Spatial-temporal Cluster Analysis Research Articles

A Spatial-Temporal Analysis of Brain Activation to Explain the Take-Over Failure in Conditionally Automated Driving.

This study aims to investigate the causes of take-over failures in conditional automated driving with spatial-temporal analysis of brain zone activation. Take-over requires a human driver to resume the control of the vehicle when its automation system disengages. Existing studies have found that take-over failures occur frequently on some drivers, but the causes have not been thoroughly studied. In a driving simulator experiment, 40 drivers took over in critical freeway cut-in situations. Functional near-infrared spectroscopy (fNIRS) data were collected before and during the take-over process to evaluate brain zone activation. Successful and failed take-overs were compared with changes in fNIRS data based on spatial-temporal comparisons and cluster analysis. The results suggested a significant difference in temporal brain activation between take-over failure and success conditions. Take-over failure conditions are mostly related to earlier and longer brain activation in most brain zones and repeated activation of the cognition brain zones. Drivers' attention switches, steering, and braking patterns are also related to different brain zone activation orders. The results indicate the need to reduce the mental workload caused by the sudden system disengagement to prevent take-over failure. Future research and implementation should focus on earlier warnings of upcoming hazards and driver education in dealing with sudden system disengagement.

Epidemiological Characteristics and Spatiotemporal Patterns of Visceral Leishmaniasis in Xinjiang, China, during 2004-2021.

The spread of visceral leishmaniasis (VL), a serious global zoonotic parasitic disease, is mostly under control; however, several cases have been reported in recent decades in Xinjiang, China. This study aimed to analyze the epidemiological status and spatiotemporal clustering characteristics of VL in Xinjiang, China, between 2004 and 2021 to provide a basis for the development and implementation of surveillance and response measures. Data on VL incidence during 2004-2021 were collected from the National Diseases Reporting Information System of China. Global spatial autocorrelation analysis, identification of local indicators of spatial association, and spatial-temporal clustering analysis were conducted to identify the distribution and high-risk areas. A total of 2034 VL cases were reported, with a mean annual incidence of 0.50 per 100,000. There was a general decreasing trend in the incidence of VL during our study period. The majority of the cases were reported from October to February of the following year, and fewer cases were reported from April to July. Spatial autocorrelation analysis revealed that the incidence of VL was spatially clustered within a few counties. Significant differences were observed during the study period (Moran's I = 0.74, Z = 4.900, p < 0.05). The male-to-female ratio was 1.37:1, and most patients were in the age group 0-3 years. Cases were primarily distributed in seven regions and two autonomous prefectures, and Kashgar reported the highest number of cases (1688, 82.98%). Spatial analysis revealed that the aggregation of VL was predominantly observed in southwest Xinjiang. This was in alignment with the high-risk areas identified by spatiotemporal clustering analysis. The H-H clustering region was primarily observed in Gashi, Atushi, Shufu, Injisha, Kashgar, Yepuhu, and Bachu. These findings indicate that integrated control measures must be taken in different endemic areas to strengthen the VL control program in Xinjiang, China.

Prevalence and spatial distribution characteristics of human echinococcosis: A county-level modeling study in southern Xinjiang, China

ObjectivesHuman echinococcosis remains an important public health problem. The aim of this study was to analyze the prevalence and spatial distribution characteristics of human echinococcosis cases in southern Xinjiang, China from 2005 to 2021. MethodsHuman echinococcosis cases were collected from the National Infectious Disease Reporting System. Joinpoint regression analysis was performed to explore the trends. Spatial autocorrelation, hot spot analysis, as well as spatial-temporal clustering analysis were conducted to confirm the distribution and risk factors. ResultsA total of 4580 cases were reported in southern Xinjiang during 2005–2021, with a mean annual incidence of 2.56/100,000. Echinococcosis incidence showed an increasing trend from 2005 to 2017 (APC = 17.939, 95%CI: 13.985 to 22.029) and a decreasing trend from 2017 to 2021 (APC = −18.769, 95%CI: 28.157 to −8.154). Echinococcosis cases had a positive spatial autocorrelation in 2005–2021 (Moran's I = 0.19, P < 0.05). The disease hotspots were located in the east and west in these areas, then returned to the east clusters, including Hejing, Heshuo, Wuqia, Atushi, Aheqi, and Yanqi Hui Autonomous County. Meanwhile, spatial-temporal analysis identified the first cluster comprised of five counties (cities): Yanqi Hui Autonomous County, Korla City, Bohu County, Hejing County, and Heshuo County. And secondary clusters 1–3 are predominantly in Wushi County, Aheqi County, Keping County, Atushi City, Wuqia County and Cele County. ConclusionsOur findings suggest that echinococcosis is still an important zoonotic parasitic disease in southern Xinjiang, yet it showed a certain degree of spatial clustering. It is crucial to implement comprehensive prevention and control measures to effectively combat the epidemic of echinococcosis.

