雙城記s001

Keywords: Data science, Machine learning, Python, Web scraping, Foursquare

關(guān)鍵字：數(shù)據(jù)科學(xué)，機(jī)器學(xué)習(xí)，Python，Web抓取，Foursquare

https://br.pinterest.com/pin/92816442292506979/https://br.pinterest.com/pin/92816442292506979/

The post covers the methodology and analysis used for the final capstone project in the IBM Data Science Professional course. Detailed report, code and results can be found on Github and are linked towards the end of the post.

這篇文章涵蓋了 IBM Data Science Professional 課程中 用于最終頂點(diǎn)項(xiàng)目的方法和分析 。 可以在Github上找到詳細(xì)的報(bào)告，代碼和結(jié)果，并在文章末尾鏈接。

1.簡(jiǎn)介 (1. Introduction)

Picking a city, when it comes to London and Paris is always a hard decision as both these cities are truly global, multicultural, and cosmopolitan cities found at the heart of two European nations. Along with being two of Europe’s most important diplomatic centres, they are major centres for finance, commerce, sciences, fashion, arts, culture and gastronomy. Both London (capital of the United Kingdom) and Paris (capital of France) have a rich history and are two of the most visited and sought-after cities in Europe. London is the largest city within the UK and stands on River Thames in South East England. Paris, on the other hand, is located in the north-central part of the nation. Similar to London, the city also stands along a river, commonly known as the Seine River.

在倫敦和巴黎時(shí)，選擇一個(gè)城市始終是一個(gè)艱難的決定，因?yàn)檫@兩個(gè)城市都是真正的全球化，多元文化和國(guó)際化城市，位于兩個(gè)歐洲國(guó)家的心臟地帶。 它們是歐洲最重要的兩個(gè)外交中心，也是金融，商業(yè)，科學(xué)，時(shí)尚，藝術(shù)，文化和美食的主要中心。 倫敦(英國(guó)首都)和巴黎(法國(guó)首都)都有著悠久的歷史，并且是歐洲訪問量最大，最受歡迎的兩個(gè)城市。 倫敦是英國(guó)最大的城市，位于英格蘭東南部的泰晤士河上。 另一方面，巴黎位于該國(guó)中北部。 與倫敦類似，這座城市也沿著一條河流而行，通常被稱為塞納河。

Our goal is to perform a comparison of the two cities to see how similar or dissimilar they are. Such techniques allow users to identify similar neighbourhoods among cities based on amenities or services being offered locally, and thus can help in understanding the local area activities, what are the hubs of different activities, how citizens are experiencing the city, and how they are utilising its resources.

我們的目標(biāo)是對(duì)兩個(gè)城市進(jìn)行比較，以了解它們之間的相似程度或相異程度。 此類技術(shù)使用戶可以根據(jù)本地提供的便利設(shè)施或服務(wù)在城市中識(shí)別相似的社區(qū)，從而有助于理解本地活動(dòng)，不同活動(dòng)的樞紐是什么，市民如何體驗(yàn)城市以及他們?nèi)绾卫盟馁Y源。

What kind of clientele would benefit from such an analysis?

什么樣的客戶將從這種分析中受益？

A potential job seeker with transferable skills may wish to search for jobs in selective cities which provide the most suitable match for their qualifications and experience in terms of salaries, social benefits, or even in terms of a culture fit for expats.

潛在的具有可轉(zhuǎn)讓技能的求職者可能希望在選擇的城市中尋找工作，這些城市在薪資，社會(huì)福利乃至適合外籍人士的文化方面提供與其資格和經(jīng)驗(yàn)最匹配的職位。

Further, a person buying or renting a home in a new city may want to look for recommendations for locations in the city similar to other cities known to them.

此外，在新城市中購買或租賃房屋的人可能希望尋找與他們所熟悉的其他城市相似的城市中的位置的推薦。

Similarly, a large corporation looking to expand its locations to other cities might benefit from such an analysis.

