Analysis of earthquake distribution in the past 10 years with Python~

Hello everyone , I'm brother CAI .

Some time ago, something happened in the Yellow Sea 5.2 level The earthquake , At that time, friends in East China were all “ shock ” 了 .

today , Let's take a look at the past 10 Distribution of earthquakes in ！

1. Data acquisition

Directly in China earthquake networks You can download it to In the past 10 year The earthquake data .

China earthquake networks ：

news.ceic.ac.cn/index.html?…

You're right here History query Interior selection Time range After the inquiry , Click on Save to local You can get the data .

There are local data previews as follows ：

2. Data processing

In data acquisition , We found that the data obtained contains Time 、 Magnitude 、 Longitude and latitude 、 depth as well as Reference position . The longitude and latitude are under Baidu map , Considering that the longitude and latitude of different maps will be different , And our subsequent drawing adopts Gaode map , So here we need to convert the latitude and longitude .

2.1. Latitude and longitude conversion

Gaode open platform has corresponding Latitude and longitude conversion Of API, We can call （ The free quota is enough ）.

import requests
import pandas as pd
#  Reading data 
df = pd.read_excel(r'eqList.xlsx')

longitude_list = []
latitude_list = []
#  Baidu map longitude and latitude is converted to Gaode map longitude and latitude 
for i , location in enumerate(df[[' longitude (°)',' latitude (°)']].values):
    location = str(location[0])+','+str(location[1])
    url = 'https://restapi.amap.com/v3/assistant/coordinate/convert?'
    
    parames = {
        'locations':location,
        'coordsys':'baidu',
        'key':' Yours key',
        }
    
    r = eval(requests.get(url, params=parames).json()['locations'])
    #  longitude 
    longitude_list.append(r[0])
    #  latitude 
    latitude_list.append(r[1])
    print(f'\r{i+1}',end='')

df[' longitude (°)'] = longitude_list
df[' latitude (°)'] = latitude_list
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such , We have successfully converted the longitude and latitude coordinates of Baidu map system into the longitude and latitude coordinates of Gaode map system .

2.2. Data processing at the time of earthquake occurrence

For the moment of earthquake , I want to be accurate to the month and hour , For subsequent statistical analysis .

#  Convert to time format 
df[' The moment of the earthquake '] = pd.to_datetime(df[' The moment of the earthquake '])
#  Get the date 
df[' month '] = df[' The moment of the earthquake '].apply(lambda x: str(x)[:7])
#  For hours 
df[' Hours '] = df[' The moment of the earthquake '].dt.hour
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Here, I feel that my operation of getting the month and year is a little low, If you have a better plan, please leave a message , I want to learn .

2.3. Earthquake location （ Provinces ）

Because the reference position in the original data can not be easily analyzed to the province and city , I'm going to use the longitude and latitude information through Gaode's API To get . Refer to previous 《》.

citys = []
provinces = []
for i , location in enumerate(df[[' longitude (°)',' latitude (°)']].values):
    location = str(location[0])+','+str(location[1])
    url = 'https://restapi.amap.com/v3/geocode/regeo?'
    params = {
        'location':location,
        'key':' Yours key',
        'extensions':'base',
        'batch':'false',
        'roadlevel':0,
        }
    
    r = requests.get(url, params=params)
    data = r.json()['regeocode']
    city = data['addressComponent']['city']
    province = data['addressComponent']['province']
    if len(city)==0:
        city = province
    citys.append(city)
    provinces.append(province)
    print(f'\r{i+1}',end='')
df[' City '] = citys
df[' province '] = provinces
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After the above treatment , We finally got the following data ：

3. Statistics and visualization

In this part, we only make a simple statistical display , Do not make similar predictions or other in-depth analysis , After all, based on the existing raw data, we can't draw much valuable conclusions .

3.1. Number of earthquakes over the years

Number of small and medium earthquakes

according to [2, 4.6] As the range of medium and small earthquakes , near 10 In all 6188 Time , Average annual 600 Remaining times ！

#  Small and medium earthquakes （[2,4.6]）
df[' year '] = df[' The moment of the earthquake '].dt.year
df_cn = df[df[' province ']!='[]']
df_xiao = df_cn.query('2<=` Magnitude (M)`<=4.6')
df_xiao.groupby(' year ')[' The moment of the earthquake '].count().to_frame(' frequency ').reset_index()
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Number of destructive earthquakes

according to [4.7, ∞] As the range of medium and small earthquakes , near 10 In all 505 Time , Average annual 50 Remaining times ！

3.2. Number of earthquakes in each province

Considering that some earthquakes occur in the sea , It is unified into Chinese waters , In addition, we don't participate in the events that take place abroad .

in addition , We only see Destructive earthquake Distribution , It can be found that in our country xinjiang 、 Tibet 、 yunnan 、 Taiwan and sichuan It is a zone with high earthquake incidence ！

