K-DBSCAN: Identifying Spatial Clusters with Differing Density Levels

2015 ◽  
Author(s):  
Madhuri Debnath ◽  
Praveen Kumar Tripathi ◽  
Ramez Elmasri
Keyword(s):  
Spatial Clusters

scholarly journals Short-Time Recurrences of Plasmodium vivax Malaria as a Public Health Proxy for Chloroquine-Resistance Surveillance: A Spatio-Temporal Study in the Brazilian Amazon

2021 ◽  
Vol 18 (10) ◽  
pp. 5061
Author(s):  
Antonio A. S. Balieiro ◽  
Andre M. Siqueira ◽  
Gisely C. Melo ◽  
Wuelton M. Monteiro ◽  
Vanderson S. Sampaio ◽  
...  
Keyword(s):  
Plasmodium Vivax ◽  
Multilevel Models ◽  
Malaria Incidence ◽  
Chloroquine Resistance ◽  
Spatial Clusters ◽  
Temporal Study ◽  
Plasmodium Vivax Malaria ◽  
Spatio Temporal ◽  
Spatially Varying ◽  
Short Time

In Brazil, malaria caused by Plasmodium vivax presents control challenges due to several reasons, among them the increasing possibility of failure of P. vivax treatment due to chloroquine-resistance (CQR). Despite limited reports of CQR, more extensive studies on the actual magnitude of resistance are still needed. Short-time recurrences of malaria cases were analyzed in different transmission scenarios over three years (2005, 2010, and 2015), selected according to malaria incidence. Multilevel models (binomial) were used to evaluate association of short-time recurrences with variables such as age. The zero-inflated Poisson scan model (scanZIP) was used to detect spatial clusters of recurrences up to 28 days. Recurrences compose less than 5% of overall infection, being more frequent in the age group under four years. Recurrences slightly increased incidence. No fixed clusters were detected throughout the period, although there are clustering sites, spatially varying over the years. This is the most extensive analysis of short-time recurrences worldwide which addresses the occurrence of P. vivax CQR. As an important step forward in malaria elimination, policymakers should focus their efforts on young children, with an eventual shift in the first line of malaria treatment to P. vivax.

scholarly journals Space–Time Clustering Characteristics of Malaria in Bhutan at the End Stages of Elimination

2021 ◽  
Vol 18 (11) ◽  
pp. 5553
Author(s):  
Kinley Wangdi ◽  
Kinley Penjor ◽  
Tobgyal ◽  
Saranath Lawpoolsri ◽  
Ric N. Price ◽  
...  
Keyword(s):  
Control Program ◽  
Space Time ◽  
Data Repository ◽  
Spatial Clusters ◽  
Scan Statistic ◽  
Disease Control Program ◽  
Risk Areas ◽  
Vector Borne ◽  
Program Data ◽  
Time Scan

Malaria in Bhutan has fallen significantly over the last decade. As Bhutan attempts to eliminate malaria in 2022, this study aimed to characterize the space–time clustering of malaria from 2010 to 2019. Malaria data were obtained from the Bhutan Vector-Borne Disease Control Program data repository. Spatial and space–time cluster analyses of Plasmodium falciparum and Plasmodium vivax cases were conducted at the sub-district level from 2010 to 2019 using Kulldorff’s space–time scan statistic. A total of 768 confirmed malaria cases, including 454 (59%) P. vivax cases, were reported in Bhutan during the study period. Significant temporal clusters of cases caused by both species were identified between April and September. The most likely spatial clusters were detected in the central part of Bhutan throughout the study period. The most likely space–time cluster was in Sarpang District and neighboring districts between January 2010 to June 2012 for cases of infection with both species. The most likely cluster for P. falciparum infection had a radius of 50.4 km and included 26 sub-districts with a relative risk (RR) of 32.7. The most likely cluster for P. vivax infection had a radius of 33.6 km with 11 sub-districts and RR of 27.7. Three secondary space–time clusters were detected in other parts of Bhutan. Spatial and space–time cluster analysis identified high-risk areas and periods for both P. vivax and P. falciparum malaria. Both malaria types showed significant spatial and spatiotemporal variations. Operational research to understand the drivers of residual transmission in hotspot sub-districts will help to overcome the final challenges of malaria elimination in Bhutan.

