Introduction I am exploring the relationship between BlueBikes trips on rainy days versus trips on nonrainy days from BlueBikes September 2022 bike trip data. While we would expect to see a very strong correlation between the frequency of trips on rainy days and the frequency of trips on nonrainy days, I am interested to see … More BlueBikes: Correlation of Trips on Rainy Days and Nonrainy Days
Introduction In this phase of analysis, we shift our focus from the district level, as used in previous investigations, to a more granular station-level exploration of urban bike-sharing dynamics. The pivot to the station level is motivated by the pursuit of a more detailed and expansive dataset, providing a richer sample for a nuanced examination. … More Correlation – Blue Bikes
Introduction For my second city exploration, I have selected Salem as my destination of choice. The decision to explore Salem was prompted by a comprehensive analysis I conducted, focusing on the latent construct of “Tourism Impact” and the intriguing insights derived from examining the proportion of casual riders compared to members who complete their rides. … More City Exploration 2 – Blue Bikes in Salem
Mia Scholes, Nov. 1, 2023 Background I have been analyzing Blue Bike data mainly from August and September of 2022 to understand use trends. Inspired by one of my recent assignments showing that the City of Salem had a significantly lower average trip distance (in miles) than any of the other 10 towns represented, I … More City Exploration 2: Blue Bikes in Salem
Shannon Sullivan Introduction I have been working with BlueBikes September 2022 trip data. A few weeks ago, I noticed a significant decline in the number of trips on random throughout the month. I found that on these days, it rained. I also work for a transportation nonprofit, and I speak with people about the … More City Walk #2: BlueBikes
Bryce Russell-Benoit Introduction When the high school students visited our classroom, they seemed rather uninterested in the BlueBikes dataset. None of them had used the system themselves and didn’t understand why it was worth the cost when they could drive wherever they needed instead. This drew my attention to the relationship between money and ridership. … More City Exploration 2: BlueBikes
Bryce Russell-Benoit Background In this data workshop, I will begin building the manifest variables required to uncover the latent constructs from last week’s assignment. My latent construct of interest is whether BlueBikes high cost is discouraging use in low-income areas. When talking with the high school students that visited class, none of them had used … More Building Latent Constructs: BlueBike
Introduction This study aims to quantify the concept of ‘Tourism Impact,’ a measure that enables the evaluation of a bike-sharing system’s influence, such as Blue Bikes, on local tourism. Gaining a comprehensive understanding of this impact is crucial, as it can unveil significant insights into the economic, environmental, and social advantages of these systems within … More Building Latent Constructs – Blue Bikes
The latent construct I am exploring is peoples’ behavior regarding riding BlueBikes on days when there is precipitation. Because of a graph I previously made that showed a large dip in the number of trips on days when there was rain, I chose to examine this latent construct. That graph is included below; it shows … More Latent Constructs: BlueBikes
Mia Scholes, Oct. 25th 2023 Background This week, I started integrating the station information dataset, which includes station District location. Using District as the aggregation variable, I sought to answer questions about how users in different areas use Blue Bikes in terms of trip quantity, time of day and duration in August of 2022. Understanding … More Building Latent Constructs: Blue Bikes
Seeing Boston Neighborhoods through Administrative Data 