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The degree of social vulnerability may vary according to the conditions and backgrounds of different locations, yet spatial clustering phenomena may exist when nearby spatial units exhibit similar characteristics. This study applied spatial autocorrelation statistics to analyze the spatial association of vulnerability among townships in Taiwan. The vulnerability was first assessed on the basis of a social vulnerability index that was constructed using Fuzzy Delphi and analytic hierarchy process methods. Subsequently, the corresponding indicator variables were applied to calculate standardized vulnerability assessment scores by using government data. According to the results of the vulnerability assessment in which T scores were normalized, the distribution of social vulnerabilities varied among the townships. The scores were further analyzed using spatial autocorrelation statistics for spatial clustering of vulnerability distribution. The LocalGstatistic identified 42 significant spatial association pockets, whereas the GlobalGstatistic indicated no spatial phenomenon of clustering. This phenomenon was verified and explained by applying Moran'sIstatistics to examine the homogeneity and heterogeneity of spatial associations. Although both statistics were originally designed to identify the existence of spatial clustering, they serve diverse purposes, and the results can be compared to obtain additional insights into the distribution patterns of social vulnerability. [ABSTRACT FROM AUTHOR]

Cluster analysis continues to be an important exploratory technique in scientific inquiry. It is used widely in geography, public health, criminology, ecology, and many other fields. Spatial cluster detection is driven by geographic information corresponding to the location of activities, requiring appropriate and meaningful treatment of space and spatial relationships combined with observed attributes of location and events. To date, this has meant utilizing dedicated measures and techniques to structure and account for distance, neighbors, contiguity, irregular geographic morphology, and so on. Unfortunately, all spatial clustering approaches, regardless of their theoretical underpinning, statistical foundation, or mathematical specification, have limitations in accuracy, sensitivity, and the computational effort required for identifying clusters. As a result, a major challenge in practice is determining which technique(s) will provide the most meaningful insights for a particular substantive issue or planning context. The purpose of this article is to provide an overview and evaluation of spatial clustering techniques, identifying the strengths and weaknesses of the most widely applied approaches. Results suggest that performance varies significantly in terms of accuracy, sensitivity, and computational expense. This is noteworthy because the misidentification of clusters, whether false positives or false negatives, has the potential to bias not only hypothesis formulation but also pragmatic facets of policy, process, and planning efforts within a region. [ABSTRACT FROM AUTHOR]

Abstract: The objective was to analyze the spatial food environment of the Washington, DC metropolitan area. Poorer food sources (convenience stores, fast food, and pharmacies) were the most spatially clustered. Co-Location results suggest strong positive autocorrelation among food environment establishment categories, and competitive effects between pharmacies and convenience stores. Grocery stores were present throughout the region. Changes in the retail food environment, a measure potentially important for diet-related health, occurred through the changing ratio of restaurants to fast food as well as the increased presence of convenience stores. [Copyright &y& Elsevier]

The creation of a spatial weights matrix by a procedure called AMOEBA, A Multidirectional Optimum Ecotope-Based Algorithm, is dependent on the use of a local spatial autocorrelation statistic. The result is (1) a vector that identifies those spatial units that are related and unrelated to contiguous spatial units and (2) a matrix of weights whose values are a function of the relationship of the ith spatial unit with all other nearby spatial units for which there is a spatial association. In addition, the AMOEBA procedure aids in the demarcation of clusters, called ecotopes, of related spatial units. Experimentation reveals that AMOEBA is an effective tool for the identification of clusters. A comparison with a scan statistic procedure (SaTScan) gives evidence of the value of AMOEBA. Total fertility rates in enumeration districts in Amman, Jordan, are used to show a real-world example of the use of AMOEBA for the construction of a spatial weights matrix and for the identification of clusters. Again, comparisons reveal the effectiveness of the AMOEBA procedure. [ABSTRACT FROM AUTHOR]

A new method for analysing spatial patterns was designed based on the variance of moving window averages (VMWA), which can be directly calculated in geographical information systems or a spreadsheet program (e.g. MS Excel). Different types of artificial data were generated to test the method. Regardless of data types, the VMWA method correctly determined the mean cluster sizes. This method was also employed to assess spatial patterns in historical plant disease survey data encompassing both airborne and soilborne diseases. The results obtained using the VMWA method were generally different from those obtained with Lloyd's index of patchiness and beta-binomial distribution methods, were in partial agreement with the results from spatial analysis by distance indices, and were highly consistent with the results from semivariogram and spatial autocorrelation analysis methods. Results demonstrated that the VMWA method can be applied to many types of data, including binomial diseased or healthy plant counts, incidence, severity, and number of diseased plants or pathogen propagules although directional and edge effects may limit its application. [ABSTRACT FROM AUTHOR]

Background: Spatial analysis has been vital in mapping the spread of diseases and assisting in policy making. Targeting diarrhea transmission hotspots is one of the potential strategies for reducing diarrhea cases. This study aimed to examine the spatial-temporal variations and to identify the modifiable determinants of diarrhea while controlling for the spatial dependence in the data.Methods: An ecological study on diarrhea data from DLHS-3 and NFHS- 4 in India. Moran's I and LISA were used to detect the spatial clustering of diarrhea cases and to test for clustering in the data. Spatial regression was used to identify the modifiable factors associated with the prevalence of diarrhea. The study comprised of the prevalence of diarrhea among the children below the age of five years (U-5 s) across different states in India. The determinants of diarrhea were obtained using spatial lag models. The software used were GeoDa 1.6.6 and QGIS 2.0.Results: The presence of spatial autocorrelation in DLHS-3 and NFHS-4 (Moron's I = 0.577 and 0.369 respectively) enforces the usage of geographical properties while modeling the diarrhea data. The geographic clustering of high-prevalence districts was observed in the state of UP consistently. The spatial pattern of the percentage of children with diarrhea was persistently associated with the household with a sanitation facility (%) (p = 0.023 and p = 0.011). Compared to the diarrhea cases in the period 2007-2008, no much reduction was observed in the period 2015-2016. The prevalence of diarrhea and percentage of household with sanitation were ranging between 0.1-33.8% and 1.3-96.1% in the period 2007-2008 and 0.6-29.1% and 10.4-92.0% in the period 2015-2016 respectively. The least and highest prevalence of diarrhea being consistently from Assam and UP respectively.Conclusion: Despite improvements in controlling spread of diarrheal disease, the burden remains high. Focus on widespread diarrheal disease control strategy by addressing the social determinants of health like basic sanitation is crucial to reduce the burden of diarrhea among U-5 s in India. The identification of hotspots will aid in the planning of control strategies for goal setting in the targeted regions. [ABSTRACT FROM AUTHOR]