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The importance of the distribution of accommodation businesses over a certain area has grown remarkably, especially if such distribution is mapped using tools and techniques that utilize the territory as a variable in the analysis. The purpose of this paper is to demonstrate, by means of a geographical information system (GIS) and spatial statistics, that it is possible to better define the groupings of rural accommodation available in Extremadura, Spain, especially if these are conceptualized by dint of their lodging capacity. To do so, two specific techniques have been used: hotspot analysis and outlier analysis, which yield results that prove the existence of homogeneous and heterogeneous groups of accommodation businesses, based not only on their spatial proximity but also on their lodging capacity. On the basis of this analysis, the regional administration can devise tourist policies and strategic plans in order to improve the management and efficiency of each business. Despite the applicability of the present results, this study also addresses the difficulties in using these techniques—Where establishing the spatial relationships and the boundary distance are key concepts. In the case study here, the ideal configuration utilizes a fixed distance of six miles.

Use of groundwater for drinking purpose poses serious hazards of arsenic contamination particularly in plains of western Himalayan region. Therefore, current study was designed to investigate the level of Arsenic (As) in the water obtained from tubewells in a metropolitan city of Lahore, Pakistan and assess the human health risk. So, a total of 73 tubewells were sampled randomly in the manner that the whole study region was covered without any clustering. The water samples were analyzed for As using atomic absorption spectrophotometer. These samples were also tested for total dissolved solids, chlorides, pH, alkalinity, turbidity, hardness and calcium. GIS based hotspots analysis technique was used to investigate the spatial distribution patterns. Our results revealed that only one sample out of total 73 had arsenic level below the WHO guideline of 10 μg/L. The spatial distribution map of arsenic revealed that the higher concentrations of arsenic are present in the north-western region of Lahore. The cluster and outlier analysis map using Anselin Local Moran's I statistic indicated the presence of an arsenic cluster in the west of River Ravi. Furthermore, the optimized hotspot analysis based on Getis-Ord Gi* statistics confirmed the statistical significance (P < 0.05) and (P < 0.01) of these samples from the vicinity of River Ravi. Regression analysis showed that variables such as turbidity, alkalinity, hardness, chlorides, calcium and total dissolved solids were significantly (all P < 0.05) associated with level of Arsenic in tubewells. Whereas, PH and electrical conductivity and other variables like town, year of installation, depth and diameter of the wells were not significantly associated with Arsenic concentrations in tubewells. Principal component analysis (PCA) exhibited that the random distribution of tubewell samples showed no distinct clustering with towns studied. Health risk assessment based on hazard and Cancer risk index revealed serious risk of developing carcinogenic and non-carcinogenic diseases particularly in children. The health risk due to prevalence of high As concentration in tubewells’ water need to be mitigated immediately to avoid worst consequences in future.

Malaria is one of the main causes of morbidity and mortality in developing countries like Pakistan. Current study is based on geospatial analysis of malaria across Bahawalpur district of Pakistan. The key purpose is to measure spatial patterns which might be helpful for generating local environmental etiological hypothesis for malaria. Union council level epidemiological data for malaria was collected through 115 health centers from the study area for the period of six years 2012–2017. Techniques of spatial autocorrelation were applied to find results. Local Moran’s I statistics was used to perform cluster and outlier analysis of malaria. Presence of local clustering was further assessed by using Getis Ord Gi* statistics to assess intensity of hotspots and cold spots at the union council level. However, Inverse Distance Weighing (IDW) was used to interpolate and predict the spatial pattern of malaria cases in study area. Results showed spatial heterogeneity of malaria incidence in the district identifying both high (hotspots) and low (cold spots) clusters. Highest statistical significance has been revealed in northwestern rural areas of the district defining them as malaria hotspots. Contrary, extreme northern areas and urban centers of tehsils were found to be cold spot during all the six years. Finally, this study provides also a set of suggestions addressing the local environmental issues and to minimize the incidence of malaria through administrative environmental management and community participation. In addition, it will not only provide a base for advance geospatial research of malaria but can also be applied in other malaria endemic districts of the country.

