Existence Of Spatial Autocorrelation Research Articles

DETECTION OF SPATIAL AUTOCORRELATION OF DIARRHOEA IN EAST JAVA USING THE MORAN'S INDEX TEST

Background: Diarrhoea is an infectious disease with a high level of morbidity and mortality in the community. Spatial analysis is one of the analytical techniques for managing diarrhoea data and helping the government formulate policies.
 
 Objectives: This study aims to detect the spatial autocorrelation of diarrhoea in East Java using the Moran's Index.
 
 Methods: This study was non-reactive. This study used secondary data sourced from the 2021 East Java Health Profile. The population in the study was all districts/cities in East Java. The sampling technique was carried out by total sampling. The research sample was 38 districts/cities. Data analysis through mapping and spatial autocorrelation detection.
 
 Results: Mapping of diarrhoea showed that out of 38 districts/cities, there were 13 areas with a category of high diarrhoea, 12 areas with a category of moderate diarrhoea, and 13 areas with a category of low diarrhoea. The significance value of Moran's I test was obtained at 0.03 (<α=0.05). There were three areas included in the High-High category, one area included in the Low-Low category, and three areas included in the Low-High category.
 
 Conclusion: The diarrhoea in districts/cities in East Java Province could affect the diarrhoea in adjacent districts/cities. The existence of spatial autocorrelation in diarrhoea should be a serious concern for all elements, both the government and the people of East Java. We need further research to identify factors affecting diarrhoea in East Java.

Evaluating urban environment quality (UEQ) for Class-I Indian city: an integrated RS-GIS based exploratory spatial analysis

Urban environmental quality consisting of ecological, physical, and socio-economic components, often deteriorates due to rapid urbanization. Therefore, using Remote sensing and GIS environment, a composite measure is applied to quantify the spatial heterogeneity of urban environmental quality for the Class-1 Indian city (Siliguri). In this study, the Urban Environmental Quality Index was constructed using 15 indicators and three interconnected dimensions (eco-environment, landscape and built-up, and socio-economy). The three domains and Urban Environmental Quality Index were computed utilizing Principal Component Analysis with average aggregation techniques. Exploratory Spatial Data Analysis includes Moran’s I and Local indicator of spatial auto-correlation, were used to leverage the information of spatial clusters, spatial heterogeneity, and outliers based on the Urban Environmental Quality Index. The results show that Siliguri’s northern, north-western, and southern parts experience good environmental quality. The effectiveness of the employed model was checked using R2 (0.832), providing a good fit for the model. Moreover, the spatial pattern of urban environmental quality and the constructed domains (except socio-economy) revealed that the Low-Low values were predominantly clustered in the city centre, while High-High patterns are concentrated towards the periphery. Also, the value of Moran’s I indicated the existence of spatial autocorrelation and non-randomness pattern in Siliguri City. The results obtained from the analysis indicate spatial heterogeneity and spatial differentiation across the study area. The study’s outcome is relevant for urban planning, frequent monitoring of urban environmental quality, urban governance, and the well-being of urban inhabitants for a sustainable urban space.

Spatial analysis of determinants affecting the total number of Covid-19 cases of provinces in Turkey

The Covid-19 which is accepted as a pandemic by the World Health Organisation, has created a global panic effect all over the world. To stop this epidemic, in which more than 4 million people died as of July 2021, researches are being carried out on all kinds of issues related to the disease. In this study, a spatial econometric analysis of the determinants of the total number of Covid-19 cases in the provinces in Turkey between February 8, 2021, and May 7, 2021, was conducted. The existence of spatial autocorrelation was investigated through the Moran I test, and as a result, the Spatial Lagged Model (SAR) was found to be the most appropriate model. According to the results of the spatial analysis, it has been determined that the change in the total number of cases in a province will be in the same direction in the neighboring provinces of that province. A spatial interaction finding was obtained between the provinces and a significant and positive relationship was found between the total number of Covid-19 cases and the population density and the number of people over the age of sixty. Similarly, a significant and negative relationship was found with the average temperature and the total number of healthcare workers, and no significant relationship was found with the literacy rate.

