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

VGI 和时空轨迹的不确定性和信任感知集成，用于理解动物行为

• 批准号: 314671965
• 负责人: Professor Dr. Daniel Keim
• 金额: --
• 依托单位: Arbeitsgruppe Datenanalyse und Visualisierung
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2016
• 资助国家: 德国
• 起止时间: 2015-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/314671965?language=en
• 关键词: Uncertainty; Trust; Aware; Integration; VGI

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.

位置感测设备（例如，传感器和智能电话）和在线平台（例如，eBird.org和ornitho.de）促进了也称为嵌入式地理信息（VGI）的大型空间数据源的生成。公民自愿提供的异构数据包含GPS数据、图像、注释等。大型VGI源包含有价值的信息，但由于其数据质量、不同的细节水平、不确定性和异构性，VGI的处理和分析具有挑战性。缺乏将联合收割机VGI与其他数据源相结合的方法，也缺乏可扩展的交互式地理可视化来调查它们。因此，在这项提案中，我们计划开发新的可视化分析方法，以半自动地整合和分析具有时空跟踪数据的大型VGI数据集。我们将开发新的方法来联合收割机结合观鸟平台的大量数据（例如，ebird.org和ornithon.de）和ICARUS（利用太空进行动物研究国际合作组织）项目，以调查动物的行为和运动特征。将提供语义背景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。这一结合将有助于解决许多重要问题，如果不整合VGI和非VGI信息，这些问题就很难回答。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，土地使用变化，入侵物种，疾病传播，最终气候变化。目前，还没有系统可以自动集成和分析这些大量的异构数据源，同时解决必要的不确定性和信任方面。我们建议遵循可视化分析方法，将人类和计算机的优势联合收割机结合起来，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地集成，匹配，注释和分析从VGI和动物跟踪数据生成的模型。