共享短租平台的概念发展、市场影响和空间交互关系研究综述

基于互联网技术的“共享经济”不仅在众多新兴市场上创造出了经济增长点,还通过潜移默化地改变大众消费行为,对城市经济和空间产生了巨大影响。正如以Airbnb为代表的共享短租平台在冲击了传统旅游住宿业与城市住房市场的同时,也引起了城市空间的变化。为了深入了解这一现象,论文首先概述了共享短租平台的发展背景、概念与现状,阐述它所带来的影响和因此引发的争论;接着探讨了共享短租平台(以Airbnb为例)与酒店业和住房市场的关系,以及其进一步商业化可能带来的影响与问题;最后,通过分析大量实践案例得出共享短租平台与城市空间存在双向互动关系,即城市空间中的社会经济与物质环境因子决定了共享短租平台房源的空间分布特征,而共享短租平台会以旅游绅士化、商业结构改变和城市设施负担加重的方式逐渐改变城市社会经济和物质环境空间。     

京津冀地区耕地利用转型时空分异及驱动机制

耕地利用转型代表耕地利用形态的趋势性变化。论文以京津冀地区157个县域空间为基本研究单元,从显性形态与隐性形态双重属性出发构建耕地利用形态指标体系,运用冷热点、空间变差函数等方法分析1990—2015年耕地利用形态变化的时空特征,在此基础上,利用空间误差模型(spatial error model, SEM)对耕地利用转型的驱动机制进行了定量分析。结果表明：① 京津冀地区耕地利用形态指数空间分布呈东南高、西北低的格局,与该地区地形分布格局基本一致,且因坝上高原特色农业发展形成独立高值区。② 京津冀地区耕地利用形态指数空间分异呈现逐渐缩小的趋势,显著热点区与显著冷点区分布相对稳定且面积不断缩小,耕地利用转型较为平稳。显性形态值普遍呈现先升高后降低趋势,隐性形态值则呈升高趋势,表明京津冀耕地出现空间收缩、功能优化式转型。③ 高程是影响京津冀地区耕地利用形态空间特征的主要自然环境因素,二三产业占比、地均固定资产投资与城镇化率等经济发展和城镇建设因素则是驱动1990—2015年京津冀地区耕地转型的主导因素,但不同时段、不同县域单元耕地转型动力机制存在差异,各驱动因子作用强度也不完全一致。     

2007-2016年四川省土壤干旱时空动态分析

四川作为农业大省,旱灾是导致农业减产最主要的因素。通过遥感和GIS手段进行四川省土壤干旱程度的时空分析,提高干旱的空间可视化程度,加强干旱监测的时效性尤为重要。本研究基于四川省2007—2016年逐季度的MODIS数据和1961—2011年40个气象站的月降水资料,采用温度植被干旱指数(TVDI)计算得到四川省干旱等级分布情况,辅以标准化降雨指数(SPI)进行相关性分析,并通过线性回归、反距离权重空间插值、GIS空间分析模型重建等方法,分析近十年来四川省地区以季度为时间尺度的土壤干旱时空变化特征,制作各时相土壤干旱分布图展示其微变化。结果表明:(1)在月时间尺度上,SPI-1与TVDI呈中等至强负相关关系,即TVDI值越小,SPI值越大,干旱程度越轻;验证结果表明TVDI都能够较好地对四川省的干旱空间分布状况进行反映。(2)四川省各区域、各季节干旱分布不均:空间上,干旱频发的区域集中在四川盆地及攀西南部区域。时间上,在春季,四川盆地区域的土壤干旱程度大致呈现加剧—持续—减缓的趋势;夏季,四川盆地的干旱变化趋势是加剧—减缓—加剧;秋季,四川盆地的干旱变化趋势是加剧—减缓—持续减缓;冬季,全川干旱程度变化不明显。本文的研究结果对四川省开展农业防灾减灾,引导农业灌溉具有指导意义。     