Analyzing China's provincial environmental emissions and its influencing factors: a spatial analysis.

In-depth analyses of the spatial heterogeneity in environmental emissions and the causes of differences are of great importance to provide a reference for reduction policies. However, a spatial analysis of the specific mechanisms of China's environmental emissions is rarely scarce. Using the province-level data of 30 provinces in China over 2005-2017, this paper constructs a spatial Durbin model (SDM) to empirically address the existence and spatial mechanisms of environmental emissions. The results show that: first, China's environmental emissions show significant characteristics of spatial dependence and clustering from global and local perspectives, indicating the existence of spatial autocorrelation in environmental emissions across regions. Second, both per capita GDP and urbanization have positive impacts on environmental emissions, but the impact of environmental regulation is not significant. Third, urbanization not only directly influences environmental emissions, but also indirectly influences environmental emissions. These analyses provide comprehensive policy implications for government and policymakers to promote environmental quality.

Исследование пространственной автокорреляции: случай российской региональной инфляции

We apply APLE statistic to explore spatial autocorrelation of Russian regional inflationary processes. APLE is discussed to be the fine alternative to Moran’s I. To conduct this study we modify statistics of spatial dependence for panel data structure. We use time series of Russian regional CPIs (i.e. quantitative measure of inflation) of food, non-food, services baskets. We find evidence to confirm the hypothesis of the existence of spatial autocorrelation of regional inflationary processes on the horizon of our study.

Geospatial clustering of childhood IgA vasculitis and IgA vasculitis-associated nephritis

ObjectivesResearch on spatial variability of the incidence of IgA vasculitis (IgAV) in children and its potential implications for elucidation of the multifactorial aetiology and pathogenesis is limited. We intended to...

Residual-Based Surface Segmentation for Monitoring Topographic Variations

Monitoring topographic variations in the engineered surface is crucial for quality engineers since the change in the surface finish is closely related to the performance of products. However, several challenging issues such as the existence of spatial autocorrelation within the surface, and changes of topographic features such as position and shape of peaks and valleys across defect-free surfaces make it difficult to monitor variations in the surface. In addition, existing monitoring approaches fail to detect local changes in the surface. In this article, we present a new approach for monitoring topographic variations in surfaces. We develop a residual-based separation deviation (RBSD) model to effectively identify local surface changes. Residuals are obtained through the fit surface prediction model, which characterizes the generic behavior of defect-free surfaces, and binarized by the RBSD model to distinguish the defective region where residuals are autocorrelated. A spatial randomness-based monitoring statistic is introduced to evaluate binary patterns in order to detect surface anomalies. Numerical simulation and a case study of the coated paper surface monitoring are provided to demonstrate the effectiveness of the proposed approach. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —An increasing number of optical measuring instruments provide quality engineers with surface topographic data of the product, and enable precise and accurate inspection of surface variations during the manufacturing process. However, spatial and arbitrary geometric features on topographic data often make monitoring the surface changes a challenging task. This article proposes a new monitoring approach for detecting topographic variations in the surface. We mathematically characterize the generic behavior of the surface under the normal process, and present a surface segmentation model to detect local variations in the surface. In practice, our approach is applicable to real-life paper surface variations monitoring. The proposed approach is expected to provide a new idea to practitioners who work on surface topography inspection in various fields as well as the pulp industry.

