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In this paper, we present an algorithm for clustering multidimensional data, which we named TreeKDE. It is based on a tree structure decision associated with the optimization of the one-dimensional kernel density estimator function constructed from the orthogonal projections of the data on the coordinate axes. Among the main features of the proposed algorithm, we highlight the automatic determination of the number of clusters and their insertion in a rectangular region. Comparative numerical experiments are presented to illustrate the performance of the proposed algorithm and the results indicate that the TreeKDE is efficient and competitive when compared to other algorithms from the literature. Features such as simplicity and efficiency make the proposed algorithm an attractive and promising research field, which can be used as a basis for its improvement, and also for the development of new clustering algorithms based on the association between decision tree and kernel density estimator.

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INTRODUCTION: In Brazil, scorpion stings are recorded in high numbers with an increasing epidemiological situation in most municipalities. In the present study, data between 1998 and 2018 in Americana, São Paulo, were analyzed. METHODS: In total, 4122 records on scorpion stings were georeferenced using a Garmin eTREX 30X global positioning system device, with WGS84 datum projection and Universal Transverse Mercator zone 23S. Multiple Poisson regression was used to explore the relationship between the incidence rates of stings and urban planning areas (UPAs). Eight quantitative variables were used to establish the environmental and anthropic characterization of UPAs associated or not associated with scorpionism. A spatial analysis was performed for geoprocessing maps of Americana using spatial statistics tools (optimized hotspot analysis and kernel density function) from cartographic clusters in the ArcMap software, version 10.5. RESULTS: The optimized hotspot analysis tool identified spatial clusters with high values of the incidence of scorpion stings in the surroundings of all UPAs in the municipality. The estimation of the kernel function of event intensity showed a wide distribution of stings across the area of the entire municipal territory, with UPA-02 and UPA-06 exhibiting the highest occurrence of scorpion stings. Six significant hotspots were established as highest-event-density areas (with occurrences of 160-270) and were contiguous to 4 environmental protection areas, located in more peripheral regions, and to 2 municipal cemeteries, which were located in urban central areas. CONCLUSIONS: This analysis showed that the risk of scorpion stings in different Americana's UPAs has increased occurrence and incidence intensity.

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Pedestrians are vulnerable road users that are directly exposed to road traffic crashes with high odds of resulting in serious injuries and fatalities. Therefore, there is a critical need to identify the risk factors associated with injury severity in pedestrian crashes to promote safe and friendly walking environments for pedestrians. This study investigates the risk factors related to pedestrian, crash, and built environment characteristics that contribute to different injury severity levels in pedestrian crashes in Santiago, Chile from a spatial and statistical perspective. First, a GIS kernel density technique was used to identify spatial clusters with high concentrations of pedestrian crash fatalities and severe injuries. Subsequently, partial proportional odds models were developed using the crash dataset for the whole city and the identified spatial clusters to examine and compare the risk factors that significantly affect pedestrian crash injury severity. The model results reveal higher increases in the fatality probability within the spatial clusters for statistically significant contributing factors related to drunk driving, traffic signage disobedience, and imprudence of the pedestrian. The findings may be utilized in the development and implementation of effective public policies and preventive measures to help improve pedestrian safety in Santiago.

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In this paper, we propose the MulticlusterKDE algorithm applied to classify elements of a database into categories based on their similarity. MulticlusterKDE is centered on the multiple optimization of the kernel density estimator function with multivariate Gaussian kernel. One of the main features of the proposed algorithm is that the number of clusters is an optional input parameter. Furthermore, it is very simple, easy to implement, well defined and stops at a finite number of steps and it always converges regardless of the data set. We illustrate our findings by implementing the algorithm in R software. The results indicate that the MulticlusterKDE algorithm is competitive when compared to K-means, K-medoids, CLARA, DBSCAN and PdfCluster algorithms. Features such as simplicity and efficiency make the proposed algorithm an attractive and promising research field that can be used as basis for its improvement and also for the development of new density-based clustering algorithms.

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BACKGROUND: Currently syphilis is considered an epidemic disease worldwide. The objective of this study was to identify intra-urban differentials in the occurrence of congenital and acquired syphilis and syphilis in pregnant women in the city of Natal, in northeast Brazil. METHODS: Cases of syphilis recorded by the municipal surveillance system from 1 January 2011 to 30 December 2018 were analysed. Spatial statistical analyses were performed using the kernel density estimator of the quadratic smoothing function (weighted). SaTScan software was applied for the calculation of risk based on a discrete Poisson model. RESULTS: There were 2163 cases of acquired syphilis, 738 cases of syphilis in pregnant women and 1279 cases of congenital syphilis. Kernel density maps showed that the occurrence of cases is more prevalent in peripheral areas and in areas with more precarious urban infrastructure. In 2011-2014 and 2015-2018, seven statistically significant clusters of acquired syphilis were identified. From 2011 to 2014, the most likely cluster had a relative risk of 3.54 (log likelihood ratio [LLR] 38 895; p<0.001) and from 2015 to 2018 the relative risk was 0.54 (LLR 69 955; p<0.001). CONCLUSIONS: In the municipality of Natal, there was a clustered pattern of spatial distribution of syphilis, with some areas presenting greater risk for the occurrence of new cases.

