Use Of Spatial Statistics Research Articles

Spatial perspectives on student profiling to inform open distance e-learning (ODeL) in various geographical contexts: a case study from the Global South

Sustainable Development Goal (SDG) 4 underpins the other SDGs and emphasizes the importance of increasing access to inclusive, equitable, quality education. Equitable and inclusive access to education remains a pressing issue in many parts of the world, especially in the Global South. Developments and challenges experienced in the Global South have increased awareness of the potential of open distance e-learning to increase access to higher education. This potential has to be considered from the perspective of the digital divide. Analyzing student data can play an important role in obtaining insights into students' lives to improve the quality of the educational services provided.This paper aims to reflect on the use of spatial statistics in compiling a profile of the students registered for a Geography module at the University of South Africa. This paper has two objectives. The first is to specify the broad student profile in terms of the geographical location of students. The second objective is to reflect on the lifeworlds of the students in the different clusters and the challenges they may face in successfully completing their studies. Four representative student profiles could be compiled using lifestyle data. Marked differences in lifestyle factors between the four identified profile types were observed. These may directly impact students' ability to progress with their studies.

Generation of random microstructure in fiber reinforced composites and its comparison with carbon/carbon composite test samples through statistical analysis

ABSTRACT The elastic properties are important parameters for design of components using carbon/carbon composites. These elastic properties are required to be estimated with acceptable level of accuracy. These elastic properties are normally determined numerically. Estimation of elastic properties using numerical methods require accurate representation of microstructure of fibres in composite materials. Fibre arrangement is an important factor in the microstructure that shall be agree closely to the real composites. One of the method to compare the fibre arrangements in composites is through use of spatial statistics. In the present work the fibre arrangement in unidirectional composites is generated with volume fraction of 0.52–0.58 using an algorithm based on random removal of fibres. The fibre arrangements in carbon/carbon composite test samples are taken from microscopy images of unidirectional carbon/carbon composite, with same volume fraction of fibres. The simulated fibre arrangements are compared with carbon/carbon composites samples using statistical descriptors. The statistical descriptors are chosen from the spatial statistics. All the statistical descriptors show close agreement between the real and simulated microstructures.

The use of spatial statistics in the analysis of salaries at poviat level in Poland

The spatial differentiation of salaries is the subject of many scientific studies, both theoretical and empirical. One of the factors determining remuneration in Poland is the structure and type of business activity, specific for a given region and depending on its poviats (counties) in terms of the level of the average gross monthly salary by means of spatial autocorrelation statistical methods. The analysed statistical data for 2010–2019 come from the Local Data Bank (Bank Danych Lokalnych – BDL) of Statistics Poland. Global and local measures were used in the analysis. The calculation of the global parameters of spatial autocorrelation was based on the I Moran and C Geary statistics, while the Ii Moran statistic, which belongs to local spatial indicators from the LISA group (Local Indicators of Spatial Association), was used to identify the local autocorrelation. The statistical significance of the global statistics was verified by means of a randomisation approach based on theoretical moments. The I Moran and C Geary global statistics indicated a significant (very weak or weak) and positive spatial autocorrelation between poviats in terms of the level of average gross monthly salaries in 2010–2019, which shows the existence of spatial poviat structures of similar values, i.e. clusters with high or low values of average salaries. The increase in I Moran’s statistics and the growth of the C Geary in the analysed period indicate a decrease in the differentiation of average monthly salaries between poviats, thus signifying an increase in the dependence of spatial autocorrelation. The analysis of the results of the obtained local statistics allowed the determination of clusters of similar poviats in Poland, e.g. Mazowiecki, Pomorski and Śląski. Furthermore, the results of the analysis indicated the presence of outlier poviats.

