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The development of visual tools for the timely identification of spatio-temporal clusters will assist in implementing control measures to prevent further damage. From January 2015 to June 2020, a total number of 1463 avian influenza outbreak farms were detected in Taiwan and further confirmed to be affected by highly pathogenic avian influenza subtype H5Nx. In this study, we adopted two common concepts of spatio-temporal clustering methods, the Knox test and scan statistics, with visual tools to explore the dynamic changes of clustering patterns. Since most (68.6%) of the outbreak farms were detected in 2015, only the data from 2015 was used in this study. The first two-stage algorithm performs the Knox test, which established a threshold of 7 days and identified 11 major clusters in the six counties of southwestern Taiwan, followed by the standard deviational ellipse (SDE) method implemented on each cluster to reveal the transmission direction. The second algorithm applies scan likelihood ratio statistics followed by AGC index to visualize the dynamic changes of the local aggregation pattern of disease clusters at the regional level. Compared to the one-stage aggregation approach, Knox-based and AGC mapping were more sensitive in small-scale spatio-temporal clustering.

Extended from the spatially defined nearest neighbor index, the nearest neighbor index measures the levels of spatiotemporal clustering of a set of points, using only their spatial locations and th...

Objective:To determine whether Clostridioides difficile infection (CDI) exhibits spatiotemporal interaction and clustering.Design:Retrospective observational study.Setting:The University of Iowa Hospitals and Clinics.Patients:This study included 1,963 CDI cases, January 2005 through December 2011.Methods:We extracted location and time information for each case and ran the Knox, Mantel, and mean and maximum component size tests for time thresholds (T = 7, 14, and 21 days) and distance thresholds (D = 2, 3, 4, and 5 units; 1 unit = 5–6 m). All tests were implemented using Monte Carlo simulations, and random CDI cases were constructed by randomly permuting times of CDI cases 20,000 times. As a counterfactual, we repeated all tests on 790 aspiration pneumonia cases because aspiration pneumonia is a complication without environmental factors.Results:Results from the Knox test and mean component size test rejected the null hypothesis of no spatiotemporal interaction (P < .0001), for all values of T and D. Results from the Mantel test also rejected the hypothesis of no spatiotemporal interaction (P < .0003). The same tests showed no such effects for aspiration pneumonia. Our results from the maximum component size tests showed similar trends, but they were not consistently significant, possibly because CDI outbreaks attributable to the environment were relatively small.Conclusion:Our results clearly show spatiotemporal interaction and clustering among CDI cases and none whatsoever for aspiration pneumonia cases. These results strongly suggest that environmental factors play a role in the onset of some CDI cases. However, our results are not inconsistent with the possibility that many genetically unrelated CDI cases occurred during the study period.

Many fluorescence super-resolution techniques, such as (d)STORM, PALM, and DNA-PAINT, generate datasets wherein multiple localizations across many camera frames may arise from a single blinking event of an emitter. These repeated localizations not only hinder interpretation and analysis of such datasets, but also represent an incomplete use of the fluorescence photons. Such localizations are typically combined into a single localization either by clustering with hard distance and time thresholds, or by classical hypothesis testing assuming Gaussian localization errors. In this work, we describe a method for clustering that accounts for localization precision, local emitter density estimates, and a kinetic model for blinking which is used to optimize connections within a group of spatiotemporally colocated localizations.

Using an open-access spatiotemporal analytics program, we mapped spatiotemporal heterogeneity loci in tuberculosis (TB) cases (clusters) and dynamic changes, and characterized the drug-resistant TB clustering risk using routine microbiological data from KwaZulu-Natal, South Africa. The data may provide insight into transmission dynamics and support efficient deployment of public health resources.

An increase in the size of data repositories of spatiotemporal data has opened up new challenges in the fields of spatiotemporal data analysis and data mining. Foremost among them is “spatiotemporal clustering,” a subfield of data mining that is increasingly becoming popular because of its applications in wide-ranging areas such as engineering, surveillance, transportation, environmental and seismology studies, and mobile data analysis. This review paper presents a comprehensive review of spatiotemporal clustering approaches and their applications as well as a brief tutorial on the taxonomy of data types in the spatiotemporal domain and patterns. Additionally, the data pre-processing techniques, access methods, cluster validation, space–time scan statistics, software tools, and datasets used by various spatiotemporal clustering algorithms are highlighted.

Considering the critical role of trajectory data in Big Data era for dynamic geographical processes, human behaviour analysis and meteorological prediction, trajectory clustering has attracted growing attention. Many literatures have discussed the spatiot emporal clustering method of simple trajectories (i.e., has no branches, e.g. vehicle trajectories), yet there are few researches for clustering complex trajectories (i.e., has at least one split and/or merger and/or split -merger branch, e.g. ocean eddy trajectories, rainstorm trajectories). For addressing this issue, we propose a Process-Oriented Spatiotemporal Clustering Method (POSCM) for clustering complex trajectory data. The POSCM includes three parts: the first uses the semantic of process-sequence-state to represent the complex trajectories; the second proposes a Hierarchical Similarity Measurement Method (HSMM) to get the similarity between any two complex trajectories; in the last step, the complex trajectories clustering pattern is extracted through density-based clustering algorithm. Experiments on simulated trajectories are used to evaluate the POSCM and demonstrate the advantage by comparing against that of the VF2 algorithm. The POSCM is applied to the sea surface temperature abnormal variations trajectories from January 1950 to December 2017 in the Pacific Ocean. As shown in this case study, some new mined spatiotemporal patterns can provide new references for understanding the behaviours of marine abnormal variations under the background of the global change.

