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Substitutes or complements? Examining effects of urban rail transit on bus ridership using longitudinal city-level data

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TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE 2023年第1期174卷

作者： Yang, Chao Yu, Chengcheng Dong, Wentao Yuan, Quan Tongji Univ Urban Mobil Inst Shanghai 201804 Peoples R China Tongji Univ Key Lab Rd & Traff Engn Minist Educ Shanghai 201804 Peoples R China

Despite massive investments, bus ridership has constantly declined in many cities worldwide. As public transit systems are evolving from bus-based to mixed structured, the longitudinal interactions between bus and urban rail transit (URT) have attracted the growing attention of transportation planners in many cities. Based on complex network theory and minimum spanning tree method, we examine network and scale effects of rail transit on conventional bus. Fixed effect models (FEMs) and generalized additive models (GAMs) are developed to identify non-linear causal effects of explanatory variables on bus transit ridership, rail transit ridership and total transit ridership, using China's city-level data during 2005-2020. The results showed when GDP per capita exceeded 75,000 yuan, residents would start switching away from bus;(2) for cities with advanced URT networks, simply improving bus service quality or constructing the rail network is not as effective as optimizing both together. Therefore, we recommend that for cities where the URT was less developed, financial resources need to be strategically invested in the bus transit networks so as to accommodate citizens' travel demand. Meanwhile, collaborative multimodal public transportation planning is essential for sustainable urban transportation development. The research outcomes may inform policymakers of the critical relationship between bus and rail transit and help create wise decisions on spending on public transit.

关键词： Multimodal public transport system Bus ridership generalized additive model Urban rail transits Network externality

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Effects of pollen concentration on allergic rhinitis in children: A retrospective study from Beijing, a Chinese megacity

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ENVIRONMENTAL RESEARCH 2023年第1期229卷 115903页

作者： Zhao, Yuxin Sun, Zhaobin Xiang, Li An, Xingqin Hou, Xiaoling Shang, Jing Han, Ling Ye, Caihua Nanjing Univ Informat Sci & Technol Sch Atmospher Phys Nanjing Nanjing 210044 Jiangsu Peoples R China Chinese Acad Meteorol Sci State Key Lab Severe Weather CMA Beijing 100081 Peoples R China China Meteorol Adm Key Lab Urban Meteorol Beijing 100089 Peoples R China Capital Med Univ Beijing Childrens Hosp Childrens Natl Med Ctr Natl Clin Med Res Ctr Resp DisMinist EducDept An Beijing 100045 Peoples R China Chinese Ctr Dis Control & Prevent Natl Inst Communicable Dis Control & Prevent State Key Lab Infect Dis Prevent & Control Beijing 102206 Peoples R China Beijing Meteorol Bur Beijing Meteorol Serv Ctr Beijing 100089 Peoples R China Capital Med Univ Beijing Childrens Hosp 56 South Lishi Rd Beijing 100045 Peoples R China Chinese Acad Meteorol Sci ZhongGuanCun South Ave 46 Beijing 100081 Peoples R China

With global climate change and rapid urbanization, the prevalence of allergic diseases caused by pollen is rising dramatically worldwide with unprecedented complexity and severity, especially for children in mega-cities. However, because of the lack of long time-series pollen concentrations data, the accurate evaluation of the impact of pollen on allergic rhinitis (AR) was scarce in the Chinese metropolis. A generalized additive model was used to assess the effect of pollen concentration on pediatric AR outpatient visits in Beijing from 2014 to 2019. A stratified analysis of 10 pollen species and age-gender-specific groups was also conducted during the spring and summer-autumn peak pollen periods separately. Positive associations between pollen concentration and pediatric AR varied with the season and pollen species were detected. Although the average daily pollen concentration is higher during the spring tree pollen peak, the influence was stronger at the summer-autumn weed pollen peak with the maximum relative risk 1.010 (95% CI 1.009, 1.011), which was higher than the greatest relative risk, 1.003 (95% CI 1.002, 1.004) in the spring peak. The significant adverse effects can be sustained to lag10 during the study period, and longer in the summer-autumn peak (lag13) than in the spring peak (lag8). There are thresholds for the health effects and they varied between seasons. The significant effect appeared when the pollen concentration was higher than 3.74 x 10(5) grain center dot m(-2)center dot d(-1) during the spring tree pollen peaks and 4.70 x 10(4) grain center dot m(-2)center dot d(-1) during the summer-autumn weed pollen peaks. The stratified results suggested that the species-specific effects were heterogeneous. It further highlights that enough attention should be paid to the problem of pollen allergy in children, especially school-aged children aged 7-18 years and weed pollen in the summer-autumn peak pollen period. These findings provide a more accurate r

