Map-based Educational Tools for Algorithm Learning (METAL)

led by Dr. James D. Teresco

Motivation

Nearly any topic in a data structures or algorithms course will be
more interesting for students if they can apply what they are learning
to real-world data and visualize results in a meaningful way. When
using graph data, it needs to be small enough to be manageable, but
large enough to be interesting. This might consist of a small road
system, airline flight schedules, or even the layout of a campus or
building. Map-based Educational Tools for
Algorithm Learning (METAL) is a system that
allows students to experiment with graph algorithms using
the Google Maps API
and highway routing data from
the Travel Mapping (TM) Project.
Students can implement graph algorithms and display, in Google Maps,
the results of computations using those algorithms. METAL's newest
features are interactive algorithm visualization capabilities intended to
aid student understanding of graph and other algorithms.

Wouldn't it be more fun and interesting
to work with this graph
than this one?

Graph Data

TM's data is updated daily, and
an updated collection of
graphs is generated almost as frequently from this data. These
graphs represent the highways in various regions, countries, and
highway systems, and are provided in
two formats. Those interested can
read more about where it comes from and how it is
generated. The highway systems available
in Travel Mapping (TM), from which
METAL derives its data, vary in size, meaning graph data can be
generated at a variety of scales. They vary in size from just a
handful of vertices and edges for systems in small island nations to
hundreds of thousands for the entire data set.

The Highway Data Examiner

Google has made an application programmer interface (API) available to
allow customized plots of data in Google Maps. It is implemented as a
set of Javascript classes and methods, and is fairly easy to learn and
use. It requires free registration to obtain an API key. However,
students, or instructors for that matter, need not learn the Google
Maps API or obtain their own key to make use of this system. The
Highway Data Examiner (HDX) is built using the Google Maps API,
and that allows plotting of the TM and derived data, and supports
interactive algorithm visualizations.

Usage in Class Assignments

These graph data files can be used as input for class examples or
assignments in a data structures, algorithms, or other courses. This
data was first used in a laboratory assignment for a data structures
course at Mount Holyoke College where students were required to build
a graph structure representing a highway system then perform
Dijkstra's algorithm to compute the shortest route between two
specified waypoints. Student programs generated output files listing
the road segments along the shortest path. These were then uploaded
to a course web server, where an instructor-provided program used the
Google Maps API to visualize their results. Students were able to
develop and debug their programs using small data sets like the Hawaii
Interstates, then use those programs to compute much more complex
routes using the larger data sets. The software has been updated and
extended and used in several courses at Siena College and The College
of Saint Rose. Successful assignments include the creation of custom
classes to store the information about the points and connections
between them, storing these points and connection in a data structure
and then searching within and sorting those points (a great way to
motivate comparators), finding convex hulls, performing graph
traversals, and more advanced graph algorithms including Dijkstra's
algorithm.

Interactive Algorithm Visualization

The latest version of HDX includes support
for interactive algorithm visualization. After
loading a graph data file into HDX, a user can select the "Algorithm
Visualization" option. From there, an algorithm can be chosen, along with
any needed parameters (e.g., a starting vertex for a traversal). A set
of controls are used to start, pause, reset, or change the speed of
the simulation. During the simulation, the data is displayed in both
tabular and map form. Our expanding set of algorithms supported
include simple vertex- and edge-based searches, graph traversals,
finding connected components, finding convex hulls, and Dijkstra's
algorithm for single-source shortest paths.