Statistics for Spatial Data.

Generative modeling of brain maps with spatial autocorrelation

Hand, foot and mouth disease (HFMD) is one of the highest reported infectious diseases with several outbreaks across the world This study aimed at describing epidemiological characteristics, investigating spatio-temporal clustering changes, and identifying determinant factors in different clustering areas of HFMD Descriptive statistics was used to evaluate the epidemic characteristics of HFMD from 2009 to 2015 Spatial autocorrelation and spatio-temporal cluster analysis were used to explore the spatial temporal patterns An autologistic regression model was employed to explore determinants of HFMD clustering The incidence rates of HFMD ranged from 5431/10 million to 31806/10 million between 2009 and 2015 in Hunan Cases were mainly prevalent in children aged 5 years and even younger, with an average male-to-female sex ratio of 166, and two epidemic periods in each year Clustering areas gathered in the northern regions in 2009 and in the central regions from 2010 to 2012 They moved to central-southern regions in 2013 and 2014 and central-western regions in 2015 The significant risk factors of HFMD clusters were rainfall (OR = 2187), temperature (OR = 4329) and humidity (OR = 2070) The protect factor was wind speed (OR = 0258) The HFMD incidence from 2009 to 2015 in Hunan showed a new spatiotemporal clustering tendency, with the shifting trend of clustering areas toward south and west Meteorological factors showed a strong association with HFMD clustering, which may assist in predicting future spatial-temporal clusters

/pdf/spatio-temporal-clustering-analysis-and-its-determinants-of-4hesvl2kfi.pdf

Spatio-temporal clustering analysis and its determinants of hand, foot and mouth disease in Hunan, China, 2009-2015

Background 
Schistosomiasis remains a major public health problem in China. The major endemic areas are located in the lake and marshland regions of southern China, particularly in areas along the middle and low reach of the Yangtze River. Spatial analytical techniques are often used in epidemiology to identify spatial clusters in disease regions. This study assesses the spatial distribution of schistosomiasis and explores high-risk regions in Hubei Province, China to provide guidance on schistosomiasis control in marshland regions.


Methods 
In this study, spatial autocorrelation methodologies, including global Moran’s I and local Getis–Ord statistics, were utilized to describe and map spatial clusters and areas where human Schistosoma japonicum infection is prevalent at the county level in Hubei province. In addition, linear logistic regression model was used to determine the characteristics of spatial autocorrelation with time.


Results 
The infection rates of S. japonicum decreased from 2009 to 2013. The global autocorrelation analysis results on the infection rate of S. japonicum for five years showed statistical significance (Moran’s I > 0, P < 0.01), which suggested that spatial clusters were present in the distribution of S. japonicum infection from 2009 to 2013. Local autocorrelation analysis results showed that the number of highly aggregated areas ranged from eight to eleven within the five-year analysis period. The highly aggregated areas were mainly distributed in eight counties.


Conclusions 
The spatial distribution of human S. japonicum infections did not exhibit a temporal change at the county level in Hubei Province. The risk factors that influence human S. japonicum transmission may not have changed after achieving the national criterion of infection control. The findings indicated that spatial–temporal surveillance of S. japonicum transmission plays a significant role on schistosomiasis control. Timely and integrated prevention should be continued, especially in the Yangtze River Basin of Jianghan Plain area.

/pdf/spatial-analysis-of-schistosomiasis-in-hubei-province-china-1gzd32suli.pdf

Spatial analysis of Schistosomiasis in Hubei Province, China: a GIS-based analysis of Schistosomiasis from 2009 to 2013.

Spatial variations in extreme precipitation events make hydrological, climatological, social, environmental and agricultural effects on a country. This study presents the spatiotemporal autocorrelation analysis of extreme precipitation events over Iran using gridded data on daily precipitation for the period 1961–2010. The 95th percentile is considered as extreme precipitation factor. The spatial autocorrelation of extreme precipitation is examined by three commonly used spatial autocorrelation statistics, the G
 i
 statistic index, Moran’s I global index, and Local Moran’s I (LISA) index, at the 95 and 99% significant confidence level. The results showed a strong significant spatial autocorrelation for extreme precipitation events with the highest Moran’s I value in January. The positive significant autocorrelation of extreme precipitation is observed over the southern parts of the Caspian Sea and Zagros Mountains ranges, while the negative significant autocorrelation is observed over the central and eastern parts of country. In spring and summer the positive autocorrelation cores displace from the Zagros Mountains ranges to the northwestern and southeastern parts.

Spatial autocorrelation analysis of extreme precipitation in Iran

Summary. We propose a method to test the correlation of two random fields when they are both spatially autocorrelated. In this scenario, the assumption of independence for the pair of observations in the standard test does not hold, and as a result we reject in many cases where there is no effect (the precision of the null distribution is overestimated). Our method recovers the null distribution taking into account the autocorrelation. It uses Monte-Carlo methods, and focuses on permuting, and then smoothing and scaling one of the variables to destroy the correlation with the other, while maintaining at the same time the initial autocorrelation. With this simulation model, any test based on the independence of two (or more) random fields can be constructed. This research was motivated by a project in biodiversity and conservation in the Biology Department at Stanford University.

/pdf/assessing-the-significance-of-global-and-local-correlations-idwdol9bkr.pdf

Júlia Viladomat

Papers

Assessing the significance of global and local correlations under spatial autocorrelation: A nonparametric approach