Data Mechanics

Data repository and service platform for educational and research efforts that address how data can traverse institutions and pervasive computational infrastructures to inform decisions and operations within systems such as smart cities.

Spring 2017 Course Offering

Boston University, College of Arts and Sciences
Computer Science 591 L1: Data Mechanics for Pervasive Systems and Urban Applications
Tue. & Thu. 9:30 - 10:45 AM in EPC 209

The third iteration of the course will once again follow a similar structure, but will encourage use of a broader collection of data sets and data sources. Students will also experiment with a more decentralized architecture for the data repository and overall platform.


Fall 2016 Course Offering

Boston University, College of Arts and Sciences
Computer Science 591 L1: Data Mechanics for Pervasive Systems and Urban Applications

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.

Statistical Correlation of Drug Crime and Youth in the City of Boston.
Aditi Dass, Andrew Lee, Benjamin Li, and Tony Yao.
GitHub | Poster
Using k-means to Determine New Hospital Locations.
Michael Gerakis, Patrick Gomes, and Raphael Baysa.
GitHub | Poster | Report
Algorithmic Optimizations of Boston's Public Bus System.
Adrian Law, Mark Bestavros, and Tyrone Hou.
GitHub | Poster
Reducing the Number of Car Accidents.
Ivan Uvarov and Stephanie Chiao.
GitHub | Poster | Report
What is a Community?
Asselya Aliyeva, Benjamin Owens, David Wang, and Jennifer Tsui.
GitHub | Poster
Average Income in Neighborhoods and Other Factors.
Shreya Ramesh.
GitHub | Poster | Report
How Streetlights Deter Crime.
Aleksander Skjoelsvik and Ying Hang Eng.
GitHub | Poster
Crimes and Firearm Recovery in Boston.
Nathan Galloway, Amanda Doss, and Sanam Patel.
GitHub | Poster
Optimal Trash Collection Installation Sites.
Jacquelyn Andrade and Joseph Cho.
GitHub | Poster | Report
Crime Clustering, its Shifting Pattern, and General Housing Evaluation.
Li Liu and Sibo Zhu.
GitHub | Poster | Report
Exploring New York City Transit.
Anurag Prasad and Jarrod Lewis.
GitHub | Poster
Factors that Influence the Number of Crime Incidents.
Joe Zhou and Ekaterina Prokopeva.
GitHub | Poster
Relationship Between Crimes and Service Requests.
Arjun Lamba.
GitHub | Poster | Report
Measuring Child-Friendliness in Boston Neighborhoods.
Ji Eun Yang and Robin Liu.
GitHub | Poster | Report
How Income Per Capita Affects Alternative Methods of Transporation.
Liam DeBeasi, Simon Nichols, Arman Sanentz, and Molly Shopper.
GitHub | Poster | Report
City of Boston Services: Optimal Police Patrol Allocation and Inspection Analysis.
Stephanie Alibrandi and Javier Arguello.
GitHub | Poster | Report
Public Facilities, Crimes, and Average Income.
Yehui Huang, Yingqiao Xiong, Hongyu Zhou, and Chang Gao.
GitHub | Poster | Report
Boston Crime Rates in Relation to Hospitals, Police Districts, and Property Value.
George Gelinas.
GitHub | Poster
Research of Factors Correlated with Crime Incidence in the Boston Area.
Bowen Yang, Jiadong Chen, Xiao Lu.
GitHub | Report
Optimal Advertisement Placements in Boston.
Kristel Tan, Nisa Gurung, Yao Zhang, Emily Hou.
GitHub | Poster | Report
A Characterization of Neighborhood Wealth and Optimization of Resource Distribution in Boston.
Cody Karjadi and John Gonsalves.
GitHub | Poster | Report
Evaluation of Crime Incidences in Boston.
Calvin Liang and Kevin Leung.
GitHub | Poster

Spring 2016 Course Offering

Boston University, College of Arts and Sciences
Computer Science 591 L1: Data Mechanics for Pervasive Systems and Urban Applications

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.

Drones for Medical and Police Services.
Tabor Beaudry.
GitHub | Poster | Report
Transportation and Food Establishments.
Yui Chi Tiffany Lo.
GitHub | Report
Highway out of the Danger Zone.
Erik Brakke and Tyler Waltze.
GitHub | Poster | Report
Crime Incidence and Lighting.
Thomas Hsu and Nicholas Louie.
GitHub | Poster | Report
Evaluating Boston Neighborhoods.
Raaid Arshad and Michael Clawar.
GitHub | Poster | Report
Average Income and Crime Incidence.
Linshan Jiang and Tianyou Luo.
GitHub | Poster | Report
Characterizing MBTA Stop Safety.
Adam Elass.
GitHub | Poster | Report
Neighborhood Classification.
Jasper Burns and Daren McCulley.
GitHub | Report
Optimizing Green Line T Stops.
Cristina Estupinan and Steven Jarvis.
GitHub | Poster | Report
Hospitals, Traffic Jams, and Property Values.
Joshua Mah, Joseph Muruguru, and Timothy Pacius.
GitHub | Poster | Report
City Employee Earnings over Time.
Yihong Guo.
GitHub | Report
Graph Metrics for the Public Transportation Network.
Nikolaj Volgushev.
GitHub | Poster | Report
Boston Food Resources in Relation to Demographics.
Johnson Lam and Kathleen McKay.
GitHub | Report
Improving Bicycle Route Safety.
Enze Yan.
GitHub | Poster | Report
Geosocial Data of Boston.
Benjamin Lawson.
GitHub | Poster | Report
Scoring Age-Friendly Neighborhoods in Boston.
Jacqueline You.
GitHub | Poster | Report
Classifying and Identifying Zipcodes by Constraints.
Jonathan Liu and Kyle Mann.
GitHub | Report
Health and Safety of Boston Restaurants.
Erica Wivagg and Yu Zhou.
GitHub | Poster | Report

Overview

The term data mechanics refers to the study of how data can move through institutions and computational infrastructures to inform decisions and operations (often in real time) within large systems such as cities, which can contain a variety of widely distributed embedded sensors and computational devices. 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., traffic modelling and management, sensor networks, smart power grids, and so on):

  • programming tools and paradigms for assembling decision-making, optimization, and analytics algorithms that can operate on large amounts of static or streaming data;
  • consolidation, synchronization, and summarization of multiple data streams;
  • formal techniques for modeling and ensuring predictable, reliable, and provably correct behavior;
  • crowdsourcing and socio-adaptive system;
  • online and offline visualizations for presenting and examining data.

In each iteration of a data mechanics course, students will apply the tools and methods presented to build 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.


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 (lapets@bu.edu).