Smart card datasets in the public transit network provide opportunities to analyse the behaviour of passengers as individuals or as groups. Studying passenger behaviour in both spatial and temporal space is important because it helps to find the pattern of mobility in the network. Also, clustering passengers based on their trips regarding both spatial and temporal similarity measures can improve group-based transit services such as Demand-Responsive Transit (DRT). Clustering passengers based on their trips can be carried out by different methods, which are investigated in this paper. This paper sheds light on differences between sequential and combined spatial and temporal clustering alternatives in the public transit network. Firstly, the spatial and temporal similarity measures between passengers are defined. Secondly, the passengers are clustered using a hierarchical agglomerative algorithm by three different methods including sequential two-step spatial-temporal (S-T), sequential two-step temporal-spatial (T-S), and combined one-step spatiotemporal (ST) clustering. Thirdly, the characteristics of the resultant clusters are described and compared using maps, numerical and statistical values, cross correlation techniques, and temporal density plots. Furthermore, some passengers are selected to show how differently the three methods put the passengers in groups. Four days of smart card data comprising 80,000 passengers in Brisbane, Australia, are selected to compare these methods. The analyses show that while the sequential methods (S-T and T-S) discover more diverse spatial and temporal patterns in the network, the ST method entails more robust groups (higher spatial and temporal similarity values inside the groups).

The categorization of multidimensional data into clusters is a common task in statistics. Many applications of clustering, including the majority of tasks in ecology, use data that is inherently spatial and is often also temporal. However, spatiotemporal dependence is typically ignored when clustering multivariate data. We present a finite mixture model for spatial and spatiotemporal clustering that incorporates spatial and spatiotemporal autocorrelation by including appropriate Gaussian processes (GP) into a model for the mixing proportions. We also allow for flexible and semiparametric dependence on environmental covariates, once again using GPs. We propose to use Bayesian inference through three tiers of approximate methods: a Laplace approximation that allows efficient analysis of large datasets, and both partial and full Markov chain Monte Carlo (MCMC) approaches that improve accuracy at the cost of increased computational time. Comparison of the methods shows that the Laplace approximation is a useful alternative to the MCMC methods. A decadal analysis of 253 species of teleost fish from 854 samples collected along the biodiverse northwestern continental shelf of Australia between 1986 and 1997 shows the added clarity provided by accounting for spatial autocorrelation. For these data, the temporal dependence is comparatively small, which is an important finding given the changing human pressures over this time.

Visual analytics combines advanced visualisation methods with intelligent analysis techniques in order to explore large data sets whose complexity, underlying structure and inherent dynamics are beyond what traditional visualisation techniques can handle. The ultimate goal is to expose relevant patterns and relationships from the data, since not everything can be exposed easily through intelligent analysis techniques. On the contrary, the human eye can outperform algorithms in grasping and interpreting subtle patterns, provided it is supported by intelligent visualisations. In this paper, we propose three novel visual analytics techniques for analysing spatio-temporal data. First, we present a fingerprinting technique for discovering and rapidly interpreting temporal and recurring patterns by use of circular heat maps. Next, we present a technique supporting comparisons in time or space by use of circular heat map subtraction. Finally, we propose a technique enabling to characterise and get insights of thetemporal behaviour of the phenomenon under study by use of label maps. The potential of the proposed approach to reveal interesting patterns is demonstrated in a case study using traffic data, originating from multiple inductive loops in the Brussels-Capital Region, Belgium.

Summary Many behaviors that are critical for survival and reproduction are expressed over extended time periods. The ability to inexpensively record and store large volumes of video data creates new opportunities to understand the biological basis of these behaviors and simultaneously creates a need for tools that can automatically quantify behaviors from large video datasets. Here, we demonstrate that 3D Residual Networks can be used to classify an array of complex behaviors in Lake Malawi cichlid fishes. We first apply pixel-based hidden Markov modeling combined with density-based spatiotemporal clustering to identify sand disturbance events. After this, a 3D ResNet, trained on 11,000 manually annotated video clips, accurately (>76%) classifies the sand disturbance events into 10 fish behavior categories, distinguishing between spitting, scooping, fin swipes, and spawning. Furthermore, animal intent can be determined from these clips, as spits and scoops performed during bower construction are classified independently from those during feeding., Graphical Abstract, Highlights • A dataset of more than 14,000 annotated animal behavior videos was created • 3D residual networks can be used to classify animal behavior • Different intents of similar behavioral actions can be distinguished • A working solution to study long-term behaviors was established, Piscine Behavior; Zoology; Computer Science; Artificial Intelligence

One of the document analyzer's essential functions is dividing input documents into stroke groups that belong to the particular type of handwriting. In this paper, we analyze existing approaches for clustering input strokes of the handwritten document and present a new one that establishes a novel state of the art result. It is the first approach where a grouping of input strokes is considered as a clustering problem and standard metrics for it are provided. This research could be used as a baseline for further ones in the clustering of handwritten documents.