Epidemiological investigation, determination of related factors, and spatial-temporal cluster analysis of wild type pseudorabies virus seroprevalence in China during 2022.

Pseudorabies virus (PRV) is a linear DNA virus with a double-stranded structure, capable of infecting a diverse array of animal species, including humans. This study sought to ascertain the seroprevalence of Pseudorabies Virus (PRV) in China by conducting a comprehensive collection of blood samples from 16 provinces over the course of 2022. The presence of PRV gE antibodies was detected through the utilization of an enzyme-linked immunosorbent assay (ELISA) technique. Logistic regression analysis was conducted to identify potential related factors associated with the serologic status of PRV gE at the animal level. Additionally, the SaTScan 10.1 software was used to analyze the spatial and temporal clusters of PRV gE seroprevalence. A comprehensive collection of 161,880 samples was conducted, encompassing 556 swine farms throughout the country. The analysis revealed that the seroprevalence of PRV gE antibodies was 12.36% (95% confidence interval [CI], 12.20% to 12.52%) at the individual animal level. However, at the swine farm level, the seroprevalence was considerably higher, reaching 46.22% (95% CI, 42.08% to 50.37%). Related factors for PRV infection at the farm level included the geographic distribution of farms and seasonal variables. Moreover, five distinct high seroprevalence clusters of PRV gE were identified across China, with the peak prevalence observed during the months of April through June 2022. Our findings serve as a valuable addition to existing research on the seroprevalence, related factors, and temporal clustering of PRV gE in China. Furthermore, our study provides a reference point for the development of effective strategies for the prevention and control of pseudorabies and wild virus outbreaks.

Epidemiological characteristics and spatio-temporal analysis of brucellosis in Shandong province, 2015–2021

BackgroundBrucellosis is one of the major public health problems in China, it not only causes huge economic losses to the society, but also threatens the human’s physical and mental health. The reported cases of brucellosis in Shandong province were at a high level, therefore, it is necessary for us to understand the epidemic characteristics and distribution trend of Brucellosis in Shandong province. This study aims to describe the epidemiological characteristics and spatial clustering characteristics of brucellosis in Shandong Province, provide a reference for the scientific prevention and control.MethodsHuman brucellosis data in Shandong province from 2015 to 2021 were obtained from the China Information System for Disease Control and Prevention, the data were analyzed by descriptive epidemiological methods, spatial autocorrelation analysis and spatial-temporal cluster analysis methods use ArcGIS and SaTScan software, the results were presented in ArcMap.ResultsA total of 22,251 human cases of brucellosis were reported, the annual incidence ranged between 2.41/100,000 and 4.07/100,000 from 2015 to 2021 in Shandong province, incidence has been decreasing year by year, while there was a significant increase in 2021. The distribution of brucellosis was of a seasonal trend, mainly concentrating during March to August. The age of the cases was mainly concentrated in the 30–74 age ranges, the average annual incidence rate was significantly higher in males than in females. The spatial analysis showed that the epidemics were mainly concentrated in the north and southwest. For the spatial autocorrelation analysis, a high global autocorrelation was observed at the county level, and the high–high clusters mainly distributed in the north and southwest region. For the spatio-temporal scanning, the most likely cluster areas mainly distributed in the north area, and then gradually moved southward, and the radius of clustered narrowed.ConclusionsHuman brucellosis remains a common challenge, particularly in northern region in spring and summer. More disease prevention and control measures should be taken in high-risk populations, and such higher-risk susceptible areas to reduce the incidence of brucellosis and ensure the health of the people.

