areas, which provides new insights into human spatial interactions with urban space.Comment: 32 pages, 7 figures, International Journal of Geographic Information Scienc

Understanding Mobility and Transport Modal Disparities Using Emerging Data Sources: Modelling Potentials and Limitations

Transportation presents a major challenge to curb climate change due in part to its ever-increasing travel demand. Better informed policy-making requires up-to-date empirical mobility data to model viable mitigation options for reducing emissions from the transport sector. On the one hand, the prevalence of digital technologies enables a large-scale collection of human mobility traces, providing big potentials for improving the understanding of mobility patterns and transport modal disparities. On the other hand, the advancement in data science has allowed us to continue pushing the boundary of the potentials and limitations, for new uses of big data in transport.This thesis uses emerging data sources, including Twitter data, traffic data, OpenStreetMap (OSM), and trip data from new transport modes, to enhance the understanding of mobility and transport modal disparities, e.g., how car and public transit support mobility differently. Specifically, this thesis aims to answer two research questions: (1) What are the potentials and limitations of using these emerging data sources for modelling mobility? (2) How can these new data sources be properly modelled for characterising transport modal disparities? Papers I-III model mobility mainly using geotagged social media data, and reveal the potentials and limitations of this data source by validating against established sources (Q1). Papers IV-V combine multiple data sources to characterise transport modal disparities (Q2) which further demonstrate the modelling potentials of the emerging data sources (Q1).Despite a biased population representation and low and irregular sampling of the actual mobility, the geolocations of Twitter data can be used in models to produce good agreements with the other data sources on the fundamental characteristics of individual and population mobility. However, its feasibility for estimating travel demand depends on spatial scale, sparsity, sampling method, and sample size. To extend the use of social media data, this thesis develops two novel approaches to address the sparsity issue: (1) An individual-based mobility model that fills the gaps in the sparse mobility traces for synthetic travel demand; (2) A population-based model that uses Twitter geolocations as attractions instead of trips for estimating the flows of people between regions. This thesis also presents two reproducible data fusion frameworks for characterising transport modal disparities. They demonstrate the power of combining different data sources to gain new insights into the spatiotemporal patterns of travel time disparities between car and public transit, and the competition between ride-sourcing and public transport

Geo-located Twitter as the proxy for global mobility patterns

In the advent of a pervasive presence of location sharing services researchers gained an unprecedented access to the direct records of human activity in space and time. This paper analyses geo-located Twitter messages in order to uncover global patterns of human mobility. Based on a dataset of almost a billion tweets recorded in 2012 we estimate volumes of international travelers in respect to their country of residence. We examine mobility profiles of different nations looking at the characteristics such as mobility rate, radius of gyration, diversity of destinations and a balance of the inflows and outflows. The temporal patterns disclose the universal seasons of increased international mobility and the peculiar national nature of overseen travels. Our analysis of the community structure of the Twitter mobility network, obtained with the iterative network partitioning, reveals spatially cohesive regions that follow the regional division of the world. Finally, we validate our result with the global tourism statistics and mobility models provided by other authors, and argue that Twitter is a viable source to understand and quantify global mobility patterns.Comment: 17 pages, 13 figure