Spatial Statistical Techniques Research Articles

Malocclusion in adolescents in the State of São Paulo: spatial and hierarchical analysis

The scope of this study was to analyze the spatial distribution of malocclusion (MO), estimate the prevalence and evaluate the associated factors in adolescents. It was a study with results of 5,558 adolescents aged 15 to 19 from the São Paulo Oral Health (SB) 2015 survey. The outcome was MO. Sociodemographic aspects, access to dental services, dental caries and tooth loss were the independent variables. A total of 162 municipalities in the state of São Paulo were included and spatial statistics techniques were applied. Hierarchical logistic regression models were performed. The prevalence of MO was 29.3%. There was a spread pattern between the types of MO and positive detachment (p<0,05). Non-white adolescents (OR=1.32, 95%CI: 1.24-1.42), with less years of schooling (OR=1.30, 95%CI: 1.22-1.42), with teeth extracted due to caries (OR=1.40, 95%CI: 1.03-1.88) were more likely to have MO. Adolescent access to dental consultation did not contribute to reducing the chance of developing MO, regardless of whether the dental consultation occurred less (OR=2.02, 95%CI: 1.65-2.47) or more than one year before (OR=1.63, 95%CI: 1.31-2.03). Thus, the occurrence of MO is unequally distributed in the state of São Paulo and associated with sociodemographic conditions, access to consultations and tooth loss due to caries.

태양광발전시스템의 발전량 추정을 위한 역거리 가중 보간(IDW) 기술 기반의 다중회귀 모형 개발

Meteorological data measured at the location of the system is commonly used to improve the accuracy of predicting the power output of a solar power system. If there is no observation or data is missing, public data such as an automated synoptic observing system (ASOS) and automatic weather station (AWS) can be used. However, since the public observatories are far from the PV system, the uncertainty of the prediction due to the distance difference is expected to increase. To solve this problem, we propose a multiple regression analysis technique that predicts power output based on inverse distance weighted (IDW) interpolation. This spatial statistics technique can estimate the value of a location where data is not observed. The proposed method was demonstrated through a case study, and the prediction accuracy of solar power output was reviewed. The prediction accuracy varies depending on the case, but the accuracy of the case, which is within 15 km from the public observatory, was good because the mean absolute percentage error (MAPE) of the prediction of power output was less than 4.2%.

The temporal and spatial relationships between professional sport events and reported vehicular crashes: an analysis of Cleveland, Ohio

ABSTRACT Road safety is one of the world’s greatest public health challenges, with more than 3,500 deaths on the roads each day and estimated 50 million injuries annually (World Health Organization, 2021). This study explores the relationship between professional sporting events and vehicular crashes by examining crash data, game times, and venues using longitudinal data from Cleveland, Ohio (2017–2019). We employ two multivariate modeling analyses and spatial statistical techniques to examine the extent to which sporting events are related to car crashes before, during, and after events and the spatial relationship between where the venues are located and the number of crashes. The temporal analyses (n = 28,260) show that crashes with damage reported a significant increase, particularly after the more attended NFL games. Meanwhile, a spatial analysis (n=741) shows that the location of the sports venue also demonstrates associations with the number of crashes, while the significance varies across spatial distances. From a scholarly perspective, our study identifies the relationship between sports events and car crashes nearby sports venues, which adds to the broader literature on vehicular crashes and society. Practically, addressing this relationship can provide a concise strategy for both the public and private sectors to reduce car crashes.

Bacteria spatial tracking in Urban Park soils with MALDI-TOF Mass Spectrometry and Specific PCR

Urban parks constitute one of the main leisure areas, especially for the most vulnerable people in our society, children, and the elderly. Contact with soils can pose a health risk. Microbiological testing is a key aspect in determining whether they are suitable for public use. The aim of this work is to map the spatial distribution of potential dangerous Enterobacteria but also bioremediation useful (lipase producers) isolates from soils in an urban park in the area of Valencia (Spain). To this end, our team has collected 25 samples of soil and isolated 500 microorganisms, using a mobile application to collect information of the soil samples (i.e. soil features, temperature, humidity, etc.) with geolocation. A combined protocol including matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) and 16S rDNA sequencing PCR has been established to characterize the isolates. The results have been processed using spatial statistical techniques (using Kriging method), taking into account the number of isolated strains, also proving the reactivity against standard pathogenic bacterial strains (Escherichia coli, Bacillus cereus, Salmonella, Pseudomonas and Staphylococcus aureus), and have increased the number of samples (to 896 samples) by interpolating spatially each parameter with this statistical method. The combined use of methods from biology and computer science allows the quality of the soil in urban parks to be predicted in an agile way, which can generate confidence in its use by citizens.