Analysis of the spatial-temporal distribution characteristics of hepatitis E in Jiangsu province from 2005 to 2020.

This study attempts to analyze the spatial clustering and spatial-temporal distribution characteristics of hepatitis E (HE) at the county (city and district) level in Jiangsu province to provide a scientific basis for the prevention and control of HE. The information on HE cases reported in the Chinese Center for Disease Control and Prevention Information System from 2005 to 2020 was collected for spatial autocorrelation analysis and spatial-temporal clustering analysis. From 2005 to 2020, 48,456 HE cases were reported in Jiangsu province, with an average annual incidence rate of 3.87/100,000. Male cases outnumbered female cases (2.46:1), and the incidence was highest in the 30-70 years of age group (80.50%). Farmers accounted for more than half of all cases (59.86%), and in terms of the average annual incidence, the top three cities were all in Zhenjiang city. Spatial autocorrelation analysis showed that Global Moran's I of HE incidence varied from 0.232 to 0.513 for the years. From 2005 to 2020, 31 counties (cities and districts) had high and statistically significant HE incidence, and two clustering areas were detected by spatial-temporal scanning. HE incidence in Jiangsu province from 2005 to 2020 was stable, with age and gender differences, regional clustering, and spatial-temporal clustering. Further investigation of HE clustering areas is necessary to formulate corresponding targeted prevention and control measures.

Prediction Method for Mine Earthquake in Time Sequence Based on Clustering Analysis

Under the background of the intelligent construction of a coal mine, how to efficiently extract effective information from the massive monitoring data of mine earthquakes, and improve prediction accuracy, is a research hotspot in the field of coal mine safety production. In view of this problem, more and more machine learning methods are being applied to the prediction on mine earthquakes. Considering that clustering analysis can enhance the correlation between microseism data, we propose a method whose main idea is to cluster microseism data before establishing the prediction model, and then train the model, so as to improve prediction accuracy. Specifically, microseism events on a working face are divided into clusters in advance by the Spatial Temporal-DBSCAN(ST-DBSCAN) algorithm, then a prediction model is established with Support Vector Regression (SVR) to predict the occurrence location and daily frequency of high-energy mine earthquake events. A set of engineering experiments were conducted in H Coal Mine, and the results show that the spatial-temporal clustering analysis of microseism events can indeed improve the prediction accuracy of machine learning methods on mine earthquakes.

Epidemiological features and spatial-temporal clustering of visceral leishmaniasis in mainland China from 2019 to 2021.

BackgroundVisceral leishmaniasis (VL) is a serious vector-borne disease in central and western China. In recent years, the number of VL cases increased gradually, particularly the mountain-type zoonotic visceral leishmaniasis (MT-ZVL). This study clarified the epidemiological features and spatial-temporal clustering of VL in China between 2019 and 2021, identified the risk areas for VL transmission, and provided scientific evidence for the prevention and control of VL.Materials and methodsThe information on VL cases in 2019–2021 was collected from the Infectious Disease Reporting Information Management System of the Chinese Center for Disease Control and Prevention. The epidemiological characteristics of VL cases were analyzed. The global Moran’s I and Getis-ORD Gi* statistical data were processed for spatial autocorrelation and hotspot analysis in ESRI ArcGIS software. Also, spatial-temporal clustering analysis was conducted with the retrospective space–time permutation scan statistics.ResultsA total of 608 VL cases were reported from 2019 to 2021, with 158, 213, and 237 cases reported each year, respectively. Of the 608 cases, there were 10 cases of anthroponotic visceral leishmaniasis (AVL), 20 cases of desert-type zoonotic visceral leishmaniasis (DT-ZVL), and 578 cases of MT-ZVL. The age of VL cases was mainly distributed in the group of subjects aged ≥ 15 years. Peasants and infants were the dominant high-risk population. The incidence peak season of VL occurred between March and May. The cases were mainly distributed in Shanxi (299 cases), Shaanxi (118 cases), and Gansu (106 cases) Provinces, accounting for 86.02% (523/608) of the total reported cases in China. Spatial analysis revealed that clustering of infection is mainly located in eastern Shanxi Province and Shaanxi–Shanxi border areas, as well as southern Gansu and northern Sichuan Province. In addition, new reemergence hotspots in Shanxi, Henan, and Hebei Provinces have been detected since 2020. Spatio-temporal clustering analysis revealed an increase in the degree of infection aggregation in eastern Shanxi Province and Shaanxi–Shanxi border areas.ConclusionThe AVL and DT-ZVL were endemic at a lower level in western China, whereas MT-ZVL rebounded rapidly and showed a resurgence in historically endemic counties. The spatial-temporal clustering analysis displayed that the high-incidence areas of VL have shifted to central China, particularly in Shanxi and Shaanxi Provinces. Integrated mitigation strategies targeting high-risk populations are needed to control VL transmission in high-risk areas.