Salinivibrio sp.YH4胞外丝氨酸蛋白酶EYHS耐盐性及生物信息学分析

摘要：目的 探讨菌株Salinivibrio sp.YH4分泌的丝氨酸蛋白酶EYHS的耐盐性及结构特征。方法 明胶底物酶谱法分析EYHS的耐盐性。应用生物信息学手段对EYHS及6种耐盐的S8家族丝氨酸蛋白酶结构特征进行分析。结果 EYHS在4 mol/L的NaCl溶液中仍具有活性,属于耐盐蛋白酶。EYHS及6种S8家族丝氨酸蛋白酶分子表面的loop区等无规则卷曲所占比例较高,α-螺旋与β-片层则主要位于酶分子内部。EYHS分子表面酸性氨基酸含量较高,且具有弱疏水内核。多序列比对发现蛋白酶的催化三联体两侧存在高度保守的基序和保守的极性氨基酸及芳香族氨基酸,并存在多个保守的Gly与Ala。同源模建和表面电荷分布显示,α螺旋和β片层围成了蛋白酶的催化腔,EYHS活性中心包含由Asp32、His65与Ser215组成的催化三联体,且催化位点区域表面静电势为负。结论 上述结构特征可能有助于耐盐丝氨酸蛋白酶EYHS在高盐环境下维持其稳定性和适度柔性,并有助于其催化功能的发挥,为深入研究耐盐丝氨酸蛋白酶的高盐环境适应性提供了一定的理论依据。     

Population,urbanization and economic scenarios over the Belt and Road region under the Shared Socioeconomic Pathways

The countries throughout the Belt and Road region account for more than 60% of the world's population and half of the global economy. Future changes in this area will have significant influences on the global economic growth, industrial structure and resource allocation. In this study, the proportion of the urban population to the total population and the gross domestic product were used to represent the levels of urbanization and economic development, respectively. The population, urbanization and economic levels of the Belt and Road countries for 2020–2050 were projected under the framework of the IPCC's shared socioeconomic pathways(SSPs), and the following conclusions are drawn.(1) The population, urbanization and economic levels in the Belt and Road region will likely increase under all five pathways. The population will increase by 2%–8%/10 a during 2020–2050 and reach 5.0–6.0 billion in 2050. Meanwhile, the urbanization rate will increase by 1.4%–7.5%/10 a and reach 49%–75%. The GDP will increase by 17%–34%/10 a and reach 134–243 trillion USD.(2) Large differences will appear under different scenarios. The SSP1 and SSP5 pathways demonstrate relatively high urbanization and economic levels, but the population size is comparatively smaller; SSP3 shows the opposite trend. Meanwhile, the economy develops slowly under SSP4, but it has a relatively high urbanization level, while SSP2 exhibits an intermediate trend.(3) In 2050, the population will increase relative to 2016 in most countries, and population size in the fastest growing country in Central Asia and the Middle East countries will be more than double. Urbanization will develop rapidly in South Asia, West Asia and Central Asia, and will increase by more than 150% in the fastest growing countries. The economy will grow fastest in South Asia, Southeast Asia and West Asia, and increase by more than 10 times in some counties with rapid economic development.     

A case study on Landscape Component Niche based on Landscape Pattern Indices: Yanchi,Ningxia Province,China

Yanchi County is located in the agro-pastoral ecotone and belongs to the ecologically fragile area of Northwest China.It is important to study the evolution of landscape pattern to curb its environmental degradation.In order to intuitively show how the landscape pattern of the study area changes over time,Landsat Thematic Mappers(TM)and Landsat Operational Land Imager(OLI)data of 1991,2000,2010 and 2017 were used.This paper attempts to apply niche theories and methods into landscape ecology,and constructs a niche model of landscape components by using"n-dimentional hypervolume niche theory"and landscape pattern indices.By evaluating the spatial and temporal evolution of niche from the perspective of two-dimensional space to reflect the changes of landscape pattern in the study area over the past 26 years,new theories and methods were introduced for the characterization of landscape pattern.The results indicate that:1)The larger the attribute and dominance value of landscape components,the higher the ecological niche and the stronger the control effect on the overall landscape.2)The ecological niche of each landscape component was significantly different,just as its control effect on the overall landscape.3)The dynamic change of the ecological niche of each landscape component was different,with grassland,unused land and arable land always in a high dominant position,although the ecological niche of construction land and water area was always low.In general,the introduction of niche theory into the landscape ecology provided a new method to study the changes in regional landscape pattern.     