Distribución espacio-temporal de la atención hospitalaria por diabetes Mellitus: el caso de los Servicios de Salud Metropolitanos Occidente y Sur, Chile

Hospital care for chronic diseases, such as diabetes mellitus, represents a constant demand on the health system. The importance of monitoring and detection of socio-spatial gaps in the supply of hospital beds play a key role in territorial health diagnoses. The methodology uses a set of spatial statistical techniques applied to demographic and clinical factors in the communes that make up the Western Metropolitan Health Service and the Southern Metropolitan Health Service, together with the space-time examination of hospital occupancy due to diabetes in the five-year period 2015-2019. The results allow us to know the cyclical evolution of hospital demand according to the seasonal transition of diabetes in the territory, the demographic composition and the needs of the population, offering a space-time diagnosis based on logics of imbalances and territorial rebalancing that can offer a Valuable planning vision for health teams

Interactions between oceanographic variables and population structure of the yellowfin tuna Thunnus albacares (Bonnaterre, 1788) in the Western Central Atlantic

AbstractPelagic fish species, such as tunas, represent important resources for the fishing fleets of several countries. Identifying spatiotemporal distributions of abundance, structure of sizes, weights and influences of environmental variables is extremely important to promote sustainable catches and efficient management plans for fisheries stocks. This present study aimed to analyse the spatiotemporal distribution of Thunnus albacares, their relationships with oceanographic variables, as well as size and weight structure, and the proportions of life stages/phases in catches, mainly within the Brazilian Exclusive Economic Zone. Data were obtained on longline fleet catches; the sea surface temperature (SST) and chlorophyll‐a (Chla) were measured in an area ranging from 42.2° to 24.8° W and 5.9° S to 8.8° N; the lunar cycle was also studied. Generalised Additive Models and spatial statistics techniques were used to model the effect of the variables on catch per unit effort (CPUE) and on length structure. The results indicate that the distribution, abundance and length structure of T. albacares are strongly associated with environmental (SST and Chla), temporal (year, months, seasons and moon phases) and spatial (latitude, longitude and hemisphere) variables. The comparison of catch sizes with the first length at maturity (L50) indicated that approximately half of the catches are on young specimens, showing great variation over the years, reaching 74.8% in 2012. These results show that providences should be taken focusing on mitigating young specimens' capture and the sustainable exploitation of tuna in the Atlantic. The patterns of CPUE and length strongly support the feeding and spawning migration hypotheses.

Application of Technology to Develop a Framework for Predicting Power Output of a PV System Based on a Spatial Interpolation Technique: A Case Study in South Korea

To increase the accuracy of photovoltaic (PV) power prediction, meteorological data measured at a plant’s target location are widely used. If observation data are missing, public data such as automated synoptic observing systems (ASOS) and automatic weather stations (AWS) operated by the government can be effectively utilized. However, if the public weather station is located far from the target location, uncertainty in the prediction is expected to increase owing to the difference in distance. To solve this problem, we propose a power output prediction process based on inverse distance weighting interpolation (IDW), a spatial statistical technique that can estimate the values of unsampled locations. By demonstrating the proposed process, we tried to improve the prediction of photovoltaic power in random locations without data. The forecasting accuracy depends on the power generation forecasting model and proven case, but when forecasting is based on IDW, it is up to 1.4 times more accurate than when using ASOS data. Therefore, if measured data at the target location are not available, it was confirmed that it is more advantageous to use data predicted by IDW as substitute data than public data such as ASOS.

