Cities have adopted evolving urban digitization strategies, and most of those increasingly focus on data, especially in the field of public transportation. Transportation data have intuitively spatial and temporal characteristics, for they are often described with when and where the trips occur. Since a trip is often described with many attributes, the transportation data can be presented with a tensor, a container which can house data in $N$-dimensions. Unlike a traditional data frame, which only has column variables, tensor is intuitively more straightforward to explore spatio-temporal data-sets, which makes those attributes more easily interpreted. However, it requires unique techniques to extract useful and relatively correct information in attributes highly correlated with each other. This work presents a mixed model consisting of tensor decomposition combined with seasonal vector autoregression in time to find latent patterns within historical taxi data classified by types of taxis, pick-up and drop-off times of services in NYC, so that it can help predict the place and time where taxis are demanded. We validated the proposed approach using the experiment evaluation with real NYC tax data. The proposed method shows the best prediction among alternative models without geographical inference, and captures the daily patterns of taxi demands for business and entertainment needs.

This study will be investigating the relationship between short-term and long-term interest rates with the OMX30 stock return expressed in percentage, as well as the effect that the interest rates have on the stock return. The data used in this study has been collected from the dataprogram Datastream with monthly observations from January 2003 until December 2022 resulting in 240 different variables within all three factors over a period of 20 years. While performing OLS estimation, the result estimated by using R-studio shows a negative correlation between the interest rates and the percentage return of OMX30. Furthermore, the Granger causality test shows that the short-term interest rate does have an impact on the market whilst the long-term interest rate does not have any direct effect on the stock market in Sweden.

This research studies the relationships in Electric Energy Consumption, Carbon Dioxide Emission and Theil Index in Brazil. The period of analysis includes annual data from 1980 to 2011 in a total of 31 observations. The series presented order of integration equal one with the presence of cointegration thus to measure these influences we used a vector error correction model (VEC). By Function Impulse Response (FIR) and Variance Decomposition Analysis (ADV) we observed how each variable behaves to an abrupt change. To analyze the behavior of variables, methods of vector autoregressive (VAR) and residues control charts were used. The VAR modeling revealed that there is a significant interrelationship among the variables under study, thus showing that there is a short-term relationship between these variables. As for the residues control chart to individual measures, a problem in the original variables was avoided tha were the the autocorrelation, and showed that all variables had a period of instability and also enabled the identification of this period. The emission of carbon dioxide and Theil Index are determining factors in the explanation of environmental impacts as well as the development of the country. The variance decomposition indicates that the carbon dioxide emission is primarily responsible for mainly caused damage to the environment. / Esta pesquisa estudou as relações existentes no Consumo de Energia Elétrica, Emissão de Dióxido de Carbono e Índice de Theil no Brasil. O período de análise se refere a dados anuais de 1980 a 2011 perfazendo um total de 31 observações do Brasil. As séries apresentaram ordem de integração igual a um com a presença de cointegração, assim, para mensurar essas influências foi utilizado um modelo de Vetor de Correção de Erros (VEC). Por meio da Função Impulso Resposta (FIR) e Análise de Decomposição da Variância (ADV) foi possível verificar como cada variável se comporta a uma mudança abrupta. Para analisar o comportamento das variáveis, foram utilizadas as metodologias de vetores auto regressivos (VAR) e gráficos de controle de resíduos. Já a modelagem VAR revelou que há um inter-relacionamento significativo entre as variáveis em estudo, mostrando assim que há uma relação de curto prazo entre estas variáveis. Quanto aos gráficos de controle de medidas individuais aos resíduos, contornou-se um problema presente nas variáveis originais que era o de autocorrelação, e mostrou-se que todas as variáveis apresentaram um período de instabilidade o que também possibilitou a identificação deste período. A Emissão de Dióxido de Carbono e o Índice de Theil são fatores determinantes na explicação dos impactos ambientais, assim como no desenvolvimento do país. A decomposição da variância indica que a Emissão de Dióxido de Carbono é o principal responsável pelos danos causados principalmente ao meio ambiente.

