Many of the recent studies of vegetation spread and the invasion of introduced species incorporate GIS and some form of dynamic modelling in one- or two-dimensional space. Most work, however, concentrates on presenting a predetermined model in an abstract computer simulation environment. The focus has been on the system’s ability to display the model (results), as opposed
to its ability to develop or compose a model, acting as a creative tool in an applied situation. The research presented in this paper uses a GIS-based cellular automata (CA) framework to study and create an applied and ecologically significant model of spread for the invasive plant species Rhamnus alaternus.
Instead of conventionally testing a previously created model, parameters such as generation time, fruiting phenology, fruigivore abundance, reproductive rate and vegetation cover are examined through simulation for their contribution to spread and the resulting spatial pattern. Initial simulation runs focus on the different effects of stochastic versus deterministic parameter values. Further studies examine the effect of different relationships, conditions and interactions
between parameters. Upper and lower limits, the interactions between, and the relative importance of each parameter are examined in a number of combinations. Each parameter is then accepted or declined to create a conceptual model outlining the ecologically most significant features of the
species. This method effectively examines and tests the ability for models of generic invasion and vegetation dynamics to be transposed into a contextually rich environment through a GIS-CA framework of model creation.

1. Introduction
Agent-based modelling (ABM) is the computational study of social agents as evolving systems of autonomous interacting agents. ABM is a tool for the study of social systems from the complex adaptive system perspective. From this perspective, the researcher is interested in how macro phenomena are emerging from micro level behaviour among a heterogeneous set of interacting agents (Holland, 1992). By using ABM as computational laboratories, one may test in a systematic way different hypotheses related to attributes of the agents, their behavioural rules, and the types of interactions, and their effect on macro level stylized facts of the system.
Since the early 1990s ABM has increasingly been used in most of the social sciences. I shall focus on the applications of ABM related to ecological economics (Janssen, 2002, 2005; Janssen and Ostrom, 2005). ABM of ecological economic systems can be defined as systems that are populated with heterogeneous population of agents, who determine their interactions with other agents and with their environment, on the basis of internalized social norms and mental models, internal behavioural rules and cognitive abilities, formal and informal institutional rules that affect how agents interact, individual and social learning, etc.
2. Motivations for Agent-based Modelling
Some readers may question why we need complex approaches such as ABM. Are equation-based models not sufficient? Other readers may argue that ABM is not new. My response to these queries is that it all depends on the type of questions one is interested in. For many problems, equation-based models are excellent tools to study the problem of concern. However, for a problem like coordination or strategic interaction, multiple agents need to be distinguished.
Traditional game theory has been very successful in addressing strategic interaction by a small number (mainly two) (types of) players, using equation-based models. Unfortunately, traditional game theory is rather restrictive: Agents are required to have high cognitive abilities, the rules of the game are fixed, and the structure of the interactions is on a rigid lattice or fully random. But from empirical studies it is known that humans are boundedly rational, the rules of the
game change, and social interactions have complex social structures (e.g.,Gigerenzer and Selten, 2001; Janssen and Ostrom, in press). It is no surprise that ABM has been widely applied to games since the early 1980s (e.g., Axelrod, 1984).
Indeed, models of individual units were developed long ago, such as statistical mechanics and micro-simulations. But these methods assume no interaction, or random interaction, between the agents. A key element in ABM is the possibility of complex structures of social interactions. In some systems, the macroscale properties are sensitive to the structure of interactions between agents and social
networks. In equation-based models, the agents are frequently, implicitly, assumed to be well mixed, the mean-field assumption, and thus these approaches miss the opportunity to investigate the sensitivities of the structure of interactions.
Finally, within integrated modelling of ecological economic systems, one of the key problems is how to match the scale of social and ecological dynamics (Levin, 1992; Gibson et al., 2000). By the use of agents, we derive tools that make it possible to integrate processes and interactions at different levels of scale, for agent-agent and agent-environment interactions.
3. ABM Methodology
Most ABMs applied within ecological economics consist of two elements:
cellular automata and agents. I will now discuss briefly both elements.
Cellular Automata
Originally, the cellular automata (CA) approach was introduced by John von
Neumann and Stanislaw Ulam at the end of the 1940s. Since the early 1970s,
CA have been used by many disciplines to study complex dynamic behaviour of
