Spatial Outliers Research Articles

Generalized sinh-Gaussian random fields

The aim of this contribution is to propose new flexible spatial models based on a class of generalized sinh transformations. Our proposal greatly extends the Gaussian random field mainly in terms of asymmetry, heavy tail behavior, and bimodality. We demonstrate that we obtain a valid random field. We ensure that it is mean square continuous. We derive explicit expressions for the moments, the covariance function, the skewness, and the kurtosis of the random fields. In comparison with the available skew-Gaussian models, the proposed models could capture a greater amount of skewness and also accommodate clustered spatial outliers. The proposed models are computationally very tractable within the Bayesian framework here adopted. They are compared with the Gaussian and skew-Gaussian spatial models through simulation studies and an application to the spatial prediction of weekly rainfall near Darwin, Australia. The result suggests that the predictive performance in terms of different criteria under our method tends to be smaller than those obtained from the competing ones.

Discovering Sericulture Clusters Through the Use of Local Indicators of Spatial Association (LISA)

The aim of this study is to analyze the spatial pattern of sericulture in Türkiye with spatial analysis approaches, taking into account the data of 81 provinces, by creating a database based on Geographic Information Systems (GIS). This study employed data from the TURKSTAT, which included the amount of fresh cocoon produced and the silkworm egg boxes opened, between 2004-2022. In order to evaluate the relationship between the cocoon yield in each province and the cocoon yield in neighboring provinces, Moran's I measurements were made for the relevant time periods, and each period's Moran's I scatter plot was created. The Local Spatial Association Indicator (LISA) was used to assess the spatial structure of fresh cocoon yield. There was determined spatial clustering and a positive spatial association for cocoon yield, and all of the Moran's I values were found to be more than 0. The LISA maps were demonstrated the distinct local clustering patterns in the cocoon yield. More local clusters were identified (High-High or Low-Low) compared to local spatial outliers (High-Low or Low-High). High-High clusters were generally identified in the Marmara Region. Important implications have been obtained in terms of sustainability, such as how to take advantage of hotspot spillover effects and prioritize hotspot locations when allocating resources. As a result, spatial relations should be taken into account in the policies to be developed for sericulture and more activities should be carried out on sericulture.

Fostering green technology innovation with green credit: Evidence from spatial quantile approach

Green technology is an important path to achieve low-carbon development, and green credit provides financial support for green technology innovation. Existing literature often fails to pay attention to the important role of spatial factors and outliers in green technology innovation. Based on 2005–2022 provincial panel data in China, this paper uses a novel spatial lag quantile model to explore the impact of green credit on green technology innovation and its impact mechanism. The empirical results indicate that green credit exerts a greater positive impact on green technology in the provinces with moderate technical level. Technological innovation has the characteristic of spatial spillover. The spatial spillover of technology contributes more to green technology innovation in the provinces with low- and medium-tech level. This result has been proven even after robustness test of the changes in sample units, and the replacement of core variable values. Further mechanistic analysis demonstrates that banking market structure and enterprise R&D investment both produces the greater impact on green technology innovation in the low-tech provinces such as Qinghai, Ningxia, and Hainan. This article provides policy reference for local governments to formulate green finance policies and promote carbon neutrality strategies.

Spatial Functional Outlier Detection in Multivariate Spatial Functional Data

Multivariate spatial functional data consists of multiple functions of time-dependent attributes observed at each spatial point. This study focuses on detecting spatial outliers in spatial functional data. Firstly, we develop a new method called Mahalanobis Distance Spatial Outlier (MDSO) to detect functional outliers in the data. The method introduces the multivariate functional Mahalanobis semi-distance and multivariate pairwise functional Mahalanobis semi-distance metrics based on the multivariate functional principal components analysis to calculate the dissimilarity between functions at each spatial point. Via simulation, we show that MDSO performs better than the other competing methods. Secondly, MDSO has been extended to detect spatial functional outliers as well. The functional outliers can now be categorized as global or/and local functional outliers. The appropriate number of neighbors and the cut-off point for the degree of isolation are determined via simulation. Finally, we demonstrate the application of the MDSO on a water quality data set obtained from Sungai Klang basin in Malaysia. The results can be used to support the authority in making better decisions on the management of the river basin or other spatial data with time-independent attributes.

