Spatial Statistical Analysis and Comprehensive Evaluation of High-Tech Industry Development

After 30 years of economic development, the high-tech industry has played an important role in China’s national economy. The development of high-level technological industry plays a leading role in guiding the transformation of China’s economy from “investment-driven” to “technology-driven”. The high-tech industry represents the future industrial development direction and plays a positive role in promoting the transformation of traditional industries. The rapid development of high-tech industry is the key to social progress. In this paper, the traditional analytical model of statistics is combined with principal component analysis and spatial analysis, and R language is used to express the analytical results intuitively on the map. Finally, a comprehensive evaluation is established.

Reflections on the current state of spatial statistics education in the United States: 2014

This paper surveys the current state of teaching spatial statistics in the United States(US),with commentary about the future teaching of such a course.It begins with a historical overview,and proposes what constitutes suitable content for a contemporary spatial statistics course.It notes that contemporary university-level spatial statistics courses are mostly taught across myriad units,including biology/ecology,climatology,economics(as spatial econometrics),environmental studies,epidemiology/public health,forestry,geography,geosciences/earth sciences,geospatial information sciences,mathematics,quantitative social science,soil science,and statistics.It discusses the diffusion of this course across the US,which began in the mid-1980s.One result it reports is a model spatial statistics course offering.

Fractals and Spatial Statistics of Point Patterns

The relationship between fractal point pattern modeling and statistical methods of pa- rameter estimation in point-process modeling is reviewed. Statistical estimation of the cluster fractal dimension by using Ripley＇s K-function has advantages in comparison with the more commonly used methods of box-counting and cluster fractal dimension estimation because it corrects for edge effects, not only for rectangular study areas but also for study areas with curved boundaries determined by re- gional geology. Application of box-counting to estimate the fractal dimension of point patterns has the disadvantage that, in general, it is subject to relatively strong ＂roll-off＂ effects for smaller boxes. Point patterns used for example in this paper are mainly for gold deposits in the Abitibi volcanic belt on the Canadian Shield. Additionally, it is proposed that, worldwide, the local point patterns of podiform Cr, volcanogenic massive sulphide and porphyry copper deposits, which are spatially distributed within irregularly shaped favorable tracts, satisfy the fractal clustering model with similar fractal dimensions. The problem of deposit size （metal tonnage） is also considered. Several examples are provided of cases in which the Pareto distribution provides good results for the largest deposits in metal size-frequency distribution modeling.

The Spatial Statistics Analysis of Housing Market Bubbles

With the incorporation of spatial statistic method, this paper constructs a state-space model of housing market bubbles, discussing the spatial pattern of housing market bubbles in China,and identifying the dynamic evolution process. The results show that: The bubbles of housing market walked along a path from low level to high level and then downsized to a low level during the period of 2009 and 2014, and the highest level stayed at 2011. From overall, the level of housing market bubbles had shown significant spatial autocorrelation and spatial agglomeration. In detail, the direction of North-South in China showed the inverted U shape, i.e., Central region was with high bubbles, and two ends contained low bubbles; from East-West direction, the East had high bubbles and the West contained comparatively low bubbles. Local spatial test indicates that there were some approximate spatial features in housing market bubbles among the adjacent regions. Observed from the level of housing market bubbles, China contained 3 plates: The first was the plate with low bubble level,including 3 provinces in North-East China(provinces of Jilin, Heilongjiang and Liaoning were included,but Dalian in Liaoning province was excluded; the second was the Central and West plate(the provinces of Yunnan, Guizhou, Sichuan, Guangdong, Guangxi, Hunan, Hubei, Gansu, Fujian, Jiangxi and Hainan were included in this plate), which was also featured with low bubble; and the third was Central East plate(provinces or provincial regions of Beijing, Tianjin, Hebei, Jiangsu, Zhejiang, Shanghai, Shandong,Anhui, Shanxi, Shaanxi and Inner Mongolia were included), which was characterized as high bubble region.

Narrative minireview of the spatial epidemiology of substance use disorder in the United States:Who is at risk and where?

