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0.0.2 Question 1b For this assignment, we’ll be using this data to study bike usage in Washington, DC. Based on the granularity and the variables present in the data, what might some limitations of using this data be? What are two additional data categories/variables that one could collect to address some of these limitations? One drawback is the absence of comprehensive meteorological information, especially on precipitation amounts, which can greatly affect the quantity of bikes rented on a daily basis. Furthermore, the dataset lacks detailed geographic information that would be necessary to comprehend regional trends of bike uti- lization and pinpoint high-demand regions, such as the precise locations or bike stations inside the city. Precipitation data would improve the dataset and offer important insights into how weather influences bike rentals. Incorporating location data with precise start and finish locations for bike rentals may also help with the placement and supply of bikes across the city, uncover popular routes, and allow for a more thorough examination of user behavior and citywide mobility patterns. 3

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0.0.5 Question 3c The density plots do not show us how the counts for registered and casual riders vary together. Use sns.lmplot (documentation) to make a scatter plot to investigate the relationship between casual and registered counts. This time, let’s use the bike DataFrame to plot hourly counts instead of daily counts. The lmplot function will also try and draw a linear regression line (just as you saw in Data 8). Color the points in the scatterplot according to whether or not the day is a working day (your colors do not have to match ours exactly, but they should be different based on whether the day is a working day). Hints: * Check out this helpful tutorial on lmplot . * There are many points in the scatter plot, so make them small to help reduce overplotting. Check out the scatter_kws parameter of lmplot . * You can set the height parameter if you want to adjust the size of the lmplot . * Add a descriptive title and axis labels for your plot. * You should be using the bike DataFrame to create your plot. * It is okay if the scales of your x and y axis (i.e., the numbers labeled on the two axes) are different from those used in the provided example. In [35]: sns . set(font_scale =1 ) # This line automatically makes the font size a bit bigger on the plot. Y sns . lmplot(x = 'casual' , y = 'registered' , data = bike, hue = 'workingday' , scatter_kws = { 's' : 0.5 }) plt . title( 'Comparison of Casual vs Registered Riders on Working and Non-working Days' ) plt . xlabel( 'Casual Riders' ) plt . ylabel( 'Registered Riders' ) plt . show() 9

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0.0.8 Question 4b With some modification to your 4a code (this modification is not in scope), we can generate the plot above. In your own words, describe what the lines and the color shades of the lines signify about the data. What does each line and color represent? Hint : You may find it helpful to compare it to a contour or topographical map as shown here . The plot displays the concentration of rides made by both casual and registered users, with clear differences between workdays and non-workdays. For example, workdays (red tones) may have a higher concentration of registered rides than non-workdays (blue tones), suggesting that workdays are when people use the service more frequently for routine or commute-related purposes. On the other hand, the distribution may be more dispersed or have a different center on non-workdays, indicating a more flexible or recreational use of the service. 15

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0.2 6: Understanding Daily Patterns 0.2.1 Question 6a Let’s examine the behavior of riders by plotting the average number of riders for each hour of the day over the entire dataset (that is, bike DataFrame ), stratified by rider type. Your plot should look like the plot below. While we don’t expect your plot’s colors to match ours exactly, your plot should have a legend in the plot and different colored lines for different kinds of riders, in addition to the title and axis labels. In [21]: average_counts = bike . groupby( 'hr' )[[ 'casual' , 'registered' ]] . mean() . reset_index() plt . figure(figsize = ( 12 , 6 )) sns . lineplot(x = 'hr' , y = 'casual' , data = average_counts, label = 'casual' ) sns . lineplot(x = 'hr' , y = 'registered' , data = average_counts, label = 'registered' ) plt . title( 'Average Count of Casual vs. Registered by Hour' ) plt . xlabel( 'Hour of the Day' ) plt . ylabel( 'Average Count' ) plt . legend() plt . show() 21

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0.2.2 Question 6b What can you observe from the plot? Discuss your observations for both types of riders, and hypothesize about the meaning of the peaks in the registered riders’ distribution. We can infer different use patterns for casual and registered riders during the day by looking at the plot for question 6a. It appears that casual ridership rises gradually in the morning, peaks in the early afternoon, and then declines as dusk draws near. This implies that non-commuters or tourists who use the service occasionally prefer to use it for errands or leisure during the day. In contrast, there are two clear peaks in the registered ridership, one in the early morning and another in the late afternoon, which correspond to regular work commute times. This suggests that commuters who use the service to get to work in the morning and go home in the evening are probably among the registered riders. The midday and late night troughs indicate that demand is lower during working hours and when most people are at home. The work commute could be the cause of the peaks in the registered riders’ distribution; the morning peak would be associated with the rush to get to work, and the evening peak would be associated with the mass return home. This pattern highlights potential opportunities for targeted promotions or increased service availability during peak hours, underscoring the importance of the service for regular commuters. 23

