Ensemble Based Temporal Weighting and Pareto Ranking (ETP) Model for Effective Root Cause Analysis

Root-cause identification plays a vital role in business decision making by providing effective future directions for the organizations.Aspect extraction and sentiment extraction plays a vital role in identifying the rootcauses.This paper proposes the Ensemble based temporal weighting and pareto ranking(ETP)model for Root-cause identification.Aspect extraction is performed based on rules and is followed by opinion identification using the proposed boosted ensemble model.The obtained aspects are validated and ranked using the proposed aspect weighing scheme.Pareto-rule based aspect selection is performed as the final selection mechanism and the results are presented for business decision making.Experiments were performed with the standard five product benchmark dataset.Performances on all five product reviews indicate the effective performance of the proposed model.Comparisons are performed using three standard state-of-the-art models and effectiveness is measured in terms of F-Measure and Detection rates.The results indicate improved performances exhibited by the proposed model with an increase in F-Measure levels at 1%–15%and detection rates at 4%–24%compared to the state-of-the-art models.

Geographically and Temporally Weighted Regression in Assessing Dengue Fever Spread Factors in Yunnan Border Regions

Objective This study employs the Geographically and Temporally Weighted Regression(GTWR)model to assess the impact of meteorological elements and imported cases on dengue fever outbreaks,emphasizing the spatial-temporal variability of these factors in border regions.Methods We conducted a descriptive analysis of dengue fever’s temporal-spatial distribution in Yunnan border areas.Utilizing annual data from 2013 to 2019,with each county in the Yunnan border serving as a spatial unit,we constructed a GTWR model to investigate the determinants of dengue fever and their spatio-temporal heterogeneity in this region.Results The GTWR model,proving more effective than Ordinary Least Squares(OLS)analysis,identified significant spatial and temporal heterogeneity in factors influencing dengue fever’s spread along the Yunnan border.Notably,the GTWR model revealed a substantial variation in the relationship between indigenous dengue fever incidence,meteorological variables,and imported cases across different counties.Conclusion In the Yunnan border areas,local dengue incidence is affected by temperature,humidity,precipitation,wind speed,and imported cases,with these factors’influence exhibiting notable spatial and temporal variation.

Spatiotemporal variation of ecological environment quality and extreme climate drivers on the Qinghai-Tibetan Plateau

Protecting the ecological security of the Qinghai-Tibet Plateau(QTP)is of great importance for global ecology and climate.Over the past few decades,climate extremes have posed a significant challenge to the ecological environment of the QTP.However,there are few studies that explored the effects of climate extremes on ecological environment quality of the QTP,and few researchers have made quantitative analysis.Hereby,this paper proposed the Ecological Environmental Quality Index(EEQI)for analyzing the spatial and temporal variation of ecological environment quality on the QTP from 2000 to 2020,and explored the effects of climate extremes on EEQI based on Geographically and Temporally Weighted Regression(GTWR)model.The results showed that the ecological environment quality in QTP was poor in the west,but good in the east.Between 2000 and 2020,the area of EEQI variation was large(34.61%of the total area),but the intensity of EEQI variation was relatively low and occurred mainly by a slightly increasing level(EEQI change range of 0.05-0.1).The overall ecological environment quality of the QTP exhibited spatial and temporal fluctuations,which may be attributed to climate extremes.Significant spatial heterogeneity was observed in the effects of the climate extremes on ecological environment quality.Specifically,the effects of daily temperature range(DTR),number of frost days(FD0),maximum 5-day precipitation(RX5day),and moderate precipitation days(R10)on ecological environment quality were positive in most regions.Furthermore,there were significant temporal differences in the effects of consecutive dry days(CDD),consecutive wet days(CWD),R10,and FD0 on ecological environment quality.These differences may be attributed to variances in ecological environment quality,climate extremes,and vegetation types across different regions.In conclusion,the impact of climate extremes on ecological environment quality exhibits complex patterns.These findings will assist managers in identifying changes in the ecological environment quality of the QTP and addressing the effects of climate extremes.

