Interactive influence of water level,sediment heterogeneity,and plant density on the growth performance and root characteristics of Carex brevicuspis

Water level, sediment heterogeneity, and plant density are important factors that determine plant growth, distribution, and community structure. In the present study, we investigated the effects of these factors on the growth and root characteristics of Carex brevicuspis. We conducted an outdoor experiment to monitor biomass accumulation and allocation, relative root distribution mass ratio, longest root length, and total N and P contents of C. brevicuspis plants. We used a factorial design with two water levels (0 cm and −15 cm relative to the soil surface, named high and low water level treatments, respectively), three sediment types (sand/clay sediment with 0–15 cm of sand and 15–30 cm of clay; mixed sediment with 0–30 cm mixture of sand and clay with 1:1 volumw ratio; and clay/sand sediment with 0–15 cm of clay and 15–30 cm of sand), and three plant densities (88 plants per m², 354 plants per m², and 708 plants per m²). Biomass accumulation decreased with increasing plant density and was significantly higher in the low water level and the clay/sand sediment than in the high water level and the other two sediment types. The shoot:root ratio was markedly higher in the high water level than in the low water level and decreased with increasing plant density; further, in the high water level, it was significantly lower in the sand/clay sediment than in the other two sediment types. The relative root distribution mass ratio was markedly higher in the high water level treatments than in the low water level treatments. Further, in the high water level treatments, the relative root distribution mass ratio increased with increasing plant density in the clay/sand sediment and was lower in the sand/clay sediment than in the other two sediment types. The longest root length was significantly lower in the high water level than in the low water level and increased with increasing plant density in the sand/clay sediment in the high water level. Total N content in the plants was influenced only by sediment type; on the other hand, total P content was markedly higher in the high water level than in the low water level. Our data indicate that growth of C. brevicuspis was limited by higher water level, higher density and sand/clay sediment. Plants can increase shoot:root ratio and develop shallow root system to acclimate to high water level and thus could adjust shoot:root ratio and root characteristics, e.g. decrease their shoot:root ratio and allocating more root and increasing root length to the nutrient rich layer to acclimate to conditions of higher density and sediment heterogeneity.     

RESEARCH ON SELF-MEDIA INFORMATION MINING MODEL FOR EARTHQUAKE EMERGENCY RESPONSE

From the events of catastrophic natural disasters that have occurred in recent years, it can be found that social media platforms are increasingly becoming the most important and most convenient way for the general public to timely release and obtain information on disasters. The information obtained from such platforms contains a large amount of information in the form of texts, pictures, etc. that record the current situation of the disaster. And it also has characteristics of high efficiency and high spatial distribution to serve the rapid emergency after the earthquake. In this paper, we firstly make a statistical analysis of 32 689 pieces of historical disaster data acquired from 5 earthquakes with obvious characteristics, such as post-earthquake disaster events, user's expression habits and so on, and adopts cross-validation method. Then information classification system which includes seven first-level categories and more than 50 second-level categories is constructed. The information classification system and evaluation system of crisis degree for post-earthquake emergency response are constructed both using cross-validation method. The former is referred to the thought of existing classification basis and the experience knowledge of several emergency experts. Based on the five indicators of subject word, action word, degree word, time and position measurement, an evaluation system of critically with four levels of severity, moderate intensity, mildness and others was constructed. Considering the sparse features of self-media information and the large difference in the number of training sets, a naive Bayes model for information classification is trained based on the classification system and evaluation system. Its accuracy rate is 73.6%. At the same time, the classification method of feature fusion of machine learning model and semantic calculation model is used to evaluate the criticality of the disaster information. The accuracy rate of the evaluation model is 89.2%, higher than 85.2% of the semantic computing model and 77% of the naive Bayesian model. The evaluation model has combined the advantages of semantic computing method which can evaluate all index features with machine learning method which has high classification efficiency and accuracy. The thresholds for classification between mild and moderate intensity, moderate intensity and severe intensity were 15.2 and 27.39. The model realized in this paper can crawl, classify and evaluate the disaster information in the media in real time after an earthquake, and realizes mining of a small amount of critical and important information from the massive self-media information, thus, to assist in earthquake intensity rapid reporting and accurate rescue. Finally, taking the Jiuzhaigou earthquake on August 8, 2017 as an example, 17 432 pieces of data were crawled in real time within 48 hours after the earthquake. At the same time, based on ArcGIS, the mining information is visualized in time and space, and the availability of the data is evaluated from two perspectives of earthquake intensity quick reporting and accurate rescue after the earthquake. The disaster information of Jiuzhaigou County in the earthquake area is obviously more than that of the non-earthquake area in terms of quantity and emergency degree. The results show that the self-media information with high spatial distribution can effectively find the severer disaster grade area after the earthquake, shorten the time of earthquake intensity prediction, effectively classify and extract information, provide real-time information for precise rescue, and improve the efficiency of emergency response after the earthquake.     

