Did the number of store closures increase due to the impact of the new coronavirus?

Recently, store sales have been sluggish due to the new coronavirus, and the store is in danger of closing. .. .. I see a lot of news articles like that, so I tried to find out what it was actually like! This is a series of articles I tried, so please close your eyes for small gabber. .. ..

The outline is like this. --Data acquisition source: https://kaiten-heiten.com/ --Method: Tag the data and plot the number of closed stores for each tag. ――What to see: How many stores are closed in April 2020 compared to the usual month? --Evaluation period: January 1, 2019 to April 28, 2020 --Understood (1) The number of rental cars and hotels closed in April 2020 is higher than usual (2) The number of restaurants and retail stores closed every month is large, and April 2020 is not particularly large. ③ The number of stores opened is decreasing overall

Data organization

Data acquisition

The data was obtained from the store opening and closing.com (https://kaiten-heiten.com/).

Tagging

The following four types of tags are attached to the tags.

--Industry (for example, restaurants, retail stores, etc.) --Small industry (grilled meat, Genghis Khan, etc. at restaurants) --Region (Hokkaido, Tohoku, etc.) --Prefectures (Kyoto, Kagoshima, etc.)

Below are some screenshots of the opening and closing .com articles. If you look at the bottom of the image, you can see that there are four types of tags that you want to get this time. Use this by scraping.

tag.png

Information such as opening and closing is taken from the article title. In the image above, it will be "closed". In addition, the relationship between industry-small industry and the relationship between region-prefecture are created from the {Open / Close} information- {Industry / Region} page in Open / Close.com. The screenshots on those pages are below, and you can see the relationship by looking at them.

group.png region.png

Tagging problems and precautions

There were some issues with tagging. Note that this solution is arbitrary and can distort the results.

There is a small industry tag in the article tag, but there is no industry tag

For example, "Shoe store" is attached as a small industry as shown below, but the industry is not classified.

no-groups.png

To solve this, create a dictionary of {industry: small industry group} from the page that describes the industry-small industry relationship introduced in the previous section image. Using this, I adopted the industry to which the tagged small industry belongs as the industry for the article.

The method of creating a dictionary was created by creating a dictionary for each of store opening information-industry and store closing information-industry, and creating a union of the elements. The reason for taking the union is that there is a difference in the display on the {opening, closing} information page.

#Create a dictionary from the url of the opening / closing page
def get_group(url):
    html = urlopen(url)
    bsObj = BeautifulSoup(html, 'html.parser')
    
    body = bsObj.find('div', attrs={'class': 'post_body'})
    titles = bsObj.find('div', attrs={'class': 'post_body'}).find_all('h3')
    group = [title.text for title in titles]
    elems = bsObj.find('div', attrs={'class': 'post_body'}).find_all('p')
    small_group = [[a.text for a in elem.find_all('a')] for elem in elems if elem.find('a')]
    dict_group_to_small_group = {g: set(sg) for g, sg in zip(group, small_group)}
    return dict_group_to_small_group

g_kaiten = get_group('https://kaiten-heiten.com/kaiten/kaiten-gyousyubetsu/')
g_heiten = get_group('https://kaiten-heiten.com/heiten/heiten-gyousyubetsu/')

#Create a dictionary with the sum of the elements of the created dictionary
group_to_small_group = dict(g_kaiten)
for key in g_heiten.keys():
    if key in group_to_small_group.keys():group_to_small_group[key] |= g_heiten[key]
    else: group_to_small_group[key] = g_heiten[key]

{Opening, closing} Information-There are a lot of small industry tags that are not by industry

There are a lot of tags that I forgot to type and tags that are not on the industry page. For example, the tag "Doujin Shop" is not listed in the {Open, Closed} Information-Industry page. However, there are actually articles with the "Doujin Shop" tag. If you search for "doujin shop" tag, many articles have retail store as an industry tag.

no-tag.png

Therefore, in the case of Doujinshi, add the industry "Retailer" to the ** {Industry: Small Industry Group} dictionary by hand as follows.

group_to_small_group['retail store'] |= set(['Doujin shop'])

We did this for ** almost all tags ** that are not in the {industry: small industry group} dictionary. The only small industry tag I didn't do was "blog". This is because it was unclear what kind of industry it belongs to, and the small industry tag "blog" is ** ignored ** this time.

I haven't done it this time, but as a smarter method, I think it would have been better to put off those without an industry tag and attach the industry tag with the largest number of cases with the desired small industry tag. .. ..

The article tag has a prefecture tag but no region tag

For example, the following screenshots have the prefecture "Osaka" tag, but do not have the desired area tag "Kinki".

no-region.png

This can be done by creating and using a {region: prefecture group} dictionary in the same way as "the article tag has a small industry tag but no industry tag".

g_kaiten = get_group('https://kaiten-heiten.com/kaiten/area-open/')
g_heiten = get_group('https://kaiten-heiten.com/heiten/area-close/')

region_to_pref = dict(g_kaiten)
for key in g_heiten.keys():
    if key in region_to_pref.keys():region_to_pref[key] |= g_heiten[key]
    else: region_to_pref[key] = g_heiten[key]

There is a region tag that is not on the region page

This happens frequently with tags in Hokkaido. Just like in the case of "Article tag has prefecture tag but no region tag", you can add it to the dictionary every time you find it. For example, you can do as follows.

region_to_area['Hokkaido'] |= set(['Ashibetsu'])

Data visualization

important point

In this analysis, the date the article was created is the date it was {opened, closed}. However, as a matter of fact, some of the articles posted on Open / Close.com are created by providing information, so the date when the article was created does not necessarily correspond to the {Open / Close} date. Please try to grasp the outline.

