Week 08/09 Lab Exercise Weighted Graphs and Geo Data




COMP2521 20T1 Data Structures and Algorithms
Week 08/09 Lab Exercise
Weighted Graphs and
Geo Data
to implement a variant of path nding in a weighted graph
to see how graphs might be used with real-world data
5=outstanding, 4=very good, 3=adequate, 2=sub-standard, 1=hopeless
in the Week 10 Lab
give cs2521 lab08 Graph.c or via WebCMS
submit by Tue 21 April 2020, 20:00:00
Geographic data is widely available, thanks to sites such as GeoNames. For this lab, we have
downloaded data les from City Distance Dataset by John Burkardt in the Department of Scientic
Computing at Florida Statue University. The dataset that we will use contains information about
distances between 30 prominent cities/locations around the world. It measures “great circle” distances;
we’ll assume that these measure the distances that an aircraft might y between the two cities. The
data that we have forms a complete graph in that there is a distance recorded for every pair of cities.
The following diagram shows a subset of the data from the City Distance Dataset.
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Map from “BlankMap-World-v2” by original uploader: Roke
Own work. Licensed under Creative Commons Attribution-Share Alike 3.0 via Wikimedia Commons
The data comes in two les:
This le contains a matrix of distances between cities. This is essentially the adjacency matrix for a
weighted graph where the vertices are cities and the edge weights correspond to distances
between cities. As you would expect for an adjacency matrix, the leading diagonal contains all
zeroes (in this case, corresponding to the fact that a city is zero distance from itself).
This le contains one city name per line. If we number the lines starting from zero, then the line
number corresponds to the vertex number for the city on that line. For example, the Azores is on
line 0, so vertex 0 corresponds to the Azores, and the rst line in the distance le gives distances
from the Azores to the other 29 cities. The last line (line 29) tells us that Tokyo is vertex 29, and
the last line in the distance les gives distances between Tokyo and all other cities.
Setting Up
Set up a directory for this lab under your COMP2521 labs directory, change into that directory, and run
the following command:
$ unzip /web/cs2521/20T1/labs/week08/
If you’re working at home, download and then work on your local machine.
If you’ve done the above correctly, you should now nd the following les in the directory:
a set of dependencies used to control compilation
main program to load and manipulate the graph
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interface to Graph ADT
implementation of Graph ADT
interface to Queue ADT
implementation of Queue ADT
denition of Items (Edges)
the city name le described above
the distance matrix le described above
The Makefile produces a le called travel based on the main program in travel.c and the functions
in Graph.c. The travel program takes either zero or two command line arguments.
$ ./travel
… displays the entire graph …
… produces lots of output, so either redirect to a file or use less …
$ ./travel from-city to-city
… display a route to fly between specified cities …
If given zero arguments, it simply displays the graph (in the format described below). If given two
arguments, it treats the rst city as a starting point and the second city as a destination, and determines
a route between the two cities, based on “hops” between cities with direct ights.
Read the main() function so that you understand how it works, and, in particular, how it invokes the
functions that you need to implement.
The Graph ADT in this week’s lab has a GraphRep data structure that is a standard adjacency matrix
representation of the kind we looked at in lectures. However, some aspects of it are dierent to the
GraphRep from lectures.
Note that city names are not stored as part of the GraphRep data structure. The Graph ADT deals with
vertices using their numeric id. The main program maintains the list of city names and passes this list to
the showGraph() function when it is called to display the graph. This means that the calling interface for
the showGraph() function is dierent to the showGraph() function from the Graph ADT in lectures.
Another dierence between this Graph ADT and the one used in lectures is that the values stored in the
matrix are not simply zero and one, but represent the distances between vertices. In other words, we’re
dealing with a weighted graph. Note, however, that we are not actually using the weights except to
indicate the existence of an edge. A weight value of zero indicates no edge between two vertices, while
a non-zero weight indicates an edge.
The Graph ADT includes a sub-ADT for Edges. The implementation of insertEdge() in lectures only
required the vertices at the endpoints of the Edge (i.e. insertEdge(g,v,w)). The version of
insertEdge() for this lab also requires a weight for the edge (i.e. insertEdge(g,v,w,weight)).
