黄土高原县域贫困空间格局及地形影响因素研究

借鉴空间贫困研究的基本理论,以2017年的社会经济数据为基础,对国务院扶贫办认定的贫困县进行标识,探讨黄土高原县域贫困格局及其受地形环境的影响。结果表明:(1)黄土高原贫困县沿太行山、吕梁山、六盘山三座山脉和毛乌素沙漠一带聚集分布,呈现“三山一带”的空间分布格局。(2)贫困县的空间分布格局与人口、GDP、人均GDP的空间分布格局大相径庭,在人均GDP较高的鄂尔多斯地区也出现了贫困县的集中分布,说明仅靠社会经济因素不能很好地解释贫困县的空间分布。(3)地形环境对贫困县的空间分布有显著的影响。地形环境越恶劣的地方,贫困县分布越多。68%的贫困县分布在15°～25°高坡度范围内,76%的贫困县位于50～105 m的较大地形起伏度范围内。     

中国县域人口集聚空间格局及影响因素的空间异质性研究

以县域为研究单元,采用人口普查等数据,分析了2000年和2010年中国人口集聚的空间格局、影响因素及空间异质性,结果表明:1)人口集聚空间格局在总体上没有变化,仍保持以"胡焕庸线"为界、东南密西北疏的特征,但空间分布的不均衡性在弱化。2)逐步多元回归模型表明,年均气温、年降水量、第二产业占GDP比重、固定资产投资完成额等是吸引人口的主要因素,海拔高度、距河流距离、距高等级城市最近距离等是驱动人口外流的主要因素。3)地理加权回归模型表明,与县域人口集聚度呈正相关的影响因素有年均气温、第二产业占GDP比重、固定资产投资完成额、人口自然增长率、城镇化率等,与县域人口集聚度呈负相关的影响因素则有年降水量、距河流距离、日照时数、海拔高度等;同时,这些影响因素对人口集聚的影响存在地区差异,具有明显的空间异质性特征。     

2000—2019年越南人口时空分异特征及其 演变类型

越南是中国重要的海陆邻国,开展其人口分布的研究对揭示越南人口发展规律和服务“一带一路”建设具有重要意义。基于越南2000—2019年人口统计数据,采用GIS空间分析方法揭示了越南21世纪以来人口时空分布特征,并依据人口密度和人口增长速度进一步划分其人口演变类型,最后从地形起伏度,人均GDP和外商投资总额3个因素分析了越南人口时空分异的影响机制。结果表明：①越南人口总量即将突破1亿大关,但人口增长率不断下降,近年来逐渐稳定在1.10%左右。②“南北多中间少,东部沿海多,西部内陆少”是越南人口空间分布总体特征,这与其国土形状与地形特征紧密吻合。近20年越南人口高密度地区基本稳定,全国人口空间分布格局变化较小。③越南人口分布演变类型以中高密度慢速增长为主（61.91%）,而人口快速增长地区可以分为社会经济发达地区的中高密度快速增长与西北部边境和西原地区的低密度快速增长两种类型。④以地形起伏度为代表的自然地理要素决定了越南人口分布的基本格局,以人均GDP和外商投资总额为代表的经济发展水平是人口集疏过程的根本动力,但越南边境地区的人口快速增长则明显受到政府宽松的生育政策和对主体民族京族移民实边政策的影响。     

浅谈某县花岗岩地区地质灾害形成特点

某县位于云南省西南边陲地区,区内地质构造活动频繁,地震活动强烈,地形地貌复杂多变,山高谷深,地形坡度陡峭,花岗岩岩体分布范围广泛。作为云南省地质灾害较为发育的县份之一,该县花岗岩地区的地质灾害发育受人为因素和自然因素的影响控制,二者中人为因素占到主导作用。由于构造、地震、地形地貌、人类活动及经济落后等各种内外作用因素,使得该县的地质灾害形成容易,预防、治理困难重重。     