Spatial modeling of multi-hazard threat to cultural heritage sites

Cultural heritage is the foundation upon which global and historical values are based on. It connects us to the legacy left by our ancestors and identifies who we are as part of the modern society. Globally and specifically in the northeastern Romania, the landscape where cultural heritage sites were built on is constantly evolving due to mass wasting processes. Among these processes, landslide and gullies can disrupt the gravitational equilibrium directly or around these sites, threatening their very existence and our capacity to pass them on to future generations. Because landsliding and gullying are stochastic processes, the use of spatial statistics has often been employed to map locations at risk. In this work, we make use of advanced spatial Bayesian statistics to model landslide and gully erosion susceptibilities, separately. And, we ultimately combine these two outputs into a unified multi-hazard susceptibility model which we cross with the known cultural heritage sites in a study area close to the city of Iaşi, in Romania. Specifically, we implement a Bayesian version of a Generalized Additive Model (GAM) which assumes that the two separate landslide and gully presence/absence distributions to behave according to a Bernoulli probability distribution. Contrary to common practices in the literature, the two susceptibility models both feature fixed and random effects, including covariates acting at a latent level. We do this to also capture the unexplained but spatially coherent distribution of properties not directly included in the model. As for the properties directly expressed as covariates, our GAM features terrain attributes obtained from a LIDAR survey, in addition to land use and soil layers.The two single models outstandingly perform (AUC > 0.9) both during the calibration and validation phases.This modeling procedure ensures that the probability of occurrence of the two mass wasting processes under consideration is well estimated and therefore can be used to reliably plan conservation practices for local cultural heritage sites deemed at risk.

Spatial analysis of cerebral palsy in children and adolescents and its association with health vulnerability.

Cerebral Palsy (CP) is commonly associated with low socioeconomic status. Use of spatial statistics and a Geographic Information Systems (GIS) are scarce and may contribute to the understanding of CP in a social context. To that end a spatial analysis of CP in children and adolescents was performed to analyze the association of CP with levels of vulnerability in a city (Aracaju, Sergipe) in north-eastern Brazil. In addition, an ecological study was conducted with data obtained from a populationbased survey and secondary data. Exploratory spatial data analysis and linear regression were used. A total of 288 CP cases were identified, with a prevalence of 1.65/1,000 and differences among city neighbourhoods ranging from 0-4/1,000. The mean age of cases studied was 9 years 1 month, with a standard deviation of 5 years 2 months. Most study subjects with cerebral palsy (163) were male (56.4%). The distribution of CP in the study population was not homogeneous throughout the territory. Some areas had clusters, with more cases associated with areas of high vulnerability. Spatial data analysis using GIS was useful to gain an epidemiological understanding of CP distribution that can guide decisionmaking with respect to production, distribution, and regulation of health goods as well as services at the local level.

The Use of Spatial Statistics to Control Human and Sex Trafficking

The Use of Spatial Statistics to Control Human and Sex Trafficking

The Dehesas of Extremadura, Spain: A Potential for Socio-Economic Development Based on Agritourism Activities

Dehesas form an agroforestry system which is highly developed in Extremadura as they occupy over 1,000,000 hectares. This is an extensive production system based on exploitation by stockbreeding with certain complements of agricultural and extractive products. It is however underexploited for activities other than agrolivestock production. As a result this research starts from the hypothesis that greater socio-economic development is possible in areas near this type of ecosystem if we consider that it is extremely attractive to tourism, at least in some of its forms. Complementing agrolivestock exploitation with tourist exploitation means that it is necessary to be familiar with the situation of the current availability of accommodation. In order to do so we analyse the main parameters characterising rural accommodation in Extremadura, Spain, the results of which reflect low occupancy for a large part of the year and consequently seasonal variation is high. This fact contrasts with the huge potential of dehesas for agritourism. In order to carry out this study we resort to the use of spatial statistics, in particular the grouping analysis. In its configuration we consider the location of the rural accommodation and its proximity to areas of dehesas made up of holm oaks (Quercus ilex) and cork oaks (Quecus suber) together with protected natural spaces, basing ourselves also on proximity to livestock trails and natural swimming pools. The results lead to the creation of 5 homogeneous groups of which 3 correspond to accommodation establishments capable of setting up agritourism and agriecotourism initiatives; this affects 45% of the establishments. However, no current initiatives of this type exist despite the fact that the National Reference Centre for Agritourism is located in Extremadura. From this can be inferred the need for implementing tourist policies to encourage the generating of tourist products specifically intended to exploit the potential of the dehesa as a complement to the current availability of rural accommodation.