面向公共安全事件的网络文本大数据结构化研究

网络文本中所包含的相关信息目前已成为公共安全事件紧急救援与影响评估的重要信息源。现有的方法虽然可定向地提取文本信息中事件的各类要素信息,但由于缺乏面向事件的整体建模与解析框架,难以从网络文本中获取系统的事件要素的结构化信息,即所提取的事件要素信息要么不够完整,要么与目标事件不匹配,由此产生的遗漏与谬误难以支撑针对公共安全事件信息的系统分析。为解决该问题,本文提出了面向公共安全事件的网络文本大数据结构化理论框架,首先,建立了公共安全事件的语义框架,并以地震事件为例构建了相应的结构化表结构;其次,应用训练语料的关联标注解决了事件要素与事件无法匹配的难点;最后,通过使用可融合关联信息的文本解析算法,系统提取了事件类型、事件名称、事件时间、事件位置及其他属性,基本实现了网络文本中不同事件信息的结构化。本文以云南邵通鲁甸地震为例,展示了地震事件的网络文本信息的结构化过程与结果,为分析地震所受的关注程度以及救援状况提供了重要参考。在上述研究的基础上,开发了面向公共安全事件的网络文本信息挖掘系统,展示了地震事件文本的结构化解析以及由此实施的事件关注度分析。     

Spatial-temporal Distribution Characteristics of Global Seismic Clusters and Associated Spatial Factors

Earthquakes exhibit clear clustering on the earth. It is important to explore the spatial-temporal characteristics of seismicity clusters and their spatial heterogeneity. We analyze effects of plate space, tectonic style, and their interaction on characteristic of cluster.Based on data of earthquakes not less than moment magnitude(M_w) 5.6 from 1960 to 2014, this study used the spatial-temporal scan method to identify earthquake clusters. The results indicate that seismic spatial-temporal clusters can be classified into two types based on duration: persistent clusters and burst clusters. Finally, we analysed the spatial heterogeneity of the two types. The main conclusions are as follows: 1) Ninety percent of the persistent clusters last for 22-38 yr and show a high clustering likelihood;ninety percent of the burst clusters last for 1-1.78 yr and show a high relative risk. 2) The persistent clusters are mainly distributed in interplate zones, especially along the western margin of the Pacific Ocean. The burst clusters are distributed in both intraplate and interplate zones, slightly concentrated in the India-Eurasia interaction zone. 3) For the persistent type, plate interaction plays an important role in the distribution of the clusters’ likelihood and relative risk. In addition, the tectonic style further enhances the spatial heterogeneity. 4) For the burst type,neither plate activity nor tectonic style has an obvious effect on the distribution of the clusters’ likelihood and relative risk. Nevertheless,interaction between these two spatial factors enhances the spatial heterogeneity, especially in terms of relative risk.     