Breast cancer is the first cause of cancer-related death in Portuguese women. This study aimed to characterize female breast cancer mortality in Portugal in the period between 2002 and 2013, with a special focus on spatiotemporal patterns.The breast cancer mortality rate was studied using descriptive analysis (unadjusted and age-adjusted), and spatiotemporal clustering analyses.In 2002 - 2013 the breast cancer mortality rate was 28.47/100 000 inhabitants and the age-adjusted mortality rate was 19.46/100 000 inhabitants. In this period the Lisbon region (urban), Alentejo and Algarve (rural) presented higher breast cancer mortality rate, but Madeira (urban), Lisbon and Algarve had higher age-adjusted mortality rate. In the spatiotemporal analysis, the overall mortality rate showed an increasing trend of 1.218%/year, without spatial variations. Also, different patterns were detected in the50, 50 - 64 and ≥ 65 age-groups (+ 0.725%, - 1.781% and + 0.896%, respectively). One temporal (2004 - 2006) and one spatiotemporal cluster (North coast) presented significantly lower mortality rate than expected for the period and/or area (26.2 and 16.1/100 000 inhabitants, respectively). Conversely, two spatiotemporal clusters, located in the city of Lisbon (2002 - 2007) and in the Centre region (2008 - 2013), presented significantly higher breast cancer mortality rate than expected (48.6 and 34.9/100 000 inhabitants, relative risk: 1.74 and 1.26, respectively).The annual female crude and adjusted breast cancer mortality rate matched previous publications. However the annual increase detected in the unadjusted rate clashes with the published literature. Overall, the presence of spatiotemporal clusters supports the uneven distribution of the breast cancer mortality reported previously in the different Portuguese regions.This study identified areas and trends of the female breast cancer mortality rate, showing high spatiotemporal variations that must support further detailed studies/interventions.Introdução: O cancro da mama é a primeira causa de morte relacionada com cancro em mulheres portuguesas. Este estudo pretende caracterizar a mortalidade feminina por cancro da mama em Portugal no período de 2002 a 2013, com enfoque nos padrões espácio-temporais. Material e Métodos: A taxa de mortalidade por cancro da mama foi estudada com recurso a análise descritiva (bruta e ajustada para a idade), e análise de clustering espácio-temporal. Resultados: Em 2002 – 2013 a taxa de mortalidade por cancro da mama foi 28,47/100 000 habitantes e a taxa de mortalidade ajustada pela idade foi de 19,46/100 000 habitantes. Neste período a região de Lisboa (urbana), Alentejo e Algarve (rural) apresentaram taxas de mortalidade mais elevadas, mas após ajustamento pela idade a Madeira (urbana), Lisboa e Algarve demonstraram taxas de mortalidade superiores. Na análise espácio-temporal, a taxa de mortalidade geral apresentou um crescimento de 1,218%/ano, sem variações espaciais. Adicionalmente, padrões diferentes foram detetados nos grupos de mulheres com50, 50 - 64 e ≥ 65 anos (+ 0,725%, - 1,781% e + 0,896%, respetivamente). Um cluster temporal (2004 – 2006) e um espácio-temporal (costa Norte) apresentaram taxa de mortalidade significativamente mais baixas que o esperado para o período e/ou área (26,2 e16,1/100 000 habitantes, respetivamente). Por outro lado, dois clusters espácio-temporais, localizados na cidade de Lisboa (2002 – 2007) e na zona Centro (2008 – 2013), apresentaram taxas de mortalidade por cancro da mama superiores às expectáveis (48,6 e 34,9/100 000 habitantes, risco relativo: 1,74 e 1,26, respetivamente). Discussão: A taxa anual bruta e ajustada para a idade de mortalidade por cancro da mama aproximam-se das anteriormente publicadas. No entanto, o aumento anual nas taxas brutas contrasta com a literatura neste tópico. A presença de clusters espácio-temporais suporta a distribuição variável da taxa de mortalidade por cancro da mama nas diferentes regiões do país. Conclusão: Este estudo identificou áreas e tendências na taxa de mortalidade feminina por cancro da mama, demonstrando variações espácio-temporais nesta taxa que suportam estudos e intervenções mais detalhadas nesta área.

Residential locations play an important role in understanding the form and function of urban systems. However, it is impossible to release this detailed information publicly, due to the issue of privacy. The rapid development of location-based services and the prevalence of global position system (GPS)-equipped devices provide an unprecedented opportunity to infer residential locations from user-generated geographic information. This article compares different approaches for predicting Twitter users' home locations at a precise point level based on temporal and spatial features extracted from geo-tagged tweets. Among the three deterministic approaches, the one that estimates the home location for each user by finding the weighted most frequently visited (WMFV) cluster of that user always provides the best performance when compared with the other two methods. The results of a fourth approach, based on the support vector machine (SVM), are severely affected by the threshold value for a cluster to be identified as the home.

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 195329, “A General Spatiotemporal Clustering-Based Nonlocal Formulation for Multiscale Modeling of Compartmentalized Reservoirs,” by Soheil Esmaeilzadeh, Stanford University, and Amir Salehi and Gill Hetz, Quantum Reservoir Impact, et al., prepared for the 2019 SPE Western Regional Meeting, San Jose, California, 23-26 April. The paper has not been peer reviewed. In the complete paper, a novel hybrid approach is presented in which a physics-based nonlocal modeling framework is coupled with data-driven clustering techniques to provide a fast and accurate multiscale modeling of compartmentalized reservoirs. The research adds to the literature by presenting a comprehensive work on spatiotemporal clustering for reservoir-studies applications that considers the clustering complexities, the intrinsic sparse and noisy nature of the data, and the interpretability of the outcome. Introduction History matching is the most time-consuming phase in any reservoir-simulation study. As a means of accelerating reservoir simulations, a 2018 study proposed an approach in which a reservoir is treated as a combination of multiple interconnected compartments that, under a range of uncertainty, can capture the reservoir’s response during a recovery process. In this work, the authors extend that approach to represent a reservoir in a multiscale form consisting of multiple interconnected segments. To identify segments of the reservoir, spatial, temporal, and spatiotemporal unsupervised data-mining clustering techniques are used. Then, a novel nonlocal formulation for flow in porous media is presented in which the reservoir is represented by an adjacency matrix describing the neighbor and non-neighbor connections of comprising compartments. The reservoir is divided automatically into distinct compartments in which direction-dependent multiphase-flow communication is a function of nonlocal phase potential differences. With the segmented reservoir and the uncertain parameters, a robust history-matching technique is used to reproduce the reservoir’s historical response. Finally, for the segmented and history-matched reservoir, forecasting is performed. The complete paper first describes the spatiotemporal clustering framework, followed by an overview of the compartmentalized reservoir simulation framework, in which multitank history material balance and multi tank predictive material balance are explained. Next, the history-matching approach is described. These sections are not included in the current synopsis. The complete paper concludes with results of clustering, multitank material balance, and history matching for a real-world reservoir.

Global positioning system (GPS) data generated from taxi trips is a valuable source of information that offers an insight into travel behaviours of urban populations with high spatio-temporal resolution. However, in its raw form, GPS taxi data does not offer information on the purpose (or intended activity) of travel. In this context, to enhance the utility of taxi GPS data sets, we propose a two-layer framework to identify the related activities of each taxi trip automatically and estimate the return trips and successive activities after the trip, by using geographic point-of-interest (POI) data and a combination of spatio-temporal clustering, Bayesian inference, and Monte Carlo simulation. Two million taxi trips in New York, the United States of America, and ten million taxi trips in Shenzhen, China, are used as inputs for the two-layer framework. To validate each layer of the framework, we collect 6,003 trip diaries in New York and 712 questionnaire surveys in Shenzhen. The results show that the first layer of the framework performs better than comparable methods published in the literature, while the second layer has high accuracy when inferring return trips.