关键词： Pollen concentration Allergic rhinitis generalized additive model Children's health

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Assessing the sustainability of threadfin bream fishery along South-eastern coast of the Arabian Sea: A comprehensive analysis of climate change impact and fishing frontiers

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REGIONAL STUDIES IN MARINE SCIENCE 2024年 71卷

作者： Balachandran, Radhika Zacharia, P. U. Purushottama, G. B. Sudarsan, K. S. Shafeeque, Muhammad Kumar, R. Ratheesh Varghese, Eldho Joseph, Alphonsa Rahul, R. Kishore, Nanda Bright, Rose P. Seetha, P. K. George, Grinson ICAR Res Complex Cent Marine Fisheries Res Inst PB 1603 Cochin 682018 Kerala India Kerala Univ Fisheries & Ocean Studies Panangad Rd Kochi 682506 Kerala India King Fahd Univ Petr & Minerals Appl Res Ctr Environm & Marine Studies Dhahran 31261 Saudi Arabia

The study aimed to assess the cumulative impact of climate change and sustainable fishing levels on the longterm sustainability of threadfin breams, a demersal species inhabiting along south-eastern coast of the Arabian Sea (SEAS). We utilized the dataset derived from the commercial fisheries in the SEAS region and addressed the influence of dynamics in fishing and environmental factors. The threadfin breams stock status was estimated by analyzing the fluctuations in Catch Per Unit Effort and by employing methods, viz. the Monte Carlo Catch- Maximum Sustainable Yield and Bayesian Schaefer model. The investigation found that the catch rate of threadfin breams declined as fishing intensity increased in terms of effectiveness of advanced fishing methods. This empirical inquiry leveraged the generalized additive model to assess the impact of environmental parameters on the fish stock. The analysis unveils a notable positive influence on this fish group, signifying a substantial response to increased precipitation. In contrast, deviations in Sea Surface Temperature, Sea Surface Salinity, ocean isothermal layer depth, and surface currents revealed adverse effects surpassing the optimal threshold. The targeted stock of threadfin breams exhibits a medium level of resilience, as indicated by the existing data, the investigation concludes that optimizing fishing practices could aid in replenishing the stock, fostering future sustainability. This conclusion holds true even in the face of potential negative influences from environmental parameters to a certain extent. Overall, the study provides important insights into the impact of climate change and sustainable fishing practices on the sustainability of a demersal resource in the SEAS and emphasizes the innovative ways to manage fishery resources in a manner that is ecologically sustainable and yet economically viable and socially acceptable.

关键词： Catch Per Unit Effort Fishing effort generalized additive model Overfishing Southwest coast of India Stock assessment

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National-scale data-driven rainfall induced landslide susceptibility mapping for China by accounting for incomplete landslide data

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Geoscience Frontiers 2021年第6期12卷 262-276页

作者： Qigen Lin Pedro Lima Stefan Steger Thomas Glade Tong Jiang Jiahui Zhang Tianxue Liu Ying Wang Institute for Disaster Risk Management School of Geographical SciencesNanjing University of Information Science&TechnologyNanjing 210044China Department of Geography and Regional Research University of ViennaUniversitätsstraße 71010 ViennaAustria Institute for Earth Observation Eurac ResearchViale Druso 139100 Bolzano-BozenItaly Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education/Academy of Disaster Reduction and Emergency Management Faculty of Geographical ScienceBeijing Normal UniversityBeijing 100875China