Analysis of spatial-temporal distribution of notifiable respiratory infectious diseases in Shandong Province, China during 2005\u20132014

BackgroundLittle comprehensive information on overall epidemic trend of notifiable respiratory infectious diseases is available in Shandong Province, China. This study aimed to determine the spatiotemporal distribution and epidemic characteristics of notifiable respiratory infectious diseases.MethodsTime series was firstly performed to describe the temporal distribution feature of notifiable respiratory infectious diseases during 2005–2014 in Shandong Province. GIS Natural Breaks (Jenks) was applied to divide the average annual incidence of notifiable respiratory infectious diseases into five grades. Spatial empirical Bayesian smoothed risk maps and excess risk maps were further used to investigate spatial patterns of notifiable respiratory infectious diseases. Global and local Moran’s I statistics were used to measure the spatial autocorrelation. Spatial-temporal scanning was used to detect spatiotemporal clusters and identify high-risk locations.ResultsA total of 537,506 cases of notifiable respiratory infectious diseases were reported in Shandong Province during 2005–2014. The morbidity of notifiable respiratory infectious diseases had obvious seasonality with high morbidity in winter and spring. Local Moran’s I analysis showed that there were 5, 23, 24, 4, 20, 8, 14, 10 and 7 high-risk counties determined for influenza A (H1N1), measles, tuberculosis, meningococcal meningitis, pertussis, scarlet fever, influenza, mumps and rubella, respectively. The spatial-temporal clustering analysis determined that the most likely cluster of influenza A (H1N1), measles, tuberculosis, meningococcal meningitis, pertussis, scarlet fever, influenza, mumps and rubella included 74, 66, 58, 56, 22, 64, 2, 75 and 56 counties, and the time frame was November 2009, March 2008, January 2007, February 2005, July 2007, December 2011, November 2009, June 2012 and May 2005, respectively.ConclusionsThere were obvious spatiotemporal clusters of notifiable respiratory infectious diseases in Shandong during 2005–2014. More attention should be paid to the epidemiological and spatiotemporal characteristics of notifiable respiratory infectious diseases to establish new strategies for its control.

An Ensemble Energy Consumption Forecasting Model Based on Spatial-Temporal Clustering Analysis in Residential Buildings

