Global Autocorrelation Statistics Research Articles

Spatial-temporal characteristics of other infectious diarrhea in Jiangsu province, 2010-2017

Objective: To analyze the spatial-temporal characteristics of other infectious diarrhea (OID) in Jiangsu province from 2010 to 2017, and to provide evidence for setting up prevention and control programs of the disease. Methods: Data was from the Chinese Center for Disease Control and Prevention and the Statistics Bureau of Jiangsu province. Descriptive methods were used to illustrate the epidemiological characteristics of OID from 2010 to 2017. Global autocorrelation statistics method (Moran's I) was used to detect the spatial autocorrelation of OID, annually. Kulldorff M spatiotemporal scan statistics was used to analyze the spatial-temporal clustering of OID. ArcGIS 10.0 software, SaTScan 9.4 software and Excel 2017 software were also applied. Results: A total of 126 341 OID cases were found in Jiangsu province from 2010 to 2017 with an average annual incidence as 19.96/100 000. Children under five accounted for 55.08% (69 590/126 341) of the total cases. Obvious seasonal backshift with the increasing trends of the OID was noticed. There appeared four areas with high incidence of OID in the whole province, including Wuxi, Suzhou, Yancheng and Xuzhou. OID showed positive spatial autocorrelation at the county level with higher Moran's I from 0.19 to 0.33 (P<0.01). There appeared four positive clusters, all occurred in the high incidence period of OID, including the cluster area from the intersection areas of Changzhou and Wuxi (RR=7.61, LLR=2 605.80, P<0.01), respectively. Conclusion: With the increasing trends and the seasonal backshift of OID cases, pathogen surveillance programs set for those scattered children under five, in clustered regions and epidemic seasons should be strengthened.

An exploratory spatial analysis of overweight and obesity among children and adolescents in Shandong, China

ObjectiveIdentifying the spatial patterns of childhood overweight/obesity (OW/OB) can help to guide resource allocation for preventive intervention in China. This study aims to estimate rates of childhood OW/OB across counties...

Discover the Spatio-temporal Process of Typhoon Disaster Using Micro blog Data

When a disaster occurs, a large number of images and texts attached geographic information often flood the social network in the Internet quickly. All these information provide a new data source for timely awareness of disaster situations. However, due to the regional variation in the number of social media users and characteristics of information propagate in cyberspace, new problems arose in the pattern analysis of spatial point process represented by the check-in data, such as the correlation between check-in points density and disasters events density, the spatial relation between check-in points, the spatial heterogeneity of point pattern and associated influences. In this study, we took the No. 201614 Typhoon as an example and collected Sina Weibo data between September 14 and September 17, 2016 using keywords “Typhoon” and “Meranti”. We classified the Weibo texts using Support Vector Machine(SVM) algorithms, and constructed a disaster database containing relevant check-in information. In addition, considering the spatial heterogeneity of Weibo users, we proposed a weighted model based on user activity at the check-in points. Using Moran’s I of the global autocorrelation statistics, we compared the check-in data before and after adding weights and discovered obvious spatial autocorrelation of the check-in data in real geographical locations. We tested our model on Weibo data with keyword “rain” and “power failure”. The results show that series map generated by our model can reflect the typhoon disaster spatio-temporal process trends well.

Different effects of meteorological factors on hand, foot and mouth disease in various climates: a spatial panel data model analysis.

