Spatio-temporal modeling of traffic risk mapping on urban road networks

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesOver the past few years, traffic collisions have been one of the serious issues all over the world. Global status report on road safety, reveals an increasing number of fatalities due to traffic accidents, especially on urban roads. The present research work is conducted on five years of accident data in an urban environment to explore and analyze spatial and temporal variation in the incidence of road traffic accidents and casualties. The current study proposes a spatio-temporal model that can make predictions regarding the number of road casualties likely on any given road segments and can generate a risk map of the entire road network. Bayesian methodology using Integrated Nested Laplace Approximation (INLA) with Stochastic Partial Differential Equations (SPDE) has been applied in the modeling process. The novelty of the proposed model is to introduce "SPDE network triangulation" precisely on linear networks to estimate the spatial autocorrelation of discrete events. The result risk maps can provide geospatial baseline to identify safe routes between source and destination points. The maps can also have implications for accident prevention and multi-disciplinary road safety measures through an enhanced understanding of the accident patterns and factors. Reproducibility self-assessment : 3, 1, 1, 3, 2 (input data, preprocessing, methods, computational environment, results)

A Bayesian machine learning approach for spatio-temporal prediction of COVID-19 cases

Modeling the spread of infectious diseases in space and time needs to take care of complex dependencies and uncertainties. Machine learning methods, and neural networks, in particular, are useful in modeling this sort of complex problems, although they generally lack of probabilistic interpretations. We propose a neural network method embedded in a Bayesian framework for modeling and predicting the number of cases of infectious diseases in areal units. A key feature is that our combined model considers the impact of human movement on the spread of the infectious disease, as an additional random factor to the also considered spatial neighborhood and temporal correlation components. Our model is evaluated over a COVID-19 dataset for 245 health zones of Castilla-Leon (Spain). The results show that a Bayesian model informed by a neural network method is generally able to predict the number of cases of COVID-19 in both space and time, with the human mobility factor having a strong influence on the model, together with the number of infections and deaths in nearby areas.J. Mateu has been partially funded by grants PID2019-107392RB-I00 from Ministry of Science and Innovation and UJI-B2018-04 from University Jaume I. P. Niraula has been funded through the Erasmus Mundus programme by the European Commission under the Framework Partnership Agreement, FPA-2016-2054

Spatiotemporal modeling of traffic risk mapping: A study of urban road networks in Barcelona, Spain

Accidents on the road have always been a major concern in modern society. According to the World Health Organization, globally road traffic collisions are one of the leading and fastest growing causes of disability and death. The present research work is conducted on ten years of traffic accident data in an urban environment to explore and analyze spatial and temporal variation in the accidents and related injuries. The proposed spatiotemporal model can make predictions regarding the number of injuries incurred on individual road segments. Bayesian methodology using Integrated Nested Laplace Approximation (INLA) with Stochastic Partial Differential Equations (SPDE) has been applied to generate a predicted risk map for the entire road network. The current study introduces INLA- SPDE modeling to perform spatiotemporal predictive analysis on selected areas, precisely on road networks instead of traditional continuous regions. Additionally, the result risk maps act as a baseline to identify the safe routes in a spatiotemporal context. The methodology can be adapted and applied to enhanced INLA-SPDE modeling of spatial point processes precisely on road networks

Data on CO2, temperature and air humidity records in Spanish classrooms during the reopening of schools in the COVID-19 pandemic

In order to reduce the advance of the pandemic produced by COVID-19, many actions and restrictions have been applied and the field of education has been no exception. In Spain, during the academic year 2020–2021, face-to-face teaching generally continued in both primary and secondary schools. Throughout the year, different measures have been taken to reduce the likelihood of contagion in classrooms, one of which was to improve ventilation by opening windows and doors. One of the most commonly used techniques to check for good ventilation has been CO2 monitoring. This work provides a set of 80,000 CO2 concentration records collected by low-cost Internet of Things nodes, primarily located within twelve classrooms in two primary schools. The published observations were collected between 1 May 2020 and 23 June 2021. Additionally, the same dataset includes temperature, air humidity and battery level observations

Association between the New COVID-19 Cases and Air Pollution with Meteorological Elements in Nine Counties of New York State