同樣，希望將其地點(diǎn)擴(kuò)展到其他城市的大型公司可能會(huì)從這種分析中受益。

Many within-city urban planning computations might also benefit from modelling a city’s relationship to other cities.

城市內(nèi)部許多城市規(guī)劃計(jì)算也可能會(huì)受益于對(duì)城市與其他城市的關(guān)系進(jìn)行建模。

2.數(shù)據(jù)準(zhǔn)備 (2. Data Preparation)

To solve the problem at hand, data extraction was done as follows:

為了解決當(dāng)前的問題，數(shù)據(jù)提取按以下方式進(jìn)行：

Web scraping: City data was extracted from the respective Wikipedia pages [1][2] using the Requests and BeautifulSoup libraries.

Web抓取：使用Requests和BeautifulSoup庫從相應(yīng)的Wikipedia頁面[1] [2]中提取城市數(shù)據(jù)。

## URL to extract City neighbourhood information
urlL = "https://en.wikipedia.org/wiki/List_of_London_boroughs"
urlP = "https://en.wikipedia.org/wiki/Arrondissements_of_Paris"
pageLondon = urllib.request.urlopen(urlL)
pageParis = urllib.request.urlopen(urlP)wikitablesL = read_html(urlL, attrs={"class":"wikitable"})
print ("Extracted {num} wikitables from London Wikipedia".format(num=len(wikitablesL)))
wikitablesP = read_html(urlP, attrs={"class":"wikitable"})
print ("Extracted {num} wikitables from Paris Wikipedia".format(num=len(wikitablesP)))dfP = wikitablesP[0]
dfL0 = wikitablesL[0]
dfL1 = wikitablesL[1]## Request & response
s = requests.Session()
responseL = s.get(urlL,timeout=10)

Further data cleaning was performed on the extracted data in order to store relevant information about its neighbourhoods, their corresponding coordinates, area and population, and the corresponding borough numbers.

為了對(duì)提取的數(shù)據(jù)進(jìn)行進(jìn)一步的數(shù)據(jù)清理，以便存儲(chǔ)有關(guān)其鄰域，其相應(yīng)坐標(biāo)，面積和人口以及相應(yīng)區(qū)號(hào)的相關(guān)信息。

def scrapeLondonTable(table):
c1=[]
c2=[]
c3=[]
c4=[]
c5=[]
c6=[]
c7=[]
c8=[]
c9=[]
c10=[]
c11=[]
for row in table.findAll("tr"):
cellsL = row.findAll('td')
if len(cellsL)==10: #Only extract table body not heading
c1.append(cellsL[0].find('a').text)
c2.append(cellsL[1].find(text=True))
c3.append(cellsL[2].find(text=True))
c4.append(cellsL[3].find(text=True))
c5.append(cellsL[4].find(text=True))
c6.append(cellsL[5].find(text=True))
c7.append(cellsL[6].find(text=True))
c8.append(cellsL[7].find(text=True))
c9.append(cellsL[8].find('span',{'class': 'geo'}))
c10.append(cellsL[9].find(text=True))
# create a dictionary
d = dict([(x,0) for x in headerL])
# append dictionary with corresponding data list
d['Borough'] = c1
d['Inner']= c2
d['Status']=c3
d['Local authority']=c4
d['Political control']=c5
d['Headquarters']=c6
d['Area (sq mi)']=c7
d['Population (2013 est)[1]']=c8
d['Co-ordinates']=c9
d[' Nr. in map']=c10
# convert dict to DataFrame
dfL_table = pd.DataFrame(d)
## convert Area units to kms^2
#dfL["Area"] = 2.59 * (pd.to_numeric(dfL["Area"]))
Latitude = []
Longitude = []
for i in range(len(dfL_table)):
locationSplit = dfL_table['Co-ordinates'].iloc[i].getText().split("; ")
Latitude.append(locationSplit[0])
Longitude.append(locationSplit[1])
dfL_table['Latitude'] = Latitude
dfL_table['Longitude'] = Longitude
dfL_table.drop(['Inner','Status','Local authority','Political control','Headquarters','Co-ordinates'],axis=1,inplace=True)
dfL = dfL_table.rename(columns={'Area (sq mi)': 'Area', 'Population (2013 est)[1]': 'Population',' Nr. in map':'District Number'})
dfL['District Number'] = dfL['District Number'].astype(int)
for i in range(len(dfL)):
dfL['Area'].iloc[i] = float(re.findall(r'[\d\.\d]+', dfL['Area'].iloc[i])[0])
dfL['Population'].iloc[i] = int(dfL['Population'].iloc[i].replace(',',''))
return dfL

