多尺度空间同位模式挖掘的点过程分解方法

doi:10.11947/j.AGCS.2022.20200548

测绘学报 ›› 2022, Vol. 51 ›› Issue (2): 258-268.doi: 10.11947/j.AGCS.2022.20200548

多尺度空间同位模式挖掘的点过程分解方法

邓敏, 谌恺祺, 石岩, 陈袁芳, 郭艺文

中南大学地球科学与信息物理学院地理信息系, 湖南长沙 410083

收稿日期:2020-11-11 修回日期:2021-03-25 发布日期:2022-02-28
通讯作者: 石岩 E-mail:csu_shiy@csu.edu.cn
作者简介:邓敏(1974-),男,教授,主要研究方向为时空大数据挖掘。E-mail:dengmin@csu.edu.cn
基金资助:
国家重点研发计划（2018YFB1004603）；国家自然科学基金（41730105；42071452）；中南大学中央高校基本科研业务费专项资金（2020zzts687）；湖南省自然科学基金（2020JJ4696）

Point process decomposition method for multi-scale spatial co-location pattern mining

DENG Min, CHEN Kaiqi, SHI Yan, CHEN Yuanfang, GUO Yiwen

Department of Geo-Informatics, School of Geosciences and Info-physics, Central South University, Changsha 410083, China

Received:2020-11-11 Revised:2021-03-25 Published:2022-02-28
Supported by:
The National Key Research and Development of China (No. 2018YFB1004603); The National Natural Science Foundation of China(Nos. 41730105;42071452); The Fundamental Research Funds for the Central Universities of Central South University(No. 2020zzts687);The Natural Science Foundation of Hunan Province, China(No. 2020JJ4696)

摘要/Abstract

摘要： 空间同位模式挖掘旨在发现多类地理要素或事件频繁互邻近形成的关联规则，对于理解复杂地理现象内在发生机理具有重要价值。针对现有基于关联规则的空间同位模式挖掘方法难以对地理数据特性（如多尺度性）进行有效建模的缺陷，本文提出了一种基于点过程建模与分解的多尺度空间同位模式挖掘方法。首先通过构建一个随机变量将多类要素实例分布数据表达为混合空间点过程，并引入一个非参统计指标对同位模式进行特征尺度判别；基于此，定义一种条件概率密度分布函数，利用点过程分解思想挖掘多尺度空间同位模式及其实例分布。试验分析结果表明本文方法可以准确挖掘空间同位模式在不同尺度的空间分布形态，并且有效降低了人为设定参数的主观性。

关键词: 多类型地理要素, 空间同位模式, 点过程分解, 空间多尺度

Abstract: Spatial co-location pattern mining aims to discover association rules formed by multiple types of geographic elements or events frequently adjacent to each other, which is the key for understanding the internal occurrence mechanism of complex geographic phenomena. Aiming at the shortcomings of existing spatial colocation pattern mining methods in the effective modeling of geographic data characteristics (such as the multi-scale characteristic), this paper proposes a multi-scale spatial co-location pattern mining method based on point process decomposition. Firstly, the spatial distribution of geographical elements with multiple types is modeled as a mixed spatial point process by constructing a random variable, and a non-parametric statistical index is introduced to discriminate the characteristic scale of the co-location patterns. On this basis, we define a conditional probability density distribution function to mine multi-scale spatial co-location patterns using points process decomposition. The experimental analysis results show that the proposed method can accurately depict the spatial distribution of spatial co-location patterns at different scales, and effectively reduce the subjectivity of artificially setting parameters.

Key words: multi-type geographical elements, spatial co-location patterns, point process decomposition, spatial multi-scales

中图分类号:

P227

邓敏, 谌恺祺, 石岩, 陈袁芳, 郭艺文. 多尺度空间同位模式挖掘的点过程分解方法[J]. 测绘学报, 2022, 51(2): 258-268.

DENG Min, CHEN Kaiqi, SHI Yan, CHEN Yuanfang, GUO Yiwen. Point process decomposition method for multi-scale spatial co-location pattern mining[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(2): 258-268.

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多尺度空间同位模式挖掘的点过程分解方法

Point process decomposition method for multi-scale spatial co-location pattern mining

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