Drug overdose is the leading cause of death by injury in the United States.The incidence of substance use disorder(SUD)in the United States has increased steadily over the past two decades,becoming a major public health problem for the country.The drivers of the SUD epidemic in the United States have changed over time,characterized by an initial heroin outbreak between 1970 and 1999,followed by a painkiller outbreak,and finally by an ongoing synthetic opioid outbreak.The nature and sources of these abused substances reveal striking differences in the socioeconomic and behavioral factors that shape the drug epidemic.Moreover,the geospatial distribution of the SUD epidemic is not homogeneous.The United States has specific locations where vulnerable communities at high risk of SUD are concentrated,reaffirming the multifactorial socioeconomic nature of this epidemic.A better understanding of the SUD epidemic under a spatial epidemiology framework is necessary to determine the factors that have shaped its spread and how these patterns can be used to predict new outbreaks and create effective mitigation policies.This narrative minireview summarizes the current records of the spatial distribution of the SUD epidemic in the United States across different periods,revealing some spatiotemporal patterns that have preceded the occurrence of outbreaks.By analyzing the epidemic of SUD-related deaths,we also describe the epidemic behavior in areas with high incidence of cases.Finally,we describe public health interventions that can be effective for demographic groups,and we discuss future challenges in the study and control of the SUD epidemic in the country.

Statistical study on the spatial - temporal distribution features of the arctic sea ice extent

On the basis of the arctic monthly mean sea ice extent data set during 1953-1984, the arctic region is divided into eight subregions,and the analyses of empirical orthogonal functions, power spectrum and maximum entropy spectrum are made to indentify the major spatial and temporal features of the sea ice fluctuations within 32-year period. And then, a brief appropriate physical explanation is tentatively suggested. The results show that both seasonal and non-seasonal variations of the sea ice extent are remarkable, and iis mean annual peripheral positions as well as their interannu-al shifting amplitudes are quite different among all subregions. These features are primarily affected by solar radiation, o-cean circulation, sea surface temperature and maritime-continental contrast, while the non-seasonal variations are most possibly affected by the cosmic-geophysical factors such as earth pole shife, earth rotation oscillation and solar activity.

Spatial epidemiology of diabetes: Methods and insights

Diabetes mellitus(DM)is a growing epidemic with global proportions.It is estimated that in 2019,463 million adults aged 20-79 years were living with DM.The latest evidence shows that DM continues to be a significant global health challenge and is likely to continue to grow substantially in the next decades,which would have major implications for healthcare expenditures,particularly in developing countries.Hence,new conceptual and methodological approaches to tackle the epidemic are long overdue.Spatial epidemiology has been a successful approach to control infectious disease epidemics like malaria and human immunodeficiency virus.The implementation of this approach has been expanded to include the study of non-communicable diseases like cancer and cardiovascular diseases.In this review,we discussed the implementation and use of spatial epidemiology and Geographic Information Systems to the study of DM.We reviewed several spatial methods used to understand the spatial structure of the disease and identify the potential geographical drivers of the spatial distribution of DM.Finally,we discussed the use of spatial epidemiology on the design and implementation of geographically targeted prevention and treatment interventions against DM.

A Statistical Approach for Predicting Grassland Degradation in Disturbance-Driven Landscapes

Maintaining a land base that supports safe and realistic training operations is a significant challenge for military land managers which can be informed by frequent monitoring of land condition in relation to management practices. This study explores the relationship between fire and trends in tallgrass prairie vegetation at military and non-military sites in the Kansas Flint Hills. The response variable was the long-term linear trend (2001-2010) of surface greenness measured by MODIS NDVI using BFAST time series trend analysis. Explanatory variables included fire regime (frequency and seasonality) and spatial strata based on existing management unit boundaries. Several non-spatial generalized linear models (GLM) were computed to explain trends by fire regime and/or stratification. Spatialized versions of the GLMs were also constructed. For non-spatial models at the military site, fire regime explained little (4%) of the observed surface greenness trend compared to strata alone (7% - 26%). The non-spatial and spatial models for the non-military site performed better for each explanatory variable and combination tested with fire regime. Existing stratifications contained much of the spatial structure in model residuals. Fire had only a marginal effect on surface greenness trends at the military site despite the use of burning as a grassland management tool. Interestingly, fire explained more of the trend at the non-military site and models including strata improved explanatory power. Analysis of spatial model predictors based on management unit stratification suggested ways to reduce the number of strata while achieving similar performance and may benefit managers of other public areas lacking sound data regarding land usage.