The principal objective of this article is to assess the possible association between the number of COVID-19 infected cases and the concentrations of fine particulate matter (PM2.5) and ozone (O3), atmospheric pollutants related to people’s mobility in urban areas, taking also into account the effect of meteorological conditions. We fit a generalized linear mixed model which includes spatial and temporal terms in order to detect the effect of the meteorological elements and COVID-19 infected cases on the pollutant concentrations. We consider nine counties of the state of New York which registered the highest number of COVID-19 infected cases. We implemented a Bayesian method using integrated nested Laplace approximation (INLA) with a stochastic partial differential equation (SPDE). The results emphasize that all the components used in designing the model contribute to improving the predicted values and can be included in designing similar real-world data (RWD) models. We found only a weak association between PM2.5 and ozone concentrations with COVID-19 infected cases. Records of COVID-19 infected cases and other covariates data from March to May 2020 were collected from electronic health records (EHRs) and standard RWD sources

Modeling Influence of Soil Properties in Different Gradients of Soil Moisture: The Case of the Valencia Anchor Station Validation Site, Spain

The prediction of spatial and temporal variation of soil water content brings numerous benefits in the studies of soil. However, it requires a considerable number of covariates to be included in the study, complicating the analysis. Integrated nested Laplace approximations (INLA) with stochastic partial differential equation (SPDE) methodology is a possible approach that allows the inclusion of covariates in an easy way. The current study has been conducted using INLA-SPDE to study soil moisture in the area of the Valencia Anchor Station (VAS), soil moisture validation site for the European Space Agency SMOS (Soil Moisture and Ocean Salinity). The data used were collected in a typical ecosystem of the semiarid Mediterranean conditions, subdivided into physiohydrological units (SMOS units) which presents a certain degree of internal uniformity with respect to hydrological parameters and capture the spatial and temporal variation of soil moisture at the local fine scale. The paper advances the knowledge of the influence of hydrodynamic properties on VAS soil moisture (texture, porosity/bulk density and soil organic matter and land use). With the goal of understanding the factors that affect the variability of soil moisture in the SMOS pixel (50 km × 50 km), five states of soil moisture are proposed. We observed that the model with all covariates and spatial effect has the lowest DIC value. In addition, the correlation coefficient was close to 1 for the relationship between observed and predicted values. The methodology applied presents the possibility to analyze the significance of different covariates having spatial and temporal effects. This process is substantially faster and more effective than traditional kriging. The findings of this study demonstrate an advancement in that framework, demonstrating that it is faster than previous methodologies, provides significance of individual covariates, is reproducible, and is easy to compare with models

Relación entre contaminantes atmosféricos, variables meteorológicas y casos de COVID-19 en ciudades europeas

Ponència presentada al 15º Congreso Nacional de Medio Ambiente (CONAMA 2020)La preocupación por conocer la relación entre los niveles de contaminación del aire y las muertes por enfermedades virales no es nueva. Algunos estudios encontraron que los pacientes con SARS, un virus respiratorio estrechamente relacionado con COVID-19, tenían un 84% más de probabilidades de morir si vivían en áreas con altos niveles de contaminación. Según el último informe de evaluación rápida del Centro Europeo para la Prevención y el Control de Enfermedades, Italia y España son los países más afectados de la Unión Europea por la pandemia COVID-19. A 31 de mayo de 2020, el número total de casos infectados por millón de habitantes de España es 5.120,95 y 3.848,11 para Italia. Este estudio se centra en las ciudades de Madrid, Barcelona, Milán y Roma durante el periodo anterior, durante y posterior del confinamiento. El objetivo de este trabajo es proporcionar un conocimiento más profundo que pueda ayudar a tomar decisiones futuras. Además, para ampliar la literatura existente, este estudio también incorpora variables meteorológicas que también pueden afectar la calidad del aire (PM10 y NO2) y está directamente relacionado con casos infectados con COVID-19. El estudio actual se ha llevado a cabo para identificar la relación entre los niveles de contaminación del aire con el valor esperado de las víctimas de COVID-19 y las covariables meteorológicas, utilizando un análisis jerárquico bayesiano. Como resultados se ha obtenido que todas estas variables son significativas respecto a la calidad del aire. La propuesta de este análisis bayesiano mejora las herramientas estadísticas que se han utilizado en este campo hasta ahora. Con su aplicación, podemos obtener avances importantes en los efectos de la salud y la calidad del aire en ambientes urbanos

