Have you ever collected pictures of your town? How it was in the past and how is it now?
The world is changing every single day in term of land use.As you can see in the fifties Lisbon was a city full of trees. This was the modern aspect at the time.
Lisbon! At night! Taken from the International Space Station by Canadian Astronaut Commander Chris Hadfield.
Fuzzy logic is a form of many-valued logic or probabilistic logic; it deals with reasoning that is approximate rather than fixed and exact
The macro-model consists of 4 strongly linked sub-models representing the Economic, Demographic, Land use and Transportation sub-systems. The economic sectors are aggregated into four main categories:Industry,Services,Commerce, andPort activitiesThe population is assigned to four residential categories:Residential continuous dense,Residential continuous medium,Residential discontinuous urban,Residential discontinuous sparse
Let’s see what happens when we don’t see everything surrounding: http://www.youtube.com/watch?v=8HJQ7XXYJSA
The agents or component parts "live" in some topological space (e.g. farmers, political institutions, predators and prey may live in a two or three dimensional world). Agents perform their activities in this environment. Agents communicate among each other and can cooperate in fulfilling their activities. The communication and actions of the autonomous agents is rule-based. Every agent possesses rules that enable it to deal with specific situations. For example, if an agent is approached by another agent and is asked whether it wants to participate in an exchange, the agent will search for a rule within its “register” that applies to the proposed exchange. If the agent has selected a rule, this rule allows the agent to negotiate in the exchange. A rule is a method of the agent.
This representation is established by defining rules, which the agent uses to pursue a goal (or multiple goals). The rules together represent the ‘rational’ behaviour of the agent. In order to simulatean agent model we let the agents communicate with each other. Communication in agent simulation is how an agent modeller intuitively sees the interactions between real-life (e.g. other agents, but also the environment) entities. Agents must be able to communicate among each other, dependent on the behaviour-rules one applies in the model; also agents communicate with the simulated environment.
Modelling urban dynamics
Presented by Rui LimaAngers, France, February 27th 2013
Human actions are altering the terrestrial environment at unprecedented rates, magnitudes, and spatial scales. Land-cover change stemming from human land uses represents a major source and a major element of global environmental change. The world population is increasing day by day, this increases the demand for food and housing. The cities nowadays are growing to points where before were agricultural areas, agricultural areas are moving to forest areas, the space will end at some point. Avoiding the collapse starts with good planning. To help planning one must be able to predict. Our crystal balls are complex computer models.
Land cover: attributes of the Earth‟s land surface and immediate subsurface, including biota, soil, topography, surface and groundwater, and human (mainly built-up) Structures Land cover conversions: replacement of one cover type by another and are measured by a shift from one land-cover category to another, as is the case of agricultural expansion, deforestation, or change in urban extent. [Cabral 2012]
Land cover and changes are visible in remotely-sensed data or by generating evidence from secondary statistics, such as (agricultural) census data Land-use as well as land-management information, in contrast, is mainly gained through detailed ground-based analysis, although land use can be inferred in remotely- sensed data under certain circumstances [Cabral 2012]
Main tropical deforestation fronts in the1980s and 1990s.The map is based on three data sets:(a) the deforestation hotspots in the humid tropics of the Tropical Ecosystem Environment Observation by Satellite (TREES) project,(b) a time series analysis of tree cover based on 8-km resolution data from the National Oceanic and Atmospheric Administration‟s advanced very high resolution radiometer (AVHRR, and(c) the Amazon Basin deforestation maps derived from time series of Landsat Thematic Mapper (LandsatTM) data.These maps were overlaid and combined to identifyareas where high rates of deforestation were measuredby several of the datasets. Green areas are intactforests. The map indicates the number of times each0.18grid was identified as being affected by rapiddeforestation by the different datasets (orange, pixelsdetected as hotspot by one dataset, red, pixelsdetected as hotspot by two datasets, black, pixelsdetected as hotspot by three datasets) [Lambin 2003]
Legend of previous slide‟s picture:Population density in 1995 and most populated and changing citiesfrom 1990 to 2000.The map is based on the 2001 revision of the “World UrbanizationProspects”, which provides population estimates in cities of morethan 750,000 inhabitants for the years 1990 and 2000, and the“gridded population of the world”, which provides populationestimates in 1995.The first dataset focuses on megacities whereas the secondincludes less populated areas.Green circles represent the most changing cities between 1990 and2000, blue circles the most populated cities in 2000, and redcircles the most changing and populated cities. The backgroundcolor scale represents the population densities in 1995 (from lessthan five inhabitants in gray to more than 1750 inhabitants/km 2indark orange) [Lambin 2003]