Dengue fever mapping in Bangladesh: A spatial modeling approach

AbstractBackgroundEpidemics of the dengue virus can trigger widespread morbidity and mortality along with no specific treatment. Examining the spatial autocorrelation and variability of dengue prevalence throughout Bangladesh's 64 districts was the focus of this study.MethodsThe spatial autocorrelation is evaluated with the help of Moran and Geary . Local Moran was used to detect hotspots and cold spots, whereas local Getis Ord G was used to identify only spatial hotspots. The spatial heterogeneity has been detected using various conventional and spatial models, including the Poisson‐Gamma model, the Poisson‐Lognormal Model, the Conditional Autoregressive (CAR) model, the Convolution model, and the BYM2 model, respectively. These models are implemented using Gibbs sampling and other Bayesian hierarchical approaches to analyze the posterior distribution effectively, enabling inference within a Bayesian context.ResultsThe study's findings show that Moran and Geary analysis provides a substantial clustering pattern of positive spatial autocorrelation of dengue fever (DF) rates between surrounding districts at a 90% confidence interval. The Local Indicators of Spatial Autocorrelation cluster mapped spatial clusters and outliers based on prevalence rates, while the local Getis‐Ord G displayed a thorough breakdown of high or low rates, omitting outliers. Although Chattogram had the most dengue cases (15,752), Khulna district had a higher prevalence rate (133.636) than Chattogram (104.796). The BYM2 model, determined to be well‐fitted based on the lowest Deviance Information Criterion value (527.340), explains a significant association between spatial heterogeneity and prevalence rates.ConclusionThis research pinpoints the district with the highest prevalence rate for dengue and the neighboring districts that also have high risk, allowing government agencies and communities to take the necessary precautions to mollify the risk effect of DF.

Outlier detection in spatial error models using modified thresholding-based iterative procedure for outlier detection approach

BackgroundOutliers, data points that significantly deviate from the norm, can have a substantial impact on statistical inference and provide valuable insights in data analysis. Multiple methods have been developed for outlier detection, however, almost all available approaches fail to consider the spatial dependence and heterogeneity in spatial data. Spatial data has diverse formats and semantics, requiring specialized outlier detection methodology to handle these unique properties. For now, there is limited research exists on robust spatial outlier detection methods designed specifically under the spatial error model (SEM) structure.MethodWe propose the Spatial-Θ-Iterative Procedure for Outlier Detection (Spatial-Θ-IPOD), which utilizes a mean-shift vector to identify outliers within the SEM. Our method enables an effective detection of spatial outliers while also providing robust coefficient estimates. To assess the performance of our approach, we conducted extensive simulations and applied it to a real-world empirical study using life expectancy data from multiple countries.ResultsSimulation results showed that the masking and JD (Joint Detection) indicators of our Spatial-Θ-IPOD method outperformed several commonly used methods, even in high-dimensional scenarios, demonstrating stable performance. Conversely, the Θ-IPOD method proved to be ineffective in detecting outliers when spatial correlation was present. Moreover, our model successfully provided reliable coefficient estimation alongside outlier detection. The proposed method consistently outperformed other models (both robust and non-robust) in most cases. In the empirical study, our proposed model successfully detected outliers and provided valuable insights in the modeling process.ConclusionsOur proposed Spatial-Θ-IPOD offers an effective solution for detecting spatial outliers for SEM while providing robust coefficient estimates. Notably, our approach showcases its relative superiority even in the presence of high leverage points. By successfully identifying outliers, our method enhances the overall understanding of the data and provides valuable insights for further analysis.

