\documentclass[acmtocl,acmnow]{acmtrans2m}
\usepackage{graphicx}
\newtheorem{theorem}{Theorem}[section]
\newtheorem{conjecture}[theorem]{Conjecture}
\newtheorem{corollary}[theorem]{Corollary}
\newtheorem{proposition}[theorem]{Proposition}
\newtheorem{lemma}[theorem]{Lemma}
\newdef{definition}[theorem]{Definition}
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\markboth{Nigel Stanger}{...}
\title{Scalability of Techniques for Online Geovisualization of Web Site Hits}
\author{NIGEL STANGER \\ University of Otago}
\begin{abstract}
A useful approach to visualising the geographical distribution of web
site hits is to geolocate the IP addresses of hits and plot them on a
world map. This can be achieved by dynamic generation and display of map
images at the server and/or the client. In this paper we compare the
scalability with respect to source data size of four techniques for
dynamic map generation and display: generating a single composite map
image, overlaying transparent images on an underlying base map,
overlaying CSS-enabled HTML on an underlying base map and generating a
map using Google Maps. These four techniques embody a mixture of
different display technologies and distribution styles. The results show
that all four techniques are suitable for small data sets, but that the
latter two techniques scale poorly to larger data sets.
\end{abstract}
\category{C.4}{Performance of Systems}{Performance attributes}
\category{C.2.4}{Computer-Communication Networks}{Distributed Systems}[distributed applications]
\category{H.3.5}{Information Storage and Retrieval}{Online Information Services}[web-based services]
\terms{Experimentation, Measurement, Performance}
\keywords{geolocation, geovisualization, scalability, GD, Google Maps}
\begin{document}
\bibliographystyle{acmtrans}
\begin{bottomstuff}
Author's address: N. Stanger, Department of Information Science,
University of Otago, PO Box 56, Dunedin 9054, New Zealand.
\end{bottomstuff}
\maketitle
\section{Introduction}
\label{sec-introduction}
When administering a web site, it is quite reasonable to want
information on the nature of traffic to the site. Information on the
geographic sources of traffic can be particularly useful in the right
context. For example, an e-commerce site might wish to determine the
geographical distribution of visitors to its site, so that it can decide
where best to target its marketing resources. One approach to doing so
is to plot the geographical location of web site hits on a map.
Geographical information systems (GIS) were already being used for these
kinds of purposes prior to the advent of the World Wide Web
\cite{Beau-JR-1991-GIS}, and it is a natural extension to apply these
ideas to online visualization of web site hits.
Our interest in this area derives from implementing a pilot digital
institutional repository at the University of
Otago\footnote{\url{http://eprints.otago.ac.nz/}} in November 2005
\cite{Stan-N-2006-running}, using the GNU
EPrints\footnote{\url{http://www.eprints.org/}} repository management
software. This repository quickly attracted interest from around the
world and the number of abstract views and document downloads began to
steadily increase. We were obviously very interested in tracking this
increase, particularly with respect to where in the world the hits were
coming from. The EPrints statistics management software developed at the
University of Tasmania \cite{Sale-A-2006-stats} proved very useful in
this regard, providing us with detailed per-eprint and per-country
download statistics; an example of the latter is shown in
Figure~\ref{fig-tas-stats}. However, while this display provides an
ordered ranking of the number of hits from each country, it does not
provide any greater detail than to the country level, nor does it
provide any visual clues as to the distribution of hit sources around
the globe.
\begin{figure}
\begin{center}
\includegraphics[scale=0.65]{tasmania_stats}
\end{center}
\caption{A portion of the by-country display for the Otago EPrints
repository, generated by the Tasmania statistics software.}
\label{fig-tas-stats}
\end{figure}
We therefore began to explore possible techniques for plotting our
repository hit data onto a world map, with the aim of adding this
capability to the Tasmania statistics package. Our preference was for a
technique that could be used within a modern web browser without the
need to manually install additional client software, so as to make the
new feature available to the widest possible audience and reduce the
impact of wide variation in client hardware and software environments
\cite[pp.\ 27--28]{Offu-J-2002-quality}.