The data period is from January 1, 2019 to April 28, 2020.

DataFrame I summarized the obtained data in a DataFrame of pandas. Some of them are below.

Date Group:1 Group:2 Group:3 Group:4 Name Prefecture:1 Prefecture:2 Prefecture:3 Prefecture:4 Prefecture:5 Region:1 Region:2 Region:3 SmallGroup:1 SmallGroup:2 SmallGroup:3 SmallGroup:4 State URL Year/Month
2020-04-27 restaurant NaN NaN NaN DYNAMIC KITCHEN Yonenozo Kumamoto NaN NaN NaN NaN Kyushu-Okinawa NaN NaN Izakaya NaN NaN NaN closed https://kaiten-heiten.com/dynamic-kitchen-yonenokura 2020/04
2020-04-27 retail store NaN NaN NaN Daiso York Benimaru Okaido Miyagi NaN NaN NaN NaN Tohoku NaN NaN 100 yen.300 yen shop NaN NaN NaN Opening https://kaiten-heiten.com/daiso-yorkbeni-ookaido 2020/04
2020-04-27 restaurant NaN NaN NaN Tonkatsu Masaya Aichi NaN NaN NaN NaN Tokai / Hokuriku NaN NaN Tonkatsu, beef cutlet, katsudon NaN NaN NaN Opening https://kaiten-heiten.com/tonkatsu-masaya 2020/04

--Date: The date the article was created --Group: i: i-th industry (because some industries have multiple industries) --SmallGroup: i: i-th small industry (because some industries have multiple small industries) --Name: Store name --Region: i: i-th region (in the case of simultaneous closing of multiple stores, i = 2 and 3 also have values) --Prefecture: i: i-th prefecture (i = 1, ..., 5 is the same as the area and has a value when multiple stores are closed at the same time) --State: Opening, closing, closing, etc.

As you can see from the above, the data may be tagged with multiple industries. Therefore, here, when classifying by tag, it is assumed that elements with multiple tags belong to all of those multiple tags. For example, if a store has multiple industry tags (retailers, restaurants), it belongs to both the DataFrame, which is a collection of retail stores only, and the DataFrame, which is a collection of only restaurants. This function is provided by the following function.

def compile_columns_to_one_column(df, columns={'Group:{}'.format(i) for i in range(1, 4+1)}, result_column_name='Group'):
    #Create a set of elements of the target column
    groups = set([])
    for col in columns:
        groups |= set(df[col].dropna())

    #Create a df for each element of the created set and concat
    group_df = pd.DataFrame()
    for g in groups:
        group_df = pd.concat([group_df] + [df[g == df[col]].assign(tmp=g) for col in columns])
    
    return group_df.rename(columns={'tmp': result_column_name})

#DataFrame by industry and small industry
group_df = compile_columns_to_one_column(base_df, {'Group:{}'.format(i) for i in range(1, 4+1)}, result_column_name='Group')
sgroup_df = compile_columns_to_one_column(group_df, {'SmallGroup:{}'.format(i) for i in range(1, 4+1)}, result_column_name='SmallGroup')
group_df = group_df.loc[:, ['Date', 'Year/Month', 'Group', 'Name', 'State', 'URL']]
sgroup_df = sgroup_df.loc[:, ['Date', 'Year/Month', 'Group', 'SmallGroup', 'Name', 'State', 'URL']]

#Create DataFrame for each region and prefecture
region_df = compile_columns_to_one_column(base_df, {'Region:{}'.format(i) for i in range(1, 3+1)}, result_column_name='Region')
pref_df = compile_columns_to_one_column(region_df, {'Prefecture:{}'.format(i) for i in range(1, 5+1)}, result_column_name='Prefecture')
region_df = region_df.loc[:, ['Date', 'Year/Month', 'Region', 'Name', 'State', 'URL']]
pref_df = pref_df.loc[:, ['Date', 'Year/Month', 'Region', 'Prefecture', 'Name', 'State', 'URL']]

For example, the elements of group_df that summarizes the industries are as follows.

Date Year/Month Group Name State URL
2020-04-18 2020/04 restaurant Fukumaru coffee closed https://kaiten-heiten.com/marufuku-coffee
2020-04-18 2020/04 restaurant Starbucks Coffee WITH HARAJUKU Opening https://kaiten-heiten.com/starbucks-with-harajuku
2020-04-24 2020/04 restaurant Doutor Coffee Shop Keikyu Heiwajima closed https://kaiten-heiten.com/doutor-coffee-shop-keikyuheiwajima

Number of stores closed

By industry

The list of industries is as follows. --Service --Sports

Whole industry

The code and results counted and plotted by industry are as follows.

tmp = group_df[group_df['State'] == 'closed']
tmp = tmp.groupby(['Year/Month', 'Group']).size().to_frame('Number of stores closed').reset_index()
plt.figure(figsize=(15, 10))
sns.barplot(x='Year/Month', y='Number of stores closed', data=tmp, hue='Group')
plt.legend(ncol=3)
plt.show()

group.png

Looking at this, we can see that:

――The number of restaurants and retail stores closed was originally very large, and March and April are not yet large. ――It seems that the number of rental, sightseeing / accommodation / travel / leisure closures has increased.

