Configuring the neighbourhood effect in irregular cellular automata based models

Cellular automata (CA) models have been widely employed to simulate urban growth and land use change. In order to represent urban space more realistically, new approaches to CA models have explored the use of vector data instead of traditional regular grids. However, the use of irregular CA-based models brings new challenges as well as opportunities. The most strongly affected factor when using an irregular space is neighbourhood. Although neighbourhood definition in an irregular environment has been reported in the literature, the question of how to model the neighbourhood effect remains largely unexplored. In order to shed light on this question, this paper proposed the use of spatial metrics to characterise and measure the neighbourhood effect in irregular CA-based models. These metrics, originally developed for raster environments, namely the enrichment factor and the neighbourhood index, were adapted and applied in the irregular space employed by the model. Using the results of these metrics, distance-decay functions were calculated to reproduce the push-and-pull effect between the simulated land uses. The outcomes of a total of 55 simulations (five sets of different distance functions and eleven different neighbourhood definition distances) were compared with observed changes in the study area during the calibration period. Our results demonstrate that the proposed methodology improves the outcomes of the urban growth simulation model tested and could be applied to other irregular CA-based models

Accuracy and computational efficiency of 2D urban surface flood modelling based on cellular automata

There is an emerging abundance freely available of high resolution (one meter or less) LIDAR data due to the advent of remote sensing, which enables wider applications of detailed flood risk modelling and analysis. Digital terrain surface data often comes in raster form, i.e., a square regular grid, and often requires conversion into a specific computational mesh for two-dimensional (2D) flood modelling that adopts triangular irregular meshes. 2D modelling of flood water movement through urban areas requires resolution of complex flow paths around buildings, which requires both high accuracy and computational efficiency. Water distribution and Wastewater systems in the UK contain over 700,000 km of water distribution and sewer pipes, which represents a large risk exposure from flooding caused by sewer surcharging or distribution pipe breaks. This makes it important for utilities to understand and predict where clean or dirty water flows will be directed when they leave the system. In order to establish risk assessment many thousands of simulations may be required calling for the most computational efficient models possible. Cellular Automata (CA) represents a method of running simulations based on a regular square grid, thus saving set-up time of configuring the terrain data into an irregular triangular mesh. It also offers a more uniform memory pattern for very fast modern, highly parallel hardware, such as general purpose graphical processing units (GPGPU). In this paper the performance of the CADDIES, a CA platform and associate flood modelling software caFloodPro, using a square regular grid and Von Neumann neighbourhood, is compared to industry standard software using triangular irregular meshes for similar resolutions. A minimum time step is used to control the computational complexity of the algorithm, which then creates a trade-off between the processing speeds of simulations and the accuracy resulting from the limitations used within the local rule to cope with relatively large time steps. This study shows that using CA based methods on regular square grids offers process speed increases in terms of 5-20 times over that of the industry standard software using irregular triangular meshes, while maintaining 98-99% flooding extent accuracy.This is the final version of the article. Available from Elsevier via the DOI in this record.There is an emerging abundance freely available of high resolution (one meter or less) LIDAR data due to the advent of remote sensing, which enables wider applications of detailed flood risk modelling and analysis. Digital terrain surface data often comes in raster form, i.e., a square regular grid, and often requires conversion into a specific computational mesh for two-dimensional (2D) flood modelling that adopts triangular irregular meshes. 2D modelling of flood water movement through urban areas requires resolution of complex flow paths around buildings, which requires both high accuracy and computational efficiency. Water distribution and Wastewater systems in the UK contain over 700,000 km of water distribution and sewer pipes, which represents a large risk exposure from flooding caused by sewer surcharging or distribution pipe breaks. This makes it important for utilities to understand and predict where clean or dirty water flows will be directed when they leave the system. In order to establish risk assessment many thousands of simulations may be required calling for the most computational efficient models possible. Cellular Automata (CA) represents a method of running simulations based on a regular square grid, thus saving set-up time of configuring the terrain data into an irregular triangular mesh. It also offers a more uniform memory pattern for very fast modern, highly parallel hardware, such as general purpose graphical processing units (GPGPU). In this paper the performance of the CADDIES, a CA platform and associate flood modelling software caFloodPro, using a square regular grid and Von Neumann neighbourhood, is compared to industry standard software using triangular irregular meshes for similar resolutions. A minimum time step is used to control the computational complexity of the algorithm, which then creates a trade-off between the processing speeds of simulations and the accuracy resulting from the limitations used within the local rule to cope with relatively large time steps. This study shows that using CA based methods on regular square grids offers process speed increases in terms of 5-20 times over that of the industry standard software using irregular triangular meshes, while maintaining 98-99% flooding extent accuracy