Due to the availability of smart metering infrastructure, high-resolution electric consumption data is readily available to study the dynamics of residential electric consumption at finely resolved spatial and temporal scales. Analyzing the electric consumption data enables the policymakers and building owners to understand consumer’s demand-consumption behaviors. Furthermore, analysis and accurate forecasting of electric consumption are substantial for consumer involvement in time-of-use tariffs, critical peak pricing, and consumer-specific demand response initiatives. Alongside its vast economic and sustainability implications, such as energy wastage and decarbonization of the energy sector, accurate consumption forecasting facilitates power system planning and stable grid operations. Energy consumption forecasting is an active research area; despite the abundance of devised models, electric consumption forecasting in residential buildings remains challenging due to high occupant energy use behavior variability. Hence the search for an appropriate model for accurate electric consumption forecasting is ever continuing. To this aim, this paper presents a spatial and temporal ensemble forecasting model for short-term electric consumption forecasting. The proposed work involves exploring electric consumption profiles at the apartment level through cluster analysis based on the k-means algorithm. The ensemble forecasting model consists of two deep learning models; Long Short-Term Memory Unit (LSTM) and Gated Recurrent Unit (GRU). First, the apartment-level historical electric consumption data is clustered. Later the clusters are aggregated based on consumption profiles of consumers. At the building and floor level, the ensemble models are trained using aggregated electric consumption data. The proposed ensemble model forecasts the electric consumption at three spatial scales apartment, building, and floor level for hourly, daily, and weekly forecasting horizon. Furthermore, the impact of spatial-temporal granularity and cluster analysis on the prediction accuracy is analyzed. The dataset used in this study comprises high-resolution electric consumption data acquired through smart meters recorded on an hourly basis over the period of one year. The consumption data belongs to four multifamily residential buildings situated in an urban area of South Korea. To prove the effectiveness of our proposed forecasting model, we compared our model with widely known machine learning models and deep learning variants. The results achieved by our proposed ensemble scheme verify that model has learned the sequential behavior of electric consumption by producing superior performance with the lowest MAPE of 4.182 and 4.54 at building and floor level prediction, respectively. The experimental findings suggest that the model has efficiently captured the dynamic electric consumption characteristics to exploit ensemble model diversities and achieved lower forecasting error. The proposed ensemble forecasting scheme is well suited for predictive modeling and short-term load forecasting.

What can influence a county’s resilience and susceptibility to coronavirus in Nebraska?

This study sought to classify and assign the 93 counties in Nebraska into distinctive spatial-temporal clusters based on their trends in the number of COVID-19 confirmed cases and deaths. A secondary goal of this study was to assess the potential role of preexisting sociodemographic, environmental, and health conditions in determining the county’s resilience and susceptibility to 2019-nCov. Spatial-temporal cluster analysis yielded three distinct confirmed cases and death clusters. Results from spatial-temporal cluster and regression analyses suggested that COVID-19 and HIV and obesity prevention should be integrated. Emergency response planning and mitigation should focus on counties characterized by noticeable increases in the number of COVID-19 confirmed cases and deaths and/or counties with relatively high population and ethnic minorities.

Human distribution and spatial-temporal clustering analysis of human brucellosis in China from 2012 to 2016

BackgroundBrucellosis is a major public health issue in China, while its temporal and spatial distribution have not been studied in depth. This study aims to better understand the epidemiology of brucellosis in the mainland of China, by investigating the human, temporal and spatial distribution and clustering characteristics of the disease.MethodsHuman brucellosis data from the mainland of China between 2012 and 2016 were obtained from the China Information System for Disease Control and Prevention. The spatial autocorrelation analysis of ArcGIS10.6 and the spatial-temporal scanning analysis of SaTScan software were used to identify potential changes in the spatial and temporal distribution of human brucellosis in the mainland of China during the study period.ResultsA total of 244 348 human brucellosis cases were reported during the study period of 2012–2016. The average incidence of human brucellosis was higher in the 40–65 age group. The temporal clustering analysis showed that the high incidence of brucellosis occurred between March and July. The spatial clustering analysis showed that the location of brucellosis clustering in the mainland of China remained relatively fixed, mainly concentrated in most parts of northern China. The results of the spatial-temporal clustering analysis showed that Heilongjiang represents a primary clustering area, and the Tibet, Shanxi and Hubei provinces represent three secondary clustering areas.ConclusionsHuman brucellosis remains a widespread challenge, particularly in northern China. The clustering analysis highlights potential high-risk human groups, time frames and areas, which may require special plans and resources to monitor and control the disease.

Assessing the spatial-temporal clustering and health implications of fine particulate matter (PM2.5)

This study seeks to examine changes in the mean annual exposure of PM2.5 air pollution from 1990 to 2000 and how they are associated with change in mortality due to tuberculosis, trachea/bronchus/lung cancer, respiratory diseases, and cardiovascular diseases from 2000 to 2016. A secondary goal of this study is to use spatial-temporal clustering to examine how PM2.5 trends differ across countries. The empirical work of this study is based on the World Bank Open Data and the World Health Organization’s Global Health Observatory (GHO) Data Repository. Quantile regression and spatial-temporal cluster analysis are used to assess the aims of this study. Findings from the quantile regression revealed that the number of deaths associated with these diseases differ according to the different levels of changes in PM2.5 concentration. Findings also reveal that countries assigned to different clusters are markedly different in terms of PM2.5 trends. Findings imply that air pollution inequality should be considered a factor in the elimination of health inequality. Efforts to integrate disease surveillance and control programs with those that reduce exposure to fine particulate matter, improve air quality, and increase people’s awareness of the health implications of air pollution should be continued or encouraged.

