{"id":17256,"date":"2025-01-30T13:05:30","date_gmt":"2025-01-30T11:05:30","guid":{"rendered":"https:\/\/spatialworld.fi\/?page_id=17256"},"modified":"2025-02-05T18:06:15","modified_gmt":"2025-02-05T16:06:15","slug":"17076-2-2-3-2-2","status":"publish","type":"page","link":"https:\/\/spatialworld.fi\/sv\/17076-2-2-3-2-2\/","title":{"rendered":"Superset Druid Transport"},"content":{"rendered":"[vc_row type=”in_container” full_screen_row_position=”middle” column_margin=”default” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” scene_position=”center” text_color=”dark” text_align=”left” row_border_radius=”none” row_border_radius_applies=”bg” overflow=”visible” overlay_strength=”0.3″ gradient_direction=”left_to_right” shape_divider_position=”bottom” bg_image_animation=”none”][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” column_element_direction_desktop=”default” column_element_spacing=”default” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”none” column_link_target=”_self” column_position=”default” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1\/1″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid”][vc_column_text css=”” text_direction=”default”]\n

Analyzing Bus Accessibility using FME, Druid, and Superset: A Step-by-Step Tutorial<\/span><\/h1>\n[\/vc_column_text][\/vc_column][\/vc_row][vc_row type=”in_container” full_screen_row_position=”middle” column_margin=”default” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” scene_position=”center” text_color=”dark” text_align=”left” row_border_radius=”none” row_border_radius_applies=”bg” overflow=”visible” overlay_strength=”0.3″ gradient_direction=”left_to_right” shape_divider_position=”bottom” bg_image_animation=”none”][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” column_element_direction_desktop=”default” column_element_spacing=”default” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”none” column_link_target=”_self” column_position=”default” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1\/1″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid”][image_with_animation image_url=”17280″ image_size=”medium_large” animation_type=”entrance” animation=”None” animation_movement_type=”transform_y” hover_animation=”none” alignment=”center” border_radius=”none” box_shadow=”none” image_loading=”default” max_width=”75%” max_width_mobile=”default”][\/vc_column][\/vc_row][vc_row type=”in_container” full_screen_row_position=”middle” column_margin=”default” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” scene_position=”center” text_color=”dark” text_align=”left” row_border_radius=”none” row_border_radius_applies=”bg” overflow=”visible” overlay_strength=”0.3″ gradient_direction=”left_to_right” shape_divider_position=”bottom” bg_image_animation=”none”][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” column_element_direction_desktop=”default” column_element_spacing=”default” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”none” column_link_target=”_self” column_position=”default” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1\/1″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid”][vc_column_text css=”” text_direction=”default”]\n

Context<\/h2>\n

Understanding urban bus accessibility requires analyzing both spatial and temporal data. This tutorial demonstrates how to manage and visualize such complex data efficiently using Superset, FME, and Druid.<\/p><\/blockquote>\n

In urban areas, spatial-temporal dynamics like commuting present complex challenges that require robust tools to analyze multiple variables and vast amounts of data effectively. Understanding bus accessibility involves not only the geospatial distribution of bus stops but also the temporal aspects of transit schedules. Time is a crucial component, as accessibility varies throughout the day, week, and in different areas, impacting how well different population segments are served.<\/p>\n

In the realm of modern business intelligence, integrating and analyzing complex geospatial and temporal data remains a significant challenge. Superset, coupled with FME and Druid, provides a powerful solution for managing these complexities efficiently. This combination allows for the integration of multiple sources of complex data at scale while maintaining low latency, minimal coding requirements, and robust visualization capabilities.<\/p>\n

Goal<\/h2>\n

We aim to generate a data point for each bus arrival at every stop over the next three months, resulting in multiple entries per stop to capture the complete schedule with FME. We will then slice through this data to get insights really fast with Druid through Superset.<\/p>\n

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Input Data<\/h2>\n

GTFS Files: This specification is used to exchange planned transport. it is used in many countries. <\/strong><\/p>\n

For Helsinki, data is accessible at : https:\/\/www.hsl.fi\/en\/hsl\/open-data#public-transport-network-and-timetables-gtfs<\/a>.<\/p>\n

We used the following files for our study:<\/p>\n