Visceral leishmaniasis (VL) remains a serious public health problem in China. To explore the temporal, spatial, and spatiotemporal characteristics of visceral leishmaniasis (VL), the spatial and spatiotemporal clustering distribution and their relationships with the surrounding geographic environmental factors were analyzed. In this study, the average nearest-neighbor distance (ANN), Ripley&rsquo, s K-function and Moran&rsquo, s I statistics were used to evaluate spatial autocorrelation in the VL distribution of the existing case patterns. Getis&ndash, Ord Gi* was used to identify the hot-spot and cold-spot areas based on Geographic Information System (GIS), and spatiotemporal retrospective permutation scan statistics was used to detect the spatiotemporal clusters. The results indicated that VL continues to be a serious public health problem in Kashi Prefecture, China, particularly in the north-central region of Jiashi County, which is a relatively high-risk area in which hot spots are distributed. Autumn and winter months were the outbreak season for VL cases. The detection of spatial and spatiotemporal patterns can provide epidemiologists and local governments with significant information for prevention measures and control strategies.

Effective air quality forecasting models are helpful for timely prevention and control of air pollution. However, the spatiotemporal distribution characteristics of air quality have not been fully considered in previous model development. This study attempts to establish a multi-time, multi-site forecasting model of Beijing’s air quality by using deep learning network models based on spatiotemporal clustering and to compare them with a back-propagation neural network (BPNN). For the overall forecasting, the performances in next-hour forecasting were ranked in ascending order of the BPNN, the convolutional neural network (CNN), the long short-term memory (LSTM) model, and the CNN-LSTM, with the LSTM as the optimal model in the multiple-hour forecasting. The performance of the seasonal forecasting was not significantly improved compared to the overall forecasting. For the spatial clustering-based forecasting, cluster 2 forecasting generally outperforms cluster 1 and the overall forecasting. Overall, either the seasonal or the spatial clustering-based forecasting is more suitable for the improvement of the forecasting in a certain season or cluster. In terms of model type, both the CNN-LSTM and the LSTM generally have better performance than the CNN and the BPNN.

This study aims to explore the effects of firearm arrests in reducing gun violence within a micro-environment in Houston, Texas. The Knox test is extended to a bivariate spatiotemporal clustering test, and it is utilized to analyze the citywide space–time interaction between shootings and firearm arrests. Also, an event-level analysis examines specific arrests generating deterrence or escalation effect, and describes their spatial distributions. A deterrence tendency is observed several days after, and at a considerable distance from, a prior arrest. About one-third of all arrests generate possible deterrence or escalation effect, and they are most densely clustered in a unique geographical area in Southwestern Houston.

With the development of E-government in China, urban grid management systems produce mass events data every day which are reported by city officers and citizens. Spatiotemporal big data can reveal the major issues in urban operation facing community. To perceive the insights on urban operation data, we build a Season Autoregressive Integrated Moving Average model and improve the spatiotemporal clustering algorithm for discovering the trend of events in time and space. We collaborate with a city management system and work on one year's data. Our spatiotemporal analysis gains high accuracy on events prediction and provides data support for community resource allocation and city governance planning.

Detailed epidemiologic examination of the distribution of Kawasaki disease (KD) cases could help elucidate the etiology and pathogenesis of this puzzling condition. Location of residence at KD admission was obtained for patients diagnosed in Canada (excluding Quebec) between March 2004 and March 2015. We identified 4,839 patients, 164 of whom (3.4%) developed a coronary artery aneurysm (CAA). A spatiotemporal clustering analysis was performed to determine whether non-random clusters emerged in the distributions of KD and CAA cases. A high-incidence KD cluster occurred in Toronto, ON, between October 2004 and May 2005 (116 cases; relative risk (RR) = 3.43; p

We briefly review five articles exploring spatiotemporal clustering of pediatric cancer cases in the state of Florida for the years 2000–2010. We review the general motivating question of interest (Are there clusters in the data?) and compare approaches with specific attention to the statistical quantification of this question, the epidemiologic insight gained about disease patterns, and potential policy responses to the collective results.

Introduction Approximately 3500 people commit suicide every year in Spain. The main aim of this study is to explore if a spatial and temporal clustering of suicide exists in the region of Antequera (Malaga, Espana). Methods Sample and procedure. All suicides from January 1, 2004 to December 31, 2008 were identified using data from the Forensic Pathology Department of the Institute of Legal Medicine, Malaga (Espana). Geolocalisation. Google Earth was used to calculate the coordinates for each suicide decedent's address. Statistical analysis. A spatiotemporal permutation scan statistic and the Ripley's K function were used to explore spatiotemporal clustering. Pearson's chi-squared was used to determine whether there were differences between suicides inside and outside the spatiotemporal clusters. Results A total of 120 individuals committed suicide within the region of Antequera, of which 96 (80%) were included in our analyses. Statistically significant evidence for seven spatiotemporal suicide clusters emerged within critical limits for the 0–2.5 km distance and for the first and second semanas ( p χ 2 = 4.13; df = 1; p Conclusions There are spatiotemporal suicide clusters in the area surrounding Antequera. Patients diagnosed with current psychotic disorder are less likely to be influenced by the factors explaining suicide clustering.

Advances of IoT facilitates leverage of heterogeneous sensing data over the Internet, such as remote sensing data, traffic data and SNS data. Integrated analysis of IoT data is crucial part for urban emergency management in smart cities in order to predict various social events co-occurring with a natural disaster event. Discovering of spatiotemporal co-occurrence patterns is a task of integrated analysis of IoT data and has received a lot of attention. However, such spatiotemporal co-occurrence patterns can fail to capture local events that occur in limited regions and limited time intervals.In this paper, we consider the problem of mining spatiotemporal co-occurrence patterns from IoT sensing data, each of which is annotated with a valid spatial and temporal region. Our idea is to incorporate spatiotemporal clustering with the frequent itemset (pattern) discovery process to reduce spatiotemporal bias of event distributions and we repeat this process in greedy approach in order to capture patterns with difference scales. By this way, our algorithm improves accuracy of the frequent itemsets. We applied our method to discovery and prediction of traffic disaster events co-occurring with torrential rain events in Kansai area, Japan. Our experimental result shows 31% improvement of prediction performance on F-measure against a baseline.