There have been several prior efforts to geovisualize web activity.
\citeN{Lamm-SE-1996-webvis} developed a sophisticated system for
real-time visualization of web traffic on a 3D globe, but this was
intended for use within a virtual reality environment, thus limiting its
general applicability. \citeN{Papa-N-1998-Palantir} described a similar
system (Palantir), which was written as a Java applet and thus able to
be run within a web browser, assuming that a Java virtual machine was
available. \citeN[pp.\ 100--103]{Dodg-M-2001-cybermap} describe these
and several other related systems for mapping Web and Internet traffic.
These early systems suffered from a distinct limitation in that there
was no public infrastructure in place for geolocating IP addresses (that
is, translating them into latitude/longitude coordinates). They
generally used \texttt{whois} lookups or parsed the domain name in an
attempt to guess the country of origin, with fairly crude results
\cite{Lamm-SE-1996-webvis}. Locations outside the United States were
typically aggregated by country and mapped to the capital city
\cite{Lamm-SE-1996-webvis,Papa-N-1998-Palantir,Jian-B-2000-cybermap}.
Reasonably accurate and detailed databases were commercially available
at the time \cite[p.\ 1466]{Lamm-SE-1996-webvis}, but were not generally
available to the public at large, thus limiting their utility.
The situation has improved considerably in the last five years, however,
with the advent of freely available and reasonably accurate geolocation
services\footnote{Such as \url{http://www.maxmind.com/} or
\url{http://www.ip2location.com/}.} with worldwide coverage and
city-level resolution. For example, Maxmind's \emph{GeoLite City}
database is freely available and claims to provide ``60\% accuracy on a
city level for the US within a 25 mile radius''
\cite{Maxm-G-2006-GeoLiteCity}. Their commercial \emph{GeoIP City}
database claims 80\% accuracy for the same parameters.
The techniques used by these systems can generally be divided into two
classes. The first class of techniques generate a single bitmap image
that contains both the map and the icons representing web hits. This can
be achieved by programmatically plotting points onto a base map image;
the composite image is displayed at the client. We shall henceforth
refer to this class of techniques as \emph{image generation} techniques.
The second class of techniques separately return both a base map image
and some kind of overlay containing the plotted points. The overlay is
then combined with the base map at the client. We shall henceforth refer
to this class of techniques as \emph{overlay} techniques.
Both classes of techniques have been used in the aforementioned systems,
but the overlay technique appears to have been particularly popular. For
example, Palantir used an overlay technique, where a Java applet running
at the client overlaid graphic elements onto a base map image retrieved
from the now-defunct Xerox online map server
\cite{Papa-N-1998-Palantir}. A more recent example is the Google Maps
API \cite{Goog-M-2006-maps}, which enables web developers to easily
embed dynamic, interactive maps within web pages. Google Maps is a
dynamic overlay technique that has only become feasible relatively
recently with the advent of widespread support for CSS positioning and
Ajax technologies in many browsers.
Overlay techniques enjoy a particular advantage over image generation
techniques, in that they provide the potential for a more flexible GIS-like
interaction with the map, with multiple layers that can be activated and
deactivated as desired. This flexibility could explain why such techniques
appear more prevalent in the literature. However, overlay techniques tend
to rely on more recent web technologies such as CSS2 and Ajax, whereas
image generation techniques generally do not. Image generation techniques
should therefore be portable to a wider range of client and server
environments.
Each technique comprises a specific technology or collection of
technologies (such as transparent bitmap overlays), implemented using a
specific distribution style. For example, one image generation technique
might be implemented completely server-side while another might use a
mixture of server-side and client-side processing. Similarly, overlay
techniques may adopt different distribution styles, and the overlays
themselves might take the form of transparent images, absolutely
positioned HTML elements, dynamically generated graphics, etc.