After the necessary data cleaning, we get the following data frames for our study:

經(jīng)過必要的數(shù)據(jù)清潔后，我們將獲得以下數(shù)據(jù)框用于研究：

List of boroughs in the city of London.倫敦市的自治市列表。 List of districts in the city of Paris (Districts 1–4 are combined as 1).巴黎市的地區(qū)清單(區(qū)1-4合并為1)。

Foursquare location data: Foursquare is a social location service that allows users to explore the world around them. Foursquare API provides location based experiences with diverse information about venues, users, photos, and check-ins. We took advantage of Foursquare data to extract venue based information for all of the neighbourhoods under study. The call to the API returns a JSON file and we need to turn that into a data-frame. The location data was then used to find the top and unique venues in each of the neighbourhoods. I decided to pick 100 most popular spots in each neighbourhood within a radius of 1 km.

Foursquare位置數(shù)據(jù)： Foursquare是一種社交位置服務(wù)，允許用戶探索周圍的世界。 Foursquare API提供基于位置的體驗(yàn)，其中包含有關(guān)場(chǎng)所，用戶，照片和簽到的各種信息。 我們利用Foursquare數(shù)據(jù)為正在研究的所有街區(qū)提取基于場(chǎng)所的信息。 對(duì)該API的調(diào)用會(huì)返回一個(gè)JSON文件，我們需要將其轉(zhuǎn)換為數(shù)據(jù)幀。 然后使用位置數(shù)據(jù)查找每個(gè)街區(qū)中最重要的地點(diǎn)。 我決定在方圓1公里以內(nèi)的每個(gè)社區(qū)中挑選100個(gè)最受歡迎的景點(diǎn)。

3.探索性數(shù)據(jù)分析 (3. Exploratory Data Analysis)

城市資料 (City data)

Wikipedia data provided us with information about the coordinates, area, and population of each borough in the respective cities. One can use this information to visualise a map of the city marking each Borough according to its population density. ‘Geocoder’ and the ‘folium map’ libraries were used for data visualisation on leaflet maps, where boroughs were marked according to its population density. The higher the density, the bigger the radius of the marker.

維基百科的數(shù)據(jù)為我們提供了有關(guān)各個(gè)城市中每個(gè)自治市鎮(zhèn)的坐標(biāo)，面積和人口的信息。 可以使用此信息來可視化根據(jù)自治市鎮(zhèn)的人口密度標(biāo)記每個(gè)自治市鎮(zhèn)的城市地圖。 “ 地理 編碼器 ”和“ 葉片地圖 ”庫用于在傳單地圖上進(jìn)行數(shù)據(jù)可視化，在傳單地圖上根據(jù)人口密度對(duì)行政區(qū)進(jìn)行標(biāo)記。 密度越高，標(biāo)記的半徑越大。

Population density based maps for the city of London (left) and Paris (right). (Note: Markers on the map of London have been scaled down 1/1000 times and up to 1/2000 times on the map of Paris).基于人口密度的倫敦市(左)和巴黎(右)的地圖。 (注意：倫敦地圖上的標(biāo)記已縮小1/1000倍，巴黎地圖上已放大至1/2000倍)。

場(chǎng)地?cái)?shù)據(jù) (Venue data)

Foursquare location data gave information about the list of venues within a 1 km radius of each borough. This is a reasonable distance to understand the characteristics of the neighbourhood.