A flock-based model for ad hoc communication networks

We introduce a model for simulating the movement of semi-autonomous mobile units that exhibit swarm-based behaviour and collectively form a mobile ad-hoc communication network. The mobility model is used to study how the topological properties of the resulting communication network change over time. The connectivity graphs are determined by allowing each unit to communicate with others inside a given radius. By varying the free parameters of the mobility model, qualitatively different regimes of movement can be emulated. A number of properties of the graphs (e.g., the size of largest connected component, overall network efficiency and the number of isolated units) are calculated and compared for the different regimes. Finally, we present several directions for future work, both in terms of further applications and extensions of the present model. 1

Cellular Automata

Modelling and simulation are disciplines of major importance for science and engineering. There is no science without models, and simulation has nowadays become a very useful tool, sometimes unavoidable, for development of both science and engineering. The main attractive feature of cellular automata is that, in spite of their conceptual simplicity which allows an easiness of implementation for computer simulation, as a detailed and complete mathematical analysis in principle, they are able to exhibit a wide variety of amazingly complex behaviour. This feature of cellular automata has attracted the researchers' attention from a wide variety of divergent fields of the exact disciplines of science and engineering, but also of the social sciences, and sometimes beyond. The collective complex behaviour of numerous systems, which emerge from the interaction of a multitude of simple individuals, is being conveniently modelled and simulated with cellular automata for very different purposes. In this book, a number of innovative applications of cellular automata models in the fields of Quantum Computing, Materials Science, Cryptography and Coding, and Robotics and Image Processing are presented

Projecting land use changes using parcel-level data : model development and application to Hunterdon County, New Jersey

This dissertation is to develop a parcel-based spatial land use change prediction model by coupling various machine learning and interpretation algorithms such as cellular automata (CA) and decision tree (DT). CA is a collection of cells that evolves through a number of discrete time steps according to a set of transition rules based on the state of each cell and the characteristics of its neighboring cells. DT is a data mining and machine learning tool that extracts the patterns of decision process from observed cell behaviors and their affecting factors. In this dissertation, CA is used to predict the future land use status of cadastral parcels based on a set of transition rules derived from a set of identified land use change driving factors using DT. Although CA and DT have been applied separately in various land use change models in the literature, no studies attempted to integrate them. This DT-based CA model developed in this dissertation represents the first kind of such integration in land use change modeling. The coupled model would be able to handle a large set of driving factors and also avoid subjective bias when deriving the transition rules. The coupled model uses the cadastral parcel as a unit of analysis, which has practical policy implications because the responses of land use changes to various policy usually take place at the parcel level. Since parcel varies by their sizes and shapes, its use as a unit of analysis does make it difficult to apply CA, which initially designed to handle regular grid cells. This dissertation improves the treatment of the irregular cell in CA-based land use change models in literature by defining a cell\u27s neighborhood as a fixed distance buffer along the parcel boundary. The DT-based CA model was developed and validated in Hunterdon County, New Jersey. The data on historical land uses and various land use change driving factors for Hunterdon County were collected and processed using a Geographic Information System (GIS). Specifically, the county land uses in 1986, I995 and 2002 were overlaid with a parcel map to create parcel-based land use maps. The single land use in each parcel is based on a classification scheme developed thorough literature review and empirical testing in the study area. The possible land use status considered for each parcel is agriculture, barren land, forest, urban, water or wetlands following the land use/land cover classification by the New Jersey Department of Environment Protection. The identified driving factors for the future status of the parcel includes the present land use type, the number of soil restrictions to urban development, and the size of the parcel, the amount of wetlands within the parcel, the distribution of land uses in the neighborhood of the parcel, the distances to the nearest streams, urban centers and major roads. A set of transition rules illustrating the land use change processes during the period 1986-1995 were developed using a TD software J48 Classifier. The derived transition rules were applied to the 1995 land use data in a CA model Agent Analyst/RePast (Recursive Porous Agent Simulation Toolkit) to predict the spatial land use pattern in 2004, which were then validated by the actual land use map in 2002. The DT-based CA model had an overall accuracy of 84.46 percent in terms of the number of parcels and of 80.92 percent in terms of the total acreage in predicting land use changes. The model shows much higher capacity in predicting the quantitative changes than the locational changes in land use. The validated model was applied to simulate the 2011 land use patterns in Hunterdon County based on its actual land uses in 2002 under both business as usual and policy scenarios. The simulation results shows that successfully implementing current land use policies such as down zoning, open space and farmland preservation would prevent the total of 7,053 acres (741 acres of wetlands, 3,034 acres of agricultural lands, 250 acres of barren land, and 3,028 acres of forest) from future urban development in Hunterdon County during the period 2002-2011. The neighborhood of a parcel was defined by a 475-foot buffer along the parcel boundary in the study. The results of sensitivity analyses using two additional neighborhoods (237- and 712-foot buffers) indicate the insignificant impacts of the neighborhood size on the model outputs in this application