Identification of priority areas for surveillance of cutaneous leishmaniasis using spatial analysis approaches in Southeastern Brazil

BackgroundCutaneous leishmaniasis (CL) is an important public health problem in Brazil and in several tropical regions of the world. In the Americas, Brazil is the country with the highest number of registered cases. In Brazil, the state of Minas Gerais has the highest number of cases in the southeastern region. In the present study, we used spatial analysis in the State of Minas Gerais to identify municipalities of priority during a nine-year period (2007–2015), which might be used to guide surveillance and control measures.MethodsAn ecological study with spatial analysis of autochthonous cases of CL was performed in the state of Minas Gerais between 2007 and 2015. We calculated incidence rates, used Empirical Bayesian smoothing for each municipality, and divided the analyses into three-year intervals. In order to analyze the existence of spatial autocorrelation, and to define priority areas, Moran’s Global Index and Local Indicators of Spatial Association (LISA) were used.ResultsThe mean incidence rate for the entire state was 6.1/100,000 inhabitants. For Minas Gerais, analysis of CL cases over time revealed a successive increase of indicated mesoregions with high priority municipalities. Eight of the designated mesoregions contained municipalities classified as high priority areas in any of the three evaluated trienniums, and four mesoregions had high priority municipalities throughout the entire investigation.ConclusionsWithin the southeastern region of Brazil, Minas Gerais State stands out, with highest CL incidence rates. Using spatial analysis, we identified an increasing numbers of cases in the municipalities classified as high priority areas in different mesoregions of the state. This information might be of value to direct surveillance and control measures against CL and to understand the dynamics of the expansion of CL in Minas Gerais. Similar approaches might be used to map CL in other regions throughout Brazil, or in any other country, where national notification and control programs exist.

Application of Moran'sI Autocorrelation in Spatial-Temporal Analysis of PM2.5 Pollutant (A case Study: Tehran City)

n Background and Objectives: PM2.5 particulate matter is one of the major air pollutants in cities of Iran especially Tehran, which threaten health of Iranian people due to numerous health hazards. Risk levels of these particulate depend on spatial-temporal changes in the air. Hence, this study aimed spatial-temporal analysis of PM2.5 concentration in Tehran's air using the Geographic Information System. Material and methods: Hourly data of PM2.5 concentration was collected in 2016-2017 from 38 air pollution monitoring stations and was analyzed monthly, seasonally and annually. Inverse Distance Weighting interpolation method was used in order to present concentration maps of the PM2.5. Existence of spatial autocorrelation in data was analyzed by Moran's I and data clustering was accomplished by Global and Local Moran's I and finally Getis-Ord-Gi index was used to determine hot spots of this pollutant in Tehran. Results: Results of concentration maps of PM2.5 and global and local spatial autocorrelation with PM2.5 concentration hot spot analysis showed that the concentration of this pollutant has an incremental mode from the north to the south of Tehran so that areas in south of Tehran especially Ray stations were the most polluted areas in Tehran. In addition, results showed that the concentration of this pollutant was more in the two colder seasons so that most hot spots were identified in these seasons. Conclusion: Using several spatial analyses simultaneously showed that Tehran can be separated into two parts of non-polluted north and polluted south, which this issue should be considered by urban planners to improve Tehran's air quality.

Investigating the Existence of Second Order Spatial Autocorrelation in Crash Frequency across Adjacent Freeway Segments

This study developed spatial Poisson model to incorporate spatial autocorrelation in crash frequency across contagious freeway segments. Spatial autocorrelation is the presence of spatial pattern in crash frequency over space due to geographic proximity. Usually crash caused congestion on a freeway segment propagates upstream and creates chance of occurring secondary crashes. This phenomenon makes the crash frequency on the contiguous freeway segments correlated. This correlation makes the distributional assumption of independence of crash frequency invalid. The existence of spatial autocorrelation is investigated by using Conditional autoregressive models (CAR models). The models are set up in a Bayesian modeling framework, to include terms which help to identify and quantify residual spatial autocorrelation for neighboring observation units. Models which recognize the presence of spatial dependence help to obtain unbiased estimates of parameters quantifying safety levels since the effects of spatial autocorrelation are accounted for in the modeling process. Based on CAR models, approximately 51% of crash frequencies across contiguous freeway segments are spatially auto-correlated. The incident rate ratios revealed that wider shoulder and weaving segments decreased crash frequency by factors of 0.84 and 0.75 respectively. The marginal impacts graphs showed that an increase in longitudinal space for segments with two lanes decreased crash frequency. However, an increase of facility width above three lanes results in more crashes, which indicates an increase in traffic flows and driving behavior leading to crashes. These results call an important step of analyzing contagious freeway segments simultaneously to account for the existence of spatial autocorrelation.

