VISUALIZATION OF POINT DISTRIBOTION IN URBAN AND REGIONAL GIS

在这项研究中，我们首先分析了一个模型，该模型代表了在点对象分布中感知的空间集群。研究认为，距离、聚集度和密度变化三个空间因素会引起集群感知。为了度量这些因素，提出了两个基于点对象密度的空间概念：局部密度和相对局部密度。这些密度被定义为地图中位置的函数，并指示点如何在该位置周围分布。将聚类感知表示为由相对局部密度得到的度量的概率函数，并将其似然表示用于模型估计。通过聚类感知实验验证了该模型的有效性，并对地图阅读器在聚类感知中的异质性进行了实证研究，分析了点状物体分布中空间差异的认知。空间变异是地理信息系统传达的基本空间概念之一，已被广泛应用于地理、生物学、流行病学等相关领域。然而，要传播这一概念，需要正确理解它的认知，并正确选择形象化的方法。因此，我们通过两个实验分析了点状物体分布中的空间变异认知，并建立了空间变异认知模型。最后，我们以宏语言程序的形式将上述模型应用于商业地理信息系统。预测模型的运行结果表明，该系统对于实现地理信息系统中点分布的可视化具有实用价值。

In this research we first analyzed a model representing spatial clusters perceived in the distribution of point objects. Three spatial factors, that is, proximity, clustering intensity, arid density change, were considered to cause cluster perception. To measure these factors, two spatial notions based on the density of point objects were proposed : local density and relative local density. These densities were defined as the functions of a location in maps, and indicate how points are distributed around the location. Cluster perception was represented as a probabilistic function of the measures derived from the relative local density, and its likelihood was shown for model estimation. An experiment of cluster perception indicates that the model was significantly valid, and some empirical findings related to the heterogeneity of map readers in cluster perception were shown.We then analyzed the cognition of spatial variance in the distribution of point objects. Spatial variance is one of the fundamental spatial concepts communicated by GIS, and has been widely used in its relevant fields such as geography, biology, and epidemiology. Communication of this concept, however, requires correct understanding of its cognition and proper choice of visualizing method. We hence employed two experiments to analyze the cognition of spatial variance in the distribution of point objects and built a model of the cognition of spatial variance. The obtained model was proved to be valid for representing the cognition of spatial variance.We finally implemented the above models into a commercial GIS as a Macro-language program. The prediction models worked successfully, and the system turned out to be useful for visualizing point distributions in GIS in a practical manner.