Effect of previous anticoagulant treatment on risk of COVID-19

Introduction: Little is known about the role played by anticoagulants in COVID-19. Objective: The aim of this study was to assess the impact of previous anticoagulant treatment on risk of hospitalization due to COVID-19, progression to severe COVID-19 and susceptibility to COVID-19 infection. Methods: We conducted a multiple population-based case–control study in northwest Spain, in 2020, to assess (1) risk of hospitalization: cases were all patients admitted due to COVID-19 with PCR confirmation, and controls were a random matched sample of subjects without a positive PCR; (2) progression: cases were hospitalized COVID-19 subjects, and controls were all non-hospitalized COVID-19 patients; and (3) susceptibility: cases were patients with a positive PCR (hospitalized and non-hospitalized), and the controls were the same as for the hospitalization model. Adjusted odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated using a generalized linear mixed modelS

Analysis and modeling spatiotemporal events on complex spatial regions

Spatial statistics is traditionally based on stationary models like Matérn fields. However, applying stationary models to complex spatial regions having physical barriers like islands or coastal areas can result in inappropriate smoothing of such regions. Additionally, in many environmental applications such as stream systems or urban road networks, it is essential to define statistical models on linear networks. The current research thesis explores the benefits and limitations of integrated nested Laplace approximations (INLA) along with traditional stochastic partial differential equation (SPDE) for Bayesian spatiotemporal modeling. The study focuses on complex distributed spatial regions having physical barriers, as well as linear networks like urban road networks. The motivation behind the initial research article is to design an application to monitor the dynamics of COVID-19 pandemic in a spatiotemporal context in the region of Catalonia, Spain. In this case, we have used INLA-SPDE but in continuous spatial region. The following two articles involved utilizing explicit network triangulation to explore and analyse the occurrences of traffic accidents on urban road networks in UK and Spain. We proposed the novel concept of spatial triangulation restricted to linear networks. But complex boundary regions create fictitious spatial structures resulting in artificial spatial dependencies. In the following proposed articles, we have explored alternative computational strategies to design nonstationary barrier models. Initially, we have used barrier model to analyse spatial variation of tsunami risk in the Republic of Maldives. Then we implemented barrier models on linear networks. But in both cases, boundaries lie within the spatial domain of interest, preventing the high boundary effects from being reduced. The final proposed article presents a novel strategy for utilizing non-Euclidean metric on graph structures, as an alternative to the conventional Euclidean distance methodology. In this case, it is challenging to find flexible classes of functions that are positive definite to formulate Gaussian fields on metric graphs. Utilizing the mentioned concept, a novel category of Gaussian processes has been developed on compact metric graphs. The Whittle-Matérn fields employed in this approach are defined through a fractional SPDE on a metric graph. The proposed fields are a natural extension of Gaussian fields with Matérn covariance functions on Euclidean domains to non-Euclidean metric graph settings. A ten-year period (2010-2019) of daily traffic-accident records from Barcelona, Spain have been used to evaluate the three models referred above. While comparing model performance using evaluation metrics, we observed that the proposed fractional SPDE on metric graph model outperform network triangulation and barrier models. Due to this flexibility, it can be applied to a wide range of environmental issues, especially those involving complex or distributed spatial regions, such as islands, road networks, or areas demarcated by boundariesL'estadística espacial es basa tradicionalment en models estacionaris com els camps de Matérn. Tot i això, l'aplicació de models estacionaris a regions espacials complexes que tenen barreres físiques com illes o àrees costaneres pot resultar en un suavitzat inadequat d'aquestes regions. A més, en moltes aplicacions ambientals, com a sistemes de rierols o xarxes de carreteres urbanes, és essencial definir models estadístics en xarxes lineals. La tesi de recerca actual explora els beneficis i les limitacions de les aproximacions de Laplace imbricades integrades (INLA) juntament amb l'equació diferencial parcial estocàstica tradicional (SPDE) per al modelatge espaciotemporal bayesià. L'estudi se centra en regions espacials distribuïdes complexes que tenen barreres físiques, així com en xarxes lineals com les xarxes de carreteres urbanes. La motivació darrere de l'article de recerca inicial és dissenyar una aplicació per monitoritzar la dinàmica de la pandèmia de COVID-19 en un context espaitemporal a la regió de Catalunya, Espanya. En aquest cas, hem utilitzat INLA-SPDE, però en regió espacial contínua. Els dos articles següents van involucrar l'ús de triangulació de xarxa explícita per explorar i analitzar l'ocurrència d'accidents de trànsit a les xarxes vials urbanes al Regne Unit i Espanya. Vam proposar el nou concepte de triangulació espacial restringida a xarxes lineals. Però les regions frontereres complexes creen estructures espacials fictícies que donen com a resultat dependències espacials artificials. Als següents articles proposats, hem explorat estratègies computacionals alternatives per dissenyar models de barrera no estacionaris. Inicialment, hem utilitzat el model de barrera per analitzar la variació espacial del risc de tsunami a la República de Maldives. Després implementem models de barrera a xarxes lineals. Però en tots dos casos, els límits es troben dins del domini espacial d'interès, cosa que impedeix que es redueixin els efectes dels límits alts. L'article final proposat presenta una estratègia nova per utilitzar mètriques no euclidianes en estructures gràfiques, com a alternativa a la metodologia de distància euclidiana convencional. En aquest cas, és un desafiament trobar classes flexibles de funcions que siguin definides positives per formular camps gaussians en gràfics mètrics. Utilitzant el concepte esmentat, s'ha desenvolupat una nova categoria de processos gaussians en gràfics mètrics compactes. Els camps de Whittle-Matérn emprats en aquest enfocament es defineixen mitjançant un SPDE fraccionari en un gràfic mètric. Els camps proposats són una extensió natural dels camps gaussians amb funcions de covariància de Matérn en dominis euclidians a configuracions gràfiques mètriques no euclidianes. S'ha fet servir un període de deu anys (2010-2019) de registres diaris d'accidents de trànsit de Barcelona, Espanya, per avaluar els tres models esmentats anteriorment. En comparar el rendiment del model, utilitzant mètriques d'avaluació, observem que l'SPDE fraccional proposat al model de gràfic de mètriques supera la triangulació de xarxa i els models de barrera. A causa d'aquesta flexibilitat, es pot aplicar a una àmplia gamma de problemes ambientals, especialment aquells que involucren regions espacials complexes o distribuïdes, com ara illes, xarxes de carreteres o àrees delimitades per límitsPrograma de Doctorat en Biologia Molecular, Biomedicina i Salu

