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Dengue in Balangan Regency has the 2nd highest incidence rate (IR) in South Kalimantan at 126.64 per 100,000 population. This study aimed to determine the distribution pattern and conduct spatial analysis of dengue with population density in Balangan Regency. This quantitative research requires secondary data of dengue cases and Population Density in time series 2017-2021 with spatial analysis methods. The results showed that high dengue cases were in areas that had high density. Based on spatial analysis, dengue cases have a positive spatial relationship in 2017-2019 and a negative spatial relationship in 2020-2021 where dengue cases have a non-clustered distribution pattern (spread). Paringin City, East Paringin, Bungin, Maradap, Panggung, and Awayan villages are categorized as high-highly vulnerable to dengue. The findings of this study indicate that there is an influence of population density on the incidence of dengue. Monitoring of areas with high population density using spatial analysis is necessary for early detection of areas with high potential for dengue incidence.

IQ study by Davide Piffer

Includes references

This article is from International Journal of Health Geographics , volume 12 . Abstract Background: The majority of maternal deaths, stillbirths, and neonatal deaths are concentrated in a few countries, many of which have weak health systems, poor access to health services, and low coverage of key health interventions. Early and consistent antenatal care (ANC) attendance could significantly reduce maternal and neonatal morbidity and mortality. Despite this, most Kenyan mothers initiate ANC care late in pregnancy and attend fewer than the recommended visits. Methods: We used survey data from 6,200 pregnant women across six districts in western Kenya to understand demand-side factors related to use of ANC. Bayesian multi-level models were developed to explore the relative importance of individual, household and village-level factors in relation to ANC use. Results: There is significant spatial autocorrelation of ANC attendance in three of the six districts and considerable heterogeneity in factors related to ANC use between districts. Working outside the home limited ANC attendance. Maternal age, the number of small children in the household, and ownership of livestock were important in some districts, but not all. Village proportions of pregnancy in women of child-bearing age was significantly correlated to ANC use in three of the six districts. Geographic distance to health facilities and the type of nearest facility was not correlated with ANC use. After incorporating individual, household and village-level covariates, no residual spatial autocorrelation remained in the outcome. Conclusions: ANC attendance was consistently low across all the districts, but factors related to poor attendance varied. This heterogeneity is expected for an outcome that is highly influenced by socio-cultural values and local context. Interventions to improve use of ANC must be tailored to local context and should include explicit approaches to reach women who work outside the home.

This article is from International Journal of Environmental Research and Public Health , volume 9 . Abstract A spatial autocorrelation analysis method is adopted to process the spatial dynamic change of industrial Chemical Oxygen Demand (COD) discharge in China over the past 15 years. Studies show that amount and intensity of industrial COD discharges are on a decrease, and the tendency is more remarkable for discharge intensity. There are large differences between inter-provincial discharge amount and intensity, and with different spatial differentiation features. Global spatial autocorrelation analysis reveals that Global Moran’s I of discharge amount and intensity is on the decrease. In space, there is an evolution from an agglomeration pattern to a discretization pattern. Local spatial autocorrelation analysis shows that the agglomeration area of industrial COD discharge amount and intensity varies greatly in space with time. Stringent environmental regulations and increased funding for environmental protections are the crucial factors to cut down industrial COD discharge amount and intensity.

This article is from International Journal of Environmental Research and Public Health , volume 9 . Abstract A spatial autocorrelation analysis method is adopted to process the spatial dynamic change of industrial Chemical Oxygen Demand (COD) discharge in China over the past 15 years. Studies show that amount and intensity of industrial COD discharges are on a decrease, and the tendency is more remarkable for discharge intensity. There are large differences between inter-provincial discharge amount and intensity, and with different spatial differentiation features. Global spatial autocorrelation analysis reveals that Global Moran’s I of discharge amount and intensity is on the decrease. In space, there is an evolution from an agglomeration pattern to a discretization pattern. Local spatial autocorrelation analysis shows that the agglomeration area of industrial COD discharge amount and intensity varies greatly in space with time. Stringent environmental regulations and increased funding for environmental protections are the crucial factors to cut down industrial COD discharge amount and intensity.

Moran's I statistic, a popular measure of spatial autocorrelation, is revisited. The exact range of Moran's I is given as a function of spatial weights matrix. We demonstrate that some spatial weights matrices lead the absolute value of upper (lower) bound larger than 1 and that others lead the lower bound larger than -0.5. Thus Moran's I is unlike Pearson's correlation coefficient. It is also pointed out that some spatial weights matrices do not allow Moran's I to take positive values regardless of observations. An alternative measure with exact range [-1,1] is proposed through a monotone transformation of Moran's I.

