目的 鸟类跟踪技术的成熟发展使得鸟类专家可以轻松获得大量鸟类运动数据。然而，数据规模的增加使得传统方法难以有效完成数据检索和分析。研究如何辅助专家有效地分析这些数据，挖掘其中的有用信息，具有很强的实用价值。本文基于国家Ⅰ级重点保护物种朱鹮的卫星跟踪数据，从鸟类专家对数据分析的需求出发，提出了一种运动轨迹的可视分析方法。方法 基于二维地图进行多视图协同展示的交互布局方式，以及聚类分析方法等对朱鹮运动轨迹进行可视分析，挖掘朱鹮的生活状态和习性。在以上工作的基础上，设计实现了一个朱鹮运动轨迹可视分析系统。结果 本文提出的可视分析方法，允许用户从时空维度和时期（繁殖期、游荡期、越冬期）、状态（夜宿、觅食）等具有生态学意义的维度观察朱鹮运动轨迹，对运动数据进行统计分析，了解朱鹮运动行为。与现有朱鹮数据分析方法相比，本文提出的可视分析方法能够同时从多个不同维度对运动数据进行分析，针对朱鹮的生活状态和生活习性进行更深入的分析挖掘。结论 案例分析表明，基于本文提出的方法，鸟类专家可以从多个角度对朱鹮运动轨迹数据进行综合分析，达到对鸟类习性和状态进行研究挖掘的目的，并能够为其他鸟类跟踪数据分析工作提供思路和方法。

Objective The study of bird satellite tracking data has positive implications for the conservation of both the birds themselves and the ecological environment. To effectively conserve bird species and to better understand their habitat suitability，it is necessary to study the spatial and temporal characteristics of bird populations. It is essential for recognizing the dominants of species distribution and their dynamics and its relevant conservation. The development of satellite tracking technology can be used to improve the ability of ornithologists to remotely collect large amounts of track movement data for birds，and global positioning system（GPS）-based track migration data is one of commonly-used collected types of data today. Analysis of acquired satellite track data can help solve many problems，such as how individual birds interact with each other，the foraging strategies，migration and movement routes of individuals in different time dimensions，and the effects of environmental changes due to climate and human factors. With recent technological advances，the frequency of positioning satellite transmitters and the variety of data collected have increased greatly，and a major challenge that has arisen is how to adequately and effectively analyze these large data. Ornithologists use existing data analysis methods to analyze data using Excel or R libraries，or plotting data points on satellite maps directly. Data visualization and visual analysis techniques，as a way to present large amounts of data，can yield users to gain better understanding and insight into datasets，providing them with an emerging tool，which can uncover complex patterns contained in the data and inspire new hypotheses and analyses. Method Nipponia nippon is a world-endangered species and a national class I key protected animal，mainly distributed in the Hanzhong Nipponia Nippon National Nature Reserve and surrounding counties in Shaanxi. With continuous conservation efforts，the wild population of Nipponia nippon has steadily increased in recent years，and its distribution has spread to the periphery of the reserve further. In order to follow the trend of the spreading activities of the Nipponia nippon and its adaptation to the contextual of the reserve，bird-related expertise has conducted a satellite tracking study of the Nipponia nippon from 2013—2019. The transmitter accounts for about 1. 5% of the Nipponia nippon’s body weight and is worn on its back to receive information on its activity loci and status at regular intervals. Based on the satellite tracking data of Nipponia nippon，as a national class I key protected species，we develop an in-depth demand analysis of the visualization and visual analysis of tracking data based on the needs of ornithologists for data analysis and around the concerns of ornithologists for Nipponia nippon. The distribution and changes of foraging and nocturnal sites of Nipponia nippon are as an important basis for analyzing its living environment and living condition；the changes of daily foraging movement and the distance of foraging movement of Nipponia nippon can reflect the ease of access to food and the activity level of individuals of Nipponia nippon on that day，and thus we investigate the visual analysis method in detail. Furthermore， we propose a visual analysis method of movement trajectory through the interactive layout of multi-view collaborative display based on 2D map and visual analysis of the cluster analysis-based movement trajectory of Nipponia nippon. Correlative ornithologists can observe and explore the tracking data of one or more Nipponia nippon，and the influence of individual living states，behavioral characteristics，living environment conditions of Nipponia nippon can be explored，and the differences among individuals to facilitate corresponding conservation measures. In addition，due to the problems of sensing equipment and communication conditions，some of Nipponia nippon-related data collected by the transmitter are missing for satellite tracking data，and the missing data are random in terms of period length and distribution. This affects the analysis and mining of the data inevitably，and it is not conducive to the exploration of the life habits of Nipponia nippon by experts. Therefore，we interpolate the missing data in the Nipponia nippon tracking dataset based on long short-term memory（LSTM） deep learning method. Result A visual analysis system for the movement of Nipponia nippon is designed and implemented. Based on the visual analysis method proposed，users are able to observe the movement trajectory of Nipponia nippon from multiple dimensions spatiotemporally，and the night-time and foraging sites are analyzed for Nipponia nippon in related to its dimensions with different ecological significance，and daily foraging activity distance indexes of interest is analyzed and revealed their changes over time. Compared with the existing data analysis methods for Nipponia nippon，the visual analysis method proposed can be used to analyze the dynamic data from several different dimensions at the same time，and more in-depth analysis and mining are conducted for the living condition and habits of Nipponia nippon. Conclusion The case study shows that based on the method proposed，ornithologists can analyze Nipponia nippon movement track data comprehensively from multiple perspectives. The system is oriented to a visual analysis system for comprehensive analysis of Nipponia nippon tracking data，which can meet the requirements for analysis of Nipponia nippon movement trajectories and an effective method is offered for research utilization of tracking data. Its potentials can be implicated for other related flying bird tracking data.