Modeling spatial dependencies of natural hazards in coastal regions: a nonstationary approach with barriers

Natural hazards like floods, cyclones, earthquakes, or, tsunamis have deep impacts on the environment and society causing damage to both life and property. These events can cause widespread destruction and can lead to long-term socio-economic disruption often affecting the most vulnerable populations in society. Computational modeling provides an essential tool to estimate the damage by incorporating spatial uncertainties and examining global risk assessments. Classical stationary models in spatial statistics often assume isotropy and stationarity. It causes inappropriate smoothing over features having boundaries, holes, or physical barriers. Despite this, nonstationary models like barrier model have been little explored in the context of natural disasters in complex land structures. The principal objective of the current study is to evaluate the influence of barrier models compared to classical stationary models by analysing the incidence of natural disasters in complex spatial regions like islands and coastal areas. In the current study, we have used tsunami records from the island nation of Maldives. For seven atoll groups considered in our study, we have implemented three distinct categories of stochastic partial differential equation meshes, two for stationary models and one that corresponds to the barrier model concept. The results show that when assessing the spatial variance of tsunami incidence at the atoll scale, the barrier model outperforms the other two models while maintaining the same computational cost as the stationary models. In the broader picture, this research work contributes to the relatively new field of nonstationary barrier models and intends to establish a robust modeling framework to explore spatial phenomena, particularly natural hazards, in complex spatial regions having physical barriers