Análisis de Sensibilidad aplicado a modelos de crecimiento urbano basados en autómatas celulares de estructura irregular

This work presents a Sensitivity Analysis (SA), as part of a validation process that is applied on an urban growth Cellular Automata (CA) based-model with irregular structure (MUGICA, Model for Urban Growth simulation using an Irregular Cellular Automata). This model has been developed to simulate urban growth of three municipalities located in an important industrial corridor (Corredor del Henares) in the central area of Spain. Although the methodology employed in this work has already been applied in models based on a raster structure, it is intended to verify its viability in the previous simulation phase, that is, in the calibration period (2000-2010) in a model of irregular structure such as MUGICA, which uses the cadastral plot as reference unit. This procedure aims to explore the degree of influence of each of the parameters on the results of the model, individually and as a whole. For this purpose, a successive elimination of the parameters is performed to evaluate if the absence of one or several of them implies a significant alteration of the results. The results show, firstly, the viability to apply this methodology in a vector environment. On the other hand, it has been possible to verify the significative influence of the suitability and accessibility factors in the development of urban land, as would be expected in a model of these characteristics.En el presente trabajo se aplica un Análisis de Sensibilidad (AS), como parte de un proceso de validación, sobre un modelo de simulación del crecimiento urbano basado en Autómatas Celulares (AC) de estructura irregular (MUGICA, Model for Urban Growth simulation using an Irregular Celular Automata). Este modelo ha sido desarrollado para simular el crecimiento urbano de tres municipios pertenecientes a un importante corredor urbano-industrial (Corredor del Henares) localizado en la zona central peninsular de España. Si bien la metodología utilizada en este trabajo ya ha sido aplicada en modelos basados en una estructura raster, se pretende comprobar su viabilidad en un modelo de estructura irregular como MUGICA, el cual emplea la parcela catastral como unidad de referencia. El objetivo es explorar el grado de influencia de cada uno de los factores en los resultados del modelo, de manera individual y en sus diferentes combinaciones. Para ello se realiza una eliminación sucesiva de los factores con el fin de evaluar si la ausencia de uno o varios de ellos supone una alteración significativa de los resultados. Los resultados muestran, en primer lugar, la viabilidad de la aplicación de esta metodología en este tipo de entorno irregular. Por otro lado, se ha podido constatar la gran influencia de los factores accesibilidad y aptitud en el desarrollo de suelo urbano, como sería de esperar en un modelo de estas características

Aplicación de un modelo basado en autómatas celulares irregulares para la simulación de escenarios futuros de cambios de uso de suelo urbano