GPS tracking has made ski injury data abundant, yet few studies have mapped where incidents actually occur or how those patterns differ between skiers and snowboarders. To address this gap, we analyzed 8719 GPS-located incidents (4196 skier; 4523 snowboarder) spanning four seasons (2017–2022, excluding 2019–2020 due to COVID-19) at a large West Coast resort in California. Incidents were aggregated into 45 m hexagons and analyzed using Getis–Ord Gi* hot spot analysis, Local Outlier Analysis (LOA), and a space–time cube with time-series clustering. Hot spot analysis identified both activity-specific and overlapping high-injury concentrations at the 99% confidence level (p < 0.01). The LOA revealed no spatial overlap between skier and snowboarder High-High classifications (areas with high incident counts surrounded by other high-count areas) at the 95% confidence level. Temporal analysis exposed distinct patterns by activity: Time Series Clustering revealed skier incidents concentrated at holiday-sensitive locations versus stable zones, while snowboarder incidents separated into sustained high-activity versus baseline areas. These findings indicate universal safety strategies may be insufficient; targeted, activity-specific interventions may warrant investigation. The methodology provides a reproducible framework for spatial injury surveillance applicable across the ski industry.

Terrorism perpetrated in any country by either internal or external actors jeopardizes the country’s security, economic growth, societal peace, and harmony. Hence, accurate modelling of terrorism has become a necessary component of the national security mission of most nations. This research extracted and analyzed high impact attacks (HIAs) perpetrated by terrorists in India and its neighboring countries since 1970 using the Global Terrorism Database (GTD). We evaluated the extraction efficacy of the Global Terrorism Index Impact Score (GTI-IS) against the GTD measure “nkill” using the iterative outlier analysis (IOA) heuristic. The heuristic identified 6117 common HIAs using nkill or GTI-IS attributes. GTI-IS extracted 1718 exclusive HIAs that nkill missed, while nkill extracted 2233 exclusive HIAs. We further classified the extracted HIAs into lethal and non-lethal attacks. Next, we conducted a rigorous spatiotemporal exploratory analysis of countries that reported the most HIAs. Though Afghanistan, India, and Sri Lanka exhibited global spatial autocorrelation, Pakistan did not. Ripley’s G function suggested the recurrence of lethal attacks near other similar events. This analysis showed that lethal and non-lethal attacks in those countries follow different statistical distributions, which can aid in focused counterterrorism tactics.

Anomaly or outlier detection is consider as one of the vital application of data mining, which deals with anomalies or outliers. Anomalies are considered as data points that are dramatically different from the rest of the data points. In this survey, we comprehensively present anomaly detection algorithms in an organized manner. We begin this survey with the definition of anomaly, then provide essential elements of anomaly detection, such as different types of anomaly, different application domains, and evaluation measures. Such anomaly detection algorithms are categorized in seven categories based on their working mechanisms, which includes total of 52 algorithms. The categories are anomaly detection algorithms based on statistics, density, distance, clustering, isolation, ensemble and subspace. For each category, we provide the time complexity of each algorithm and their general advantages and disadvantages. In the end, we compared all discussed anomaly detection algorithms in detail.

Structural damage detection using unsupervised learning methods has been a trending topic in the structural health monitoring (SHM) research community during the past decades. In the context of SHM, unsupervised learning methods rely only on data acquired from intact structures for training the statistical models. Consequently, they are often seen as more practical than their supervised counterpart in implementing an early-warning damage detection system in civil structures. In this article, we review publications on data-driven structural health monitoring from the last decade that relies on unsupervised learning methods with a focus on real-world application and practicality. Novelty detection using vibration data is by far the most common approach for unsupervised learning SHM and is, therefore, given more attention in this article. Following a brief introduction, we present the state-of-the-art studies in unsupervised-learning SHM, categorized by the types of used machine-learning methods. We then examine the benchmarks that are commonly used to validate unsupervised-learning SHM methods. We also discuss the main challenges and limitations in the existing literature that make it difficult to translate SHM methods from research to practical applications. Accordingly, we outline the current knowledge gaps and provide recommendations for future directions to assist researchers in developing more reliable SHM methods.

