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所在平台: Udemy |
课程主页: https://www.udemy.com/course/fire-hotspots-analysis-using-gis/
课程评论：没有评论
课程名称：利用GIS进行火灾热点分析 课程概述：本课程旨在探索和下载特定研究区域的VIIRS-375m活火数据。可见光红外成像辐射仪套件（VIIRS）由NASA的火灾信息资源管理系统（FIRMS）开发，是一种最新的中等分辨率传感器，自2012年以来提供了每天全球范围内的活火产品，具有375m的更高空间分辨率和强大的火灾敏感性。因此，VIIRS-375m火灾产品能够高效地检测较冷和较小的火灾。此外，利用ArcGIS的空间统计工具，识别获取的活火数据集中的火灾热点区域。热点分析用于显示您正在处理的数据中哪里有聚类，哪里没有聚类。换句话说，它找出空间上以非随机方式聚集在一起的，与平均值非常不同的高或低值聚集地。热点分析工具返回三种置信水平，即90%、95%和99%。某个特征属于一个高值的非随机聚类（即热点）或一个低值的非随机聚类（即冷点）。此外，空间自相关有助于理解一个对象与其他邻近对象的相似程度。莫兰指数（Moran's I）用于测量空间自相关。正空间自相关是指相似值在地图上聚集在一起，负空间自相关则是指不同值在地图上聚集在一起。莫兰指数可以分为正自相关、负自相关和没有自相关。正自相关发生在莫兰指数接近+1时，莫兰指数为0通常表示没有自相关。通过准备图表、图形和模式等统计分析，可以提供有关火灾数据在空间和时间上的分布的附加信息。课程最后将集中在制作信息丰富、全面的火灾热点地图布局设计上。通过在ArcGIS平台上妥善编排每个分析数据，最终得出更好和更具信息性的地图设计和呈现。地图构成的过程始于为地图准备适当的布局。除了数据，地图还有某些其他基本组件，使其成为有效且清晰沟通的整体。
The course is designed for explore and download of VIIRS-375m Active Fire Data of specific study area. The Visible Infrared Imaging Radiometer Suite (VIIRS), which was produced by NASA's Fire Information for Resource Management System (FIRMS), is a recent developed moderate resolution sensor provides daily global active fire products at finer spatial resolution of 375m with strong fire sensitivity since 2012. So, the VIIRS-375m fire product have high capability to detect cooler and smaller fires. Further, using of Spatial Statistics Tools of ArcGIS, fire hotspots area were identified for the acquired active fire dataset. Hot spot analysis that performs to show where you have clusters and where you don't have clusters in any data that you are working with. In other words, it finds places where values that are very different from the average, either really high or really low, cluster together spatially in a nonrandom way. The Hotspot analysis tool returns three levels of confidence ie 90%, 95% and 99% confident. A feature belongs to a nonrandom cluster of high values ie a hot spot or to a nonrandom cluster of low values ie a cold spot. In addition, Spatial autocorrelation helps to understand the degree to which one object is similar to other nearby objects. Moran's I (Index) measures spatial autocorrelation. Positive spatial autocorrelation is when similar values cluster together in a map. Negative spatial autocorrelation is when dissimilar values cluster together in a map. Moran's I can be classified as positive, negative, and no spatial auto-correlation. Positive spatial autocorrelation occurs when Moran's I is close to +1. A value of 0 for Moran's I typically indicates no autocorrelation. The statistical analysis such as preparation of chart, graph and pattern provides an addition information about the fire data distribution in space and time. The course is ended with the preparation of an informative and comprehensive fire hotspots map layout design. The proper compilation of each analyzed data into ArcGIS platform finally concluded into a better and informative map design and presentation. The process of map composition starts with preparation of appropriate layout for the map. Apart from the data, a map has certain other essential components that make map, a package of effective and clear communication.
