The document discusses a study conducted by the authors to determine the relationship between weather and bike ridership in a city. It describes the methodology used, which included gathering datasets on bike rental counts, weather data from 2011-2012, and conducting linear regressions. The results showed weather has a significant impact on bike usage, with rental counts being most strongly correlated with maximum temperature, average wind, precipitation, visibility, and snow levels. Including the year as a variable also improved the accuracy of the regression model.

1. Mother Nature’s Impact
on Bike Ridership
Jackie Zajac
Kays Fattal
Naumaan Nasir

2. Does weather have a relationship
with bike ridership?
Can we predict bike usage based
on weather?

3. INTRODUCTION
• Our team
• Research questions
• Picking datasets
• Our audience

4. METHODOLOGY
• Why linear regression?
• How we manipulated the data
• MySQL engine aggregated
3M table into sum of rental
counts and duration
• Mashed up with 731 rows of
weather data (2011, 2012)
• Added a Year field
• Tools: Excel, MySQL database,
R (Rattle)

5. METHODOLOGY
• Picking our best configuration
• Categoric vs. numeric variables
• Must decide how to measure bike usage
• Must pick best variables
• Error analysis

6. PHASE I
• Began with a broad study of six regressions
• Two target variables (rental counts, duration)
• Three temperature measures
• Minimum, Average, Maximum
• Chunked the day into three time ranges to reflect
temperature during bike rides
• Evaluated multiple weather variables’ affect on
regressions
• Ignored Date field

7. Plots

8. PHASE II
• Combining the data sets
• Picking best variables:
• Bike rental counts as sole target variable
• Maximum temperature
• Utilized date/year field
• Switched Snow to categoric variable
• Analyzed and refined our regression
• Higher accuracy – R-squared = .8374 or 83.74%

9. MSE and R-squared
• A measure of accuracy in one dataset
predicting another
• Relationship between R-squared and MSE

10. X X
X

11. FINAL MODEL
Weight Variable
-4004.501 Intercept
62.118 Maximum Temperature
-132.741 Average Wind
93.162 Precipitation
416.818 Visibility
2063.069 Year
-161.038 Snow [0.0-1.2] inches
-4.945 Snow [1.2-2.0] inches
-588.349 Snow [2.0-3.1] inches
-5.390 Snow [3.1-3.9] inches
Y=

12. LESSONS LEARNED
• Too many independent variables to incorporate
crime dataset in addition to weather dataset
• Means Squared Error (MSE), R-squared
• Only two years’ worth of data was available due to
Bikeshare’s short history (2011, 2012)
• Final model would be even more accurate with
additional historical data

13. CONCLUSION
• Our hypotheses proved true: weather does affect
bike ridership
• Why is Maximum Temperature better?
• Why does the Year improve accuracy?
• The categorical range of snow inches

14. QUESTIONS?
Thanks!