Spatial-temporal Evolution of the Urban-rural Coordination Relationship in Northeast China in 1990-2018

To comprehensively understand the law of urban-rural relationship and propose scientific measures of urban-rural coordinated development in Northeast China,this study uses the coupling coordination degree model and geographically and temporally weighted regression(GTWR)model to analyze the spatial-temporal patterns and the corresponding driving mechanisms of its urban-rural coordination since 1990.The results are as follows.First,the urban-rural coupling coordination degree in Northeast China was very low and improved slowly,but its stages of evolution is a good interpretation of the strategic arrangements of China's urbanization.Second,the urban-rural coupling coordination degree in Northeast China had spatial differences and was characterized by central polarization,converging on urban agglomeration,which was high in the south and low in the north.Moreover,the gap between the north and south weakened.Third,the spatial-temporal evolution of the urban-rural coordination relationship in Northeast China was influenced by pulling from the central cities,pushing from rural transformation,and government regulations.The influence intensity of the three mechanisms was weak,but the pulling from the central cities was stronger than that of the other two mechanisms.Furthermore,the spatial difference between the three mechanisms determines the spatial pattern and its evolution of the urban-rural coordination relationship in Northeast China.Fourth,to promote the development of urban-rural coordination in Northeast China,it is essential to advance urban-rural economic correlation,enhance the government^role in regulating and guiding,and adopt different policies for each region in Northeast China.

Elaborating Spatiotemporal Associations Between the Built Environment and Urban Vibrancy:A Case of Guangzhou City,China

This study applies multi-source datasets(i.e.,Baidu Heat Map data,points of interest(POIs)data,and floor area and land use data)and geographically and temporally weighted regression(GTWR)models to elaborate the spatiotemporal relationships between the built environment and urban vibrancy on both weekdays and weekends,using Guangzhou City as a case.First,we verified the spatially and temporally nonstationary nature of the built environment correlates,which have been largely ignored in previous studies based on local regression techniques.The spatially and temporally heterogeneous effects of the built environment on urban vibrancy are then presented and visualized,based on the GTWR results.We found that the elasticity of location(i.e.,distance),land use mix(i.e.,diversity),building intensity and numbers of POIs with various functions(i.e.,density)are different across time(2-h intervals within a day)and space(grids),due to people’s everyday lifestyle,time-space constraints,and geographical context(e.g.,spatial structure).The findings highlight the importance of a better understanding of the local geography on the spatiotemporal relationships for urban planners and local governments so as to put forward decision-making support for fostering and maintaining urban vibrancy.

Vegetation recovery trends under dual dominance of climate change and anthropogenic factors in the severely damaged areas of the Wenchuan earthquake

The occurrence of the Wenchuan earthquake caused the degradation of regional ecosystems,including vegetation destruction.However,the post-seismic vegetation recovery and its driving forces on the spatial-temporal scale are still vague,especially in the severely damaged areas(including Wenchuan,Beichuan,Mianzhu,Shifang,Qingchuan,Maoxian,Anzhou,Dujiangyan,Pingwu and Pengzhou).Here,we detected vegetation recovery in the severely damaged areas by using Ensemble Empirical Mode Decomposition(EEMD)to analyze the time series characteristics of the Enhanced Vegetation Index(EVI),and explored the driving effects of climate,land use types,nighttime light,water system,slope,and clay content on vegetation recovery based on Geographically and Temporally Weighted Regression(GTWR)model.The results indicated that the post-seismic vegetation recovery rate increased rapidly(acceleration>0)but slowed down after 2013.And the areas of best vegetation recovery(EVI increments>0.1)were distributed in the north of the study area,the Minjiang River Basin,and front fault and central fault of the Longmenshan Fault Zone.While the areas with the worst vegetation recovery(EVI increments<-0.1)were concentrated in the southern high-altitude areas and the Chengdu Plain.Additionally,a process attribution of the driving forces of vegetation recovery indicated that accumulated precipitation and maximum temperature promoted vegetation recovery(regression coefficients>0),but the impacts weakened after the earthquake,possibly due to the increase of secondary disasters induced by precipitation and the rise in maximum temperature.The impact of cultivated land on vegetation recovery was mostly positive(regression coefficients>0),which may be related to the implementation of the Grain for Green Project.The nighttime light inhibited vegetation recovery(regression coefficients<0),which could be closely associated with urbanization.The results indicated that more attention should be paid to the nonlinear variations of post-earthquake vegetation recovery trends,and the effects of climatic and anthropogenic factors on vegetation recovery also should not be underestimated.

