Cold Spot Areas Research Articles

Spatial Pattern Evolution of Coal Consumption at Provincial Level in China

Exploratory Spatial Data Analysis was used to analyze the evolvement of spatia1 pattern on coal consumption in China since 2002. General spatial autocorrelation of coal consumption in 31 provinces of China was analyzed by Morans I and Getis-Ord General G. Getis-0rd Gi* was used to test the local spatial dependence, identifying the spatial distribution of hot spots and cold spots. The results show that coal consumption per capita of 31 provinces in China exhibits an enhanced trend of spatial autocorrelation. The areas with similar level of coal consumption are clustered in space. The coal consumption activity can be affected by the neighborhoods and their own regions. Hotspot areas are mainly concentrated in North and Northeast China and continuously increase with time, coldspot areas are mainly concentrated in South China and constantly decrease by time. So government needs to consider the spatial interaction mechanism of coal consumption when establishing the energy management policy.

Spatial and temporal variation and hotspot detection of kala-azar disease in Vaishali district (Bihar), India

BackgroundAn improved understanding in transmission variation of kala-azar is fundamental to conduct surveillance and implementing disease prevention strategies. This study investigated the spatio-temporal patterns and hotspot detection for reporting kala-azar cases in Vaishali district based on spatial statistical analysis.MethodsEpidemiological data from the study area during 2007–2011 was used to examine the dynamic space-time pattern of kala-azar outbreaks, and all cases were geocoded at a village level. Spatial smoothing was applied to reduce random noise in the data. Inverse distance weighting (IDW) is used to interpolate and predict the pattern of VL cases distribution across the district. Moran’s I Index (Moran’s I) statistics was used to evaluate autocorrelation in kala-azar spatial distribution and test how villages were clustered or dispersed in space. Getis-Ord Gi*(d) was used to identify the hotspot and cold spot areas within the study site.ResultsMapping kala-azar cases or incidences reflects the spatial heterogeneity in the incidence rate of kala-azar affected villages in Vaishali district. Kala-azar incidence rate map showed most of the highest endemic villages were located in southern, eastern and northwestern part of the district; in the middle part of the district generally show the medium occurrence of VL. There was a significant positive spatial autocorrelation of kala-azar incidences for five consecutive years, with Moran’s I statistic ranging from 0.04-0.17 (P <0.01). The results revealed spatially clustered patterns with significant differences by village. The hotspots showed the spatial trend of kala-azar diffusion (P < 0.01).ConclusionsThe results pointed to the usefulness of spatial statistical approach to improve our understanding the spatio-temporal dynamics and control of kala-azar. The study also showed the north-western and southern part of Vaishali district is most likely endemic cluster region. To employ exact and geographically suitable risk-reduction programmes, apply of such spatial analysis tools should suit a vital constituent in epidemiology research and risk evaluation of kala-azar.