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0.2.3 Question 7b In our case, with the bike ridership data, we want 7 curves, one for each day of the week. The x-axis will be the temperature (as given in the 'temp' column), and the y-axis will be a smoothed version of the proportion of casual riders. You should use statsmodels.nonparametric.smoothers_lowess.lowess just like the example above. Un- like the example above, plot ONLY the lowess curve. Do not plot the actual data, which would result in overplotting. For this problem, the simplest way is to use a loop. You do not need to match the colors on our sample plot as long as the colors in your plot make it easy to distinguish which day they represent. Hints: * Start by plotting only one day of the week to make sure you can do that first. Then, consider using a for loop to repeat this plotting operation for all days of the week. • The lowess function expects the y coordinate first, then the x coordinate. You should also set the return_sorted field to False . • You will need to rescale the normalized temperatures stored in this dataset to Fahrenheit values. Look at the section of this notebook titled ‘Loading Bike Sharing Data’ for a description of the (normalized) temperature field to know how to convert back to Celsius first. After doing so, convert it to Fahrenheit. By default, the temperature field ranges from 0.0 to 1.0. In case you need it, Fahrenheit = Celsius × 9 5 + 32 . Note: If you prefer plotting temperatures in Celsius, that’s fine as well! Just remember to convert accordingly so the graph is still interpretable. In [34]: from statsmodels.nonparametric.smoothers_lowess import lowess plt . figure(figsize = ( 10 , 8 )) bike[ 'temp_F' ] = bike[ 'temp' ] . apply( lambda x: x * 41 * ( 9 / 5 ) + 32 ) for day in bike[ 'weekday' ] . unique(): data = bike[bike[ 'weekday' ] == day] line = lowess(data[ 'prop_casual' ], data[ 'temp_F' ]) plt . plot(line[:, 0 ], line[:, 1 ], label = day) plt . xlabel( 'Temperature (Fahrenheit)' ) plt . ylabel( 'Casual Rider Proportion' ) plt . title( 'Temperature vs Casual Rider Proportion by Weekday' ) plt . legend() plt . show() 25

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0.2.4 Question 7c What do you observe in the above plot? How is prop_casual changing as a function of temperature? Do you notice anything else interesting? The percentage of casual riders (prop_casual) in the plot generally rises with temperature. The fact that this pattern holds true on all days suggests that the arrival of warmer weather will probably lead to an increase in the casual usage of ride-sharing services. In addition, the plot indicates that on weekends (Saturday and Sunday) as opposed to weekdays, the slope of the increase appears to be steeper. This may suggest that weekend temperatures have a greater impact on casual riders, who may have more time to engage in leisure activities such as using ride-sharing services during weekends. Furthermore, the beginning points of the curves for each day differ, with weekdays starting lower than weekend values, even though the increase in prop_casual is clearly visible as temperatures rise. This may suggest that there is a base level of casual ridership that is independent of temperature, perhaps for essential travel, but that leisure travel—which is more temperature-dependent, particularly on weekends—contributes to higher levels of casual ridership. 27

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0.2.5 Question 8a Imagine you are working for a bike-sharing company that collaborates with city planners, transportation agencies, and policymakers in order to implement bike-sharing in a city. These stakeholders would like to reduce congestion and lower transportation costs. They also want to ensure the bike-sharing program is implemented equitably. In this sense, equity is a social value that informs the deployment and assessment of your bike-sharing technology. Equity in transportation includes: Improving the ability of people of different socio-economic classes, gen- ders, races, and neighborhoods to access and afford transportation services and assessing how inclusive transportation systems are over time. Do you think the bike data as it is can help you assess equity? If so, please explain. If not, how would you change the dataset? You may discuss how you would change the granularity, what other kinds of variables you’d introduce to it, or anything else that might help you answer this question. Note : There is no single “right” answer to this question – we are looking for thoughtful reflection and commentary on whether or not this dataset, in its current form, encodes information about equity. Even though the current bike dataset might shed light on usage trends, it might not be enough to conduct a thorough assessment of equity. The dataset would need to contain geographic data to determine where the service is being used and who has access to it, as well as demographic data about riders, such as age, gender, income level, and race, in order to address equity. Surveys of riders’ satisfaction, accommodations for people with disabilities, and multilingual accessibility are possible additional factors. Adjustments to collect longitudinal data may demonstrate shifts over time, illustrating the effects of policy adjustments or service expansions. The dataset would need to measure accessibility and usage across various demographics and reflect the diversity of the community in order to accurately assess equity. 29

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0.2.6 Question 8b Bike sharing is growing in popularity , and new cities and regions are making efforts to implement bike- sharing systems that complement their other transportation offerings. The goals of these efforts are to have bike sharing serve as an alternate form of transportation in order to alleviate congestion, provide geographic connectivity, reduce carbon emissions, and promote inclusion among communities. Bike-sharing systems have spread to many cities across the country. The company you work for asks you to determine the feasibility of expanding bike sharing to additional cities in the US. Based on your plots in this assignment, would you recommend expanding bike sharing to additional cities in the US? If so, what cities (or types of cities) would you suggest? Please list at least two reasons why, and mention which plot(s) you drew your analysis from. Note : There isn’t a set right or wrong answer for this question. Feel free to come up with your own conclusions based on evidence from your plots! The analysis suggests that expanding bike-sharing to more US cities is a promising idea. The most important graphs that help with this decision might be those that demonstrate heavy use during commute hours, which suggests that bike sharing can reduce peak traffc, and the positive relationship between pleasant weather and higher casual usage, which points to the possibility of bike sharing becoming popular in cities with temperate climates. Expansion, however, ought to take into account the particular requirements of every city, taking into account the local transit system, the density of the city, and the cultural perspectives on cycling. Bike-sharing schemes would probably be most beneficial to cities with strong public transportation networks and a dedication to environmentally friendly mobility. Additionally, bike-sharing might be used by smaller or mid-sized cities where traffc is starting to become an issue as a preventative measure. 31

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