Revealing intra-urban spatial structure through an exploratory analysis by combining road network abstraction model and taxi trajectory data

The unprecedented urbanization in China has dramatically changed the urban spatial structure of cities. With the proliferation of individual-level geospatial big data, previous studies have widely used the network abstraction model to reveal the underlying urban spatial structure. However, the construction of network abstraction models primarily focuses on the topology of the road network without considering individual travel flows along with the road networks. Individual travel flows reflect the urban dynamics, which can further help understand the underlying spatial structure. This study therefore aims to reveal the intra-urban spatial structure by integrating the road network abstraction model and individual travel flows. To achieve this goal, we 1) quantify the spatial interaction relatedness of road segments based on the Word2Vec model using large volumes of taxi trip data, then 2) characterize the road abstraction network model according to the identified spatial interaction relatedness, and 3) implement a community detection algorithm to reveal sub-regions of a city. Our results reveal three levels of hierarchical spatial structures in the Wuhan metropolitan area. This study provides a data-driven approach to the investigation of urban spatial structure via identifying traffic interaction patterns on the road network, offering insights to urban planning practice and transportation management

Spatial and Temporal Dynamics of Influenza

Despite the significant amount of research conducted on the epidemiology of seasonal influenza, the patterns in the annual oscillations of influenza epidemics have not been fully described or understood. Furthermore, the current understanding of the intrinsic properties of influenza epidemics is limited by the geographic scales used to evaluate the data. Analyses conducted at larger spatial scales may potentially conceal local trends in disease structure which may reveal the effect of population structure or environmental factors on disease spread. By using influenza incidence data from the Commonwealth of Pennsylvania and United States influenza mortality data, this dissertation characterizes seasonal influenza epidemics, evaluates factors that drive local influenza epidemics, and provides an initial assessment in how administrative borders influence surveillance for local and regional influenza epidemics.Evidence of spatial heterogeneity existed in the distribution of influenza epidemics for Pennsylvania counties resulting in a cluster of elevated incidence in the South Central region of the state that persisted during the entire study period (2003-2009). Lower monthly precipitation levels during the influenza season (OR = 0.52, p = 0.0319), fewer residents over age 64 (OR = 0.27, p = 0.01) and fewer residents with more than a high school education (OR = 0.76, p = 0.0148) were significantly associated with membership in this cluster. In addition, significant synchrony in the timing of epidemics existed across the entire state and decayed with distance (regional correlation r = 62%). Synchrony as a function of population size displayed evidence of hierarchical spread with more synchronized epidemics occurring among the most populated counties. A gravity model describing movement between two populations was the best predictor of influenza spread suggesting that non-routine and leisure travel drive local epidemics. Within the United States, clusters of epidemic synchronization existed, most notably in densely populated regions where connectivity is stronger. Observation of county and state epidemic clusters highlights the importance and necessity of correctly identifying the ontologic unit of epidemicity for influenza and other diseases. Recognition of the appropriate geographic unit to implement effective surveillance and prevention methods can strengthen the public health response and minimize inefficient mechanisms

A Geovisual Analytic Approach to Understanding Geo-Social Relationships in the International Trade Network

The world has become a complex set of geo-social systems interconnected by networks, including transportation networks, telecommunications, and the internet. Understanding the interactions between spatial and social relationships within such geo-social systems is a challenge. This research aims to address this challenge through the framework of geovisual analytics. We present the GeoSocialApp which implements traditional network analysis methods in the context of explicitly spatial and social representations. We then apply it to an exploration of international trade networks in terms of the complex interactions between spatial and social relationships. This exploration using the GeoSocialApp helps us develop a two-part hypothesis: international trade network clusters with structural equivalence are strongly ‘balkanized’ (fragmented) according to the geography of trading partners, and the geographical distance weighted by population within each network cluster has a positive relationship with the development level of countries. In addition to demonstrating the potential of visual analytics to provide insight concerning complex geo-social relationships at a global scale, the research also addresses the challenge of validating insights derived through interactive geovisual analytics. We develop two indicators to quantify the observed patterns, and then use a Monte-Carlo approach to support the hypothesis developed above

Assessing the determinants of COVID-19 burden to address disease-control policy decisions