Given the many possible techniques that were available, the next
question was which techniques would be most suitable for our purposes?
Scalability is a key issue for web applications in general \cite[p.\
28]{Offu-J-2002-quality}, and online activity visualization in
particular \cite[p.\ 50]{Eick-SG-2001-sitevis}, so we were particularly
interested in techniques that could scale to a large number of points.
For example, at the time of writing the Otago EPrints repository had
been accessed from over 10,000 distinct IP addresses, each potentially
representing a distinct geographical location. Separating out the type
of hit (abstract view versus document download) increased that figure to
nearly 13,000.
We first narrowed down the range of techniques to just four (server-side
image generation, server-side image overlay, server-side HTML overlay
and Google Maps); the selection process and details of the techniques
chosen are discussed in Section~\ref{sec-techniques}. We then set about
testing the scalability of these four techniques, in order to determine
how well each technique handled large numbers of points. A series of
experiments was conducted on each technique with progressively larger
data sets, and the elapsed time and memory usage were measured. The
experimental design is discussed in Section~\ref{sec-experiment}.
Our initial intuition was that server-side image generation and
server-side image overlay techniques would scale best, and this was
borne out by the results of the experiments, which show that both
techniques scale reasonably well to very large numbers of points. The
other two techniques proved to be reasonable for relatively small
numbers of points (generally less than about 500--1,000), but their
performance deteriorated rapidly beyond this. The results are discussed
in more detail in Section~\ref{sec-results}.
It should be noted that the intent of the experiments was not to
identify statistically significant differences between techniques. It
was expected that variations across techniques would be obvious, and the
experiments were designed to test this expectation. However, the two
best performing techniques, server-side image generation and server-side
image overlay, produced very similar results, so a more formal
statistical analysis of these techniques may be warranted. This and
other possible future directions are discussed in
Section~\ref{sec-future}.
\section{Technique selection}
\label{sec-techniques}
In this section we discuss in more detail the four techniques that we
chose for testing, and how we decided upon these particular techniques.
First, we discuss the impact of distribution style on the choice of
technique. Then, for each of the four chosen techniques, we examine how
the technique works in practice, its implementation requirements, its
relative advantages and disadvantages, and any other issues peculiar to
the technique.
\subsection{Distribution style}
\label{sec-distribution}
\citeN{Wood-J-1996-vis} and \citeN{MacE-AM-1998-GIS} identified four
distribution styles for web-based geographic visualization software. The
\emph{data server} style is where the server only supplies raw data, and
all manipulation, display and analysis takes place at the client. In
other words, this is primarily a client-side processing model, as
illustrated in Figure~\ref{fig-distribution-styles}(a). For example,
Palantir implemented an overlay technique using this distribution style
\cite{Papa-N-1998-Palantir}, where the source data were generated at the
server and the map was generated, displayed and manipulated by a Java
applet running at the client. The data server distribution style can
provide a very dynamic and interactive environment to the end user, but
clearly requires support for executing application code within the web
browser, typically using something like JavaScript, Java applets or
Flash. JavaScript is now tightly integrated into most browsers, but the
same cannot be said for either Java or Flash. That is, we cannot
necessarily guarantee the existence of a Java virtual machine or Flash
plugin in every browser, which violates our requirement to avoid manual
installation of additional client-side software. We can therefore
eliminate Java- or Flash-based data server techniques from
consideration, but JavaScript-based data server techniques may still be
feasible.