Foursquare位置數(shù)據(jù)提供了有關(guān)每個(gè)市鎮(zhèn)1公里半徑內(nèi)的場(chǎng)所列表的信息。 這是理解社區(qū)特征的合理距離。

## Extracting Foursquare venue data
LIMIT = 100 # limit of number of venues returned by Foursquare API
radius = 1000 # define radiusdef getNearbyVenues(names, latitudes, longitudes, radius=500):

venues_list=[]
for name, lat, lng in zip(names, latitudes, longitudes):
print(name)

# create the API request URL
url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(
CLIENT_ID,
CLIENT_SECRET,
VERSION,
lat,
lng,
radius,
LIMIT)

# make the GET request
results = requests.get(url).json()["response"]['groups'][0]['items']
# return only relevant information for each nearby venue
venues_list.append([(
name,
lat,
lng,
v['venue']['name'],
v['venue']['location']['lat'],
v['venue']['location']['lng'],
v['venue']['categories'][0]['name']) for v in results])

nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])
nearby_venues.columns = ['Borough',
'Borough Latitude',
'Borough Longitude',
'Venue',
'Venue Latitude',
'Venue Longitude',
'Venue Category']

return(nearby_venues)print("Getting venues inside the following Boroughs of London:")
London_venues = getNearbyVenues(names=tableL['Borough'],
latitudes=tableL['Latitude'],
longitudes=tableL['Longitude']
)

In total, there were around 219 unique venue categories in London and 180 in Paris. I used this information to visualise the most common venues in some of the central London and central Paris boroughs on a bar chart.

總體而言，倫敦大約有219種獨(dú)特的場(chǎng)館類別，而巴黎則有180種。 我使用此信息以條形圖可視化了倫敦市中心和巴黎市中心的一些最常見場(chǎng)所。

## Picking major districts inside inner London and inner Paris respectively
districtsL = ['City of London','Westminster','Kensington and Chelsea','Hammersmith and Fulham', 'Wandsworth']
districtsP = ['Louvre, Bourse, Temple, H?tel-de-Ville', 'Panthéon', 'Luxembourg', 'Palais-Bourbon', 'élysée']def nearbyvenues_count(venues_df, district):
venues_count = venues_df
venues_count = venues_count.rename(columns={'Venue Category': 'Category'})
venues_count = venues_count.groupby(['Borough']).Category.value_counts().to_frame("Counts")
fig, ax = plt.subplots(1,1,figsize=(20, 7))
fig.subplots_adjust(left=0.115, right=0.88)
venues_plot = venues_count['Counts'].loc[district][:10].head(10)
pos = np.arange(len(venues_plot))
ax.set_title(district,size=20)
freqchart = ax.barh(pos, venues_plot,align='center',height=0.5,tick_label=venues_plot.index)

return freqchartMost common venues for boroughs from inner London (top) and inner Paris (bottom).倫敦內(nèi)(上)和巴黎內(nèi)(下)的行政區(qū)最常見的場(chǎng)所。

While the most common venues in central London varied between cafes/coffee shops, pubs, juice bars or hotels, the most common venues in Boroughs in the central or major areas of Paris were mostly French or Italian restaurants along with hotels.

倫敦市中心最常見的場(chǎng)所因咖啡館/咖啡店，酒吧，果汁吧或酒店而異，而巴黎市中心或主要地區(qū)的自治市鎮(zhèn)最常見的場(chǎng)所主要是法國(guó)或意大利餐廳以及酒店。

In order to explore the venue data in a more comprehensive way and further use it for analysis, foursquare venue data was arranged into pandas data frame as follows:

為了更全面地探索場(chǎng)館數(shù)據(jù)并進(jìn)一步用于分析，將Foursquare場(chǎng)館數(shù)據(jù)整理到熊貓數(shù)據(jù)框中，如下所示：