scholarly journals sparrpowR: a flexible R package to estimate statistical power to identify spatial clustering of two groups and its application

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Ian D. Buller ◽  
Derek W. Brown ◽  
Timothy A. Myers ◽  
Rena R. Jones ◽  
Mitchell J. Machiela
Keyword(s):  
Relative Risk ◽  
Statistical Power ◽  
Risk Function ◽  
R Package ◽  
Cancer Epidemiology ◽  
Spatial Cluster ◽  
Cluster Detection ◽  
Detection Technique ◽  
Power Calculation ◽  
Spatial Clusters

Abstract Background Cancer epidemiology studies require sufficient power to assess spatial relationships between exposures and cancer incidence accurately. However, methods for power calculations of spatial statistics are complicated and underdeveloped, and therefore underutilized by investigators. The spatial relative risk function, a cluster detection technique that detects spatial clusters of point-level data for two groups (e.g., cancer cases and controls, two exposure groups), is a commonly used spatial statistic but does not have a readily available power calculation for study design. Results We developed sparrpowR as an open-source R package to estimate the statistical power of the spatial relative risk function. sparrpowR generates simulated data applying user-defined parameters (e.g., sample size, locations) to detect spatial clusters with high statistical power. We present applications of sparrpowR that perform a power calculation for a study designed to detect a spatial cluster of incident cancer in relation to a point source of numerous environmental emissions. The conducted power calculations demonstrate the functionality and utility of sparrpowR to calculate the local power for spatial cluster detection. Conclusions sparrpowR improves the current capacity of investigators to calculate the statistical power of spatial clusters, which assists in designing more efficient studies. This newly developed R package addresses a critically underdeveloped gap in cancer epidemiology by estimating statistical power for a common spatial cluster detection technique.

Spatial Clusters of Diabetes and Physical Inactivity: Do Neighborhood Characteristics in High and Low Clusters Differ?

2019 ◽  
Vol 31 (7) ◽  
pp. 612-621
Author(s):  
Joanna Sara Valson ◽  
V. Raman Kutty ◽  
Biju Soman ◽  
V. T. Jissa
Keyword(s):  
Built Environment ◽  
Rural Areas ◽  
Physical Inactivity ◽  
Geographical Information Systems ◽  
Communicable Diseases ◽  
Geographical Information ◽  
Sample Population ◽  
Spatial Clusters ◽  
Non Communicable Diseases ◽  
Urban And Rural Areas

This study aims to find spatial clusters of diabetes and physical inactivity among a sample population in Kerala, India, and evaluate built environment characteristics within the high and low spatial clusters. Spatial clusters with a higher and lower likelihood of diabetes and physical inactivity were identified using spatial scan statistic at various radii. Built environment characteristics were captured at panchayat level and 1600 m buffer around participant location using Geographical Information Systems. Comparison of sociodemographic and built environment factors was carried out for participants within high and low spatial clusters using t tests. Ten high and 8 low spatial clusters of diabetes and 17 high and 23 low spatial clusters of physical inactivity were identified in urban and rural areas of Kerala. Significant differences in built environment characteristics were consistent for low spatial clusters of diabetes and physical inactivity in the urban scenario. Built environment characteristics were found to be relevant in both urban and rural areas of Kerala. There is an urgent call to explore spatial clustering of non-communicable diseases in Kerala and undo the one-size-fits-all approach for prevention and control of non-communicable diseases.

scholarly journals Spatial clusters of autism births and diagnoses point to contextual drivers of increased prevalence