洞庭湖区旅游城镇化系统耦合协调性时空格局及影响因素

基于旅游产业、城镇化和生态环境耦合协调作用机理，运用耦合协调模型分析洞庭湖区2000—2018年17个县域旅游城镇化系统耦合协调性的时空格局及影响因素。结果表明：① 时序特征上，近20 a来耦合度和协调度呈现同步上升态势，两者均出现“一波三折”过程变化特点。耦合度增强的同时，协调度也随之增强，但滞后于耦合度的变化，协调水平有待提升。② 空间格局上，协调度与耦合度均呈现出“中部低、四周高”的分布特点。17个县域2个典型年份的耦合度和协调度出现了不同类型的转变，高层次耦合类型和协调类型的县域集中分布在洞庭湖外围地区，低层次的县域则集中分布在洞庭湖中部地区。③ 耦合协调类型的空间组合差异明显，由低层次向高层次转变的县域达到9个，组合类型的空间分布格局与耦合度和协调度的分异特征基本相似。④ 相关分析表明，协调度与表征旅游产业发展水平、城镇化发展水平和生态环境治理水平因子的较强相关性是形成这种时空分异格局的主要影响因素。     

松嫩高平原县域土地利用空间格局地形梯度特征分析——以巴彦县为例

以黑龙江省巴彦县为研究区,基于研究区DEM数据,运用土地利用类型的分布指数、土地利用结构的信息熵、均衡度和优势度,明确研究区土地利用空间格局的地形梯度特征及地形因子对土地利用空间格局的影响。结果表明：① 研究区土地利用空间格局在地形因子的作用下存在明显的梯度特征,旱地、水田、草地、水域、建设用地及其他用地主要分布在较低地形级别区,林地主要分布在较高地形级别区。② 除林地以外,其他土地利用类型分布的优势地形位均位于较低地形级别区,林地分布的优势地形位位于较高地形级别区,各土地利用类型分布的优势区域不尽相同。③ 研究区土地利用结构的信息熵及均衡度变化趋势相同,优势度的变化趋势与信息熵、均衡度的变化趋势相反,土地利用空间格局与地形因子具有显著的相关性。     

中国县域城镇化发展格局及其影响因素——基于2000和2010年全国人口普查分县数据

利用2000 和2010 年中国人口普查分县数据,借助空间分析方法揭示近期中国常住人口城镇化格局特征及其动态变化,结合多元回归模型探讨城镇化空间分异的主要影响因素,基于主成分分析和聚类分析进行中国城镇化发展类型区划分。结果表明：① 2000-2010 年,中国常住人口城镇化率不断提高,年均增加1.3 个百分点,32%的县域城镇化水平年均增长超过1.5 个百分点;② 中国城镇化发展存在区域差异性和“低快高慢”的收敛性,东部、东北、中部、西部四大区域城镇化率年均分别增加1.4、0.5、1.4 和1.3 个百分点;③ 60%的中国县域城镇化发展集中在低城镇化率—高增长率、低城镇化率—中高增长率和低城镇化率—中增长率三种类型,且以中、西部地区分布最为密集;④ 中国县域城镇化格局基本未变,仍以珠三角、长三角、京津冀三大增长极的城镇化发展较为突出,东部地区及内陆省会城市周边的城镇化水平也相对较高;⑤ 经济发展水平、产业结构特征、人力资本状况、人口集聚能力、公共服务水平、地理区位特征对县域城镇化空间分异具有显著而稳健的影响;⑥ 中国城镇化格局可分为具有明显区类一致性和区外异质性的5 大类、20 个区域。新时期的城镇化发展应凸显地域功能、提升承载能力、夯实产业基础、强调节约集约。     

京津冀地区经济发展的空间溢出效应

在实施京津冀协同发展战略的背景下,将修正的Conley-Ligon模型与空间马尔可夫链分析法相结合,探讨了京津冀县域经济发展过程中的溢出效应空间格局及其对经济水平空间格局演变的影响。研究发现：2000-2013年,京津冀地区的县域间存在显著的空间溢出效应,溢出效应在空间上呈现出与经济发展水平和空间距离紧密相关的具有梯次性特征的核心-外围结构,经济发达的地区发出正向溢出效应的同时也获取了正向溢出效应,但总体上县域溢出效应的类型以负向溢出为主;受溢出效应的空间格局和以负向溢出为主要类型的影响,京津冀县域经济水平类型发生转变的概率较小,经济水平较低的县域难以发生类型的转移,经济水平类型发生转移的县域多分布在地级以上城市市辖区的邻接地带;在邻接县域间显著的空间溢出效应作用下,相邻县域的经济水平类型的转移具有显著的同向性特征。     