Infuence of urbanization on schistosomiasis infection risk in Anhui Province based on sixteen year’s longitudinal surveillance data:a spatio-temporal modelling study

Background Urbanization greatly afects the natural and social environment of human existence and may have a multifactoral impact on parasitic diseases.Schistosomiasis,a common parasitic disease transmitted by the snail Oncomelania hupensis,is mainly found in areas with population aggregations along rivers and lakes where snails live.Previous studies have suggested that factors related to urbanization may infuence the infection risk of schistosomiasis,but this association remains unclear.This study aimed to analyse the efect of urbanization on schistosomiasis infection risk from a spatial and temporal perspective in the endemic areas along the Yangtze River Basin in China.Methods County-level schistosomiasis surveillance data and natural environmental factor data covering the whole Anhui Province were collected.The urbanization level was characterized based on night-time light data from the Defense Meteorological Satellite Program Operational Linescan System(DMSP-OLS)and the National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite(NPP-VIIRS).The geographically and temporally weighted regression model(GTWR)was used to quantify the infuence of urbanization on schistosomiasis infection risk with the other potential risk factors controlled.The regression coefcient of urbanization was tested for signifcance(α=0.05),and the infuence of urbanization on schistosomiasis infection risk was analysed over time and across space based on signifcant regression coefcients.Variables studied included climate,soil,vegetation,hydrology and topography.Results The mean regression coefcient for urbanization(0.167)is second only to the leached soil area(0.300),which shows that the urbanization is the most important infuence factors for schistosomiasis infection risk besides leached soil area.The other important variables are distance to the nearest water source(0.165),mean minimum temperature(0.130),broadleaf forest area(0.105),amount of precipitation(0.073),surface temperature(0.066),soil bulk density(0.037)and grassland area(0.031).The infuence of urbanization on schistosomiasis infection risk showed a decreasing trend year by year.During the study period,the signifcant coefcient of urbanization level increased from−0.205 to−0.131.Conclusions The infuence of urbanization on schistosomiasis infection has spatio-temporal heterogeneous.The urbanization does reduce the risk of schistosomiasis infection to some extend,but the strength of this infuence decreases with increasing urbanization.Additionally,the efect of urbanization on schistosomiasis infection risk was greater than previous reported natural environmental factors.This study provides scientifc basis for understanding the infuence of urbanization on schistosomiasis,and also provides the feasible research methods for other similar studies to answer the issue about the impact of urbanization on disease risk.

Monthly gravity field recovery from GRACE orbits and K-band measurements using variational equations approach

The Gravity Recovery and Climate Experiment（GRACE） mission can significantly improve our knowledge of the temporal variability of the Earth＇s gravity field.We obtained monthly gravity field solutions based on variational equations approach from GPS-derived positions of GRACE satellites and K-band range-rate measurements.The impact of different fixed data weighting ratios in temporal gravity field recovery while combining the two types of data was investigated for the purpose of deriving the best combined solution.The monthly gravity field solution obtained through above procedures was named as the Institute of Geodesy and Geophysics（IGG） temporal gravity field models.IGG temporal gravity field models were compared with GRACE Release05（RL05） products in following aspects：（i） the trend of the mass anomaly in China and its nearby regions within 2005-2010; （ii） the root mean squares of the global mass anomaly during 2005-2010; （iii） time-series changes in the mean water storage in the region of the Amazon Basin and the Sahara Desert between 2005 and 2010.The results showed that IGG solutions were almost consistent with GRACE RL05 products in above aspects（i）-（iii）.Changes in the annual amplitude of mean water storage in the Amazon Basin were 14.7 ± 1.2 cm for IGG,17.1 ± 1.3 cm for the Centre for Space Research（CSR）,16.4 ± 0.9 cm for the GeoForschungsZentrum（GFZ） and 16.9 ± 1.2 cm for the Jet Propulsion Laboratory（JPL） in terms of equivalent water height（EWH）,respectively.The root mean squares of the mean mass anomaly in Sahara were 1.2 cm,0.9 cm,0.9 cm and 1.2 cm for temporal gravity field models of IGG,CSR,GFZ and JPL,respectively.Comparison suggested that IGG temporal gravity field solutions were at the same accuracy level with the latest temporal gravity field solutions published by CSR,GFZ and JPL.

