I checked the number of closed and opened stores nationwide by Corona

How many stores did Corona shut down?

Information on stores opened and closed nationwide is collected on a site called Opened and closed.com. For studying scraping, let's color map the percentage of store closures in March-April 2020 and 2019 for each prefecture.

Method

Let's go in the direction of counting the number of stores corresponding to each period in each region on the above site. Fortunately, it is categorized by region and each page is saved in descending order, so use that. Also, scraping uses python's beautifulsoup4.

Results / Discussion

Let's look at the results first (implementation is below). The result is as follows. The numbers for sidebar are for each prefecture ratio = $ (N_ {opened} --N_ {closed}) $ / $ (N_ {closed} + N_ {opened}) $ It is standardized in.

Looking at this, there is no significant difference due to coronavirus. It is necessary to look at the future trends to see whether the impact is simply small or the impact will appear with a time lag. I hope that the damage will be reduced somehow.

Implementation

First, collect the data from the above url address.

shop_openup_closedown_ratio_1.py

from bs4 import BeautifulSoup
from urllib import request
import datetime
import numpy as np

def period(year,month,year_s = 2019,year_e = 2019,month_s = 3,month_e = 4):
    res = False
    if (year<=year_e) & (year >=year_s) & (month>=month_s) & (month<=month_e):
        res = 2
    if year >= year_e:
        if year > year_e:
            res = 1
        else:
            if month>month_e:
                res = 1

    return res



def main(year_s = 2020,year_e = 2020,month_s = 4,month_e = 4):
    dic = {}
    states = ['close','open']
    for state in range(len(states)):
        url = 'https://kaiten-heiten.com/heiten/area-' + states[state] + '/'  
        response = request.urlopen(url)
        soup = BeautifulSoup(response,'html.parser')

        for a in soup.find_all('a', class_="links"):   
            link = a.get('href')
            region = a.text 
            print(region)

            if dic.get(region) is None:
                dic[region] = [0,0]



            url = link
            response = request.urlopen(url)
            soup = BeautifulSoup(response,'html.parser')

            shop_list = soup.find_all('span', class_='post_time')

            year_last = int(shop_list[-1].text[:5])
            month_last = int(shop_list[-1].text[6:8])

            for a in shop_list:        

                year = int(a.text[:5])
                month = int(a.text[6:8])


                if period(year, month,year_s,year_e,month_s,month_e) == 2:
                    dic[region][state] += 1

                cout = 0
                flag = 0

                while period(year_last, month_last,year_s,year_e,month_s,month_e)>=1:

                    next_p = soup.find('a',class_='next page-numbers')
                    
                    if soup.find_all('a',class_='next page-numbers') is not None:
                        link = next_p.get('href')
                    else:
                        break

                    url = link
                    response = request.urlopen(url)
                    soup = BeautifulSoup(response,'html.parser')


                    shop_list = soup.find_all('span', class_='post_time')

                    year_last = int(shop_list[-1].text[:5])
                    month_last = int(shop_list[-1].text[6:8])


                    print(year_last,month_last)

                    for a in shop_list:        

                        year = int(a.text[:5])
                        month = int(a.text[6:8])


                        if period(year, month):
                            dic[region][state] += 1

                            
    regions = list(dic.keys())
    vals_ = np.array(list(dic.values()))

    hk = int(22)

    hk_name = ['Hokkaido']
    hk_vals = np.array([vals_[:hk,0].sum(), vals_[:hk,1].sum()])

    regions = hk_name + regions[hk:]
    vals = [list(hk_vals)] + list(vals_[hk:])


    # ratio == N_o - N_c / N

    ratio = np.zeros(len(regions))

    for i in range(len(regions)):
        ratio[i] = (vals[i][1] - vals[i][0]) / (vals[i][1] + vals[i][0])
    
    return regions, vals, ratio, vals_


regions_2020, vals_2020, ratio_2020, vals_2020 = main(2020,2020,3,4)
regions_2019, vals_2019, ratio_2019, vals_2019 = main(2019,2019,3,4)

Get the data with.

shop_openup_closedown_ratio_2.py

import numpy as np
import cv2
from PIL import Image
import matplotlib.colors
import matplotlib.pyplot as plt
from japanmap import *



def mapping(regions,ratio,name,a=0.1,b=1):

    n_min = a
    n_max = b

    cmap = plt.cm.rainbow
    norm = matplotlib.colors.Normalize(vmin=n_min, vmax=n_max)

    def color_scale(r):
        tmp = cmap(norm(r))
        return (tmp[0]*255, tmp[1]*255, tmp[2]*255)

    dic = {}
    for k in range(len(regions)):
        map_val = color_scale(ratio[k])
        dic[regions[k]] = map_val

    lab = name + ' 3~4'
    fig = plt.figure(figsize=(15,9))
    plt.title(lab,fontsize=15)
    plt.imshow(picture(dic))


    sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
    
    plt.colorbar(sm)
    plt.show()
    
    fig.savefig(name)

Arrange the data with. Plot below.

shop_openup_closedown_ratio_3.py

a = list(ratio_2020)+list(ratio_2019)

max_n = max(a)
min_n = min(a)
mapping(regions_2019,ratio_2019,'2019')
mapping(regions_2020,ratio_2020,'2020',min_n,max_n)

Data source

Opening and closing.com