This article is from Chinese Journal of Cancer , volume 30 . Abstract Spatial analytical techniques and models are often used in epidemiology to identify spatial anomalies (hotspots) in disease regions. These analytical approaches can be used to identify not only the location of such hotspots, but also their spatial patterns. We used spatial autocorrelation methodologies, including Global Moran's I and Local Getis-Ord statistics, to describe and map spatial clusters and areas in which nine malignant neoplasms are situated in Taiwan. In addition, we used a logistic regression model to test the characteristics of similarity and dissimilarity between males and females and to formulate the common spatial risk. The mean found by local spatial autocorrelation analysis was used to identify spatial cluster patterns. We found a significant relationship between the leading malignant neoplasms and well-documented spatial risk factors. For instance, in Taiwan, the geographic distribution of clusters where oral cavity cancer in males is prevalent was closely correspond to the locations in central Taiwan with serious metal pollution. In females, clusters of oral cavity cancer were closely related with aboriginal townships in eastern Taiwan, where cigarette smoking, alcohol drinking, and betel nut chewing are commonplace. The difference between males and females in the spatial distributions was stark. Furthermore, areas with a high morbidity of gastric cancer were clustered in aboriginal townships where the occurrence of Helicobacter pylori is frequent. Our results revealed a similarity between both males and females in spatial pattern. Cluster mapping clarified the spatial aspects of both internal and external correlations for the nine malignant neoplasms. In addition, using a method of logistic regression also enabled us to find differentiation between gender-specific spatial patterns.

This article is from Evolution; International Journal of Organic Evolution , volume 66 . Abstract Social structure, limited dispersal, and spatial heterogeneity in resources are ubiquitous in wild vertebrate populations. As a result, relatives share environments as well as genes, and environmental and genetic sources of similarity between individuals are potentially confounded. Quantitative genetic studies in the wild therefore typically account for easily captured shared environmental effects (e.g., parent, nest, or region). Fine-scale spatial effects are likely to be just as important in wild vertebrates, but have been largely ignored. We used data from wild red deer to build “animal models” to estimate additive genetic variance and heritability in four female traits (spring and rut home range size, offspring birth weight, and lifetime breeding success). We then, separately, incorporated spatial autocorrelation and a matrix of home range overlap into these models to estimate the effect of location or shared habitat on phenotypic variation. These terms explained a substantial amount of variation in all traits and their inclusion resulted in reductions in heritability estimates, up to an order of magnitude up for home range size. Our results highlight the potential of multiple covariance matrices to dissect environmental, social, and genetic contributions to phenotypic variation, and the importance of considering fine-scale spatial processes in quantitative genetic studies.

This article is from Evolution; International Journal of Organic Evolution , volume 66 . Abstract Social structure, limited dispersal, and spatial heterogeneity in resources are ubiquitous in wild vertebrate populations. As a result, relatives share environments as well as genes, and environmental and genetic sources of similarity between individuals are potentially confounded. Quantitative genetic studies in the wild therefore typically account for easily captured shared environmental effects (e.g., parent, nest, or region). Fine-scale spatial effects are likely to be just as important in wild vertebrates, but have been largely ignored. We used data from wild red deer to build “animal models” to estimate additive genetic variance and heritability in four female traits (spring and rut home range size, offspring birth weight, and lifetime breeding success). We then, separately, incorporated spatial autocorrelation and a matrix of home range overlap into these models to estimate the effect of location or shared habitat on phenotypic variation. These terms explained a substantial amount of variation in all traits and their inclusion resulted in reductions in heritability estimates, up to an order of magnitude up for home range size. Our results highlight the potential of multiple covariance matrices to dissect environmental, social, and genetic contributions to phenotypic variation, and the importance of considering fine-scale spatial processes in quantitative genetic studies.