Based on this, we will look at small rental and tourism industries.

Small industry

I will not write because there are many small industries. Please confirm that the rental and travel small industries are written in the figure below.

First, make a similar plot on the rental system.

rentals = sgroup_df[(sgroup_df['State'] == 'closed') & (sgroup_df['Group'] == 'rental')]
rentals_count = rentals.groupby(['Year/Month', 'SmallGroup']).size().to_frame('Number of stores closed').reset_index()

max_count = rentals_count['Number of stores closed'].max()
min_ym, max_ym = rentals_count['Year/Month'].min(), rentals_count['Year/Month'].max()

plt.figure(figsize=(15, 10))
plt.xlim(min_ym, max_ym)
plt.ylim(0, max_count*1.1)
sns.barplot(x='Year/Month', y='Number of stores closed', data=rentals_count, hue='SmallGroup')
plt.legend(ncol=2)
plt.show()

rentals.png

Looking at this, you can see that "rent-a-car / car-sharing" is increasing very much. Looking at the data based on this, we found that a large number of Toyota Rent-A-Cars closed on April 18. Below is the entire code and raw data.

rentals_rentalcar = rentals[(rentals['SmallGroup'] == 'Car rental / car sharing') & (rentals.Date >= datetime.date(2020, 4, 1))]
writer = pytablewriter.MarkdownTableWriter()
writer.from_dataframe(rentals_rentalcar.loc[:, ['Date', 'Name', 'URL']])
writer.write_table()
Date Name URL
2020-04-11 TSUTAYA Asahikawa Nagayama store https://kaiten-heiten.com/tsutaya-asahikawanagayama
2020-04-18 Toyota Rent-A-Car Kichijoji Ekimae Store https://kaiten-heiten.com/toyota-rentacar-kichijojiekimae
2020-04-18 Toyota Rent-A-Car Minowa https://kaiten-heiten.com/toyota-rentacar-minowa
2020-04-18 Toyota Rent-A-Car Kanamachi https://kaiten-heiten.com/toyota-rentacar-kanamachi
2020-04-18 Toyota Rent-A-Car Kanda Jimbocho https://kaiten-heiten.com/toyota-rentacar-kandajinbocho
2020-04-18 Toyota Rent-A-Car Kanjo-dori Higashi Naebo https://kaiten-heiten.com/toyota-rentacar-kanjodorihigashinaeho
2020-04-18 Toyota Rent-A-Car Asagaya Ekimae Store https://kaiten-heiten.com/toyota-rentacar-asagayaekimae
2020-04-18 Toyota Rent-A-Car Ashiya https://kaiten-heiten.com/toyota-rentacar-ashiya
2020-04-18 Toyota Rent-A-Car Ikegami Daini Keihin Store https://kaiten-heiten.com/toyota-rentacar-dai2keihin
2020-04-18 Toyota Rent-A-Car Ayase https://kaiten-heiten.com/toyota-rentacar-ayase
2020-04-18 Toyota Rent-A-Car Urafune https://kaiten-heiten.com/toyota-rentacar-urafune
2020-04-18 Toyota Rent-A-Car Takadanobaba Store Return Counter Store https://kaiten-heiten.com/toyota-rentacar-takadanobabahenkyaku
2020-04-18 Toyota Rent-A-Car Oizumi Gakuen https://kaiten-heiten.com/toyota-rentacar-ooizumigakuen
2020-04-18 Toyota Rent a Car Shimbashi Ekimae Building Store https://kaiten-heiten.com/toyota-rentacar-shinbashiekimae-bld
2020-04-18 Toyota Rent-A-Car Sugamo https://kaiten-heiten.com/toyota-rentacar-sugamo
2020-04-18 Toyota Rent-A-Car Takadanobaba https://kaiten-heiten.com/toyota-rentacar-takadanobaba
2020-04-18 Toyota Rent a Car Ichibancho University Entrance Store https://kaiten-heiten.com/toyota-rentacar-ichibanchodaigakuiriguchi
2020-04-18 Toyota Rent-A-Car Kameido https://kaiten-heiten.com/toyota-rentacar-kameidp
2020-04-18 Toyota Rent-A-Car Chukan Ibaraki https://kaiten-heiten.com/toyota-rentacar-nakakanibaraki
2020-04-18 Toyota Rent-A-Car Tsuruse Ekimae Store https://kaiten-heiten.com/toyota-rentacar-tsuruseekimae
2020-04-18 Toyota Rent-A-Car Gojo Ohashi https://kaiten-heiten.com/toyota-rentacar-gojooohashi
2020-04-18 Toyota Rent-A-Car Harajuku Meiji-dori https://kaiten-heiten.com/toyota-rentacar-harajukumeijidori
2020-04-18 Toyota Rent-A-Car Aoto https://kaiten-heiten.com/toyota-rentacar-aoto
2020-04-25 Times Car Takamatsu https://kaiten-heiten.com/timescar-takamatsu
2020-04-25 Times Car Okayama Station Store https://kaiten-heiten.com/timescar-okayamaekimae
2020-04-25 Times Car Dobashi https://kaiten-heiten.com/timescar-dobashi
2020-04-25 Nissan Rent-A-Car Onna https://kaiten-heiten.com/nissan-rentacar-onna
2020-04-25 Nissan Rent-A-Car Chatan https://kaiten-heiten.com/nissan-rentacar-chatan
2020-04-26 Sky Rent-A-Car Okinawa Chubu Store https://kaiten-heiten.com/skyrent-okinawakoza

