divvy bike use data analysis and recommendations using tableau

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Use Analysis Hanbit Choi and Recommendations

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Page 1: Divvy Bike Use Data Analysis and Recommendations using Tableau

Use Analysis

Hanbit Choi

and Recommendations

Page 2: Divvy Bike Use Data Analysis and Recommendations using Tableau

# 1 Overview# 2 Analysis - Peak season- User type- Weekday vs Weekend- Popular stations- Community areas# 3 limitations# 4 Recommendations

Use Analysis

Agenda

Page 3: Divvy Bike Use Data Analysis and Recommendations using Tableau

Divvy is a bike sharing system operated in Chicago. It offers two type of service, 24hr pass and annual membership. Customers should change bike in a dock within 30 mins each to avoid being charged extra fee.

About Divvy

Use Analysisand Business Recommendations

Page 4: Divvy Bike Use Data Analysis and Recommendations using Tableau

Use Analysisand Business Recommendations

I like Divvy! because I learned how to ride a bike with Divvy although I didn’t have my own bike.

There are 577 stations in service currently but I experienced that I couldn’t get a bike near the lake and it happened again in the museum campus area on the same day.

So I would like to analyze Divvy use and get useful insights.In addition, I would like to present recommendations for business.

Page 5: Divvy Bike Use Data Analysis and Recommendations using Tableau

# 1 Overview Use Analysisand Business Recommendations

• Data Resources : City of Chicago(Chicago data portal), Divvy

• Time frame of data : Divvy use - 2015 ver, Population by community area (Census) - 2010 ver

• Divvy trip total record in 2015 : 3,183,439

• Community area : 77

• Available Divvy bike stations : 577

For analysis

-Only Q1 & Q2 available for 2016 data, so working on 2015 full dataset

-Created SQL database with 3 tables : Stations, Divvy, and Community

-Figured out relationship between Divvy station and community area

based on polygon custom mapping in Tableau 9.3

-Population level by community is represented by color saturation

-Analyzed data based on “from station” which customers took Divvy bike for trip start

-Subscribers could provide their birth year and gender information

Page 6: Divvy Bike Use Data Analysis and Recommendations using Tableau

In 2015, Summer Season (June to August) accounted for 45.15% of total divvy trip records.Customers were much more likely to ride Divvy in Summer time.

Avg. trip duration per month

% of total count of trip_starttime_month

# 2 Analysis - Peak season Use Analysisand Business Recommendations

Page 7: Divvy Bike Use Data Analysis and Recommendations using Tableau

# 2 Analysis - User type Use Analysisand Business Recommendations

Male subscribers appeared 1,686,117 times in the dataset but female subscribers showed 567, 351 records only.People who are in young professional age, such as from mid- twenties to mid – thirties, used Divvy much more than other generations.

Page 8: Divvy Bike Use Data Analysis and Recommendations using Tableau

total trip duration(seconds)

Avg. trip duration (seconds)

# 2 Analysis - User type Use Analysisand Business Recommendations

Avg. trip duration of one time customer was longer than subscriber's one but subscriber's total trip duration was more longer than others type of users.

Page 9: Divvy Bike Use Data Analysis and Recommendations using Tableau

# 2 Analysis - User type / Popular stations Use Analysisand Business Recommendations

According to top 10 popular stations by user type, Non- subscribers(one time customers and kids) seem that they rode Divvy along the lake. On the other hand, subscribers rode Divvy in the city area. And top of popular stations for subscriber were loved much more than top of popular stations for Non- subscribers.

Non-subscribers

Subscriber

Page 10: Divvy Bike Use Data Analysis and Recommendations using Tableau

# 2 Analysis - Weekday vs Weekend / Popular stations Use Analysisand Business Recommendations

According to top 5 popular stations by day of the week, customers were likely to ride Divvy along the lake on weekend.

weekend

weekday

Page 11: Divvy Bike Use Data Analysis and Recommendations using Tableau

# 2 Analysis - Weekday vs Weekend / Popular stations Use Analysisand Business Recommendations

weekend

weekday

According to top 5 popular stations by day of the week, customers usually rode Divvy in the city area, especially, in Near West Side community on weekdays. And why the West Loop, among other neighborhoods in the community area, specifically?

Page 12: Divvy Bike Use Data Analysis and Recommendations using Tableau

# 2 Analysis – Divvy became a trip mate and a commuting option Use Analysisand Business Recommendations

On weekend, customers rode Divvy for a longer time than weekdays use. Therefore, it is assumed that they traveled Chicago along the lake with divvy on weekend and commute for not too far distance in the downtown area on weekdays. Divvy was used as one of commuting options by mainly subscribers.

total trip duration(seconds)

Avg. trip duration (seconds)

Page 13: Divvy Bike Use Data Analysis and Recommendations using Tableau

# 2 Analysis – Chicago community area Use Analysisand Business Recommendations

As mentioned, according to top 5 popular stations by day of the week, customers usually took Divvy bike from Near West Side community area on weekdays. And why the West Loop, among other neighborhoods in the community area, specifically?

Ogilvie Metra Station

Union Amtrak/ Metra Station

Because…there are Ogilvie and Union Metra station in the West Loop so people who lived in suburb cities got off from trains and took Divvy there to go to work.

Page 14: Divvy Bike Use Data Analysis and Recommendations using Tableau

# 2 Analysis – Chicago community area Use Analysisand Business Recommendations

More bike stations are available in the Loop generally andTop 10 big stations(47 docks ~ 31 docks) are mostly located in/near the Loop as well but the West Loop area has only one big one called Canal St & Adams St which was no.2 among top 5 popular stations on weekdays.

Page 15: Divvy Bike Use Data Analysis and Recommendations using Tableau

- Divvy data doesn’t provide community area information for each station location and Tableau 9.3 doesn’t support two sets of latitude & longitude (stations / community area boundaries) map in a same view so overlaid two separated maps manually by exporting as images from Tableau

- Population information by community area based on 2010 census which is the latest census data

# 3 Limitations

Use Analysisand Business Recommendations

Page 16: Divvy Bike Use Data Analysis and Recommendations using Tableau

Use Analysisand Business Recommendations

# 4 Recommendations

• New station installing in Near West Side (the West Loop)

: Especially, near the Metra stations. Although Near West Side has not a high level of population, there are big transportation centers, which are Ogilvie and Union, so many commuters from suburb areas will take and return Divvy bikes in this area. Also, compared to the Loop, there are less Divvy docks.

• Divvy - Metra joint pass: Divvy offers annual membership and Metra offers

monthly pass. Divvy has become a powerful commuting option so Divvy & Metra joint pass would have marketability.

Page 17: Divvy Bike Use Data Analysis and Recommendations using Tableau

T H A N K Y O U