Epidemiological characteristics and spatial-temporal clusters of hand, foot, and mouth disease in Qingdao City, China, 2013-2018.

BackgroundHand, foot, and mouth disease (HFMD) has become one of the most important infectious diseases recent years. Qingdao City has suffered from serious HFMD epidemic. This study aimed to describe epidemiological characteristics and investigate spatial-temporal distribution at town level in Qingdao City.MethodThe surveillance data of HFMD during 2013–2018 were collected from the National Notifiable Disease Surveillance System. The global Moran’s I statistic was used to detect the spatial autocorrelation of HFMD cases by ArcGis 10.0 software. Purely spatial and spatial-temporal analysis was used to detect epidemic clusters by SatScanTM v9.6 software.ResultsThe annual average incidence of HFMD cases in Qingdao City from 2013 to 2018 was 123.16 per 100000, while the incidence rate of children≤5years old was 2879.80 per 100000. The majority (88.97%) of HFMD cases were aged within 0–5 years old and the males were 60.20%. Other enterovirus (EV), enteriovirus 71(EV71), and Coxsackievirus A16 (CA16) accounted for 48.75%, 30.91% and 20.34%. The seasonal peak was between May and October. HFMD had positive spatial autocorrelation at town level with global Moran’s I from 0.19 to 0.31(P<0.001). Spatial-temporal cluster analysis detected six most likely clusters and three secondary clusters from 2013 to 2018. The most likely cluster was located in urban and urban-rural fringe areas.ConclusionsUrban and urban-rural fringe areas were the major locations of the clusters with other EV as the dominant pathogen between May and October. The findings suggested that the prevention and control of HFMD in Qingdao City should be focus on these high-risk periods and locations which had important public health significance for the allocation of public health resources.

Spatial-Temporal Cluster Analysis of Childhood Cancer in California.

The observance of nonrandom space-time groupings of childhood cancer has been a concern of health professionals and the general public for decades. Many childhood cancers are suspected to have initiated in utero; therefore, we examined the spatial-temporal randomness of the birthplace of children who later developed cancer. We performed a space-time cluster analysis using birth addresses of 5,896 cases and 23,369 population-based, age-, sex-, and race/ethnicity-matched controls in California from 1997 to 2007, evaluating 20 types of childhood cancer and three a priori designated subgroups of childhood acute lymphoblastic leukemia (ALL). We analyzed data using a newly designed semiparametric analysis program, ClustR, and a common algorithm, SaTScan. We observed evidence for nonrandom space-time clustering for ALL diagnosed at 2-6 years of age in the South San Francisco Bay Area (ClustR P = 0.04, SaTScan P = 0.07), and malignant gonadal germ cell tumors in a region of Los Angeles (ClustR P = 0.03, SaTScan P = 0.06). ClustR did not identify evidence of clustering for other childhood cancers, although SaTScan suggested some clustering for Hodgkin lymphoma (P = 0.09), astrocytoma (P = 0.06), and retinoblastoma (P = 0.06). Our study provides evidence that childhood ALL diagnosed at 2-6 years and malignant gonadal germ cell tumors sporadically occurs in nonrandom space-time clusters. Further research is warranted to identify epidemiologic features that may inform the underlying etiology.