Toward tuberculosis elimination by understanding epidemiologic characteristics and risk factors in Hainan Province, China

Background The disease burden of tuberculosis(TB)was heavy in Hainan Province,China,and the information on transmission patterns was limited with few studies.This atudy aims to further explore the epidemiological charac-teristics and influencing factors of TB in Hainan Province,and thereby contribute valuable scientific evidences for TB elimination in Hainan Province.Methods The TB notification data in Hainan Province from 2013 to 2022 were collected from the Chinese National Disease Control Information System Tuberculosis Surveillance System,along with socio-economic data.The spatial-temporal and population distributions were analyzed,and spatial autocorrelation analysis was conducted to explore TB notification rate clustering.In addition,the epidemiological characteristics of the cases among in-country migrants were described,and the delay pattern in seeking medical care was investigated.Finally,a geographically and tem-porally weighted regression(GTWR)model was adopted to analyze the relationship between TB notification rate and socio-economic indicators.The tailored control suggestions in different regions for TB elimination was provided by understanding epidemiological characteristics and risk factors obtained by GTWR.Results From 2013 to 2022,64,042 cases of TB were notified in Hainan Province.The estimated annual percent-age change of TB notification rate in Hainan Province from 2013 to 2020 was-6.88%[95%confidence interval(CI):-5.30%,-3.69%],with higher rates in central and southern regions.The majority of patients were males(76.33%)and farmers(67.80%).Cases among in-country migrants primarily originated from Sichuan(369 cases),Heilongjiang(267 cases),Hunan(236 cases),Guangdong(174 cases),and Guangxi(139 cases),accounting for 53%.The majority(98.83%)of TB cases were notified through passive case finding approaches,with delay in seeking care.The GTWR analysis showed that gross domestic product per capita,the number of medical institutions and health personnel per 10,oo0 people were main factors affecting the high TB notification rates in some regions in Hainan Province.Dif-ferent regional tailored measures such as more TB specialized hospitals were proposed based on the characteristics of each region.Conclusions The notification rate of TB in Hainan Province has been declining overall but still remained high in central and southern regions.Particular attention should be paid to the prevalence of TB among males,farmers,and outof-province migrant populations.The notification rate was also influenced by economic development and medical conditions,indicating the need of more TB specialized hospitals,active surveillance and other tailored prevention and control measures to promote the progress of TB elimination in Hainan Province.

基于重心-GTWR模型的京津冀城市群研究

Research into urban expansion patterns and their driving forces is of great significance for urban agglomeration development planning and decision-making.In this paper,we reveal the multi-dimensional characteristics of urban expansion patterns,based on the intensity index of the urban expansion,the differentiation index of the urban expansion,the fractal dimension index,the land urbanization rate,and the center of gravity model,by taking the Beijing-Tianjin-Hebei(Jing-Jin-Ji)urban agglomeration as an example.We then build the center of gravity-geographically and temporally weighted regression(GTWR)model by coupling the center of gravity model with the GTWR model.Through the analysis of the temporal and spatial patterns and by using the center of gravity-GTWR model,we analyze the driving forces of the urban land expansion and summarize the dominant development modes and core driving forces of the Jing-Jin-Ji urban agglomeration.The results show that:1)Between 1990 and 2015,the expansion intensity of the Jing-Jin-Ji urban agglomeration showed a down-up-down trend,and the peak period was in 2005-2010.Before 2005,high-speed development took place in Beijing,Tianjin,Baoding,and Langfang;after 2005,rapid development was seen in Xingtai and Handan.2)Although the barycenter of cities in the Jing-Jin-Ji urban agglomeration has shown a divergent trend,the local interaction between cities has been enhanced,and the driving forces of urban land expansion have shown a characteristic of spatial spillover.3)The spatial development mode of the Jing-Jin-Ji urban agglomeration has changed from a dual-core development mode to a multi-core development mode,which is made up of three functional cores:the transportation core in the northern part,the economic development core in the central part,and the investment core in the southern part.The synergistic development between each functional core has led to the multi-core development mode.4)The center of gravity-GTWR model combines the analysis of spatial and temporal nonstationarity with urban spatial interaction,and analyzes the urban land expansion as a space-time dynamic system.The results of this study show that the model is a feasible approach in the analysis of the driving forces of urban land expansion.