Free Trade Agreement between Tunisia and the European Union, Do Institutions Matter?: An Empirical Validation by A Gravity Model

Tunisia has signed a free trade agreement with the European Union in 1996, which provides for the reduction of tariff barriers between Tunisia and the EU. In this article, we aim to know and test whether the similarity of the institutional framework has to stimulate international trade between Tunisia and the European Union. In this context, we built a variable called “Institutional distance” to valid the institutional dimension of international trade, near borders effects reported in the literature. To this end, a gravity model was used initially (Tunisia and 21 European countries). Secondly, the estimate shows the existence of spatial autocorrelation. The latter has been corrected using spatial econometrics. The results show that the geographical distance remains more important than the institutions in this type of agreement between north and south shores of the Mediterranean.

Evaluation of three spatial interpolation methods to estimate forest volume in the municipal forest of the Greek island Skyros

Forest volume is of great interest to forest managers. Plot observations of forest volume are available from planning reports. For managers, however, what is relevant are forest volume surfaces. In this study, three interpolation methods were employed to construct continuous surfaces for the evaluation of forest volume at positions for which no measurements are available. For the Municipal Forest of Skyros Island, we compared spatial predictions derived from Inverse Distance Weighting (IDW), Block Kriging (BK), and Block Co-Kriging (BCK) interpolation methods, as applied to data from 120 sample plots. The existence of spatial autocorrelation in the data was identified using correlograms of Moran’s I index. Spatial outliers were identified and excluded from the analysis using the local Moran’s I index. Only slope, of the examined environmental factors, showed an acceptable correlation with forest volume and was used as an auxiliary variable for BCK. The performance of the three methods was evaluated, using an independent validation set and comparing these indices: mean error (ME) and root mean square error. Additionally, for BK and BCK, the indices, standardized ME, average standard error, and the standardized root-mean-square error were used. For the three interpolation methods, the BK method gave the more accurate results; BCK is the next more accurate method and IDW the third.