The Advantages of Comparative LISA Techniques in Spatial Inequality Research: Evidence from Poverty Change in the United States

Although scholarship regarding spatial inequality has grown in recent years, past research has seen limited use of spatial statistics—let alone comparison between spatial statistical techniques. Comparing and contrasting the application and use of spatial statistics is valuable in research because it allows for more precise identification of spatial patterns, and highlights results that may be hidden when only using a single method. This study serves as a demonstration on how the use of multiple LISA statistics can benefit inequality related research. Analyzing changes in county level poverty in the rural United States from 1990 to 2015 serves as a tool to demonstrate these techniques and this study examined how the geographic distribution of poverty has changed, and well as if there is evidence of diffusion effects. The three featured techniques utilized Local Indicators of Spatial Association (LISA) statistics. The techniques are Bivariate LISA, LISA Cluster Transitions, and LISA Diffusion Transitions, with the last technique specifically designed for this study. Each technique varies in how it reports the changes in the spatial structure of poverty. Bivariate LISA and LISA Cluster Transitions are complementary to each other—with the former technique providing a single global statistic while the latter is more easily interpretable. Diffusion Transitions show how the highest and lowest values of a variable may be spreading over time. The study also produces new findings regarding rural poverty, with poverty in Mountain-West and rural Sun Belt counties on the rise. Analysis shows a diffusion effect for poverty in Southeastern metropolitan fringe counties.

Analyzing valve interstitial cell mechanics and geometry with spatial statistics

Understanding cell geometric and mechanical properties is crucial to understanding how cells sense and respond to their local environment. Moreover, changes to cell mechanical properties under varied micro-environmental conditions can both influence and indicate fundamental changes to cell behavior. Atomic Force Microscopy (AFM) is a well established, powerful tool to capture geometric and mechanical properties of cells. We have previously demonstrated substantial functional and behavioral differences between aortic and pulmonary valve interstitial cells (VIC) using AFM and subsequent models of VIC mechanical response. In the present work, we extend these studies by demonstrating that to best interpret the spatially distributed AFM data, the use of spatial statistics is required. Spatial statistics includes formal techniques to analyze spatially distributed data, and has been used successfully in the analysis of geographic data. Thus, spatially mapped AFM studies of cell geometry and mechanics are analogous to more traditional forms of geospatial data. We are able to compare the spatial autocorrelation of stiffness in aortic and pulmonary valve interstitial cells, and more accurately capture cell geometry from height recordings. Specifically, we showed that pulmonary valve interstitial cells display higher levels of spatial autocorrelation of stiffness than aortic valve interstitial cells. This suggests that aortic VICs form different stress fiber structures than their pulmonary counterparts, in addition to being more highly expressed and stiffer on average. Thus, the addition of spatial statistics can contribute to our fundamental understanding of the differences between cell types. Moving forward, we anticipate that this work will be meaningful to enhance direct analysis of experimental data and for constructing high fidelity computational of VICs and other cell models.

Accelerated estimation and permutation inference for ACE modeling.

There are a wealth of tools for fitting linear models at each location in the brain in neuroimaging analysis, and a wealth of genetic tools for estimating heritability for a small number of phenotypes. But there remains a need for computationally efficient neuroimaging genetic tools that can conduct analyses at the brain‐wide scale. Here we present a simple method for heritability estimation on twins that replaces a variance component model‐which requires iterative optimisation‐with a (noniterative) linear regression model, by transforming data to squared twin‐pair differences. We demonstrate that the method has comparable bias, mean squared error, false positive risk, and power to best practice maximum‐likelihood‐based methods, while requiring a small fraction of the computation time. Combined with permutation, we call this approach “Accelerated Permutation Inference for the ACE Model (APACE)” where ACE refers to the additive genetic (A) effects, and common (C), and unique (E) environmental influences on the trait. We show how the use of spatial statistics like cluster size can dramatically improve power, and illustrate the method on a heritability analysis of an fMRI working memory dataset.