Background Hand, foot and mouth disease (HFMD) is one of the highest reported infectious diseases with several outbreaks across the world. This study aimed at describing epidemiological characteristics, investigating spatio-temporal clustering changes, and identifying determinant factors in different clustering areas of HFMD. Methods Descriptive statistics was used to evaluate the epidemic characteristics of HFMD from 2009 to 2015. Spatial autocorrelation and spatio-temporal cluster analysis were used to explore the spatial temporal patterns. An autologistic regression model was employed to explore determinants of HFMD clustering. Results The incidence rates of HFMD ranged from 54.31/10 million to 318.06/10 million between 2009 and 2015 in Hunan. Cases were mainly prevalent in children aged 5 years and even younger, with an average male-to-female sex ratio of 1.66, and two epidemic periods in each year. Clustering areas gathered in the northern regions in 2009 and in the central regions from 2010 to 2012. They moved to central-southern regions in 2013 and 2014 and central-western regions in 2015. The significant risk factors of HFMD clusters were rainfall (OR = 2.187), temperature (OR = 4.329) and humidity (OR = 2.070). The protect factor was wind speed (OR = 0.258). Conclusions The HFMD incidence from 2009 to 2015 in Hunan showed a new spatiotemporal clustering tendency, with the shifting trend of clustering areas toward south and west. Meteorological factors showed a strong association with HFMD clustering, which may assist in predicting future spatial-temporal clusters. Electronic supplementary material The online version of this article (10.1186/s12879-017-2742-9) contains supplementary material, which is available to authorized users.

Introduction Colorectal cancer (CRC) is a common type of neoplasm. This study examined the spatio-temporal distribution of the CRC incidence in Guangzhou during 2010–2014. Methods Colorectal cancer incidence data were obtained from the Guangzhou Cancer Registry System. Spatial autocorrelation analysis and a retrospective spatio-temporal scan were used to assess the spatio-temporal cluster distribution of CRC cases. Results A total of 14,618 CRC cases were registered in Guangzhou during 2010–2014, with a crude incidence of 35.56/100,000 and an age-standardized rate of incidence by the world standard population (ASRIW) of 23.58/100,000. The crude incidence increased by 19.70% from 2010 (32.88/100,000) to 2014 (39.36/100,000) with an average annual percentage change (AAPC) of 4.33%. The AAPC of ASRIW was not statistically significant. The spatial autocorrelation analysis revealed a CRC incidence hot spot in central urban areas in Guangzhou City, which included 25 streets in southwestern Baiyun District, northwestern Haizhu District, and the border region between Liwan and Yuexiu Districts. Three high- and five low-incidence clusters were identified according to spatio-temporal scan of CRC incidence clusters. The high-incidence clusters were located in central urban areas including the border regions between Baiyun, Haizhu, Liwan, and Yuexiu Districts. Conclusions This study revealed the spatio-temporal cluster pattern of the incidence of CRC in Guangzhou. This information can inform allocation of health resources for CRC screening.

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 geog...

We present a spatiotemporal clustering method, namely SEFCM, which is a generalization of the extended fuzzy C-Means (EFCM) method for detecting hotspots in spatial analysis. Each pattern is formed by three features: the geographical coordinates and the period in which a certain event is occurred. This method is applied to a spatial dataset (formed by earthquake epicenters occurred in Southern Italy since 2001 till to 2014) for prediction of the hotspots obtained in a given year. Comparisons of the prediction results are also made with the ones obtained by applying the known ST-DBSCAN algorithm.

In this work we present two methods that can extract habitual movement patterns and reconstruct the underlying movement of users from their call detail records (CDR) in a way that works for users with only moderate numbers of CDRs and that does not make any prior assumptions on the behaviour of the users. The methods allow for a more comprehensive user base in large-scale studies due to the fact that users that might otherwise have to be discarded can also be analysed. The first one is computationally not overly intense and is based on association mining. The second one, which we named DAMOCLES, is based on extracting idiosyncratic daily patterns from clustered daily activities. The methods are evaluated on real data of 140 users over an average of 200 days against benchmarks using assumptions commonly found in the literature such as a work week from Monday to Friday on GPS ground truth. Both methods clearly outperform the benchmarks and for many users retrieve similar regularities. Additionally a simulation study is performed that allows to evaluate the methods in a more controlled environment.

Spatio-temporal clustering, which is a process of grouping objects based on their spatial and temporal similarity, is increasingly gaining more scientific attention. Research in spatio-temporal clustering mainly focuses on approaches that use time and space in parallel. In this paper, we introduce a serial spatio-temporal clustering algorithm, called ST-DPOLY, which creates spatial clusters first and then creates spatio-temporal clusters by identifying continuing relationships between the spatial clusters in consecutive time frames. We compare this serial approach with a parallel approach named ST-SNN. Both ST-DPOLY and ST-SNN are density-based clustering approaches: while ST-DPOLY employs a density-contour based approach that operates on an actual density function, ST-SNN is based on well-established generic clustering algorithm Shared Nearest Neighbor (SNN). We demonstrate the effectiveness of these two approaches in a case study involving a New York city taxi trip dataset. The experimental results show that both ST-DPOLY and ST-SNN can find interesting spatio-temporal patterns in the dataset. Moreover, in terms of time and space complexity, ST-DPOLY has advantages over ST-SNN, while ST-SNN is more superior in terms of temporal flexibility; in terms of clustering results, results of ST-DPOLY are easier to interpret, while ST-SNN obtains more clusters which overlap with each other either spatially or temporally, which makes interpreting its clustering results more complicated.