Geographical epidemiology of antibacterials in the preschool age

BackgroundThematic maps allow a more rapid and immediate reading of the geographical differences in the distribution of data referred to a specific territory. The aim of this study was to show, for the first time, the application of some statistical and cartographic tools in the analysis of drug utilization in the pediatric population of an Italian region, and to assess the intra-regional difference in the antibiotic prescriptions.MethodsTo assess the type of geographic distribution of the prescriptions, the analyses were based on the standardized prevalence rate (z-score) calculated at the local health unit, health district, and municipality levels. Pearson’s coefficient of correlation was used to evaluate the correlation with hospitalization and the Moran’s I index was used to evaluate the existence of spatial autocorrelation. With the use of Getis-Ord’s G statistic, clusters of areas with high and low levels of prevalence were identified and mapped. The probability of receiving at least one prescription of antibacterials during the year for all the children included in the study was evaluated with a logistic regression model.ResultsWith the use of the maps it was possible to see that the prescriptions were not correlated with the health status of the population, but with the tendency of the pediatrician to prescribe drugs. This was also confirmed by the logistic regression model constructed to estimate the probability of receiving at least one prescription of antibacterials considering, as independent variables: age, sex, prevalence of hospitalizations in the district of residence, prescriptive attitude of the pediatrician, sex of the pediatrician, pediatrician’s age group, and duration of the pediatrician’s contract with the local health unit (LHU).ConclusionsThe priority actions to rationalize the use of antibacterials in the preschool age should concentrate on the active participation of the pediatricians in permanent education activities. Moreover, the competent authorities should increasing their efforts to limit unnecessary prescriptions and increase appropriateness of prescribing.RiassuntoIntroduzioneLe mappe tematiche consentono una più rapida ed immediata lettura delle differenze geografiche nella distribuzione di dati riferiti ad un territorio specifico. Lo scopo dello studio è mostrare, per la prima volta, l’applicazione di alcuni strumenti statistici e cartografici, nell’analisi dell’uso dei farmaci nella popolazione pediatrica di una regione italiana e valutare le differenze intra-regionali.MetodiPer valutare il tipo di distribuzione geografica delle prescrizioni, sono stati calcolati i tassi di prevalenza standardizzati (punteggi-z) a livello di ASL, Distretti Sanitari e Comuni. Per valutare la correlazione con le ospedalizzazioni è stato usato il coefficiente di correlazione di Pearson; per valutare l’esistenza di autocorrelazione spaziale è stato usato l’indice I di Moran. Tramite l’uso della statistica G di Getis-Ord sono stati identificati cluster di aree ad alto e basso livello di prevalenza. Infine con un modello di regressione logistica è stata stimata la probabilità di ricevere almeno una prescrizione nel corso dell’anno per tutti i pazienti inclusi nello studio.RisultatiCon l’uso delle mappe è possibile vedere che le prescrizioni non sono correlate con lo stato di salute della popolazione, ma sono correlate con l’attitudine prescrittiva del pediatra. Questo è confermato anche dal modello di regressione logistica costruito per stimare la probabilità di ricevere almeno una prescrizione considerando come variabili indipendenti l’età, il sesso, la prevalenza di ricoveri nel distretto di residenza, l’attitudine prescrittiva del pediatra, la classe di età del pediatra e la durata della convenzione del pediatra con l’Azienda Sanitaria Locale (ASL).ConclusioniGli interventi primari per razionalizzare l’uso degli antibiotici in età prescolare dovrebbero concentrarsi sulla partecipazione attiva dei pediatri in attività di educazione permanente. Inoltre il contenimento delle prescrizioni di ASL e Distretti dovrebbe prevedere un ulteriore controllo da parte delle autorità competenti in merito all’appropriatezza prescrittiva.

A Simplified Indicator of Social Well-Being in the United States: Examining the Ecological Impact of Family Formation within a County Level Framework

In 1995, a study entitled “Does Marriage Matter?” was published by Linda Waite in the journal of Demography, which was concerned with the direction of such causal relationships. While Waite’s examination of the causal relationships associated with marriage, and most other analyses of this type, is primarily concerned with the individual level effects of marriage on a variety of outcomes, little is understood concerning the ecological effect of community marriage rates on levels of aggregate well-being. This study aims to contribute to this gap through the implementation of a recent conceptualization of social well-being as a multi-dimensional measure incorporating both biological, operationalized as average life expectancy, and social phenomena, operationalized as, community level crime rates (Raphael, Making the links: what do health promotion, crime prevention, and social development have in common? in 2004). It is important to understand such aggregate level effects in the face of the existing literature, which relies heavily on relational associations which could be subject to ecological fallacy. Analytic techniques incorporate Exploratory Spatial Data Analysis and spatial regression techniques, due to the high existence of spatial autocorrelation often evident in census data, as a way of understanding the effect of the aggregate level marriage rate on the constructed social well being indicator.

