A sequential sparse polynomial chaos expansion using Bayesian regression for geotechnical reliability estimations

Polynomial chaos expansions (PCEs) have been widely employed to estimate failure probabilities in geotechnical engineering. However, PCEs suffer from two deficiencies: (a) PCE coefficients are solved by the least-square minimization method which easily causes overfitting issues; (b) building a high order PCE is often computationally expensive. In order to overcome the aforementioned drawbacks, the Bayesian regression technique is employed to evaluate PCE coefficients, which not only provides a sparse solution but also avoids overfitting. With the aid of the predictive means and variances given by Bayesian analysis, a learning function is proposed to sequentially select the most informative samples that are critical to build a PCE. This sequential learning scheme can highly enhance the computational efficiency of PCEs. Besides, importance sampling (IS) is incorporated into the sequential learning (SL)-PCEs to deal with geotechnical problems with small failure probabilities. The proposed method of SL-PCE-IS is applied to three illustrative examples, which shows that the improved PCE method is more effective and efficient than the common PCEs method, leading to accurate estimations of small failure probabilities using fewer training samples.     

青藏高原"一江两河"农区居民食物消费结构与特征

居民食物消费受自然地理环境、城镇化等因素影响,同时通过市场机制影响区域粮食安全和农牧业发展。以西藏“一江两河”地区为例,基于分层抽样,通过2019年实地入户调研,获取了262户农村居民上年度食物消费数据,对青藏高原农区居民的食物消费结构及其影响因素进行分析。结果表明：① 样本内居民的植物性食物的消费量是动物性食物的3.19倍,植物性食物以蔬菜和粮食为主,动物性食物以肉类和牛奶为主;青稞及青稞酒在藏民饮食中占有重要地位。② 居民食物消费规模和结构与食物自给率指标密切相关,自给自足特征显著。③ 不同地区、家庭规模、务工规模、收入水平以及家庭年龄之间,居民的家庭食物消费结构均有所差异,且不同家庭之间面粉和水果的消费差异最显著。④ 区域差异、家庭规模类型和家庭务工规模是影响样本农区居民食物消费综合差异的主要因素。结果可为青藏高原地区居民食物消费结构改善、膳食营养提升以及农牧业转型发展提供科学基础和决策借鉴。     

粤港澳大湾区生态系统服务价值的时空演化及其影响机制

生态系统服务是生态要素、结构、功能和产品造福于人类社会的媒介和通道。根据生态系统服务价值（ESV）及其影响机制调节人类社会对生态系统的利用强度和保护方式,对于实现人地和谐的可持续发展目标具有重要意义。本文以粤港澳大湾区作为研究对象,依托遥感数据修订ESV核算结果,更加准确地揭示2000—2015年粤港澳大湾区ESV的时空演变特征,并引入面板分位数回归深入探索生态系统服务影响因素的分段效应,增加对生态系统服务影响机制的认识。结果显示：① 2000—2015年粤港澳大湾区ESV减速下降,下降区域主要分布在粤港澳大湾区中部和珠江入海口两岸等城市快速扩张区。② 在城市交界处,土地利用变化会带来更为强烈的ESV变化。③ 在ESV水平不同的区域,影响因素具有不同的影响效果。用地完整度仅在ESV的低值区可以促进生态系统服务能力提升。气温对生态系统服务能力的正向作用随ESV的提高而增强,经济密度对生态系统服务能力的负向作用随ESV的提高而减弱,二者均会导致ESV高值区与低值区间的差距增大,形成“自然马太效应”。     

中国城镇化对老年人健康的影响

基于2010年第六次全国人口普查微观数据和夜间灯光数据等,运用多层级logistic回归模型估计中国城镇化水平和速度与老年人自评健康的关联,分析城镇化健康效应在不同老年人群体间的差异,并运用倾向得分匹配方法,控制“自选择”机制对分析结果的干扰。结果表明：1）中国县区城镇化水平和速度与老年人自评健康呈显著正相关关系,居住于城镇化水平中等和城镇化速度较快县区的老年人更可能自评为健康;2）城镇化水平和速度与老年人自评健康的关系在不同受教育程度和户口状态的老年人群体间存在显著差异,城镇化水平和速度对受教育程度较低、持有本地户口老年人健康水平的提升效益更大;3）控制了老年人居住“自选择”的干扰后,县区城镇化水平和速度与老年人自评健康的正向关联依然稳健,表明“自选择偏误”对中国县区城镇化水平和速度与老年人自评健康的关系没有显著的影响。     

Spatially filtered ridge regression (SFRR): A regression framework to understanding impacts of land cover patterns on urban climate

Understanding the impacts of land cover pattern on the heat island effect is essential for sustainable urban development. Conventional model fitting methods have restricted ability to produce accurate estimates of the land cover‐temperature association due to the lack of procedures to address two important issues: spatial dependence in proximal spatial units and high correlations among predictor variables. In this study, we seek to develop an effective framework called spatially filtered ridge regression (SFRR) to estimate the variations in the quantity and distribution of land surface temperature (LST) in response to various land cover patterns. The SFRR effectively integrates spatial autoregressive models and ridge regression, and it achieves reliable parameter estimates with substantially reduced mean square errors. We show this by comparing the performance of the SFRR to other widely adopted models using Monte Carlo simulation followed by an empirical study over central Phoenix. Results highlight the great potential of the SFRR in producing accurate statistical estimates, providing a positive step toward informed and unbiased decision‐making across a wide variety of disciplines. (Code and data to reproduce the results in the case study are available at: https://github.com/cfan13/SFRRTGIS.git .)     