A novel method for evaluating brain function and microstructural changes in Parkinson's disease

In this study,microstructural brain damage in Parkinson＇s disease patients was examined using diffusion tensor imaging and tract-based spatial statistics.The analyses revealed the presence of neuronal damage in the substantia nigra and putamen in the Parkinson＇s disease patients.Moreover,disease symptoms worsened with increasing damage to the substantia nigra,confirming that the substantia nigra and basal ganglia are the main structures affected in Parkinson＇s disease.We also found that microstructural damage to the putamen,caudate nucleus and frontal lobe positively correlated with depression.Based on the tract-based spatial statistics,various white matter tracts appeared to have microstructural damage,and this correlated with cognitive disorder and depression.Taken together,our results suggest that diffusion tensor imaging and tract-based spatial statistics can be used to effectively study brain function and microstructural changes in patients with Parkinson＇s disease.Our novel findings should contribute to our understanding of the histopathological basis of cognitive dysfunction and depression in Parkinson＇s disease.

Correlation between white matter damage and gray matter lesions in multiple sclerosis patients

We observed the characteristics of white matter fibers and gray matter in multiple sclerosis patients, to identify changes in diffusion tensor imaging fractional anisotropy values following white matter fiber injury. We analyzed the correlation between fractional anisotropy values and changes in whole-brain gray matter volume. The participants included 20 patients with relapsing-remitting multiple sclerosis and 20 healthy volunteers as controls. All subjects underwent head magnetic resonance imaging and diffusion tensor imaging. Our results revealed that fractional anisotropy values decreased and gray matter volumes were reduced in the genu and splenium of corpus callosum, left anterior thalamic radiation, hippocampus, uncinate fasciculus, right corticospinal tract, bilateral cingulate gyri, and inferior longitudinal fasciculus in multiple sclerosis patients. Gray matter volumes were significantly different between the two groups in the right frontal lobe（superior frontal, middle frontal, precentral, and orbital gyri）, right parietal lobe（postcentral and inferior parietal gyri）, right temporal lobe（caudate nucleus）, right occipital lobe（middle occipital gyrus）, right insula, right parahippocampal gyrus, and left cingulate gyrus. The voxel sizes of atrophic gray matter positively correlated with fractional anisotropy values in white matter association fibers in the patient group. These findings suggest that white matter fiber bundles are extensively injured in multiple sclerosis patients. The main areas of gray matter atrophy in multiple sclerosis are the frontal lobe, parietal lobe, caudate nucleus, parahippocampal gyrus, and cingulate gyrus. Gray matter atrophy is strongly associated with white matter injury in multiple sclerosis patients, particularly with injury to association fibers.

Land use intensity dynamics in the Andhikhola watershed, middle hill of Nepal

Land use intensity is a valuable concept to understand integrated land use system, which is unlike the traditional approach of analysis that often examines one or a few aspects of land use disregarding multidimensionality of the intensification process in the complex land system. Land use intensity is based on an integrative conceptual framework focusing on both inputs to and outputs from the land. Geographers’ non-stationary data-analysis technique is very suitable for most of the spatial data analysis. Our study was carried out in the northeast part of the Andhikhola watershed lying in the Middle Hills of Nepal, where over the last two decades, heavy loss of labor due to outmigration of rural farmers and increasing urbanization in the relatively easy accessible lowland areas has caused agricultural land abandonment. Our intention in this study was to ascertain factors of spatial pattern of intensity dynamism between human and nature relationships in the integrated traditional agricultural system. High resolution aerial photo and multispectral satellite image were used to derive data on land use and land cover. In addition, field verification, information collected from the field and census report were other data sources. Explanatory variables were derived from those digital and analogue data. Ordinary Least Square(OLS) technique was used for filtering of the variables. Geographically Weighted Regression(GWR) model was used to identify major determining factors of land use intensity dynamics. Moran’s I technique was used for model validation. GWR model was executed to identify the strength of explanatory variables explaining change of land use intensity. Accordingly, 10 variables were identified having the greatest strength to explain land use intensity change in the study area, of which physical variables such as slope gradient, temperature and solar radiation revealed the highest strength followed by variables of accessibility and natural resource. Depopulation in recent decades has been a major driver of land use intensity change but spatial variability of land use intensity was highly controlled by physical suitability, accessibility and availability of natural resources.