#  The provinces   Destructive earthquake 
df_province = df_cn.query('` Magnitude (M)`>=4.7').groupby(' province ')[' The moment of the earthquake '].count().to_frame(' frequency ').sort_values(by=' frequency ',ascending=False).reset_index()
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Province	Number of destructive earthquakes
xinjiang	98
Tibet	63
yunnan	47
Taiwan	47
sichuan	45
qinghai	32
gansu	10
Ji Lin	9
Inner Mongolia	7
guangxi	4
hubei	3
guangdong	2
hebei	2
guizhou	2
Chongqing	2
heilongjiang	2
jiangsu	1
fujian	1
liaoning	1
shaanxi	1

3.3. Number of earthquakes in each city

In our country 233 There have been earthquakes in three cities , Most of the cities with the most earthquakes are concentrated in xinjiang 、 sichuan .

notes ： The statistics here are all seismic data

Like Xinjiang Kizilsu Kirgiz Autonomous Prefecture 、 Hotan Area

Sichuan Yibin and Ya'an etc.

province	City	frequency
Xinjiang Uygur Autonomous Region	Kizilsu Kirgiz Autonomous Prefecture	491
Xinjiang Uygur Autonomous Region	Hotan Area	431
Xinjiang Uygur Autonomous Region	Aksu Area	308
Tibet Autonomous Region	Naqu city	257
Sichuan Province	Yibin City	237
Xinjiang Uygur Autonomous Region	Kashgar area	234
Xinjiang Uygur Autonomous Region	Bayingolin Mongol Autonomous Prefecture	206
Sichuan Province	Ya'an City	188
Taiwan Province	Taiwan Province	167
Sichuan Province	Aba Tibetan and Qiang Autonomous Prefecture	167

【 Moving graph 】

3.4. Scatter diagram of earthquake distribution

We only see Destructive earthquake Distribution , Draw according to the longitude and latitude coordinate information

import folium.plugins as plugins
import folium

df = df[df[' Magnitude (M)']>=4.7]
ss1 = [[latitude,longitude] for latitude,longitude in df[[' latitude (°)', ' longitude (°)']].values.tolist()]
m = folium.Map([39.904989, 116.405285],
              tiles='https://webrd01.is.autonavi.com/appmaptile?&x={x}&y={y}&z={z}&lang=zh_cn&size=1&scale=1&style=7',
               zoom_start=6,
               attr=' Gao de ')
groups = folium.FeatureGroup('')
for l,label in zip(ss1,df[' Reference position '].to_list()):
    groups.add_child(
        folium.CircleMarker(
            location=l,
            radius=1,
            color='red',
            fill=True,
            fill_color='red',
            fill_opacity=0.4,
            popup=folium.Popup(html=label,max_width=100),
        )
    )
m.add_child(groups)
m.add_child(folium.LatLngPopup()) 
m.save(' Earthquake distribution .html')               
 Copy code 

3.5. Address distribution diagram

You can see the southwest of Xinjiang , And the eastern waters of Taiwan Province are earthquake prone areas .

data_all = df[[' latitude (°)', ' longitude (°)',' Magnitude (M)']].values.tolist()
m = folium.Map([39.904989, 116.405285],
               tiles='https://webrd01.is.autonavi.com/appmaptile?&x={x}&y={y}&z={z}&lang=zh_cn&size=1&scale=1&style=7',
               zoom_start=6,
               attr=' Gao de ')
hm = plugins.HeatMap(data_all, radius=10, 
                     gradient={.1: 'green', .6: 'yellow', 1: 'red'},
                     )
hm.add_to(m)
hm.save(' Seismic thermal map .html')
 Copy code 

3.6. near 10 Annual monthly earthquake distribution map （ dynamic ）

notes ： The statistics here are all seismic data

【 Video Number 】

Draw code

data_move = []
date_list = df[' month '].sort_values().unique()
for month in date_list:
    data_move.append(df[df[' month '] == month][[' latitude (°)', ' longitude (°)',' Magnitude (M)']].values.tolist())
m = folium.Map([39.904989, 116.405285],
               tiles='https://webrd01.is.autonavi.com/appmaptile?&x={x}&y={y}&z={z}&lang=zh_cn&size=1&scale=1&style=7',
               zoom_start=6,
               attr=' Gao de ')
time_index = df[' month '].sort_values().unique().tolist()
hm = plugins.HeatMapWithTime(data_move, 
                             index=time_index,
                             radius=10)

hm.add_to(m)
hm.save(' Seismic dynamic thermodynamic diagram .html')
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4. Supplementary knowledge

Distribution map of seismic zone

We screen 5 The distribution map of earthquakes above M is as follows ：

Basically match ~

That's all of this , If you're interested , Order one Looking at The private chat Xiaobian can get data Ha ~