\begin{figure}
\begin{center}
\begin{tabular}{ccc}
\includegraphics[scale=1]{data_server} &
\qquad &
\includegraphics[scale=1]{image_server} \\
\footnotesize (a) Data server &
\qquad &
\footnotesize (b) Image server \\
\\
\\
\includegraphics[scale=1]{model_interaction} &
\qquad &
\includegraphics[scale=1]{shared} \\
\footnotesize (c) Model interaction environment &
\qquad &
\footnotesize (d) Shared environment \\
\end{tabular}
\end{center}
\caption{Distribution styles for web-based geographic visualization
\protect\cite{Wood-J-1996-vis}. (F = filtering, M = mapping, R =
rendering.)}
\label{fig-distribution-styles}
\end{figure}
In contrast, the \emph{image server} style is where the display is
created entirely at the server and is only viewed at the client. In
other words, this is primarily a server-side processing model, as
illustrated in Figure~\ref{fig-distribution-styles}(b). Consequently,
techniques that use this style require no additional client-side
software, and thus meet our requirements. The downside is that the
resultant visualization can tend to be very static and non-interactive
in nature, as it is just a simple bitmap image.
The \emph{model interaction environment} style is where a model created
at the server can be explored at the client, as illustrated in
Figure~\ref{fig-distribution-styles}(c). \citeN{Wood-J-1996-vis}
originally referred to this as the ``3D model interaction'' style, but
this seems slightly out of place in the current context. They originally
intended this distribution style to apply to VRML models for GIS
applications, but it could be equally applied to any situation where an
interactive model is generated at the server, then downloaded to and
manipulated at the client. This is very similar to what happens with
many Flash-based applications, for example. ``Model interaction
environment'' therefore seems a more appropriate name for this style.
The key distinguishing feature of this style is that there is no further
interaction between the client and server after the model has been
downloaded. This means that while the downloaded model can be very
dynamic and interactive, changing the underlying data requires a new
model to be generated at the server and downloaded to the client.
Similar restrictions apply to techniques using this style as to the
data server style, so Java- and Flash-based model interaction
environment techniques can be eliminated from consideration. For similar
reasons, we can also eliminate solutions that require browser plugins
such as VRML or SVG (although native support for the latter is beginning
to appear in some browsers). It may be possible to implement this
distribution style using only client-side JavaScript, but it is presently
unclear as to how effective this might be.
% future work: implement model interaction using JavaScript?
Finally, the \emph{shared environment} style is where data manipulation
is done at the server, but control of that manipulation, rendering, and
display all occur at the client, as illustrated in
Figure~\ref{fig-distribution-styles}(d). This is similar to the model
interaction environment style, but with the addition of a feedback loop
from the client to the server, thus enabling a more flexible and dynamic
interaction. This is essentially the distribution style provided by Ajax
technologies [REF]. We can eliminate techniques based on the same
criteria as applied to the other three styles.
\subsection{Image generation techniques}
\label{sec-image-gen}
As noted earlier, image generation techniques work by directly plotting
geolocated IP addresses onto a base map image, then displaying the
composite image at the client. A typical example of the kind of output
that might be produced is shown in Figure~\ref{fig-image}. Such
techniques require two specific components: software to programmatically
create and manipulate bitmap images (for example, the GD image
library\footnote{\url{http://www.boutell.com/gd/}}); and software to
transform raw latitude/longitude coordinates into projected map
coordinates on the base map (for example, the PROJ.4 cartographic
projections library\footnote{\url{http://www.remotesensing.org/proj/}}).
\begin{figure}
\begin{center}
\includegraphics[width=0.95\textwidth,keepaspectratio]{ImageGeneration-full}
\end{center}
\caption{Sample output from the server-side image generation technique.}
\label{fig-image}
\end{figure}
Image generation techniques could use any of the distribution styles
discussed in Section~\ref{sec-distribution}. However, all but the image
server style would require the installation of additional client-side
software for generating images and performing cartographic projection
operations, so we will only consider image generation using an image
server distribution style (or ``server-side image generation'') from
this point on.
The server-side image generation technique provides some distinct
advantages. It is relatively simple to implement and is fast at
producing the final image, mainly because it uses existing,
well-established technologies. It is also bandwidth efficient: the size
of the generated map image is determined by the total number of pixels
and the compression method used, rather than by the number of points to
be plotted. The amount of data to be sent to the client should therefore
remain more or less constant, regardless of the number of points
plotted.
