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\markboth{Nigel Stanger}{...}

\title{Scalability of Methods for Online Geovisualisation of Web Site Hits}
            
\author{NIGEL STANGER \\ University of Otago}
            
\begin{abstract} 
A common technique for visualising the geographical distribution of
web site hits is to geolocate the IP addresses of hits and plot them on
a world map. This is typically achieved by dynamic generation of images
on the server. In this paper we compare this method with two others:
overlaying CSS-enabled HTML on an underlying image and using Google
Maps. The results show that all three methods are suitable for small
data sets, but that the latter two methods do not scale well to large
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, geovisualisation, 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 running a web site, it is quite reasonable to want information on
the nature of traffic to the site. 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 visualisation 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 repository management
software\footnote{\url{http://www.eprints.org/}}. The 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 monitoring this increase, particularly with
respect to where in the world the hits were coming from. The EPrints
statistics 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 a numerical ranking of the number of hits from
each country, it does not provide any visual clues as 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 various 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, thus providing us
with the widest possible audience and reducing the impact of wide
variation in client hardware and software environments.

There have been several prior efforts to plot web activity
geographically. \citeN{Lamm-SE-1996-webvis} developed a sophisticated
system for real-time visualisation of web traffic on a 3D globe, but
this was intended for use with a virtual reality interface, thus
limiting its general applicability. \citeN{Papa-N-1998-Palantir}
describe a similar system (Palantir) that is written in Java, and thus
able to be run within a web browser, asssuming that a Java virtual
machine is available. \citeN[pp.\ 100--103]{Dodg-M-2001-cybermap}
describe these and several other related systems.

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, but these produced fairly crude
results. 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}. Reasonably accurate
databases were commercially available at the time \cite[p.\
1466]{Lamm-SE-1996-webvis}, but were not 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
databases\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.

Based on the literature, it appeared that a Palantir-style model would
be suitable for our purposes. The Palantir software itself appears to no
longer be available, but we would probably not have used it anyway as it
requires the client machine to have a Java virtual machine installed.
Palantir worked by plotting web hits directly onto a base map image,
then displaying the composite image, all within a client-side Java
applet. However, the basic technique can just as easily be implemented
as a server-side application that returns a bitmap image to the client.
We shall henceforth refer to this technique as \emph{image generation};
it will be discussed further in Section~\ref{sec-imagegen}.

However, there are alternative techniques that have become possible only
relatively recently, and are therefore unlikely to be in wide use (if at
all). One possible technique is to load a base image into the browser,
then overlay points onto the image using using absolutely positioned
HTML \verb|<DIV>| elements. This technique raises the potential for a
more GIS-like style of interaction with the map, with multiple layers
that can be activated and deactivated as necessary.  We refer to this
technique as \emph{HTML overlay}; it will be discussed further in
Section~\ref{sec-overlay}.

Another recent development is the release of the Google Maps API
\cite{Goog-M-2006-maps}, which enables web developers to easily embed
dynamic, interactive maps within web pages. These maps have an obvious
visual appeal and provide quite powerful interactive functionality
(including pan and zoom) out of the box. They also offer significant
customisability to the developer. This technique will be discussed
further in Section~\ref{sec-google}.

% This technique
% requires a browser that supports the Cascading Style Sheet (CSS)
% positioning properties; such support has only appeared relatively
% recently.
% 
% The former technique, which we shall henceforth refer to as \emph{base
% map + HTML overlay}, involves overlaying points onto a base map image on
% the client side, using HTML \verb|<DIV>| elements that are absolutely
% positioned via CSS. The latter technique 
% 
% 
% 
% We therefore
% considered options that did not require additional client software byond what
% was provided by the web browser. We identified three
% possible techniques for generating such a map:

% \begin{description}
% 
% 	\item[Image generation] An image (e.g., a JPEG or PNG) is generated
% 	at the server by plotting points directly onto a base map. The final
% 	image is then sent to the client.
% 	
% 	\item[Base map + HTML overlay] A base map image is sent to the
% 	client. Points are then overlaid on this map at either the client or
% 	the server using HTML \verb|<DIV>| elements that are absolutely
% 	positioned via CSS.
% 	
% 	\item[Google Maps] The Google Maps API is used at the client to
% 	generate a base map and plot points on the map. The data for this
% 	map are generated at the server.
% 
% \end{description}

% We will describe these techniques in more detail in
% Section~\ref{sec-techniques}. The first technique (image generation)
% appears to be fairly widespread and has been in use for some time,
% whereas the latter two do not appear to have been widely used (we will
% examine possible reasons for this shortly).

The identification of these three techniques immediately raised the
question of which was the best for our purposes. The greatest concern
was whether these techniques 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. Taking into consideration
the type of hit (abstract view versus document download) increased that
figure to nearly 13,000. Ideally we wanted a technique that could plot a
large number of points as quickly as possible.

We therefore set about testing the scalability of the three techniques
to determine how well each technique handled large numbers of points. A
series of experiments was conducted using 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 the image generation technique would
prove the most scalable, and this was borne out by the results of the
experiments, which show that image generation scales 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), but their performance deteriorated rapidly beyond this. The
results are discussed in more detail in Section~\ref{sec-results}.


\section{The techniques in more detail}
\label{sec-techniques}

In this section we discuss in more detail each of the three techniques
outlined in the previous section. For each technique, 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{Image generation}
\label{sec-imagegen}

As noted earlier, this technique works by directly plotting geolocated
IP addresses onto a base map image, then displays the composite image at
the client, as shown in Figure~\ref{fig-image}. It requires two
additional pieces of software: one that can create and manipulate bitmap
images programmatically (for example, the GD image
library\footnote{\url{http://www.boutell.com/gd/}}); and one that can
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]{gd_map}
	\end{center}
	\caption{Example output from the image generation technique.}
	\label{fig-image}
\end{figure}


The Palantir system implemented a distributed architecture for this
technique, where the source data were generated at the server and the
map was generated by a Java applet at the client
\cite{Papa-N-1998-Palantir}. Alternatively, the map image can be
generated entirely on the server. Both architectures are illustrated in
Figure~\ref{fig-image-architecture}. We have adopted the latter approach
in our experiments (server-side image generation), as the former would
require installing additional software at the client (for generating
images and performing cartographic projection operations).


\begin{figure}
	\caption{Distributed vs.\ server-side architectures for the image
	generation technique.}
	\label{fig-image-architecture}
\end{figure}


This technique provides some distinct advantages. If a server-side
architecture is adopted, the technique 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 plotted. The amount of data generated 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 for the map image
can impact on the quality of the final result. For example, lossy
compression methods such as JPEG can make the points plotted on the map
appear distinctly fuzzy (see Figure~\ref{fig-image-quality}). A
lossless compression method such as PNG will avoid this problem, but
will produce larger image files. Finally, it is harder to provide
interactive map manipulation features with this technique, as the output
is a 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 with a very high
resolution base map image, but the number of zoom levels may be
restricted.


\subsection{HTML overlay}
\label{sec-overlay}

This technique also involves plotting points onto a base map image, but
it differs from the image generation technique in that the points are
not plotted directly onto the base map image. Rather, the points are
plotted as an independent overlay on the base map image, using HTML
\verb|<DIV>| elements that are absolutely positioned via CSS. This
technique thus requires a web browser that supports the appropriate CSS
positioning attributes, but such support is now standard in many
browsers. (The output looks essentially identical to that from the image
generation technique, so we have not provided an example.)