Geospatial Analysis of Building Structures in Megacity Dhaka: the Use of Spatial Statistics for Promoting Data-driven Decision-making

Information on spatial building structures is limited, but it can support efficient planning and management in the context of fast-growing big cities in many developing countries. In this paper, we present a spatial analysis approach that includes an estimate of building intensity in the megacity of Dhaka and a spatial analysis using spatial statistics. The entire city was divided into regular grids and the building intensity (both horizontal and vertical) was extracted using vector type building information; the spatial statistics were calculated on the basis of Moran’s I and Gini indices. The variability of the estimated spatial statistics is interpreted according to co-relationship or clustering patterns with the location of the central business district (CBD) area as well as the public bus transit infrastructure (routes and stops). The results show that the residential building structure intensity is prominent and the concentrations are distributed all over the city. The mixed-use type building structures show highest clustering, with fewer outliers in the old part of the city. The vertical-use intensities indicate extreme clustering within highly intensified building activity in the nearby CBD area. The higher presence of low-low clustering of horizontal intensity indicated low development at the suburban area. However, the strongly clustered grid cells within residential sector as well as horizontal development classes are less accessible by bus transit within a defined catchment area, whereas the service sector and vertical development type seem to be more accessible. This type of geographic approach, visualization, and statistical information can help in making data-driven planning decisions with the advantage of monitoring urban development; however, the modeling sensitivity and uncertainties in the building data set remain open for further investigation.

Use of spatial statistics to identify hotspots of lead and copper in selected soils from north of Khuzestan Province, southwestern Iran

ABSTRACTTo identify areas with a high priority for remediation action, regarding soil pollution with Pb and Cu in southwestern Iran, 234 soil samples (0–10 cm) from industrial, urban, agricultural, range and forest land utilization types were collected and analyzed for total concentration of Pb and Cu, using an atomic absorption spectrometer. The results indicated that 55% of the soil samples had Pb concentrations greater than background values; for Cu, this figure was 45%. The highest pollution index values for Pb and Cu found in the industrial areas, while the forests and rangelands exhibited the lowest values. Spatial distribution analysis of the soils, using the indicator kriging approach, showed spherical, Gaussian and exponential equations as the best-fitted models for the description of Pb and Cu pollution in the study area. Accordingly, with a probability of greater than 0.8, approximately 8,875 and 3,106 km2, and 7,148 and 832 km2 of the study area classified as low- and highly-polluted soils, in terms of Pb and Cu, respectively. Using the Moran’s I index, 20 and 18 points identified as hotspots for Pb and Cu, respectively, which strongly correlate to the locations of the industrial towns and urban districts of Ahvaz, Masjed-Soleiman, and Izeh.

Cultural landscap’s changes of Solyna commune

The main aim of the present work was to analyze the dynamics of settlements from the 2nd half of the 18th century to 2nd half of 20th century in Solyna commune (in polish Solina), in Lesko district, Podkarpackie voivodeship in Eastern Poland. There was analyzed the Solyna commune in terms of the changes of settlements and applied maps together with published historical data for the analysis of changes in its structure. The first was the historical map, in other words the tactical map of «WIG- Military Institute of Geography». Next maps from the «WMS-Web Map Service, Geoportal» were analyzed. Changes in the number of villages and households population dynamics for Greek-catholics, «Latynnykys» and Jews for the year 1785 were evaluated applying ArcGIS 10.3 program. Ukrainian ones were evaluated using ArcGIS program in comparison with the number of Poles and Jews for the year 1939. Ethnic and religious composition from the 2nd half of the 18th century to 2nd half of 20th century and the character of settlements distribution in Solyna commune was evaluated based on the study of spatial distribution of settlements with the use of Spatial Statistics (Spatial Autocorrelation Global Moran's test, Standard Deviational Ellipse and Mean Centre). In 1785 there were 80,55% of Greek-catholics and in 1939 there were 89,82% of Ukrainians in Solyna commune. There was confirmed a similar configuration of Standard Deviation Ellipse and Mean Center for Greek-catholic for the year 1785 and Ukrainians for the year 1939 in Solyna commune. The scale and results of such changes are essential for future research, mainly in terms of the change of traditional village system infrastructure and culture.

