Abstract:

To explore the application of Ripley′s L function and the nearest neighbor hierarchical clustering ‘hot spots’ analysis in epidemiological spots map analysis. MethodsArcGIS90 was used for data management and analysis. The experimental epidemiology method in combination with Ripley′s L function analysis and nearest neighbor spatial clustering analysis compositely reflected the disease spatial heterogeneity and its dynamic characters. ResultsThe average radius of the host rat was 429 meters, of the strongest cluster district was 1443 meters, and of the biggest cluster district of host rat was 8626 meters. The numbers of the “hot spot” in different villages greatly differed. The undulation range of the first order was 38 and of the second order was 01. The average radius of Mus norvegicus was 486 meters, of the strongest cluster district was 2114 meters, and of the biggest cluster district was 9257 meters. Numbers of the “hot spot” in different villages greatly differed. The undulation山东大学学报(医学版)47卷3期

高杰，等.Ripley′s L 指数与最近邻空间热点分析在流行病学标点地图分析中的应用[HT5”SS〗
range of the first order was 112 and of the second order was 02. The average radius of Rattus norvegicus was 500 meters, of the strongest cluster district was 3271 meters and of the biggest cluster district was 9386 meters. Numbers of the“hot spot” in different villages greatly differed. The undulation range of the first order was 311, and there was no cluster hot spot of the second order. ConclusionIn the spatial epidemiological field, Ripley′s L index analysis in combination with the nearest neighbor spatial clustering analysis can provide statistical evidence for clarifying the spatial distribution of the host rat and controlling the HFRS infection source.

Key words: Ripley′s L function, Cluster analysis, Spatial epidemiology