The Battle of Neighborhood: Tokyo Hostels
By: Dhoomil Sheta
Tags: Data Science, Hostel, Capstone, Clustering, Folium

As someone who loves travelling, finding ways to save money and travel low-cost is essential. There’s little to save while booking flights and I’d rather pay more to traveling comfortably on a 16Hr flight than try to save and wear myself down even before the trip begins! But we can always save a few bucks on accomodation.

A hostel is a low-budget accommodation shared among travelers. Staying at hostels is an extremely affordable option for travelers. Not only that, It also gives you a chance to expereince which you would never at a hotel. I’ve found that experiencing hostel life one of the best ways to travel the world on a budget and here’s why:

You’ll save a lot of money trust me. Meet other like-minded travellers and make new friends around the world! Most hostels organize lot of social activites

Hence, I selected this as a topic for my Capstone Project for IBM Data Sciene Professional Certificate. In this post, I’ll give a summary of my findings from the project. Tourism in Japan in on a rise. It is expected that the number of foreign tourists coming to Japan will be increasing till 2020 when Olympic will be held in Tokyo. Hence, for this project, I looked at hostels in Japan, in particular, Tokyo.

Problem Description

Hostel accomodation is a booming industry with the latest rise in tourism worldwide, especially in Asia. Over 20% of the total tourists worldwide are aged between 19–25. And around 80% of them prefer to spend less on accommodation by opting for hostels. This means the demand for hostels is only going to grow further and more people would want to get their legs in the game.

How should a new business person decide where to open a hostel?

What factors should he look at before investing?

Which neighborhood venues affect a user’s rating for location of hostel?

At the same time, it is difficult for a travellers, especially first-timers, to select a hostel from among many options. Hostel reviews are subjective and differ from person-to-person and one cannot solely depend on them to make a decision. It is especially important to consider other aspects like price and neighborhood, which can greatly influence one’s experience of the city/country. I will try to answer the following questions

How does price vary with location?

Where are the ‘value of money’ hostels located?

How does proximity to transportation affect hostel rating?

Which hostels are most secure and where are they located?

Suggest similar hostel but which cheaper price

Data Requirements

One city was analysed in this project : Tokyo.

Following are the datasets used in the project along with the reasons for choosing them:

1. Japan Hostel Dataset: This dataset was web scraped from Hostelworld by Koki Ando and is available on Kaggle. This is the core dataset with which I worked.

2. Foursquare API: This API helped me get the venues around the hostel which I will use for EDA and clustering.

3. Tokyo Land Price: I scraped this website to get land prices of various neighborhoods in Tokyo.

Exploratory Data Analysis

Number of hostels by City in Japan (kaggle)

Tokyo has the most number of hostels which is understandable since it is one of the largest cities of the world and hence might attract a lot of visitors. Also, the two airports in Tokyo make it an important hub in the pacific rim. I visualized all the hostels on map using Folium and Open Street Maps. Below is the geo-visualization of the hostels in Tokyo

Hostels in Japan

For a person interested in opening a new hostels, it is essential to identify the right area to do so. The main factors to consider while making such a decision is demand, supply and cost.

Hostel Density in Tokyo

Demand and supply usually go hand-in-hand in such cases i.e. we can say that the more the hostels in a region, the higher the demand. It is evident from the above map that Taito and Chuo are host to many hostels. Hostel density is highest in these neighborhoods. However, if we compare the land prices in these regions, they are amongst the most costly localities to buy property.

Taito and Chuo contain the highest number of hostels from our dataset. This means that these wards must attract lot of tourists, no other reason appropriately justifies why they house so many hostels inspite of being very costly. Sumida is a neighbour of Taito and is almost 41% cheaper than Taito. It also contains decent amount of hostels but not as many as Taito or Chuo, which leaves space for new opportunities.

Coming to the next question…

Does proximity to city centre affect the price of a spot at hostel?

Well I thought so, but the analysis did not support my view.

We can see a very weak negative correlation between the price and distance of hostel from city centre. Our intuition is supported mathematically by the Pearson coefficient which turned out to be -0.3. The correlation coefficient is very less to make any strong inferences.

Which wards have hostels with very high rating for security?

For this, I plotted hostels with security rating above 9.5

Left: Hostels rated high(>9.5) for security in green and hostels with low security(<8) in red. Right: Wards with most secure hostels.

Most of the hostels rated high for their security are in Katsushika, Kita, Meguro, Shibuya and Shinagawa.

Which wards/regions house the most “value for money” hostels?

The darker the shade, the higher is the average value for money rating of its hostels

Ota-ku and Taito-ku have high average value for money rating which can be explained by the fact that Taito has many landmarks and tourist places, and Ota-ku is home to Haneda International Airport.

What type of venues in the neighborhood affect a hostel’s rating?

Of course, the venues in the neighborhood alone are not a strong predictor of rating, but I aimed to find out some rough patterns. Following are the things I found out:

Proximity to a mode of transportation seemed to greatly affected the ratings, since 50% of hostels near to a bus/metro station were rated Fabulous (highest rating)

Proximity to convenience store did not have any effect.

Hostels near museums and malls had a lower rating than the overall average. Only 25% of hostels in such areas were rated high compared to 32% overall.

Clustering

I clustered the hostels twice. Once depending on the different rating parameters, price and distance from city centre. Second one based on the hostel’s neighborhood.

Consider the following case, that a traveller has selected a hostel, but he finds that it is full! What options does he have? One can either go for a hostel which is similar in rating and price to the one he/she has selected. Or he/she can consider hostels which boast similar neighborhood.

Clusters based on rating and price

I used K-Means algorithm and found out K to be 6 using the elbow method, scope of which is outside the scope of this port. You can see the color-coded clusters below and the distinguishing features of each cluster.

Cluster 0:

Very High Cost. Close to City centre. Very high cleanliness and security ratings

Cluster 1:

Medium cost. Far from city centre. High Value for Money score.

Cluster 2:

Low Cost. Average overall score between 8.5 and 9. Low atmosphere rating.

Cluster 3:

Low Cost. Close to city centre. Low facilities and staff score.

Cluster 4:

High Cost, On average 7km of City centre. All ratings moderately high

Cluster 5:

Very low cost. Far away from city centre. High value for money and location ratings.

Clusters based on neighborhood

This time, I fixed K in our K-Means algorithm to be 4 since otherwise each hostel would be assigned a unique cluster which defeats the purpose.

I examined each cluster and determine the discriminating venue categories that distinguish each cluster. Based on the defining categories, I then assigned a name to each cluster.

Summary

In the above study, I explored and analysed various aspects of the hostels scene in Tokyo, Japan using data science. We used an existing dataset and combined it with data collected from Foursquare API as well as data scraped from a website. We performed EDA and clustering on these datasets in our pursuit of solutions. We were able to find satisfactory answers to the questions we posed before the study.

The study is based on limited data, but it is nevertheless a significant step in shedding light on the hostel scene in Tokyo. This study can be repeated easily for other cities of the world.

The notebook and persisted datasets can be found on my github repository here.

Happy Learning!