\documentclass[acmnow]{acmtrans2m}
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\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. This paper compares 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{downloads, geolocation, geovisualization, scalability, Google
Maps, distribution style, dynamic map generation}
\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 the site, so as to decide
where best to target 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.
The author's interest in this area derives from implementing a pilot
digital institutional repository for the University of Otago School of
Business\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. There was great interest 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 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}
\centering
\includegraphics[scale=0.65]{tasmania_stats}
\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}
The author therefore began to explore possible techniques for plotting
repository hit data onto a world map, with the aim of adding this
capability to the Tasmania statistics package. Preference was given to
techniques 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 prior 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 graphics representing web hits.
This can be achieved by programmatically plotting points onto a base map
image; the composite image is then displayed at the client. This class
of techniques shall henceforth be referred to as \emph{single-layer}
techniques. The second class of techniques separately return both a base
map image and some kind of overlay containing the plotted points. The
overlay and the base map are then displayed as separate items at the
client. This class of techniques shall henceforth be referred to as
\emph{multi-layer} techniques.
Both classes of techniques have been used in the aforementioned systems,
but multi-layer techniques appear to have been particularly popular. For
example, Palantir used a multi-layer 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 multi-layer technique that has only become feasible relatively
recently with the advent of widespread support for CSS positioning and
Ajax technologies in many browsers.
Multi-layer techniques enjoy a particular advantage over single-layer
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. As we shall see
shortly, however, web-based multi-layer techniques tend to rely on more
recent web technologies such as CSS and Ajax, whereas single-layer
techniques generally do not. Single-layer techniques should therefore be
portable to a wider range of client and server environments.
Each map generation and display technique comprises a specific
technology or collection of technologies (such as transparent bitmap
overlays + CSS positioning), implemented using a specific distribution
style. For example, a particular single-layer technique might be
implemented completely server-side while another might use a mixture of
server-side and client-side processing. Similarly, multi-layer
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 wide variety of possible techniques that were available, the
next question was which techniques would be most suitable? Ideally, a
technique should not only efficiently fulfil the task of plotting
repository hits on a map, but also provide tangible benefits to
end-users. 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 techniques that could
scale to a large number of points were of particular interest. 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. Informal testing with these data suggested that a
single-layer composite map image would perform well with this volume of
data, taking at most a few seconds to load and display a page.
Conversely, it appeared that Google Maps would not perform well, taking
on the order of minutes to load and display a large number of points.
The range of techniques was first narrowed down 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}. The
scalability of these four techniques was then tested 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}.
Informal tests suggested that the server-side image generation and the
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 in performance across the
four techniques. It was expected that variations across techniques would
be reasonably clear-cut, 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-conclusion}.
\section{Technique selection}
\label{sec-techniques}
In this section the four techniques that were chosen for testing are
discussed in more detail, along with the reasons for choosing these
particular techniques. First, the impact of distribution style on the
choice of technique is discussed. This is followed by an examination of
how each 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 a multi-layer 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, the existence of
a Java virtual machine or Flash plugin cannot necessarily be guaranteed
in every browser, which violates the requirement to avoid manual
installation of additional client-side software. Java- or Flash-based
data server techniques can therefore be eliminated from consideration,
but JavaScript-based data server techniques are feasible. Indeed, Google
Maps is an example of such a technique (see Section~\ref{sec-overlay}).
\begin{figure}
\centering
\begin{tabular}{ccc}
\includegraphics[scale=0.9]{data_server} &
\qquad &
\includegraphics[scale=0.9]{image_server} \\
\footnotesize (a) Data server &
\qquad &
\footnotesize (b) Image server \\
\\
\\
\includegraphics[scale=0.9]{model_interaction} &
\qquad &
\includegraphics[scale=0.9]{shared} \\
\footnotesize (c) Model interaction environment &
\qquad &
\footnotesize (d) Shared environment \\
\end{tabular}
\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. The downside is that the resultant visualization can tend to
be very static and non-interactive in nature, as it is typically 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 such as VRML or SVG that require external
browser plugins (although native support for SVG 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.
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. Ajax technologies \cite{Garr-JJ-2005-Ajax} can easily
support this kind of distribution style. For example,
\citeN{Saya-A-2006-GISWS} use Ajax to integrate Google Maps with
existing GIS visualization web services. We can eliminate specific
shared environment techniques from consideration based on the same
criteria as were applied to the other three styles (e.g., no Java- or
Flash-based techniques).