Assessment of Intra-Urban Heat Island in a Densely Populated City Using Remote Sensing: A Case Study for Manila City

Changes in the environment occur in cities due to increased urbanization and population growth. Sustainable Development Goal (SDG) 11 is intrinsically linked to the environment, one facet of which is the need for universal access to secure, inclusive, and accessible green and public places. As urban heat islands (UHI) have the potential to negatively influence cities and their residents, existing resources and data must be used to identify and quantify these effects. To address this, we present the use of satellite-derived (2013–2022) and meteorological data (2014–2020) to assess intra-urban heat islands in Manila City, Philippines. The assessment includes (a) understanding the temporal variability of air temperature measurements and outdoor thermal comfort based on meteorological data, (b) comparative and correlative analysis between common Land-Use Land Cover indicators (Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Normalized Difference Built-up Index (NDBI)) and Land Surface Temperature (LST), (c) spatial and temporal analysis of LST using spatial statistics techniques, and (d) generation of an intra-urban heat island (IUHI) map with a recommended class of action using a suitability analysis model. Finally, the areas that need intervention are compared to the affected population, and suggestions to enhance the thermal characteristics of the city and mitigate the effects of UHI are established.

Moderating Effects of Racial Segregation on the Associations of Cardiovascular Outcomes with Walkability in Chicago Metropolitan Area.

Cardiovascular diseases (CVDs), as the leading cause of death in the U.S., pose a disproportionate burden to racial/ethnic minorities. Walkability, as a key concept of the built environment, reflecting walking and physical activity, is associated with health behaviors that help to reduce CVDs risk. While the unequal social variation and spatial distribution inequality of the CVDs and the role of walkability in preventing CVDs have been explored, the moderating factors through which walkability affects CVDs have not been quantitatively analyzed. In this paper, the spatial statistical techniques combined with the regression model are conducted to study the distribution of the CVDs' health outcomes and factors influencing their variation in the Chicago metropolitan area. The spatial statistical results for the CVDs' health outcomes reveal that clusters of low-value incidence are concentrated in the suburban rural areas and areas on the north side of the city, while the high-value clusters are concentrated in the west and south sides of the city and areas extending beyond the western and southern city boundaries. The regression results indicate that racial segregation reduced the positive association between health outcomes and walkability, although both racial segregation and walkability factors were positively associated with CVDs' health outcomes.

PENGARUH PERUBAHAN TATA GUNA LAHAN TERHADAP DISPARITAS REGIONAL ANTARKECAMATAN DI KOTA TANGERANG SELATAN

Regional disparity is a phenomenon of economic differences between regions (Bakri, et.al 2016). The Bappeda of Banten Province stated that South Tangerang City experienced a relatively high regional disparity level (Noviar, 2021). Triyanto, et.al (2019) stated that land use changes influenced regional disparity. However, this influence has not been identified in South Tangerang City. This study aims to identify the influence of land use change on regional disparity between sub-districts in South Tangerang City in 2015 and 2019. The data collection technique uses secondary data from related agencies with regional disparity, spatial and multiple linear regression statistical analysis techniques. The results of the analysis show that regional disparity consist of low, medium and high levels. Then, land use changes in 2019 are built-up land use change with an area of ​​493.29 ha (84.75%) and non-built-up land use change with an area of ​​88.73 ha (15.25%) out of a total of 582, 02 ha (100%). Furthermore, the influence of built-up land use change (X1) and non-built-up land use change (X2) simultaneously or partially has a significant influence on regional disparity (Y) which is 86%, while the remaining 14% is influenced by other independent not examined variables.

Region matters: Mapping the contours of undernourishment among children in Odisha, India

Levels of child undernutrition and its correlates exhibit considerable spatial variation at different levels of granularity. In India, such variations and their interrelation have not been studied at the sub-district level primarily due to the non-availability of good quality granular data. Given the sheer regional diversity in India, it is essential to develop a region-specific evidence base at the micro-level. The current study utilised, for the first time, a sub-district level survey data (Concurrent Child Monitoring Survey-II, 2014-15) to investigate the statistically significant clusters and spatial patterns of burden of undernutrition among children. The emergence of distinct patterns at the level of natural geographical regions of the state-coastal, southern and northern regions, lead to a region-specific analysis to measure the impact of various demographic, socio-economic and maternal factors on the prevalence of undernutrition specific to the three regions, using the National Family Health Survey-IV unit-level data. The spatial dependence and clustering of child undernourishment across sub-districts in Odisha were studied using various spatial statistical techniques, including spatial econometric models. Binary logistic regression was applied in the region-specific analysis. Findings indicated statistically significant spatial clustering of undernutrition among children in specific geographic pockets with poor sanitation, low institutional and skilled deliveries, poor maternal health reinforcing the need for inter-sectoral coordination. Disparities across the three natural-regions, suggest that the parameters requiring priority for intervention may differ across levels of overall development. The spatial clustering of different socio-demographic indicators in specific geographic pockets highlights the differential impact of these determinants on child undernutrition thereby reinforcing a strong need for targeted intervention in these areas. Present analysis and the evidence-based micro-level analysis can be utilised as a model for other Indian states and low-resource countries, making interventions more effective through multiple, synergistic and a multi-sectoral approach.