Background Understanding how C. diffcile infection (CDI) is acquired in healthcare settings is key to designing interventions to mitigate CDI. The goal of this research is apply statistical methods, typically used to investigate regional outbreaks, to study spatio-temporal clustering of in-hospital CDI incidence. Methods We analyzed 1,804 CDI cases (out of 241,248 in-patient visits) at the University of Iowa Hospitals and Clinics (UIHC) during January 2005–December 2011. Letting T and D be time and space parameters, we constructed an observed CDI cluster graph by connecting pairs of CDI cases whose positive CDI tests occur within T days and D distance units of each other. In Experiment 1, for each CDI case, we replaced its actual time stamp by one picked uniformly at random from the time interval [January 2005, December 2011] and constructed a random CDI cluster graph. We tested the UIHC CDI case counts for seasonality and observed none, but did observe that the CDI counts increased significantly (weekly mean: 4.12–>8.11) starting in December 2009, when the C. diffcile Toxin A&B test was replaced by the C. diffcile Toxin PCR. So, we performed an Experiment 2 in which we sampled time stamps from a mixture of two uniform distributions, representing the periods of the two tests. Results We report sizes of connected components in the table below, for 10,000 trials of Experiments 1 and 2, for T = 14 days and varying D, a one setting in which D is set to the unit in which the CDI case occurs. The plots show the distribution of the mean and maximum component size (blue curves) for Experiment 2, for D = 2. D ObservedMean Comp. Size Experiment 1 Expected Mean Comp. Size (P-value) Experiment 2 Expected Mean Comp. Size (P-value) Observed Max. Comp. Size Experiment 1 Expected Max. Comp. Size (P-value) Experiment 2 Expected Max. Comp Size (P-value) 2 1.12 1.07 (0) 1.06 (0) 6 3.72 (0.01) 4.07 (0.05) 3 1.19 1.11 (0) 1.11 (0) 7 4.61 (0.03) 5.17 (0.1) 5 1.29 1.18 (0) 1.20 (0) 11 6.6 (0.01) 8.10 (0.1) 10 1.93 1.68 (0) 1.52 (0) 29 17.13 (0.02) 28.08 (0.4) Unit 1.63 1.38 (0) 1.01 (0) 23 9.74 (0) 12.87 (0.01) Conclusion Our analysis of the UIHC CDI cases shows significant spatio-temporal clustering in the observed CDI cluster graph. These results suggest that direct or environmental transmission may play a significant role in CDI acquisition at the UIHC. Funded by the CDC MInD-Healthcare. Disclosures All authors: No reported disclosures.

Porcine epidemic diarrhea (PED) was detected for the first time in seven years in Japan in October 2013 in Okinawa Prefecture. By December 2013, PED had spread into Miyazaki and Kagoshima Prefectures in the southern part of Kyushu, one of the regions with the highest farm density in the country. The objective of the study here was to assess the spatial dynamics of PED spread during the 8 months of the epidemic in the southern part of Kyushu between December 2013, the month observed first case in the studied region, and July 2014. Information on location and capacity of all farms in the prefectures (n=1269) was obtained from a government database containing demographic information for livestock producers. Additionally, data on PED detection (positive or negative) was obtained from the regional Livestock Hygiene Service Center. The Cuzick-Edwards (CE) test, the Knox test, the directional test, and the permutation model of the scan statistic were used to assess the spatio-temporal distribution of the epidemic. PED cumulative farm level incidence was 19.5% (248/1269) through the study period. The highest density of positive farms was observed in the most farm-populated areas of the prefecture. The CE test revealed an extensive degree of spatial clustering, with clustering of positive sites being significant (P0.01) up to the 35th level of neighborhood (approximately 5km in the studied data). The observed-to-expected ratio of cases was maximized at short spatio-temporal distances, with values of the observed-to-expected ratio of cases maximized when the thresholds were set at 2km and 10days, respectively. A significant (P0.01) direction of spread was detected towards the northeastern direction. The permutation model detected five significant (P0.01) clusters occurring at different stages of the epidemic wave. The strong spatio-temporal clustering of PED-infected farms during the first 6 months of the epidemic in the southern part of Kyushu is consistent with results obtained elsewhere and demonstrates the rapid spread of the virus in naïve populations.

The recent development in Global Positioning System (GPS) techniques has started a new era in predation studies. Estimates of kill rates based on animal movements and GPS relocation clusters have proven to be valid in several obligatory carnivores. The main focus has been to obtain accurate mean predation estimates for the management of wildlife populations. We present a model to estimate individual kill rates of moose calves by adult female brown bears in Sweden, based on spatiotemporal clustering of 30,889 bear GPS relocations and 71 moose calves verified killed during 714 field investigations in 2004-2006. In this virtually single-predator single large prey system, the omnivorous brown bear is an efficient predator on moose calves up to 4 weeks of age. The top model set only included models with cluster radii of 30 m or 50 m, indicating very high kill-site fidelity. The best model included a cluster radius of 30 m and number of periods of bear activity at the kill site as a single covariate. The mean estimated individual kill rate of 7.6 � 0.71 (n ¼ 18, xSE) moose calves per calving season is comparable to the estimate of 6.8 from a previous study of radio- tracked moose in our study area, though at a lower moose/bear ratio. The mean annual kill rates varied from 6.1 to 9.4 calves per bear. The estimated individual kill rates ranged from 2 to 15 calves per season, indicating a large individual variation in hunting skills and possibly effort. Predation and livestock depredation represent a core conflict between humans and carnivores in rural Scandinavia. Accurate predation estimates represent an important step in quantifying costs of carnivores and reducing human-carnivore conflicts. Our technique may be applied in the exploration of predation mechanisms and predator-prey interactions, and contribute to the old and global debate of problem individuals in livestock depredation. 2012 The Wildlife Society.

The plasma time-activity curve is often required as the input function for dynamic quantitative FDG PET studies to estimate the metabolic rate of glucose. The invasive gold standard arterial blood sampling has been suggested, however, it has many inconveniences and challenges in clinical and pre-clinical settings. Thus, the image-derived input function has been proposed to obtain the input function from dynamic images non-invasively. This method often needs a manual drawing of one or two regions of interest (ROIs), which is an operator-dependent and time-consuming task. The aim of the presented study was to capture the spatial and temporal patterns of dynamic PET images for automatic ROI extraction. Our proposed approach tries to overcome the main limitation of image clustering methods: the loss of temporal information for dynamic PET ROI definition. The experiments showed that the proposed automatic ROI method can be used for dynamic PET parameter estimation.