[ES] La presente tesis doctoral propone la arquitectura de un sistema de Geo-visualización Espaciotemporal de actividad delictiva y criminal, para ser aplicada a Sistemas de Comando y Control (C2S) específicamente dentro de sus Sistemas de Información de Comando y Control (C2IS). El sistema de Geo-visualización Espaciotemporal se basa en el análisis masivo de datos reales de actividad delictiva, proporcionado por la Policía Nacional Colombiana (PONAL) y está compuesto por dos aplicaciones diferentes: la primera permite al usuario geo-visualizar espaciotemporalmente de forma dinámica, las concentraciones, tendencias y patrones de movilidad de esta actividad dentro de la extensión de área geográfica y el rango de fechas y horas que se precise, lo cual permite al usuario realizar análisis e interpretaciones y tomar decisiones estratégicas de acción más acertadas; la segunda aplicación permite al usuario geo-visualizar espaciotemporalmente las predicciones de la actividad delictiva en periodos continuos y cortos a modo de tiempo real, esto también dentro de la extensión de área geográfica y el rango de fechas y horas de elección del usuario. Para estas predicciones se usaron técnicas clásicas y técnicas de Machine Learning (incluido el Deep Learning), adecuadas para el pronóstico en multiparalelo de varios pasos de series temporales multivariantes con datos escasos. Las dos aplicaciones del sistema, cuyo desarrollo se muestra en esta tesis, están realizadas con métodos novedosos que permitieron lograr estos objetivos de efectividad a la hora de detectar el volumen y los patrones y tendencias en el desplazamiento de dicha actividad, mejorando así la conciencia situacional, la proyección futura y la agilidad y eficiencia en los procesos de toma de decisiones, particularmente en la gestión de los recursos destinados a la disuasión, prevención y control del delito, lo cual contribuye a los objetivos de ciudad segura y por consiguiente de ciudad inteligente, dentro de arquitecturas de Sistemas de Comando y Control (C2S) como en el caso de los Centros de Comando y Control de Seguridad Ciudadana de la PONAL. / [CA] Aquesta tesi doctoral proposa l'arquitectura d'un sistema de Geo-visualització Espaitemporal d'activitat delictiva i criminal, per ser aplicada a Sistemes de Comandament i Control (C2S) específicament dins dels seus Sistemes d'informació de Comandament i Control (C2IS). El sistema de Geo-visualització Espaitemporal es basa en l'anàlisi massiva de dades reals d'activitat delictiva, proporcionada per la Policia Nacional Colombiana (PONAL) i està composta per dues aplicacions diferents: la primera permet a l'usuari geo-visualitzar espaitemporalment de forma dinàmica, les concentracions, les tendències i els patrons de mobilitat d'aquesta activitat dins de l'extensió d'àrea geogràfica i el rang de dates i hores que calgui, la qual cosa permet a l'usuari fer anàlisis i interpretacions i prendre decisions estratègiques d'acció més encertades; la segona aplicació permet a l'usuari geovisualitzar espaciotemporalment les prediccions de l'activitat delictiva en períodes continus i curts a mode de temps real, això també dins l'extensió d'àrea geogràfica i el rang de dates i hores d'elecció de l'usuari. Per a aquestes prediccions es van usar tècniques clàssiques i tècniques de Machine Learning (inclòs el Deep Learning), adequades per al pronòstic en multiparal·lel de diversos passos de sèries temporals multivariants amb dades escasses. Les dues aplicacions del sistema, el desenvolupament de les quals es mostra en aquesta tesi, estan realitzades amb mètodes nous que van permetre assolir aquests objectius d'efectivitat a l'hora de detectar el volum i els patrons i les tendències en el desplaçament d'aquesta activitat, millorant així la consciència situacional , la projecció futura i l'agilitat i eficiència en els processos de presa de decisions, particularment en la gestió dels recursos destinats a la dissuasió, prevenció i control del delicte, la qual cosa contribueix als objectius de ciutat segura i per tant de ciutat intel·ligent , dins arquitectures de Sistemes de Comandament i Control (C2S) com en el cas dels Centres de Comandament i Control de Seguretat Ciutadana de la PONAL. / [EN] This doctoral thesis proposes the architecture of a Spatiotemporal Geo-visualization system of criminal activity, to be applied to Command and Control Systems (C2S) specifically within their Command and Control Information Systems (C2IS). The Spatiotemporal Geo-visualization system is based on the massive analysis of real data of criminal activity, provided by the Colombian National Police (PONAL) and is made up of two different applications: the first allows the user to dynamically geo-visualize spatiotemporally, the concentrations, trends and patterns of mobility of this activity within the extension of the geographic area and the range of dates and times that are required, which allows the user to carry out analyses and interpretations and make more accurate strategic action decisions; the second application allows the user to spatially visualize the predictions of criminal activity in continuous and short periods like in real time, this also within the extension of the geographic area and the range of dates and times of the user's choice. For these predictions, classical techniques and Machine Learning techniques (including Deep Learning) were used, suitable for multistep multiparallel forecasting of multivariate time series with sparse data. The two applications of the system, whose development is shown in this thesis, are carried out with innovative methods that allowed achieving these effectiveness objectives when detecting the volume and patterns and trends in the movement of said activity, thus improving situational awareness, the future projection and the agility and efficiency in the decision-making processes, particularly in the management of the resources destined to the dissuasion, prevention and control of crime, which contributes to the objectives of a safe city and therefore of a smart city, within architectures of Command and Control Systems (C2S) as in the case of the Citizen Security Command and Control Centers of the PONAL. / Salcedo González, ML. (2023). Arquitectura de un sistema de geo-visualización espacio-temporal de actividad delictiva, basada en el análisis masivo de datos, aplicada a sistemas de información de comando y control (C2IS) [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/192685