• First, create a data-frame with pandas one hot encoding for each of the venue categories

  首先，為每個(gè)場(chǎng)所類別的熊貓創(chuàng)建一個(gè)數(shù)據(jù)編碼的熱編碼

• Obtain the mean of each one-hot encoded venue categories using pandas groupby method on the borough column

  使用自治市鎮(zhèn)列上的pandas groupby方法獲得每個(gè)熱門編碼場(chǎng)所類別的平均值

• Use the venue category mean to obtain a venue based data frame for each city giving the ten most common venues for each borough
  使用場(chǎng)所類別的意思是獲取每個(gè)城市的場(chǎng)所數(shù)據(jù)框架，并給出每個(gè)行政區(qū)的十個(gè)最常見場(chǎng)所

## Categorizing venues into pandas# one hot encoding
London_onehot = pd.get_dummies(London_venues[['Venue Category']], prefix="", prefix_sep="")# add neighborhood column back to dataframe
London_onehot['Borough'] = London_venues['Borough'] # move neighborhood column to the first column
fixed_columns = [London_onehot.columns[-1]] + list(London_onehot.columns[:-1])
London_onehot = London_onehot[fixed_columns]
London_onehot.head()
London_grouped = London_onehot.groupby('Borough').mean().reset_index()def return_most_common_venues(row, num_top_venues):
row_categories = row.iloc[1:]
row_categories_sorted = row_categories.sort_values(ascending=False)
return row_categories_sorted.index.values[0:num_top_venues]num_top_venues = 10
indicators = ['st', 'nd', 'rd']# create columns according to number of top venues
columns = ['Borough']for ind in np.arange(num_top_venues):
try:
columns.append('{}{} Most Common Venue'.format(ind+1, indicators[ind]))
except:
columns.append('{}th Most Common Venue'.format(ind+1))# create a new dataframe for London
Londonboroughs_venues_sorted = pd.DataFrame(columns=columns)
Londonboroughs_venues_sorted['Borough'] = London_grouped['Borough']for ind in np.arange(London_grouped.shape[0]):
Londonboroughs_venues_sorted.iloc[ind, 1:] = return_most_common_venues(London_grouped.iloc[ind, :], num_top_venues)
Londonboroughs_venues_sorted.head(10)Section of Foursquare venue data arranged in pandas data frame for London (left) and Paris (right).在倫敦(左)和巴黎(右)的pandas數(shù)據(jù)框中排列的Foursquare場(chǎng)地?cái)?shù)據(jù)部分。

The information providing the most common venue data per borough was then used to categorise the neighbourhoods into clusters using ‘k-means’.

然后使用每個(gè)行政區(qū)提供最常見場(chǎng)地?cái)?shù)據(jù)的信息，使用“ k均值 ”將鄰域分類為聚類。

4.聚集鄰里 (4. Clustering Neighbourhoods)

I performed a clustering analysis using the ‘k-means’ algorithm in order to categorise similar neighbourhoods into clusters based on the similarities provided by the venue categories. To gain some understanding, I decided to do some investigation into the number of clusters (k) to be used as follows:

我使用“ k-means ”算法進(jìn)行了聚類分析，以便根據(jù)會(huì)場(chǎng)類別提供的相似性將相似的鄰域分類為聚類。 為了獲得一些了解，我決定對(duì)要使用的簇?cái)?shù)(k)進(jìn)行如下調(diào)查：

Elbow method: I tried to determine the effective number of clusters (k) using the elbow method for London clustering analysis and saw a small kink around k = 6 (although not clear and sharp). The elbow method uses Within-Cluster-Sum of Squared Errors (WSS) for different values of k and one can choose the value of k for which WSS starts to diminish and can be seen as an elbow in the WSS-versus-k plot. However for Paris data, no kink was explicitly seen so I tried to look into the Silhouette score for clustering for each value of k. The silhouette value measures how similar a point is to its own cluster (cohesion) compared to other clusters (separation). A value of around k = 2 gave the global maximum for the silhouette score. Although one can see from these studies that we do not have clearly clustered data, I decided to categorise London neighbourhoods into 6 set of clusters and Paris neighbourhoods into 2 set of clusters for the purpose of our analysis. It might be useful to look into a more detailed analysis to optimise k in the future for such studies.