Generalized spatial fusion model framework for joint analysis of point and areal data

The availability of geo-referenced data increased dramatically in recent years, motivating the use of spatial statistics in a variety of research fields, including epidemiology, environmental science, remote sensing, and economics. Combining data measured at both point and areal support can improve parameter estimation and increase prediction accuracy. We propose a new generalized spatial fusion model framework for jointly analyzing point and areal data. Assuming a common latent spatial process, we take a Bayesian hierarchical approach to model both types of data without distributional constraints. The models are implemented with nearest neighbor Gaussian process in Stan modeling language to increase computational efficiency and flexibility. Our simulation study shows that generalized fusion models under this framework model the latent process better than spatial process models. We identify scenarios where fusion models can offer large improvements. We then apply the framework to epidemiological data to identify the spatial risk pattern of respiratory diseases and lung cancer in Canton of Zurich, Switzerland.

Аналіз поселень ґміни Лютовска від ІІ половини ХVІІІ століття із застосуванням GIS

The study was conducted within the actual area of Lutoviska commune in Bieszczady district, Podkarpackie voivodeship in Eastern Poland. The dynamics of settlements in Lutoviska commune from the 2nd half of the 18th century was discussed. Maps and historical data were analyzed in the ArcGIS 10.3 program. In the study there were presented possibilities of applying the ArcGIS for Desktop 10.3 program. We analyzed the Lutoviska commune in terms of the changes of settlements and used maps for the analysis of changes in its structure. The first of these was the historical map, namely the tactical map of “WIG” in scale 1:100 000 from 1936. Next we analyzed maps from 1965 and 2014, which were prepared applying the “WMS” server. The changes in the number of villages and households were presented. Population dynamics for Greek-Catholics, “Latynnykys” and Jews for the year 1785 and Ukrainians in comparison with the number of Poles and Jews for the year 1939 were shown. Ethnic and religious composition from the 2nd half of the 18th century was evaluated. Based on the study of spatial placement of settlements with the use of Spatial Statistics (Spatial Autocorrelation Global Moran’s test, Standard Deviational Ellipse and Mean Centre) the character of settlements distribution in Lutoviska commune was presented. There was confirmed a similar configuration of Standard Deviation Ellipse and Mean Center for Greek-Catholics-Ukrainians in 1785 and Ukrainians in 1939 in Lutoviska commune. The scale and results of such changes are interesting for future research, mainly in terms of the change of traditional village system infrastructure and culture. Key words: dynamics, village, GIS, Lutoviska commune.

Estatística Espacial Aplicada aos Estudos de Incidência de Câncer

O Sistema de Informação Geográfica (SIG) como ferramenta em estudos epidemiológicos tem se tornado algo frequente na área da Saúde Coletiva. Em estudos sobre incidência de câncer, o uso da análise espacial ainda é incipiente no país, entretanto possuí grande potencial para auxílio nos modelos de planejamento em saúde. O objetivo deste trabalho é apresentar um método empírico de análise de padrões espaciais para os casos de incidência de cânceres considerados raros que acometem a população com idades entre 0 e 19 anos, discutindo a aplicabilidade da estatística espacial e as possíveis utilizações da interpretação da disposição espacial dos eventos. Utilizou-se o SIG para georreferenciar as taxas de incidências de casos de cânceres de alguns grupos da Classificação Internacional de Câncer Infantil (CICI), ajustadas por idade e sexo, segundo os Centros de Saúde de suas residências. Foram estimadas as razões padronizadas de incidência (Standardized Morbidity Ratio-SMR). Para controlar a instabilidade das taxas devido ao pequeno número de casos, foi feita uma suavização pelo método Empirical Bayes. Comparando as taxas anuais, suavizadas pelo referido método Bayesiano, constatou-se diferenças entre elas quanto à distribuição espacial, com concentração destas nas regiões Sudoeste e Noroeste do município de Campinas. Conclui-se que o uso de estatística espacial como ferramenta de apoio à decisão, permite identificar a distribuição de casos de cânceres do grupo da CICI, contribuindo para o planejamento em saúde.