China is one of the countries where landslides caused the most fatalities in the last decades. The threat that landslide disasters pose to people might even be greater in the future, due to climate change and the increasing urbanization of mountainous areas. A reliable national-scale rainfall induced landslide susceptibility model is therefore of great relevance in order to identify regions more and less prone to landsliding as well as to develop suitable risk mitigating strategies. However, relying on imperfect landslide data is inevitable when modelling landslide susceptibility for such a large research area. The purpose of this study is to investigate the influence of incomplete landslide data on national scale statistical landslide susceptibility modeling for China. In this context, it is aimed to explore the benefit of mixed effects modelling to counterbalance associated bias propagations. Six influencing factors including lithology, slope,soil moisture index, mean annual precipitation, land use and geological environment regions were selected based on an initial exploratory data analysis. Three sets of influencing variables were designed to represent different solutions to deal with spatially incomplete landslide information: Set 1(disregards the presence of incomplete landslide information), Set 2(excludes factors related to the incompleteness of landslide data), Set 3(accounts for factors related to the incompleteness via random effects). The variable sets were then introduced in a generalized additive model(GAM: Set 1 and Set 2) and a generalized additive mixed effect model(GAMM: Set 3) to establish three national-scale statistical landslide susceptibility models: models 1, 2 and 3. The models were evaluated using the area under the receiver operating characteristics curve(AUROC) given by spatially explicit and non-spatial cross-validation. The spatial prediction pattern produced by the models were also investigated. The results show that the landslide inve

关键词： Statistical modelling Landslide susceptibility generalized additive model Mixed-effects model China

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Fast Automatic Smoothing for generalized additive models

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JOURNAL OF MACHINE LEARNING RESEARCH 2019年第1期20卷 1-27页

作者： El-Bachir, Yousra Davison, Anthony C. Ecole Polytech Fed Lausanne FSB MATH STAT Stn 8 CH-1015 Lausanne Switzerland

generalized additive models (GAMs) are regression models wherein parameters of probability distributions depend on input variables through a sum of smooth functions, whose degrees of smoothness are selected by L-2 regularization. Such models have become the de-facto standard nonlinear regression models when interpretability and flexibility are required, but reliable and fast methods for automatic smoothing in large data sets are still lacking. We develop a general methodology for automatically learning the optimal degree of L-2 regularization for GAMs using an empirical Bayes approach. The smooth functions are penalized by hyper-parameters that are learned simultaneously by maximization of a marginal likelihood using an approximate expectation-maximization algorithm. The latter involves a double Laplace approximation at the E-step, and leads to an efficient M-step. Empirical analysis shows that the resulting algorithm is numerically stable, faster than the best existing methods and achieves state-of-the-art accuracy. For illustration, we apply it to an important and challenging problem in the analysis of extremal data.

关键词： Automatic L-2 Regularization Empirical Bayes Expectation-maximization Algorithm generalized additive model Laplace Approximation Marginal Maximum Likelihood

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Evaluating the eff ect of input variables on quantifying the spatial distribution of croaker Johnius belangerii in Haizhou Bay,China

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Journal of Oceanology and Limnology 2021年第4期39卷 1570-1583页

作者： Yunlei ZHANG Ying XUE Binduo XU Chongliang ZHANG Xiaoxiao ZAN College of Fisheries Ocean University of ChinaQingdao 266003China Marine College Shandong UniversityWeihai 264209China Field Observation and Research Station of Haizhou Bay Fishery Ecosystem Ministry of EducationQingdao 266003China

A habitat model has been widely used to manage marine species and analyze relationship between species distribution and environmental *** predictive skill in habitat model depends on whether the models include appropriate explanatory *** to limited habitat range,low density,and low detection rate,the number of zero catches could be very large even in favorable *** zeroes will increase the bias and uncertainty in estimation of ***,appropriate explanatory variables need to be chosen first to prevent underestimate or overestimate species abundance in habitat *** addition,biotic variables such as prey data and spatial autocovariate(SAC)of target species are often ignored in species distribution ***,we evaluated the eff ects of input variables on the performance of generalized additive models(GAMs)under excessive zero catch(>70%).Five types of input variables were selected,i.e.,(1)abiotic variables,(2)abiotic and biotic variables,(3)abiotic variables and SAC,(4)abiotic,biotic variables and SAC,and(5)principal component analysis(PCA)based abiotic and biotic variables and ***’s croaker Johnius belangerii is one of the dominant demersal fish in Haizhou Bay,with a large number of zero catches,thus was used for the case *** show that the PCA-based GAM incorporated with abiotic and biotic variables and SAC was the most appropriate model to quantify the spatial distribution of the *** variables and SAC were important and should be incorporated as one of the drivers to predict species *** study suggests that the process of input variables is critical to habitat modelling,which could improve the performance of habitat models and enhance our understanding of the habitat suitability of target species.