A Framework for Spatial-Temporal Trajectory Cluster Analysis Based on Dynamic Relationships

In spatial-temporal data analysis, location data and its evolution through time are investigated with the goal of uncovering important information to provide novel insights. These insights, for example, may involve congestion identification in transportation, mobility patterns in urban computing, and storm prediction in weather forecasting. Clustering, one data analysis technique, groups spatial-temporal data based on location. Current spatial-temporal data analysis techniques fail to investigate relationships between spatial-temporal clusters, such as splitting from a cluster and merging with another one because of a change of properties over time. These relationships could hold valuable information about the existence of a cluster and its interactions with other clusters and trajectories. In this paper, we introduce a framework to identify, process, and analyze relationships between clusters of spatial-temporal data (e.g. enter, merge, or split). We describe its architecture and components, as well as a proposed clustering technique, the different approaches for distance calculation, and how we calculate cluster similarity of temporally separated clusters. The result of these operations are used in the identification of cluster relationships over space and time. The analysis of these relationships helps uncover hidden values that could support novel approaches to more effective decision-making. We evaluate our framework with two case studies, based on truck and human trajectories.

Spatial Temporal Characteristics and Cluster Analysis of Chemical Components for Ambient PM2.5 in Wuhan

The characteristics of chemical components of particulate matter are good indicators for analyzing sources and causes of pollution. The spatial and temporal distribution characteristics of particulate matter can reflect regional pollution problems in urban development, providing a basic dataset to support effective control of particulate matter sources. We collected PM2.5 and analyzed its concentration and chemical components at eight sites during different seasons. The results indicated that the average concentration of PM2.5 in Wuhan reached 70.7 μg·m-3. The concentration of PM2.5 in winter (103.1 μg·m-3) was significantly higher than that of other seasons, and the lowest concentration was in autumn (52.4 μg·m-3). The concentrations of PM2.5 in Donghu Gaoxin, Zhuankou New Area, and Qingshan Ganghua Station were significantly higher than those at the other sites. The main chemical components in PM2.5 were OC and SO42-, accounting for 15.4% and 14.2%, respectively. The OC concentration was the highest in winter, whereas SO42-concentration was the highest in summer. The average annual OC/EC ratio was up to 2.80, lower in winter and spring, and higher in summer and autumn. Material reconstruction showed that secondary particles and organic matter (OM) were major substances, accounting for 32.34% and 20.44% of PM2.5 mass, respectively. Coal combustion and vehicle exhaust might be the main contributors to ambient PM2.5. The highest fractions for OM were at the Wujiashan and Donghu Gaoxin sites, whereas the fraction of secondary particles was higher at each site, suggesting that secondary pollution had obvious regional characteristics in Wuhan. Cluster analysis based on the characteristics of chemical components showed that the eight sites were divided into three clusters:1 Hanyang Yuehu, Haze, Donghu Liyuan, and Huangpi sites, where the main characteristics were that the concentrations of components at each point were low; ② Zhuankou New Area and Qingshan Ganghua, which were characterized by higher nitrogen components; and ③ Donghu Gaoxin and Wujiashan, where not only industrial sources were heavily polluted in Wuhan, but also motor vehicles and dust pollution greatly contributed.

Spatial-Temporal Clustering Analysis of Dating Violence in Gwanak-gu, Seoul and Its Policy Implications for Dating Violence Victim Protection