This technique also has some disadvantages, however. First, a suitable
base map image must be acquired. This could be generated from a GIS, but
if this is not an option an appropriate image must be obtained from a
third party. Care must be taken in the latter case to avoid potential
copyright issues. Second, the compression method used to produce the
final composite map image can have a significant impact on visual
quality. For example, lossy compression methods such as JPEG can make
the points plotted on the map appear distinctly fuzzy, as shown in
Figure~\ref{fig-image-quality}. A lossless compression method such as
PNG will avoid this problem, but will tend to produce larger image
files. Finally, it is harder to provide interactive map manipulation
features with this technique, as the output is a simple static image.
Anything that changes the content of the map (such as panning or
changing the visibility of points) will require the entire image to be
regenerated. Zooming could be achieved if a very high resolution base
map image was available, but the number of possible zoom levels might be
restricted.
\begin{figure}
\begin{center}
\includegraphics[scale=1.25]{jpeg_detail}\medskip
\includegraphics[scale=1.25]{overlay_detail}
\end{center}
\caption{Image quality of JPEG image generation (top) vs.\ PNG image
overlay (bottom).}
\label{fig-image-quality}
\end{figure}
\subsection{Overlay techniques}
\label{sec-overlay}
% Look for publications regarding the DataCrossing Ajax client.
% See <http://datacrossing.crs4.it/en_Documentation_Overlay_Example.html>.
% They use <IMG> rather than <DIV>, which has the advantage of the image
% being loaded only once, but makes it harder to dynamically change the
% appearance of markers. The amount of data generated will still be
% proportional to the number of points (one <IMG> per point).
Overlay techniques also involve plotting points onto a base map image,
but they differ from image generation techniques in that the points are
not composited directly onto the base map image. Rather, the points are
displayed as an independent overlay on top of the base map image. This
provides a significant advantage over image generation techniques, as it
enables the possibility of multiple independent overlays that can be
individually shown or hidden. This is very similar to the multi-layer
functionality provide by GIS, and is an effective way to provided
interactive visualizations of geographic data
\cite{Wood-J-1996-vis,MacE-AM-1998-GIS}. We still have the problem of
finding a suitable base map image, however.
Until relatively recently, implementing overlay techniques would likely
have required additional software at the client, but most modern
browsers now support absolute positioning of elements using CSS. This
enables us to create a map overlay using nothing more than HTML, CSS and
a few bitmap images. We have identified two main alternatives for
producing such an overlay, which we have termed \emph{image overlay} and
\emph{HTML overlay}.
An image overlay comprises a transparent bitmap image into which the
points are plotted, which is then overlaid on the base map image (in our
implementation, the output looks essentially identical to that shown in
Figure~\ref{fig-image}). This requires the overlay image to be in either
PNG or GIF format, as JPEG does not support transparency. Fortunately
the overlay image is likely to contain a lot of ``white space'', which
compresses very well, so use of a lossless compression method should not
be an issue. This also eliminates the ``fuzziness'' issue noted earlier
(see Figure~\ref{fig-image-quality}). The size of the image overlay will
generally be proportional to the number of points to be plotted, but the
image compression should have a moderating effect on this.
As noted earlier, generating images at the client would require
additional software to be installed, so we will only consider the data
server distribution style for image overlays. That is, both the base map
image and the overlay(s) are generated at the server.
An HTML overlay comprises a collection of HTML elements corresponding to
the points to be plotted, which are positioned over the base map image
using CSS absolute positioning. There is considerable flexibility as to
the types of elements that could be used to construct the overlay. One
possibility is to use \verb|<IMG>| elements to place icons on the base
map; this appears to be the approach adopted by Google Maps (see
Figure~\ref{fig-google}). Another possibility is to use appropriately
sized and colored \verb|<DIV>| elements, which then appear as colored
blocks ``floating'' over the base map image (in our implementation, the
output looks essentially identical to that shown in
Figure~\ref{fig-image}).
\begin{figure}
\begin{center}
\includegraphics[width=0.95\textwidth,keepaspectratio]{GoogleMap-full.png}
\end{center}
\caption{Sample output from the Google Maps technique.}
\label{fig-google}
\end{figure}
HTML overlays may be generated at either the server or the client.
