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As most studies of segregation rely on the evenness dimension, this current study proposes a graph embedding approach to explore the usefulness of employing the isolation-exposure dimension to evaluate income segregation. While most segregation studies analyzed the static distribution of population subgroups, current study attempts to classify neighborhoods based on house value as a proxy of income, residents' exposure to people of different income levels as constrained by their mobility patterns, and amenities available in the neighborhood. This study exploits the graph embedding method to classify neighborhoods by combining their various attributes, static population distribution and mobility data provided by smart cards to analyze income segregation in Shenzhen, China. Results identify four types of communities with different economic statuses, mobility patterns, and amenity characteristics. They provide rich descriptions about the connections between income segregation patterns, population dynamics, and neighborhood characteristics. The study found that the more segregated communities, which are composed of the poorest and richest groups, are mostly in the peripheral regions of the city while the inner city has lower levels of segregation, mainly due to differentials in transit accessibility. The study demonstrates the great potential of the proposed method to incorporate multiple aspects to evaluate segregation. • Exposure between residents in communities of different house values reflects income segregation. • Use graph embedding data mining method to incorporate mobility and neighborhood characteristics. • Use smart card, house value and amenity data of Shenzhen, China to derive four community types. • Less segregated communities near city center and more segregated communities in city periphery. • Demonstrated a method with potential to combine multiple aspects in evaluating segregation. [ABSTRACT FROM AUTHOR]

• A transit mobility community detection problem is formulated. • Mobility communities are detected via embedding multiple-source transit data. • The proposed approach was evaluated using transit data from Shenzhen, China. Detecting urban mobility structure, i.e., segmenting urban areas into disjoint clusters with similar mobility patterns, can facilitate our understanding of how a city is organized and how different parts of a city interact with each other, which underpin informed decision-making in achieving sustainable urban transportation development and resilient society. In this study, we propose to tackle a novel transit mobility structure detection problem, which hinges on high-level mobility patterns that characterize collective movement dynamics across the study region. To this end, we propose a machine learning-based approach to discover meaningful urban mobility structure using big transit data. We contend that both transit mobility patterns and local urban function information should be considered during the detection of transit mobility structure. By integrating different sources of urban data, we model the network of transit mobility as an attributed graph: local static urban functions are described by attributed features for graph nodes whereas travel dynamics are captured via a transit mobility pattern matrix. Similarities of both attributed features and transit mobility patterns are jointly embedded to derive compact low-dimensional vector representations via graph auto-encoder. Mobility structure is then extracted using unsupervised clustering and gap statistics. The proposed approach is capable of synthesizing both mobility and static information in a data-driven manner, preserving original urban topological structure and resident movement dynamics. The proposed approach was evaluated using real-world transit data collected in Shenzhen City, China. Experimental results and analyses demonstrate that the proposed approach has the applicability of unveiling meaningful transit mobility structure in large metropolitan areas. The detected mobility community maps present a holistic overview of public transit movement structure, enabling decision makers to make informed decisions on sustainable urban development and transit management. [ABSTRACT FROM AUTHOR]

Background: Rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Wuhan, China, prompted heightened surveillance in Shenzhen, China. The resulting data provide a rare opportunity to measure key metrics of disease course, transmission, and the impact of control measures.Methods: From Jan 14 to Feb 12, 2020, the Shenzhen Center for Disease Control and Prevention identified 391 SARS-CoV-2 cases and 1286 close contacts. We compared cases identified through symptomatic surveillance and contact tracing, and estimated the time from symptom onset to confirmation, isolation, and admission to hospital. We estimated metrics of disease transmission and analysed factors influencing transmission risk.Findings: Cases were older than the general population (mean age 45 years) and balanced between males (n=187) and females (n=204). 356 (91%) of 391 cases had mild or moderate clinical severity at initial assessment. As of Feb 22, 2020, three cases had died and 225 had recovered (median time to recovery 21 days; 95% CI 20-22). Cases were isolated on average 4·6 days (95% CI 4·1-5·0) after developing symptoms; contact tracing reduced this by 1·9 days (95% CI 1·1-2·7). Household contacts and those travelling with a case were at higher risk of infection (odds ratio 6·27 [95% CI 1·49-26·33] for household contacts and 7·06 [1·43-34·91] for those travelling with a case) than other close contacts. The household secondary attack rate was 11·2% (95% CI 9·1-13·8), and children were as likely to be infected as adults (infection rate 7·4% in children <10 years vs population average of 6·6%). The observed reproductive number (R) was 0·4 (95% CI 0·3-0·5), with a mean serial interval of 6·3 days (95% CI 5·2-7·6).Interpretation: Our data on cases as well as their infected and uninfected close contacts provide key insights into the epidemiology of SARS-CoV-2. This analysis shows that isolation and contact tracing reduce the time during which cases are infectious in the community, thereby reducing the R. The overall impact of isolation and contact tracing, however, is uncertain and highly dependent on the number of asymptomatic cases. Moreover, children are at a similar risk of infection to the general population, although less likely to have severe symptoms; hence they should be considered in analyses of transmission and control.Funding: Emergency Response Program of Harbin Institute of Technology, Emergency Response Program of Peng Cheng Laboratory, US Centers for Disease Control and Prevention. [ABSTRACT FROM AUTHOR]