Next, let's look at the travel system.

travels = sgroup_df[(sgroup_df['State'] == 'closed') & (sgroup_df['Group'] == 'Sightseeing / Accommodation / Travel / Leisure')]
travels_count = travels.groupby(['Year/Month', 'SmallGroup']).size().to_frame('Count').reset_index()

max_count = travels_count.Count.max()
min_ym, max_ym = rentals_count['Year/Month'].min(), rentals_count['Year/Month'].max()

#Since there are many types, plot by size
size = 9
small_groups = travels_count['SmallGroup'].unique()
plot_num = int(len(small_groups)/size)+1
for i in range(plot_num):
    target_groups = small_groups[i*size:(i+1)*size]
    
    plt.figure(figsize=(15, 5))
    plt.xlim(min_ym, max_ym)
    plt.ylim(0, max_count*1.1)
    sns.barplot(x='Year/Month', y='Count', data=travels_count[travels_count['SmallGroup'].isin(target_groups)], hue='SmallGroup')
    plt.legend(ncol=3)
    plt.show()

カフェ・レストラン カラオケ クラブ ゲームセンター ダンスホール パチンコ・スロット ホテル・ビジネスホテル ライブハウス レンタルDVD・CD.png 体験型アトラクション 動物園 旅館・民宿 書店・文具 自動車・販売・買取・修理 買取・質屋 遊園地・テーマパーク CD・DVD ギャラリー.png 映画館 温泉・足湯 鉄道ジオラマ・RCコース カプセルホテル ダーツ 商業ビル・施設 観光案内所 VR・ゲーム施設 ボウリング場.png 工場見学・企業ミュージアム 物産展示販売 自転車・販売・買取・修理 遊歩道・展望台 ガソリンスタンド キャンプ場・グランピング バーべQ ビリヤード 占い.png 釣堀・釣り場・釣舟 音楽ホール バー・バル・ダイニング 劇場 おもちゃ 雀荘 寄席 水族館 漫喫・複合カフェ.png バッティングセンター 和食・割烹・懐石 銭湯・健康ランド ゴルフ練習場 史跡 展示施設.png

Looking at this, we can see that the number of "hotels, business hotels" and "capsule hotels" closed in April has increased compared to the usual month. Similarly, when the raw data is output, it is as follows. The hotel is conspicuously closed in the first cabin. At other hotels, the same vendors are not closed together, and it seems that tourist destination hotels nationwide are closed. Is it a sharp decrease in tourists? .. ..

Date SmallGroup Name URL
2020-04-02 Hotel Business Hotel Green Squalle Sekigane https://kaiten-heiten.com/green-squalle
2020-04-02 Hotel Business Hotel Hotel Numazu Castle https://kaiten-heiten.com/numazu-castle
2020-04-02 Hotel Business Hotel Harazuru Onsen Hakuseikaku https://kaiten-heiten.com/kanseikaku
2020-04-02 Hotel Business Hotel Oku Hida Yakushi no Yu Honjin https://kaiten-heiten.com/okuhida-yakushinoyuhonjin
2020-04-02 Hotel Business Hotel Sakurajima Youth Hostel https://kaiten-heiten.com/sakurajima-yh
2020-04-02 Hotel Business Hotel Itako Fujiya Hotel https://kaiten-heiten.com/itako-fujiyahotel
2020-04-04 Hotel Business Hotel Towada Fujiya Hotel https://kaiten-heiten.com/towadafujiyahotel
2020-04-04 Hotel Business Hotel Takeo Century Hotel https://kaiten-heiten.com/takeocenturyhotel
2020-04-04 Hotel Business Hotel Yumura Onsen Private source inn Tomiya https://kaiten-heiten.com/jikagensen-tomiya
2020-04-04 Hotel Business Hotel A remote inn with an open-air bath https://kaiten-heiten.com/hoshitaru
2020-04-05 Hotel Business Hotel Izumigatake Onsen Yamaboshi https://kaiten-heiten.com/yamabousi
2020-04-06 Hotel Business Hotel Hagi Grand Hotel Tenku https://kaiten-heiten.com/hagi-gh
2020-04-08 Hotel Business Hotel Hotel Axia Kushikino https://kaiten-heiten.com/axia-kushikino
2020-04-10 Hotel Business Hotel Hachinohe Seagull View Hotel Hana to Tsuki no Nagisa https://kaiten-heiten.com/hsv-hotel
2020-04-10 Hotel Business Hotel Kawaguchi Onsen Ousanso https://kaiten-heiten.com/kawaguchi-ouusansou
2020-04-16 Hotel Business Hotel First Cabin ST.Kyoto Umekoji RYOKAN https://kaiten-heiten.com/first-cabin-st-kyotoumekouji-ryokan
2020-04-16 Hotel Business Hotel First Cabin Station Abeno-so https://kaiten-heiten.com/firstcabin-st-abenosou
2020-04-16 Hotel Business Hotel First Cabin Station Wakayama Station https://kaiten-heiten.com/first-cabin-wakayama
2020-04-18 Hotel Business Hotel Haga Fudotaki Park Kaede Kaso https://kaiten-heiten.com/fukasou
2020-04-19 Hotel Business Hotel Omachi Onsenkyo Hotel Karamatsuso https://kaiten-heiten.com/hotel-karamatsuso
2020-04-22 Hotel Business Hotel Kitsuregawa Onsen Hotel New Sakura https://kaiten-heiten.com/kitsuregawa-hotel-new-sakura
2020-04-24 Hotel Business Hotel First Cabin Kyobashi https://kaiten-heiten.com/first-cabin-kyobashi
2020-04-26 Hotel Business Hotel Hotel Kayotei https://kaiten-heiten.com/hotel-kayoutei
Date SmallGroup Name URL
2020-04-16 capsule hotel Nine Hours Kyoto https://kaiten-heiten.com/ninehours-kyoto
2020-04-24 capsule hotel First Cabin Kyoto Arashiyama https://kaiten-heiten.com/first-cabin-kyotoarashiyama
2020-04-24 capsule hotel First Cabin Kyoto Kawaramachi Sanjo https://kaiten-heiten.com/first-cabin-kyotokawarasanjo
2020-04-24 capsule hotel First Cabin Kashiwanoha https://kaiten-heiten.com/first-cabin-kashiwanoha
2020-04-24 capsule hotel First Cabin Tsukiji https://kaiten-heiten.com/first-cabin-tsukiji