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BACKGROUND: Leprosy causes a range of symptoms, and most diagnoses are established based on the clinical picture. Therefore, false negative and positive diagnoses are relatively common. We analyzed the spatial pattern of leprosy misdiagnosis and associated factors in Brazil. METHOD: Exploratory analyses of Kernel density of the new case detection rate (NCDR) and proportion of misdiagnosis in Brazil, 2003-2017. Factors associated with misdiagnosis were identified by logistic regression at the 5% significance level. RESULT: A total of 574,181 new leprosy cases were recorded in Brazil within the study period, of which 7,477 (1.3%) were misdiagnoses. No spatial correlation was observed between the proportion of misdiagnoses and the NCDR. The likelihood of misdiagnosis was elevated for females [OR: 1.58 (1.51-1.66)], children [OR: 1.49 (1.36-1.64)]; paucibacillary [OR: 1.08 (1.02-1.13)], indeterminate clinical forms [OR: 2.37 (2.15-2.62)], for cases diagnosed in the frame of mass screenings [OR: 3.36 (3.09- 3.73)] and contact examination [OR: 2.30 (2.13-2.49)] and for cases with affected nerves but no skin lesions [OR: 2.47 (2.19-2.77)] when compared with those presenting both skin lesion and affected nerves. CONCLUSION: Misdiagnosis of leprosy is not correlated with the endemicity level in Brazil but rather with personal, diagnosis-related and disease characteristics.

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Technological advances in the field of underwater video have led to an exponential increase in the use of drifting cameras (DC) and remotely operated vehicles (ROVs) to monitor the diversity, abundance, and size structure of marine life. Main advantages of DCs relative to ROVs are their lower costs and the much simpler logistics required to operate them. This study compares the performance of a new low-cost DC system equipped with a novel measuring device with that of a standard DC bearing an array of laser pointers. The new DC, which can be operated from a small boat, carries a pair of parallel steel "whiskers" that are dragged on the seabed within the field of view of the camera, providing a scale for measuring and estimating the density of benthic biota. An experiment conducted using an array of objects of known sizes laid on the bottom showed that its performance in terms of both size and density estimation was similar to that of the standard technique based on laser pointers. Measurement errors had a negligible negative bias (- 2.3%) and a standard deviation that ranged between 13 and 8% for objects from 25 to 110 mm in size. The whiskers offered a simplified method for density estimation that avoids the need to calculate the width of the field of view, thus reducing the video processing time by around 60% with respect to the standard method. Briefly, the new system offers an efficient low-cost alternative for benthic ecology studies conducted on soft or non-irregular bottoms.

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BACKGROUND: Haiti has one of the world's highest maternal mortality ratios. Comprehensive obstetric services could prevent many of these deaths, though most births in Haiti occur outside health facilities. Demand-side factors like a mother's socioeconomic status are understood to affect her access or choice to deliver in a health facility. However, analyses of the role of supply-side factors like health facility readiness have been constrained by limited data and methodological challenges. We sought to address these challenges and determine whether Haiti could increase rates of facility-based birth by improving facility readiness to provide delivery services. METHODS: Our task was to characterize facility delivery readiness and link it to nearby births. We used birth data from the 2012 Haiti DHS and facility data from the 2013 Haiti SPA. Our outcome of interest was facility-based birth. Our predictor of interest was delivery readiness at the DHS sampling cluster level. We derived a novel likelihood function that used Kernel Density Estimation to estimate cluster-level readiness alongside the coefficients of a logistic regression. RESULTS: We analyzed data from 389 facilities and 1,991 births. Rural facilities were less ready than urban facilities to provide delivery services. Women delivering in health facilities were younger, more educated, wealthier, less likely to live in rural areas, and had fewer previous children. Our model estimated that rural facilities (σ = 12.28, standard error [SE] = 0.16) spread their readiness over larger areas than urban facilities (σ = 7.14, SE = 0.016). Cluster-level readiness was strongly associated with facility-based birth (adjusted log-odds = 0.031; p = 0.005), as was socioeconomic status (adjusted log-odds = 0.78; p < 0.001). CONCLUSIONS: Health system policymakers in Haiti could increase rates of facility-based birth by supporting targeted interventions to improve facility readiness to provide delivery-related services, alongside efforts to reduce poverty and increase educational attainment among women.