关键词： generalized additive model principal component analysis biotic variables spatial autocovariate

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Spatial distribution of northern shrimp Pandalus eous in the Sea of Japan in relation to the seafloor environment

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JOURNAL OF THE MARINE BIOLOGICAL ASSOCIATION OF THE UNITED KINGDOM 2024年 104卷 e98-e98页

作者： Naito, Taiga Sakuma, Kay Iida, Masaya Japan Fisheries Res & Educ Agcy Fisheries Resources Inst Niigata 9518121 Japan

To explore relationships between spatial distributions of northern shrimp Pandalus eous and environmental factors along the coastline of Honshu Island in the Sea of Japan, we built delta-type two-step generalized additive models (delta-GAM) based on bottom trawl surveys conducted in 2013-2022. The models provide the first quantitative analysis of the species' habitat, showing that its distribution is associated with bottom sediment type, depth, slope and topographic position index (TPI) alongside the effects of year and region. From the delta-GAM response plots, species habitat preferences in the Sea of Japan were estimated as follows: seafloor deeper than 283 m;muddy rather than sandy bottoms;gently sloped to flat bottoms (<0.8 degrees in slope);and valleys rather than ridges (TPI < 0.9). These results were reviewed in detail along with previously reported distribution records of northern shrimp. Standardized density (s-density) per fishing grid cell (10 ' square latitude-longitude mesh) estimated from the delta-GAM results indicated that this species is widely distributed on the continental slope along the coast of Honshu Island. To test plausibility of the s-density analysis, we compared s-density per fishing grid cell with nominal CPUE (kg per haul) per fishing grid cell based on official logbook data from large offshore trawl fisheries. The two were generally positively correlated by year, and the delta-GAM results were assumed to be fairly robust. Finally, potential fishing grounds were explored based on the delta-GAM results.

关键词： bottom sediment delta-type two-step model generalized additive model Pandalus eous species distribution model slope Sea of Japan topographic position index

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The association between ambient air pollution and the risk of incident nasopharyngeal carcinoma in Hangzhou, China

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SCIENTIFIC REPORTS 2024年第1期14卷 1-10页

作者： Chen, Zesheng Cheng, Zongxue Wu, Yaoyao Yu, Zhecong Qin, Kang Jiang, Caixia Xu, Jue Hangzhou Hlth Supervis Inst Hangzhou Ctr Dis Control & Prevent Dept Chron & Noncommunicable Dis 568 Ming Shi Rd Hangzhou 310021 Zhejiang Peoples R China

Ambient air pollution exposure was associated with an increased risk of incident cancer, but few previous studies have focused on the associations between ambient air pollution and nasopharyngeal carcinoma (NPC). Thus, our goal is to examine whether exposure to ambient air pollution in Hangzhou, which includes sulfur dioxide (SO2), nitrogen dioxide (NO2), and inhalable particles (PM10), will have an impact the risk of incident cancer. We collected data on daily ambient air pollution data, climate, and daily incidence of NPC in Hangzhou from Jan 1, 2013, to Dec 31, 2022. We applied a generalized additive model (GAM) based on the Poisson distribution to investigate the effect of ambient air pollution on the risk of incident NPC. The effects of ambient air pollution exposure on NPC were also discussed in subgroups by age, gender, region, and season. A total of 3121 NPC incident cases were included during the study period. We discovered that the risk of incident NPC was increased by 0.75% (95% CI: 0.01-1.58), 0.36% (95% CI: 0.03-0.69), and 0.14% (95% CI: 0.01-0.28) for every 1 mu g/m3 increase in the concentration of SO2, NO2, and PM10, respectively. These pollutants continued to have a substantial impact on the risk of incident NPC even after controlling for other ambient air pollutants. A noteworthy affirmative connection was a significant positive correlation between SO2 and NPC in male, warm season, urban areas, and elderly subgroups. In contrast to SO2, there was a significant positive correlation between PM10 and NPC in female, warm season, rural areas, non-elderly, and elderly subgroups. The association between NO2 and NPC was significantly positively correlated in male, female, rural areas, and elderly subgroups. In conclusion, our study's findings demonstrated that exposure to airborne SO2, NO2, and PM10 can negatively impact the risk of incident NPC.