彎頭法 ：我嘗試使用彎頭法確定有效的聚類數(shù)(k)進(jìn)行倫敦聚類分析，并在k = 6處發(fā)現(xiàn)了一個(gè)小扭結(jié)(盡管不清楚且清晰)。 彎頭方法對(duì)k的不同值使用平方誤差的簇內(nèi)和之和(WSS)，并且可以選擇WSS開始減小的k值，并且可以將其視為WSS-vs-k圖中的彎頭。 但是，對(duì)于巴黎數(shù)據(jù)，沒有明確看到扭結(jié)，因此我嘗試查看Silhouette得分以對(duì)k的每個(gè)值進(jìn)行聚類。 輪廓值衡量的是一個(gè)點(diǎn)與其自身的群集(內(nèi)聚)相比其他群集(分離)的相似程度。 k = 2左右的值給出了輪廓分?jǐn)?shù)的全局最大值。 盡管可以從這些研究中看出我們沒有明確的聚類數(shù)據(jù)，但出于分析的目的，我還是決定將倫敦社區(qū)分為6組聚類，將巴黎社區(qū)分為2組聚類。 對(duì)于此類研究，將來進(jìn)行更詳細(xì)的分析以優(yōu)化k可能會(huì)很有用。

5.結(jié)果討論 (5. Discussion of Results)

In order to solve the business problem at hand, we considered the 33 boroughs of inner and outer London and 16 districts of inner Paris. Although each of these neighbourhoods might be unique, some of them might be more similar in terms of the kind of amenities they offer. A clustering analysis was performed to find those similar neighbourhoods. One thing to note is that the data extracted from the London Wiki gave information about boroughs in inner and outer London and thus taking the suburban area into account. While the data provided by the Paris Wiki, only gave information about the districts inside inner Paris and not including much information about the large suburbs surrounding Paris where a decent fraction of the population is spread. Thus, the analysis performed in this study would need more information to compare the cities on an equal footing.

為了解決當(dāng)前的業(yè)務(wù)問題，我們考慮了倫敦內(nèi)部和外部的33個(gè)行政區(qū)和巴黎內(nèi)部的16個(gè)區(qū)。 盡管這些社區(qū)中的每一個(gè)都可能是唯一的，但就其提供的便利設(shè)施而言，其中一些社區(qū)可能更加相似。 進(jìn)行聚類分析以找到那些相似的社區(qū)。 要注意的一件事是，從倫敦Wiki提取的數(shù)據(jù)提供了有關(guān)倫敦內(nèi)部和外部倫敦自治市鎮(zhèn)的信息，因此考慮了郊區(qū)。 盡管巴黎Wiki提供的數(shù)據(jù)僅提供了有關(guān)巴黎內(nèi)部地區(qū)的信息，但并未提供有關(guān)巴黎周圍人口分散的大郊區(qū)的大量信息。 因此，在這項(xiàng)研究中進(jìn)行的分析將需要更多信息，以平等地比較城市。

Some of the inferences which were drawn from the explanatory analysis are:

