Search

MobileGIS refers to the use of geographic data in the field on mobile devices like networked personal digital assistant (PDA). The main components for MobileGIS are global positioning system (GPS), mobile device i.e. mobile phone, and communication network with GIS acting as the backbone. Map generalization is the process by which small scale maps are to be derived from large scale maps. This requires the appropriate use of map generalization operations to be performed subsequent to scale reduction to reduce the graphic conflict. This paper looks at how human factors requirements can be considered in the context of graphic conflict reduction for MobileGIS applications. Currently, this reduction is achieved by using schematic mapping techniques. The application of schematic mapping can be thought of as a data reduction technique for large scale datasets to make it suitable for rendering in mobile applications. This work makes use of simulated annealing (SA) based technique. At the start of the optimization process SA is presented with an initial approximate solution (or state). In the case of the schematic map problem, this will be the initial network (line features with travel time, each made up of constituent vertices). The initial state is then evaluated using a cost function C; this function assigns to the input state a score that reflects how well it measures up against a set of given constraints (topological, angle, minimum edge length, clearance). If the initial cost is greater than some user defined threshold (i.e. the constraints are not met adequately) then the algorithm steps into its optimization phase. This part of the process is iterative. At each iteration, the current state (i.e. the current network) is modified to make a new, alternative, approximate solution. The current and new states are said to be neighbours. The neighbours of any given state are generated usually in an application-specific way. The iterative process continues until stopping criteria are met (e.g. a suitably good solution is found or a certain amount of time has passed). Prototype software for producing schematic maps tailored for MobileGIS has been developed. The software makes use of the simulated annealing optimization technique. The software is currently implemented as a VBA script within ArcGIS. This technique has been used previously to control operations of displacement, deletion, reduction and enlargement of multiple map objects to help resolve spatial conflict arising due to scale reduction. These maps are subsequently displayed within the ArcPad application on a HP iPAQ PDA and tests have been carried out using different datasets. The results of applying simulated annealing based approach for automated schematic map generation is promising and further work will be done in enhancing the software with more functionality. Development of automated schematic map generation techniques and cartographic specification for large scale digital geographic datasets suitable for MobileGIS applications was done and various tests carried out. Spatial conflict between feature classes at the specified scale ranges are to be dealt with by applying simulated annealing metaheuristic optimization technique. [ABSTRACT FROM AUTHOR]

Computational Ecology and Software ISSN 2220-721X URL: http://www.iaees.org/publications/journals/ces/online-version.asp RSS: http://www.iaees.org/publications/journals/ces/rss.xml E-mail: ces@iaees.org Editor-in-Chief: WenJun Zhang Aims and Scope COMPUTATIONAL ECOLOGY AND SOFTWARE (ISSN 2220-721X) is an open access, peer-reviewed online journal that considers scientific articles in all different areas of computational ecology. It is the transactions of the International Society of Computational Ecology. The journal is concerned with the ecological researches, constructions and applications of theories and methods of computational sciences including computational mathematics, computational statistics and computer science. It features the simulation, approximation, prediction, recognition, and classification of ecological issues. Intensive computation is one of the major stresses of the journal. The journal welcomes research articles, short communications, review articles, perspectives, and book reviews. The journal also supports the activities of the International Society of Computational Ecology. The topics to be covered by CES include, but are not limited to: •Computation intensive methods, numerical and optimization methods, differential and difference equation modeling and simulation, prediction, recognition, classification, statistical computation (Bayesian computing, randomization, bootstrapping, Monte Carlo techniques, stochastic process, etc.), agent-based modeling, individual-based modeling, artificial neural networks, knowledge based systems, machine learning, genetic algorithms, data exploration, network analysis and computation, databases, ecological modeling and computation using Geographical Information Systems, satellite imagery, and other computation intensive theories and methods. •Artificial ecosystems, artificial life, complexity of ecosystems and virtual reality. •The development, evaluation and validation of software and algorithms for computational ecology. The development and evaluation of apparatus, instruments and machines for ecological and environmental analysis, investigation and monitoring based on the software of computational ecology. •Methodological applications of computational ecology in the researches of ecology and environmental sciences. Authors can submit their works to the email box of this journal, ces@iaees.org. All manuscripts submitted to this journal must be previously unpublished and may not be considered for publication elsewhere at any time during review period of this journal. Authors are asked to read Author Guidelines before submitting manuscripts. In addition to free submissions from authors around the world, special issues are also accepted. The organizer of a special issue can collect submissions (yielded from a research project, a research group, etc.) on a specific research topic, or submissions of a scientific conference for publication of special issue.