Exploring spatially varying and scale-dependent relationships between soil contamination and landscape patterns using geographically weighted regression

Landscape pattern is an important determinant of soil contamination at multiple scales, and a proper understanding of their relationship is essential for alleviating soil contamination and making decisions for land planners. Both soil contamination and landscape patterns are heterogeneous across spaces and scale-dependent, but most studies were carried out on a single scale and used the conventional multivariate analyses (e.g. correlation analysis, ordinary least squared regression-OLS) that ignored the issue of spatial autocorrelation. To move forward, this paper examined spatially varying relationships between agricultural soil trace metal contamination and landscape patterns at three block scales (i.e. 5 km × 5  km, 10 km × 10 km, 15 km × 15 km) in the Pearl River Delta (PRD), south China, using geographically weighted regression (GWR). This paper found that GWR performed better than OLS in terms of increasing R square of the model, lowering Akaike Information Criterion values and reducing spatial autocorrelation. GWR results revealed great spatial variations in the relationships across scales, with an increasing explanatory power of the model from small to large block scales. Despite a few negative correlations, more positive correlations were found between soil contamination and different aspects of landscape patterns of water, urban land and the whole landscape (i.e. the proportion, mean patch area, the degree of landscape fragmentation, landscape-level structural complexity, aggregation/connectivity, road density and river density). Similarly, more negative correlations were found between soil contamination and landscape patterns of forest and the distance to the river and industry land (p < 0.05). Furthermore, most significant correlations between soil contamination and landscape variables occurred in the western PRD across scales, which could be explained by the prevailing wind, the distribution of pollutant sources and the pathway of trace metal inputs.     

贵州省乡村贫困空间格局与形成机制分析

以贫困态势严峻、区域内部贫困差异大的贵州省为研究区,分析了贵州省区县层面乡村贫困的空间异质性和空间依赖性格局,定量测度了乡村贫困空间差异的影响因素和因素效应的空间差异性,进而归纳了贵州省乡村贫困的形成机制。结果发现,贵州省区县乡村贫困具有时空稳定性,呈现出东、南、西部高而中、北部低的“马蹄”形空间异质性格局。区县贫困存在较强的空间依赖性,“高-高”型贫困地域即空间贫困陷阱区域,集聚分布在贵州省的东南部、南部。定量模型发现,坡度、到所在市中心的距离、青少年人口占比、少数民族人口占比是导致贵州区县层面乡村贫困空间差异的显著因素,且这些因素的效应水平呈现出不同的空间模式。产业发展受限、劳动力流动性差、金融和人力资本积累不足是贵州贫困空间形成的主导机制。最后建议扶贫政策层面应将基于地方和基于人的政策相结合。     

黑河中游地区耕地景观演变及社会经济驱动力分析

选取1987~2010年的土地利用数据,运用变化轨迹分析方法以及景观指数表征黑河中游地区耕地景观的演变过程,采用偏最小二乘回归模型分析耕地景观演变的驱动力。结果表明：从耕地的变化轨迹来看,研究期间耕地的扩张是黑河中游地区耕地景观演变的主要趋势;从空间上来看,扩张型耕地和缩减型耕地分布趋向集中,主要集中在稳定型耕地边缘,同时耕地扩张的方向趋向河流;从驱动因素分析来看,总人口、城市化、产业发展、机电井数等是促使耕地景观演变的主要社会经济驱动因素,但是各个驱动因素的驱动作用均有差异。     

中国住宅出让地价发育特征及其影响因素分析

基于中国土地市场网2009~2013年招、拍、挂出让的住宅用地数据,以县级行政区域作为研究单元,建立区域住宅地价综合模型,选取地价水平、地价增长率和交易宗数作为评价指标,采用Ward系统聚类法,综合分析了中国住宅出让地价的发育特征,利用多分类Logistic回归模型探索了各类地价发育形态的潜在影响因素。结果表明：① 研究期内中国住宅出让地价总体呈现东高西低、沿海高于内陆、城市群带动周边区域发展、城市群内部围绕中心城市增长等空间格局特征;中西部地价高增长的单元多于东部,西北、东北中南部、四川盆地、河西走廊以及长江中下游地区是地价高增长的聚集区;住宅市场活跃度呈阶梯状变化,活跃度较高的地区主要分布在山东半岛、长三角、长江中游、辽宁中部、哈尔滨、成渝、滇中、呼包鄂等城市群。② 根据综合特征,中国住宅出让地价空间发育可分为成熟稳健型、发展完善型、成长发展型、萌芽起步型和成长受阻型5类,相邻发育形态在空间关系上表现出互为邻里的特征。③ 各类发育区的主要影响因素差异显著。区位条件、居民收入和财政收入是地价发育成熟度的主要影响因素;人均GDP、国土开发度提升将促进地价形态发育程度;而人口吸引力不足、基础设施建设相对滞后等将导致地价形态发育受阻。     

转型期广州市居民职住模式的群体差异及其影响因素

职住关系是城市研究领域重要的议题之一。体制改革后中国社会分层结构特殊,检验不同阶层居民在职住地选择偏好的差异,有助于理解居民职住格局形成的内部机制。利用广州市入户问卷调查、建成环境和人口普查等数据,采用两步聚类和多项logistic回归,对广州市居民进行阶层划分,对比居民职住模式的群体差异及其影响因素。结果表明：职住决策时,体制外工薪阶层追求低生活成本,受职住地建成环境影响显著;体制内阶层习惯于传统单位制下社会关系密切的社区,受邻里环境影响显著,还受个人属性影响;无固定工作者决策自由和平衡程度高,受少量建成环境因素影响。研究有助于了解居民职住格局形成的制度性机制,为优化居民职住格局提供思路。