Basin scale monitoring of microplastics and phthalates in sediments from the Persian Gulf and the Gulf of Makran using GIS-based algorithms: Insights towards spatial variation and potential risk assessment

Information on sedimentary microplastics and phthalates has been restricted to the coastal regions of the Persian Gulf and the Gulf of Makran. Our basin-wide study monitored their levels, spatial behaviors, and potential risks using GIS-based techniques. Microplastics and phthalates ranged from 5 to 75 particles/kg d.w and 0.004–1.219 μg g−1 d.w, respectively. Microplastics were in the size category of 100 μm to 3 mm, and black microfibers (< 1 mm) and high-density polymers were dominant. The total number of microplastics was between 356.333 × 1012 and 469.075 × 1012 particles in the surface sediments of the studied regions (confidence interval = 99 %). Diethylhexyl phthalate (DEHP) and Di-isobutyl phthalate contributed 88 % of detected phthalates. Significant correlations among microplastic abundance, total phthalates, and DEHP were distinguished (p < 0.05). Overall, the findings reiterated the widespread presence of microplastics and a potential link between phthalates and microplastics. Semi-variogram, cluster Voronoi polygons, and Trend analysis identified spatial outliers and major deposition sites of microplastics and phthalates and consequently outlined the localities where upcoming studies should be concentrated. A hotspot of potential risks was marked using Fuzzy logic and GIS-based algorithms in the Sea of Makran, covering an area equal to 342. 99 km2.

Data-driven evolution of water quality models: An in-depth investigation of innovative outlier detection approaches-A case study of Irish Water Quality Index (IEWQI) model

Recently, there has been a significant advancement in the water quality index (WQI) models utilizing data-driven approaches, especially those integrating machine learning and artificial intelligence (ML/AI) technology. Although, several recent studies have revealed that the data-driven model has produced inconsistent results due to the data outliers, which significantly impact model reliability and accuracy. The present study was carried out to assess the impact of data outliers on a recently developed Irish Water Quality Index (IEWQI) model, which relies on data-driven techniques. To the author's best knowledge, there has been no systematic framework for evaluating the influence of data outliers on such models. For the purposes of assessing the outlier impact of the data outliers on the water quality (WQ) model, this was the first initiative in research to introduce a comprehensive approach that combines machine learning with advanced statistical techniques. The proposed framework was implemented in Cork Harbour, Ireland, to evaluate the IEWQI model's sensitivity to outliers in input indicators to assess the water quality. In order to detect the data outlier, the study utilized two widely used ML techniques, including Isolation Forest (IF) and Kernel Density Estimation (KDE) within the dataset, for predicting WQ with and without these outliers. For validating the ML results, the study used five commonly used statistical measures.The performance metric (R2) indicates that the model performance improved slightly (R2 increased from 0.92 to 0.95) in predicting WQ after removing the data outlier from the input. But the IEWQI scores revealed that there were no statistically significant differences among the actual values, predictions with outliers, and predictions without outliers, with a 95 % confidence interval at p < 0.05. The results of model uncertainty also revealed that the model contributed <1 % uncertainty to the final assessment results for using both datasets (with and without outliers). In addition, all statistical measures indicated that the ML techniques provided reliable results that can be utilized for detecting outliers and their impacts on the IEWQI model. The findings of the research reveal that although the data outliers had no significant impact on the IEWQI model architecture, they had moderate impacts on the rating schemes' of the model. This finding indicated that detecting the data outliers could improve the accuracy of the IEWQI model in rating WQ as well as be helpful in mitigating the model eclipsing problem. In addition, the results of the research provide evidence of how the data outliers influenced the data-driven model in predicting WQ and reliability, particularly since the study confirmed that the IEWQI model's could be effective for accurately rating WQ despite the presence of the data outliers in the input. It could occur due to the spatio-temporal variability inherent in WQ indicators.However, the research assesses the influence of data input outliers on the IEWQI model and underscores important areas for future investigation. These areas include expanding temporal analysis using multi-year data, examining spatial outlier patterns, and evaluating detection methods. Moreover, it is essential to explore the real-world impacts of revised rating categories, involve stakeholders in outlier management, and fine-tune model parameters. Analysing model performance across varying temporal and spatial resolutions and incorporating additional environmental data can significantly enhance the accuracy of WQ assessment. Consequently, this study offers valuable insights to strengthen the IEWQI model's robustness and provides avenues for enhancing its utility in broader WQ assessment applications.Moreover, the study successfully adopted the framework for evaluating how data input outliers affect the data-driven model, such as the IEWQI model. The current study has been carried out in Cork Harbour for only a single year of WQ data. The framework should be tested across various domains for evaluating the response of the IEWQI model's in terms of the spatio-temporal resolution of the domain. Nevertheless, the study recommended that future research should be conducted to adjust or revise the IEWQI model's rating schemes and investigate the practical effects of data outliers on updated rating categories. However, the study provides potential recommendations for enhancing the IEWQI model's adaptability and reveals its effectiveness in expanding its applicability in more general WQ assessment scenarios.

