Complex Geographical Phenomena Research Articles

Geovisualization of tropical cyclone behaviour in the South Pacific

Abstract A combination of computational power, dynamic graphics and geographical information system (GIS) packages creates a powerful platform for advanced visualization tools to explore complex geographical phenomena in an interactive computer environment – known as geovisualization. Geovisualization is a relatively new computer-based approach that refers to a set of methods and techniques to support geospatial data analysis through the use of four-dimensional (4D), multi-variable and interactive visualization. In this paper, we illustrate and discuss the value of several spatialization techniques that are used to perform analysis of spatial structures of climatic and meteorological elements, especially through the geovisualization of characteristics and behaviour of tropical cyclones in the South Pacific Ocean. Preliminary findings are encouraging, allowing patterns and dependencies between chosen cyclone features to be identified, in turn indicating the enormous potential of geovisualization for analysing multi-variate spatial attributes within large tropical cyclone datasets.

A New Concept of Multi-Scenario, Multi-Component Animated Maps for the Visualization of Spatio-Temporal Landscape Evolution

A New Concept of Multi-Scenario, Multi-Component Animated Maps for the Visualization of Spatio-Temporal Landscape Evolution In this paper, we propose a new approach to the presentation of the spatio-temporal evolution of landscape using a multi-component multi-scenario animated map system. The concept of multi-scenario map was introduced with a few conceptual level objectives. Firstly, to facilitate understanding of geographic spatio-temporal changeability (especially landscape changeability) by the use of complex cartographic animations. Secondly, to investigate factors which influence an intuitive and effective use of multi-component cartographic applications. In relation to understanding processes, the overriding purpose was to build up a generic approach that allows users to recognize features of complex geographic phenomena. Finally, since the implementation of the concept was of importance, a prototype has been prepared.

GIScience Research at the Thirtieth Annual ESRI International User Conference

GIScience Research at the Thirtieth Annual ESRI International User Conference

CAUSTA: Clifford Algebra‐based Unified Spatio‐Temporal Analysis

AbstractIntroducing Clifford algebra as the mathematical foundation, a unified spatio‐temporal data model and hierarchical spatio‐temporal index are constructed by linking basic data objects, like pointclouds and Spatio‐Temporal Hyper Cubes of different dimensions, within the multivector structure of Clifford algebra. The transformation from geographic space into homogeneous and conformal space means that geometric, metric and many other kinds of operators of Clifford algebra can be implemented and we then design the shortest path, high‐dimensional Voronoi and unified spatial‐temporal process analyses with spacetime algebra. Tests with real world data suggest these traditional GIS analysis algorithms can be extended and constructed under Clifford Algebra framework, which can accommodate multiple dimensions. The prototype software system CAUSTA (Clifford Algebra based Unified Spatial‐Temporal Analysis) provides a useful tool for investigating and modeling the distribution characteristics and dynamic process of complex geographical phenomena under the unified spatio‐temporal structure.

A bottom‐up approach to discover transition rules of cellular automata using ant intelligence

This paper presents a new method to discover transition rules of geographical cellular automata (CA) based on a bottom‐up approach, ant colony optimization (ACO). CA are capable of simulating the evolution of complex geographical phenomena. The core of a CA model is how to define transition rules so that realistic patterns can be simulated using empirical data. Transition rules are often defined by using mathematical equations, which do not provide easily understandable explicit forms. Furthermore, it is very difficult, if not impossible, to specify equation‐based transition rules for reflecting complex geographical processes. This paper presents a method of using ant intelligence to discover explicit transition rules of urban CA to overcome these limitations. This ‘bottom‐up’ ACO approach for achieving complex task through cooperation and interaction of ants is effective for capturing complex relationships between spatial variables and urban dynamics. A discretization technique is proposed to deal with continuous spatial variables for discovering transition rules hidden in large datasets. The ACO–CA model has been used to simulate rural–urban land conversions in Guangzhou, Guangdong, China. Preliminary results suggest that this ACO–CA method can have a better performance than the decision‐tree CA method.

Discovery of transition rules for geographical cellular automata by using ant colony optimization

A new intelligent algorithm of geographical cellular automata (CA) based on ant colony optimization (ACO) is proposed in this paper. CA is capable of simulating the evolution of complex geographical phenomena, and the core of CA models is how to define transition rules. However, most of the transition rules are defined by mathematical equations, and are hence not explicit. When the study area is complicated, it is much more difficult to extract parameters for geographical CA. As a result, ACO is applied to geographical CA to automatically and intelligently obtain transition rules in this paper. The transition rules extracted by ACO are defined as logical expressions rather than implicit mathematical equations to describe the complex relationships of the nature, and easy for people to understand. The ACO-CA model was applied to simulating rural-urban land conversions in Guangzhou City, China, and appropriate simulation results were generated. Compared with See5.0 decision tree model, ACO-CA is more suitable to discovering transition rules for geographical CA.

