Open e-commerce 1.0, five years of crowdsourced U.S. Amazon purchase histories with user demographics.

This is a first-of-its-kind dataset containing detailed purchase histories from 5027 U.S. Amazon.com consumers, spanning 2018 through 2022, with more than 1.8 million purchases. Consumer spending data are customarily collected through government surveys to produce public datasets and statistics, which serve public agencies and researchers. Companies now collect similar data through consumers' use of digital platforms at rates superseding data collection by public agencies. We published this dataset in an effort towards democratizing access to rich data sources routinely used by companies. The data were crowdsourced through an online survey and shared with participants' informed consent. Data columns include order date, product code, title, price, quantity, and shipping address state. Each purchase history is linked to survey data with information about participants' demographics, lifestyle, and health. We validate the dataset by showing expenditure correlates with public Amazon sales data (Pearson r = 0.978, p < 0.001) and conduct analyses of specific product categories, demonstrating expected seasonal trends and strong relationships to other public datasets.

YJMob100K: City-scale and longitudinal dataset of anonymized human mobility trajectories.

Modeling and predicting human mobility trajectories in urban areas is an essential task for various applications including transportation modeling, disaster management, and urban planning. The recent availability of large-scale human movement data collected from mobile devices has enabled the development of complex human mobility prediction models. However, human mobility prediction methods are often trained and tested on different datasets, due to the lack of open-source large-scale human mobility datasets amid privacy concerns, posing a challenge towards conducting transparent performance comparisons between methods. To this end, we created an open-source, anonymized, metropolitan scale, and longitudinal (75 days) dataset of 100,000 individuals' human mobility trajectories, using mobile phone location data provided by Yahoo Japan Corporation (currently renamed to LY Corporation), named YJMob100K. The location pings are spatially and temporally discretized, and the metropolitan area is undisclosed to protect users' privacy. The 90-day period is composed of 75 days of business-as-usual and 15 days during an emergency, to test human mobility predictability during both normal and anomalous situations.

Behavioral changes during the COVID-19 pandemic decreased income diversity of urban encounters.

Diversity of physical encounters in urban environments is known to spur economic productivity while also fostering social capital. However, mobility restrictions during the pandemic have forced people to reduce urban encounters, raising questions about the social implications of behavioral changes. In this paper, we study how individual income diversity of urban encounters changed during the pandemic, using a large-scale, privacy-enhanced mobility dataset of more than one million anonymized mobile phone users in Boston, Dallas, Los Angeles, and Seattle, across three years spanning before and during the pandemic. We find that the diversity of urban encounters has substantially decreased (by 15% to 30%) during the pandemic and has persisted through late 2021, even though aggregated mobility metrics have recovered to pre-pandemic levels. Counterfactual analyses show that behavioral changes including lower willingness to explore new places further decreased the diversity of encounters in the long term. Our findings provide implications for managing the trade-off between the stringency of COVID-19 policies and the diversity of urban encounters as we move beyond the pandemic.

Toward data-driven, dynamical complex systems approaches to disaster resilience.

With rapid urbanization and increasing climate risks, enhancing the resilience of urban systems has never been more important. Despite the availability of massive datasets of human behavior (e.g., mobile phone data, satellite imagery), studies on disaster resilience have been limited to using static measures as proxies for resilience. However, static metrics have significant drawbacks such as their inability to capture the effects of compounding and accumulating disaster shocks; dynamic interdependencies of social, economic, and infrastructure systems; and critical transitions and regime shifts, which are essential components of the complex disaster resilience process. In this article, we argue that the disaster resilience literature needs to take the opportunities of big data and move toward a different research direction, which is to develop data-driven, dynamical complex systems models of disaster resilience. Data-driven complex systems modeling approaches could overcome the drawbacks of static measures and allow us to quantitatively model the dynamic recovery trajectories and intrinsic resilience characteristics of communities in a generic manner by leveraging large-scale and granular observations. This approach brings a paradigm shift in modeling the disaster resilience process and its linkage with the recovery process, paving the way to answering important questions for policy applications via counterfactual analysis and simulations.

Early warning of COVID-19 hotspots using human mobility and web search query data.