Confounded Local Inference: Extending Local Moran Statistics to Handle Confounding

Local statistical analysis has long been of interest to social and environmental scientists who analyze geographic data. Research into local spatial statistics experienced a step-change in the mid-1990s, which provided a large class of local statistical methods and models. The local Moran statistic is one commonly used local indicator of spatial association, able to detect both areas of similarity and observations that are very dissimilar from their surroundings. From this, many further local statistics have been developed to characterize spatial clusters and outliers. These statistics have seen limited adoption because they do not sufficiently model the relationships involved in confounded spatial data, where the analyst seeks to understand the local spatial structure of a given outcome variable that is influenced by one or more additional factors. Recent innovations used to do joint multivariate local analysis also do not model this kind of conditional local structure in data. This article provides tools to rigorously characterize confounded local inference and a new and different class of multivariate conditional local Moran statistics that can account for confounding. To do this, we return to the Moran scatterplot as the critical tool for local Moran-style covariance statistics. Extending this concept, a new method is available directly from a “Moran-form” multiple regression. We show the empirical and theoretical properties of this statistic, show how some existing heuristic approaches arise naturally from this framework, and show how the use of conditional inference can change interpretations in an empirical analysis of rent and housing stock in a rapidly changing neighborhood.

A raster-based spatial clustering method with robustness to spatial outliers

Spatial clustering is an essential method for the comprehensive understanding of a region. Spatial clustering divides all spatial units into different clusters. The attributes of each cluster of the spatial units are similar, and simultaneously, they are as continuous as spatially possible. In spatial clustering, the handling of spatial outliers is important. It is necessary to improve spatial integration so that each cluster is connected as much as possible, while protecting spatial outliers can help avoid the excessive masking of attribute differences This paper proposes a new spatial clustering method for raster data robust to spatial outliers. The method employs a sliding window to scan the entire region to determine spatial outliers. Additionally, a mechanism based on the range and standard deviation of the spatial units in each window is designed to judge whether the spatial integration should be further improved or the spatial outliers should be protected. To demonstrate the usefulness of the proposed method, we applied it in two case study areas, namely, Changping District and Pinggu District in Beijing. The results show that the proposed method can retain the spatial outliers while ensuring that the clusters are roughly contiguous. This method can be used as a simple but powerful and easy-to-interpret alternative to existing geographical spatial clustering methods.

Safety: A spatial and feature mixed outlier detection method for big trajectory data

Trajectories, as sequential data records generated by continuously collecting sample points from positioning sensors, have the capability to effectively depict the motion patterns of mobile entities. The primary objective of trajectory outlier detection is to identify entities that exhibit aberrant behavior. However, outlier detection for massive trajectories still faces challenges in terms of computational power and algorithms. Specifically: 1. (Computational Power): Trajectory outlier detection, as a computationally intensive application, has encountered significant obstacles in traditional single-machine computing environments. 2. (Algorithmic Challenges): The accuracy of trajectory outlier detection is influenced by various factors. For instance, vehicle trajectories are constrained by the road network structure, which can lead to spatial similarities between abnormal and normal trajectories. To address the above issues, we propose a spatial and feature mixed outlier detection method for big trajectory data, named Safety. Our approach tackles the Computational Power challenge by designing data structures that facilitate trajectory load distribution and result correctness assurance within a distributed parallel processing engine, making it compatible with parallel processing architectures. Regarding Algorithmic Challenges, Safety utilizes feature-based detection within clusters to identify finer-grained feature anomalies, thus improving result accuracy. Extensive experiments on Beijing and Chengdu datasets show that the proposed Safety consistently outperforms existing baselines. In the distributed environment, Safety reduces latency by 29.3% and 27.94% on these two datasets, respectively. In terms of F-measure, Safety has achieved an improvement of 29.55% compared to the baseline on the Beijing dataset.