Obtaining morphometric variables from gullies using two methods of interpolation laser scanner data:the case study of Vassouras,Brazil

Gully erosion is a worldwide problem of land degradation and water quality,and it is also frequent in Brazil.Typically,anthropic influence is the major driver of gully evolution.To study and monitor gullies it is necessary to use specific instruments and methods to obtain accurate information.The objective of this study was to use Terrestrial Laser Scanning(TLS) to create digital elevation model(DEM) accurately and define morphometric variables that characterize gullies in a mountainous relief.Two different interpolations were evaluated using the Topogrid and GridSurfaceCreate algorithms to elaborate DEM.Topographic profile for gullies was used to assess modeling quality.The DEM of the Gully 1(G1) from the Topogrid algorithm estimated soil loss of 49%,whereas the GridSurfaceCreate algorithm estimated a soil loss of97%,in a period of 1 year.The estimated soil loss for the Gully 2(G2) was 14% from the Topogrid,and 8%from the GridSurfaceCreate algorithm.The GridSurfaceCreate algorithm underestimated the volume to area ratio for G2 due to a failure on interpolating a region of low point representativity.The Topogrid algorithm represented better the terrain irregularities,as observed through the topographic profiles traced in three regions of G1 and G2.Statistical analysis showed that the GridSurfaceCreate algorithm presented lower accuracy in estimating elevations.The underestimation trend of this algorithm was also observed in G2.The gullies showed considerable soil losses,which may reduce the areas suitable for agricultural activities,and silting up of water courses.The Topogrid algorithm presented satisfactory results,denoting great potential to produce morphometric data of gullies.

Evaluation of CHIRPS Satellite Gridded Dataset as an Alternative Rainfall Estimate for Localized Modelling over Uganda

The Ugandan economy is largely dependent on rural-based and rain-fed agriculture. This creates a critical need to understand the rainfall dynamics at the local scale. However, the country has a sternly sparse and unreliable rain gauge network. This research, therefore, sets out to evaluate the use of the CHIRPS satellite gridded dataset as an alternative rainfall estimate for local modelling of rainfall in Uganda. Complete, continuous and reliable in situ station observations for the period between 2012 and 2020 were used for the comparison with CHIRPS satellite data models in the same epoch. Rainfall values within the minimum 5 km and maximum 20 km radii from the in situ stations were extracted at a 5 km interval from the interpolated in situ station surface and the CHIRPS satellite data model for comparison. Results of the 5 km radius were adopted for the evaluation as it’s closer to the optimal rain gauge coverage of 25 km2. They show the R2 = 0.91, NSE = 0.88, PBias = -0.24 and RSR = 0.35. This attests that the CHIRPS satellite gridded datasets provide a good approximation and simulation of in situ station data with high collinearity and minimum deviation. This tallies with related studies in other regions that have found CHIRPS datasets superior to interpolation surfaces and sparse rain gauge data in the comprehensive estimation of rainfall. With a 0.05° * 0.05° (Latitude, longitude) spatial resolution, CHIRPS satellite gridded rainfall estimates are therefore able to provide a comprehensive rainfall estimation at a local scale. Essentially these results reward research science in regions like Uganda that have sparse rain gauges networks characterized by incomplete, inconsistent and unreliable data with an empirically researched alternative source of rainfall estimation data. It further provides a platform to scientifically interrogate the rainfall dynamics at a local scale in order to infuse local policy with evidence-based formulation and application.

Spatiotemporal heterogeneity of phytoplankton diversity and its relation to water environmental factors in the southern waters of Miaodao Archipelago,China

To study the water quality influenced by the anthropogenic activities and its impact on the phytoplankton diversity in the surface waters of Miaodao Archipelago, the spatiotemporal variations in phytoplankton communities and the environmental properties of the surface waters surrounding the Five Southern Islands of Miaodao Archipelago were investigated, based on seasonal field survey conducted from November 2012 to August 2013. During the survey, a total of 109 phytoplankton species from 3 groups were identified in the southern waters of Miaodao Archipelago, of which 77 were diatoms, 29 were dinoflagellates, and 3 were chrysophytes. Species number was higher in winter（73）, moderate in autumn（70）, but lower in summer（31） and spring（27）. The species richness index in autumn（5.92） and winter（4.28） was higher than that in summer（2.83） and spring（1.41）.The Shannon-Wiener diversity index was high in autumn（2.82）, followed by winter（1.99） and summer（1.92）, and low in spring（0.07）. The species evenness index in autumn（0.46） and summer（0.39） was higher than that in winter（0.32） and spring（0.02）. On the basis of principal component analysis（PCA） and redundancy analysis（RDA）, we found that dissolved inorganic nitrogen（DIN） and chemical oxygen demand（COD） in spring, COD in summer, p H in autumn, and salinity and oil pollutant in winter, respectively, showed the strongest association with the distribution of phytoplankton diversity. The spatial heterogeneity of the southern waters of Miaodao Archipelago was quite obvious, and three zones, i.e., northeastern, southwestern and inter-island water area, were identified by cluster analysis（CA） based on key environmental variables.