“In 2000, towns and cities housed more than 2.9 bilion people,nearly half of the world population.”“Urban population has been growing more rapidly than ruralpopulation worldwide”“the number of megacities, defined here as cities with more than 10million inhabitants, has changed from one in 1950 (New York) to 17 in2000, the majority of which are in developing countries”“ It is estimated that 1 to 2 million ha of cropland are being takenout of production every year in developing countries to meet the landdemand for housing, industry, infrastructure, and recreation ” [Lambin & Geist 2006]
Guia de Portugal in 1924:« Finalmente, no extremo N. dessa longa artéria, como remate da cidade moderna, abre-se o diadema da Rotunda(Praça Marquês de Pombal), de pavimento em empedrado lisboeta, com letreiros alusivos à acção reformadora doestadista, plantada de acácias do Japão (Sophora japonica), e de onde a vista se enfia através da Avenida e as ruas daBaixa até morrer nos montes azuis da outra banda. De aí irradiam em diferentes direcções uma série de avenidas: ade Brancaamp, que vai dar à praça do Brasil [Largo do Rato]; a de Fontes Pereira de Melo que comunica com a praçado Duque de Saldanha, servindo de ligação aos modernos bairros conhecidos por Avenidas Novas; e a do Duque deLoulé, que liga directamente a Rotunda com o largo do Matadouro, servindo de limite N. ao bairro de Camões.» Praça Marquês de Pombal, Lisbon, 192... Source: unkown
Praça Marquês de Pombal, Lisbon, 195...Photo by: António Passaporte, in Postais de Lisboa, [Lisbon], C.M.L., .
Praça Marquês de Pombal, Lisbon, 196... 197...Source: unkown
Rome was not built in a day.Europeans still travel on Roman roads.
Planning for the best use of land and its resources should take fully into consideration the long-term consequences of each type of use in order to stretch out most beneficially the well-beingof society in the future, and to protect the integrity of the land and its biota.Reversible land-use: leaves the land, after use, essentially as it was before; little or no man-induced modification remains.Terminal land-use: commits the land to a chosen particular use, and any attempt at reversalrequires either time-scales that are long compared with the expected lifespan of the social andpolitical institution, or a commitment of resources that is too high for society to consider worthbearing. Examples of terminal land-use are location of metropolises and sites of toxic and/orradioactive waste disposals; by its nature the list grows monotonically.In between these two extremes of reversible and terminal land-use, the bulk of land-use issequential, in which each use of land changes its potentials and configurations, and thesechanges are mainly irreversible.
Major transport investments tend to be the most durable and also involve the longest time lags between planning and completion. The same is true for the appropriation of open space for human settlements. The common feature of these changes is their virtual irreversibility.
Provision and lack of food, feed, fiber and timber Disease risk and human health Atmospheric Chemistry, Climate Regulation and life support functions Agro diversity and biodiversity loss Soil quality Fresh water hydrology, agricultural water use and coastal zones [Lambin & Geist 2006]
Several factors can be used to explain a land use change in general: ◦ Biophysical factors Climate, relief, hydrology, and vegetation ... ◦ Economic and technological factors input and output prices, taxes, subsidies, production and transportation costs, capital flows and investments, credit access, trade … ◦ Demographic factors population composition and distribution, namely changes in urbanization and in household size acknowledge the importance of indirect or consumptive demands on the land by an increasingly urbanized population ◦ Institutional factors Institutions (political, legal, economic, and traditional) and their interactions with individual decision-making. In particular, government policy plays a ubiquitous role in land change, either directly causative or in mediating fashion [Cabral 2012]
“ A model is a an abstraction of an object,system or process that permits knowledge tobe gained about reality by conductingexperiments on the model ” [Clarke 2003]
“Modeling is essential for the analysis, andespecially for the prediction, of the dynamics ofurban growth.Yet the successful application of a model in oneparticular geographical area does not necessarilyimply its successful use in another setting wherelocal characteristics, territorial constraints and theclassic site and situation properties of economicgeography ensure that different developmentpaths have been followed.” [Silva 2001]
In the last decades different modeling techniques have been developed for better understanding and predicting urban expansion, such as Cellular Automata (CA). CA-based models have been commonly used in exploring various urban phenomena, such as urbanization, urban form change, urban growth effect, etc. CA is individual-based models designed to simulate systems in which states, time, and space are discrete. [MadhanMohan 2012]
Simply stated, an automaton (plural: automata) is a self-operating machine. CA is a discrete dynamical system that is composed of an array of cells, each of which behaves like a finite-state automaton.