Identification of spatial clusters and spatial outliers of HIV/AIDS using local Moran’s I statistic

Background: Human immunodeficiency virus (HIV) remains a major global public health issue, having claimed 40.4 million lives so far with ongoing transmission in all countries globally; with some countries reporting increasing trends in new infections when previously on the decline. This study aims to use the local Moran’s I statistic to investigate the spatial clusters and spatial outliers of HIV/AIDS cases 63 provinces/cities in Vietnam in 2017. Methods: The local Moran’s I statistic is first employed to measure spatial auto-correlation between the number of HIV/AIDS cases in each province/city, and then identify the spatial clusters and spatial outliers of HIV/AIDS cases. Spatial distribution of HIV/AIDS clusters and outliers will be mapped with the help of a GIS. Finally, the main findings will be discussed and summarised. Results: High numbers of HIV/AIDS cases were mainly concentrated in the provinces of the north central region, Da Nang and provinces in the south of Vietnam. Low numbers of HIV/AIDS cases were detected in the northeastern provinces, central and southeastern provinces of Vietnam. Specifically, one high-high cluster and six low-low spatial clusters, and four low-high and high-low spatial outliers of HIV/AIDS cases were successfully detected. Whereas, the only high-high spatial cluster was discovered in Binh Duong province with 3598 HIV/AIDS cases. Conclusions: Local Moran’s I statistic can help to effectively identify spatial clusters and spatial outlier of HIV/AIDS. Findings in this study provide an insight into how to use spatial statistics to study the spread of HIV/AIDS.

Assertion of Soil Data Consistency by Detecting and Removing Spatial Outliers Using Iterative Techniques for Precision Agriculture

Assertion of Soil Data Consistency by Detecting and Removing Spatial Outliers Using Iterative Techniques for Precision Agriculture

Exploring the spatial pattern of animal bites in Iran (2021–2022)

ObjectiveKnowledge of the spatial pattern of animal bites can be helpful for targeted resource allocation and to develop and deliver effective intervention programs. The aim of this study was to explore the spatial pattern of the animal bites in Iran during 2021–2022. MethodsAnimal bite cases from all provinces and counties in Iran were obtained from a nationally based registry. Global Moran's I was applied to check spatial autocorrelation. The spatially adjusted standardized incidence ratios (SIRs) were estimated using the Besag, York, and Mollie (BYM) model. Spatial clusters were identified by local indicators of spatial association (LISA) and purely spatial scan statistic. ResultsA total of 260,470 animal bites were registered during the study period. There was a positive spatial autocorrelation (global Moran's I=0.27, p-value=0.001). The majority of SIRs greater than 1.00 was found in counties in the northern belt of Iran (e.g., observed greater than expected animal bites). LISA found that approximately 7% of counties in the north and northeast, 18% in the west and south, and 3% in the central part of Iran were significant hot spots, cold spots, and spatial outliers (p-value≤0.05). Spatial scan statistic detected primary hot spot cluster in the counties in the Mazandaran and Alborz provinces (Relative Risk=2.56, p-value<0.001), while primary cold spot cluster involved counties in Kurdistan and Kermanshah province (0.37, <0.001). ConclusionAnimal bites were unevenly distributed in Iran. Further prevention and control programs as well as appropriately resource allocation are needed in order to reduce the observed animal bites spatial disparity.

Community genetics of the key plant species Carex gayana in high Andean wetlands and conservation implications

