Spatiotemporal clustering of cutaneous leishmaniasis in Fars province,Iran

Objective: To assess the spatiotemporal trait of cutaneous leishmaniasis(CL) in Fars province, Iran.Methods: Spatiotemporal cluster analysis was conducted retrospectively to find spatiotemporal clusters of CL cases. Time-series data were recorded from 29 201 cases in Fars province, Iran from 2010 to 2015, which were used to verify if the cases were distributed randomly over time and place. Then, subgroup analysis was applied to find significant sub-clusters within large clusters. Spatiotemporal permutation scans statistics in addition to subgroup analysis were implemented using Sa TScan software.Results: This study resulted in statistically significant spatiotemporal clusters of CL(P < 0.05). The most likely cluster contained 350 cases from 1 July 2010 to 30 November2010. Besides, 5 secondary clusters were detected in different periods of time. Finally,statistically significant sub-clusters were found within the three large clusters(P < 0.05).Conclusions: Transmission of CL followed spatiotemporal pattern in Fars province,Iran. This can have an important effect on future studies on prediction and prevention of CL.

Network SpaceTime AI:Concepts,Methods and Applications

SpacetimeAI and GeoAI are currently hot topics,applying the latest algorithms in computer science,such as deep learning,to spatiotemporal data.Although deep learning algorithms have been successfully applied to raster data due to their natural applicability to image processing,their applications in other spatial and space-time data types are still immature.This paper sets up the proposition of using a network(&graph)-based framework as a generic spatial structure to present space-time processes that are usually represented by the points,polylines,and polygons.We illustrate network and graph-based SpaceTimeAI,from graph-based deep learning for prediction,to space-time clustering and optimisation.These applications demonstrate the advantages of network(graph)-based SpacetimeAI in the fields of transport&mobility,crime&policing,and public health.

DBSTC:an effective method for discovering cluster features with different spatiotemporal densities

Spatiotemporal clustering is one of the most advanced research topics in geospatial data mining.It has been challenging to discover cluster features with different spatiotemporal densities in geographic information data set.This paper presents an effective density-based spatiotemporal clustering algorithm(DBSTC).First,we propose a method to measure the degree of similarity of a core point to the geometric center of its spatiotemporal reachable neighborhood,which can effectively solve the isolated noise point misclassification problem that exists in the shared nearest neighbor methods.Second,we propose an ordered reachable time window distribution algorithm to calculate the reachable time window for each spatiotemporal point in the data set to solve the problem of different clusters with different temporal densities.The effectiveness and advantages of the DBSTC algorithm are demonstrated in several simulated data sets.In addition,practical applications to seismic data sets demonstrate the capability of the DBSTC algorithm to uncover clusters of foreshocks and aftershocks and help to improve the understanding of the underlying mechanisms of dynamic spatiotemporal processes in digital earth.

High-risk spatiotemporal patterns of cutaneous leishmaniasis:a nationwide study in Iran from 2011 to 2020

Background Cutaneous leishmaniasis(CL)is a wide-reaching infection of major public health concern.Iran is one of the six most endemic countries in the world.This study aims to provide a spatiotemporal visualization of CL cases in Iran at the county level from 2011 to 2020,detecting high-risk zones,while also noting the movement of high-risk clusters.Methods On the basis of clinical observations and parasitological tests,data of 154,378 diagnosed patients were obtained from the Iran Ministry of Health and Medical Education.Utilizing spatial scan statistics,we investigated the disease’s purely temporal,purely spatial,spatial variation in temporal trends and spatiotemporal patterns.At P=0.05 level,the null hypothesis was rejected in every instance.Results In general,the number of new CL cases decreased over the course of the 9-year research period.From 2011 to 2020,a regular seasonal pattern,with peaks in the fall and troughs in the spring,was found.The period of September–February of 2014–2015 was found to hold the highest risk in terms of CL incidence rate in the whole country[relative risk(RR)=2.24,P<0.001)].In terms of location,six signifcant high-risk CL clusters covering 40.6%of the total area of the country were observed,with the RR ranging from 1.87 to 9.69.In addition,spatial variation in the temporal trend analysis found 11 clusters as potential high-risk areas that highlighted certain regions with an increasing tendency.Finally,fve space-time clusters were found.The geographical displacement and spread of the disease followed a moving pattern over the 9-year study period afecting many regions of the country.Conclusions Our study has revealed signifcant regional,temporal,and spatiotemporal patterns of CL distribution in Iran.Over the years,there have been multiple shifts in spatiotemporal clusters,encompassing many diferent parts of the country from 2011 to 2020.The results reveal the formation of clusters across counties that cover certain parts of provinces,indicating the importance of conducting spatiotemporal analyses at the county level for studies that encompass entire countries.Such analyses,at a fner geographical scale,such as county level,might provide more precise results than analyses at the scale of the province.