Mathematical modeling has been crucial to address fundamental issues related to COVID-19 disease-control policy decisions. This thesis deals with the robust quantification of the disease burden associated with COVID-19 across different socio-demographic settings. The presented work includes the statistical analysis of novel epidemiological records to provide solid estimates describing the clinical course of SARS-CoV-2 infections and the simulation of data-driven models to forecast the potential impact of COVID-19 in rural and urban areas of Ethiopia. Obtained estimates show that being older than 60 years of age is associated with about 40% likelihood of developing symptoms after SARS-CoV-2 infection and 1% risk of requiring intensive care. The analysis of potential SARS-CoV-2 transmission in Ethiopia suggests that the low prevalence and mortality observed during 2020 can be explained by combined effect of younger demography and a reduced transmission generated by school closures implemented in response to the pandemic. Provided estimates highlight that in this country, after the launch of vaccination in 2021, the highest fraction of severe cases is expected to arise from the interaction between children (who are the main responsible for the spread of the disease) with the elderly (representing the most vulnerable population segment). Remarkably, prioritizing the vaccination of the elderly emerged as the best strategy to reduce the number of critical patients, irrespectively to the limited number of doses made available to low-income settings

Urban landscape and infection risk in free-roaming cats

Altres ajuts: Fundação para a Ciência e a Tecnologia (FCT) CEECIND/01428/2018. FEDER UID/AMB/50017/2019Despite public concern on the role of free-roaming cats as reservoirs of zoonotic agents, little is known about the influence of urban and peri-urban landscapes on the exposure risk. We evaluated the seroprevalence of three zoonotic agents (Chlamydia felis, Coxiella burnetii and Toxoplasma gondii) in domestic cats (Felis catus). Two hundred and ninety-one free-roaming cats were trapped in Murcia municipality (Southeast Spain), and their sera were tested for specific antibodies against T. gondii using a modified agglutination test (MAT), and for C. felis, C. burnetii and feline immunodeficiency virus (FIV) antibodies with ELISA technique. Pathogen seroprevalence at 95% CI was calculated for each sex and age category (up to and over 12 months) and compared with a chi-squared test. The role of human population density and urban landscape characteristics on the risk of pathogen exposure in the cat population was explored using generalized linear models. Seropositivity against a single pathogen was found in 60% of the cats, while 19% was seropositive for two or three pathogens. Seroprevalence of C. felis was 8% (CI: 5-11), 37% (CI: 31-42) for C. burnetii and 42% (CI: 36-47) for T. gondii. In addition to these three pathogens, FIV seropositivity was low (1%, CI: −0.1 to 2) and adult cats were more likely to be seropositive to C. burnetii than young individuals (OR: 2.3, CI: 1.2-4.2). No sex or age class differences in seroprevalence were observed for the rest of the pathogens. Seropositivity was correlated with water surface areas for C. felis, and not with crop areas. Coxiella burnetii seropositivity was correlated with the percentage of urban areas (continuous with only buildings and discontinuous, that include buildings, parks, and pedestrian and urban green areas), human population size and peri-urban areas with shrubs, and not correlated with other agricultural landscapes (orchards and crop areas). However, the seroprevalence of T. gondii was only associated with agricultural landscapes such as orchards. The detection of hotspot areas of high pathogen exposure risk is the basis for municipal services to implement surveillance and risk factor control campaigns in specific-risk areas, including (a) efficient health management of urban cat colonies by geographical location, population census and health status monitoring of the components of each cat colony, (b) improvement of hygiene and sanitary conditions at the feeding points of the cat colony and (c) free-roaming cat trapping for health monitoring and, in the long term, to know the evolution of the health status of their populations

Proximity and post-COVID-19 urban development: Reflections from Milan, Italy

2noopenThis paper aims to describe the impact of the COVID-19 pandemic on the socioeconomic structure of cities, and discuss the possible responses of a place-based agenda of urban policies built around the concept of "proximity economy". With this objective, the present work provides an interpretative framework for understanding the impact on urban economy with respect to Milan and Italy, by observing the emergence of the perspective of proximity in the debate to respond to the healthcare crisis due to the current pandemic. On this conceptual basis, the paper suggests measures and gives examples for implementing a proximity-based urban development agenda, concluding with a final framework of policy recommendations relating to the implementation of these policies.openTricarico, Luca; De Vidovich, LorenzoTricarico, Luca; De Vidovich, Lorenz