By region

Next, let's look at each region. However, I could not find such a difference in each region. .. ..

tmp = region_df[region_df['State'] == 'closed']
tmp = tmp.groupby(['Year/Month', 'Region']).size().to_frame('Number of stores closed').reset_index()
plt.figure(figsize=(15, 5))
sns.barplot(x='Year/Month', y='Number of stores closed', data=tmp, hue='Region')
plt.legend(ncol=4)
plt.show()

region.png

Number of stores opened

By industry

The type of business is the same as the one handled at the closing. In April, the number of stores opened in all industries seems to be smaller than usual.

tmp = group_df[group_df['State'] == 'Opening']
tmp = tmp.groupby(['Year/Month', 'Group']).size().to_frame('Number of stores opened').reset_index()
plt.figure(figsize=(15, 5))
sns.barplot(x='Year/Month', y='Number of stores opened', data=tmp, hue='Group')
plt.legend(ncol=3)
plt.show()

group_open.png

By region

The areas and prefectures are the same as those handled when the store was closed. As with each industry, the number of stores opened in all regions seems to be lower than usual.

tmp = region_df[region_df['State'] == 'Opening']
tmp = tmp.groupby(['Year/Month', 'Region']).size().to_frame('Number of stores opened').reset_index()
plt.figure(figsize=(15, 5))
sns.barplot(x='Year/Month', y='Number of stores opened', data=tmp, hue='Region')
plt.legend(ncol=4)
plt.show()

region_open.png

Summary and impressions

What i remembered --Japanese display on seaborn!

Other things I was interested in and investigated

The main thing I investigated was the sudden changes other than April.

June 2019 "Public Facilities / Transportation / Finance" Tag

Looking at the breakdown of the "Public Facilities / Transportation / Finance" tags for this month, it looks like the result of Nomura Securities closing many branches.

The reason why only Nomura Securities appears significantly is that Daiwa Securities has not consolidated or abolished large-scale branches, and other bank-affiliated securities have joint stores with banks, so they are not recognized as closed. Or is it simply because Nomura Securities has more branches than other securities and there are many consolidations and abolitions?

