新冠肺炎疫情前后游客出行行为的时空特征研究——以中国香港为例

新型冠状病毒肺炎疫情对香港旅游业产生了巨大的冲击,掌握疫情期间游客出行的时空变化特征,对后疫情时代旅游业复苏和发展至关重要。本文基于2019年和2020年社交媒体的旅游评论数据,采用时序统计、复杂网络分析等方法,对比分析疫情前后香港旅游网络的演化特征,并结合旅游目的地语义刻画游客出行规律。结果表明：(1)游客出行规律呈现出一定程度变化,与疫情前相比,游客在一次出游行程中所访问的旅游目的地数量大幅下降;(2)热门目的地仍然在香港旅游网络中承担着连接其他目的地的重要作用,而围绕热门目的地形成的旅游网络社区由疫情前的空间显著集聚转为相对稀疏分布,但网络社区内部目的地的空间分布较疫情前变得更加集聚;(3)在游客选择出行目的地方面,热门目的地访问量急剧减少,而以往游客较少且地理位置较为偏僻的目的地,在游客出行目的地选择时变得更被关注,但产业目的地始终是香港旅游中最热门的旅游目的地类型。     

建成环境对步行通勤通学的影响——以中国香港为例

步行是一种重要的交通方式,也是体力活动的重要组成部分。然而,现代城市居民步行频率持续下降,相应地带来体力活动水平的持续降低,伴随着肥胖等慢性非传染病广泛蔓延,值得警惕。西方很多研究证实了建成环境(常以“3D”或“5D”等模型刻画)对步行行为的影响。基于西方的结论对于中国香港、北京和上海等具有与西方城市大相迥异的建成环境的城市是否适用?不同的人群由于具有不同的社会经济属性和生活节奏、习惯,其步行行为受建成环境的影响在方向、强度上是否存在差异?为解决上述问题,论文以香港为案例地,以职员和学生2类人群的步行通勤通学行为为研究对象,利用香港人口普查数据、Open Street Map数据以及中原地图数据等,采用传统的线性回归和空间计量模型进行分析,发现：①通勤通学距离是影响职员和学生步行通勤通学行为的最重要变量;②以“5D”模型刻画的建成环境对香港居民步行通勤通学行为的影响,与西方情境下的结论存在一定的差异,如,在香港,距地铁站的距离与职员和学生步行通勤比例相关性均不显著;③建成环境对步行通勤通学行为的影响,在职员与学生两类人群之间在方向和强度上存在显著差异。例如人口密度与职员步行通勤比例负相关,但与学生步行通学比例正相关。研究凸显了在“建成环境-交通行为”关系研究中情境(context)和人群区分(segmentation)的重要性。     

“一国两制”下香港居民跨境消费行为特征和影响因素研究

深港边界是全球跨境活动最频繁的地区之一。以通过深圳湾口岸前往蛇口消费的香港居民为研究对象,采用定性、定量和空间计量相结合的方法分析香港居民来往深圳的消费模式和行为影响因素,发现在蛇口的跨境港人多来自“新界西”的屯门、元朗、荃湾三地,呈现以公交出行为主、出行频率高、消费低、时间短、老龄化、女性多的特点,其跨境行为主要受到个人年龄及收入、出行距离、区位等因素影响;港人在深港边界的跨境活动开始出现与收入、年龄相关的空间分异现象,呼吁展开针对不同过境群体的进一步研究,厘清其行为特点和影响因素,这将有助于建构粤港“优质生活圈”、促进粤港两地间更便利的跨境活动和更深层次的协作。     

Landslide susceptibility evaluation based on BPNN and GIS: a case of Guojiaba in the Three Gorges Reservoir Area

