\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}
\newdef{remark}[theorem]{Remark}
\markboth{Nigel Stanger}{...}
\title{Scalability of Techniques for Online Geovisualization 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 technique with two others:
overlaying CSS-enabled HTML on an underlying image and using Google
Maps. The results show that all three techniques are suitable for small
data sets, but that the latter two techniques 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, 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. 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 tracking 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 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 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 \cite[pp.\
27--28]{Offu-J-2002-quality}.
There have been several prior efforts to plot web activity
geographically. \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) written in Java, which was 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, 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,Jian-B-2000-cybermap}.
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.
The techniques used by previous systems can generally be divided into two
classes. Techniques of the first class 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,
which is then displayed at the client. We shall henceforth refer to this
class of techniques as \emph{image generation} techniques. Techniques of the
second class 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 previously mentioned
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 recently become feasible with the
advent of support for CSS positioning and Ajax technologies in most
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.
Within each class, there are several alternative approaches to
implementing these techniques. For example, an image generation technique
might be completely server-side or use a mixture of server-side and
client-side processing. Overlay techniques can also adopt different
distribution styles, and the overlays themselves might take the form of
transparent images, absolutely positioned HTML elements, dynamically
generated graphics, etc.
% With regard to our own visualization needs, we could quickly eliminate
% several methods from consideration because they did not meet our
% requirement to avoid manual installation of additional client-side
% software (see Section~\ref{sec-methods} for further discussion). For
% example, we eliminated the Palantir approach of using a client-side Java
% applet because we could not guarantee the existence of a Java virtual
% machine in every browser. We ultimately settled on the following four
% methods that appeared to meet our requirements:
% \begin{itemize}
%
% \item server-side image generation (discussed further in
% Section~\ref{sec-image-gen});
%
% \item overlay using transparent images (discussed further in
% Section~\ref{sec-image-overlay});
%
% \item overlay using absolutely positioned HTML elements (discussed
% further in Section~\ref{sec-html-overlay}); and
%
% \item overlay using the Google Maps API (discussed further in
% Section~\ref{sec-google}).
%
% \end{itemize}
Given the many possible techniques that were available, the next question
was which of these 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, image overlay, 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
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 intuition was that server-side image generation and image overlay
would prove the most scalable, 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}.
\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 briefly 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}
\citeN{Wood-J-1996-vis} and \citeN{MacE-AM-1998-GIS} identify four
distribution styles for web-based geographic visualization software:
\begin{itemize}
\item the \emph{data server} style where the server only supplies
raw data, and all manipulation, display and analysis takes place at
the client (i.e., primarily client-side);
\item the \emph{image server} style where the display is created at
the server and is viewed at the client (i.e., primarily server-side);
\item the \emph{3D model interaction environment} style where a 3D
model created at the server can be explored at the client; and
\item the \emph{shared environment} style where data manipulation is
done at the server, but control of that manipulation, rendering and
display all occur at the client.
\end{itemize}
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 cient-side software. We can therefore immediately eliminate
Java- or Flash-based data server techniques from consideration.
In contrast, the image server distribution style is primarily server
based, and thus require no additional client-side software. The downside
is that the resultant visualization tends to be very static and
non-interactive in nature.
%%!! HERE
The term ``3D model interaction environment'' seems a little out of place
in the current context. \citeN{Wood-J-1996-vis}
originally intended this to apply to VRML models for GIS applications,
but this distribution style could
be equally applied to any situation where an interactive model is downloaded
something like a Flash application, where a self-contained
seems more appropriate to
GIS-style applications than to visualization of web hits.
, but the
remaining three styles are all appropriate for this application area.
For example, Palantir implemented an overlay technique using a data
server 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 at the client.
In contrast, techniques that use the image server style are primarily
server based, and thus require no additional client-side software.
Techniques that use a shared environment style fall somewhere in the
middle, as they may or may not require additional client-side software
(such as an image generation library), depending on how they are
implemented.
\subsection{Image generation techniques}
\label{sec-image-gen}
As noted earlier, these techniques work by directly plotting geolocated IP
addresses onto a base map image, then displaying the composite image at
the client, as 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}
% There are several different architectures for implementing web-based
% visualization software \cite{Wood-J-1996-vis,MacE-AM-1998-GIS}. One possibility is
% to use a distributed architecture, where the source data are
% generated at the server and the map is generated and manipulated by a
% Java applet at the client; \citeN{MacE-AM-1998-GIS} refers to this as
% the \emph{shared environment} architecture. Alternatively, the map image
% can be generated entirely on the server, with the client responsible
% only for display; \citeN{MacE-AM-1998-GIS} refers to this as the
% \emph{image server} architecture, which is the standard form of
% interaction for many web sites. Both architectures are illustrated in
% Figure~\ref{fig-image-architecture}. We have adopted the latter
% architecture (server-side image generation) in our experiments, as the
% former would require installing additional client-side software for
% generating images and performing cartographic projection operations.
We could implement image generation techniques using either the data
server, shared environment or image server distribution styles. However,
the data server style would require the installation of additional
client-side software for generating images and performing cartographic
projection operations, so solutions based on this style can be eliminated
from consideration.