Kinetic Modelling of Epidemic Dynamics: Social Contacts, Control with Uncertain Data, and Multiscale Spatial Dynamics

In this survey we report some recent results in the mathematical modelling of epidemic phenomena through the use of kinetic equations. We initially consider models of interaction between agents in which social characteristics play a key role in the spread of an epidemic, such as the age of individuals, the number of social contacts, and their economic wealth. Subsequently, for such models, we discuss the possibility of containing the epidemic through an appropriate optimal control formulation based on the policy maker’s perception of the progress of the epidemic. The role of uncertainty in the data is also discussed and addressed. Finally, the kinetic modelling is extended to spatially dependent settings using multiscale transport models that can characterize the impact of movement dynamics on epidemic advancement on both one-dimensional networks and realistic two-dimensional geographic settings

Thirty-second Annual Symposium of Trinity College Undergraduate Research

2019 annual volume of abstracts for science research projects conducted by students at Trinity College

Planning Towards Equal Spatial Accessibility of NCI Cancer Centers Across Geographic Areas and Demographic Groups in the U.S.

The Cancer Centers designated by the National Cancer Institute (NCI) form the “backbone” of the cancer care system in the United States. Awarded via a peer-review process and being re-evaluated every 3 to 5 years, an NCI Cancer Center receives substantial financial support from NCI grants. When the quality standard is not compromised, we argue that an additional criterion for improving and promoting equal accessibility should be factored into the designation and planning process of NCI Cancer Centers. With the help of regression and dummy variables, this research evaluates geographic disparities in spatial accessibility of the NCI Cancer Centers across geographic area, divisions and urbanicity. It also evaluates demographic disparities across ethnic and poverty groups. Then this research examines two planning objectives to minimize the inequalities in accessibility. One is to minimize the geographic inequality while the other is to minimize the racial disparities. Two types of optimization scenarios are considered in this exploratory research for the objective of minimizing inequality of spatial accessibility. One is to allocate additional resources to existing NCI Cancer Centers, and the other is to designate new centers from the most likely candidates (e.g., existing academic medical centers or AMCs). Quadratic Programming (QP) and Particle Swarm Optimization (PSO) are used to solve different optimization problems. Several scenarios are used to illustrate the impact of optimization on reducing geographic and demographic disparities. Results from the study may inform the public policy decision making process in planning of the NCI Cancer Centers towards equal accessibility

Assessing vulnerability and modelling assistance: using demographic indicators of vulnerability and agent-based modelling to explore emergency flooding relief response

Flooding is a significant concern for much of the UK and is recognised as a primary threat by most local councils. Those in society most often deemed vulnerable: the elderly, poor or sick, for example, often see their level of vulnerability increase during hazard events. A greater knowledge of the spatial distribution of vulnerability within communities is key to understanding how a population may be impacted by a hazard event. Vulnerability indices are regularly used – in conjunction with needs assessments and on-the-ground research – to target service provision and justify resource allocation. Past work on measuring and mapping vulnerability has been limited by a focus on income-related indicators, a lack of consideration of accessibility, and the reliance on proprietary data. The Open Source Vulnerability Index (OSVI) encompasses an extensive range of vulnerability indicators supported by the wider literature and expert validation and provides data at a sufficiently fine resolution that can identify vulnerable populations. Findings of the OSVI demonstrate the potential cascading impact of a flood hazard as it impacts an already vulnerable population: exacerbating pre-existing vulnerabilities, limiting capabilities and restricting accessibility and access to key services. The OSVI feeds into an agent-based model (ABM) that explores the capacity of the British Red Cross (BRC) to distribute relief during flood emergencies using strategies based upon the OSVI. A participatory modelling approach was utilised whereby the BRC were included in all aspects of the model development. The major contribution of this work is the novel synthesis of demographics analysis, vulnerability mapping and geospatial simulation. The project contributes to the growing understanding of vulnerability and response management within the NGO sector. It is hoped that the index and model produced will allow responder organisations to run simulations of similar emergency events and adjust strategic response plans accordingly