Unlike the techniques discussed previously, however, HTML overlays can
be generated at the client without the need for additional software,
because only HTML (i.e., text) is being generated, not images. This can
be easily achieved using client-side JavaScript, so HTML overlays can
use any of the distribution styles discussed in
Section~\ref{sec-distribution} without violating our requirements. We
have therefore adopted two representative overlay techniques for our
experiments: server-side HTML overlays (using the image server
distribution style) and Google Maps (using the data server distribution
style). Since Google Maps uses \verb|<IMG>| elements, we have used
\verb|<DIV>| elements for the server-side HTML overlay.
Server-side HTML overlays are actually slightly simpler to implement
than either server-side image generation or image overlays, because we
do not need to write any code to generate or manipulate images (the base
map image is static and thus requires no additional processing). All
that is required is code to transform latitude/longitude coordinates
into projected map coordinates and produce corresponding \verb|<DIV>|
elements.
Google Maps \cite{Goog-M-2006-maps} is a more complex proposition. This
technique uses the data server distribution style, where JavaScript code
running within the browser enables the client to manipulate the base map
and its overlays. Data and map images are requested asynchronously from
the server as required, using Ajax technologies, which seems to imply
that Google Maps in fact uses the shared environment distribution style.
However, the server has no involvement beyond simply supplying data to
the client. In the shared environment distribution style, the server is
directly involved in manipulating the map, under the control of the
client. This is clearly not the case with Google Maps.
The primary advantage of Google Maps is the powerful functionality it
provides for generating and interacting with the map. Users may pan the
map in any direction and zoom in and out to many different levels. A
satellite imagery view is also available. In addition, further
information about each point plotted (such as the name of the city, for
example) can be displayed in a callout attached to the point, as shown
in Figure~\ref{fig-google}.
However, there are also some significant disadvantages to the Google
Maps technique\footnote{Interestingly, the Google Earth application
addresses many of these issues, but since it is not a browser-based
solution it falls outside the scope of our consideration. However, for
interest's sake we did an informal comparison between Google Earth and
the four techniques that we have tested, and this has been included in
the results in Section~\ref{sec-results}.}. First, it is a distributed
application, thus making it more complex to implement, test and debug
[REF]. Second, the server must have a registered API key from Google,
which is verified every time that a page attempts to use the API.
Similarly, the client must connect to Google's servers in order to to
download the API's JavaScript source. This means that the technique must
have an active Internet connection in order to work. Finally, the Google
Maps API does not currently provide any way to toggle the visibility of
markers on the map, so it is not possible to implement the interactive
``layers'' mentioned at the start of this section. (It is possible, of
course, that Google will implement this feature in a later version of
the API.)
The most significant disadvantage of all HTML overlay techniques,
however, is that the size of the HTML overlay is directly proportional
to the number of points to be plotted. There will be one overlay element
(\verb|<DIV>| or \verb|<IMG>|) per point, so a very large number of
points will result in an even larger amount of HTML source being
generated. We expect that this will lead to excessive browser memory
usage, and consequently that these techniques will not scale well at the
high end. However, they may still be useful for smaller data sets that
require interactive manipulation.