• Self-fertilisation has consequences for variation across the genome as it reduces effective population size, effect recombination rates and pollen flow, with implications for local adaptation. • We conducted simulations of divergent stabilising selection on a quantitative trait with drift, pollen flow, mutation, recombination and different outcrossing rates. We quantified trait divergence and the genetic architecture of adaptation. We conducted an FST outlier analysis to identify candidate loci and quantified the impact of mating system on detectability. • Selfing promoted trait divergence mainly through reductions in pollen flow. Moreover, trait architecture became more diffuse with selfing. Average effect size of trait loci was lower, while the number of loci, and their clustering distance increased. The genetic architecture of selfers was also more diffuse than outcrossers for equivalent migration rates. However, when deleterious alleles were included, architectures became more concentrated in selfers, likely to be because of reductions in population size caused by mutational meltdown and impacts of background selection on Nₑ. • Our simulations demonstrate that mating system has important impacts on adaptive divergence of traits and the genetic landscape underlying that divergence. Selfing has a significant effect on detectability of regions of the genome important for adaptation because of neutral divergence and diffuse trait architecture.

Outlier mining constitutes an essential aspect of modern data analytics, focusing on the identification and interpretation of anomalous observations. Conventional density-based local outlier detection methodologies frequently exhibit limitations due to their inherent lack of data preprocessing capabilities, consequently demonstrating degraded performance when applied to novel or heterogeneous datasets. Moreover, the computation of the outlier factor for each sample in these algorithms results in considerably higher computational cost, especially in the case of large datasets. This paper introduces a local outlier detection method named FOLOF (FCM Objective Function-based LOF) through an examination of existing algorithms. The approach starts by applying the elbow rule to determine the optimal number of clusters in the dataset. Subsequently, the FCM objective function is employed to prune the dataset to extract a candidate set of outliers. Finally, a weighted local outlier factor detection algorithm computes the degree of anomaly for each sample in the candidate set. For the analysis, the Golden Section method was used to classify the outliers. The underlying causes of these outliers can be revealed by exploring the anomalous properties of each outlier data point through the outlier factors of each dimension property. This approach has been validated on artificial datasets, the UCI dataset, and an NBA player dataset to demonstrate its effectiveness.

The target slope is generally divided into grids to predict landslide susceptibility; however, it is difficult to acquire all geotechnical properties for each grid. The objective of this study is to examine oversampling characterization for each grid using geostatistical method and oversampling algorithms. Kriging, which is widely used in geotechnical engineering, is selected as a geostatistical method, and the synthetic minority oversampling technique (SMOTE) and conditional tabular generative adversarial network (CTGAN) are applied to perform oversampling as deep learning algorithms. The target area is divided into 900, 1800, 3600, 9000, 18,000, and 180,000 grids to determine the oversampling behavior for each grid size. The soil cohesion, slope angle, soil density, soil depth, and friction angle, which are input parameters in an infinite slope stability model, are measured through laboratory and field tests, and then the oversampling is performed. The distributions of oversampled data are analyzed with a comparison of mean and standard deviation, and the SMOTE showed a similar distribution with measured values at both 1800 and 3600 grids. Outlier analysis is also performed to suggest a reasonable confidence level for each input parameter, and the resolution of each geotechnical parameter is increased at the 5% confidence level. Finally, the mean absolute error (MAE) is reduced to around 62–69% and 41–43% for arithmetical mean and standard deviation. This study shows that not only kriging but also deep learning algorithms can be used when oversampling is required in the fields of geotechnical and geological engineering.