systems. The essential properties of a CA are:
• a regular n-dimensional lattice (n is in most cases of one or two dimensions),
where each cell of this lattice has a discrete state,
• a dynamical behaviour, described by so called rules. These rules describe the
state of a cell for the next time step, depending on the states of the cells in the
neighbourhood of the cell.
The basic element of a CA is the cell that is represented by states. In the
simplest case, each cell can have the binary states 1 or 0. In more complex
simulations, the cells can have more different states. These cells are arranged in
a lattice. The most common CAs are built in one or two dimensions. The cells
can change state by transition rules, which determine the state of the cells for the
next time step. In cellular automata, a rule defines the state of a cell in
dependence of the neighbourhood of the cell.
With regard to our interest for ecological economics, the application of CA can
be rather straightforward. In fact, CA can be used to produce a dynamic
Geographical Information System (GIS). The lattice represents a map of a certain
area, with each possible state of a cell representing a possible land use. Due to
physical restrictions, cells on some locations may be restricted to a limited
number of states; for example, a secondary forest cannot turn back into a
primary forest. Transition rules determine when a certain land use of a cell
changes into another land use. Cell changes can be influenced by local rules; for
example, if the cell is a forest-cell, and if one of the neighbour cells is on fire,
then the cell turns to fire. However, global rules are also possible, since land use
changes can be influenced by demand for certain land on a higher level of scale.
For example, demand for extra agricultural land can be translated as changing
those cells to agriculture that are the most suitable.
A drawback of using CA for representing social agents is its simplicity. For
example, social networks are more complex than the local neighbours on a
lattice. The number of possible states in which a social agent can be might be too
large to be efficiently represented as a CA. Within land use models, landowners
may own multiple cells and make decisions on the land use of their cells. Thus a
cell-based rule that ignores parcel boundaries is inadequate. The study of agents
has been a topic of research for a long time in computer science, which has
developed its own tools and frameworks.
Agents
The architecture of agents in ABM has been much influenced by work on multi-
agent systems in Artificial Intelligence (AI). Multi-agent systems research studies
the behaviour of adaptive autonomous agents in the physical world (robots) or in
cyberspace (software agents). Wooldridge (2002) argues that intelligent agents
are able to act flexibly and autonomously. By flexibility we mean that agents are
goal-directed (satisfying or maximizing their utility), reactive (responding to
changes in the environment) and capable of interacting with other agents. One of
the difficulties is in balancing reactive and goal-directed behaviour. Developing
models with agents who have only reactive behaviour is relatively simple, and
individual-based ecological modelling addresses problems by simulating non-
human agents as reactive objects (e.g., DeAngelis and Gross, 1992).
However, humans combine reactive and goal-directed behaviour.
Conventional economics assumes the selfish rational actor to describe individual
behaviour. Although this agent model provides a good description of human
behaviour in highly competitive markets, as is confirmed in experimental studies,
it is not satisfactory for the description of behaviour in various decision situations
of importance for ecological economics (Gintis, 2000). For decision situations
such as economic valuation and collective action, motivation, fairness and
preferences play an important role, and the characteristics may vary within the
population of human agents. Furthermore, decision problems related to
environmental management are often so complex that it is not likely that one has
full information and understanding of the problem and is able to evaluate all
possible options. Models of bounded rationality have been used as an alternative
in economics (Simon, 1955). Furthermore, using concepts from psychology, we
are able to include dimensions of economic agents such as emotions,
motivations, and perceptions. A problem is that loosening the tight framework of
the selfish rational actor leads to many possible frameworks. Within behavioural
economics, there is mainly attention to models of learning that explain observed
behaviour in experiments (Camerer, 2003). Others focus on fast and frugal
heuristics, of how individuals make a choice in simple problems under time
pressure (Gigerenzer et al., 1999).
A scheme of a simple model of two agents interacting with each other and
their environment is given in Figure 1, which provides the simplest description of
ABM applied to ecological economics. Agents derive information from the
environment that informs the perception they have about the state of the
environment. Based on the goals and attributes of the agents they make
decisions on actions to perform and these actions affect the environment. The
agents can interact indirectly, for example by affecting the common resource, or
directly by communication. This communication might be used to exchange
information about possible strategies, knowledge about the resource and
agreements how to solve collective action problems.