\section{Experimental design}
\label{sec-experiment}
After some preliminary testing with live data from the Otago School of
Business repository, we proceeded with a series of experiments to test
the scalability of the four techniques. Each technique was tested using
progressively larger synthetic data sets. The first data set comprised
one point at the South Pole (latitude \(-90^{\circ}\), longitude
\(-180^{\circ}\)). Each successive data set was twice the size of its
predecessor, building up a regular grid of latitude/longitude points at
one degree intervals\footnote{The entire grid has 64,800 points, so the
five largest data sets have many duplicate points.}. A total of
twenty-one data sets were created in this way, with the number of points
ranging from one to 1,048,576 (\(=2^{20}\)). The result of plotting the
16,384-point data set is shown in Figure~\ref{fig-grid-points}.
\begin{figure}
\begin{center}
\includegraphics[width=0.95\textwidth,keepaspectratio]{ImageGeneration-full}
\end{center}
\caption{The 16,384-point data set plotted on the base map.}
\label{fig-grid-points}
\end{figure}
The focus on scalability meant that we were primarily interested in
measuring page load times, memory usage and the amount of data
generated (which impacts on both storage and network bandwidth). Page
load time can be further broken down into the time taken to generate the
map data, the time taken to transfer the map data to the client across
the network, and the time taken by the client to display the map.
Unfortunately, the Google Maps technique requires an active Internet
connection (as noted in Section~\ref{sec-overlay}), so we were unable to
run the experiments on an isolated network. This meant that traffic on
the local network was a potential confounding factor. We therefore
decided to eliminate network performance from the equation by running
both the server and the client on the same machine\footnote{A Power
Macintosh G5 1.8\,GHz with 1\,GB RAM, running Mac OS X 10.4.7, Apache
2.0.55, PHP 4.4 and Perl 5.8.6.}. This in turn enabled us to measure the
time taken for data generation and page display independently, thus
simplifying the process of data collection and also ensuring that the
client and server processes did not unduly interfere with each other,
despite running on the same machine.
It could be argued that network performance would still have a
confounding effect on the Google Maps technique, but this would only be
likely for the initial download of the API (comprising about 235\,kB of
JavaScript source and images), which would be locally cached thereafter.
The API key verification does occur every time the map is loaded, but
the amount of data involved is very small, so it is less likely that
this would be significantly affected by network performance. Any such
effect would also be immediately obvious as it would simply block the
server from proceeding.
For each data set generated, we recorded its size, the time taken to
generate it, the time taken to display the resultant map in the browser,
and the amount of memory used during the test by the browser. We also
intended to measure the memory usage of the server, but this proved more
difficult to isolate than we expected, and was thus dropped from the
experiments. The data set generation time and browser memory usage were
measured using the \texttt{time} and \texttt{top} utilities respectively
(the latter was run after each test run to avoid interference). The map
display time was measured using the ``page load test'' debugging feature
of Apple's Safari web browser, which can repetitively load a set of
pages while recording various statistics, in particular the time taken
to load the page. Tests were run up to twenty times each where feasible,
in order to reduce the impact of random variations. Some tests were run
fewer times because they took a very long time (several minutes for a
single test run). We typically broke off further testing when a single
test run took longer than about five minutes, as by this stage
performance had already deteriorated well beyond usable levels.
While it is really beyond the scope of this work, out of interest some
informal tests were also undertaken using the Google Earth application.
A Perl script was used to generate a collection of KML files
corresponding to the data sets described above. Each data set was then
loaded into Google Earth, and a stopwatch was used to measure how long
it took to load the data set, defined as the period during which the
dialog box ``\textsf{Loading myplaces.kml, including enabled overlays}''
was displayed on screen.
\subsection{Technique implementation}
As noted in Sections~\ref{sec-image-gen} and \ref{sec-overlay}, the
server-side image generation, server-side image overlay and server-side
HTML overlay techniques were all implemented using the image server
distribution style. A separate dispatcher page was written in PHP for
each technique, which enabled arguments---such as the number of points
to be plotted---to be passed from the client to a corresponding Perl
script for each technique. The final page was then constructed as
follows:
\begin{description}
\item[server-side image generation] The Perl script generated a JPEG
composite map image that was displayed using a standard \verb|<IMG>|
element.