High Andean wetlands are crucial for biodiversity conservation, climate change mitigation and adaptation, and the well-being of local communities in the Andes. Although community genetics provides an evolutionary viewpoint on ecological processes and allows for potential new understandings of the mechanisms that influence biodiversity and ecosystem function and their response to environmental changes, no research has yet explored the ecological effects of genetic variation in high Andean species. Here, we searched for single-nucleotide-polymorphism (SNP) loci of Carex gayana, an abundant plant species that fulfills crucial ecological functions in high Andean wetlands, that are associated with plant and aquatic macroinvertebrate diversity, and then investigated the spatial distribution of their alleles across a latitudinal gradient of ca. 600 km in Chile's Norte Chico. To identify SNP loci of C. gayana associated with community diversity, we analyzed a genomic dataset of SNP loci obtained through genotyping-by-sequencing of 158 individuals from 17 Andean wetlands using a genome scan approach. We found 30 structure and spatial outlier loci in C. gayana that were also linked with community parameters. We performed spatially-based causal modelling to infer the most probable origin of the observed relationships between SNP allele frequencies and community diversity parameters, resulting in 11 SNP candidates with possible extended effects on communities. The prevalence of alleles linked to greater taxonomic diversity varied greatly across wetlands. Some wetlands exhibited high frequencies of these alleles, while others had very low frequencies. These findings suggest that certain sites may be less resilient to global changes, while others could potentially serve as a source for restoration initiatives. Functional analysis of candidate genes using reference genomes of closely related Carex species allowed the identification of genes involved mainly in plant metabolism, responses to biotic and abiotic stress, defense mechanisms, growth and development, with functions commonly found in high-altitude plant species, and suggests that extended genetic effects of C. gayana genes to high Andean communities could be related to flower/reproductive development, defense mechanisms against herbivore insects and stress response.

Improving assessment quality of soil natural attenuation capacity at the point and regional scales.

Soil natural attenuation capacity (NAC) is an important ecosystem service that maintains a clean environment for organisms in the soil, which in turn supports other services. However, spatially varying indicator weights were rarely considered in the traditionally-used soil NAC assessment model (e.g., ecosystem-service performance model) at the point scale. Moreover, in the spatial simulation of soil NAC, the traditionally-used geostatistical models were usually susceptible to spatial outliers and ignored valuable auxiliary information (e.g., land-use types). This study first proposed a novel soil NAC assessment method based on the ecosystem-service performance model and moving window-entropy weight method (MW-EW) (NACMW-EW). Next, NACMW-EW was used to assess soil NAC in a typical area in Guixi City, China, and further compared with the traditionally-used NACtra and NACEW. Then, robust sequential Gaussian simulation with land-use types (RSGS-LU) was established for the spatial simulation of NACMW-EW and compared with the traditionally-used SGS, SGS-LU, and RSGS. Last, soil NAC's spatial uncertainty was evaluated based on the 1000 realizations generated by RSGS-LU. The results showed that: (i) MW-EW effectively revealed the spatially varying indicator weights but EW couldn't; (ii) NACMW-EW obtained more reasonable results than NACtra and NACEW; (iii) RSGS-LU (RMSE = 0.118) generated higher spatial simulation accuracy than SGS-LU (RMSE = 0.123), RSGS (RMSE = 0.132), and SGS (RMSE = 0.135); and (iv) the relatively high (P[NACMW-EW(u) > 0.57] ≥ 0.95) and low (P[NACMW-EW(u) > 0.57] ≤ 0.05) threshold-exceeding probability areas were mainly located in the south and east of the study area, respectively. It is concluded that the proposed methods were effective tools for soil NAC assessment at the point and regional scales, and the results provided accurate spatial decision support for soil ecosystem service management.

Spatial distribution and factors associated with health insurance subscription among women in Ghana.

This study examined the spatial distribution and factors associated with health insurance subscription among women in Ghana. We analysed a representative sample of 9380 women aged 15-49 y from the 2014 Ghana Demographic and Health Survey. Descriptive and multilevel regression analyses were performed. The study also employed spatial analysis techniques, including spatial autocorrelation, hotspot analysis, cluster and outlier analysis, as well as geographically weighted regression to explore the geographic distribution and predictors of non-subscription to health insurance. The analysis revealed a moderately high prevalence of health insurance subscription among women in Ghana (62.4%). The spatial analyses indicated substantial variations in health insurance subscription across different regions in Ghana. The coastal and middle zones reported higher rates of non-subscription compared with the northern zone. We observed that young women (aged 20-24 y) had a lower likelihood of subscribing compared with adolescent girls (aged 15-19 y). Also, higher educational attainment, listening to the radio, being in a marital union and higher wealth status were positively associated with health insurance subscription. The study calls for targeted interventions and policies to promote equitable access to healthcare services, focusing on improving health infrastructure in coastal and middle zones, educational campaigns for individuals with lower education levels, leveraging media platforms for health insurance awareness and implementing equitable and affordable processes for individuals in poorer households.