Cake is a rapid development framework for PHP that uses well-known design patterns and provides a structured framework that enables PHP users at all levels to rapidly develop robust web applications, without any loss of flexibility. It means you can code faster, your code is better, and it makes writing Web 2.0-style apps a snap.
This book builds on your knowledge as a PHP developer to provide a fast-paced, step-by-step tutorial in building CakePHP applications. The book is packed with example code and demo applications, so that you can see techniques in action.

A PHP Framework
A PHP framework is a collection of code, libraries, classes, and run-time environment that helps developers build web applications faster. The main idea behind using frameworks is to provide the developers with commonly used functionalities and a basic structure that they can build their application on. Most PHP developers, who have some experience, have their own set of libraries and structure to help them develop faster, as they can use their code base (and experience) from one project to another. But, building a web application on an open-source framework like CakePHP has its obvious benefits. By doing so, the developers not only use their own experience, but also the experiences of many other developers who have used and developed the framework. Also, the framework is much more reliable as it is used and tested by many application developers. Besides, using a framework like CakePHP has the advantage that all the developers involved in a project have to follow the same conventions and rules to structure the application. It makes it very easy for new developers to settle down if they are familiar with the conventions of the framework.

• PHP Programmers are widely available. Most project have tight deadlines, and you want team members who can quickly pick up a new piece of technology.
• Cake PHP is easy to learn. You want a powerful tool taht you can easily master.
• Cake PHP has good support. Developers frequently post and reply to message on the Cake forum. And there are always some good discussions happening on the Cake IRC. (To see for yourself, simply download mIRC from, connect to  the server, and join the channel.)

When you are developing a site using Cake, you often find yourself trawling through tutorials online to see how things are done. We’ve done that ourselves many times. However, despite the power of the Internet, we still like to look through books. And we think you will find this book a great help in your Cake development endeavors, in addition to all of the material available online.
Most of the applications in this book have been written as a result of some real-world development we have done in the past. We focus on projects that we think are relevant to the future of web development.
Let’s take mashups, for example. We should all take an interest in this ever-expanding area of web development. We can honestly say that any successful online web site in the future will need to easily communicate with other applications. Application designers will need to bear this in mind. Matters such as search engine optimization need to be built into the application itself. Cake allows us to think in terms of the high-level architecture instead of the nuts and bolts of a web application.

by: Ibu Claire

The main market of Brussels has been used as a market, since its creation beginning 10th century.
Vegetables and poultry were sold, but also birds maintained into cages for their singing…

At the end of 17th century, King Louis the 14th of France willing to conquer the Low Countries (name of Belgium at that time), bombed in 1685 the center of the City and the market. Most of the houses were made out of wood and burned for more than a week. Only the Town Hall made out of stone remained. As most of the market-houses were the siege of the craft-corporations, these competed to have their house built up again as fast as could be.They managed to do so between 1690 and 1693. All the buiding craftmen rushed to Brussels all over from Germany and France. They built all the houses, we can admire now.

Most of the houses were café’s, where people could meet, talk, and bargain after the market. The upper levels were habitations. The French poet Victor Hugo lived there being a refugee in 1848 as he couldn’t bear Napoleon the III. Nowadays, they are very expensive flat appartments

The house where Victor Hugo (19th C.French writer and poet on exile) remained.