Computational Ecology and Software ISSN 2220-721X URL: http://www.iaees.org/publications/journals/ces/online-version.asp RSS: http://www.iaees.org/publications/journals/ces/rss.xml E-mail: ces@iaees.org Editor-in-Chief: WenJun Zhang Aims and Scope COMPUTATIONAL ECOLOGY AND SOFTWARE (ISSN 2220-721X) is an open access, peer-reviewed online journal that considers scientific articles in all different areas of computational ecology. It is the transactions of the International Society of Computational Ecology. The journal is concerned with the ecological researches, constructions and applications of theories and methods of computational sciences including computational mathematics, computational statistics and computer science. It features the simulation, approximation, prediction, recognition, and classification of ecological issues. Intensive computation is one of the major stresses of the journal. The journal welcomes research articles, short communications, review articles, perspectives, and book reviews. The journal also supports the activities of the International Society of Computational Ecology. The topics to be covered by CES include, but are not limited to: •Computation intensive methods, numerical and optimization methods, differential and difference equation modeling and simulation, prediction, recognition, classification, statistical computation (Bayesian computing, randomization, bootstrapping, Monte Carlo techniques, stochastic process, etc.), agent-based modeling, individual-based modeling, artificial neural networks, knowledge based systems, machine learning, genetic algorithms, data exploration, network analysis and computation, databases, ecological modeling and computation using Geographical Information Systems, satellite imagery, and other computation intensive theories and methods. •Artificial ecosystems, artificial life, complexity of ecosystems and virtual reality. •The development, evaluation and validation of software and algorithms for computational ecology. The development and evaluation of apparatus, instruments and machines for ecological and environmental analysis, investigation and monitoring based on the software of computational ecology. •Methodological applications of computational ecology in the researches of ecology and environmental sciences. Authors can submit their works to the email box of this journal, ces@iaees.org. All manuscripts submitted to this journal must be previously unpublished and may not be considered for publication elsewhere at any time during review period of this journal. Authors are asked to read Author Guidelines before submitting manuscripts. In addition to free submissions from authors around the world, special issues are also accepted. The organizer of a special issue can collect submissions (yielded from a research project, a research group, etc.) on a specific research topic, or submissions of a scientific conference for publication of special issue.

Abstract: Road selection is a prerequisite to effective road network generalization. This article introduces a novel algorithm for road network selection in map generalization, which take four types of information into consideration: statistical, metric, topological, and thematic at three spatial scales: macro-scale which describes the general pattern of networks, mezzo-scale that handles relationships among road segments, and micro-scale that focuses on individual roads’ properties. A set of measures is selected to quantify these different types of information at various spatial levels. An algorithm is then developed with the extraction of these measures based on Voronoi diagrams and a perceptual grouping method called “stroke”. The selection process consists of three consecutive steps: measuring network information based on Voronoi partitioning and stroke generation, selecting roads based on information extraction in the first step with strokes as selection unit, and assessing selection results. The algorithm is further tested with a real-world dataset: road network map at 1:10,000 scale and its generalized version at 1:50,000 scale in Wuhan, China. The result reveals that the algorithm can produce reasonable selection results and thus has the potential to be adopted in road selection in map generalization. [Copyright &y& Elsevier]

Commercial Geographical Information Systems (GISs) incorporate line simplification functionality to generate generalised versions of maps. A commonly used algorithm is the classical Ramer-Douglas-Peucker (RDP) algorithm. Such an algorithm does not guarantee the preservation of topological consistency and therefore requires a posteriori checks to rectify unwanted intersections. To overcome this problem, Saalfeld proposed a modification based on the fact that, while generalising a polyline, conflicts can only occur with vertices of other polylines that lie within its convex hull. We have developed an improved version of Saalfeld's algorithm to detect possible self-intersections more efficiently. Although this improves the performance, especially when generalising very large data sets, the processing time is still not acceptable for real-time web mapping. Therefore, we have integrated our algorithm into a web-mapping system that relies on progressive transmission. In this paper we describe our improved algorithm and the experimental results. We also discuss the progressive transmission approach. [ABSTRACT FROM AUTHOR]