최근 데이트폭력이 사회적 주목을 끌면서 관련 연구도 활발히 이루어지고 있다. 다만, 데이트폭력을 분석한 종래의 연구들은 주로 미시적 수준에서 개인의 특성과 데이트 폭력 간의 상관관계에 초점을 두고 연구를 진행한 측면이 있다. 반면, 국외에서는 데이 트폭력을 사회구조적 관점에서 분석, 접근을 시도하는 실증연구들이 증가하고 있는 추세이다. 이러한 연구는 데이트폭력에 대한 단순한 분석을 넘어 예방에 관한 시사점을 제공한다는 점에서 중요한 의미를 갖는다. 그러나 그러한 국외연구에서조차 데이트폭력의 공간적 군집성에만 착안한 나머지, 시간적 분포에 대한 분석에는 한계를 보이고 있다. 이에, 본 연구는 시공간 순열검정 통계량(space-time permutation scan statistics, STPSS) 기법을 활용하여 데이트폭력의 시공간적 군집성을 분석하였다. STPSS모형은 독립변수의 영향력에 대한 가정 없이 데이트폭력의 유의미한 시공간적 분포를 분석할 수있는 모형으로, 이를 통해 본 연구는 데이트폭력이 시공간적으로 군집을 이루고 있을 뿐만 아니라, 특정 주거지에서 반복하여 발생함을 추정하였다. 이러한 추정을 토대로 데이 트폭력이 시공간적으로 군집을 이루는 지역에 대해서는 적절한 초기 대응 및 재발 방지를 통한 피해자 보호가 필요하며, 그러한 활동을 뒷받침할 수 있는 법적 근거가 마련되 어야 함을 제안하였다.Recently, a growing number of research has actively studied dating violence as several atrocious dating violence cases attracted social attention. However, previous researches examining dating violence mainly focused on the relationship between personal characteristics and dating violence at the individual-level. On the other hand, there is an increasing number of empirical researches attempting to understand dating violence at the structural-level. These previous researches are important in that they provide several policy implications beyond individual-levels. However, even these structural researches failed to consider temporal aspect of dating violence while only focusing on the spatial aspect of dating violence. Therefore, this study aimed to analyze both spatial and temporal aspect of dating violence by using space-time permutation scan statistics (STPSS) technique. The STPSS is useful for analyzing the possible spatial and temporal clusters of dating violence without assuming the influence of other socio-structural variables. This study found that dating violence occurred repeatedly in a specific residential area. Based on these assumptions and findings, this study suggested that victims of dating violence should be provided with more active police response at the early stage of dating violence incidence, and that legal basis should be established to support such police activities.

Spatial-temporal clustering analysis of yaws on Lihir Island, Papua New Guinea to enhance planning and implementation of eradication programs

BackgroundIn the global program for the eradication of yaws, assessments of the prevalence of the disease are used to decide where to initiate mass treatment. However, the smallest administrative unit that should be used as the basis for making decisions is not clear. We investigated spatial and temporal clustering of yaws to help inform the choice of implementation unit.Methodology/Principal findingsWe analyzed 11 years of passive surveillance data on incident yaws cases (n = 1448) from Lihir Island, Papua New Guinea. After adjusting for age, sex, and trends in health-seeking, we detected three non-overlapping spatial-temporal clusters (p < 1 × 10−17, p = 1.4 × 10−14, p = 1.4 × 10−8). These lasted from 28 to 47 months in duration and each encompassed between 4 and 6 villages. We also assessed spatial clustering of prevalent yaws cases (n = 532) that had been detected in 7 biannual active case finding surveys beginning in 2013. We identified 1 statistically significant cluster in each survey. We considered the possibility that schools that serve multiple villages might be loci of transmission, but we found no evidence that incident cases of yaws among 8- to 14-year-olds clustered within primary school attendance areas (p = 0.6846).Conclusions/SignificanceThese clusters likely reflect transmission of yaws across village boundaries; villages may be epidemiologically linked to a degree such that mass drug administration may be more effectively implemented at a spatial scale larger than the individual village.

Epidemiological characteristics and spatial-temporal clustering analysis on pulmonary tuberculosis in Changsha from 2013 to 2016

To analyze the epidemic characteristics and to explore the spatial-temporal clusters of pulmonary tuberculosis (PTB) in Changsha from 2013 to 2016. Methods: Descriptive analysis and space-time permutation scan statistic were used to analyze the reported PTB cases in Changsha from 2013 to 2016. Results: Between 2013 and 2016, a total of 17 721 PTB cases were reported in Changsha, with annual reported incidence rate at 60.87 per 100 000 population. Males and individuals aged 15 to <60 years accounted for higher proportion of PTB cases compared to females and other age groups. The number of reported PTB cases reached peak from March to May in each year. The space-time permutation scan statistic identified one most likely cluster and two secondary clusters of PTB cases. The most likely cluster covered most areas of Liuyang City and the North-east part of Changsha County from October 1, 2013 to February 28, 2014. The first cluster occupied 12 towns (streets) in Kaifu District and Changsha County in December 2016. The second cluster included four towns (streets) in Yuhua District and Tianxin District from March 1 to September 30, in 2013. Conclusion: Between 2013 and 2016, significant space-time clusters of PTB cases were identified in Changsha. These findings could provide a guide for development of regional intervention strategies for PTB control.