A unified multi-view multi-person tracking framework

Despite significant developments in 3D multi-view multi-person (3D MM) tracking, current frameworks separately target footprint tracking, or pose tracking. Frameworks designed for the former cannot be used for the latter, because they directly obtain 3D positions on the ground plane via a homography projection, which is inapplicable to 3D poses above the ground. In contrast, frameworks designed for pose tracking generally isolate multi-view and multi-frame associations and may not be sufficiently robust for footprint tracking, which utilizes fewer key points than pose tracking, weakening multi-view association cues in a single frame. This study presents a unified multi-view multi-person tracking framework to bridge the gap between footprint tracking and pose tracking. Without additional modifications, the framework can adopt monocular 2D bounding boxes and 2D poses as its input to produce robust 3D trajectories for multiple persons. Importantly, multi-frame and multi-view information are jointly employed to improve association and triangulation. Our framework is shown to provide state-of-the-art performance on the Campus and Shelf datasets for 3D pose tracking, with comparable results on the WILDTRACK and MMPTRACK datasets for 3D footprint tracking.

Exploring spatiotemporal patterns of geosocial media data for urban functional zone identification

Urban Functional Zones(UFZs)can be identified by measuring the spatiotemporal patterns of activities that occur within them.Geosocial media data possesses abundant spatial and temporal information for activity mining.Identifying UFZs from geosocial media data aids urban planning,infrastructure,resource allocation,and transportation modernization in the complex urban system.In this work,we proposed an integrated approach by combining the spatiotemporal clustering method with a machine learning classifier.The spatiotemporal clustering method was used to mine the spatiotemporal patterns of activities,of which the distinctive features were extracted as inputs into a machine learning classifier for UFZ identification.The results show that more than 80%of the UFZs can be correctly identified by our proposed method.It reveals that this work serves as a functional groundwork for future studies,facilitating the understanding of urban systems as well as promoting sustainable urban development.

Spatial-temporal pattern of cutaneous leishmaniasis in Brazil

Background:Cutaneous leishmaniasis(CL)is a vector-borne disease classified by the World Health Organization as one ofthe most neglected tropical diseases.Brazil has the highest incidence of CL in America and is one of the ten countries in the world with the highest number of cases.Understanding the spatiotemporal dynamics of CL is essential to provide guidelines for public health policies in Brazil.In the present study we used a spatial and temporal statistical approach to evaluate the dynamics ofCL in Brazil.Methods:We used data of cutaneous leishmaniasis cases provided by the Ministry of Health of Brazil from 2001 to 2017.We calculated incidence rates and used the Mann-Kendall trend test to evaluate the temporal trend of CL in each municipality.In addition,we used Kuldorff scan method to identify spatiotemporal clusters and emerging hotspots test to evaluate hotspot areas and their temporal trends.Results:We found a general decrease in the number of CL cases in Brazil(from 15.3 to 8.4 cases per 100000 habitants),although 3.2%of municipalities still have an increasing tendency of CL incidence and 72.5%showed no tendency at all.The scan analysis identified a primary cluster in northern and central regions and 21 secondary clusters located mainly in south and southeast regions.The emerging hotspots analysis detected a high spatial and temporal variability of hotspots inside the main cluster area,diminishing hotspots in eastern Amazon and permanent,emerging,and new hotspots in the states of Amapa and parts of Para,Roraima,Acre and Mato Grosso.The central coast the state of Bahia is one of the most critical areas due to the detection of a cluster of the highest rank in a secondary cluster,and because it is the only area identified as an intensifying hotspot.Conclusions:Using a combination of statistical methods we were able to detect areas of higher incidence of CL and understand how it changed over time.We suggest that these areas,especially those identified as permanent,new,emerging and intensifying hotspots,should be targeted for future research,surveillance,and implementation of vector control measures.