Establishment of background pollution levels and spatial analysis of moss data on a regional scale

The determination of background pollution levels is fundamental for the interpretation of the results obtained from environmental biomonitoring. In this paper we propose a new probabilistic method, based on a Gaussian mixture model, for determining the distribution of regional background levels of different pollutants. The distribution of the reference level is used to categorize the observations as “background” or “above-background” and spatial statistical techniques are then applied to determine the probability of the background level being exceeded. To exemplify its use, we applied the method to concentrations of five potentially toxic elements (Cd, Cu, Hg, Pb and Zn) measured in the moss Pseudoscleropodium purum. The proposed method was applied to data resulting from sampling at ca. 150 sampling stations in a regular grid (15 × 15 km) in Galicia (NW Spain). Sampling was carried out in June in 2000 and 2002, and in March and September in 2004, 2006, 2008 and 2014. The proposed method yielded consistent results for all of the different sampling surveys, and the pollution levels were found to be closely related to the sources of pollution identified in the study region. In short, although not an optimal solution, the proposed method seems to be suitable and realistic for the qualitative assessment of regional pollution.

Spatiotemporal pattern of road traffic fatalities in Africa: the effect of economic development

ABSTRACT This study aims to analyse the spatiotemporal trends and effect of economic development on road traffic fatalities (RTF) across 48 countries in Africa using annual data between 2007 and 2016. Data on Gross Domestic Product (GDP) per capita, population density, road fatalities and registered vehicles were obtained from WHO and World Development Indicators. Spatial autocorrelation and clustering of mortality rates were analysed using spatial statistical techniques. Average mortality rate declined from 32.2 per 100,000 population in 2007 to 26.6 in 2016. There was wide spatial variation in mortality rates across countries. The year 2010 exhibited a statistically significant spatial dependence on mortality rates. Locally, hotspots of mortality rates were located in 10 countries in 2010 and in 2 countries in 2016. Significant ‘coldspots’ were exhibited in four countries during the study period. Mixed-effect negative binomial regression analysis results reveal significant negative relationship between GDP per capita and road fatalities for both highly developed and less developed countries. Furthermore, 1% increase in registered vehicles leads to a rise in RTF. The government needs to invest more funds in road infrastructure, safe public transport, enforcement of regulations and post-crash emergency care.

Degeneration of the Sensorimotor Tract in Degenerative Cervical Myelopathy and Compensatory Structural Changes in the Brain.

Degenerative cervical myelopathy is a progressive neurodegenerative disease, that has become increasingly prevalent in the aging population worldwide. The current study determined the factors affecting degeneration in the sensorimotor tract with degenerative cervical myelopathy and its relationship with brain structure. We divided patients into hyperintensity (HS) and non-hyperintensity (nHS) groups and measured the fractional anisotropy and apparent diffusion coefficients of the lateral corticospinal tract (CST), fasciculus gracilis and fasciculus cuneatus (FGC). Voxel-based morphometry (VBM) and tract-based spatial statistics (TBSS) techniques were used to estimate brain structure changes. Correlation of the modified Japanese Orthopaedic Association (mJOA) score, light touch, pinprick, motor score, and fractional anisotropy (FA) ratios of the CST at different levels were analyzed. Compared to healthy controls, the FA ratios of CST in the HS and nHS groups were decreased at all levels, and the apparent diffusion coefficient (ADC) ratio was increased only at C4/5 levels in the HS group. The FA ratio of FGC was decreased at the C3/4 and C4/5 levels in the HS group and only decreased at the C4/5 level in the nHS group. The ADC ratio was decreased only at the C4/5 level in the HS group. VBM analysis revealed that the volume of the precentral gyrus, postcentral gyrus, and paracentral lobule increased in patients compared to controls. TBSS analysis found no statistical significance between the sensory and motor tracts in white matter. The volume of clusters in HS and nHS groups negatively correlated with the C1/2 FA ratio of the CST. The results showed that the degeneration distance of the CST was longer than the FGC, and the degeneration distance was related to the degree of compression and spinal cord damage. Structural compensation and the neurotrophin family may lead to enlargement of the brain.