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Given the rapid population decline and recent petition for listing of the monarch butterfly (Danaus plexippus L.) under the Endangered Species Act, an accurate estimate of the Eastern, migratory population size is needed. Because of difficulty in counting individual monarchs, the number of hectares occupied by monarchs in the overwintering area is commonly used as a proxy for population size, which is then multiplied by the density of individuals per hectare to estimate population size. There is, however, considerable variation in published estimates of overwintering density, ranging from 6.9-60.9 million ha-1. We develop a probability distribution for overwinter density of monarch butterflies from six published density estimates. The mean density among the mixture of the six published estimates was ∼27.9 million butterflies ha-1 (95% CI [2.4-80.7] million ha-1); the mixture distribution is approximately log-normal, and as such is better represented by the median (21.1 million butterflies ha-1). Based upon assumptions regarding the number of milkweed needed to support monarchs, the amount of milkweed (Asclepias spp.) lost (0.86 billion stems) in the northern US plus the amount of milkweed remaining (1.34 billion stems), we estimate >1.8 billion stems is needed to return monarchs to an average population size of 6 ha. Considerable uncertainty exists in this required amount of milkweed because of the considerable uncertainty occurring in overwinter density estimates. Nevertheless, the estimate is on the same order as other published estimates. The studies included in our synthesis differ substantially by year, location, method, and measures of precision. A better understanding of the factors influencing overwintering density across space and time would be valuable for increasing the precision of conservation recommendations.

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As well as being of global cultural importance (from local tribal folklore to being an iconic species for conservation), the tapir plays an important role in its ecosystem as a herbivore and seed disperser. However, the ecology and ethnozoology of the endangered Baird's tapir in the north of Oaxaca, Mexico is poorly understood. We used camera traps to estimate its relative abundance and density and to describe the activity patterns of the northernmost population of Baird's tapir in the Sierra Madre de Oaxaca. Local knowledge concerning the tapir was also documented, along with the conservation strategies undertaken by the 2 indigenous communities that own the land where the study site is located. Only adult tapirs were photographed, and these were active 14 h per day, but were mainly nocturnal and crepuscular. The estimated relative abundance (12.99 ± 2.24 events/1000 camera days) and density values (0.07-0.24 individuals/km(2) ) were both similar to those found in another site in Mexico located within a protected area. Semi-structured interviews revealed that people have a basic understanding of the eating habits, activity and main predators of the tapir. There were reports of hunting, although not among those respondents who regularly consume bush meat. Thus, the relative abundance and density estimates of tapir at the study site could be related to the favorable condition of the forest and the absence of hunting and consumption of tapir meat. Fortunately, the local people are conducting initiatives promoting the conservation of this ungulate and its habitat that combine to constitute a regional trend of habitat and wildlife protection.

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This paper describes the evolutionary split and merge for expectation maximization (ESM-EM) algorithm and eight of its variants, which are based on the use of split and merge operations to evolve Gaussian mixture models. Asymptotic time complexity analysis shows that the proposed algorithms are competitive with the state-of-the-art genetic-based expectation maximization (GA-EM) algorithm. Experiments performed in 35 data sets showed that ESM-EM can be computationally more efficient than the widely used multiple runs of EM (for different numbers of components and initializations). Moreover, a variant of ESM-EM free from critical parameters was shown to be able to provide competitive results with GA-EM, even when GA-EM parameters were fine-tuned a priori.

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For many marine species, locations of key foraging areas are not well defined. We used satellite telemetry and switching state-space modeling (SSM) to identify distinct foraging areas used by Kemp's ridley turtles (Lepidochelys kempii) tagged after nesting during 1998-2011 at Padre Island National Seashore, Texas, USA (PAIS; N = 22), and Rancho Nuevo, Tamaulipas, Mexico (RN; N = 9). Overall, turtles traveled a mean distance of 793.1 km (±347.8 SD) to foraging sites, where 24 of 31 turtles showed foraging area fidelity (FAF) over time (N = 22 in USA, N = 2 in Mexico). Multiple turtles foraged along their migratory route, prior to arrival at their "final" foraging sites. We identified new foraging "hotspots" where adult female Kemp's ridley turtles spent 44% of their time during tracking (i.e., 2641/6009 tracking days in foraging mode). Nearshore Gulf of Mexico waters served as foraging habitat for all turtles tracked in this study; final foraging sites were located in water <68 m deep and a mean distance of 33.2 km (±25.3 SD) from the nearest mainland coast. Distance to release site, distance to mainland shore, annual mean sea surface temperature, bathymetry, and net primary production were significant predictors of sites where turtles spent large numbers of days in foraging mode. Spatial similarity of particular foraging sites selected by different turtles over the 13-year tracking period indicates that these areas represent critical foraging habitat, particularly in waters off Louisiana. Furthermore, the wide distribution of foraging sites indicates that a foraging corridor exists for Kemp's ridleys in the Gulf. Our results highlight the need for further study of environmental and bathymetric components of foraging sites and prey resources contained therein, as well as international cooperation to protect essential at-sea foraging habitats for this imperiled species.