This project consisted of performing exploratory analysis of ride-sharing data for PyBer to provide data visualizations based on the realtionship between city types and fare costs to determine which neighbourhoods need to be funded to improve access to locals for ride-sharing services.
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The data reflects logical deductions of ride-sharing trends, the further you get from city centers the less accessible ride-sharing services become. It is evident through the data results that rural cities had the least number of rides with a total of 125 as compared to Suburban with a 625 and Urban with 1625. The data also reflects that the costs of ride-sharing rises as the distance from the city center increases, which also impacts the number of Total Riders per city type and its accesibilty as only 78 people used ride-sharing services in Rural areas as compared to Urban areas with a total of 2405. The results of the analysis can be reference on the table below:
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The results of data are much easily understood with the graph below as it shows the disparity of ride-sharing services across the three different city types, and it clearly demonstrates what are most likely accessibility-influenced trends.
- In order to make ride-sharing more accessible across city types, it would be necessary to implement a ride-sharing infrastructure that could seamlessly connect the three regions. It would also be of importance that the service is affordable for all riders coming to or from any city. These services need to be accessible in terms of affordability and also in commuting time, as it's likely that high costs or commute times would discourage riders from using these services as their primary method of transportation.
Data Source: city_data.csv, ride_data.csv
Software: Jupyter Notebook