\item[server-side image overlay] The base map (a JPEG image) was
displayed using a CSS-positioned \verb|<IMG>| element. The Perl
script then generated a transparent PNG image, which was displayed
using an \verb|<IMG>| element with identical positioning attributes
to the base map.
\item[server-side HTML overlay] The base map (a JPEG image) was
displayed using a CSS-positioned \verb|<IMG>| element. The Perl
script then generated a collection of CSS-positioned \verb|<DIV>|
elements, which were included inline in the source of the dispatcher
page.
\end{description}
The Google Maps technique was implemented using the data server
distribution style. Once again, a PHP dispatcher page was used. This
time, however, the page included client-side JavaScript code to load and
initialise the Google Maps API, create the base map, and build the map
overlay. The first two steps were achieved using standard Google Maps
API calls. For the last step, the client used an \texttt{XMLHttpRequest}
object to call a server-side Perl script. This script generated and
returned to the client an XML data set containing the points to be
plotted. The client then looped through this data set and used Google
Maps API calls to create a marker on the base map corresponding to each
point.
\section{Results}
\label{sec-results}
As noted in the introduction, the intent of these experiments was not to
do a full analysis and statistical comparison of the performance of the
different techniques, but rather to identify broad trends. We have not,
therefore, carried out any statistical analysis on the results. We will
now discuss the results for data size, page load time and memory usage.
Because the data set size increases by powers of two, we have used
log-log scales for all plots.
\subsection{Data size}
The size of the data generated for each technique is shown in
Figure~\ref{fig-data-size}.
\begin{figure}
\begin{center}
\includegraphics[scale=0.66]{data_size}
\end{center}
\caption{Comparison of generated data size for each technique (log-log scale).}
\label{fig-data-size}
\end{figure}
\subsection{Page load time}
The size of the data generated for each technique is shown in
Figure~\ref{fig-data-size}.
\begin{figure}
\begin{center}
\includegraphics[scale=0.66]{data_generation_time}
\end{center}
\caption{Comparison of data generation time for each technique (log-log scale).}
\label{fig-data-generation-time}
\end{figure}
\begin{figure}
\begin{center}
\includegraphics[scale=0.66]{page_load_time}
\end{center}
\caption{Comparison of map display time for each technique (log-log scale).}
\label{fig-page-load-time}
\end{figure}
\begin{figure}
\begin{center}
\includegraphics[scale=0.66]{combined_time}
\end{center}
\caption{Comparison of combined page load time for each technique (log-log scale).}
\label{fig-combined-time}
\end{figure}
\subsection{Memory usage}
We measured both the real and virtual memory usage of the browser by
running the \texttt{top} utility after each test run and observing the
memory usage in each category. This told us the size of both the current
``working set'' and the total memory footprint of the browser process
after it had completed a test run.
The real memory data proved somewhat unreliable, however. Real memory
usage was generally consistent across several test runs, but frequently
it would fluctuate upwards by a factor of nearly two for no readily
apparent reason. We can only assume that this was a result of other
processes running on the test machine interacting with the browser
process in unexpected ways. We therefore discarded the real memory data.
The virtual memory data proved more consistent, as the virtual memory
footprint of a process is less likely to be impacted by other running
processes. The amount of virtual memory used by the browser for each
technique is shown in Figure~\ref{fig-virtual-memory}.
\begin{figure}
\begin{center}
\includegraphics[scale=0.66]{virtual_memory}
\end{center}
\caption{Comparison of virtual memory usage for each technique (log-log scale).}
\label{fig-virtual-memory}
\end{figure}
\section{Conclusion}
% The
% software extracts IP addresses from the web server logs, geolocates them
% using the free MaxMind GeoLite Country database\footnote{See
% \url{http://www.maxmind.com/app/ip-location}.}, then stores the
% resulting country information in a separate database.
% The Tasmania software, however, uses countries as its base unit of
% aggregation. We were interested in looking at the distribution on a finer
% level, down to individual cities if possible
\bibliography{Map_Visualisation}
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\end{document}