Analysis of the geographic pattern of the police reports for domestic violence in Girona (Spain)

BackgroundThere are many studies that address the issue of gender-based violence, but few analyse the police-reported cases. It is as important to analyse the temporal and geographic distribution of these incidents as it is the sociodemographic profile of the accuser. To this effect, the present study aims to analyse the sociodemographic profile of the victims of the particular case of domestic violence that report the incident to the police and to evaluate the spatial–temporal distribution of these reports.MethodsUsing the data from a database containing the police-reported incidents of domestic violence in Girona in the period 2012–2018, the risk of a police-reported incident was estimated by adjusting the two-part Hurdle model.ResultsThe risk of reporting incidents of domestic violence to the police is higher in the less deprived areas of the city, and the spatial distribution of these police reports corroborate this finding. Nevertheless, those areas with the greatest socio-economic deprivation were also the ones where there were less police reports filed. Also the prediction of less police reports in the census tract with the highest percentage of the population with an insufficient educational level coincides with the largest number of police reports made by women with a medium and university level education (56.1%) compared with 9.5% of police reports made by women with insufficient schooling.ConclusionsThese results can be useful for social protection services to design policies specifically aimed at women residing in those areas with the highest risk. Moreover, the use of spatial statistical techniques together with geographic information systems tools is a good strategy to analyse domestic violence and other types of offences because they not only allow to graphically identify the spatial distribution, but it is also a good tool to identify problems related to this type of offence.

Geographies of infections: built environment and COVID-19 pandemic in metropolitan Melbourne.

This paper uses spatial statistical techniques to reflect on geographies of COVID-19 infections in metropolitan Melbourne. We argue that the evolution of the COVID-19 pandemic, which has become widespread since early 2020 in Melbourne, typically proceeds through multiple built environment attributes – diversity, destination accessibility, distance to transit, design, and density. The spread of the contagion is institutionalised within local communities and postcodes, and reshapes movement practices, discourses, and structures of administrative politics. We demonstrate how a focus on spatial patterns of the built environment can inform scholarship on the spread of infections associated with COVID-19 pandemic and geographies of infections more broadly, by highlighting the consistency of built environment influences on COVID-19 infections across three waves of outbreaks. A focus on the built environment influence seeks to enact visions of the future as new variants emerge, illustrating the importance of understanding geographies of infections as global cities adapt to ‘COVID-normal’ living. We argue that understanding geographies of infections within cities could be a springboard for pursuing sustainable urban development via inclusive compact, mixed-use development and safe public transport.

Utilization and Verification of Inverse Distance Weighting (IDW) Interpolation Technology for Predicting Solar Radiation of Photovoltaic System

Purpose: Weather conditions around solar photovoltaic (PV) systems have a direct effect on the amount of generated power. Therefore, highly correlated meteorological environmental variables are used to accurately predict the power generation capacity of PV systems. However, it is difficult to install observation equipment due to high installation costs and maintenance. Therefore, public data from the Korea Meteorological Administration are often used. However, uncertainty may arise due to the large distance between the stations of the Korea Meteorological Administration. Therefore, in this study, inverse distance weighting (IDW) was used to predict the solar radiation of an arbitrary area to improve the uncertainty caused by the large distance between the meteorological stations. Method: In this study, IDW was used as a spatial statistical technique to predict the monthly average of daily accumulation solar radiation in South Korea. Moreover, four cases of the automated synoptic observing system (ASOS) were used as study cases to evaluate the prediction technology. The actual solar radiation observed and the predicted solar radiation was compared and analyzed in each study case, and the accuracy was evaluated as an indicator of the mean absolute percentage error (MAPE). Additionally, the MAPE was compared with the prediction results of the multiple regression model. Result: Although the results differ for each case, the average MAPE was approximately 11.06% using the inverse distance weighting interpolation method, and the predicted value by the multiple regression method yielded an average MAPE of approximately 16.12%.