COVID-19 has disrupted the global economy and well-being of people at an unprecedented scale and magnitude. To contain the disease, an effective early warning system that predicts the locations of outbreaks is of crucial importance. Studies have shown the effectiveness of using large-scale mobility data to monitor the impacts of non-pharmaceutical interventions (e.g., lockdowns) through population density analysis. However, predicting the locations of potential outbreak occurrence is difficult using mobility data alone. Meanwhile, web search queries have been shown to be good predictors of the disease spread. In this study, we utilize a unique dataset of human mobility trajectories (GPS traces) and web search queries with common user identifiers (> 450 K users), to predict COVID-19 hotspot locations beforehand. More specifically, web search query analysis is conducted to identify users with high risk of COVID-19 contraction, and social contact analysis was further performed on the mobility patterns of these users to quantify the risk of an outbreak. Our approach is empirically tested using data collected from users in Tokyo, Japan. We show that by integrating COVID-19 related web search query analytics with social contact networks, we are able to predict COVID-19 hotspot locations 1-2 weeks beforehand, compared to just using social contact indexes or web search data analysis. This study proposes a novel method that can be used in early warning systems for disease outbreak hotspots, which can assist government agencies to prepare effective strategies to prevent further disease spread. Human mobility data and web search query data linked with common IDs are used to predict COVID-19 outbreaks. High risk social contact index captures both the contact density and COVID-19 contraction risks of individuals. Real world data was collected from 200 K individual users in Tokyo during the COVID-19 pandemic. Experiments showed that the index can be used for microscopic outbreak early warning.

Spatiotemporal contact density explains the disparity of COVID-19 spread in urban neighborhoods.

The rapid early spread of COVID-19 in the US was experienced very differently by different socioeconomic groups and business industries. In this study, we study aggregate mobility patterns of New York City and Chicago to identify the relationship between the amount of interpersonal contact between people in urban neighborhoods and the disparity in the growth of positive cases among these groups. We introduce an aggregate spatiotemporal contact density index (CDI) to measure the strength of this interpersonal contact using mobility data collected from mobile phones, and combine it with social distancing metrics to show its effect on positive case growth. With the help of structural equations modeling, we find that the effect of CDI on case growth was consistently positive and that it remained consistently higher in lower-income neighborhoods, suggesting a causal path of income on case growth via CDI. Using the CDI, schools and restaurants are identified as high contact density industries, and the estimation suggests that implementing specific mobility restrictions on these point-of-interest categories is most effective. This analysis can be useful in providing insights for government officials targeting specific population groups and businesses to reduce infection spread as reopening efforts continue to expand across the nation.

Non-compulsory measures sufficiently reduced human mobility in Tokyo during the COVID-19 epidemic.

While large scale mobility data has become a popular tool to monitor the mobility patterns during the COVID-19 pandemic, the impacts of non-compulsory measures in Tokyo, Japan on human mobility patterns has been under-studied. Here, we analyze the temporal changes in human mobility behavior, social contact rates, and their correlations with the transmissibility of COVID-19, using mobility data collected from more than 200K anonymized mobile phone users in Tokyo. The analysis concludes that by April 15th (1 week into state of emergency), human mobility behavior decreased by around 50%, resulting in a 70% reduction of social contacts in Tokyo, showing the strong relationships with non-compulsory measures. Furthermore, the reduction in data-driven human mobility metrics showed correlation with the decrease in estimated effective reproduction number of COVID-19 in Tokyo. Such empirical insights could inform policy makers on deciding sufficient levels of mobility reduction to contain the disease.

Understanding post-disaster population recovery patterns.

Despite the rising importance of enhancing community resilience to disasters, our understandings on when, how and why communities are able to recover from such extreme events are limited. Here, we study the macroscopic population recovery patterns in disaster affected regions, by observing human mobility trajectories of over 1.9 million mobile phone users across three countries before, during and after five major disasters. We find that, despite the diversity in socio-economic characteristics among the affected regions and the types of hazards, population recovery trends after significant displacement resemble similar patterns after all five disasters. Moreover, the heterogeneity in initial and long-term displacement rates across communities in the three countries were explained by a set of key common factors, including the community's median income level, population, housing damage rates and the connectedness to other cities. Such insights discovered from large-scale empirical data could assist policymaking in various disciplines for developing community resilience to disasters.

Cross-comparative analysis of evacuation behavior after earthquakes using mobile phone data.