The data requirements for parameterization, calibration and validation of urban models are intense due to the complexity of the models and their objectives. A simple and well-known example of a cellular automata is John Conways Game of Life.
These models are not concern on the dynamics of multiple categories of urban land that leads to simulation errors. To overcome this drawback we use Agent Based Models (ABM). It is an effective and process based model which is used for modeling the real world applications like urban growth. [MadhanMohan 2012]
SLEUTH ◦ Stands for the input needs for driving the model: Slope Land cover Exclusion Urbanization Transportation Hillshade Uses historical geospatial data for calibration of its parameters, when running SLEUTH the rules of the CAs are calibrated to historical urban spatial data. Actually SLEUTH incorporates two different models such as Clarke Urban Growth Model (UGM) and Deltatron Land Use/Land Cover Model (DLM).
SLEUTH – Advantages ◦ SLEUTH is a self-organizing CA model and, therefore, the coefficients that control growth may vary according to numerous factors. ◦ Due to its independent scalability, transportability, and transparency which has become a popular tool in modeling, the increase of urban extent over time and recreating the past or forecasting growth into the future. SLEUTH – Drawbacks ◦ The simulations performed with SLEUTH underestimate the emergence of urban. ◦ SLEUTH produces some of the errors, because this model does not consider the dynamics of multiple categories of urban land. ◦ The number of patches generated by SLEUTH is much lower and patches are larger and more clustered. This model produces an excess underestimation of new growth and an overestimation of infill growth.
FCAUGM ◦ Stands for Fuzzy Cellular Automata Urban Growth Model ◦ Fuzzy logic combined to CA provides a proper framework for expressing and mapping the urban growth dynamics. ◦ FCAUGM is generally capable of simulating and predicting the complexities of urban growth.
FCAUGM - Advantages ◦ The fuzzy logic gives the evaluation closer to the complex reality of regional planning. ◦ Assignment of the weights to the different indicators can be taken into consideration in an environmental impact using fuzzy logic, in order to obtain a significant homogeneity and objectivity. FCAUGM – Drawbacks ◦ Direct employment of fuzzy logic lies in the way knowledge is captured, i.e. by employing man-made rules. ◦ The construction of a manual, expertly guided rule-base is a complex task due to the presence of a high number of inter-dependent variables.
MOLAND ◦ The MOLAND model represents processes at three spatial levels: Global, Regional and Local. Greater Dublin Area example. [Lavalle 2004]
The macro-model consists of 4 strongly linked sub- models representing the Economic, Demographic, Land use and Transportation sub-systems. ◦ The economic sectors are aggregated into four main categories: Industry, Services, Commerce, and Port activities The population is assigned to four residential categories: ◦ Residential continuous dense, ◦ Residential continuous medium, ◦ Residential discontinuous urban, ◦ Residential discontinuous sparse
The MOLAND macro-model consists of 4 sub-systems:Economy, Demography, Land use and Transportation [Lavalle 2004]
MOLAND – Advantages ◦ MOLAND is the model that comes nearest to real percentages of each type of growth and it has produced growth patterns closer to the real ones than the other models. ◦ Indeed, it produced more realistic urban patterns near the road network than the model of White et al. since it considered various types of roads ( consequently, various coefficients) in the calculation of the effects of road type on the transition potential, instead of a single type of road and, consequently, a single coefficient. [MadhanMohan 2012]
MOLAND – Drawbacks ◦ The cell-to-cell correspondence between the real and the map simulated using MOLAND is low. Yet, establishing the correct location of each simulated land use cell is very difficult because of path dependence and stochastic uncertainty. [MadhanMohan 2012]
There are some common limitations for all CA- based models: ◦ They focused on the reproduction of spatial patterns and this is the obscurity to model socioeconomic dynamics and decision making processes regarding land use. ◦ None of these models consider the dynamics of multiple categories of urban land and they may produce simulation errors such as those found in the visual inspection of results, caused by the lack of differentiation between a single-family detached house with a large garden and a group of single-family detached houses. ◦ These shortcomings are overcome by Agent Based Models (ABM), which are process based.
