# Event Detection and Spatio-temporal Analysis of Low-Altitude Unstable Approach
## 1 Introduction  
A stable approach is a flying stage during which the pilot establishes and maintains a constant angle glidepath towards a predetermined point on the landing runway [1]. For a safe flight, it is necessary for a flight to keep stabilized by 1000 feet above airport elevation in instrument meteorological conditions and by 500 feet in visual meteorological conditions [2]. An unstable approach (UA) can happen when the pilot fails to plan, prepare and conduct a stable approach. Although the approach process only accounts for about 4% of the entire flight process, unsafe incidents occurring during this process account for up to 49.1% of total flight accidents [3]. In particular, UA was a causal factor in 66% of 76 approach-and-landing accidents or serious incidents all over the world in 1984 through 1997 [4]. Therefore, it is of great practical significance to explore and analyze the spatio-temporal distribution of UA events and their influencing factors.  
A number of studies using physical methods have been conducted to explore the criteria of a stable approach and identify UA events. [PERSON] et al. [5] proposed a method for analyzing the criteria of a stable approach with surveillance track data. Moreover, they also explored aborted approaches and their underlying factors [6]. [PERSON] and [PERSON] [7] presented a retrospective approach to explore the causes ofUA events with historical accident data. [PERSON] and [PERSON] [8] presented a possibility measure-based model for the detection of UA via flight data analysis. [PERSON] et al. [9] proposed a Gaussian Mixture Model-based cluster analysis approach to detect abnormal flights with elevated risks. [PERSON] et al. [10] developed go-around decision making criteria for avoiding UA risks via simulation studies. These studies largely concerned mechanical factors of UA events, but ignored the flying experience of pilots, particularly for evaluating their potential risks. In this paper, a detection model based on extensive questionnaires of more than 20 experienced pilots is proposed.  
Moreover, previous studies on UA primarily focused on artificial [11], meteorological, and mechanical [12] influencing factors, such as the impact of sudden low-altitude wind shear [13]. Risks potentially caused by UA have also been widely evaluated via the gray clustering method [14], approaching angle and trajectory analysis [15], K-means clustering [13], and neural networks [16]. However, UA events and relative factors have rarely been studied from spatial and temporal perspectives [17; 18]. In this study, spatio-temporal patterns and relative factors of UA events are explored with the risk of low-altitude UA events detected and evaluated.  
In this study, with data collected from quick access recorders (QARs) [19; 20; 21], a program for detecting low-altitude UA events with their risks evaluated was developed. The spatio-temporal analyses of detected UA events were conducted via exploratory spatial data analysis (ESDA) techniques [22]. Moreover, relationships between the influencing factors of UA events were further explored to develop models for prediction and explanation of UA events. The exploratory analysis of the UA events presented in this article provides an important research basis and guidance for future quantitative causal analysis, and also shows practical significance for predicting and avoiding UA events.  
The remainder of this paper is organized as follows. Section 2 presents the methodology and data used in this study. In Section 3, results of UA detection and exploratory factors are presented with a case study. Finally, this study is summarized and future work on this topic is also anticipated in Section 4.