Coupling models of cattle and farms with models of badgers for predicting the dynamics of bovine tuberculosis (TB)

Bovine TB is a major problem for the agricultural industry in several countries. TB can be contracted and spread by species other than cattle and this can cause a problem for disease control. In the UK and Ireland, badgers are a recognised reservoir of infection and there has been substantial discussion about potential control strategies. We present a coupling of individual based models of bovine TB in badgers and cattle, which aims to capture the key details of the natural history of the disease and of both species at approximately county scale. The model is spatially explicit it follows a very large number of cattle and badgers on a different grid size for each species and includes also winter housing. We show that the model can replicate the reported dynamics of both cattle and badger populations as well as the increasing prevalence of the disease in cattle. Parameter space used as input in simulations was swept out using Latin hypercube sampling and sensitivity analysis to model outputs was conducted using mixed effect models. By exploring a large and computationally intensive parameter space we show that of the available control strategies it is the frequency of TB testing and whether or not winter housing is practised that have the most significant effects on the number of infected cattle, with the effect of winter housing becoming stronger as farm size increases. Whether badgers were culled or not explained about 5%, while the accuracy of the test employed to detect infected cattle explained less than 3% of the variance in the number of infected cattle

Challenging Issues of Spatio-Temporal Data Mining

The spatio-temporal database (STDB) has received considerable attention during the past few years, due to the emergence of numerous applications (e.g., flight control systems, weather forecast, mobile computing, etc.) that demand efficient management of moving objects. These applications record objects' geographical locations (sometimes also shapes) at various timestamps and support queries that explore their historical and future (predictive) behaviors. The STDB significantly extends the traditional spatial database, which deals with only stationary data and hence is inapplicable to moving objects, whose dynamic behavior requires re-investigation of numerous topics including data modeling, indexes, and the related query algorithms. In many application areas, huge amounts of data are generated, explicitly or implicitly containing spatial or spatiotemporal information. However, the ability to analyze these data remains inadequate, and the need for adapted data mining tools becomes a major challenge. In this paper, we have presented the challenging issues of spatio-temporal data mining. Keywords: database, data mining, spatial, temporal, spatio-tempora

Visual analytics of flight trajectories for uncovering decision making strategies

In air traffic management and control, movement data describing actual and planned flights are used for planning, monitoring and post-operation analysis purposes with the goal of increased efficient utilization of air space capacities (in terms of delay reduction or flight efficiency), without compromising the safety of passengers and cargo, nor timeliness of flights. From flight data, it is possible to extract valuable information concerning preferences and decision making of airlines (e.g. route choice) and air traffic managers and controllers (e.g. flight rerouting or optimizing flight times), features whose understanding is intended as a key driver for bringing operational performance benefits. In this paper, we propose a suite of visual analytics techniques for supporting assessment of flight data quality and data analysis workflows centred on revealing decision making preferences

Analysis of Illegal Parking Behavior in Lisbon: Predicting and Analyzing Illegal Parking Incidents in Lisbon´s Top 10 Critical Streets

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceIllegal parking represents a costly and pervasive problem for most cities, as it not only leads to an increase in traffic congestion and the emission of air pollutants but also compromises pedestrian, biking, and driving safety. Moreover, it obstructs the flow of emergency vehicles, delivery services, and other essential functions, posing a significant risk to public safety and impeding the efficient operation of urban services. These detrimental effects ultimately diminish the cleanliness, security, and overall attractiveness of cities, impacting the well-being of both residents and visitors alike. Traditionally, decision-support systems utilized for addressing illegal parking have heavily relied on costly camera systems and complex video-processing algorithms to detect and monitor infractions in real time. However, the implementation of such systems is often challenging and expensive, particularly considering the diverse and dynamic road environment conditions. Alternatively, research studies focusing on spatiotemporal features for predicting parking infractions present a more efficient and cost-effective approach. This project focuses on the development of a machine learning model to accurately predict illegal parking incidents in the ten highly critical streets of Lisbon Municipality, taking into account the hour period and whether it is a weekend or holiday. A comprehensive evaluation of various machine learning algorithms was conducted, and the k-nearest neighbors (KNN) algorithm emerged as the top performing model. The KNN model exhibited robust predictive capabilities, effectively estimating the occurrence of illegal parking in the most critical streets, and together with the creation of an interactive and user-friendly dashboard, this project contributes valuable insights for urban planners, policymakers, and law enforcement agencies, empowering them to enhance public safety and security through informed decision-making