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Analysis of spatial clusters when the phenomenon is constrained by a network space.

机译：当现象受网络空间约束时的空间聚类分析。

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摘要

The objective of this research is to develop a set of analytical procedures to detect local spatial clustering of spatial phenomena in a network space while enabling non-uniform distribution of risk population over the network to be explicitly taken into account and then to investigate the relationships between the detected clusters and the characteristics of the network space. The first stage of the analysis aims to detect local spatial clustering in the network space. L&barbelow;ocal i&barbelow;ndicators of n&barbelow;etwork-constrained c&barbelow;lusters&barbelow; (LINCS) are developed as tools of exploratory spatial data analysis. Discussed then is the treatment of the risk population that may not be distributed uniformly over the network. The second stage is to construct models that explain the detected clusters in relation to the characteristics of the network space. To carry out the second stage, this research utilizes inductive learning techniques, more specifically, decision tree induction algorithms and feedforward neural networks, together with traditional statistical techniques, such as regression and discrete choice models.; A spatial cluster in the network space can be defined in two different ways depending on the phenomenon of interest and the scale of data available. When the phenomenon has individual events as its basic element (e.g., vehicle crashes and disease cases) and those events are represented as points distributed over the network space, physical concentration of the points is referred to as a cluster. There is yet another type of spatial phenomenon that does not have countable events and is represented by attribute values associated with the network links (e.g., traffic volume and travel speed). In this case, high values of the attribute and their concentration are referred to as clusters since they imply a high incidence level of the phenomenon in those locations. Low values and their concentration can also be regarded as clusters of low values, implying a low incidence level. This research develops different analytical methods for the event-based and link-based clusters considering that their definitions and properties are quite different.; Performance of the proposed methodologies is illustrated via a case study on vehicle crash distribution in the Buffalo, NY area, in 1997.
机译：这项研究的目的是开发一套分析程序，以检测网络空间中空间现象的局部空间聚类，同时能够明确考虑网络上风险人群的不均匀分布，然后研究之间的关系。检测到的群集和网络空间的特征。分析的第一阶段旨在检测网络空间中的局部空间聚类。 L＆baral; ocal i＆barbelow;网络工作量受限的c＆barbelow;色泽＆barbelow; （LINCS）被开发为探索性空间数据分析的工具。然后讨论的是可能无法在网络上均匀分布的风险人群的处理方法。第二阶段是构建模型，该模型说明与网络空间特征有关的检测到的群集。为了进行第二阶段，本研究利用归纳学习技术，更具体地说，是决策树归纳算法和前馈神经网络，以及传统的统计技术，例如回归模型和离散选择模型。可以根据感兴趣的现象和可用数据的规模，以两种不同的方式定义网络空间中的空间集群。当现象以个别事件为基本要素（例如，车辆撞车和疾病病例）并且这些事件表示为分布在网络空间上的点时，这些点的物理集中称为簇。还有另一种类型的空间现象，它不具有可计数的事件，并由与网络链接关联的属性值（例如，交通量和行驶速度）表示。在这种情况下，属性的高值及其集中度被称为簇，因为它们暗示着这些位置的现象发生率很高。低值及其集中度也可以看作是低值的群集，这意味着发生率较低。考虑到它们的定义和属性完全不同，本研究针对基于事件的群集和基于链接的群集开发了不同的分析方法。通过1997年在纽约州布法罗市的一次车辆碰撞分布案例研究，说明了所提出方法的性能。

著录项

  • 作者

    Yamada, Ikuho.;

  • 作者单位

    State University of New York at Buffalo.;

  • 授予单位 State University of New York at Buffalo.;
  • 学科 Physical Geography.
  • 学位 Ph.D.
  • 年度 2004
  • 页码 219 p.
  • 总页数 219
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 自然地理学;
  • 关键词

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