Defining agents’ behaviors to simulate complex residential development using multicriteria evaluation

Cellular automata (CA) have been increasingly used to simulate complex geographical phenomena. These models may have limitations on reflecting individuals’ behaviors which should be considered in urban simulation. Agent-based modeling can solve some of the problems in addressing individuals’ influences in urban systems. However, there is a general lack of methodology on how to define agents’ properties. This paper uses multicriteria evaluation techniques to determine some of the parameters for the agent-based model. Empirical data from GIS are used to define agent's properties. Sensitivity analysis is also carried out to assess the influences of parameters on simulation outcomes. This model has been applied to the simulation of the residential development in a fast growing city, Guangzhou, in south China.

Data mining of cellular automata's transition rules

This paper presents a new method to discover knowledge for geographical cellular automata (CA) by using a data-mining technique. CA have the ability to simulate complex geographical phenomena. Very few studies have been carried out on how to determine and validate the transition rules of CA from observed data. The transition rules of traditional CA are usually expressed by mathematical equations. This paper demonstrates that the explicit transition rules of CA can be automatically reconstructed through the rule induction procedure of data mining. The explicit transition rules are more intuitive to decision-makers. The transition rules are obtained by applying data-mining techniques to spatial data. The proposed method can reduce the uncertainties in defining transition rules and help to generate more reliable simulation results.

Representing Complex Geographic Phenomena in GIS

Conventionally, spatial data models have been designed according to object- or field-based conceptualizations of reality. Conceptualization of complex geographic phenomena that have both object- and field-like properties, such as wildfire and precipitation, has not yet been incorporated into GIS data models. To this end, a new conceptual framework is proposed in this research for organizing data about such complex geographic phenomena in a GIS as a hierarchy of events, processes, and states. In this framework, discrete objects are used to show how events and processes progress in space and time, and fields are used to model how states of geographic themes vary in a space-time frame. Precipitation is used to demonstrate the construction and application of the proposed framework with digital precipitation data from April 15 to May 22, 1998, for the state of Oklahoma, U.S.A. With the proposed framework, two sets of algorithms have been developed. One set automatically assembles precipitation events and processes from the data and stores the precipitation data in the hierarchy of events, processes, and states, so that attributes about events, processes, and states are readily available for information query. The other set of algorithms computes information about the spatio-temporal behavior and interaction of events and processes. The proposed approach greatly enhances support for complex spatio-temporal queries on the behavior and relationships of events and processes.

Conceptualizing Space and Time: A Classification of Geographic Movement

In an increasingly complex and interactive world, the need to study, manage, and understand the critical interface between physical and human environments is becoming more urgent, precipitating technological advancements in the dynamic spatio-temporal capabilities of geographic information systems (GIS) and associated visualization and representational techniques. Such efforts require a thorough knowledge of the intrinsic characteristics associated with changes in space and time. A comprehensive understanding of the underlying dynamic structure associated with movement is necessary before accurate, integrative, and insightful analyses, explanations, and representations of complex, geographically dynamic phenomena can be performed. Using the logical subdivision method of classification, geographic movement is conceptualized by its abstract spatial and temporal components creating a matrix of 12 unique classes characterizing the form of movement. The organization is a simplified framework that offers a movement language with which to describe the movements associated with diverse geographic phenomena. Classifing geographic movement by its changes in time and space serves as a useful construct that facilitates the comprehension of the general ideas and principles intrinsic to geographical patterns and processes and provides a solid foundation for representing and understanding complex geographic phenomena.

Towards an integral strategy for modelling uncertain geospatial data

This paper endeavours to put the discussion on errors and uncertainties in geographical information systems (GISs) in a more systematic way by examining the strength and weakness of discrete objects and continous fields, the two distinct schools of spatial data modelling. In doing so, it argues that neither discrete objects nor continous fields alone provide objective and complete representations of highly complex geographical phenomena, though there are good reasons for asserting that continuous fields are better suited to modelling spatial dependence, heterogeneity and fuzzines significant in geographical reality than discrete objects. Thus, there seems to be merit in adopting an integrated model incorporating analytical capabilities of fields and generalization functions of objects, for which extended TIN (triangulated irregular network) models along with their duals (Voronoi diagrams) provide a pragmatical solution.