BackgroundMajor outbreaks of hand, foot and mouth disease (HFMD) have been reported in China since 2008, posing a great threat to the health of children. Although many studies have examined the effect of meteorological variables on the incidence of HFMD, the results have been inconsistent. This study aimed to quantify the relationship between meteorological factors and HFMD occurrence in different climates of mainland China using spatial panel data models.MethodsAll statistical analyses were carried out according to different climate types. We firstly conducted a descriptive analysis to summarize the epidemic characteristics of HFMD from May 2008 to November 2012 and then detected the spatial autocorrelation of HFMD using a global autocorrelation statistic (Moran’s I) in each month. Finally, the association between HFMD incidence and meteorological factors was explored by spatial panel data models.ResultsThe 353 regions were divided into 4 groups according to climate (G1: subtropical monsoon climate; G2: temperate monsoon climate; G3: temperate continental climate; G4: plateau mountain climate). The Moran’s I values were significant with high correlations in most months of group G1 and G2 and some months of group G3 and G4. This suggested the existence of a high spatial autocorrelation with HFMD. Spatial panel data models were more appropriate to describe the data than fixed effect models. The results showed that HFMD incidences were significantly associated with average atmospheric pressure (AAP), average temperature (AT), average vapor pressure (AVP), average relative humidity (ARH), monthly precipitation (MP), average wind speed (AWS), monthly total sunshine hours (MSH), mean temperature difference (MTD), rain day (RD) and average temperature distance (ATD), but the effect of meteorological factors might differ in various climate types.ConclusionsSpatial panel data models are useful and effective when longitudinal data are available and spatial autocorrelation exists. Our findings showed that meteorological factors were related to the occurrence of HFMD, which were also affected by climate type.

Detecting the association between meteorological factors and hand, foot, and mouth disease using spatial panel data models

The aim of this study was to quantify the relationship between meteorological factors and the occurrence of hand, foot, and mouth disease (HFMD) among children in Shandong Province, China, at a county level, using spatial panel data models. Descriptive analysis was applied to describe the epidemic characteristics of HFMD from January 2008 to December 2012, and then a global autocorrelation statistic (Moran's I) was used to detect the spatial autocorrelation of HFMD in each year. Finally, spatial panel data models were performed to explore the association between the incidence of HFMD and meteorological factors. Moran's I at the county level were high, from 0.30 to 0.45 (p < 0.001), indicating the existence of a high spatial autocorrelation on HFMD. Spatial panel data models are more appropriate to describe the data. Results showed that the incidences of HFMD in Shandong Province, China were significantly associated with average temperature, relative humidity, vapor pressure, and wind speed. Spatial panel data models are useful when longitudinal data with multiple units are available and spatial autocorrelation exists. The association found between HFMD and meteorological factors makes a contribution towards advancing knowledge with respect to the causality of HFMD and has policy implications for HFMD prevention and control.

Spatial analysis of the temperature trends in Serbia during the period 1961–2010

The spatial analysis of annual and seasonal temperature trends in Serbia during the period 1961–2010 was carried out using mean monthly data from 64 meteorological stations. Change year detection was achieved using cumulative sum charts. The magnitude of trends was derived from the slopes of linear trends using the least square method. The same formalism of least square method was used to assess the statistical significance of the determined trends. Maps of temperature trends were generated by applying a spatial regression method to visualize the detected tendencies. The obtained results indicate a negative temperature trend for the period before the change year except for winter and a more pronounced positive trend after the change year. Besides being more pronounced, the vast majority of trends after the change year were also clearly statistically significant. Our estimate of the average temperature trend over Serbia is in agreement with those obtained at the global and European scale. Calculated global autocorrelation statistics (Moran’s I) indicate an apparent random spatial pattern of temperature trends across the Serbia for both periods before and after the change year.

Detecting spatial-temporal clusters of HFMD from 2007 to 2011 in Shandong Province, China.

BackgroundHand, foot, and mouth disease (HFMD) has caused major public health concerns worldwide, and has become one of the leading causes of children death. China is the most serious epidemic area with a total of 3,419,149 reported cases just from 2008 to 2010, and its different geographic areas might have different spatial epidemiology characteristics at different spatial-temporal scale levels. We conducted spatial and spatial-temporal epidemiology analysis to HFMD at county level in Shandong Province, China.MethodsBased on the China National Disease Surveillance Reporting and Management System, the spatial-temporal database of HFMD from 2007 to 2011 was built. The global autocorrelation statistic (Moran’s I) was first used to detect the spatial autocorrelation of HFMD cases in each year. Purely Spatial scan statistics combined with Space-time scan statistic were used to detect epidemic clusters.ResultsThe annual average incidence rate was 93.70 per 100,000 in Shandong Province. Most HFMD cases (93.94%) were aged within 0–5 years old with an average male-to-female sex ratio 1.71, and the incidence seasonal peak was between April and July. The dominant pathogen was EV71 (47.35%), and CoxA16 (26.59%). HFMD had positive spatial autocorrelation at medium spatial scale level (county level) with higher Moran’s I from 0.31 to 0.62 (P<0.001). Seven spatial-temporal clusters were detected from 2007 to 2011 in the landscape of the whole Shandong, with EV71 or CoxA16 as the dominant pathogen for most hotspots areas.ConclusionsThe spatial-temporal clusters of HFMD wandered around the whole Shandong Province during 2007 to 2011, with EV71 or CoxA16 as the dominant pathogen. These findings suggested that a real-time spatial-temporal surveillance system should be established for identifying high incidence region and conducting prevention to HFMD timely.