從解釋性分析中得出的一些推論是：

• Most of the areas in inner Paris are more densely populated than similar areas in inner London. While there are 9 (out of 33) boroughs in London with a density greater than 10000, only 3 districts (out of 16) in Paris have a density of less than 10000 and 4 of the neighbourhoods above 30000.
  巴黎內(nèi)的大多數(shù)地區(qū)比倫敦內(nèi)的類似地區(qū)人口稠密。 倫敦有9個(gè)行政區(qū)(33個(gè)行政區(qū))的密度大于10000，而巴黎僅有3個(gè)區(qū)(16個(gè)行政區(qū))的密度小于10000，而4個(gè)街區(qū)的30000以上。
• The dense boroughs are more concentrated towards the inner regions of London in the map than the outer regions and for Paris the most dense boroughs lie towards north of Seine river.
  在地圖中，稠密的行政區(qū)比倫敦更集中于倫敦的內(nèi)部區(qū)域，而對(duì)于巴黎來說，最密集的行政區(qū)位于塞納河以北。
• Initial exploration of the Foursquare venue data revealed that coffee shops, cafes, pubs and juice bars are the most common venues in five main boroughs in inner London. Similarly French restaurants, Italian restaurants, and hotels were the most common venues seen in five main boroughs of inner Paris.
  對(duì)Foursquare場(chǎng)地?cái)?shù)據(jù)的初步探索顯示，咖啡店，咖啡館，酒吧和果汁吧是倫敦市中心五個(gè)主要行政區(qū)中最常見的場(chǎng)所。 同樣，法國(guó)餐廳，意大利餐廳和酒店是巴黎市中心五個(gè)主要行政區(qū)中最常見的場(chǎng)所。

Further, machine learning analysis of the venue based data revealed most of the boroughs of London can be grouped together into one cluster. The most common venues in such boroughs were always coffee shops, cafes, pubs, hotels or restaurants followed by some kind of clothing, convenience stores or pharmacies. Paris was categorised into two separate clusters in total. Although the most common venue in both the clusters was always a French restaurant, it was followed by a high number of Italian restaurants, hotels, and cafes in the first cluster and variations of other cuisine restaurants, bars, bistros, clothing stores or supermarkets in the second cluster.

此外，對(duì)基于場(chǎng)所的數(shù)據(jù)進(jìn)行的機(jī)器學(xué)習(xí)分析表明，倫敦大部分行政區(qū)都可以歸為一個(gè)集群。 在這些自治市鎮(zhèn)中，最常見的場(chǎng)所通常是咖啡店，咖啡館，酒吧，酒店或飯店，然后是某些服裝，便利店或藥店。 巴黎總共分為兩個(gè)單獨(dú)的類別。 盡管這兩個(gè)集群中最常見的場(chǎng)所始終是法國(guó)餐廳，但在第一個(gè)集群中緊隨其后的是大量意大利餐廳，酒店和咖啡館，以及其他美食餐廳，酒吧，小酒館，服裝店或超市第二個(gè)集群。

The most common type of venues in either of the cities are mostly restaurants, cafes, hotels, pubs/bars, clothing stores or parks. This in a way highlights that how similar the cities of London and Paris are in terms of services being offered.

在這兩個(gè)城市中，最常見的場(chǎng)所類型主要是餐館，咖啡館，酒店，酒館/酒吧，服裝店或公園。 這從某種程度上突出了倫敦和巴黎城市在提供服務(wù)方面的相似程度。

One can further use the venue data to compare the cities is a more comprehensive way where one can also explore different levels of spatial aggregation, namely grids, neighbourhoods, and the city as a whole. The level of spatial aggregation can be an important factor when characterising a city in terms of its venues.

人們可以進(jìn)一步使用場(chǎng)館數(shù)據(jù)來比較城市，這是一種更全面的方法，其中還可以探索不同級(jí)別的空間聚合，即網(wǎng)格，街區(qū)和整個(gè)城市。 當(dāng)根據(jù)地點(diǎn)來表征城市時(shí)，空間聚集的水平可能是重要的因素。

Some of the questions one can answer with different levels of spatial aggregation could be:

人們可以用不同級(jí)別的空間聚合來回答的一些問題可能是：

• How are the venue categories distributed inside a neighbourhood, i.e., is the neighbourhood more of a residential or a commercial one.
  場(chǎng)地類別如何在鄰里分布，即鄰里更多是住宅或商業(yè)場(chǎng)所。
• Which city has the highest number of each of the amenities (bars, restaurants, parks, universities, libraries, shopping centres, etc.)
  每種設(shè)施(酒吧，飯店，公園，大學(xué)，圖書館，購物中心等)中哪個(gè)城市擁有最多的設(shè)施