A Fractal Measure of Spatial Association between Landslides and Conditioning Factors

Measuring the relative importance and assigning weights to conditioning factors of land- slides occurrence are significant for landslide prevention and/or mitigation. In this contribution, a fractal method is introduced for measuring the spatial relationships between landslides and condition- ing factors （such as faults, rivers, geological boundaries, and roads）, and for assigning weights to condi- tioning factors for mapping of landslide susceptibility. This method can be expressed as p=C~, where d is the fractal dimension, and C is a constant. This relationship indicates a fractal relation between land- slide density （p） and distances to conditioning factors （6）. The case of d〉0 suggests a significant spatial correlation between landslides and conditioning factors. The larger the d （〉0） value, the stronger the spatial correlation is between landslides and a specific conditioning factor. Two case studies in South China were examined to demonstrate the usefulness of this novel method.

A robust discretization method of factor screening for landslide susceptibility mapping using convolution neural network,random forest,and logistic regression models

The selection of discretization criteria and interval numbers of landslide-related environmental factors generally fails to quantitatively determine orfilter,resulting in uncertainties and limitations in the performance of machine learning(ML)methods for landslide susceptibility mapping(LSM).The aim of this study is to propose a robust discretization criterion(RDC)to quantify and explore the uncertainty and subjectivity of different discretization methods.The RDC consists of two steps:raw classification dataset generation and optimal dataset extraction.To evaluate the robustness of the proposed RDC method,Lushan County of Sichuan Province in China was chosen as the study area to generate the LSM based on three datasets(optimal dataset,original dataset with continuous values,and statistical dataset)using three popular ML methods,namely,convolution neural network,random forest,and logistic regression.The results show that the areas under the receiver operating characteristic curve(AUCs)of the optimal dataset for the abovementioned ML models are 0.963,0.961,and 0.930 which are higher than those of the original dataset(0.938,0.947,and 0.900)and statistical dataset(0.948,0.954,and 0.897).In conclusion,the RDC method can extract the more representative features from environmental factors and outperform the other conventional discretization methods.

High-risk spatiotemporal patterns of cutaneous leishmaniasis:a nationwide study in Iran from 2011 to 2020

Background Cutaneous leishmaniasis(CL)is a wide-reaching infection of major public health concern.Iran is one of the six most endemic countries in the world.This study aims to provide a spatiotemporal visualization of CL cases in Iran at the county level from 2011 to 2020,detecting high-risk zones,while also noting the movement of high-risk clusters.Methods On the basis of clinical observations and parasitological tests,data of 154,378 diagnosed patients were obtained from the Iran Ministry of Health and Medical Education.Utilizing spatial scan statistics,we investigated the disease’s purely temporal,purely spatial,spatial variation in temporal trends and spatiotemporal patterns.At P=0.05 level,the null hypothesis was rejected in every instance.Results In general,the number of new CL cases decreased over the course of the 9-year research period.From 2011 to 2020,a regular seasonal pattern,with peaks in the fall and troughs in the spring,was found.The period of September–February of 2014–2015 was found to hold the highest risk in terms of CL incidence rate in the whole country[relative risk(RR)=2.24,P<0.001)].In terms of location,six signifcant high-risk CL clusters covering 40.6%of the total area of the country were observed,with the RR ranging from 1.87 to 9.69.In addition,spatial variation in the temporal trend analysis found 11 clusters as potential high-risk areas that highlighted certain regions with an increasing tendency.Finally,fve space-time clusters were found.The geographical displacement and spread of the disease followed a moving pattern over the 9-year study period afecting many regions of the country.Conclusions Our study has revealed signifcant regional,temporal,and spatiotemporal patterns of CL distribution in Iran.Over the years,there have been multiple shifts in spatiotemporal clusters,encompassing many diferent parts of the country from 2011 to 2020.The results reveal the formation of clusters across counties that cover certain parts of provinces,indicating the importance of conducting spatiotemporal analyses at the county level for studies that encompass entire countries.Such analyses,at a fner geographical scale,such as county level,might provide more precise results than analyses at the scale of the province.