Spatial pattern of recent rainfall trends in Serbia (1961–2009)

This paper examines a spatial pattern of annual, seasonal and monthly rainfall trends in Serbia. The study used data from 63 weather stations between the period of 1961–2009. The rainfall series was examined by applying the nonparametric method of the Mann–Kendall test and Sen’s method to determine the significance and magnitude of the trends. Significant trends have not been detected for the whole country at an annual scale. Seasonal trends at the confidence level of 97.5 %, however, indicate a slight decrease in winter (5 stations out of 63) and spring (7 stations out of 63) precipitation and an increase in autumn precipitation (10 stations out of 63). Results for monthly rainfall trends also generally showed a nonsignificant trend with the exception of a negative trend in May (6 stations out of 63) and positive trend for October (9 stations out of 63). Calculated global autocorrelation statistics (Moran’s I) indicate a random spatial pattern of rainfall trends on annual, seasonal and monthly timescales with exceptions for March, June and November. Overall, results suggest that only weak, mostly nonsignificant trends are present in Serbia in the period 1961–2009.

A Bayesian Poisson specification with a conditionally autoregressive prior and a residual Morans coefficient minimization criterion for quantitating leptokurtic distributions in regression-based multi-drug resistant tuberculosis treatment protocols

In this study, we employed an eigenfunction decomposition algorithm associated with a Moran’s coefficient to investigate district-level non-linearity in an empirical dataset of spatiotemporal-sampled MDR-TB parameter estimators sampled in San Juan de Lurigancho (SJL) Lima, Peru. The non-parametric technique attempted to remove the inherent autocorrelation in the model by introducing appropriate synthetic surrogate variants. We also constructed a robust Bayesian Poisson model to generate unbiased estimators for qualitatively assessing resistance to four commonly used drugs in TB treatment: isoniazid, rifampin, ethambutol, and streptomycin. Initially, data of residential addresses of individual patients with smear-positive MDR-TB were geocoded in ArcGIS. Next, the sampled data were matched automatically and interactively within the geodatabase. The MDR-TB data feature attributes were then calculated and digitally overlaid onto sub-meter resolution satellite data within a 1 km buffer of 31 georeferenced health centers using a 10 m2 grid-based algorithm. Global autocorrelation statistics were then generated by decomposing the sampled data into positive and negative spatial filter eigenvectors using the eigenfunction decomposition algorithm. Bayesian Poisson projections were then rendered employing normal priors for each of the sampled estimators. A Residual Moran’s coefficient (MC) minimization criterion was then applied to the clinical coefficients generated from the decomposition algorithm to detect any unaccounted latent autocorrelation error in the estimators. The model accounted for approximately 14% pseudo-replicated information and exhibited positive residual autocorrelation. Spatial statistics can elucidate the mechanics of MDR-TB transmission by prioritizing clinical covariates for identifying spatial distribution of high- risk populations and random heterogeneity in resistant strains. Key words: Multi-drug resistant tuberculosis, Bayesian Poisson, residual Moran’s coefficient (MC), minimization criterion, San Juan de Lurigancho (SJL) Lima, Peru.