\subsection{Single-layer techniques}
\label{sec-image-gen}
As noted earlier, single-layer 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 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}
\centering
\includegraphics[width=\textwidth,keepaspectratio]{ImageGeneration-full}
\caption{Sample output from the (single-layer) server-side image
generation technique.}
\label{fig-image}
\end{figure}
Single-layer 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 single-layer techniques that use
the image server distribution style (or \textbf{server-side image
generation}).
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, because
the size of the generated map image is determined by its pixel
dimensions 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 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 or ``muddy'',
%as shown in Figure~\ref{fig-image-quality}
even at high quality levels. Lossless compression methods such as PNG
avoid this problem, but may produce larger files for the same image.
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 certain 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}
% \centering
% \includegraphics[scale=0.98]{jpeg_detail}
% \includegraphics[scale=0.98]{overlay_detail}
% \caption{Image quality of JPEG (Q=90) image generation (left) vs.\
% PNG image overlay (right).}
% \label{fig-image-quality}
% \end{figure}
\subsection{Multi-layer techniques}
\label{sec-overlay}
Multi-layer techniques also involve plotting points onto a base map
image, but they differ from single-layer techniques in that the points
are not plotted 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 single-layer techniques, as it
enables the possibility of multiple independent layers that can be
individually shown or hidden. This is very similar to the multi-layer
functionality provided by GIS, and is an effective way to provide
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 multi-layer 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} on page~\pageref{fig-image}). This requires the
overlay image to be in either PNG or GIF format, as JPEG does not
support transparency. The overlay image is likely to contain
considerable ``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 in Section~\ref{sec-image-gen}, 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 (or
\textbf{server-side image overlay}). 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, which 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} on page~\pageref{fig-image}).
\begin{figure}
\centering
\includegraphics[width=\textwidth,keepaspectratio]{GoogleMap-full.png}
\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 HTML overlay techniques for
our experiments: \textbf{server-side HTML overlays} (using the image
server distribution style) and \textbf{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 generate 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 to many different levels of detail. A
satellite imagery view is also available. In addition, further
information about each point plotted (such as the name of the city) can
be displayed in a callout attached to the point, as shown in
Figure~\ref{fig-google}. Google Maps also has a proven record for
visualization of network resources. For example,
\citeN{Gibb-H-2006-Gridscape} use Google Maps to visualize and manage
worldwide computing grids.
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.}. First, it is
a distributed application, thus making it more complex to implement,
test and debug \cite{Bates-PC-1995-distdebug,Ensl-PH-1978-distributed}.
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 requires 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 may implement this feature in a future 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 appropriate 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. A regular grid of points at one degree
intervals was then constructed by progressively incrementing the
latitude and longitude, with each data set being twice the size of its
predecessor. 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}. The grid spacing used meant that 64,800
points were sufficient to fill the entire map, so the five largest data
sets had many duplicate points. This does not affect the results of the
experiments, however, as it is the total number of points that is
significant, not their location.
\begin{figure}
\centering
\includegraphics[width=\textwidth,keepaspectratio]{16384_points}
\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
independently measure the time taken for data generation and page
display, 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 real and virtual memory used by the browser during the
test. We also intended to measure the memory usage of the server, but
this proved more difficult to isolate than 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 an
excessive amount of time to complete (i.e., 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.
\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 dispatcher page included a
standard \verb|<IMG>| element that called the Perl script. This
script loaded a base map PNG image, plotted points directly onto it,
and returned the composite map to the client as a JPEG image (with
the ``quality'' parameter set to 90).
\item[server-side image overlay] The dispatcher page included two
\verb|<IMG>| elements, the first for the base map and the second for
the overlay, both with identical CSS positioning attributes. The
first \verb|<IMG>| simply loaded a static JPEG image representing
the base map. The second \verb|<IMG>| called the Perl script, which
generated and returned the overlay as a transparent PNG image.
\item[server-side HTML overlay] The dispatcher page included a
\verb|<IMG>| element for the base map and a \verb|<DIV>| element for
the overlay, both with identical CSS positioning attributes. As with
the previous technique, the \verb|<IMG>| simply loaded a static JPEG
image representing the base map. The \verb|<DIV>| contained inline
PHP code that called the Perl script. This in turn generated and
returned the overlay as a collection of CSS-positioned \verb|<DIV>|
elements, nested within the top-level \verb|<DIV>| element.