Despite the importance of predicting evacuation mobility dynamics after large scale disasters for effective first response and disaster relief, our general understanding of evacuation behavior remains limited because of the lack of empirical evidence on the evacuation movement of individuals across multiple disaster instances. Here we investigate the GPS trajectories of a total of more than 1 million anonymized mobile phone users whose positions were tracked for a period of 2 months before and after four of the major earthquakes that occurred in Japan. Through a cross comparative analysis between the four disaster instances, we find that in contrast to the assumed complexity of evacuation decision making mechanisms in crisis situations, an individual's evacuation probability is strongly dependent on the seismic intensity that they experience. In fact, we show that the evacuation probabilities in all earthquakes collapse into a similar pattern, with a critical threshold at around seismic intensity 5.5. This indicates that despite the diversity in the earthquakes profiles and urban characteristics, evacuation behavior is similarly dependent on seismic intensity. Moreover, we found that probability density functions of the distances that individuals evacuate are not dependent on seismic intensities that individuals experience. These insights from empirical analysis on evacuation from multiple earthquake instances using large scale mobility data contributes to a deeper understanding of how people react to earthquakes, and can potentially assist decision makers to simulate and predict the number of evacuees in urban areas with little computational time and cost. This can be achieved by utilizing only the information on population density distribution and seismic intensity distribution, which can be observed instantaneously after the shock.

Detection of metabolic abnormality in asynergic regions of ischemic human myocardium using 31P and 1H magnetic resonance spectroscopy.

To assess quantitatively phosphocreatine (PCr), adenosine triphosphate (ATP) and total creatine (CR) in asynergic regions of ischemic human myocardium using phosphorus (31P) and proton magnetic resonance spectroscopy (1H MRS). Patients were divided into two groups: 31P MRS and 1H MRS. In each group, patients were subdivided into three groups using echocardiography: a normokinetic [WN(P) (n = 6) in 31P MRS, WN(H) (n = 10) in 1H MRS], a hypokinetic [WH(P) (n = 13), WH(H) (n = 7)], and a- or dyskinetic wall motion groups [WA(P) (n = 14), WA(H) (n =6)]. They were compared with normal subjects of each group [CNP (n = 10), CN(H) (n = 10)]. 31P MRS spectra were obtained from the anterior and apical regions of the left ventricle by slice-selected 1D CSI. 1H MRS spectra were obtained from the 2 x 2 x 2-cm voxel set in the left ventricular wall by the PRESS method. In the 31P MRS group, myocardial PCr was significantly lower in the WH(P) and WA(P) groups than in the CN(P) group, but myocardial PCr in the WN(P) group did not differ from that in CN(P). A difference in ATP could not be detected among the four groups. In the 1H MRS group, myocardial CR was significantly lower in the WH(H) and WA(H) groups than in the CN(H) group. Myocardial CR in the WNH group did not differ from that in the CN(H). The noninvasive 31P and 1H MRS approach is useful in the assessment of metabolite reduction associated with wall motion abnormality.

Proton magnetic resonance spectroscopy can detect creatine depletion associated with the progression of heart failure in cardiomyopathy.

OBJECTIVES: This study noninvasively examined total creatine (CR) of the myocardium in dilated cardiomyopathy (DCM) or hypertrophic cardiomyopathy (HCM) using proton magnetic resonance spectroscopy ((1)H-MRS). BACKGROUND: Abnormalities in CR metabolism in failing hearts have been reported. A biochemical study suggested that myocardial metabolic changes are very similar in DCM and HCM despite the different heart failure (HF) mechanisms. METHODS: Using cardiac-gated (1)H-MRS with magnetic resonance image (MRI)-guided point-resolved spectroscopy (PRESS) localization, we quantitatively measured septal CR. Patients with either DCM (n = 11) or HCM (n = 7) and age-matched normal subjects (n = 14) were examined. RESULTS: Myocardial CR was significantly lower in DCM patients (16.1 +/- 4.5 micromol/g wet weight [range 10.2 to 22.9], p < 0.05) than that in subjects with normal hearts (27.6 +/- 4.1 micromol/g [range 21.4 to 36.2]). Myocardial CR in HCM patients (22.6 +/- 8.1 micromol/g [range 12.2 to 34.5]) was significantly lower than that in subjects with normal hearts (p < 0.05) but was significantly higher than that in DCM patients (p < 0.05). In 18 patients with either DCM or HCM, myocardial CR correlated positively with left ventricular ejection fraction (LVEF) (y = 0.22x + 9.8, r = 0.73, p = 0.0006) but correlated negatively with plasma B-type natriuretic peptide (BNP) levels (y = -0.012x + 22.4, r = -0.54, p = 0.022). CONCLUSIONS: This study showed that (1)H-MRS can noninvasively detect CR depletion associated with the severity of HF in cardiomyopathy.