STAMP: spatial–temporal analysis of moving polygons

Research questions regarding temporal change in spatial patterns are increasingly common in geographical analysis. In this research, we explore and extend an approach to the spatial–temporal analysis of polygons that are spatially distinct and experience discrete changes though time. We present five new movement events for describing spatial processes: displacement, convergence, divergence, fragmentation and concentration. Spatial–temporal measures of events for size and direction are presented for two time periods, and multiple time periods. Size change metrics are based on area overlaps and a modified cone-based model is used for calculating polygon directional relationships. Quantitative directional measures are used to develop application specific metrics, such as an estimation of the concentration parameter for a von Mises distribution, and the directional rate of spread. The utility of the STAMP methods are demonstrated by a case study on the spread of a wildfire in northwestern Montana.      

Toward accountable land use mapping: Using geocomputation to improve classification accuracy and reveal uncertainty

The classification of satellite imagery into land use/cover maps is a major challenge in the field of remote sensing. This research aimed at improving the classification accuracy while also revealing uncertain areas by employing a geocomputational approach. We computed numerous land use maps by considering both image texture and band ratio information in the classification procedure. For each land use class, those classifications with the highest class-accuracy were selected and combined into class-probability maps. By selecting the land use class with highest probability for each pixel, we created a hard classification. We stored the corresponding class probabilities in a separate map, indicating the spatial uncertainty in the hard classification. By combining the uncertainty map and the hard classification we created a probability-based land use map, containing spatial estimates of the uncertainty. The technique was tested for both ASTER and Landsat 5 satellite imagery of Gorizia, Italy, and resulted in a 34% and 31% increase, respectively, in the kappa coefficient of classification accuracy. We believe that geocomputational classification methods can be used generally to improve land use and land cover classification from imagery, and to help incorporate classification uncertainty into the resultant map themes.     

基于a gent的建模———地理计算的新发展

20世纪90年代后期,基于agent建模（Agent BasedModeling,ABM）的理论和技术不断发展,并且逐渐引起地理研究者的重视。ABM这种自下而上的模型策略是复杂适应系统理论、人工生命以及分布式人工智能技术的融合,目前已经成为继面向对象方法之后出现的又一种进行复杂系统分析与模拟的重要手段。ABM关注的是地理系统中大量异质性个体间的相互关系,强调进化和适应行为,主张非均衡的发展路径,我们必须为个别的决策者建立微观行为模型,并且通过观察大量的微观a gent的相互作用来研究宏观上整个地理系统的空间演化过程。将在简要回顾地理空间演化模型的基础上重点讨论ABM出现的理论背景、技术优势、研究进展以及模拟系统的开发问题。     

Computing environments for spatial data analysis

This paper describes the functionality and architecture of SpaceStat, the SpaceStat Extension for ArcView and the DynESDA Extension for ArcView. It compares the features of these packages to five other software implementations for spatial data analysis. Some ideas are formulated on generic requirements and future directions pertaining to computing environments for spatial data analysis.     

Geomorphometry on the surface of a triaxial ellipsoid: towards the solution of the problem

Existing algorithms of geomorphometry can be applied to digital elevation models (DEMs) given with plane square grids or spheroidal equal angular grids on the surface of an ellipsoid of revolution or a sphere. Computations on spheroidal equal angular grids are trivial for modelling of the Earth, Mars, the Moon, Venus, and Mercury. This is because: (a) forms of these celestial bodies can be described by an ellipsoid of revolution or a sphere and (b) for these surfaces, there are well-developed theory and algorithms to solve the inverse geodetic problem as well as to determine spheroidal trapezoidal areas. It is advisable to apply a triaxial ellipsoid for describing the forms of small moons and asteroids. However, there are no geomorphometric algorithms intended for such a surface. In this article, first, we formulate the problem of geomorphometric modelling on a triaxial ellipsoid surface. Then, we recall definitions and formulae for coordinate systems of a triaxial ellipsoid and their transformation. Next, we present analytical and computational solutions, which provide the basis for geomorphometric modelling on the surface of a triaxial ellipsoid. The Jacobi solution for the inverse geodetic problem has a fundamental mathematical character. The Bespalov solutions for determination of the length of meridian/parallel arcs and the spheroidal trapezoidal areas are computationally efficient. Finally, we describe easy-to-code algorithms for derivation of local and non-local morphometric variables from DEMs based on a spheroidal equal angular grid of a triaxial ellipsoid.     

地理计算——数量地理学的新发展

研究方法论的变革是推动地理科学发展的动力。90年代以来,作为地理科学方法论之一的数量地理学迅速向全新的地理计算学发展。凭借高性能计算软、硬件设备的支撑,一些以“整体性”、“大容量”资料所表征的地理学时、空演变问题的模拟、预测,逐渐得以实现。以国内外文献资料为基础,追溯了国外地理计算学的发展,讨论了地理计算的内涵和构成,分析了作为地理计算基础的高性能计算的基本特征,介绍了在几个地理学固有命题(空间互作用、人口、城市演变动力学)研究中凭借高性能计算所进行的成功探索,旨在为启动我国的地理计算学研究,提供一些有益的借鉴。