Integrated urban evolutionary modeling

Cellular automata models have proved rather popular as frameworks for simulating the physical growth of cities. Yet their brief history has been marked by a lack of application to real policy contexts, notwithstanding their obvious relevance to topical problems such as urban sprawl. Traditional urban models which emphasize transportation and demography continue to prevail despite their limitations in simulating realistic urban dynamics. To make progress, it is necessary to link CA models to these more traditional forms, focusing on the explicit simulation of the socio-economic attributes of land use activities as well as spatial interaction. There are several ways of tackling this but all are based on integration using various forms of strong and loose coupling which enable generically different models to be connected. Such integration covers many different features of urban simulation from data and software integration to internet operation, from interposing demand with the supply of urban land to enabling growth, location, and distributive mechanisms within such models to be reconciled. Here we will focus on developin

Can geocomputation save urban simulation? Throw some agents into the mixture, simmer and wait ...

There are indications that the current generation of simulation models in practical, operational uses has reached the limits of its usefulness under existing specifications. The relative stasis in operational urban modeling contrasts with simulation efforts in other disciplines, where techniques, theories, and ideas drawn from computation and complexity studies are revitalizing the ways in which we conceptualize, understand, and model real-world phenomena. Many of these concepts and methodologies are applicable to operational urban systems simulation. Indeed, in many cases, ideas from computation and complexity studies—often clustered under the collective term of geocomputation, as they apply to geography—are ideally suited to the simulation of urban dynamics. However, there exist several obstructions to their successful use in operational urban geographic simulation, particularly as regards the capacity of these methodologies to handle top-down dynamics in urban systems. This paper presents a framework for developing a hybrid model for urban geographic simulation and discusses some of the imposing barriers against innovation in this field. The framework infuses approaches derived from geocomputation and complexity with standard techniques that have been tried and tested in operational land-use and transport simulation. Macro-scale dynamics that operate from the topdown are handled by traditional land-use and transport models, while micro-scale dynamics that work from the bottom-up are delegated to agent-based models and cellular automata. The two methodologies are fused in a modular fashion using a system of feedback mechanisms. As a proof-of-concept exercise, a micro-model of residential location has been developed with a view to hybridization. The model mixes cellular automata and multi-agent approaches and is formulated so as to interface with meso-models at a higher scale

Simulating city growth by using the cellular automata algorithm

The objective of this thesis is to develop and implement a Cellular Automata (CA) algorithm to simulate urban growth process. It attempts to satisfy the need to predict the future shape of a city, the way land uses sprawl in the surroundings of that city and its population. Salonica city in Greece is selected as a case study to simulate its urban growth. Cellular automaton (CA) based models are increasingly used to investigate cities and urban systems. Sprawling cities may be considered as complex adaptive systems, and this warrants use of methodology that can accommodate the space-time dynamics of many interacting entities. Automata tools are well-suited for representation of such systems. By means of illustrating this point, the development of a model for simulating the sprawl of land uses such as commercial and residential and calculating the population who will reside in the city is discussed

Developing Efficient Discrete Simulations on Multicore and GPU Architectures

In this paper we show how to efficiently implement parallel discrete simulations on multicoreandGPUarchitecturesthrougharealexampleofanapplication: acellularautomatamodel of laser dynamics. We describe the techniques employed to build and optimize the implementations using OpenMP and CUDA frameworks. We have evaluated the performance on two different hardware platforms that represent different target market segments: high-end platforms for scientific computing, using an Intel Xeon Platinum 8259CL server with 48 cores, and also an NVIDIA Tesla V100GPU,bothrunningonAmazonWebServer(AWS)Cloud;and on a consumer-oriented platform, using an Intel Core i9 9900k CPU and an NVIDIA GeForce GTX 1050 TI GPU. Performance results were compared and analyzed in detail. We show that excellent performance and scalability can be obtained in both platforms, and we extract some important issues that imply a performance degradation for them. We also found that current multicore CPUs with large core numbers can bring a performance very near to that of GPUs, and even identical in some cases.Ministerio de Economía, Industria y Competitividad, Gobierno de España (MINECO), and the Agencia Estatal de Investigación (AEI) of Spain, cofinanced by FEDER funds (EU) TIN2017-89842

A review of wildland fire spread modelling, 1990-present 3: Mathematical analogues and simulation models

In recent years, advances in computational power and spatial data analysis (GIS, remote sensing, etc) have led to an increase in attempts to model the spread and behvaiour of wildland fires across the landscape. This series of review papers endeavours to critically and comprehensively review all types of surface fire spread models developed since 1990. This paper reviews models of a simulation or mathematical analogue nature. Most simulation models are implementations of existing empirical or quasi-empirical models and their primary function is to convert these generally one dimensional models to two dimensions and then propagate a fire perimeter across a modelled landscape. Mathematical analogue models are those that are based on some mathematical conceit (rather than a physical representation of fire spread) that coincidentally simulates the spread of fire. Other papers in the series review models of an physical or quasi-physical nature and empirical or quasi-empirical nature. Many models are extensions or refinements of models developed before 1990. Where this is the case, these models are also discussed but much less comprehensively.Comment: 20 pages + 9 pages references + 1 page figures. Submitted to the International Journal of Wildland Fir

Application of a cellular automata model to the metropolitan area of Barcelona

Cellular Automata (CA) models are among the most popular models for simulating land use change/growth in urban areas around the world and have experienced a significant development over the last twenty years. These models have their origins on the efforts of devising mathematical rules for biological systems and for evolution developed by mathematicians von Neumann and Stanislaw Ulam in the 1940s. Two main features made CA interesting for urban studies, to which they were introduced by Waldo Tobler in the early 1970s. First, their intrinsic spatiality, which is suitable for the simulation of a variety of geographic phenomena. Second, the possibility of simulating complex patterns of, for example, land use starting from a simple conceptual framework that includes the definition of a cell space, a neighborhood, and a finite set of transition rules applied to a finite set of cell states. These models have been developed for different urban contexts and are mainly based on the use of regular cells derived from remote sensing imagery. This is a simplification in the representation of urban areas: on the one hand, regular cells do not represent common urban form and, on the other hand, they do not held information of any type other than land use, obtained from automatic classification. These issues suggested the consideration of irregular cells linked to reliable information, that is, census blocks. Census blocks are drawn considering the form of urban areas and they are the most reliable source of data on a wide variety of subjects, being a natural choice for the design of CA cells. We present in this paper an application of a CA model to simulate urban change in the Metropolitan Area of Barcelona, in a prospective analysis of 20 years. The model uses irregular cells designed considering census blocks.We describe the main features of the model and the calibration process, as well as the simulation results. We also discuss some new features that are the core of our current research on CA.Peer Reviewe