nomura.png

Date Name URL
2019-06-04 ENEOS Wing Muecho SS https://kaiten-heiten.com/eneos-wing-hijiecho
2019-06-07 ENEOS Wing Route 17 Konosu TS https://kaiten-heiten.com/eneos-wingroot17konosu-ts
2019-06-08 Nomura Securities Co., Ltd. Nakameguro Branch https://kaiten-heiten.com/nomura-nakameguro
2019-06-08 Nomura Securities Co., Ltd. Kishiwada Branch https://kaiten-heiten.com/nomura-kishiwada
2019-06-08 Nomura Securities Co., Ltd. Musashi Kosugi Branch https://kaiten-heiten.com/nomura-musashikosugi
2019-06-08 Nomura Securities Co., Ltd. Aobadai Branch https://kaiten-heiten.com/nomura-aobadai
2019-06-08 Nomura Securities Co., Ltd. Kawanishi Branch https://kaiten-heiten.com/nomura-kawanishi
2019-06-08 Nomura Securities Co., Ltd. Tanashi Branch https://kaiten-heiten.com/nomura-tanashi
2019-06-08 Nomura Securities Co., Ltd. Kanayama Branch https://kaiten-heiten.com/nomura-kanayama
2019-06-08 Nomura Securities Co., Ltd. Uehonmachi Branch https://kaiten-heiten.com/nomura-kamihoncho
2019-06-08 Nomura Securities Co., Ltd. Tsukaguchi Branch https://kaiten-heiten.com/nomura-tsukaguchi
2019-06-08 Nomura Securities Co., Ltd. Sagamihara Branch https://kaiten-heiten.com/nomura-sagamihara
2019-06-08 Nomura Securities Co., Ltd. Shinjuku Nomura Building Branch https://kaiten-heiten.com/nomura-shinjukunomura-bldg
2019-06-08 Nomura Securities Co., Ltd. Okamoto Branch https://kaiten-heiten.com/nomura-okamoto
2019-06-08 Nomura Securities Co., Ltd. Senri Branch https://kaiten-heiten.com/nomura-senri
2019-06-08 Nomura Securities Co., Ltd. Nakano Branch https://kaiten-heiten.com/nomura-nakano
2019-06-08 Nomura Securities Co., Ltd. Denenchofu Branch https://kaiten-heiten.com/nomura-denenchofu
2019-06-08 Nomura Securities Co., Ltd. Ibaraki Branch https://kaiten-heiten.com/nomura-ibaraki
2019-06-08 Nomura Securities Co., Ltd. Kamakura Branch https://kaiten-heiten.com/nomura-kamakura
2019-06-08 Nomura Securities Co., Ltd. Takarazuka Branch https://kaiten-heiten.com/nomura-takaraduka
2019-06-08 Nomura Securities Co., Ltd. Gakuemmae Branch https://kaiten-heiten.com/nomura-gakuenmae
2019-06-08 Nomura Securities Co., Ltd. Kamata Branch https://kaiten-heiten.com/nomura-kamata
2019-06-08 Nomura Securities Co., Ltd. Yokohama Bashamichi Branch https://kaiten-heiten.com/nomura-yokohamabashamichi
2019-06-08 Nomura Securities Co., Ltd. Daito Branch https://kaiten-heiten.com/nomura-daito
2019-06-08 Nomura Securities Co., Ltd. Tsurumi Branch https://kaiten-heiten.com/nomura-tsurumi
2019-06-08 Nomura Securities Tamagawa Branch https://kaiten-heiten.com/nomura-tamagawa
2019-06-08 Nomura Securities Co., Ltd. Gotanda Branch https://kaiten-heiten.com/nomura-gotanda
2019-06-10 Dog Run & Cafe Dinny ’s Garden Dinny ’s Garden https://kaiten-heiten.com/dinnys-garden
2019-06-15 Yamamoto Oil Shindo SS https://kaiten-heiten.com/yamamoto-shindo-ss
2019-06-16 Idemitsu Kosan Tsuneishi C Values Co., Ltd. Nishimachi SS https://kaiten-heiten.com/idemitsu-tsuneishi-nishimachi-ss
2019-06-16 Showa Shell Sekiyu(Yes)Sakaguchi Oil Store Azekari SS https://kaiten-heiten.com/showa-shell-sakaguchi-ss
2019-06-16 ENEOS Tsuneishi C Values Co., Ltd. Kasugacho SS https://kaiten-heiten.com/eneos-tsuneishi-kasugacho-ss
2019-06-29 ENEOS (stock)Sanotas Hongodai SS https://kaiten-heiten.com/eneos-hongodai-ss

"Karaoke" tag from January to May 2019

Many karaoke tags were closed at this time, so I took a look at the raw data. Below is the raw data from January to May, and it seems that each karaoke company tends to close multiple stores on the same day.

This seems to be a company other than karaoke. For example, closing unprofitable franchise management all at once?