1982-2013年中国植被NDVI空间异质性的气候影响分析

为研究气候变化与植被活动之间的复杂关系,采用1982-2013年GIMMS NDVI与气象站点温度与水分的监测资料,应用基于像元的地理加权回归方法,探究了中国植被NDVI及其动态特征对气候变化响应的空间格局。中国植被NDVI与地表温度呈空间非平稳关系,在空间上的负相关关系主要集中在东北、西北及东南部分地区,空间正相关则更为集中和连片;针对不同气候指标的标准化系数对比可知,植被NDVI受水分控制作用较为显著的区域主要集中在北方地区以及青藏高原,温度的主导作用区域则分布在华东、华中及西南地区,其中年均最高气温对NDVI的主导区域范围最广;植被NDVI动态与气候变率的回归结果表明,增温速率的升高会通过加剧干旱等机制对植被活动产生抑制作用,水分变率对植被活动的强弱起到了重要的调节作用。     

SMSM: a similarity measure for trajectory stops and moves

For many years trajectory similarity research has focused on raw trajectories, considering only space and time information. With the trajectory semantic enrichment, emerged the need for similarity measures that support space, time, and semantics. Although some trajectory similarity measures deal with all these dimensions, they consider only stops, ignoring the moves. We claim that, for some applications, the movement between stops is as important as the stops, and they must be considered in the similarity analysis. In this article, we propose SMSM, a novel similarity measure for semantic trajectories that considers both stops and moves. We evaluate SMSM with three trajectory datasets: (i) a synthetic trajectory dataset generated with the Hermoupolis semantic trajectory generator, (ii) a real trajectory dataset from the CRAWDAD project, and (iii) the Geolife dataset. The results show that SMSM overcomes state-of-the-art measures developed either for raw or semantic trajectories.     

A response to ‘A comment on geographically weighted regression with parameter-specific distance metrics’

In this article, we respond to ‘A comment on geographically weighted regression with parameter-specific distance metrics’ by Oshan et al. (2019), published in this journal, where several concerns on the parameter-specific distance metric geographically weighted regression (PSDM GWR) technique are raised. In doing so, we review the developmental timeline of the multiscale geographically weighed regression modelling framework with related and equivalent models, including flexible bandwidth GWR, conditional GWR and PSDM GWR. In our response, we have tried to answer all the concerns raised in terms of applicability, veracity, interpretability and computational efficiency of the PSDM GWR model.     

城市建成环境对广州市居民幸福感的影响——基于15 min步行可达范围的分析

人民群众幸福感的提升,是新型城镇化的落脚点,改善人居环境是城镇化质量提升的必然要求。既有的社区环境与居民幸福感关系的研究大多基于行政管辖范围评估社区的建成环境状况,且忽视了城市建成环境影响居民幸福感的中间机制。论文基于广州市23个社区采集的问卷调查数据,利用多层线性回归模型、中介效应分析和分层分析,识别影响居民幸福感的建成环境因素,揭示了居住满意度所起到的中介作用,尤其关注缓冲区的重新划定对分析结果的影响,以及建成环境对幸福感影响的异质性效应。结果表明：① 相较于行政管辖范围和1000 m面要素缓冲区,基于15 min步行可达范围所提取的建成环境指标与居民主观幸福感的关联程度更高;② 人均绿地面积、POI点密度与居民幸福感呈正相关关系,人口密度与幸福感呈负相关关系;③ 居住满意度在POI点密度与居民主观幸福感之间的关系中发挥了完全中介作用;④ 异质性分析结果表明,社区建成环境与个体幸福感的关联程度因居民的户籍、住房产权和就业情况不同而呈现显著差异。     

Exploring temporal heterogeneity in an intercity travel network: A comparative study between weekdays and holidays in China

Journal of Geographical Sciences - A largely unexplored application of “Big Data” in urban contexts is using human mobility data to study temporal heterogeneity in intercity travel...