Emergent Urbanism : agent base model to predict the development (growth) of the city.


The earth is experiencing some accelerated transformations of human’s behaviors since the beginning of this century. We can see government faces to the problems of cities growth, congestion, energy, and all of this regarding the climate change and human habitat. Traditional urban master plans lack the flexibility required for the world today and perhaps for future context, with all these technologies. Our initial intention and motivation for this work is to try to find the adequate means, or system on how to understand, predict and design modern cities, how to coordinate partiality interest in a flexible and adaptive way using “agents”. This study will lead the proposition of a smart village.

What is the situation today? Why do we need to focus on the human behaviors?

Today’s cities and governments still operate according to principles developed two centuries ago. To solve problems such as exploding population growth and climate change, we need new thinking that fits with the technological era we are in. Big data and urban data can help on this way. The digital we have today reveal more about us; our daily lives provide a powerful tool for addressing social problems. We need to create such a security precaution on Data that gives individuals far more control over their own information which are today real-time accessible. The impact of urban growth on the planet and the demographic incidence of the population is undeniable. For these reason, simulate the growth of the city seems to be useful to create different scenarios regarding urban system evolution. The issue of treating cities as complex and emergent structure has been studied during the last 20 last twenty years, posing question for understanding the urban phenomena. Cities nowadays are well known to be systems of organized complexity with a distinct hierarchical order. On that way, smart cities appeared to visualize urban context and they evolve fast since then. For more than one person, smart cities are synonym to intelligent cities, information cities, data cities but always, as far as we are since the 80’s, the meaning of smart cities is connected to data and theory. In the history of urban studies and planning, the evolution of the city is guided by some factors, socials activities, business facilities, human behaviors, etc…

Base on the knowledge that urban patterns are highly complex, heterogeneous and hierarchically ordered revealing self-similarity across scales, a lot of model of analysis have been tested for cities. There is a huge amount of studies about “fractals”, ansd“stigmergy” in the urbanization processes. According to wikipedia, Fractals follow some rules regarding to the environment or other situation in area of evolution, and are complex and present some remarkable similarities to urban built-up patterns, urban boundaries, land use distribution etc.

Source : Márton Jancsó (https://www.flickr.com/photos/21315929@N07/6274716546)

Stigmergy is a mechanism of indirect coordination between agents or actions. The idea is that the trace left in the environment by an action stimulates the performance of the next action by the same or different agent. Stigmergy is a form of self-organization. It produces complex-intelligent structures, without need for any planning, control, or even direct communication between the agents.

Tokyo rail network designed by Physarum plasmodium

Why agent? How it works?

Dynamic evolution or rigid structure evolution, all geometry at the analysis of the urban development patterns has been and are investigating until now, these fits to cellular automata and the agent-based models, fractals and chaos theory (Batty 2005, Benguigui, Chamanski, Marinov 2001). We can divide the urban environment models in 2 groups. First group is complex urban simulation methods and the second is more flexible and it is dedicated to help or attest local spatial planning activities. We are interested in the distinction based on the predestination of modelling results. We need to deal with emergent or self-organizing behavior and its consequences for build-up environment, but always the key theme is available data structures which can be used for evolution simulations.

Test agent behavior ( avoid obstacle).
Test agent behavior ( exploring connection and area )

What Complexity means for a city? Last years we realized that the world is an infinitely complex place, not quite as understandable as we once thought it was through science. Hence the rise of the complexity sciences. Of course, it’s important to understand that there is a top-down intervention when we talk about complexity. These actions are in general simulated environment where there are interactions among individual actors also called agents. Whole process is simulating intelligence, behavior and decisions much greater than separated individuals. Some planners are thinking to shift the rigid conventional approaches to more complex and flexible ones. They say, we must rethink concepts of planning, we all as architects and urbanist. For many years, these actors believe that we could control growth by building elegant and livable neighborhoods. But issues connected with global phenomenon of urbanization are much more complicated. Since conditions in current cities relate to great uncertainty, planning and design in extension must be more versatile, flexible and adopting more methodologies (Bc. Pavel Paseka 2016). This involves concepts bottom-up, crowd-sourced self-organization and understanding of spontaneous order. One thing is understanding how to compete with actual situation and challenges and not being restricted by a strict and rigid urbanism. Another thing is to see how either “letting-go approach” can help to have a more dynamic city. The last concept, in terms of traditional structured urban planning thinking (Flint, 2015). Now, we will pass over this limit, we are focusing more on organization and life of the cities themselves, so then on process. From demands of inhabitants, residents, laborer and visitors of specific locality. All these individuals and groups leave every day, every hour and every second a virtual footprint through their behavior and acting in the urban environment (Bc. Pavel Paseka 2016). It is possible to understand behavior of urban populations and to read data to translate in a suitable virtual specific design. At this moment, the city planner move at a specific level more abstract and strategic. Although in current cities there is a huge amount of interactions, individual and public interest, so much relations in physical, social and economic dimension of urban environment, that the whole design process had to incorporate much more knowledge and variables than it was expected.

The condition of predictability! What if a + b = a & a != b?

The present study want to involve the identification of former city computational models, and it shows the development of an application that deals with complex urban systems from a design point of view, by using an agent-based modeling approach.

The predictability means a consistent repetition of state, course of action, even behavior making it possible to know in advance what to expect. In our case, we consider “a” as the initial step. How can we predict the “a+t” in the future time? To understand this process, lets imagine a leaf and the ant following the line drawn on the leaf. What is the probability to turn left, right or going straight? This is the real question of the evolution. To solve that, we consider some external factors, behaviors, and also interaction in environment ( attraction or repulsion forces). All this parameter are includes in the “b”. In the real life, structure plan tells landowners and promoters what the parameters of development are, which assures that their immediate investments are secure, and that the returns and use of such efforts are predictable. A Structure Plan is intended to provide owners and investors with predictable future scenarios. Cities require efficient patterns for their main infrastructure systems and utilities. The main theory “a+b = a considering that a is different than b” means that to predict, we need to control the “b” parameter, and in the leaf case, is to know when the ant will avoid the other leaf lines.

Test agent using processing and culebra library

The work is still in progress. The preliminary statement show us that we can set the agent behavior and make them react true the environment. What we see in the first image are agent moving, following rules that can be random and even emergent action.

Test agent defining area

The second picture show us the reality in the urban point. These areas can be houses, green landscape etc. In any case, the result we have should be optimize for the futuer needs.