Leptospirosis is a re-emerging disease of dogs in the United States (U.S.). This paper reports the findings of a retrospective study conducted to determine if seroreactivity to Leptospira microscopic agglutination test (MAT) among dogs in the U.S. clustered in space and time. The study utilized canine sera submitted to a commercial laboratory for leptospiral MAT from January 2000 through December 2007. There were 31,869 serum samples submitted by veterinarians from 3156 zip code locations across the U.S. Results of MAT were considered positive at titers of ≥1:1600. Spatial and spatial-temporal scan statistics were used to identify statistically significant clusters of seroreactivity to Leptospira (overall and individual serovars) using recorded test request dates and locations of the centroid of the zip code reported for each serum sample. There were 2469 positive MAT results with a titer ≥1:1600 to at least one of seven Leptospira serovars. Two relevant spatial clusters of 26.3 and 246.5 km radius were identified (P = 0.001). The primary cluster was located in the northeastern part of Illinois including Chicago and surrounding areas (232 [14.4%] of 1612 MAT positive; RR = 1.95). The secondary cluster covered the central part of Texas (292 [12.62%] of 2314 MAT positive; RR = 1.71). Eight space-time clusters of overall MAT positivity were identified (29–335 km radius; P = 0.001–0.048 and RR = 3.98–24.69) that covered different geographic locations for different time points. Spatial and space-time clusters for individual serovars were also identified for six serovars: eight each of Grippotyphosa and Pomona, seven of Bratislava, five of Autumnalis, and three each of Icterohaemorrhagiae and Canicola. In conclusion Leptospira seropositivity in dogs tended to have distinctive clusters in space and space-time. Most of the space-time clusters of overall Leptospira MAT seropositivity were associated with cluster events for individual serovars. Further investigation is warranted to explain individual serovar clusters detected in this study, as a complex interaction of incidental host, environment and reservoir host may be responsible for the occurrence of these serovar clusters.

Background: Mortality in a population may be clustered in space and time for a variety of reasons, including geography, socio-economics, environment and demographics. Analysing mortality clusters can therefore reveal important insights into patterns and risks of mortality in a particular setting. Objective and design: To investigate the extent of spatio-temporal clustering of mortality in the Butajira District, Ethiopia, from 1987 to 2008. The Health and Demographic Surveillance System (HDSS) dataset recorded 10,696 deaths among 951,842 person-years of observation, with each death located by household, in which population time at risk was also recorded. The surveyed population increased from 28,614 in 1987 to 62,322 in 2008, in an area approximately 25 km in diameter. Spatio-temporal clustering analyses were conducted for overall mortality and by specific age groups, grouping the population into a 0.01° latitude-longitude grid. Results: A number of significantly high- and low-mortality clusters were identified at various times and places. Butajira town was characterised by significantly low mortality throughout the period. A previously documented major mortality crisis in 1998-1999, largely resulting from malaria and diarrhoea, dominated the clustering analysis. Other local high-mortality clusters, appreciably attributable to meningitis, malaria and diarrhoea, occurred in the earlier part of the period. In the later years, a more homogeneous distribution of mortality at lower rates was observed. Conclusions: Mortality was by no means randomly distributed in this community during the period of observation. The clustering analyses revealed a clear epidemiological transition, away from localised infectious epidemics, over a generation. Keywords: mortality clustering; Ethiopia; malaria; meningitis; diarrhoea; demographic surveillance; epidemiological transition (Published: 30 August 2010) Citation: Global Health Action Supplement 1, 2010. DOI: 10.3402/gha.v3i0.5244 Access the supplementary material to this article: animated graphic - sequence.ppsx - displaying the clusters (viewed over time) shown in Table 1 (see Supplementary files under Reading Tools online).

BACKGROUND:Childhood diarrhea continues to be a public health problem in developing countries, including Ethiopia. Detecting clusters and trends of childhood diarrhea is important to designing effective interventions. Therefore, this study aimed to investigate spatiotemporal clustering and seasonal variability of childhood diarrhea in northwest Ethiopia. METHODS:Retrospective record review of childhood diarrhea was conducted using quarterly reported data to the district health office for the seven years period beginning July 1, 2007. Thirty three districts were included and geo-coded in this study. Spatial, temporal and space-time scan spatial statistics were employed to identify clusters of childhood diarrhea. Smoothing using a moving average was applied to visualize the trends and seasonal pattern of childhood diarrhea. Statistical analyses were performed using Excel® and SaTScan programs. The maps were plotted using ArcGIS 10.0. RESULTS:Childhood diarrhea in northwest Ethiopia exhibits statistical evidence of spatial, temporal, and spatiotemporal clustering, with seasonal patterns and decreasing temporal trends observed in the study area. A most likely purely spatial cluster was found in the East Gojjam administrative zone of Gozamin district (LLR = 7123.89, p

Objective To investigate the epidemic characteristics of human cutaneous anthrax (CA) in China, detect the spatiotemporal clusters at the county level for preemptive public health interventions, and evaluate the differences in the epidemiological characteristics within and outside clusters. Methods CA cases reported during 2005–2012 from the national surveillance system were evaluated at the county level using space-time scan statistic. Comparative analysis of the epidemic characteristics within and outside identified clusters was performed using using the χ2 test or Kruskal-Wallis test. Results The group of 30–39 years had the highest incidence of CA, and the fatality rate increased with age, with persons ≥70 years showing a fatality rate of 4.04%. Seasonality analysis showed that most of CA cases occurred between May/June and September/October of each year. The primary spatiotemporal cluster contained 19 counties from June 2006 to May 2010, and it was mainly located straddling the borders of Sichuan, Gansu, and Qinghai provinces. In these high-risk areas, CA cases were predominantly found among younger, local, males, shepherds, who were living on agriculture and stockbreeding and characterized with high morbidity, low mortality and a shorter period from illness onset to diagnosis. Conclusion CA was geographically and persistently clustered in the Southwestern China during 2005–2012, with notable differences in the epidemic characteristics within and outside spatiotemporal clusters; this demonstrates the necessity for CA interventions such as enhanced surveillance, health education, mandatory and standard decontamination or disinfection procedures to be geographically targeted to the areas identified in this study.