1. Introduction The long-term result of cooperation between environmental factors and circulation patterns determines the arrangement of type and manner in temperature heat islands in geographical areas. The knowledge about space dispersion in geographical areas provides the grounds for sound programming and proper environmental decision making. The information about time and place distribution of temperature is necessary to determine energy balance of earth, meteorology studies, and Eva transpiration; in fact, this is the reason why researchers approve of temperature studies. However, traditional trends of statistics have not clearly shown this fact. In environmental studies, we are dealing with observations which are interdependent; the dependence relates to the position and setting of the observations in the studied atmosphere. Thus, traditional trends of statistics should not be used in this type of observations because this kind of data has an interdependent structure in time and place. Therefore, this kind of data is called 'spatial data' in environmental studies and their studies need a normal approach to deal with the action of the data in time and place. 2. Study area This study is carried out to identify the spatial-temporal autocorrelation of temperature heat islands of Khorasan Razavi Province. 3. Material and methods To reach the expressed goal of the study, the station of networking data of maximum and minimum temperature of Khorasan Razavi Province was established. The data homogeneity of the stations’ temperature with the Kolmogorov-Smirnov Test was applied to SPSS software and their homogeneity was also confirmed. Then, from the data of the station a statistical period of 30 years in a daily period from 1980/1/1 until 2010/12/31 is used as the base of the present research and a network in range of 15×15 kilometers have been spread over the location under study. In reviewing the changes of temperature heat islands of Khorasan Razavi Province during a year, modern spatial statistics methods such as Spatial Auto Correlation Global Moran, Local Insulin Moral Index and Hotspots (through GIS) and Matlab were used. 4. Results and discussion The conclusions showed that Global Moran Index for each of 12 month of the year is more than 0.98 while it is one for the four months of August, September, October, and November. This point indicates that temperature in Khorasan Razavi Province based on Global Moran within the period of the study has the cluster pattern as high of 95 and 99 percent; however, the highest index of Global Moran was in August with the scale of 1/006052. The Z statistics estimated for each 12 month of the study ranges between 98 and 99. According to Global Moran, it can thus be concluded that Khorasan Razavi Province follows a very high cluster pattern during the year. To assess the autocorrelation of spatial temperature change in heat islands of Khorasan Razavi Province, the local Moran island index along with the analysis of hotspots were used. According to the both indicators, the southwest areas such as Ferdows and Boshruyeh stations and northeast areas such as Tabas station play a significant role in forming the heat islands patterns with high cluster. As such, the areas of Khorasan Razavi under study have positive spatial autocorrelation. This occurs while regions have negative spatial autocorrelation. In other words, Cold Island in 12 month of a year is limited to the high regions. On the whole, a significant area of the province in all 12 months of the study lacks significant or disciplined pattern. In fact, they statistically lack sound virtual spatial autocorrelation. The results of this research showed the islands are formed over long periods of time under local and distributional elements playing different roles. 5. Conclusion Generally, the geographical arrangement of heat islands is formed by regional factors, especially heights, latitude. In this way, the formation, structure and the role of latitude can be traced. However, we should not ignore the role of external factors in formation of heat islands because external factors including the general circulation atmosphere elements play a significant role in determining heat regime and temperature lapse. If we look at the temperature cluster of Khorasan Razavi Province, we see that the clusters in high and low levels are not the same. This contrast is due to the influence of circulation element factors. Therefore, generally we can say that heat islands are created and controlled by two systems, namely 1) Regional factors controlling the region (geographical arrangement of heat islands) and 2) external factors controlling the time (heat island regime).

This article is from Ecology and Evolution , volume 4 . Abstract Few universal trends in spatial patterns of wildlife crop-raiding have been found. Variations in wildlife ecology and movements, and human spatial use have been identified as causes of this apparent unpredictability. However, varying spatial patterns of spatial autocorrelation (SA) in human–wildlife conflict (HWC) data could also contribute. We explicitly explore the effects of SA on wildlife crop-raiding data in order to facilitate the design of future HWC studies. We conducted a comparative survey of raided and nonraided fields to determine key drivers of crop-raiding. Data were subsampled at different spatial scales to select independent raiding data points. The model derived from all data was fitted to subsample data sets. Model parameters from these models were compared to determine the effect of SA. Most methods used to account for SA in data attempt to correct for the change in P-values; yet, by subsampling data at broader spatial scales, we identified changes in regression estimates. We consequently advocate reporting both model parameters across a range of spatial scales to help biological interpretation. Patterns of SA vary spatially in our crop-raiding data. Spatial distribution of fields should therefore be considered when choosing the spatial scale for analyses of HWC studies. Robust key drivers of elephant crop-raiding included raiding history of a field and distance of field to a main elephant pathway. Understanding spatial patterns and determining reliable socio-ecological drivers of wildlife crop-raiding is paramount for designing mitigation and land-use planning strategies to reduce HWC. Spatial patterns of HWC are complex, determined by multiple factors acting at more than one scale; therefore, studies need to be designed with an understanding of the effects of SA. Our methods are accessible to a variety of practitioners to assess the effects of SA, thereby improving the reliability of conservation management actions.