Victor Hugo lived as a refugee in 1848. He took the opportunity to visit the battlefield of the Battle of Waterloo (1815) and to write a well-known epopee

The Swann
There was founded in 1895 the Workers’political party. Nextdoor on the left ,was and is still the main house of the brewers. he decoration of all the house in Barocco-style.

Manneken-Pis (a 100m. away from the Grand-Place)

This little 17th C. boy is supposed to be the oldest inhabitant of the city of Brussels. He is supposed to have gotten lost, and when found back, he was in this positon !

His family was so happy that they offered a water-fontain to the inhabitants of the neighborough. Although, it often rains in Belgium, people have to run far awy to fetch water. offering a fountain was thus a great gift !

When an important guest visits Brussels, he usually offers a costume to this little boy. He has nowaday a special museum with more than 600 costumes!

Whenever, someone comes back to Brussels, Manneken-Pis is dressed up . He is about 80cm high !

by: Ibu Claire

This square was built on the ruins of a previous royal palace, that burned out around 1715. Around 1780, huge works started in the “classical” style inspired by the castle of Versailles (France). It was supposed to be used for military parades,as the new palace was built “around the corner”

Archeological researches were made by Brussels University 10 years ago: the coronation room was found again as well a small street(for the level has been completely changed,because the huge quantity of the ruins). An underground museum was created.

Was erected in 1958 for the World Exhebition. It represents a molecule of cristal iron and symbolizes the 9 provinces of Belgium. Each sphere has a diameter of 18 m.
It is 102m. high and weighs 2400 tons. The designer was Ingenior André Waterkeyn.

For its 5Oth anniversary, it was completely restaured,with partly by private gifts, for the inhabtants of Brussels consider it as part of their city and their memory. The World Fair “Expo 58″ was an exceptional happy period after the second world war Europeans were thinking Science would solve everything and that there would be no war anymore, that there would be work and food for everyone…

Like usual people discovered far away countries.Belgian Congo showed life unhabitants(like in a zoo!!!), but had to send them back after 2 days !

One year and a half later, Congo was asking Independance and got it in June 1960 !!!

Location :15 km. South of Brussels
Last battle of Emperor Napoleon on June 18,1815 against the “allied” forces of Europe: Austria, Great-Britain, Prussia,Netherland(all old reigning families, while Napoleon wasn’t a noble man(although very intelligent), he crowned himself Emperor !
He almost won the battle, BUT his observers hadn’t noticed, many Prussian soldiers were hidden in a small valley covered by trees and bushes (le vallon d’Ohain).
They encercled SOUTH the French army, which couldn’t escape,because the rest of the Allied troops were NORTH.
Napoleon was taken prisonner and sent by Britain to the remote island of St Helena,where he finally died in 1821 (?)

In order NEVER to be invaded anymore by France, the Allied nations got together at the Congress of Vienna(1815),and remodeled the political map of Europe :Small Belgium was given to Nederland.But Belgium got rid of Nederland in 1830(Belgian revolution), because this country didn’t respect the Belgian population….!

The huge monument “Lion of Waterloo” was erected there where the King of Nederland had fallen from his horse
Peasants still do find remains(bones or pieces of uniforms) from this battle

The aim of web pages visualization is to present in a very informative and interactive way a set of web documents to the user in order to let him or her navigate through these documents. In the web context, this may correspond to several user’s tasks: displaying the results of a search engine, or visualizing a graph of pages such as a hypertext or a surf map.

In addition to web pages visualization, web pages clustering also greatly improves the amount of information presented to the user by highlighting the similarities between the documents [6]. In this paper we explore the use of a cellular automata (CA) to generate such maps of web pages.

Categories and Subject Descriptors: H.3.3 [Information Search and Retrieval]: Clustering General Terms: Algorithms.

Keywords: Web pages, visualization, unsupervised clustering, cellular automata.