Date SmallGroup Name URL
2019-01-11 karaoke Karaoke CLUB DAM Kumamoto Shimodori store https://kaiten-heiten.com/karaoke-club-dam-kumamotoshimodori
2019-01-11 karaoke Kisuke Karaoke WAO Imabari https://kaiten-heiten.com/kisuke-karaoke-wao-imabari
2019-01-15 karaoke Song Stage 19 Makishima Store https://kaiten-heiten.com/utanostage19-biwajima
2019-01-18 karaoke Shidax Himeji Kameyama Club https://kaiten-heiten.com/shidax-himegikameyama
2019-01-19 karaoke Karaoke Bang Bang Tsukuba Inarimae https://kaiten-heiten.com/karaokebanban-inarimae
2019-01-20 karaoke Shidax Shizuoka Distribution Street Club https://kaiten-heiten.com/abc-mart-iy-fukuyam
2019-01-21 karaoke Shidax Tochigi Showacho Club https://kaiten-heiten.com/shidax-tochigishowa
2019-01-25 karaoke Glare Kitakami store https://kaiten-heiten.com/glarekitakami
2019-01-31 karaoke Court d'Azur Dining Shin-Yokohama https://kaiten-heiten.com/cotedazur-diningshinyokohama
2019-01-31 karaoke Côte d'Azur Shimosuwa https://kaiten-heiten.com/cotedazur-shimosuwa
2019-01-31 karaoke Côte d'Azur Aobadai station square store https://kaiten-heiten.com/cotedazur-aobadaiekimae
2019-01-31 karaoke Côte d'Azur Katsutadai store https://kaiten-heiten.com/cotedazur-katsutadai
2019-01-31 karaoke Côte d'Azur Kuwana https://kaiten-heiten.com/cotedazur-kuwana
2019-01-31 karaoke Côte d'Azur Kanayama Station South Exit https://kaiten-heiten.com/cotedazur-kanayamaekiminamiguchi
2019-01-31 karaoke Côte d'Azur Kanazawa Station East Exit https://kaiten-heiten.com/cotedazur-kanazawaekihigashiguchi
Date SmallGroup Name URL
2019-02-18 karaoke Karaoke Manekineko Yagiri store https://kaiten-heiten.com/manekineko-yagiri
2019-02-19 karaoke Karaoke Manekineko Hofu store https://kaiten-heiten.com/karaokemanekineko-hiufu
2019-02-24 karaoke Karaoke Manekineko Hikari 2nd store https://kaiten-heiten.com/karaokemanekineko-hikari2go
2019-02-24 karaoke Karaoke Manekineko Hanshin Nishinomiya https://kaiten-heiten.com/karaokemanekineko-hanshinnishinomiya
2019-02-25 karaoke Karaoke Manekineko Furukawa Oyama store https://kaiten-heiten.com/karaokemanekineko-kogaooyama
2019-02-25 karaoke Karaoke Manekineko Ojima store https://kaiten-heiten.com/karaokemanekineko-ojima
2019-02-25 karaoke Karaoke Manekineko Hitachiota https://kaiten-heiten.com/karaokemanekineko-hitachiota
2019-02-27 karaoke Karaoke Manekineko Gifu quail store https://kaiten-heiten.com/karaokemanekineko-gifuuzura
2019-02-27 karaoke Karaoke Manekineko Tsutaka Chaya https://kaiten-heiten.com/karaokemanekineko-tsutakachaya
2019-02-27 karaoke Karaoke Manekineko Minokamo store https://kaiten-heiten.com/karaokemanekineko-minokamo
2019-02-27 karaoke Karaoke Manekineko Kisarazu Kiyomidai store https://kaiten-heiten.com/karaokemanekineko-kisaradukiyomidai
Date SmallGroup Name URL
2019-03-03 karaoke Shidax Kasukabe Yurinoki Street Club https://kaiten-heiten.com/shidax-karaokeyurinokidori
2019-03-04 karaoke Shidax Zama Sagamigaoka Club https://kaiten-heiten.com/shidax-zamasagamigaoka
2019-03-11 karaoke Shidax Kurume Central Park Club https://kaiten-heiten.com/shidax-kurumechuokouen
2019-03-11 karaoke Shidax Isehara Club https://kaiten-heiten.com/shidax-isehara
2019-03-11 karaoke Shidax Chiba Yachimata Club https://kaiten-heiten.com/shidax-chibayachimata
2019-03-11 karaoke Shidax Joto Furuichi Club https://kaiten-heiten.com/shidax-jotofuruichi
2019-03-11 karaoke Shidax Oyama Jonan Club https://kaiten-heiten.com/shidax-oyamajonan
2019-03-11 karaoke Shidax Narita New Town Club https://kaiten-heiten.com/shidax-narita-nt
2019-03-11 karaoke Shidax Higashimatsuyama Matsubacho Club https://kaiten-heiten.com/shidax-higashimatsuyamamatsubacho
2019-03-11 karaoke Shidax Yonago Yonehara Club https://kaiten-heiten.com/shidax-yonagomaibara
2019-03-13 karaoke Karaoke Court d'Azur Sannomiya station square store https://kaiten-heiten.com/cotedazur-sannomiyaekimae
2019-03-13 karaoke Karaoke Court D'AZUR Hachioji Ekimae Store https://kaiten-heiten.com/cotedazur-hachioujiekimae
2019-03-13 karaoke Karaoke Cote d'Azur Tennoji Apollo https://kaiten-heiten.com/cotedazur-tennoujiaporo
2019-03-13 karaoke Karaoke Cote d'Azur Higashi Totsuka https://kaiten-heiten.com/cotedazur-higashitotsuka
2019-03-13 karaoke Karaoke Cote d'Azur Kashiwa Matsugasaki https://kaiten-heiten.com/cotedazur-kashiwamatsugasaki
2019-03-13 karaoke Karaoke Court d'Azur Fukuoka Yukuhashi https://kaiten-heiten.com/cotedazur-fukuokayukuhashi