An agent-based model is a generalization of cellular automata in which agents are able to move around the space, rather than being confined to the cells of a raster. Agent based modeling refers to a modeling concept which is closely linked to the modeling techniques of object orientation (OO).
Agents are a representation and a simplification of complex (including human) behavior, this representation is established by defining rules used by the agents to pursue a goal (or goals). Agents must be able to communicate among each other, dependent on the behavior-rules one applies in the model; also agents communicate with the simulated environment.
An agent is „alive‟ in its environment. An agent has direct interactions with its world. The agent may act on the environment, which in turn provides perceptions to the agent. The complexity of these interactions is primarily constructed in the agent, and not in the environment. An agent observes and interprets the world. The environment may change, but the agent will have to observe these changes.
Agent-based models are useful in conceptualizing land use changes and urban growth. Each agent, in such models, acquires its momentum from factors like the configuration of the land use of its neighbors, the cost of living, cost of transportation, accessibility and other factors determining the quality of life. The spatial environment in the model includes land use attributes (slope, land use, excluded, urban, transportation, hill shade), land price distribution, surrounding environment. A spatial environment includes a virtual real estate market, social network, government policies and casualty problems
Components in an agent based LUCC model [Huigen 2003]
ABMS Software Packages ◦ AgentSheets ◦ AndroMeta ◦ AnyLogic ◦ Ascape ◦ Breve ◦ Cormas ◦ DEVS: Discrete Event System Specification ◦ EcoLab ◦ FLAME: FLexible Agent Modelling Environment ◦ GAMA: Gis& Agent-based Modelling Architecture ◦ JAS: Java Agent Based Simulation Library ◦ LSD: Laboratory for Simulation Development ◦ MAML: Multi-Agent Modelling Language ◦ MATSim ◦ MASON: Multi-Agent Simulation of Neighbourhoods ◦ MASS: Multi-Agent Simulation Suite ◦ MetaABM ◦ MIMOSE ◦ MobiDyc: Modélisation Basée sur les Individus pour la Dynamique des Communautés ◦ Modelling4all ◦ NetLogo ◦ Open StarLogo ◦ RePast: Recursive Porous Agent Simulation Toolkit ◦ Repast Simphony ◦ SimPack ◦ SimPy ◦ SOARS: Spot Oriented Agent Role Simulator ◦ StarLogo ◦ SugarScape ◦ Swarm ◦ VisualBots ◦ Xholon ◦ ...
Cellular automata (CA）modelling is one of the recent advances in spatial–temporal modelling techniques in the field of urban growth dynamics. It is commonly used in exploring various urban phenomena, such as urbanization, urban form change, urban growth effect, etc. The limitations of the existing CA models are overcome by using Agent based Modelling (ABM). ABMs would certainly provide a more realistic representation of complex urban organization, as well as provide us the flexibility to vary urban quantities and population characteristics. ABM can be used as an effective model for modeling the urban growth dynamics.
[Cabral 2012]Cabral P. (2012) - Land use and cover changes: general issues and modelling approaches - Erasmus Intensive Programm 2012 . [Lambin 2003] Lambin E. et al. – Dynamics of land-use and land-cover, change in tropical regions. [Lambin & Geist 2006] Lambin E. and Geist H. (Eds) (2006). Land-Use and Land-Cover Change: local Processes and Global Impacts. Springer
[Silva 2001] Silva, E.A & Clarke, K.C. 2001- Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal, in Computers, Environment and Urban Systems #26 (2002), pg. 525–552 [MadhanMohan 2012] MadhanMohan, S. et al. 2012 – Analysis of various urban growth models based on cellular automata, in [IJESAT] International Journal of Engineering Science & Advanced Techonology volume 2, issue 3, p.453-460
[Lavalle 2004] Lavalle, C. et al. 2004-The MOLAND model for urban and regional growth forecast - A tool for the definition of sustainable development paths, European Commission - Joint Research Centre [Huigen 2003] Huigen, M. G.A. 2003 - Agent Based Modelling in Land Use and Land Cover Change Studies – Interim Report IR-03-044, International Institute for Applied Systems Analysis Schlossplatz 1 A-2361 Laxenburg, Austria
[Mathur 2007] Mathur, P. et al. 2007- Agent- based Modeling of Urban Phenomena in GIS, University of Pennsylvania
Erasmus Intensive Programme (IP)Agents-based modeling for processes and dynamics inlandscape geography17 February until 2 March 2013At AGROCAMPUS OUEST ANGERS - France