La urbanización es uno de los fenómenos más drásticos de transformación del territorio. En las últimas décadas, este fenómeno ha experimentado un aumento vertiginoso. Según indica el informe más reciente de World Urbanization Prospects de Naciones Unidas, se estima que el 68,4% de la población mundial vivirá en zonas urbanas en 2050. Además, se prevé que dicha población se duplique en los países desarrollados y se triplique en los países en vías de desarrollo. Todo ello ha supuesto impactos irreversibles sobre el territorio, afectando enormemente al conjunto de la sociedad en términos de gestión y acceso a recursos, problemas de índole social y económica, contaminación ambiental, etc. Ante esta situación, se ha observado un creciente interés por el desarrollo y mejora de instrumentos que den soporte a la toma de decisiones y a la gestión de las áreas urbanas. Uno de los instrumentos más empleados para este fin ha sido la planificación de escenarios futuros. Este permite conocer cómo podría afectar la evolución de los usos del suelo urbano a la configuración de los patrones espaciales bajo distintas perspectivas futuras. Con este enfoque, la planificación de escenarios trata de reducir la incertidumbre facilitando la toma de medidas proactivas para minimizar los posibles impactos territoriales. No obstante, la planificación de escenarios puede verse limitada ante un futuro complejo e incierto si todos los escenarios se mantienen muy próximos a una proyección tendencial. Como ejemplo, el surgimiento de acontecimientos inesperados puede llegar a inhabilitar la utilidad de una planificación lineal basada únicamente en tendencias pasadas. Por dicha razón, y para gestionar de la mejor manera posible los futuros desarrollos urbanos (no) deseados, el pensamiento disruptivo debe formar parte del proceso de previsión, rompiendo así con la linealidad de los acontecimientos actuales para abarcar lo inesperado. Como parte del proceso de planificación urbana, los modelos de simulación intentan representar el desarrollo futuro de las ciudades para garantizar que puedan desarrollarse de manera eficiente y sostenible. De ellos, los modelos basados en Autómatas Celulares (AC) se encuentran entre los más utilizados como apoyo a la gestión de las áreas urbanas. Estos modelos han experimentado una importante flexibilización, adaptándose a entornos irregulares (parcelario catastral) para ofrecer simulaciones de cambio de uso del suelo urbano a escala local. En esta línea, son cada vez más los estudios que combinan escenarios narrativos con tareas de modelización de manera participativa, y todo ello con la finalidad de obtener resultados más realistas que contemplen los actuales retos que afronta la planificación urbana. Sin embargo, es difícil que estos modelos consideren por sí solos la amplia gama de factores que intervienen en la evolución futura de las zonas urbanas, especialmente cuando tratan de representar escenarios imaginativos y disruptivos. Ante la situación actual en la que se encuentra la planificación espacial de escenarios urbanos, la presente investigación desarrolla e implementa una metodología que trata de cubrir algunos de los huecos más notables que se observan en este ámbito de estudio. En primer lugar, se presenta un estudio basado en el diseño y cartografiado de escenarios disruptivos a través de un taller participativo donde colaboraron conjuntamente expertos de diversos ámbitos relacionados con el urbanismo y el transporte. Los resultados derivados de dicho taller se analizaron mediante un método estadístico denominado Regresión Logística Geográficamente Ponderada (RLGP) con el objetivo de determinar los principales factores que explican la localización de los usos del suelo urbano en los distintos escenarios disruptivos. Posteriormente se emplearon los resultados del análisis previo para calibrar un nuevo modelo desarrollado basado en AC vectoriales, denominado Land Parcel – Cellular Automata (LP-CA). Este se encarga de simular a futuro diferentes escenarios imaginativos y disruptivos reproduciendo dinámicas urbanas de crecimiento, cambio y pérdida de usos del suelo. Al mismo tiempo, se aplicó una metodología de validación parcial para observar la robustez del modelo respecto a la influencia de los factores en las simulaciones. Finalmente, se aplicó una metodología innovadora diseñada para evaluar los diferentes escenarios disruptivos. Esta emplea métricas espaciales multiescalares basadas en el uso de ventanas móviles aplicadas a nivel de parcela que permiten caracterizar la diversidad y el tipo de expansión urbana. La metodología desarrollada fue aplicada a un sector del Corredor del Henares (España), empleándose este como laboratorio territorial experimental. Los resultados han demostrado la utilidad de la integración de escenarios disruptivos en la planificación espacial para mostrar contrastes entre los diferentes escenarios, destacando la utilidad de las visiones y del taller de cartografiado participativo en la representación espacial de la cantidad y dirección del crecimiento de los usos urbanos y la organización de la red de transporte. De manera complementaria, el análisis estadístico mediante RLGP permitió un ajuste relevante del parámetro de aptitud en el modelo de simulación, hecho que favoreció una calibración más adaptada a cada escenario. En cuanto a los avances desarrollados para el modelo LP-CA, las simulaciones lograron reproducir satisfactoriamente dinámicas urbanas disruptivas (además de crecimiento, transformación de usos y abandono). Los patrones espaciales generados se ajustaron a los escenarios narrativos descritos. Adicionalmente, el análisis de sensibilidad constató la incidencia equilibrada de todos los factores en las simulaciones generadas por el modelo LP-CA. Por último, la evaluación de escenarios permitió caracterizar en profundidad y realizar comparaciones más detalladas de las implicaciones territoriales de cada escenario en lo que respecta a la diversidad y al tipo de expansión urbana. En conclusión, la información proporcionada por esta investigación aporta nuevas herramientas y mejora algunos de los métodos ya existentes dentro de la planificación espacial de escenarios. Concretamente, ofrece una novedosa metodología capaz de generar simulaciones de crecimiento y cambio en los usos del suelo urbano para escenarios futuros disruptivos. Los resultados facilitan la observación de la propagación espacial de la incertidumbre asociada a los eventos futuros a través de los patrones que configuran los nuevos usos del suelo. En definitiva, trata de extraer información compleja de diferentes enfoques de evolución urbana futura, presentándola de manera sencilla para que pueda ser empleada por los responsables de la toma de decisiones

