Uncertainty- and Trust-Aware Integration of VGI andSpatio-Temporal Traces for Understanding Animal Behavior

位置传感设备（如传感器和智能手机）和在线平台（如eBird.org和ornitho.de）的普及促进了大型空间数据源的生成，也称为自愿地理信息（VGI）。公民自愿提供的异构数据包括GPS数据、图像、注释等。大型VGI资源包含有价值的信息，但由于其数据质量、细节水平、不确定性和异质性，VGI的处理和分析具有挑战性。缺乏将VGI与其他数据源结合起来的方法，也缺乏可伸缩的交互式地理可视化来研究它们。因此，在本提案中，我们计划开发新的可视化分析方法，以半自动地集成和分析大型VGI数据集与时空跟踪数据。我们将开发新的方法，结合来自观鸟平台（如ebird.org和ornithon.de）和ICARUS（国际动物研究利用空间合作）项目的大量数据，调查动物的行为和运动特征。将提供语义上下文和理解的人工生成的VGI数据与ICARUS系统提供的跟踪数据的准确性和可靠性相结合，具有巨大的价值。如果不整合VGI和非VGI信息，这种结合将有助于解决许多难以回答的重要问题。特别是，我们的系统将有助于调查当地动物栖息地、生物多样性丧失、动物跨大陆迁徙、土地利用变化、入侵物种、疾病传播，以及最终的气候变化。目前，还没有系统能够自动集成和分析这些海量异构数据源，同时处理必要的不确定性和信任问题。我们建议采用可视化分析方法，将人类和计算机的优势结合起来，从这些大型数据源中生成知识。开发的可视化分析系统将有助于交互式集成、匹配、注释和分析从VGI和动物跟踪数据生成的模型。

The popularity of location-sensing devices (e.g., sensors and smartphones) and online platforms (e.g., eBird.org and ornitho.de) facilitates the generation of large spatial data sources also known as Volunteered Geographic Information (VGI). The heterogeneous data provided voluntarily from citizens contains, for instance, GPS data, images, annotations, etc. The large VGI sources contain valuable information, but the processing and analysis of VGI are challenging due to their data quality, varying level of detail, uncertainty, and heterogeneity. There is a lack of methods to combine VGI with other data sources and a lack of scalable interactive geo-visualizations to investigate them. In this proposal, we therefore plan to develop novel Visual Analytics methods to semi-automatically integrate and analyze large VGI datasets with spatiotemporal tracking data. We will develop new methods to combine the huge amounts of data from birdwatching platforms (e.g., ebird.org and ornithon.de) and the ICARUS (International Cooperation for Animal Research Using Space) project to investigate behavioral and movement traits of animals. There is an immense value in combining the human-generated VGI data providing semantic context and understanding with the accuracy and reliability of the tracking data provided by the ICARUS system. The combination will help to tackle numerous important questions that are difficult to answer without integrating the VGI and Non-VGI information. In particular, our system will help to investigate local animal habitats, biodiversity loss, animal migrations across continents, land-use change, invasive species, the spread of diseases, and ultimately climate change.Currently, there is no system that can automatically integrate and analyze these massive heterogeneous data sources while tackling the necessary uncertainty and trust aspects. We propose to follow the Visual Analytics approach to combine the strengths of humans and computers to generate knowledge from these large data sources. The developed Visual Analytics system will help to interactively integrate, match, annotate, and analyze models generated from the VGI and animal tracking data.