Introduction Video: https://www.youtube.com/watch?v=hREgA79KWEo
This project is intended for visualizing the trends and interesting patterns in Taxi ridership data (2019) in Chicago. We are choosing to look at data from 2019 data is because it is pre-COVID data and more representative of a 'typical' year.
Let us focus our attention on the left-hand side, where the controls are placed. The visualization starts with Chicago as a chosen community area, which means data from every community in Chicago is aggregated. Furthermore, the user can select the Outside Chicago Area checkbox to also include the data for when the taxi ride started or ended in Chicago. Next, the user can go ahead by selecting West Lawn as the community area, after which From and To radio buttons come to life as they are enabled. Here we can specify and aggregate data for only when the trip started from West Lawn and ended somewhere in Chicago, or when the trip started somewhere in Chicago and ended in West Lawn. This is assuming the fact that the Outside Chicago Area is deselected. If the Outside Chicago Area is selected when West Lawn is chosen as the community area then the following is how the data would be aggregated: Trip started from or ended in West Lawn regardless of destination or starting point, respectively. Next, the user can select between the time formats: 12-hour or 24-hour. These changes are reflected in the bar graph and the table where time is displayed. Next, the user can select a distance unit between either Miles or Kilometers. These changes are reflected in the bar graph and the table where time is displayed. Next, the user can select whether they want to look at data from a specific taxi company or display data from all the companies. This helps the user investigate specific statistics about an individual taxi company.
On the "About" page, some details are listed such as creators of the application, date published, data sources, data owner, etc.
Next, we will look at graphs and tables associated with the graphs. Then at the end, we will look at a map that displays ridership data per community. The data displayed in the graph adapts based on the controls we explored above.
On the left-hand side, the graph displays one bar for each day of 2019 and each bar represents rides for that day. On the right-hand side, the same data can be explored in a tabular format, where users can look at individual data points. The table can be sorted in an ascending or descending order for each column.
On the left-hand side, the graph displays one bar for each month of 2019 and each bar represents rides for that month. On the right-hand side, the same data can be explored in a tabular format, where users can look at data aggregated for each month.
Here, the graph displays aggregated data for each day of the week in 2019 starting from Monday to Sunday. Next to the graph, we can see the data shown in a table, where each column can be sorted in ascending or descending order by clicking on the column names.
Next, we have taxi rides by the hour of the day. Here, the graph shows aggregated data about each day in 2019. The x-axis adapts to a 12-hour or 24-hour format based on the controls that we explored in the earlier section. Next to the graph, we can see the data from the graph shown in a table, where the data can be sorted in ascending or descending order by clicking on the column names in the table.
Here we have a graph that shows data based on trip range. The trip distance can be adapted to two distance formats based on the controls on the left side: Miles and Kilometers. When Kilometers is selected, ranges are broken down based on the following bins: 0.8 - 2, 2 - 5, 5 - 10, 10 - 15, 15 - 25, 25 - 35, 35 - 160. When Miles is selected, ranges are broken down based on the following bins: 0.5 - 1, 1 - 3, 3 - 5, 5 - 10, 10 - 15, 15 - 20, 20 - 100. Next to the graph, we can see the data from the graph shown in a table, where the data can be sorted by trip range or rides count for each bin in ascending or descending order by clicking on the column names in the table.
Here we have a graph that shows data based on trip duration. The time duration is broken down into 7 subset: 1 - 5 min, 5 - 10 min, 10 - 15 min, 15 - 20 min, 15 - 20, 20 - 30 min, 1/2 hr - 1 hr, > 1 hr. Each bar in the graph and each table entry in the table represents each one of those bins.
Here, the x-axis represents either the destination or the starting point based on whether the user clicks the radio button To or From. The y-axis represents the percentage of riders traveling from one point to another. It makes more sense when we look at the title. Let us select West Lawn as the community area and click on the To radio button. The graph shows the percentage of riders traveling from the community area on the graph to West Lawn. On the right-hand side, we have a table that displays the same data but in a tabular format. The percentage can be sorted from low to high or vice-versa. The name can also be sorted by the pick-up area or drop-off area depending on where you chose To or From on the radio button, respectively.
Furthermore, the data we looked at in the graph is also represented on the map with a heat map. On the upper right of the map, we have legends that go from Red color to Blue color hue representing low to high percentages of riders. Additionally, the community area can be selected by clicking on the community area on the map. When you hover over the community area on the map, you can also see the name and percentage of riders traveling from or to that community area.