A heteroskedastic error covariance matrix estimator using a first-order conditional autoregressive Markov simulation for deriving asympotical efficient estimates from ecological sampled Anopheles arabiensis aquatic habitat covariates

BackgroundAutoregressive regression coefficients for Anopheles arabiensis aquatic habitat models are usually assessed using global error techniques and are reported as error covariance matrices. A global statistic, however, will summarize error estimates from multiple habitat locations. This makes it difficult to identify where there are clusters of An. arabiensis aquatic habitats of acceptable prediction. It is therefore useful to conduct some form of spatial error analysis to detect clusters of An. arabiensis aquatic habitats based on uncertainty residuals from individual sampled habitats. In this research, a method of error estimation for spatial simulation models was demonstrated using autocorrelation indices and eigenfunction spatial filters to distinguish among the effects of parameter uncertainty on a stochastic simulation of ecological sampled Anopheles aquatic habitat covariates. A test for diagnostic checking error residuals in an An. arabiensis aquatic habitat model may enable intervention efforts targeting productive habitats clusters, based on larval/pupal productivity, by using the asymptotic distribution of parameter estimates from a residual autocovariance matrix. The models considered in this research extends a normal regression analysis previously considered in the literature.MethodsField and remote-sampled data were collected during July 2006 to December 2007 in Karima rice-village complex in Mwea, Kenya. SAS 9.1.4® was used to explore univariate statistics, correlations, distributions, and to generate global autocorrelation statistics from the ecological sampled datasets. A local autocorrelation index was also generated using spatial covariance parameters (i.e., Moran's Indices) in a SAS/GIS® database. The Moran's statistic was decomposed into orthogonal and uncorrelated synthetic map pattern components using a Poisson model with a gamma-distributed mean (i.e. negative binomial regression). The eigenfunction values from the spatial configuration matrices were then used to define expectations for prior distributions using a Markov chain Monte Carlo (MCMC) algorithm. A set of posterior means were defined in WinBUGS 1.4.3®. After the model had converged, samples from the conditional distributions were used to summarize the posterior distribution of the parameters. Thereafter, a spatial residual trend analyses was used to evaluate variance uncertainty propagation in the model using an autocovariance error matrix.ResultsBy specifying coefficient estimates in a Bayesian framework, the covariate number of tillers was found to be a significant predictor, positively associated with An. arabiensis aquatic habitats. The spatial filter models accounted for approximately 19% redundant locational information in the ecological sampled An. arabiensis aquatic habitat data. In the residual error estimation model there was significant positive autocorrelation (i.e., clustering of habitats in geographic space) based on log-transformed larval/pupal data and the sampled covariate depth of habitat.ConclusionAn autocorrelation error covariance matrix and a spatial filter analyses can prioritize mosquito control strategies by providing a computationally attractive and feasible description of variance uncertainty estimates for correctly identifying clusters of prolific An. arabiensis aquatic habitats based on larval/pupal productivity.

Describing Anopheles arabiensis aquatic habitats in two riceland agro-ecosystems in Mwea, Kenya using a negative binomial regression model with a non-homogenous mean

This research illustrates a geostatistical approach for modeling the spatial distribution patterns of Anopheles arabiensis Patton (Patton) aquatic habitats in two riceland environments. QuickBird 0.61m data, encompassing the visible bands and the near-infra-red (NIR) band, were selected to synthesize images of An. arabiensis aquatic habitats. These bands and field sampled data were used to determine ecological parameters associated with riceland larval habitat development. SAS ® was used to calculate univariate statistics, correlations and Poisson regression models. Global autocorrelation statistics were generated in ArcGIS ® from georeferenced Anopheles aquatic habitats in the study sites. The geographic distribution of Anopheles gambiae s.l. aquatic habitats in the study sites exhibited weak positive autocorrelation; similar numbers of log-larval count habitats tend to clustered in space. Individual rice land habitat data were further evaluated in terms of their covariations with spatial autocorrelation, by regressing them on candidate spatial filter eigenvectors. Each eigenvector generated from a geographically weighted matrix, for both study sites, revealed a distinctive spatial pattern. The spatial autocorrelation components suggest the presence of roughly 14–30% redundant information in the aquatic habitat larval count samples. Synthetic map pattern variables furnish a method of capturing spatial dependency effects in the mean response term in regression analyses of rice land An. arabiensis aquatic habitat data.

