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
The data was obtained from the store opening and closing.com (https://kaiten-heiten.com/).
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.
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.
There were some issues with tagging. Note that this solution is arbitrary and can distort the results.
For example, "Shoe store" is attached as a small industry as shown below, but the industry is not classified.
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]
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.
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. .. ..
For example, the following screenshots have the prefecture "Osaka" tag, but do not have the desired area tag "Kinki".
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]
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'])
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 |
The list of industries is as follows. --Service --Sports
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()
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.
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()
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()
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 |
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()
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()
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()
What i remembered --Japanese display on seaborn!
The main thing I investigated was the sudden changes other than April.
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?
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 |
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 |