As with the image generation technique, we can adopt either a
distributed architecture, where source data are generated at the server
and converted into an HTML overlay at the client, or a server-side
architecture, where the HTML overlay is generated at the server. (The
base map image is static and thus requires no additional processing.)
Both architectures are illustrated in
Figure~\ref{fig-html-architectures}. Unlike the image generation
technique, however, the distributed architecture can be implemented
without additional software on the client side. JavaScript is now
standard in most browsers, and this is sufficient to implement the
client-side behaviour. Both architectures thus meet our requirement for
avoiding additional client-side software.


\begin{figure}
	\caption{Distributed vs.\ server-side architectures for the HTML
	overlay technique.}
	\label{fig-html-architectures}
\end{figure}


If a server-side architecture is adopted, this technique is actually
slightly easier to implement than the image generation technique,
because we do not need the code to generate or manipulate images.
Implementing a distributed architecture is more complex, but this has
more to do with the nature of distributed applications than the
technique itself. It does not suffer the image generation technique's
problem of fuzzy-looking points (see Figure~\ref{fig-image-quality}),
because the points are not part of the map image. Finally, the most
significant advantage of the HTML overlay technique over the image
generation technique is that 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, albeit on
a much smaller scale.

As with the image generation technique, however, we still have the
problem of finding a suitable base map image. The technique also relies
on relatively recent technologies that have not yet been fully or
consistently implemented by all browsers. The most significant
disadvantage of the HTML overlay technique, however, is that the size of
the HTML overlay will be directly proportional to the number of points
to be plotted, as there will be one \verb|<DIV>| element per point. A
very large number of points will almost certainly lead to excessive
memory usage, so this technique is unlikely to scale well at the high
end. However, it may still be useful for smaller data sets that require
interactive manipulation.


\begin{figure}
	\begin{center}
		\includegraphics[scale=1.25]{gd_detail}\medskip
		
		\includegraphics[scale=1.25]{html_detail}
	\end{center}
	\caption{Image quality of JPEG image generation (top) vs.\ HTML
	overlay (bottom).}
	\label{fig-image-quality}
\end{figure}


\subsection{Google Maps}
\label{sec-google}

This technique uses the client-side Google Maps API
\cite{Goog-M-2006-maps} to both generate the base map and plot points on
it, as shown in Figure~\ref{fig-google}. The output and interaction is
therefore significantly different in nature from that provided by the
other two techniques. Google Maps requires JavaScript support at the
client and the Google Maps software must be installed on the client.
However, since the latter happens automatically when the corresponding
web page is loaded, this technique meets our requirements.


\begin{figure}
	\begin{center}
		\includegraphics[width=0.95\textwidth,keepaspectratio]{google_map}
	\end{center}
	\caption{Example output from the Google Maps technique.}
	\label{fig-google}
\end{figure}


Google Maps by definition uses a distributed architecture, as shown in
Figure~\ref{fig-google-architecture}. Data are generated at the server,
while all map display and manipulation occurs at the client.


\begin{figure}
	\caption{Distributed architecture of the Google Maps technique.}
	\label{fig-google-architecture}
\end{figure}

The primary advantage of this technique is that it provides an appealing
visual display and powerful functionality for 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 ``speech bubble'' next
to the point, as shown in Figure~\ref{fig-google}.

However, there are also some significant disadvantages compared to the
previous two techniques. As a distrbiuted applicatiopn, it is more
complex to implement and debug. It also relies on an active Internet
connection in order to run. The server must register an API key with
Google, which is checked 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. Consequently, this technique
cannot be used on an isolated network. Finally, the Google Maps API does
not currently provide any method of toggling the visibility of markers
on the map, so it is not possible to implement the ``layers'' that are
possible with the HTML overlay technique (it is of course possible that
Google will implement this feature in a later version of the API).

Interestingly, the Google Earth application addresses several of these
issues, but this is clearly outside the scope of our work, as it
requires the manual installation of extra software and runs outside the
web browser entirely. (Just for fun, however, we will do an informal
comparison in Section~\ref{sec-results} between Google Earth and the
three techniques discussed here.)


\section{Experimental design}
\label{sec-experiment}


\section{Results}
\label{sec-results}


\subsection{Data size}


\subsection{Display time}


\subsection{Memory usage}


\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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