This article is from Ecology and Evolution , volume 4 . Abstract Few universal trends in spatial patterns of wildlife crop-raiding have been found. Variations in wildlife ecology and movements, and human spatial use have been identified as causes of this apparent unpredictability. However, varying spatial patterns of spatial autocorrelation (SA) in human–wildlife conflict (HWC) data could also contribute. We explicitly explore the effects of SA on wildlife crop-raiding data in order to facilitate the design of future HWC studies. We conducted a comparative survey of raided and nonraided fields to determine key drivers of crop-raiding. Data were subsampled at different spatial scales to select independent raiding data points. The model derived from all data was fitted to subsample data sets. Model parameters from these models were compared to determine the effect of SA. Most methods used to account for SA in data attempt to correct for the change in P-values; yet, by subsampling data at broader spatial scales, we identified changes in regression estimates. We consequently advocate reporting both model parameters across a range of spatial scales to help biological interpretation. Patterns of SA vary spatially in our crop-raiding data. Spatial distribution of fields should therefore be considered when choosing the spatial scale for analyses of HWC studies. Robust key drivers of elephant crop-raiding included raiding history of a field and distance of field to a main elephant pathway. Understanding spatial patterns and determining reliable socio-ecological drivers of wildlife crop-raiding is paramount for designing mitigation and land-use planning strategies to reduce HWC. Spatial patterns of HWC are complex, determined by multiple factors acting at more than one scale; therefore, studies need to be designed with an understanding of the effects of SA. Our methods are accessible to a variety of practitioners to assess the effects of SA, thereby improving the reliability of conservation management actions.

This article is from Ecology and Evolution , volume 4 . Abstract Few universal trends in spatial patterns of wildlife crop-raiding have been found. Variations in wildlife ecology and movements, and human spatial use have been identified as causes of this apparent unpredictability. However, varying spatial patterns of spatial autocorrelation (SA) in human–wildlife conflict (HWC) data could also contribute. We explicitly explore the effects of SA on wildlife crop-raiding data in order to facilitate the design of future HWC studies. We conducted a comparative survey of raided and nonraided fields to determine key drivers of crop-raiding. Data were subsampled at different spatial scales to select independent raiding data points. The model derived from all data was fitted to subsample data sets. Model parameters from these models were compared to determine the effect of SA. Most methods used to account for SA in data attempt to correct for the change in P-values; yet, by subsampling data at broader spatial scales, we identified changes in regression estimates. We consequently advocate reporting both model parameters across a range of spatial scales to help biological interpretation. Patterns of SA vary spatially in our crop-raiding data. Spatial distribution of fields should therefore be considered when choosing the spatial scale for analyses of HWC studies. Robust key drivers of elephant crop-raiding included raiding history of a field and distance of field to a main elephant pathway. Understanding spatial patterns and determining reliable socio-ecological drivers of wildlife crop-raiding is paramount for designing mitigation and land-use planning strategies to reduce HWC. Spatial patterns of HWC are complex, determined by multiple factors acting at more than one scale; therefore, studies need to be designed with an understanding of the effects of SA. Our methods are accessible to a variety of practitioners to assess the effects of SA, thereby improving the reliability of conservation management actions.

This article is from BMC Medical Research Methodology , volume 11 . Abstract Background: Analytic methods commonly used in epidemiology do not account for spatial correlation between observations. In regression analyses, omission of that autocorrelation can bias parameter estimates and yield incorrect standard error estimates. Methods: We used age standardised incidence ratios (SIRs) of esophageal cancer (EC) from the Babol cancer registry from 2001 to 2005, and extracted socioeconomic indices from the Statistical Centre of Iran. The following models for SIR were used: (1) Poisson regression with agglomeration-specific nonspatial random effects; (2) Poisson regression with agglomeration-specific spatial random effects. Distance-based and neighbourhood-based autocorrelation structures were used for defining the spatial random effects and a pseudolikelihood approach was applied to estimate model parameters. The Bayesian information criterion (BIC), Akaike's information criterion (AIC) and adjusted pseudo R2, were used for model comparison. Results: A Gaussian semivariogram with an effective range of 225 km best fit spatial autocorrelation in agglomeration-level EC incidence. The Moran's I index was greater than its expected value indicating systematic geographical clustering of EC. The distance-based and neighbourhood-based Poisson regression estimates were generally similar. When residual spatial dependence was modelled, point and interval estimates of covariate effects were different to those obtained from the nonspatial Poisson model. Conclusions: The spatial pattern evident in the EC SIR and the observation that point estimates and standard errors differed depending on the modelling approach indicate the importance of accounting for residual spatial correlation in analyses of EC incidence in the Caspian region of Iran. Our results also illustrate that spatial smoothing must be applied with care.

