Bryce Russell-Benoit Rationale For the last few months, I have been working to examine the drivers of BlueBikes ridership in Boston. This question came to me during my discussion with the high school students that visited class early in the semester. They only just became eligible to ride BlueBikes due to their age so … More Urban Hike: BlueBikes in Seaport
Introduction In the bustling landscape of urban mobility, understanding the factors influencing the distances covered in bike-sharing rides is paramount for optimizing transportation services. This exploration delves into the dynamic relationship between age, gender, and trip distances using Blue Bikes data. Focusing on the iconic MIT/Cambridge area, this study aims to unravel patterns that transcend … More City Exploration 3 : Urban Hike at MIT / Cambridge
Mia Scholes 12/6/23 Background I have been analyzing Blue Bike data from September 2022. In past weeks, I’ve looked at station use as defined by trips starting at each station within the month, and factors that may predict how well a station performs. I formed three new measures that I thought might explain the success … More City Exploration 3: Blue Bikes
Introduction I explored Kendall Square and the Massachusetts Institute of Technology (MIT) campus in Cambridge. I chose this area because, according to the BlueBikes September 2022 bike trip data, the MIT at Mass Ave./Amherst St. station sees a heavy volume of user traffic. This station is popular on sunny and rainy days alike. Even though … More BlueBikes City Walk #3: Kendall Square/MIT
Introduction I have been examining the relationship between the frequency of BlueBikes trips on rainy days versus nonrainy days. This week, I will use the two-sample T test and ANOVA to analyze the differences between variables. For the T-test, I compare the frequency of trips on rainy days versus nonrainy days. Then, create a new … More BlueBikes: Comparing Groups
Bryce Russell-Benoit Background This week, my goal is to look at different slices of BlueBikes data and compare the groups. I have been very focused on examining wider trends across the whole system, but comparing different groups of data can also be very illuminating. Specifically, I will explore how BlueBikes stations perform depending on their … More Comparing Groups: BlueBikes
Introduction In this analysis, we delve into the dynamics of bike-sharing activities, utilizing the total rides (TR) as a key metric to gauge the community’s usage and demand for bike-sharing services. Understanding the factors influencing total rides is essential for optimizing bike-sharing systems and tailoring services to meet varying user needs. Two distinct categorical variables, … More Comparing Groups – Blue Bikes
Mia Scholes 2023-11-20 Background This week’s addition of the t-test and ANOVA test to our toolkit allowed me to assess differences across categorical variables of a continuous variable of interest. After establishing that proximity to the bike network has a statistically significant impact on the number of trips beginning at a Blue Bike station, I … More Comparing Groups: Blue Bikes
Mia Scholes. November 15th, 2023 Background This week built off of last week’s analysis of station use as it relates to the roadway bike network. I tested the hypothesis that Blue Bike stations near safer bike infrastructure outperformed those nearest to lower quality infrastructure or no bike lanes at all. From the mapping and visualization, … More Correlation: Blue Bikes
Bryce Russell-Benoit Background For the last few weeks, I have been working towards examining the relationship between income and ridership. I have tried many different methods to try to isolate the impact of income which I can now examine and compare. In this data exploration, I aim to look at the relationships between population, income, … More BlueBikes: Correlation
Seeing Boston Neighborhoods through Administrative Data 