Visualização e análise de dados de microclima

Este trabalho descreve o projeto, desenvolvimento e avaliação de um sistema Web que permite ao usuário visualizar e analisar dados obtidos de sensores distribuídos em um ambiente fechado. O desenvolvimento deste aplicativo foi motivado pelas necessidades das instalações aviárias. Nessas instalações, pode-se identificar um ou mais microclimas, cada um com características diferentes em relação a atributos como temperatura, umidade, velocidade e direção do vento, entre outros, que podem impactar na produção aviária. Um microclima é uma área pequena ou restrita que apresenta dados climáticos diferentes de sua grande área circundante. Nesta aplicação, o usuário tem acesso aos dados climáticos de diferentes locais, podendo visualizar a localização de cada conjunto de sensores a partir de uma planta baixa. Cada estação meteorológica gera um conjunto de dados que são então representados por diferentes técnicas de visualização de dados. A partir da definição de uma data ou de um período, o usuário pode navegar dentro do histórico de dados do sensor. Além disso, filtros foram desenvolvidos para modificar os dados selecionados. Esta aplicação tem como objetivo ajudar e facilitar o monitoramento desses microclimas usando técnicas de visualização e análise de dados. Os resultados obtidos em testes com usuários demonstram que nossos objetivos foram atingidos. Os usuários monstraram serem capazes de executar o conjunto de tarefas proposto usando a maioria das visualizações disponíveis.This work describes the design and evaluation of a Web-based system that allows the user to visualize and analyze data from sensors distributed in a closed environment. We developed the system motivated by the needs of aviary facilities. In these facilities, one can identify one or more microclimates, each one having different characteristics regarding attributes such as temperature, humidity, wind velocity and direction, and others, that may impact aviary production. A microclimate is a small or restricted area that presents different climate data from its large, surrounding area. In the application, users have access to climatic data from different locations, being able to view the location of each set of sensors from a floor plan. Each station has a set of data that is represented by different visualization techniques. From the definition of a date or a time period, the user is able to browse within the sensor data history. In addition, filters were developed to modify the selected data. This application aims to help and facilitate the monitoring of these microclimates by using visualization and data analysis techniques. The results obtained from user tests shown our objectives were achieved. Users have demonstrated that they are able to perform the proposed set of tasks using most of the available visualizations

Hybrid approach on multi- spatiotemporal data framework towards analysis of long-lead upstream flood: a case of Niger State, Nigeria

Floods have become a global concern because of the vast economic and ecological havoc that ensue. Thus, a flood risk mitigation strategy is used to reduce flood-related consequences by a long-lead identification of its occurrence. A wide range of causative factors, including the adoption of hybrid multi-spatiotemporal data framework is considered in implementing the strategy. Besides the structural or homogenous non-structural factors, the adoption of various Information Systems-based tools are also required to accurately analyse the multiple natural causative factors. Essentially, this was needed to address the inaccurate flood vulnerability classifications and short time of flood prediction. Thus, this study proposes a framework named: Hybrid Multi-spatiotemporal data Framework for Long-lead Upstream Flood Analysis (HyM-SLUFA) to provide a new dimension on flood vulnerability studies by uncovering the influence of multiple factors derived from topography, hydrology, vegetal and precipitation features towards regional flood vulnerability classification and long-lead analysis. In developing the proposed framework, the spatial images were geometrically and radiometrically corrected with the aid of Quantum Geographic Information System (QGIS). The temporal data were cleaned by means of winsorization methods using STATA statistical tool. The hybrid segment of the framework classifies flood vulnerability and performs long-lead analysis. The classification and analysis were conducted using the corrected spatial images to acquire better understanding on the interaction between the extracted features and rainfall in inducing flood as well as producing various regional flood vulnerabilities within the study area. Additionally, with the aid of regression technique, precipitation and water level data were used to perform long-lead flood analysis to provide a foresight of any potential flooding event in order to take proactive measures. As to confirm the reliability and validity of the proposed framework, an accuracy assessment was conducted on the outputs of the data. This study found the influence of various Flood Causative Factors (FCFs) used in the developed HyM-SLUFA framework, by revealing the spatial disparity indicating that the slope of a region shows a more accurate level of flood vulnerability compared to other FCFs, which generally causes severe upstream floods when there is low volume of precipitation within regions of low slope degree. Theoretically, the HyM-SLUFA will serve as a guide that can be adopted or adapted for similar studies. Especially, by considering the trend of precipitation and the pattern of flood vulnerability classifications depicted by various FCFs. These classifications will determine the kind(s) of policies that will be implemented in town planning, and the Flood Inducible Precipitation Volumes can provide a foresight of any potential flooding event in order to take practical proactive measures by the local authority