Patterns in Bicycle Ownership Among Neighboring Countries.

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[vc_row type=”in_container” full_screen_row_position=”middle” scene_position=”center” text_color=”dark” text_align=”left” top_padding=”30″ overlay_strength=”0.3″ shape_divider_position=”bottom”][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=”1/1″ tablet_text_alignment=”default” phone_text_alignment=”default” column_border_width=”none” column_border_style=”solid”][vc_column_text][nectar_dropcap color=”#3452ff”]W [/nectar_dropcap]ith the rise of internet and ease in travelling, distance doesn’t deter us anymore from sharing information across the globe. An email can be sent across the world in the matter of seconds, and people in different continents can hold a face-to-face conversation online, without having to travel one step outside of their rooms. Also, numerous globalized social network systems allows us to easily share out thoughts and ideas with anyone else on the Web. But despite the easiness in sharing ideas worldwide, we still find plenty of ideas that don’t penetrate through many international borders; one example: bicycle ownership.[/vc_column_text][/vc_column][/vc_row][vc_row type=”in_container” full_screen_row_position=”middle” scene_position=”center” text_color=”dark” text_align=”left” top_padding=”30″ overlay_strength=”0.3″ shape_divider_position=”bottom”][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=”1/1″ tablet_text_alignment=”default” phone_text_alignment=”default” column_border_width=”none” column_border_style=”solid”][image_with_animation image_url=”7051″ alignment=”” animation=”Fade In” img_link_large=”yes” border_radius=”none” box_shadow=”none” max_width=”100%”][/vc_column][/vc_row][vc_row type=”in_container” full_screen_row_position=”middle” scene_position=”center” text_color=”dark” text_align=”left” top_padding=”30″ overlay_strength=”0.3″ shape_divider_position=”bottom”][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=”1/1″ tablet_text_alignment=”default” phone_text_alignment=”default” column_border_width=”none” column_border_style=”solid”][vc_column_text]Last year at MODL, we began exploring bicycle ownership around the world. By collecting and analyzing the sporadically available data, we were able to characterize 150 different countries into four levels of bicycle ownership. And in visualizing this characterization on a world map, we saw that there were many bordering countries with similar levels of ownership. So this year, we went a step further to analyze how these levels of ownership was related to the locations of the countries. To do this, we built a model network, designating each country as a point (or node) on the network, and drawing a connecting line (edge) between two points to represent neighboring countries. And in analysis of this model, we found that countries close to each other are very likely to have similar level of ownership.

Despite the likeliness of neighboring countries to save similar levels of ownership, there are some countries whose ownership level much differs from that of its neighboring countries. Burkina Faso is an example of a country that has much higher level of ownership than its neighbors, probably because of its extensive work in promoting bike tourism. On the other hand, the UK has much lower level of ownership, which may have been influenced by poor attitudes and infrastructure for biking.

This study provides a little more insight into understanding the spatial relation of bicycle ownership. But to have a better understanding of how bicycle ownership is influenced between countries, we would need to observe how ownership is affect by other factors, such as climate, topography, cultural history, and much more. Furthermore, we hope to build a more dynamic model that will help us understand how bicycle ownership has changed over time.[/vc_column_text][/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 black-link-outer” id=”Book-Chapters” 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=”1/1″ tablet_text_alignment=”default” phone_text_alignment=”default” column_border_width=”none” column_border_style=”solid”][vc_column_text css_animation=”fadeInUp” el_class=”visual-generated-div”]

Oke, O., Bhalla, K., Love, D. C., & Siddiqui, S., ( 2016 )

Spatial associations in global household bicycle ownership

Annals of Operations Research, 1-21

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Oke, O., Bhalla, K., Love, D., Siddiqui, S., ( 2015 )

Tracking global bicycle ownership patterns

Journal of Transport & Health, 2(4): 490-501

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Assume that a decision-maker’s uncertain behavior is observed. We develop a an inverse optimization framework to impute an objective function that is robust against misspecifications of the behavior. In our model, instead of considering multiple data points, we consider an uncertainty set that encapsulates all possible realizations of the input data. We adopt this idea from robust optimization, which has been widely used for solving optimization problems with uncertain parameters. By bringing robust and inverse optimization together, we propose a robust inverse linear optimization model for uncertain input observations. We aim to find a cost vector for the underlying forward problem such that the associated error is minimized for the worst-case realization of the uncertainty in the observed solutions. That is, such a cost vector is robust in the sense that it protects against the worst misspecification of a decision-maker’s behavior. 

As an example, we consider a diet recommendation problem. Suppose we want to learn the diet patterns and preferences of a specific person and make personalized recommendations in the future. The person’s choice, even if restricted by nutritional and budgetary constraints, may be inconsistent and vary over time. Assuming the person’s behavior can be represented by an uncertainty set, it is important to find a cost vector that renders the worst-case behavior within the uncertainty set as close to optimal as possible. Note that the cost vector can have a general meaning and may be interpreted differently depending on the application (e.g., monetary cost, utility function, or preferences). Under such a cost vector, any non-worst-case diet will thus have a smaller deviation from optimality.  

We introduce a new approach that combines inverse optimization with conventional data analytics to recover the utility function of a human operator. In this approach, a set of final decisions of the operator is observed. For instance, the final treatment plans that a clinician chose for a patient or the dietary choices that a patient made to control their disease while also considering her own personal preferences. Based on these observations, we develop a new framework that uses inverse optimization to infer how the operator prioritized different trade-offs to arrive at her decision. 

We develop a new inverse optimization framework to infer the constraint parameters of a linear (forward) optimization based on multiple observations of the system. The goal is to find a feasible region for the forward problem such that all given observations become feasible and the preferred observations become optimal. We explore the theoretical properties of the model and develop computationally efficient equivalent models. We consider an array of functions to capture various desirable properties of the inferred feasible region. We apply our method to radiation therapy treatment planning—a complex optimization problem in itself—to understand the clinical guidelines that in practice are used by oncologists. These guidelines (constraints) will standardize the practice, increase planning efficiency and automation, and make high-quality personalized treatment plans for cancer patients possible.

Inverse optimization is an area of study where the purpose is to infer the unknown parameters of an optimization problem when a set of observations is available on the previous decisions made in the settings of the problem. We develop a framework to effectively and efficiently infer the cost vector of a linear optimization problem based on multiple observations on the decisions made previously. 

We then test our models in the setting of a diet problem on a data-set obtained from NHANES; The data-set is accessible via the link bellow:

https://github.com/CSSEHealthcare/Dietary-Behavior-Dataset