Escenarios de la ordenación territorial: modelos prospectivos del uso de suelo en el cantón Cuenca

La generación de escenarios para la ordenación territorial mediante modelos prospectivos de uso de suelo representa una oportunidad para anticipar, prevenir y mitigar modalidades insostenibles de consumo y producción que tiene el desarrollo territorial. Esta investigación tiene por objeto contribuir a la generación de escenarios territoriales mediante la prospectiva de usos de suelo rural. La metodología se basó en la aplicación de prospectiva estratégica y el modelo CA_Markov, mismo que se sintetiza en: a) generación de mapas de cobertura y uso de suelo de los años 2000, 2008 y 2018, a1) análisis de cambios de uso de suelo entre los diferentes periodos, a2) cálculo de áreas de probabilidad de transición (Markov Chain); b) generación de mapas de idoneidad de transición a través de: identificación de factores de cambio, restricciones, funciones de membresía difusa (Fuzzy), modelos de proceso de jerarquía analítica (AHP) y evaluación multicriterio (MCE); c) evaluación del poder predictivo del modelo a través del índice de Kappa y; d) simulación de escenarios al año 2030. Los resultados para el periodo 2000 – 2018 indican disminución de coberturas naturales e incremento de zonas antrópicas y tierras agropecuarias. Por su parte, en los escenarios proyectados al año 2030 esta tendencia se mantendrá. Se concluye que el análisis espacio temporal para la construcción de escenarios territoriales representa no solo una alternativa para comprender la dinámica territorial, sino que permite influir en él, a través de la toma de decisiones basado en los criterios técnicos, académicos, planificadores y sociedad civil, cuya visión permita alcanzar un desarrollo sostenible del cantón Cuenca a futuro.The generation of scenarios for territorial planning through prospective models of land use represents an opportunity to anticipate, prevent, and mitigate unsustainable patterns of consumption and production that territorial development has. This research aims to contribute to the generation of territorial scenarios through the prospective of rural land. The methodology was based on the application of strategic prospective and the CA_Markov model, which is synthesized in a) generation of land cover and land use maps for the years 2000, 2008, and 2018, a1) analysis of changes in land use between the different periods, a2) calculation of transition probability areas (Markov Chain); b) generation of transition suitability maps through identification of change factors, restrictions, fuzzy membership functions (Fuzzy), analytical hierarchy process (AHP) models and multi-criteria evaluation (MCE); c) evaluation of the predictive power of the model through the Kappa index and; d) simulation of scenarios to the year 2030. The 2000-2018 period results indicate a decrease in natural coverage and an increase in anthropic zones and agricultural land. In the scenarios projected for the year 2030, this trend will continue. It is concluded that the space-time analysis for the construction of territorial scenarios represents not only an alternative to understanding the territorial dynamics but also allows to influence it through decision-making based on the technical, academic, planner, and civil society criteria, whose vision allows for achieving sustainable development of the Cuenca canton in the future.Magíster en Ordenación del TerritorioCuenc

