An in-depth dataset of New York bus stations, offering insights into various station features, accessibility, and geographic distribution to support urban planning and transportation research.
This dataset provides an extensive overview of bus stations across New York that dives into station features, reviews, and spatial distribution for research, planning, and decision-making.
Explore the detailed dataset on New York bus stations, featuring sample entries that highlight key station attributes, user reviews, accessibility options, and geographic information. This dataset serves as a vital resource for urban planning, transportation optimization, and informed decision-making.
This dataset provides an extensive overview of bus stations across New York that dives into station features, reviews, and spatial distribution for research, planning, and decision-making.
How can this dataset benefit you?
Leverage the dataset to analyze bus station distribution, traffic trends, and accessibility, aiding policymakers in improving transportation infrastructure and addressing transit disparities.
Provide commuters with critical information on station features, reviews, and geographic proximity, helping them plan efficient and convenient travel routes.
Use reviews and ratings data to identify well-performing stations and benchmark others, fostering improvement through data-driven insights.
The dataset provides detailed information about New York bus stations, including station names, geographic coordinates (latitude and longitude), and contact information (phone numbers and addresses). It also includes station ratings, number of reviews, and traffic-related metrics, such as daily traffic trends and accessibility data. Additionally, it captures tags related to station features and accessibility options like wheelchair availability. These variables offer valuable insights for urban planning, commuter decision-making, and resource allocation.
With a staggering total of 15,211 bus stations, New York Dataset includes their information along with the accompanying map displaying the precise geographic locations of these stations. This visualization highlights the dense clustering of stations in central urban areas, particularly in Manhattan and parts of Brooklyn, while also showcasing the spread across suburban and less populated regions.
The pie chart above illustrates the distribution of bus-related categories across New York, highlighting the proportions of bus stations, bus routes, and bus stops. Each segment represents a specific category, providing a clear view of the relative distribution within the transportation network.
By analyzing the data made available, more information about the location and availability of all bus stations in New York.
Key insights:
This bar chart illustrates the top 10 most frequently searched keywords across New York bus stations, as recorded in the “keywords_used_in_search” field. The most searched keyword, by far, was "homeless," highlighting a strong concern or interest in social issues and facilities near bus stations—perhaps a reminder that these hubs serve more than just transit needs.
Following this, other commonly searched keywords included "trip," "metro card," and "ticket," reflecting a general focus on travel services and fare management. Keywords such as "commuters," "organized," and "train" emphasize the interest in efficient transit and commuter services, while "subway," "airport," and "restrooms" underscore the importance of connectivity and essential amenities.
These search patterns provide valuable insights into the key areas of interest for transit users, potentially guiding facility management, service enhancements, and policy-making strategies.
Based on the data presented in the line graph above, it is evident that Friday marks the peak of activity at New York bus stations. The graph shows a significant increase in bus station activity on Fridays compared to other weekdays. Following Friday, there is a noticeable decline in activity levels on Saturday and Sunday, where the numbers are lower in comparison to the weekdays.
Key Insights:
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