Data Mining for Global Bicycling Trends

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Tracking global bicycle ownership patterns

In the first effort on this scale, we sourced data from 11 types of surveys spanning a period of 23 years (1989 – 2012) and covering 150 countries.


Map showing number of survey sources for each country in bicycle ownership dataset
Given that the data were sparse, we applied unsupervised learning to discover any underlying patterns that might useful to researchers and other stakeholders in sustainable active transport. Using a suite of methods: dynamic time warping (data alignment), the gap test (optimal cluster numbers) and hiearchical agglomerative clustering, we found four prevailing groups of characteristic ownership levels.

To find the group trends, we used the weighted average rolling mean over a five-year window. Extrapolating from the 1.25 billion households represented in the surveys we mined and the common question of interest: “Does your household have at least one bicycle?” we estimate that 580 million or more bicycles were available to the world’s households as of 2012.

Finally, we shed light on aggregate global trends in ownership, with a spotlight on India and China, as they together account for a quarter of the world’s households. We conclude that globally, bicycle ownership has not been on the rise.

[/vc_column_text][/tab][tab icon_family=”none” title=”Map of bicycle ownership” id=”1532330197-2-9″ tab_id=”1532330313580-2″][vc_column_text][/vc_column_text][/tab][tab icon_family=”none” title=”Trends in bicycle ownership” id=”1532335726585-2-5″ tab_id=”1532330313580-2″][vc_column_text][/vc_column_text][/tab][/tabbed_section][/vc_column][/vc_row][vc_row type=”in_container” full_screen_row_position=”middle” equal_height=”yes” content_placement=”middle” scene_position=”center” text_color=”dark” text_align=”left” top_padding=”30″ disable_element=”yes” overlay_strength=”0.3″ shape_divider_position=”bottom” shape_type=””][vc_column column_padding=”no-extra-padding” column_padding_position=”all” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_shadow=”none” column_border_radius=”none” width=”2/3″ tablet_text_alignment=”default” phone_text_alignment=”default” column_border_width=”none” column_border_style=”solid”][vc_column_text]This work has been published as Tracking global bicycle ownership patterns in the Journal of Transport and Health, which highlights work at the transport-health nexus. Click here to view the paper on ScienceDirect. Download it here.[/vc_column_text][/vc_column][vc_column column_padding=”no-extra-padding” column_padding_position=”all” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_shadow=”none” column_border_radius=”none” width=”1/3″ tablet_text_alignment=”default” phone_text_alignment=”default” column_border_width=”none” column_border_style=”solid”][image_with_animation image_url=”7027″ alignment=”right” animation=”None” border_radius=”none” box_shadow=”none” max_width=”100%”][/vc_column][/vc_row][vc_row type=”in_container” full_screen_row_position=”middle” equal_height=”yes” content_placement=”top” scene_position=”center” text_color=”dark” text_align=”left” class=”section-top-padding ” overlay_strength=”0.3″ shape_divider_position=”bottom” shape_type=””][vc_column column_padding=”no-extra-padding” column_padding_position=”all” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_shadow=”none” column_border_radius=”none” el_class=”custom-split-line-heading” width=”1/4″ tablet_text_alignment=”default” phone_text_alignment=”default” column_border_width=”none” column_border_style=”solid”][split_line_heading]Relevant

publications

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Olufolajimi Oke., Kavi Bhalla., David C.Love., Sauleh Siddiqui., ( 2015 )

Tracking global bicycle ownership patterns

Journal of Transport & Health, DOI: 10.1016/j.jth.2015.08.006

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This work was performed with the MIT/MGH Collaboration.

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This work was performed with the MIT/MGH Collaboration.

Primary care is an important piece in the healthcare system that affects the downstream medical care of patients heavily. There are specific challenges in primary care as healthcare shifts from fee-for-service to population health management and medical home, focuses on cost savings and integrates quality measures. We consider the primary care unit at a large academic center that is facing similar challenges. In this work we focus on the imbalance in workload, which is a growing regulatory burden and directly concerns any staff in primary care. It can result in missed opportunities to deliver better patient care or providing a good work-environment for the physicians and the staff. We consider the primary care unit at the large academic center and focus on their challenge in balancing staff time with quality of care through a redesign of their system. We employ optimization models to reschedule providers’ sessions to improve the patient flow, and through that, a more balanced work-level for the support staff. 

This work was performed with the MIT/MGH Collaboration.

Perioperative services are one of the vital components of hospitals and any disruption in their operations can leave a downstream effect in the rest of the hospital. A large body of evidence links inefficiencies in perioperative throughput with adverse clinical outcomes. A regular delay in the operating room (OR), may lead to overcrowding in post-surgical units, and consequently, more overnight patients in the hospital. Conversely, an underutilization of OR is not only a waste of an expensive and high-demand resource, but it also means that other services who have a demand are not able to utilize OR. This mismatch in demand and utilization may, in turn, lead to hold-ups in the OR and cause further downstream utilization. We investigate the utilization of operating rooms by each service. The null hypothesis of this work is that the predicted utilization of the OR, i.e., the current block schedule, matches completely with the actual utilization of the service. We test this hypothesis for different utilization definitions, including physical and operational utilization and reject the null hypothesis. We further analyze why a mismatch may exist and how to optimize the schedule to improve patient flow in the hospital.