Spatial sampling, data models, spatial scale and ontologies: Interpreting spatial statistics and machine learning applied to satellite optical remote sensing

This paper summarizes the development and application of spatial statistical models in satellite optical remote sensing. The paper focuses on the development of a conceptual model that includes the measurement and sampling processes inherent in remote sensing. We organized this paper into five main sections: introducing the basis of remote sensing, including measurement and sampling; spatial variation, including variation through the object-based data model; advances in spatial statistical modelling; machine learning and explainable AI; a hierarchical ontological model of the nature of remotely sensed scenes. The paper finishes with a summary. We conclude that optical remote sensing provides an important source of data and information for the development of spatial statistical techniques that, in turn, serve as powerful tools to obtain important information from the images.

Neurocomputing Modelling of Hydrochemical and Physical Properties of Groundwater Coupled with Spatial Clustering, GIS, and Statistical Techniques

Groundwater (GW) is a critical freshwater resource for billions of individuals worldwide. Rapid anthropogenic exploitation has increasingly deteriorated GW quality and quantity. Reliable estimation of complex hydrochemical properties of GW is crucial for sustainable development. Real field and experimental studies in an agricultural area from the significant sandstone aquifers (Wajid Aquifer) were conducted. For the modelling purpose, three types of computational models, including the emerging Hammerstein–Wiener (HW), back propagation neural network (BPNN), and statistical multi-variate regression (MVR), were developed for the multi-station estimation of total dissolved solids (TDS) (mg/L) and total hardness (TH) (mg/L). A geographic information system (GIS) was used for the spatial variability assessment of 32 hydrochemical and physical properties of the GW aquifer. A comprehensive visualized literature review spanning several decades was conducted in order to gain an understanding of the existing research and debates relevant to a particular GW and artificial intelligence (AI) study. The experimental data, pre-processing, and feature selection were conducted to determine the most dominant variables for AI-based modelling. The estimation results were evaluated using determination coefficient (DC), mean bias error (MBE), mean square error (MSE), and root mean square error (RMSE). The outcomes proved that TDS (mg/L) and TH (mg/L) correlated more than 90% and 70–85% with Ca2+, Cl−, Br−, NO3−, and Fe, and Na+, SO42−, Mg2+, and F− combinations, respectively. HW-M1 justified promising among all the models with MBE = 1.41 × 10−11, 1.14 × 10−14, and MSE = 7.52 × 10−2, 3.88 × 10−11 for TDS (mg/L), TH (mg/L), respectively. The accuracy proved merit for the overall development of and practical estimation of hydrochemical variables (TDS, TH) (mg/L) and decision-making benchmarks.

Spatial statistics techniques for SPEI and NDVI drought indices: a case study of Khuzestan Province.

Drought is a major water resources management issue in Iran. Khuzestan Province is in a drought state due to water shortage. Therefore, identifying areas at high risk of drought and when drought occurs is essential for drought management. For this purpose, this study used precipitation and temperature data of 12 selected stations and MODIS sensor images from the United States Geological Survey database in 2000–2017. The Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Normalized Difference Vegetation Index (NDVI) were calculated using the Hargreaves–Samani method and ENVI software. Moreover, different spatial statistics techniques were used in the ArcGIS environment to analyze the results. Also, time series diagrams were drawn, and the trend was evaluated using the Mann–Kendall test. Finally, the distribution of NDVI values was investigated using EasyFit software, and the amount of drought damage was determined using NDVI. The investigation of the cluster maps of the Anselin Local Moran’s Index along with hot and cold spots formed for both SPEI and NDVI showed that drought severity was higher at the southern stations than at the semi-northern and northwestern ones in the province. Moreover, the survey results using the EasyFit software showed that the southern stations, including the Ahvaz, Mahshahr, and Omidiyeh-Aghajari stations, were more at risk of drought than the other stations due to the drought threshold. Furthermore, the total damage caused by drought for the Ahvaz and Abadan stations showed a damage rate of 50%.