Use of spatial statistics to investigate early forest degradation activities as detected from satellite images

Selective logging gives currently a major contribution to ongoing deforestation in the Brazilian Amazonia. The spatial distribution of log landing sites (LLS), i.e. the sites where logged trees are collected, serves as a proxy to the intensity of selective logging activities. In this study we analysed the LLS pattern in a study area that has a rapid deforestation. Actual LLS locations were extracted from a Landsat image of 2000 that covers a large part of the study area. We first used the inhomogeneous J-function. A kernel bandwidth of 20 km best modelled the non-stationarity, showing a strongly clustered LLS distribution. Second, the Area-interaction point process model incorporating information about distance of LLS to roads and to clear-cut deforested areas was applied. The model well explained the clustered LLS pattern and showed a significant effect of distance to roads. We concluded that this spatial statistical study helped to quantify and better understand the LLS pattern.

Applications of spatial statistical network models to stream data

AbstractStreams and rivers host a significant portion of Earth's biodiversity and provide important ecosystem services for human populations. Accurate information regarding the status and trends of stream resources is vital for their effective conservation and management. Most statistical techniques applied to data measured on stream networks were developed for terrestrial applications and are not optimized for streams. A new class of spatial statistical model, based on valid covariance structures for stream networks, can be used with many common types of stream data (e.g., water quality attributes, habitat conditions, biological surveys) through application of appropriate distributions (e.g., Gaussian, binomial, Poisson). The spatial statistical network models account for spatial autocorrelation (i.e., nonindependence) among measurements, which allows their application to databases with clustered measurement locations. Large amounts of stream data exist in many areas where spatial statistical analyses could be used to develop novel insights, improve predictions at unsampled sites, and aid in the design of efficient monitoring strategies at relatively low cost. We review the topic of spatial autocorrelation and its effects on statistical inference, demonstrate the use of spatial statistics with stream datasets relevant to common research and management questions, and discuss additional applications and development potential for spatial statistics on stream networks. Free software for implementing the spatial statistical network models has been developed that enables custom applications with many stream databases.This article is categorized under: Water and Life > Nature of Freshwater Ecosystems

Evaluating Experimental Design of ERT for Soil Moisture Monitoring in Contour Hedgerow Intercropping Systems

Contour hedgerow intercropping systems have been proposed as an alternative to traditional agricultural practice with a single crop, as they are effective in reducing run‐off and soil erosion. However, competition for water and nutrients between crops and associated hedgerows may reduce the overall performance of these systems. To get a more detailed understanding of the competition for water, spatially resolved monitoring of soil water contents in the soil‐plant‐atmosphere system is necessary. Electrical resistivity tomography (ERT) is potentially a valuable technique to monitor changes in soil moisture in space and time. In this study, the performance of different ERT electrode arrays to detect the soil moisture dynamics in a mono‐ and an intercropping system was tested. Their performance was analyzed based on a synthetic study using geophysical measures, such as data recovery and resolution, and using spatial statistics of retrieved water content, such as an adjusted coefficient of variation and semivariances. The synthetic ERT measurements detected differences between the cropping systems and retrieved spatial structure of the soil moisture distribution, but the variance and semivariance were underestimated. Sharp water content contrasts between horizons or in the neighborhood of a root water uptake bulb were smoothened. The addition of electrodes deeper in the soil improved the performance, but sometimes only marginally. ERT is therefore a valuable tool for soil moisture monitoring in the field under different cropping systems if an electrode array is used which can resolve the patterns expected to be present in the medium. The use of spatial statistics allowed to not only identify the overall model recovery, but also to quantify the recovery of spatial structures.

Potential and Challenges in Use of Thermal Imaging for Humid Region Irrigation System Management

Thermal imaging has shown potential to assist with many aspects of irrigation management that include scheduling application of water and detecting leaking irrigation canals and delivery systems. There are particular challenges for use of thermal imaging for scheduling irrigation in humid subtropical climates, which include the need for fine delineation of canopy temperatures under low vapor pressure deficits, proper accounting for micrometeorological conditions, and altitude effects on canopy temperature represented at the camera. A review of pertinent issues involved in using thermal methods for sensing canopy temperature is presented that can be applied to the imaging problem. Altitude effects were demonstrated in a new field experiment, and multiple regression was used to indicate and model weather and altitude effects. The use of spatial statistics was shown to enhance the value of thermal imagery using sensor fusion. Thermal imagery was also useful in detecting leakage from irrigation systems in the context of overall irrigation system management.