Decomposing Malaria Mosquito Aquatic Habitat Data into Spatial Autocorrelation Eigenvectors in a SAS/GIS® Module

AbstractWe present a geostatistical approach that accounts for spatial autocorrelation in malaria mosquito aquatic habitats in two East African urban environments. QuickBird 0.61 m data, encompassing visible bands and the near infra‐red (NIR) bands, were selected to synthesize images of Anopheles gambiae s.l. aquatic habitats in Kisumu and Malindi, Kenya. Field sampled data of An. gambiae s.l. aquatic habitats were used to determine which ecological covariates were associated with An. gambiae s.l. larval habitat development. A SAS/GIS® spatial database was used to calculate univariate statistics, correlations and perform Poisson regression analyses on the An. gambiae s.l. aquatic habitat datasets. Semivariograms and global autocorrelation statistics were generated in ArcGIS®. The spatially dependent models indicate the distribution of An. gambiae s.l. aquatic habitats exhibits weak positive autocorrelation in both study sites, with aquatic habitats of similar log‐larval counts tending to cluster in space. Individual anopheline habitats were further evaluated in terms of their covariations with spatial autocorrelation by regressing them on candidate spatial filter eigenvectors. This involved the decomposition of Moran's I statistic into orthogonal and uncorrelated map pattern components using a negative binomial regression. The procedure generated synthetic map patterns of latent spatial correlation representing the geographic configuration of An. gambiae s.l. aquatic habitat locations in each study site. The Gaussian approximation spatial filter models accounted for approximately 13% to 32% redundant locational information in the ecological datasets. Spatial statistics generated in a SAS/GIS® module can capture spatial dependency effects on the mean response term of a Poisson regression analysis of field and remotely sampled An. gambiae s.l. aquatic habitat data.

Use of Spatial Autocorrelation to Investigate Clustering of Health Deprivation

SM2-PD-02 Introduction: The susceptibility of people to develop ill health can be influenced by characteristics of their neighborhood. Moreover, populations in socioeconomic deprived areas are considered more susceptible to poor health compared with those in affluent areas. Here, we aimed to use methods of spatial statistics and apply them in the context of environmental epidemiology. Specifically, we aimed to test for spatial autocorrelation to investigate clustering of indicators of health, in the Northeast of England. Methods: We abstracted data on the English Health Deprivation and Disability Index (HDDI), which comprises of 4 measures of ill health. These were downloaded for the smallest possible spatial unit comprising of a mean population of 1500, called Lower Super Output Areas (LSOA). We used Geographical Information Systems to map HDDI for the Northeast of England, as well as for each county and unitary authority (UA) in the region. We used the global Moran's I statistic to investigate spatial autocorrelation. Global autocorrelation statistics provide a single measure for clustering (positive spatial autocorrelation) or dispersion (negative spatial autocorrelation) of an attribute, in a spatial unit. A Moran's Index value near +1.0 indicates clustering, whereas an index value near −1.0 indicates dispersion. Results: We detected positive spatial autocorrelation in all spatial units assessed. A high positive z-score for all tests indicated that the observed clustering was statistically significant (P < 0.01). The strongest positive spatial autocorrelation was detected in counties Durham (Moran's I = 0.68, z = 10.4) and Northumberland (Moran's I = 0.68, z = 8.5). Clustering of health deprived areas was apparent in the eastern part of both counties. Weak positive spatial autocorrelation was found in Darlington UA (Moran's I = 0.18, z = 3.8) and Hartlepool UA (Moran's I = 0.24, z = 3.2). Moderate positive spatial autocorrelation was detected for the Northeast of England (Moran's I = 0.34, z = 22.4) and the other spatial units. Discussion and Conclusions: The global Moran's test was successful in identifying spatial autocorrelation of health indicators, separating spatial units with high, moderate, and weak clustering. In those areas where county and UA level clustering was moderate or weak, the local Moran's I test may be able to detect more localized clustering in smaller spatial units. We conclude that susceptibility to develop ill health is strongly clustered at county level in some counties but not in others.