\end{description}
For all of these techniques, the base map image was 1,024 by 520 pixels.
In PNG format it occupied approximately 1.2\,MB (but this version was
never returned to the client), while in JPEG format (Q=90) it occupied
approximately 180\,kB. The base map image was derived from an original
3,599 by 1,826 pixel image, which was part of a collection of maps
released into the public domain by the \citeN{CIA-WFB-2006}. All three
techniques used the PROJ.4 cartographic projections library to convert
latitude/longitude pairs into projected map coordinates, while the first
two techniques also used the GD graphics library to programmatically
generate and manipulate images.
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 the
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 number of points in each data set increases in powers of
two, we have used log-log scales for all plots.
\subsection{Data size}
During each test run, the data generated by the server was saved to a
file and its size in bytes recorded. In the case of the server-side
image generation and server-side image overlay techniques, the file
comprised a bitmap image; whereas for the server-side HTML overlay and
Google Maps techniques, the file comprised HTML or XML text,
respectively.
There was a certain amount of fixed overhead for each technique tested,
as summarised in Table~\ref{tab-overhead}. This overhead comprised
static files that were always downloaded to the client, regardless of
the number of points to be plotted. Typical fixed overhead included
items such as the base map image, various icons, the PHP source of the
dispatcher page and the JavaScript source for the Google Maps API.
\begin{acmtable}{11cm}
\centering
\begin{tabular}{lll}
Technique & Fixed overhead & Content \\
\hline
Server-side image generation & 629\,bytes & dispatcher (PHP)\smallskip \\
Server-side image overlay & \(\approx\) 181\,kB & dispatcher (PHP) \\
& & base map image (JPEG)\smallskip \\
Server-side HTML overlay & \(\approx\) 181\,kB & dispatcher (PHP) \\
& & base map image (JPEG)\smallskip \\
Google Maps & \(\approx\) 235\,kB & dispatcher (PHP) \\
& & base map image tiles (PNG) \\
& & API (JavaScript) \\
& & various icons (PNG) \\
\end{tabular}
\caption{Fixed overhead for each technique.}
\label{tab-overhead}
\end{acmtable}
\begin{figure}
\centering
\includegraphics[scale=0.55]{data_size}
\caption{Comparison of generated data size for each technique (log-log scale).}
\label{fig-data-size}
\end{figure}
The amount of data generated for each technique, including fixed
overhead, is shown in Figure~\ref{fig-data-size}. It is immediately
apparent from these results that there is a divergence between the two
techniques that generate images (server-side image generation and
server-side image overlay), and the two techniques that generate text
(server-side HTML overlay and Google Maps).
Both the server-side image generation and server-side image overlay
techniques scale particularly well with regard to the amount of data
generated. Interestingly, the amount of data generated by the image
generation technique increases by about 8\,kB up to the 8,192-point data
set, but then \emph{drops} by about 90\,kB over the next three data
sets. This occurs because the number of points plotted has become
sufficient to cover most of the base map. In other words, a large
portion of the composite map image is a single color (see
Figure~\ref{fig-grid-points} on page~\pageref{fig-grid-points} for an
example), which compresses more efficiently.
The amount of data generated by the image overlay technique appears
constant, but actually increases by about 2\,kB across the entire range
of tests. This has important implications for the ability of this
technique to handle multiple layers. Because the overlay images are
quite small (less than 2\,kB for up to one million points), it should be
feasible to pre-load several overlay images into a client-side array and
switch them on and off as desired.
The server-side HTML overlay and Google Maps techniques clearly do not
scale well, and begin to visibly diverge from the other two techniques
once the amount of data generated exceeds about 5\% of the fixed
overhead. For the HTML overlay technique this occurs somewhere between
64 and 128 points, whereas for Google Maps it occurs somewhere between
256 and 512 points. The divergence increases rapidly for both techniques
beyond these points, with the HTML overlay technique suffering the most.
The latter occurs because the HTML overlay technique needs to generate
additional CSS attributes (i.e., more text) in order to correctly
position the \verb|<DIV>| elements, whereas the Google Maps technique
needs only to return a more compact list of latitude/longitude
coordinates.
\subsection{Page load time}
For each test run, we recorded the length of time taken to generate the
data at the server and to display the page in the client browser. The
former is illustrated in Figure~\ref{fig-data-generation-time} and the
latter in Figure~\ref{fig-page-load-time}. The combined time (data
generation + display time) is shown in Figure~\ref{fig-combined-time}.