六，結(jié)論 (6. Conclusion)

To summarise, analysing cities using venue based data from Foursquare lead to an overall understanding of the type of venues in each neighbourhood and presented some of the key features of the cities but the level of data is not adequate to provide a comprehensive analysis for a city-to-city comparison. For a potential interested person (job-seeker or person deciding to move to either of the cities) or a bigger clientele like a business corporation or city planners, one would need to do a more detailed analysis adding features such as rents, salaries, transportation, cost of living, growth rate, economy, etc.

總而言之，使用來自Foursquare的基于場(chǎng)所的數(shù)據(jù)來分析城市，可以全面了解每個(gè)社區(qū)中場(chǎng)所的類型，并介紹了城市的一些關(guān)鍵特征，但是數(shù)據(jù)水平不足以對(duì)城市進(jìn)行全面分析到城市比較。 對(duì)于潛在的有興趣的人(求職者或決定搬到兩個(gè)城市的人)或更大的客戶(例如商業(yè)公司或城市規(guī)劃師)，您需要進(jìn)行更詳細(xì)的分析，添加諸如租金，薪水，交通等功能，生活費(fèi)用，增長(zhǎng)率，經(jīng)濟(jì)狀況等。

The capstone project provided a medium to understand in depth about how real life data science projects work and what all steps go in building a data science methodology. All steps from understanding the business problem, data understanding to data preparation, and model building were discussed in detail here. Many drawbacks of the current analysis and further ways to improve the analysis were also mentioned. This was an initial attempt to understand and solve the business problem at hand. However, there still exists a huge potential to extend this project in real life scenarios.

最高項(xiàng)目為深入了解現(xiàn)實(shí)生活中的數(shù)據(jù)科學(xué)項(xiàng)目的工作方式以及構(gòu)建數(shù)據(jù)科學(xué)方法論的所有步驟提供了一種媒介。 這里詳細(xì)討論了從理解業(yè)務(wù)問題，數(shù)據(jù)理解到數(shù)據(jù)準(zhǔn)備以及模型構(gòu)建的所有步驟。 還提到了當(dāng)前分析的許多缺點(diǎn)以及改進(jìn)分析的其他方法。 這是理解和解決當(dāng)前業(yè)務(wù)問題的最初嘗試。 但是，在現(xiàn)實(shí)生活中仍存在將這個(gè)項(xiàng)目擴(kuò)展的巨大潛力。

About Me:

關(guān)于我：

I am a Physicist and have been analysing particle physics data for several years now. I have moved for work between Karlsruhe, Geneva, Sao Paulo, and Chicago in the last few years and currently am residing in Paris. Although such a data analysis would be a stepping stone to design a methodology to compare any of the cities, comparing London and Paris was driven by a personal choice of love for those cities. ?

我是物理學(xué)家，多年來一直在分析粒子物理數(shù)據(jù)。 過去幾年中，我曾在卡爾斯魯厄，日內(nèi)瓦，圣保羅和芝加哥之間工作，目前居住在巴黎。 盡管這樣的數(shù)據(jù)分析將成為設(shè)計(jì)比較任何城市的方法的墊腳石，但倫敦和巴黎的比較是由個(gè)人對(duì)這些城市的熱愛所推動(dòng)的。 ?

You can find more about me on Linkedin.

您可以在Linkedin上找到有關(guān)我的更多信息。

Links to the project summary and code.

鏈接到項(xiàng)目摘要和代碼 。

“Doing the best at this moment puts you in the best place for the next moment.” Stay safe and well.“此時(shí)此刻，盡一切所能將您置于下一刻的最佳位置。” 保持身體健康。

翻譯自: https://towardsdatascience.com/a-tale-of-two-cities-e693c15b3ddb

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