Seed bank spatial pattern in a temperate secondary forest

Abstract. Seed bank spatial pattern was studied in a secondary forest dominated by Fagus sylvatica and Betula celtiberica in the Urkiola Natural Park (N Spain). Soil samples were taken every 2 m in a regular grid (196 points) and divided into two fractions (0–3 cm and 3–10 cm deep). The viable seed bank was studied by monitoring seedling emergence for ten months. The effect of different factors on seed bank composition and patterning was analysed using constrained ordination as a hypothesis testing tool. Furthermore, the existence of spatial autocorrelation was evaluated by geostatistical analysis. Seed density was high, 7057 seed.m−2, with a few species dominating. Species composition in the various layers were significantly correlated. The seed bank showed significant spatial structure, which was partially explainable by the spatial structure of the canopy and understorey vegetation. Spatial clumping from 0–8 m was observed in seed bank density and composition, mainly due to the pattern of two abundant taxa Juncus effusus and Ericaceae. The Ericaceae seed bank was related to the spatial distribution of dead stumps of Erica arborea. J. effusus was not present in the above‐ground vegetation, which indicates that its seed bank was formed in the past. As expected, the seed bank of this forest reflects its history, which is characterized by complex man‐induced perturbations. The seed bank appears to be structured as a consequence of contrasting driving forces such as canopy structure, understorey composition and structural and microhabitat features.

Seed bank spatial pattern in a temperate secondary forest

Abstract Seed bank spatial pattern was studied in a secondary forest dominated by Fagus sylvatica and Betula celtiberica in the Urkiola Natural Park (N Spain). Soil samples were taken every 2 m in a regular grid (196 points) and divided into two fractions (0-3 cm and 3-10 cm deep). The viable seed bank was studied by monitoring seedling emergence for ten months. The effect of different factors on seed bank composition and patterning was analysed using constrained ordination as a hypothesis testing tool. Furthermore, the existence of spatial autocorrelation was evaluated by geostatistical analysis. Seed density was high, 7057 seed.m−2, with a few species dominating. Species composition in the various layers were significantly correlated. The seed bank showed significant spatial structure, which was partially explainable by the spatial structure of the canopy and understorey vegetation. Spatial clumping from 0-8 m was observed in seed bank density and composition, mainly due to the pattern of two abundant t...

Dynamic Models of Spatial Autocorrelation

Dynamic models have been studied intensively during the last decade, particularly in the field of growth theory and diffusion analysis. Consequently, problems like temporal autocorrelation have received much attention. Recently the attention of econometricians has also concentrated on spatial autocorrelation, particularly in the field of spatial interaction models. The existence of spatial autocorrelation among spatially dispersed phenomena appears to lead to significant differences in the treatment of multiregional models. In this paper, attention will be focused on the formal aspects of dynamic spatial diffusion models. Some statistical measures for temporal-spatial autocorrelation will be constructed. In addition, a formal derivation of necessary extensions in estimation procedures for dynamic multiregional econometric models will be presented, particularly in the field of seemingly unrelated regressions and instrumental variables. The analysis will be illustrated by means of a dynamic explanatory model for the spatial dispersion of foreign workers in The Netherlands.

O patrón desigual de crecemento europeo: unha análise semiparamétrica a nivel rexional

O obxectivo deste traballo é analizar a relación entre a desigualdade e a renda dispoñible equivalente a nivel rexional e contrastar o tipo de relación existente entrambas as dúas variables na Unión Europea (UE) no contexto do modelo de crecemento neoliberal. Neste traballo estimamos un modelo con datos de panel mediante o método de mínimos cadrados ordinarios (MCO) de efectos fixos e, ademais, analizamos a posible existencia de autocorrelación espacial no modelo e a necesidade de implementación de modelos espaciais. Unha achega anovadora é a estimación mediante análise semiparamétrica, que permite non condicionar a función relacional. A análise semiparamétrica, a diferenza dunha análise non paramétrica, permitiranos introducir outras variables mediante unha función lineal definida como, por exemplo, a desigualdade inicial, a densidade de poboación, o emprego en distintos sectores (industry mix) ou as transferencias. Esta aplicación da análise semiparamétrica é una contribución relevante deste traballo, xa que aínda é unha técnica nova para o estudo da desigualdade.