The main program makes some changes to the edges implied by the distance matrix as it copies them
into the Graph. The values in the ha30_dist.txt le are given in units of “hundreds of miles”; we want
them in units of kilometres so each distance is converted before it is added to the graph as the weight
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of an edge. Since every city has a distance to every other city (except itself), this gives us a complete
As supplied, the Graph.c le is missing implementations for the findPath() function. If you compile
the travel program and try to nd any route, it will simply tell you that there isn’t one. If you run
travel with no arguments, it will print a representation of the graph (you can see what this should look
like in the le graph.txt.
Implement the findPath(g,src,dest,max,path) function. This function takes a graph g, two vertex
numbers src and dest, a maximum ight distance, and lls the path array with a sequence of vertex
numbers giving the “shortest” path from src to dest where no edge on the path is longer than max. The
function returns the number of vertices stored in the path array; if it cannot nd a path, it returns zero.
The path array is assumed to have enough space to hold the longest possible path (which would
include all vertices).
This could be solved with a standard BFS graph traversal algorithm, but there are two twists for this
The edges in the graph represent real distances, but the user of the travel program (the
traveller) isn’t necessarily worried about real distances. They are more worried about the number
of take-os and landings (which they nd scary), so the length of a path is measured in terms of
the number of edges, not the sum of the edge weights. Thus, the “shortest” path is the one with
the minimum number of edges.
While the traveller isn’t worried about how far a single ight goes, aircraft are aected by this (e.g.
they run out of fuel). The max parameter for findPath() allows a user to specify that they only
want to consider ights whose length is at most max kilometres (i.e. only edges whose weight is
not more than max).
Your implementation of findPath() must satisfy both of the above.
In implementing findPath(), you can make use of the Queue ADT that we’ve supplied. This will create a
queue of Vertex numbers.
Note that the default value for max, set in the main program is 10000 km. Making the maximum ight
distance smaller produces more interesting paths (see below), but if you make it too small (e.g. 5000km)
you end up isolating Australia from the rest of the world. With maximum ights of 6000km, the only
way out of Australia in this data is via Guam. If you make the maximum ight length large enough (e.g.
aircraft technology improves signicantly), every city will be reachable from every other city in a single
Some example routes (don’t expect them to closely match reality):
# when no max distance is given on the command line,
# we assume that planes can fly up to 10000km before refuelling
$ ./travel Berlin Chicago
Least-hops route:
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$ ./travel Sydney Chicago
Least-hops route:
$ ./travel Sydney London
Least-hops route:
$ ./travel London Sydney
Least-hops route:
$ ./travel Sydney ‘Buenos Aires’
Least-hops route:
-Buenos Aires
# if no plane can fly more than 6000km without refuelling
$ ./travel Sydney London 6000
Least-hops route:
# if no plane can fly more than 5000km without refuelling
# you can’t leave Australia under this scenario
$ ./travel Sydney ‘Buenos Aires’ 5000
No route from Sydney to Buenos Aires
# if no plane can fly more than 7000km without refuelling
$ ./travel Sydney ‘Buenos Aires’ 7000
Least-hops route:
-Panama City
-Buenos Aires
# planes can fly up to 8000km without refuelling
$ ./travel Sydney ‘Buenos Aires’ 8000
Least-hops route:
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-Mexico City
-Buenos Aires
# planes can fly up to 11000km without refuelling
$ ./travel Sydney ‘Buenos Aires’ 11000
Least-hops route:
-Buenos Aires
# planes can fly up to 12000km without refuelling
# can reach Buenos Aires on a single flight
$ ./travel Sydney ‘Buenos Aires’ 12000
Least-hops route:
-Buenos Aires
$ ./travel Bombay Chicago 5000
Least-hops route:
$ ./travel Sydney Sydney
Least-hops route:
The above routes were generated using an algorithm that checked vertices in order (vertex 0 before
vertex 1 before vertex 2, etc.). If you check in a dierent order, you may generate dierent, but possibly
equally valid, routes.
You have two weeks to complete this lab, and importantly you can use Help Sessions to get help for this
lab. The submission deadline is 8pm Tuesday of Week 10. Once you have completed the lab, you should
demonstrate your work to your tutor during your Week 10 lab time.
Have fun, jas
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