A landslide susceptibility evaluation is vital for disaster management and development planning in the Yangtze River Three Gorges Reservoir Area. In this study, with the support of remote sensing and Geographic Information System, 4 factor groups comprising 10 separate subfactors of landslide-related data layers were selected to establish a susceptibility evaluation model based on the back-propagation neural network including slope, aspect, plan curvature, strata and lithology, distance to faults, land use/land cover, Normalized Difference Vegetation Index, Normalized Difference Water Index, distance from roads, and effect of rivers. During model development, a three-layered interconnected neural network structure of 10 (input layer) × 20 (hidden layer) × 1 (output layer) was used for evaluating the landslide susceptibility in Guojiaba. At the same time, a back-propagation algorithm was applied to calculate the weights between the input layer and the hidden layer and between the hidden layer and the output layer. The results showed that the effect of slope has the highest weight value (0.2051), which is more than two times that of the other factors, followed by strata and lithology (0.1213) and then the effect of rivers (0.1201). At the end of the susceptibility evaluation, the area was divided into four zones such as very high, high, moderate and low susceptibility. For verification, the receiver operating characteristic curve for the back-propagation neural network-derived landslide susceptibility evaluation model was drawn, and the results showed that the area under the receiver operating characteristic curve was 0.8790 and the prediction accuracy was 88%. Furthermore, the results obtained from this article were then verified by comparing with the existing landslide historical data and multiple field-verified results. Lastly, the landslide susceptibility map will help decision makers in risk management, site selection, site planning, and the design of control engineering.     

Landslide scar/soil erodibility mapping using Landsat TM/ETM+ bands 7 and 3 Normalised Difference Index: A case study of central region of Kenya

Landsat series multispectral remote sensing imagery has gained increasing attention in providing solutions to environmental problems such as land degradation which exacerbate soil erosion and landslide disasters in the case of rainfall events. Multispectral data has facilitated the mapping of soils, land-cover and structural geology, all of which are factors affecting landslide occurrence. The main aim of this research was to develop a methodology to visualize and map past landslides as well as identify land degradation effects through soil erosion and land-use using remote sensing techniques in the central region of Kenya. The study area has rugged terrain and rainfall has been the main source of landslide trigger. The methodology comprised visualizing landslide scars using a False Colour Composite (FCC) and mapping soil erodibility using FCC components applying expert based classification. The components of the FCC were: the first independent component (IC1), Principal Component (PC) with most geological information, and a Normalised Difference Index (NDI) involving Landsat TM/ETM+ band 7 and 3.The FCC components formed the inputs for knowledge-based classification with the following 13 classes: runoff, extreme erosions, other erosions, landslide areas, highly erodible, stable, exposed volcanic rocks, agriculture, green forest, new forest regrowth areas, clear, turbid and salty water. Validation of the mapped landslide areas with field GPS locations of landslide affected areas showed that 66% of the points coincided well with landslide areas mapped in the year 2000. The classification maps showed landslide areas on the steep ridge faces, other erosions in agricultural areas, highly erodible zones being already weathered rocks, while runoff were mainly fluvial deposits. Thus, landuse and rainfall processes play a major role in inducing landslides in the study area.     

基于信息量模型和机器学习方法的滑坡易发性评价研究——以四川理县为例

以阿坝藏族羌族自治州地质灾害频发的理县为研究区,从地形地貌、地质环境、水文条件和人类工程活动等方面选取11个影响因子,通过皮尔森相关系数研究各因子之间的相关性,从而构建滑坡易发性评价指标体系。利用信息量模型计算各影响因子的信息量值,从信息量模型得出的极低和低易发性分区中选取非滑坡样本,在此基础上将样本数据代入随机森林和径向基函数神经网络2种机器学习模型开展滑坡易发性评价,并通过接收灵敏度（Receiver Operating Characteristic,ROC）曲线进行精度验证。结果显示：随机森林模型预测出的高易发区单位面积内分布的滑坡点数量更为集中,在仅占6.666%的区域分布了74.026%的灾害点,评价结果优于径向基函数神经网络模型。ROC曲线中两模型AUC（Area Under Curve）值分别为0.893、0.874,说明随机森林模型具有更高的可靠性,比径向基函数神经网络在该区域地质灾害易发性评价中更具优势。     