There are several different architectures for implementing web-based
visualization software \cite{Wood-J-1996-vis,MacE-AM-1998-GIS}. One possibility is
to use a distributed architecture, where the source data are
generated at the server and the map is generated and manipulated by a
Java applet at the client; \citeN{MacE-AM-1998-GIS} refers to this as
the \emph{shared environment} architecture. Alternatively, the map image
can be generated entirely on the server, with the client responsible
only for display; \citeN{MacE-AM-1998-GIS} refers to this as the
\emph{image server} architecture, which is the standard form of
interaction for many web sites. Both architectures are illustrated in
Figure~\ref{fig-image-architecture}. We have adopted the latter
architecture (server-side image generation) in our experiments, as the
former would require installing additional client-side software for
generating images and performing cartographic projection operations.
% \begin{figure}
% \caption{Shared environment vs.\ image server architectures for the
% image generation technique.}
% \label{fig-image-architecture}
% \end{figure}
This technique provides some distinct advantages. If an image server
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 technique 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 technique used for the map image
can impact on the quality of the final result. For example, lossy
compression techniques such as JPEG can make the points plotted on the map
appear distinctly fuzzy (see Figure~\ref{fig-image-quality}). A
lossless compression technique 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-html-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).
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 produced by
image generation, so we have not provided an example.)
As with image generation, we can adopt either a shared environment
architecture, where source data are generated at the server and
converted into an HTML overlay at the client, or an image server
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 image generation, however,
HTML overlay can be implemented as a shared environment architecture
without the need to install additional client-side software. JavaScript
is now standard in most browsers, and this is sufficient to implement
the necessary client-side behaviour. Both architectures thus meet our
requirement for avoiding additional client-side software.
\begin{figure}
\caption{Shared environment vs.\ image server architectures for the
HTML overlay technique.}
\label{fig-html-architectures}
\end{figure}
If an image server architecture is adopted, this technique is actually
slightly easier to implement than image generation, because we do not
need any code to generate or manipulate images. Implementing a shared
environment architecture is more complex, but this has more to do with
the nature of distributed applications than the technique itself. The
technique does not suffer image generation's problem of fuzzy-looking
points (see Figure~\ref{fig-image-quality}), because the points are not
part of the map image and thus unaffected by compression artifacts.
Finally, the most significant advantage of HTML overlay over image
generation 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, and is an effective way
to provide interactive visualizations of geographic data
\cite{Wood-J-1996-vis,MacE-AM-1998-GIS}.
As with image generation, however, we still have the problem of finding
a suitable base map image. HTML overlay 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 browser 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]{jpeg_detail}\medskip
\includegraphics[scale=1.25]{overlay_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; an example of the output is shown in Figure~\ref{fig-google}. The
output and interaction is significantly different in nature and
appearance 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. Google Maps inherently uses a shared environment
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}
\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}
\begin{figure}
\caption{Shared environment architecture of the Google Maps
technique.}
\label{fig-google-architecture}
\end{figure}
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 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 distributed application, it will be more
complex to implement, test and debug. It also relies on an active
Internet connection in order to run. 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. Finally, the Google
Maps API does not currently provide any technique of toggling the
visibility of markers on the map, so it is not possible to implement the
``layers'' that are possible with HTML overlay (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 falls outside the scope of this 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}
After some preliminary experimentation and testing with live data from
the Otago School of Business repository, we proceeded with a more formal
series of experiments to test the scalability of the three 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, comprising a regular grid of
latitude/longitude points at one degree intervals. A total of twenty-one
data sets were created in this way, with the number of points ranging
from one to 1,048,576 (\(=2^{20}\))\footnote{It should be noted that
only 64,800 points are required to fill the entire grid, so the five
largest data sets have many duplicate points.}.
Our 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). The
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, so we were unable to run the experiments on an isolated
network. This meant that traffic on the local network could be a
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\,MHz with 1\,GiB
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 of each other, thus simplifying the
process of data collection and also ensuring that the client and server
processes did not impede each other unduly.
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 155\,KiB of
JavaScript source), as the API will be locally cached thereafter. The
API key verification occurs every time the map is loaded, but the amount
of data involved is very small, so it is unlikely 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, we recorded the size of the data set, 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 both the
browser and the web server. The data set generation time and memory
usage were measured using the \texttt{time} and \texttt{top} utilities
respectively. 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.
%%!! confused!
The image generation technique was implemented as an image server
architecture. A dispatcher page written in PHP called a Perl script,
which generated a JPEG-compressed map image and returned this to the
browser.
The HTML overlay technique was implemented in two ways:
\begin{itemize}
\item as an image server architecture that worked in much the same way
as the image generation technique, except that the Perl script
returned an HTML file containing the \verb|<DIV>| elements for the
overlay, and an \verb|<IMG>| element to load the base map image; and
\item as a shared environment architecture, where client-side
JavaScript code made an asynchronous call to the server-side Perl
script, which returned
\end{itemize}
and Google Maps techniques were implemented as an image server and a
shared environment architecture respectively. The HTML overlay technique
was implemented twice; once as an image server architecture and once as
a shared environment architecture.
\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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