介绍 (Introduction)

Covid-19 pandemic has seriously impacted the world. In order to slow down the pandemic, countries issued travel restrictions, social distancing mandates, lockdown, etc., and therefore, a lot of businesses have been impacted. The traveling industry is one of the industries that has been hit very hard.

Covid-19大流行严重影响了世界。 为了减缓这种流行病的爆发速度，各国发布了旅行限制，社会疏远指令，*等措施，因此，许多企业受到了影响。 旅游行业是遭受重创的行业之一。

Because of the travel restrictions, the number of flights around the world decreased dramatically. In this post, I will analyze and visualize the flight data.

由于旅行限制，世界各地的航班数量急剧下降。 在这篇文章中，我将分析和可视化航班数据。

数据集 (Dataset)

The dataset I used is “Crowdsourced air traffic data from The OpenSky Network 2020”. It provides flight data from 2019–01–01 to 2020–07–31 in several csv files (one file per month). In this work, I used data from 2020–1–1 to 2020–07–31.

我使用的数据集是“ 来自The OpenSky Network 2020的众包空中交通数据 ”。 它以多个csv文件(每月一个文件)提供从2019-01-01到2020-07-31的航班数据。 在这项工作中，我使用了2020–1–1到2020–07–31的数据。

The dataset has the following features:

数据集具有以下功能：

callsign: the identifier of the flight displayed on ATC screens (usually the first three letters are reserved for an airline: AFR for Air France, DLH for Lufthansa, etc.)

呼号 ：在ATC屏幕上显示的航班的标识符(通常前三个字母是为航空公司保留的：法国航空的AFR，汉莎航空的DLH等)

number: the commercial number of the flight, when available (the matching with the callsign comes from public open API)

number ：航班的商业编号(如果有)(与呼号的匹配来自公共开放API)

icao24: the transponder unique identification number;

icao24 ：应答器唯一标识号；

registration: the aircraft tail number (when available);

注册 ：飞机机尾号(如果有)；

typecode: the aircraft model type (when available);

类型代码 ：飞机型号类型(如果有)；

origin: a four letter code for the origin airport of the flight (when available);

原点 ：航班原点机场的四个字母代码(如果有)；

destination: a four letter code for the destination airport of the flight (when available);

目的地 ：航班的目的地机场的四字母代码(如果有)；

firstseen: the UTC timestamp of the first message received by the OpenSky Network;

firstseen ：OpenSky网络收到的第一条消息的UTC时间戳；

lastseen: the UTC timestamp of the last message received by the OpenSky Network;

lastseen ：OpenSky网络收到的最后一条消息的UTC时间戳；

day: the UTC day of the last message received by the OpenSky Network;

day ：OpenSky网络收到的最后一条消息的UTC日期；

latitude_1, longitude_1, altitude_1: the first detected position of the aircraft;

latitude_1 ， 经度_1 ， 海拔_1 ：飞机的第一个检测位置；

latitude_2, longitude_2, altitude_2: the last detected position of the aircraft.

latitude_2 ， longitude_2 ， height_2 ：飞机最后检测到的位置。

可视化 (Visualization)

After some data processing, I was able to visualize the data.

经过一些数据处理后，我可以可视化数据。

总数 (Total count)

Let’s start with the total amount of flights.

让我们从航班总数开始。

It is very obvious the flight count decreased a lot at the beginning of March, this is the result of the global lockdown at that period of time. Since then, the number of flights has gradually increased because of slow reopenings, but still not back to pre COVID-19 level.

很明显，3月初航班数减少了很多，这是那段时间全球*的结果。 此后，由于重新开放速度缓慢，航班数量逐渐增加，但仍未恢复到COVID-19之前的水平。

I also put the data on the map as shown below. It is clear the density of the red spots decreased in March and then gradually increased.

我还将数据放在地图上，如下所示。 很明显，红色斑点的密度在3月份下降，然后逐渐增加。

乘飞机 (By airline)

Let’s break it down to see the impact per airline. There are too many airlines in the dataset so I only took some of them as examples. I took one European airline KLM (Netherlands), two American airlines AAL and DAL, two Asian airline ANA (Japan), CAL (*), and one low-cost airline EJU (Easyjet).

让我们细分一下，看看每个航空公司的影响。 数据集中的航空公司过多，因此我仅以其中一些为例。 我乘坐了欧洲的KLM(荷兰)，美国的AAL和DAL两家，亚洲的ANA(日本)，CAL(*)和美国的廉价航空公司EJU(Easyjet)。

Most of the flight count decreased pretty hard in March and April to below 25% compared to 1st Jan, Easyjet (EJU) even decreased to 0 between March to June. At the end of July, the flight count climbed back to around 50% for these airlines.

与1月1日相比，3月和4月的大多数航班数量下降幅度很大，降至25％以下，Easyjet(EJU)甚至在3月至6月之间下降到0。 在7月底，这些航空公司的航班总数回升到50％左右。

One special case is the * airline (CAL), it did not have a sharp decrease in March like others, instead, it gradually decreased since January and still maintained around 50% even at the worst period of time. This might due to the early awareness of Covid-19 by the * government (related report here). The other Asian airline (ANA) also showed an early decrease in activity.

*航空公司(CAL)是一种特殊情况，它在3月份没有像其他航空公司那样急剧下降，相反，自1月份以来逐渐下降，即使在最坏的时期也保持在50％左右。 这可能是由于**对Covid-19的早期了解( 此处是相关报告 )。 另一家亚洲航空公司(ANA)的活动也有所减少。

乘飞机 (By airport)

Let’s look at the airports. I took Amsterdam (EHAM), Taipei (RCTP), Tokyo (RJTT), Los Angels (KLAX), and New York (JFK) as examples.

让我们看看机场。 我以阿姆斯特丹(EHAM)，台北(RCTP)，东京(RJTT)，洛杉矶(KLAX)和纽约(JFK)为例。

It showed similar trends, a sharp decrease to 20% in March and April and slowly climbed back to 40–60%. Amsterdam airport decreased the activity slightly earlier than American airports (LA and New York). Tokyo airport started to decrease activity slightly earlier but at a slower rate. Taipei showed a different behavior, it started to decrease at the end of January with a gentle slope, which is similar to the * CAL airline.

它显示出类似的趋势，三月和四月急剧下降至20％，然后缓慢回升至40-60％。 阿姆斯特丹机场的活动减少时间比美国机场(洛杉矶和纽约)稍早。 东京机场稍早开始减少活动，但速度较慢。 台北表现出不同的行为，它在1月底开始以平缓的坡度下降，这类似于*的CAL航空公司。

货物 (Cargo)

How about the flights for cargo?

货运航班怎么样？

From the data of FedEx and UPS flights, it is not really impacted.

从FedEx和UPS航班的数据来看，它并没有真正受到影响。

结论 (Conclusion)

• Flight traffic decreased significantly at the beginning of the Covid-19 pandemic and has been recovered gradually.
  在Covid-19大流行初期，航班运输量显着下降，并已逐渐恢复。
• Low-cost airline (Easyjet) almost stop the flights from March to June, then recovered gradually since June.
  廉价航空公司(Easyjet)几乎从3月至6月停止航班，然后从6月开始逐渐恢复。
• Cargo flights were not impacted.
  货运航班没有受到影响。

Thanks for reading.

谢谢阅读。