\subsubsection{Data generation time}
\begin{figure}
\centering
\includegraphics[scale=0.55]{data_generation_time}
\caption{Comparison of data generation time for each technique (log-log scale).}
\label{fig-data-generation-time}
\end{figure}
The results (see Figure~\ref{fig-data-generation-time}) show that the
length of time taken to generate the source data increases in proportion
to the number of points to be plotted, as expected. It is interesting to
note the differences in data generation time for each technique,
however. Data generation for both of the ``text-based'' techniques (HTML
overlay and Google Maps) is consistently faster than for the
``image-based'' techniques (image generation and image overlay).
The results show that server-side image generation generally takes the
longest to generate its data. This is because it not only has to map
points from latitude/longitude into projected map coordinates, but also
must plot these points onto the base map image, then compress the
composite image as a JPEG. The image to be compressed is also moderately
complex, which only adds to the data generation time. Server-side image
overlay performs somewhat better because it uses a less complex
compression method (PNG) and the image to be compressed is much simpler
(a collection of colored points on a blank background).
The server-side HTML overlay technique appears faster at generating data
than either of the two image-based techniques at the low end, but is
similar in performance at the high end. In this technique the server
only needs to map latitude/longitude to projected map coordinates; no
images need to be generated and there is no compression to deal with. At
the high end, however, this advantage is clearly offset by the
significant volume of data being generated. Google Maps is faster again,
because almost all processing is carried out on the client; the server's
only involvement is to generate a simple list of latitude/longitude
coordinates.
In terms of data generation, it appears that all techniques tested scale
reasonably well. The image-based techniques perform worse at the low end
because they involve more complex processing than the text-based
techniques, but this is offset at the high end by the relatively
constant amount of data generated. Conversely, the text-based techniques
perform better at the low end, but are negatively impacted at the high
end by the sheer volume of data produced (tens or hundreds of megabytes
vs.\ hundreds of kilobytes).
\subsubsection{Map display time}
\begin{figure}
\centering
\includegraphics[scale=0.55]{page_load_time}
\caption{Comparison of map display time for each technique (log-log scale).}
\label{fig-page-load-time}
\end{figure}
These results (see Figure~\ref{fig-page-load-time}) reveal quite a
spectacular difference between the image-based and text-based
techniques. The time taken to display the map is essentially constant
for both of the image-based techniques, regardless of the number of
points to be plotted. This is not surprising given that the size of the
generated data is also essentially constant, and that the browser is
simply loading and displaying static images. The image overlay technique
appears slightly slower than the image generation technique. This is
probably because the image overlay technique has to load two images from
the server (the base map and the overlay), compared to one image for the
image generation technique.
In contrast, the text-based technique clearly do not scale well with
regards to map display time. Google Maps suffers particularly in this
regard, with display time exceeding ten seconds shortly past 512 points.
Testing was abandoned at 4,096 points, with a single test run taking
over seven minutes. The HTML overlay technique fares better, exceeding
ten seconds somewhere between 4,096 and 8,192 points. Testing was
abandoned at 32,768 points, with a single test run taking almost ten
minutes.
\subsubsection{Combined time}
\begin{figure}
\centering
\includegraphics[scale=0.55]{combined_time}
\caption{Comparison of combined page load time for each technique (log-log scale).}
\label{fig-combined-time}
\end{figure}
Combining the data generation and map display times (see
Figure~\ref{fig-combined-time}) yields little change in the curves for
the text-based techniques, because the data generation times are very
small compared to the map display times. There is a more obvious impact
on the image-based techniques, with both techniques remaining more or
less constant up to about 2,048 points, then slowing as the number of
points increases beyond that. However, the slowdown is nowhere near as
dramatic as for the text-based techniques; even the largest data set
only takes about nineteen seconds overall. The image overlay technique
does display a slight advantage of about half a second over the image
generation technique for the largest data set, but further experiments
will be required to determine whether this is a statistically
significant difference.