Hypsometric Integral (HI) dis geomorphic structures. In this st lays the effect of active tectonics and sensitivity on dy we calculated HI values for Hunza valley to investigate neotectonics, development of topographic structures and process of erosion using SRTM DEM 90m. ArcGIS and MATLAB is used to generate HI and hypsometric curve (HC). We generated HI and HC values by sing D8 algorithm in MATLAB to extract drainage basins for 5 and 6 Strahler orders. HI and HC values show the stages of erosion for instance high values of HI and convex HC displays young and tectonically active stage. We used different grid sizes in ArcGIS to calculate maximum, mean and minimum elevation utilizin different statistical techniques. We used Local Indicator of Spatial Autocorrelation (LISA) instead of Global Moran Index to determine the extent of distribution of clustered, dispersed and randomized HI values. This tec data. Regions with high HI val nique indicates high positive z score for auto correlated e indicate relative uplift, undissected and young structures while low HI values indicate sediment accumulation and shallow earthquakes.

Hypsometric Integral (HI) dis geomorphic structures. In this st lays the effect of active tectonics and sensitivity on dy we calculated HI values for Hunza valley to investigate neotectonics, development of topographic structures and process of erosion using SRTM DEM 90m. ArcGIS and MATLAB is used to generate HI and hypsometric curve (HC). We generated HI and HC values by sing D8 algorithm in MATLAB to extract drainage basins for 5 and 6 Strahler orders. HI and HC values show the stages of erosion for instance high values of HI and convex HC displays young and tectonically active stage. We used different grid sizes in ArcGIS to calculate maximum, mean and minimum elevation utilizin different statistical techniques. We used Local Indicator of Spatial Autocorrelation (LISA) instead of Global Moran Index to determine the extent of distribution of clustered, dispersed and randomized HI values. This tec data. Regions with high HI val nique indicates high positive z score for auto correlated e indicate relative uplift, undissected and young structures while low HI values indicate sediment accumulation and shallow earthquakes.

Hypsometric Integral (HI) dis geomorphic structures. In this st lays the effect of active tectonics and sensitivity on dy we calculated HI values for Hunza valley to investigate neotectonics, development of topographic structures and process of erosion using SRTM DEM 90m. ArcGIS and MATLAB is used to generate HI and hypsometric curve (HC). We generated HI and HC values by sing D8 algorithm in MATLAB to extract drainage basins for 5 and 6 Strahler orders. HI and HC values show the stages of erosion for instance high values of HI and convex HC displays young and tectonically active stage. We used different grid sizes in ArcGIS to calculate maximum, mean and minimum elevation utilizin different statistical techniques. We used Local Indicator of Spatial Autocorrelation (LISA) instead of Global Moran Index to determine the extent of distribution of clustered, dispersed and randomized HI values. This tec data. Regions with high HI val nique indicates high positive z score for auto correlated e indicate relative uplift, undissected and young structures while low HI values indicate sediment accumulation and shallow earthquakes.

Hypsometric Integral (HI) dis geomorphic structures. In this st lays the effect of active tectonics and sensitivity on dy we calculated HI values for Hunza valley to investigate neotectonics, development of topographic structures and process of erosion using SRTM DEM 90m. ArcGIS and MATLAB is used to generate HI and hypsometric curve (HC). We generated HI and HC values by sing D8 algorithm in MATLAB to extract drainage basins for 5 and 6 Strahler orders. HI and HC values show the stages of erosion for instance high values of HI and convex HC displays young and tectonically active stage. We used different grid sizes in ArcGIS to calculate maximum, mean and minimum elevation utilizin different statistical techniques. We used Local Indicator of Spatial Autocorrelation (LISA) instead of Global Moran Index to determine the extent of distribution of clustered, dispersed and randomized HI values. This tec data. Regions with high HI val nique indicates high positive z score for auto correlated e indicate relative uplift, undissected and young structures while low HI values indicate sediment accumulation and shallow earthquakes.