Robust Spatial Durbin Model Untuk Pemodelan Tingkat Pengangguran Terbuka di Provinsi Jawa Tengah

Unemployment is still a very crucial problem faced by the Indonesian state. The Central Bureau of Statistics noted that the number of unemployed in Indonesia in 2019 was 4.44 percent, an increase of 2.04 percent in 2020. The purpose of this study was to find out the characteristics of the spatial pattern and modeling the open unemployment rate in Central Java Province. Removing outliers in spatial analysis can change the composition of the spatial effects on the data. One method of solving due to outliers in the spatial regression model is using spatial robust regression. Robust Spatial Durbin Model method which is one of the methods used when spatial outliers occur. The results showed that there are spatial pattern characteristics and modeling for each dependent and independent variable which are different from the independent variables which are mutually correlated. In addition, the RSDM model is a better model than the OLS and SDM models in modeling the Open Unemployment Rate in Central Java Province in 2021 because it has an R2 value of 49.85% and the smallest AIC value of 22,563.

Spatial Patterns Of Hand-Foot And Mouth Disease In Ho Chi Minh City, Vietnam

Background: Hand-Foot-and-Mouth Disease (HFMD) is most frequently caused by Enterovirus71 (EV-A71) or Coxsackie virus A16 (CV-A16), infants and young children are at greatest risk. Describing the spatial patterns of HFMD can help develop and better target interventions. The objective of this study is to identify spatial patterns of HFMD in the first 8-months of 2023 in Ho Chi Minh city, Vietnam. Methods: The global Moran’s I statistic, Moran’s I scatterplot and local Moran’s I statistic and Boxplot will be applied to study spatial patterns of HFMD. Spatial patterns including spatial clusters (high-high and low-low) and spatial outliers (low-high and high-low) will be identified for HFMD cases and HFMD infection rates. Results: three high-high spatial clusters were mainly distributed in districts in the western region of the city such as Binh Tan, Binh Chanh, and Tan Phu. These high-high spatial clusters belonged to districts having the highest rates of HFMD infections in the city with their corresponding rates of 289, 283 and 281 cases/100,000 inhabitants, respectively. On the other hand, two low-low spatial clusters were detected in Districts 1 and 5 in the city center with their HFMD infection rates of 190 and 209 cases/100,000 inhabitants, respectively. Conclusions : findings in this study support the usefulness of boxplots, local and global Moran’s I statistics, and Moran’s I scatterplots in the identification of spatial clusters and spatial outliers of HFMD.

Spatial Clustering Analysis Of Dengue Hemorrhagic Fever In The First 9-Monthsof 2023 In Ho Chi Minh City, Vietnam

Background: Dengue hemorrhagic fever is a notable vector-borne viral disease, currently becoming the most dreaded worldwide health problem in terms of the number of people affected. The objective of this study is to investigate spatial clustering of dengue hemorrhagic fever incidence in the first 9-months of 2023 in Ho Chi Minh city, Vietnam. Methods: the global Moran’s I statistic, Moran’s I scatterplot and local statistic were employed to spatial clusters (high-high and low-low) and spatial outliers (low-high and high-low) in the study area of Ho Chi Minh city. The first and third order of contiguity were used to constructe spatial weight matrix. Results: it was found from a case study of the first 9-months of 2023 in Ho Chi Minh city, a total of four high-high clusters, two low-low spatial clusters were detected in urban area and rural areas in the north and south of the Ho Chi Minh city, respectively when using the first order contiguity (statistically significance at the 0.05 level). For the case of using the third order of contiguity, a total of six high-low, two low-high spatial clusters and one low-low spatial cluster were successfully identified. Conclusions: the study results has proven the effective use of the global Moran’s I statistic, Moran’s I scatterplot and local Moran’s I statistic in the identification of spatial clustering of dengue hemorrhagic fever incidence.