顾及属性特征的城市设施热点识别方法

研究城市设施的热点分布对把握当前城市形态具有重要意义。传统的设施热点识别方法容易忽略设施的特征尺度且多以区域识别为主，缺少精准化提取设施热点的方法体系。针对上述问题，本文提出了一种顾及属性特征的设施热点识别方法，并以北京市住宅设施为例进行了试验分析。首先将设施的属性值作为权重，进行加权核密度估计生成密度值表面，利用极值点探测模型提取极值点；然后采用Getis-Ord G_i^*统计进行空间自相关分析，生成具有显著统计学意义的热点区域，筛选极值点得到热点。结果表明，该方法能够准确有效地识别设施热点并进行合理的等级划分，为城市设施空间布局研究提供多样化视角。

A method of urban facility hot spot recognition considering attribute characteristics

Studying the hot spot distribution of urban facilities is of great significance to grasp the current urban form. Traditional facility hot spot recognition methods tend to ignore the feature scale of facilities, focus on regional research, and lack a method system for accurately extracting facility hot spots. To solve the above problems, this paper proposed a method of hot spot recognition considering attribute characteristics,and takes the residential facilities in Beijing as an example. Firstly, the attribute values of facilities are used as weights to estimate the density value surface generated by weighted kernel density estimation, and the extreme points are extracted by using the extreme point detection model. Then use Getis-Ord G_i^* statistics for spatial autocorrelation analysis to generate statistically significant hot spots, and select extreme points to obtain hotspots. The experimental analysis shows that the method can accurately and effectively identify the hot spots of the facilities and make a reasonable classification, providing a diversified perspective for the research on the spatial layout of urban facilities.