东北三省人口分布空间格局演化及其驱动因素研究

东北地区作为全国重要老工业基地,在面临新一轮振兴机遇的形势下,人口分布的集疏变化及其驱动因素亟待细致梳理。根据中国第四、第五、第六次分县人口普查资料,运用洛伦兹曲线、人口密度相对变化率和空间自相关分析方法对东北地区1990~2010年、176个县域的人口分布空间格局演化进行深入分析,在此基础上,选取10个自然和社会经济指标,使用偏最小二乘法（PLS）对东北地区人口分布空间格局演化的影响因素进行定量分析,研究发现：①从总体分布格局变化特征来看,20 a来人口密度始终是黑龙江省<吉林省<辽宁省,且以省会城市为主的多中心“T”型人口分布空间格局越来越明显;②1990~2010年,东北地区人口密度经历了从相对分散到相对集中的变化过程,且人口密度高的地区所占土地面积小,人口密度低的地区占地面积大。人口空间分布呈现显著的正的空间自相关特征,即人口密度存在高高、低低集聚现象;③从人口密度变化类型来看,1990~2010年,共80个县域属于快速增加和缓慢增加类型,快速降低和缓慢降低类型的县域共96个,其中辽宁省县域人口密度变化“两极化”现象较为明显;④自然因素与社会经济因素同时作用于人口密度变化,但社会经济发展水平是人口密度变化的主要影响因素。     

中国县域高龄人口地域分异特征及环境成因

以全国分县为基本研究单元,基于2000和2010年人口普查资料,依据高龄化系数将中国不同区域划分为未高龄化、浅度高龄化、中度高龄化和深度高龄化4个类型区,并探讨其空间上的地域分异规律。并选取5个自然环境指标,利用偏最小二乘法（PLS）,对高龄化水平区域差异的环境成因进行定量分析。结果表明：① 全国县域高龄化水平呈现“西疏东密”分布格局;高龄化系数三级阶梯状分布明显,平原、丘陵、盆地普遍高于山地及高原地区;由“凹”字型向“东西隆升、北端塌缩”格局演变。② 高龄人口空间集聚不断增强,区域间差异逐渐增大。③ 水文指数、气候指数是影响全国县域高龄人口集聚的主要因素;地形起伏度、空气质量指数对高龄人口分布影响不显著,对不同类型区影响差异显著;随着时间的推移,植被指数对高龄人口空间分布的影响趋强;社会经济因素对高龄人口分布具有扰动作用。     

Spatial pattern and influencing factors of landslide casualty events

Analysis of casualties due to landslides from 2000 to 2012 revealed that their spatial pattern was affected by terrain and other natural environmental factors, which resulted in a higher distribution of landslide casualty events in southern China than in northern China. Hotspots of landslide-generated casualties were in the western Sichuan mountainous area and Yunnan-Guizhou Plateau region, southeast hilly area, northern part of the loess hilly area, and Tianshan and Qilian Mountains. However, local distribution patterns indicated that landslide casualty events were also influenced by economic activity factors. To quantitatively analyse the influence of natural environment and human-economic activity factors, the Probability Model for Landslide Casualty Events in China (LCEC) was built based on logistic regression analysis. The results showed that relative relief, GDP growth rate, mean annual precipitation, fault zones, and population density were positively correlated with casualties caused by landslides. Notably, GDP growth rate ranked only second to relative relief as the primary factors in the probability of casualties due to landslides. The occurrence probability of a landslide casualty event increased 2.706 times with a GDP growth rate increase of 2.72%. In contrast, vegetation coverage was negatively correlated with casualties caused by landslides. The LCEC model was then applied to calculate the occurrence probability of landslide casualty events for each county in China. The results showed that there are 27 counties with high occurrence probability but zero casualty events. The 27 counties were divided into three categories: poverty-stricken counties, mineral-rich counties, and real-estate overexploited counties; these are key areas that should be emphasized in reducing landslide risk.     