地块质量特征对西南山区梯田撂荒的影响——以贵州剑河县白都村为例北大核心CSCDCSSCI

为分析西南山区梯田撂荒地块质量特征及影响因素,厘清山区梯田撂荒与可持续利用机制,本文以贵州省剑河县白都村为例,综合运用农户调查数据与无人机高分辨率影像数据探讨梯田的撂荒规模、空间分布情况及地块质量特征;构建二元Logistic回归模型,对影响梯田撂荒的各地块质量因素作用大小进行定量分析。结果表明:①白都村样本农户的梯田面积为62.518 hm^(2),多集中分布于耕作高差、坡度和耕作距离较大区域,其撂荒面积为21.354 hm^(2),撂荒率为34.16%。②梯田的地块质量因素对其撂荒的贡献程度表现为:灌溉条件>动力使用状况>田埂坍塌状况>耕作面积>综合地形条件,其中灌溉条件、动力使用状况、耕作面积与梯田撂荒均呈负相关,田埂坍塌状况和综合地形条件与梯田撂荒呈正相关。研究结果对于推进西南山区梯田的可持续利用与管理具有重要的理论及实践意义。     

县域农村贫困化空间分异及其影响因素——以陕西山阳县为例

以国家扶贫开发重点县山阳县为研究区,通过空间自相关分析和分组分析方法探究山阳县农村贫困化的空间格局和类型;利用逐步回归、地理加权回归和地理探测器模型对山阳县农村贫困化影响因素进行分析,讨论影响因素效应水平的空间异质性及其交互作用。研究表明：① 山阳县农村贫困发生率具有较强的空间集聚性,形成6个热点集聚区和4个冷点集聚区;综合考虑农村贫困程度和空间连接性,将山阳县划分为低度贫困区、中度贫困区和高度贫困区。② 水网密度、到最近公路的距离、危房比例、农民人均可支配收入、外出务工人数比例、农户入社比例6个因素是山阳县农村贫困化的主要影响因素,各因素的影响效应具有空间异质性。③ 两因素交互作用要比单因素作用于贫困发生率时影响力更显著,各主要影响因素的交互作用类型有双因子增强型和非线性增强型两种。     

一国两制下的深港跨境生活空间形成——以中英街地区为例

以深港边境中英街地区为例,运用实地调查和访谈方法,探讨一国两制下的深港边境生活空间形成、居民自下而上的再边界化、以及居民通过日常生活实践对边界的再定义过程。研究指出,中英街地区的生活空间形成是在边界效应下,边境居民对边境两侧管理差异的不断适应、调试、乃至主动运用的空间构建过程。边境管理者、居民、游客和水客等多方力量在此过程中互动角力,而中英街地区发展的不同阶段正是这一互动过程的结果;在此过程中,边境居民从被动的边界适应到主动的边界效应利用,体现了一国两制下居民的生活空间战略和自下而上的社会力量对边界及边境地区生活空间形成的作用。本研究有助于深化对于亚洲的边界和边境地区空间形成的理解,也对当下的边境治理具有积极的政策意义。     

省尺度森林植被碳储量空间分布模拟不确定性分析——以江西省为例（英文）

Forest vegetation carbon patterns are significant for evaluating carbon emission and accumulation. Many methods were used to simulate patterns of forest vegetation carbon stock in previous studies, however, uncertainty apparently existed between results of different methods, even estimates of same method in different studies. Three previous methods, including Atmosphere-vegetation interaction model 2(AVIM2), Kriging, Satellite-data Based Approach(SBA), and a new method, High Accuracy Surface Modeling(HASM), were used to simulate forest vegetation carbon stock patterns in Jiangxi Province in China. Cross-validation was used to evaluate methods. The uncertainty and applicability of the four methods on provincial scale were analyzed and discussed. The results showed that HASM had the highest accuracy, which improved by 50.66%, 33.37% and 28.58%, compared with AVIM2, Kriging and SBA, respectively. Uncertainty of simulation of forest biomass carbon stock was mainly derived from modeling error, sampling error and statistical error of forest area. Total forest carbon stock, carbon density and forest area of Jiangxi were 288.62 Tg, 3.06 kg/m~2 and 94.32×109 m~2 simulated by HASM, respectively.     