Space Changes in the Rural Tourism Area of Mufu Town,Hubei Province,China

In a society dominated by tourism consumption, space changes occurring in rural areas can generally reflect their social changes. On the theoretical basis of flow, regeneration and adaptation of rural tourism space, this paper originally and creatively proposes that the spatial elements in a rural tourist area can be classified into three categories: Attractions (A), Towns (T) and Villages (V). By analyzing the spatial transformation characteristics of A, T and V, five types of rural spatial transition modes are found, the types of heritage, theme park, those serving as scenic spots, leisure industrial clusters and ecotourism areas. These different classes emerge due to their geographical differentiation. They show the same spatial evolution trend: The Attractions are distributed throughout the whole area and characterized by diversification; supporting services facilities gather in the Towns; and the Villages are landscape images. In this area the traditional rural benefit trends toward that of compound development. Mufu Town, Hubei province, is taken as a study case, and the changing characteristics of A, T and V from 2006 to 2016 are described. Problems in the process of establishing the new spatial order are considered. In order to realize the synergy between production space, living space and ecological space, the interactive development between Attractions, Towns and Villages is recommended. The perspective of Attraction-Town-Village (ATV) can lead to a better understanding of the situation of tourism space in rural areas and provide directions for thinking about the reconstruction path for the modernization of traditional societies.     

多时相Landsat影像的平原河网区不透水面信息提取方法与应用

不透水面是衡量城市生态环境的重要指标,针对平原河网区不透水面与水体、裸土等地物较难区分的问题,以苏北里下河平原快速城镇化区为研究对象,利用多时相Landsat影像为数据源,基于像元光谱特征及其季节性变化分析,构建了基于时序NDWI、NDVI和NDBI的决策树模型,并利用GIS空间分析技术对不透水面的时空变化特征进行了分析。结果表明：多时相光谱信息可有效改善水体、裸土和稀疏植被等像元与不透水面的混分,2005年和2016年的不透水面分类总体精度和Kappa系数均在0.85以上,合理反映了不透水面以向东和向南扩张为主的趋势特征,年均扩张速度约为6.7 km²。研究成果为该区城镇化下的生态环境效应研究提供数据基础,同时可为其他平原河网区不透水面信息提供借鉴和参考。     

一种多层特征融合的道路场景实时分割方法

道路场景理解是自动驾驶领域中重要模块之一,它可以提供关于道路更丰富的信息,对于建立高精度地图和实时规划都具有重要作用。其中,语义分割可以为图像每个像素赋予类别信息,是自动驾驶场景理解中最常用的方法。但是,目前常用的语义分割算法在速度和精度上大都不能达到很好的平衡。本文在MobileNetV2的基础上,提出了一种多层次特征融合的方法,使得网络可以在实时运行的同时保证精度满足实际应用的需求,并在Cityscapes数据集上进行了试验验证和分析。     

利用CEEMD分析中国沿海GNSS站高程时序变化

利用完备经验模态分解方法（CEEMD）对我国沿海地区6个GNSS基准站（2010—2018）的高程时序数据进行了处理分析。结果表明：CEEMD在高程时间序列分析中具有一定的优越性,可准确分解出各GNSS站高程时序中存在的周、月、季节、年等变化周期项,其中周年运动是主要贡献项,各站高程时间序列的短周期变化与潮汐变化周期具有密切关联性;沿海GNSS站的地面沉降既具有区域的一致性,又存在区域间差异性,其中D区DBJO、DZJJ站呈现先下降后上升的趋势,N区NZUH、NWZU站呈下降趋势,B区的BZMW呈上升趋势,而同海区的BLHT站则呈显著的下降趋势。