Program Overview

The term data mechanics refers to the study of how data can move through institutions and computational infrastructures to inform decisions and operations (sometimes in real time) within large systems such as cities, which can contain a variety of widely distributed sources of data being updated at various time scales. Computer science and computational thinking provide a variety of tools and techniques for facilitating data collection, delivery, processing, and interpretation in application areas like urban informatics and distributed systems (e.g., public infrastructure management, traffic modeling, smart power grids, and so on):

• consolidation, synchronization, and summarization of multiple data streams;
• data provenance, maintenance, and availability;
• programming tools and paradigms for assembling decision-making, optimization, and analytics algorithms that can operate on large amounts of static or streaming data;
• crowdsourcing and socio-adaptive system;
• online and offline interactive visualizations for presenting and examining data.

In each iteration of a data mechanics course, students apply the tools and methods presented to build libraries, platforms, and applications that work with data sets dealing with aspects of urban environments such as mobility (e.g., walkability), employment, traffic and parking, emissions, energy consumption, public safety, and others. Lists of student projects for past iterations of the course can be found in the sections below.

Spring 2019 Course Offering

In this iteration of the course, a number of student teams collaborated with faculty members in other disciplines or with external organizations and initiatives in applying data mechanics, data science, and software engineering techniques to solve analysis and resource allocation problems. BU Spark! identified these project opportunities, matched student teams with partner faculty members and organizations, and supervised the collaborations.

Spring 2018 Course Offering

Fall 2017 Course Offering

Spring 2017 Course Offering

Fall 2016 Course Offering

The second iteration of the course followed a syllabus that is very similar to that of the first iteration. However, there was more extensive use of dedicated libraries and tools built specifically for creating an integrated platform that combines all the student projects. Furthermore, students had the option to use data sets made available by other cities, and were be able to draw upon, improve, and incorporate data sets and algorithms assembled by students during the previous iteration of the course.

Individual projects were programmed as forks of a shared GitHub repository, and each project was submitted via a pull request. The source for the submitted version of each of the projects listed below can be viewed by going to the corresponding subdirectory of the overall GitHub repository.

Spring 2016 Course Offering

Students applied techniques and methods presented during the course to retrieve and derive data sets, implement optimization algorithms, perform analyses, and create visualizations. All the data retrieved or generated for the individual projects was collected within a unifying framework consisting of a shared database and data set provenance tracking conventions.

Acknowledgments

This effort exists thanks to the support and cooperation of Boston University, including the Department of Computer Science, the Hariri Institute for Computing, the Software & Application Innovation Lab, and the Initiative on Cities.

This work is also supported under the SCOPE effort by the National Science Foundation under Grant No. 1430145. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

We also thank the City of Boston Department of Innovation and Technology and the MassDOT Office of Performance Management and Innovation for their advice, logistical support, and contributions of data.

To request further information or make other inquiries, please contact Andrei Lapets (a@lapets.io).