Creating Persian-like music using computational intelligence

Dastgāh are modal systems in traditional Persian music. Each Dastgāh consists of a group of melodies called Gushé, classified in twelve groups about a century ago (Farhat, 1990). Prior to that time, musical pieces were transferred through oral tradition. The traditional music productions revolve around the existing Dastgāh, and Gushe pieces. In this thesis computational intelligence tools are employed in creating novel Dastgāh-like music.There are three types of creativity: combinational, exploratory, and transformational (Boden, 2000). In exploratory creativity, a conceptual space is navigated for discovering new forms. Sometimes the exploration results in transformational creativity. This is due to meaningful alterations happening on one or more of the governing dimensions of an item. In combinational creativity new links are established between items not previously connected. Boden stated that all these types of creativity can be implemented using artificial intelligence.Various tools, and techniques are employed, in the research reported in this thesis, for generating Dastgāh-like music. Evolutionary algorithms are responsible for navigating the space of sequences of musical motives. Aesthetical critics are employed for constraining the search space in exploratory (and hopefully transformational) type of creativity. Boltzmann machine models are applied for assimilating some of the mechanisms involved in combinational creativity. The creative processes involved are guided by aesthetical critics, some of which are derived from a traditional Persian music database.In this project, Cellular Automata (CA) are the main pattern generators employed to produce raw creative materials. Various methodologies are suggested for extracting features from CA progressions and mapping them to musical space, and input to audio synthesizers. The evaluation of the results of this thesis are assisted by publishing surveys which targeted both public and professional audiences. The generated audio samples are evaluated regarding their Dastgāh-likeness, and the level of creativity of the systems involved

A Multi-Scale Flexible Framework for Urban Modelling

Ph. D. ThesisThe configuration of urban areas, and of infrastructures which serve them is central to managing the urbanisation process. Integrated assessment frameworks aim to inform decisions regarding planning, policy, and design to coordinate projects across sectors. Development of such models poses a number of challenges; (i) scenario generation, (ii) intelligibility to stakeholders, (iii) validity, (iv) control and feedback, (v) execution time, (vi) data requirements, (vii) uncertainties and, (viii) flexibility/reusability. This research has developed a multi-scale flexible framework which disaggregates projected regional employment to ward-level population, and further to rasterised development. This comprises; (i) transport network generalised cost, (ii) cost composition, (iii) spatial interaction incorporating transport accessibility, (iv) development zoning, (v) multi-criteria evaluation of development suitability, and (vi) cellular development. The framework is generically implemented, each model being specified in terms of inputs, outputs, and parameters. Modellinkage is via input/output chaining, providing the opportunity to experiment with alternative solutions. Execution is flexible/configurable to perform multiple model runs whilst varying parameters and propagating metadata through stages. Python controls execution flow, C++ provides performance, PostgreSQL manages data, and QGIS assists input/output. The framework is deployed in baseline scenarios for London and Innsbruck, and in more detailed scenario/uncertainty exploration for London. The framework’s utility is judged by criteria corresponding to the above challenges and is found to be favourable, with performance, flexibility and uncertainty support as key attributes. The framework executes models for London in ~52 seconds on modest hardware (1.6GHz, 8GB). This involves costweighted Dijkstra - 4 transport networks (~42s), cost composition and accessibility conversion (~4s), spatial interaction - 633 wards (~2s), rasterised 4-hectare development zones (~1s), 7 criteria development suitability evaluation (~1s), and cellular development - 100m scale (~2s). Combinatorial uncertainties are accommodated by a flexible, modular structure which promotes reuse, and records run configuration as well as model parameters in chained metadat