Preliminary mapping of high-resolution rural population distribution based on imagery from Google Earth: A case study in the Lake Tai basin, eastern China

There is a large rural population in China. Throughout the country, sewage from rural households is mostly discharged with minimal treatment. Knowledge of rural population distribution is essential to study the rural households’ impacts on their surrounding environment. However, high-resolution spatial datasets on rural population distribution are rarely available in China. This study explores the feasibility of using the image from Google Earth to derive a high-resolution map on rural population distribution for a town in the Lake Tai basin of eastern China. Study results show that texture analysis in conjunction with other processing procedures can extract man-made building features from the image reasonably, which can then serve as the basis for the preliminary mapping of rural population distribution in the study region. This may prove to be a promising alternative for deriving spatial datasets on rural population distribution in many parts of the world.     

基于可持续生计的精准扶贫分析方法及应用研究——以四川凉山彝族自治州为例

可持续生计框架是辩证思考贫困的影响因素及其形成过程的理论总结,有助于对精准扶贫进行规范化和系统化的研究。本文以可持续生计框架为蓝本,构建基于可持续生计的精准扶贫分析框架,并从影响农户生计的内因和外因视角,讨论精准扶贫的多维贫困识别指数(MPII)的基本构成。以四川省凉山州作为案例区域,建立具有本土特色的MPII指标体系;识别出420户贫困户,其中80.48%(338户)的建档立卡户与识别结果重叠;与建档立卡农户相比,所识别贫困农户在各单个维度和综合维度都几乎处于更劣势的水平,识别效果更加精准。根据贫困原因的相似性,可将贫困农户划分为人力资本贫困型、基础型资本贫困型、社会资本贫困型、多维资本贫困型、生计环境恶劣型等五种类型,并针对不同的贫困类型提出相应的帮扶措施。     

基于改进高斯两步移动搜索法的深圳市公园绿地可达性评价

公园绿地可达性是衡量城市宜居水平的重要指标.针对传统可达性分析多用统计数据开展评估而导致的供需尺度失衡问题,论文以深圳为例,采用公园绿地空间矢量数据和手机信令数据获取供需空间分布,采用高斯两步移动搜索法计算交通小区(TAZ)尺度公园绿地可达性,并结合可达性结果及供需、交通情况聚类,剖析可达性差异化模式及成因.主要结论如...     

邻里环境感知与个体健康对通勤模式选择的影响研究——以南京市为例

通勤是大部分城市就业者日常工作所必须面对的问题。相比宏观层面的城市建成环境,微观层面的邻里环境感知及个体健康与通勤模式选择的联系更为密切。以南京市主城区8个社区为案例地,利用结构方程模型探究邻里环境感知与个体健康对城市就业者通勤模式选择的影响。首先借助问卷调查数据,将通勤模式分为积极通勤、公共交通通勤、电动车/摩托车通勤以及小汽车通勤四类;其次使用因子分析法确定邻里环境感知的潜变量,并将个体健康分为身体健康、心理健康与健康行为3个潜变量;最后构建结构方程模型的理论框架并进行实证分析。研究发现：① 邻里环境感知变量中服务设施感知、环境品质感知对通勤模式选择存在显著的直接效应,交通安全感知对通勤模式选择存在显著的间接效应。② 个体健康变量中,心理健康与健康行为对通勤模式选择存在显著的直接效应。③ 通勤距离与社会经济属性对通勤模式选择存在显著的直接效应,同时社会经济属性对通勤模式选择亦存在显著的间接效应。基于研究结果,提出有效提升就业者对于社区环境的主观感受,进而提高就业者参与健康行为的意愿与就业者的健康水平,从而引导其选择更加健康和绿色的通勤模式。