关键词： Ambient Air pollution Risk of incident Nasopharyngeal carcinoma generalized additive model

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Exploring the effects and interactions of meteorological factors on the incidence of scrub typhus in Ganzhou City, 2008-2021

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BMC PUBLIC HEALTH 2024年第1期24卷 1页

作者： Pan, Kailun Huang, Renfa Xu, Lingui Lin, Fen Gannan Med Univ Sch Publ Hlth & Hlth Management Ganzhou 341000 Jiangxi Peoples R China Ganzhou Municipal Ctr Dis Control & Prevent Ganzhou 341000 Jiangxi Peoples R China

BackgroundScrub typhus poses a substantial risk to human life and wellbeing as it is transmitted by vectors. Although the correlation between climate and vector-borne diseases has been investigated, the impact of climate on scrub typhus remains inadequately comprehended. The objective of this study is to investigate the influence of meteorological conditions on the occurrence of scrub typhus in Ganzhou City, Jiangxi Province. MethodsFrom January 1, 2008 to December 31, 2021, we gathered weekly records of scrub typhus prevalence alongside meteorological data in Ganzhou city. In order to investigate the correlation between meteorological factors and scrub typhus incidence, we utilized distributional lag nonlinear models and generalized additive models for our *** 2008 and 2021, a total of 5942 cases of scrub typhus were recorded in Ganzhou City. The number of females affected exceeded that of males, with a male-to-female ratio of 1:1.86. Based on the median values of these meteorological factors, the highest relative risk for scrub typhus occurrence was observed when the weekly average temperature reached 26 degrees C, the weekly average relative humidity was 75%, the weekly average sunshine duration lasted for 2 h, and the weekly mean wind speed measured 2 m/s. The respective relative risks for these factors were calculated as 3.816 (95% CI: 1.395-10.438), 1.107 (95% CI: 1.008-1.217), 2.063 (95% CI: 1.022-4.165), and 1.284 (95% CI: 1.01-1.632). Interaction analyses showed that the risk of scrub typhus infection in Ganzhou city escalates with higher weekly average temperature and sunshine *** findings of our investigation provide evidence of a correlation between environmental factors and the occurrence of scrub typhus. As a suggestion, utilizing environmental factors as early indicators could be recommended for initiating control measures and response strategies.

关键词： Scrub typhus Meteorological factors Distributed lag nonlinear model generalized additive model Interactions

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Assessing abundance-suitability models to prioritize conservation areas for the dwarf caimans in South America

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ECOLOGY AND EVOLUTION 2024年第9期14卷 e70235页

作者： Rodriguez-Cordero, Andres L. Balaguera-Reina, Sergio A. Gross, Brandon A. Munn, Margaret Densmore III, Llewellyn D. Texas Tech Univ Dept Biol Sci 2500 BroadwayBox 43131 Lubbock TX 79409 USA Univ Florida Ft Lauderdale Res & Educ Ctr Ft Lauderdale FL USA

Species-environment relationships have been extensively explored through species distribution models (SDM) and species abundance models (SAM), which have become key components to understand the spatial ecology and population dynamics directed at biodiversity conservation. Nonetheless, within the internal structure of species' ranges, habitat suitability and species abundance do not always show similar patterns, and using information derived from either SDM or SAM could be incomplete and mislead conservation efforts. We gauged support for the abundance-suitability relationship and used the combined information to prioritize the conservation of South American dwarf caimans (Paleosuchus palpebrosus and P. trigonatus). We used 7 environmental predictor sets (surface water, human impact, topography, precipitation, temperature, dynamic habitat indices, soil temperature), 2 regressions methods (generalized Linear models-GLM, generalized additive models-GAM), and 4 parametric distributions (Binomial, Poisson, Negative binomial, Gamma) to develop distribution and abundance models. We used the best predictive models to define four categories (low, medium, high, very high) to plan species conservation. The best distribution and abundance models for both Paleosuchus species included a combination of all predictor sets, except for the best abundance model for P. trigonatus which incorporated only temperature, precipitation, surface water, human impact, and topography. We found non-consistent and low explanatory power of environmental suitability to predict abundance which aligns with previous studies relating SDM-SAM. We extracted the most relevant information from each optimal SDM and SAM and created a consensus model (2,790,583 km(2)) that we categorized as low (39.6%), medium (42.7%), high (14.9%), and very high (2.8%) conservation priorities. We identified 279,338 km(2) where conservation must be critically prioritized and only 29% of these areas are under protection. We con

关键词： generalized additive model generalized linear model Paleosuchus palpebrosus Paleosuchus trigonatus spatial ecology species abundance models species distribution models

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