White Matter Changes in Posttraumatic Stress Disorder Following Mild Traumatic Brain Injury： A Prospective Longitudinal Diffusion Tensor Imaging Study

Background： The ability to predict posttraumatic stress disorder （PTSD） is a critical issue in the management of patients with mild traumatic brain injury （mTBI）, as early medical and rehabilitative interventions may reduce the risks of long-term cognitive changes. The aim of the present study was to investigate how diffusion tensor imaging （DTI） metrics changed in the transition from acute to chronic phases in patients with mTBI and whether the alteration relates to the development of PTSD. Methods： Forty-three patients with mTBI and 22 healthy volunteers were investigated. The patients were divided into two groups： successful recovery （SR, n = 22） and poor recovery （PR, n = 21）, based on neurocognitive evaluation at 1 or 6 months after injury. All patients underwent magnetic resonance imaging investigation at acute （within 3 days）, subacute （10-20 days）, and chronic （1-6 months） phases after injury. Group differences of fractional anisotropy （FA） and mean diffusivity （MD） were analyzed using tract-based spatial statistics （TBSS）. The accuracy of DTI metrics for classifying PTSD was estimated using Bayesian discrimination analysis. Results： TBSS showed white matter （WM） abnormalities in various brain regions. In the acute phase, FA values were higher for PR and SR patients than controls （all P 〈 0.05）. In subacute phase, PR patients have higher mean MD than SR and controls （all P 〈 0.05）. In the chronic phase, lower FA and higher MD were observed in PR compared with both SR and control groups （all P 〈 0.05）. PR and SR groups could be discriminated with a sensitivity of 73%, specificity of 78%, and accuracy of 75.56%, in terms of MD value in subacute phase. Conclusions： Patients with mTBI have multiple abnormalities in various WM regions. DTI metrics change over time and provide a potential indicator at subacute stage for PTSD following mTBI.

Estimation of transit trip production using Factorial Kriging with External Drift:an aggregated data case study

Studies in transportation planning routinely use data in which location attributes are an important source of information.Thus,using spatial attributes in urban travel forecasting models seems reasonable.The main objective of this paper is to estimate transit trip production using Factorial Kriging with External Drift(FKED)through an aggregated data case study of Traffic Analysis Zones in São Paulo city,Brazil.The method consists of a sequential application of Principal Components Analysis(PCA)and Kriging with External Drift(KED).The traditional Linear Regression(LR)model was adopted with the aim of validating the proposed method.The results show that PCA summarizes and combines 23 socioeconomic variables using 4 components.The first component is introduced in KED,as secondary information,to estimate transit trip production by public transport in geographic coordinates where there is no prior knowledge of the values.Cross-validation for the FKED model presented high values of the correlation coefficient between estimated and observed values.Moreover,low error values were observed.The accuracy of the LR model was similar to FKED.However,the proposed method is able to map the transit trip production in several geographical coordinates of non-sampled values.

Optimal orientations of discrete global grids and the Poles of Inaccessibility

Spatial analyses involving binning often require that every bin have the same area,but this is impossible using a rectangular grid laid over the Earth or over any projection of the Earth.Discrete global grids use hexagons,triangles,and diamonds to overcome this issue,overlaying the Earth with equally-sized bins.Such discrete global grids are formed by tiling the faces of a polyhedron.Previously,the orientations of these polyhedra have been chosen to satisfy only simple criteria such as equatorial symmetry or minimizing the number of vertices intersecting landmasses.However,projection distortion and singularities in discrete global grids mean that such simple orientations may not be sufficient for all use cases.Here,I present an algorithm for finding suitable orientations;this involves solving a nonconvex optimization problem.As a side-effect of this study I show that Fuller’s Dymaxion map corresponds closely to one of the optimal orientations I find.I also give new high-accuracy calculations of the Poles of Inaccessibility,which show that Point Nemo,the Oceanic Pole of Inaccessibility,is 15 km farther from land than previously recognized.