2019-03-13 karaoke Karaoke Court D'Azur Minoh https://kaiten-heiten.com/cotedazur-minoo
2019-03-13 karaoke Karaoke Court d'Azur Wakatsuki store https://kaiten-heiten.com/cotedazur-iwatsuki
2019-03-13 karaoke Karaoke Court d'Azur Saiin Ekimae store https://kaiten-heiten.com/cotedazur-nishiinekimae
2019-03-13 karaoke Karaoke Court d'Azur Shizuoka Magarikane https://kaiten-heiten.com/cotedazur-shizuokamagarigane
2019-03-13 karaoke Karaoke Court d'Azur Tsuruhashi station square store https://kaiten-heiten.com/cotedazur-tsuruhashiekimae
2019-03-13 karaoke Karaoke Court D'Azur Mizusawa https://kaiten-heiten.com/cotedazur-mizusawa
2019-03-13 karaoke Karaoke Court D'Azur Kitaueo https://kaiten-heiten.com/cotedazur-kitaageo
2019-03-13 karaoke Karaoke Cote d'Azur Shiki Ekimae store https://kaiten-heiten.com/cotedazur-shikiekimae
2019-03-13 karaoke Karaoke Cote d'Azur Niiza Ekimae store https://kaiten-heiten.com/cotedazur-nizaekimae
2019-03-13 karaoke Karaoke Court D'Azur Kanda Station North Exit https://kaiten-heiten.com/cotedazur-kandaekikitaguchi
2019-03-13 karaoke Karaoke Cote d'Azur Nishikawaguchi https://kaiten-heiten.com/cotedazur-nishikawaguchi
2019-03-16 karaoke Karaoke Manekineko Imabari Karako https://kaiten-heiten.com/karaokemanekineko-imabarikarako
2019-03-21 karaoke Karaoke Shidax Aomori Kanko Dori Club https://kaiten-heiten.com/shidax-aomorikankodori
2019-03-23 karaoke Karaoke Shidax Toyota Kozaka Club https://kaiten-heiten.com/shidax-toyotakosaka
2019-03-28 karaoke Karaoke Shidax Sapporo Nishioka Club https://kaiten-heiten.com/shidax-sapporonishioka
Date SmallGroup Name URL
2019-04-14 karaoke Karaoke Shidax Funabashi Natsumi Club https://kaiten-heiten.com/shidax-funabashinatsumi
2019-04-14 karaoke Karaoke Shidax Akita New National Highway Club https://kaiten-heiten.com/shidax-akitashinkokudo
2019-04-19 karaoke Karaoke Shidax Kisarazu Club https://kaiten-heiten.com/shidax-kisaradu
2019-04-20 karaoke Karaoke Shidax Chofu Kokuryo Club https://kaiten-heiten.com/shidax-chofukokuryo
2019-04-22 karaoke Karaoke CLUB DAM Resort Sugamo station square store https://kaiten-heiten.com/karaoke-club-dam-resort-sugamoekimae
Date SmallGroup Name URL
2019-05-01 karaoke Karaoke Manekineko Wakayama Mukai https://kaiten-heiten.com/karaokemanekineko-wakayamamukai
2019-05-02 karaoke JOYSOUND Takaoka store https://kaiten-heiten.com/joysound-takaoka
2019-05-03 karaoke Karaoke Shidax Kishiwada Komatsuricho Club https://kaiten-heiten.com/shidax-kishiwadakomatsuzato
2019-05-04 karaoke JOYSOUND Tamatsukuri https://kaiten-heiten.com/joysound-tamatsukuri
2019-05-04 karaoke Karaoke Shidax Shinjuku Kabukicho Club https://kaiten-heiten.com/shidax-shinjukukabukicho
2019-05-04 karaoke Karaoke Room Utahiroba Ginza Miyuki Dori https://kaiten-heiten.com/utahiroba-ginzamiyukidori
2019-05-05 karaoke Karaoke Manekineko Joetsu Kida store https://kaiten-heiten.com/karaokemanekineko-jyoetukida
2019-05-05 karaoke Karaoke Manekineko Hayato store https://kaiten-heiten.com/karaokemanekineko-hayato
2019-05-18 karaoke Karaoke Shidax Osaka Sennichimae Club https://kaiten-heiten.com/shidax-osakasennichimae
2019-05-21 karaoke Karaoke Shidax Utsunomiya Takebayashi Club https://kaiten-heiten.com/shidax-utsunomiyatakebayashi
2019-05-24 karaoke Karaoke Shidax Yachiyo Narita Highway Club https://kaiten-heiten.com/shidax-yachiyonaritakaidou
2019-05-27 karaoke Karaoke Shidax Chiba Chuo Club https://kaiten-heiten.com/shidax-chibacguo
2019-05-27 karaoke Karaoke Shidax Minaminagareyama Club https://kaiten-heiten.com/shidax-minaminagareyama
2019-05-27 karaoke Karaoke Shidax Fujinomiya Yumizawa Club https://kaiten-heiten.com/shidax-fujinomiyayumisawa
2019-05-27 karaoke Karaoke Shidax Asahikawa Sanjo Club https://kaiten-heiten.com/shidax-asahikawasanjo
2019-05-27 karaoke Karaoke Shidax Honjo Club https://kaiten-heiten.com/shidax-honjo
2019-05-27 karaoke Karaoke Shidax Hamamatsu Sumiyoshi Bypass Club https://kaiten-heiten.com/shidax-hamamatsusumiyoshibypass
2019-05-27 karaoke Karaoke Shidax Fukushima station square club https://kaiten-heiten.com/shidax-fukushimaekimae
2019-05-27 karaoke Karaoke Shidax Hamura City Hall Street Club https://kaiten-heiten.com/shidax-hamurashiyakushodori
2019-05-27 karaoke Karaoke Shidax Takasaki Takazeki Club https://kaiten-heiten.com/shidax-takasakitakaseki

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