2013 ◽  
Vol 95 ◽  
pp. 87-96 ◽  
Author(s):  
Soumya Mazumdar ◽  
Alix Winter ◽  
Ka-Yuet Liu ◽  
Peter Bearman
Keyword(s):  
Spatial Clusters

scholarly journals Detecting spatial clusters of HIV and hepatitis coinfections

PLoS ONE ◽  
2018 ◽  
Vol 13 (9) ◽  
pp. e0203674 ◽  
Author(s):  
Suparna Das ◽  
Jenevieve Opoku ◽  
Adam Allston ◽  
Michael Kharfen
Keyword(s):  
Spatial Clusters

scholarly journals SPATIAL AND STATISTICAL ANALYSIS OF LEPTOSPIROSIS IN GUILAN PROVINCE, IRAN

2015 ◽  
Vol XL-1-W5 ◽  
pp. 497-502 ◽  
Author(s):  
A. Mohammadi Nia ◽  
A. Alimohammadi ◽  
R. Habibi ◽  
M. R. Shirzadi
Keyword(s):  
Spatial Distribution ◽  
Pathogenic Bacteria ◽  
Rice Field ◽  
Health Planning ◽  
World Health ◽  
Nearest Neighbour ◽  
Clustering Methods ◽  
Spatial Clusters ◽  
North Of Iran ◽  
Health Organization

The most underdiagnosed water-borne bacterial zoonosis in the world is Leptospirosis which especially impacts tropical and humid regions. According to World Health Organization (WHO), the number of human cases is not known precisely. Available reports showed that worldwide incidences vary from 0.1-1 per 100 000 per year in temperate climates to 10-100 per 100 000 in the humid tropics. Pathogenic bacteria that is spread by the urines of rats is the main reason of water and soil infections. Rice field farmers who are in contact with infected water or soil, contain the most burden of leptospirosis prevalence. In recent years, this zoonotic disease have been occurred in north of Iran endemically. Guilan as the second rice production province (average=750 000 000 Kg, 40% of country production) after Mazandaran, has one of the most rural population (Male=487 679, Female=496 022) and rice workers (47 621 insured workers) among Iran provinces. The main objectives of this study were to analyse yearly spatial distribution and the possible spatial clusters of leptospirosis to better understand epidemiological aspects of them in the province. Survey was performed during the period of 2009–2013 at rural district level throughout the study area. Global clustering methods including the average nearest neighbour distance, Moran’s I and General G indices were utilized to investigate the annual spatial distribution of diseases. At the end, significant spatial clusters have been detected with the objective of informing priority areas for public health planning and resource allocation.

scholarly journals A connectivity-constrained computational account of topographic organization in high-level visual cortex

2021 ◽  
Author(s):  
Nicholas M Blauch ◽  
Marlene Behrmann ◽  
David Plaut
Keyword(s):  
Visual Processing ◽  
Spatial Organization ◽  
Functional Organization ◽  
Spatial Clusters ◽  
Feature Extracting ◽  
Domain Specific ◽  
Visual Processes ◽  
Topographic Organization ◽  
High Level ◽  
Multiple Domain

Inferotemporal cortex (IT) in humans and other primates is topographically organized, with multiple domain-selective areas and other general patterns of functional organization. What factors underlie this organization, and what can this neural arrangement tell us about the mechanisms of high level vision? Here, we present an account of topographic organization involving a computational model with two components: 1) a feature-extracting encoder model of early visual processes, followed by 2) a model of high-level hierarchical visual processing in IT subject to specific biological constraints. In particular, minimizing the wiring cost on spatially organized feedforward and lateral connections within IT, combined with constraining the feedforward processing to be strictly excitatory, results in a hierarchical, topographic organization. This organization replicates a number of key properties of primate IT cortex, including the presence of domain-selective spatial clusters preferentially involved in the representation of faces, objects, and scenes, within-domain topographic organization such as animacy and indoor/outdoor distinctions, and generic spatial organization whereby the response correlation of pairs of units falls off with their distance. The model supports a view in which both domain-specific and domain-general topographic organization arise in the visual system from an optimization process that maximizes behavioral performance while minimizing wiring costs.

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