\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 results are shown in
Figure~\ref{fig-real-memory} and the virtual memory results are shown in
Figure~\ref{fig-virtual-memory}.
\begin{figure}
\centering
\includegraphics[scale=0.55]{real_memory}
\caption{Comparison of real memory usage for each technique (log-log scale).}
\label{fig-real-memory}
\end{figure}
\begin{figure}
\centering
\includegraphics[scale=0.55]{virtual_memory}
\caption{Comparison of virtual memory usage for each technique (log-log scale).}
\label{fig-virtual-memory}
\end{figure}
While both sets of results display similar trends, the real memory data
proved somewhat problematic. Real memory usage was generally consistent
across test runs, but would also frequently fluctuate upwards by a
factor of nearly two for no readily apparent reason. This is
particularly apparent with the HTML overlay technique beyond 1,024
points. We can only assume that this was a result of other processes on
the test machine interacting with the browser process in unexpected
ways. We are therefore somewhat wary of the real memory data, but they
are at least broadly consistent with the virtual memory data. The
virtual memory data proved more consistent overall, as the virtual
memory footprint of a process is less likely to be impacted by other
running processes.
The results show that the two image-based techniques have essentially
constant memory usage regardless of the number of points plotted. This
is to be expected, given that the size of the source data is also
essentially constant. The text-based techniques, however, clearly begin
to diverge as the number of points increases. The HTML overlay technique
starts to visibly diverge somewhere between 2,048 and 4,096 points,
while Google Maps starts to visibly diverge 64 and 128 points. This is
in line with our expectation for these techniques that memory usage
would increase in proportion to the number of points. It is intriguing
to note that for both techniques, there appears little consistency as to
where the performance of each measure begins to diverge, as shown in
Table~\ref{tab-divergence} (although Google Maps appears to exhibit
greater consistency than HTML overlay in this regard).
\begin{acmtable}{11cm}
\centering
\begin{tabular}{lccc}
Technique & Data size & Map display time & Virtual memory \\
\hline
Server-side HTML overlay & 64--128 & 128--256 & 2,048--4,096 \\
Google Maps & 256--512 & 64--128 & 64--128 \\
\end{tabular}
\caption{Approximate number of points at which each measure begins to diverge,
for the HTML overlay and Google Maps techniques.}
\label{tab-divergence}
\end{acmtable}
\section{Conclusion and future work}
\label{sec-conclusion}
In this research, we tested the scalability of four techniques for
online geovisualization of web site hits, with respect to the number of
points to be plotted on the map. The four techniques tested were
server-side image generation, server-side image overlay, server-side
HTML overlay and Google Maps. The results clearly show that the
server-side image generation and server-side image overlay techniques
scale the best from small to large data sets. The HTML overlay and
Google Maps techniques work well for small data sets, but their
performance rapidly deteriorates as the size of the data set increases,
to the point where they become unusable.
Despite this clear difference in scalability, we are still left with
some interesting questions. We did not investigate the model interaction
environment distribution style in this research, as it was unclear
whether this could be achieved using only client-side JavaScript. This
is clearly an avenue for further investigation. In addition, the
appearance of native SVG support in some browsers means that this may
also become a viable option in future.
We were somewhat surprised that the server-side HTML overlay and Google
Maps techniques exhibited no obvious consistency in where the different
measures (data size, map display time and virtual memory usage)
diverged. It seems logical that some form of correlation might exist, so
further research will be required to investigate this. One possibility
might be to implement an instrumented web browser and server in order to
gather more precise data.
Shortly after completing our experiments, we discovered \emph{msCross
Webgis}\footnote{\url{http://datacrossing.crs4.it/en_Documentation_mscross.html}},
which is an open source Google Maps clone. Its documentation implies
that it may be possible to build a fully self-contained implementation
that requires no external network access. This would enable us to test
on an isolated network with the client and server running on different
machines. We could then include measurements of network transfer time,
and eliminate any problems caused by running the client and server on
the same machine. This would require a distributed measurement
infrastructure similar to that developed by \citeN{Barf-P-1999-webperf}.
Our overall aim was to identify which was the best technique to use to
plot downloads and abstract views from the Otago School of Business
digital repository. Based on our results, both the server-side HTML
overlay and Google Maps techniques are clearly inappropriate for this
task. This leaves us with a choice between two very similarly-performing
techniques: server-side image generation and server-side image overlay.
However, the practical advantages of multi-layer techniques over
single-layer techniques, such as the ability to dynamically show and
hide multiple overlays, mean that server-side image overlay is the clear
winner in this case.
\begin{acks}
The author would like to acknowledge Dr.\ Antoni Moore and Prof.\ George
Benwell for their input into this research.
\end{acks}
\bibliography{Map_Visualisation}
\begin{received}
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\end{received}
\end{document}