In the era of big data, we are witnessing the rapid growth of a new type of information source. In particular, tweets are one of the most widely used microblogging services for situation awareness during emergencies. In our previous work, we focused on geotagged tweets posted on Twitter that included location information as well as a time and text message. We previously developed a real-time analysis system using the (e,τ)-density-based adaptive spatiotemporal clustering algorithm to analyze local topics and events. The proposed spatiotemporal analysis system successfully detects emerging bursty areas in which geotagged tweets related to observed topics are posted actively; however the system is tailor-made and specialized for a particular observed topic, therefore, it cannot identify other topics. To address this issue, we propose a new real-time spatiotemporal analysis system for enhancing local situation awareness using a density-based adaptive spatiotemporal clustering algorithm. In the proposed system, local bursty keywords are extracted and their bursty areas are identified. We evaluated the proposed system using actual real world topics related to weather in Japan. Experimental results show that the proposed system can extract local topics and events.

In vitro neuronal networks display synchronized bursting events (SBEs), with characteristic temporal width of 100-500ms and frequency of once every few seconds. We analyze such data using preprocessing by SVD for dimensional reduction, after which we apply quantum clustering to sort out the SBEs into different groups. Each SBE is described within a spatiotemporal template, allowing us to distinguish between neuronal activities (firing rates) and relative temporal variations of the spiking of each neuron within the SBE. Clustering assignments according to the two different kinds of information are very different from one another. SBEs may thus serve as carriers of different information in these two different implementations of the neuronal code.

交通工学・計画の一層の高度化に向けて, 道路上の車両動態に関する, 広域を対象とした詳細かつ効率的な観測手法が要請されている. 車両動態の詳細を把握するための最も適した方法のひとつに, 高度から撮影された動画像による計測が考えられる. 本研究は高度撮影画像を用いた車両動態の詳細を認識する手法の構築を目的とする. 具体的には, 時空間画像において背景差分値, オプティカルフローの情報, かつ, 車両挙動の連続性, 車両形状を考慮した時空間クラスタリングに基づく車両動態認識手法を構築し, その適用可能性を検討した. 航空HDTV画像 (空間分解能10cm, 時間分解能1130秒) に対し提案手法を適用し, 提案する車両動態認識手法の有効性を確認した.

Evaluating the spatial behavior of players allows for comparing design intent with emergent behavior. However, spatial analytics for game development is still in its infancy and current analysis mostly relies on aggregate visualizations such as heatmaps. In this paper, we propose the use of advanced spatial clustering techniques to evaluate player behavior. In particular, we consider the use of DEDIC OM and DESICOM, two techniques that operate on asymmetric spatial similarity matrices and can simultaneously uncover preferred locations and likely transitions between them. Our results highlight the ability of asymmetric techniques to partition game maps into meaningful areas and to retain information about player movements between these areas.

Extracting meaningful information from the growing quantity of spatial data is a challenge. The issues are particularly evident with spatio-temporal data describing movement. Such data typically corresponds to movement of humans, animals and machines in the physical environment. This article considers a special form of movement data generated through human–computer interactions with online web maps. As a user interacts with a web map using a mouse as a pointing tool, invisible trajectories are generated. By examining the spatial features on the map where the mouse cursor visits, a user's interests and experience can be detected. To analyse this valuable information, we have developed a geovisual analysis tool which provides a rich insight into such user behaviour. The focus of this paper is on a clustering technique which we apply to mouse trajectories to group trajectories with similar behavioural properties. Our experiments reveal that it is possible to identify experienced and novice users of web mapping environments using an incremental clustering approach. The results can be used to provide personalised map interfaces to users and provide appropriate interventions for completing spatial tasks. Science Foundation Ireland

Nowadays, with the increasing attention being paid to social media, a huge number of georeferenced documents, which include location information, are posted on social media sites. People transmit and collect information over the Internet through these georeferenced documents. Georeferenced documents are usually related to not only personal topics but also local topics and events. Therefore, extracting bursty areas associated with local topics and events from georeferenced documents is one of the most important challenges in different application domains. In this paper, a novel spatiotemporal clustering algorithm, called the (ϵ,τ)-density-based spatiotemporal clustering algorithm, for extracting bursty areas from georeferenced documents is proposed. The proposed clustering algorithm can recognize not only temporally-separated but also spatially-separated clusters. To evaluate our proposed clustering algorithm, geo-tagged tweets posted on the Twitter site are used. The experimental results show that the (ϵ,τ)-density-based spatiotemporal clustering algorithm can extract bursty areas as (ϵ,τ)-density-based spatiotemporal clusters associated with local topics and events.

The genesis of epileptic seizures is nowadays still mostly unknown. The hypothesis that most of scientist share is that an abnormal synchronization of different groups of neurons seems to trigger a recruitment mechanism that leads the brain to the seizure in order to reset this abnormal condition. If this is the case, a gradual transformation of the characteristics of the EEG can be hypothesized. It is therefore necessary to find a parameter that is able to measure the synchronization level in the EEG and, since the spatial dimension has to be taken into account if we aim to find out how the different areas in the brain recruit each other to develop the seizure, a spatio-temporal analysis of this parameter has to be carried out. In the present paper, a spatio-temporal analysis of EEG synchronization in 24 patients affected by absence seizure is proposed and the results are hereby reported and compared to the results obtained with a group of 40 healthy subjects. The spatio-temporal analysis is based on Permutation Entropy (PE). We found out that, ever since the interictal stages, fronto-temporal areas appear constantly associated to PE levels that are higher compared to the rest of the brain, whereas the parietal/occipital areas appear associated to low-PE. The brain of healthy subjects seems to behave in a different way because we could not see a recurrent behaviour of PE topography.