中国地质灾害伤亡事件的空间格局及影响因素（英文）

Analysis of casualties due to landslides from 2000 to 2012 revealed that their spatial pattern was affected by terrain and other natural environmental factors, which resulted in a higher distribution of landslide casualty events in southern China than in northern China. Hotspots of landslide-generated casualties were in the western Sichuan mountainous area and Yunnan-Guizhou Plateau region, southeast hilly area, northern part of the loess hilly area, and Tianshan and Qilian Mountains. However, local distribution patterns indicated that landslide casualty events were also influenced by economic activity factors. To quantitatively analyse the influence of natural environment and human-economic activity factors, the Probability Model for Landslide Casualty Events in China(LCEC) was built based on logistic regression analysis. The results showed that relative relief, GDP growth rate, mean annual precipitation, fault zones, and population density were positively correlated with casualties caused by landslides. Notably, GDP growth rate ranked only second to relative relief as the primary factors in the probability of casualties due to landslides. The occurrence probability of a landslide casualty event increased 2.706 times with a GDP growth rate increase of 2.72%. In contrast, vegetation coverage was negatively correlated with casualties caused by landslides. The LCEC model was then applied to calculate the occurrence probability of landslide casualty events for each county in China. The results showed that there are 27 counties with high occurrence probability but zero casualty events. The 27 counties were divided into three categories: poverty-stricken counties, mineral-rich counties, and real-estate overexploited counties; these are key areas that should be emphasized in reducing landslide risk.     

Susceptibility assessment of earthquake-triggered landslides in El Salvador using logistic regression

This work has evaluated the probability of earthquake-triggered landslide occurrence in the whole of El Salvador, with a Geographic Information System (GIS) and a logistic regression model. Slope gradient, elevation, aspect, mean annual precipitation, lithology, land use, and terrain roughness are the predictor variables used to determine the dependent variable of occurrence or non-occurrence of landslides within an individual grid cell. The results illustrate the importance of terrain roughness and soil type as key factors within the model — using only these two variables the analysis returned a significance level of 89.4%. The results obtained from the model within the GIS were then used to produce a map of relative landslide susceptibility.     

Prediction of landslide susceptibility using rare events logistic regression: A case-study in the Flemish Ardennes (Belgium)

In this article a statistical multivariate method, i.e., rare events logistic regression, is evaluated for the creation of a landslide susceptibility map in a 200 km² study area of the Flemish Ardennes (Belgium). The methodology is based on the hypothesis that future landslides will have the same causal factors as the landslides initiated in the past. The information on the past landslides comes from a landslide inventory map obtained by detailed field surveys and by the analysis of LIDAR (Light Detection and Ranging)-derived hillshade maps. Information on the causal factors (e.g., slope gradient, aspect, lithology, and soil drainage) was extracted from digital elevation models derived from LIDAR and from topographical, lithological and soil maps. In landslide-affected areas, however, we did not use the present-day hillslope gradient. In order to reflect the hillslope condition prior to landsliding, the pre-landslide hillslope was reconstructed and its gradient was used in the analysis. Because of their limited spatial occurrence, the landslides in the study area can be regarded as “rare events”. Rare events logistic regression differs from ordinary logistic regression because it takes into account the low proportion of 1s (landslides) to 0s (no landslides) in the study area by incorporating three correction measures: the endogenous stratified sampling of the dataset, the prior correction of the intercept and the correction of the probabilities to include the estimation uncertainty. For the study area, significant model results were obtained, with pre-landslide hillslope gradient and three different clayey lithologies being important predictor variables. Receiver Operating Characteristic (ROC) curves and the Kappa index were used to validate the model. Both show a good agreement between the observed and predicted values of the validation dataset. Based on a qualified judgement, the created landslide susceptibility map was classified into four classes, i.e., very high, high, moderate and low susceptibility. If interpreted correctly, this classified susceptibility map is an important tool for the delineation of zones where prevention measures are needed and human interference should be limited in order to avoid property damage due to landslides.     

Evaluation and comparison of landslide susceptibility mapping methods: a case study for the Ulus district,Bartın,northern Turkey

The purpose of this study was to investigate the capabilities of different landslide susceptibility methods by comparing their results statistically and spatially to select the best method that portrays the susceptibility zones for the Ulus district of the Bart?n province (northern Turkey). Susceptibility maps based on spatial regression (SR), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression (LR) method, and artificial neural network method (ANN) were generated, and the effect of each geomorphological parameter was determined. The landslide inventory map digitized from previous studies was used as a base map for landslide occurrence. All of the analyses were implemented with respect to landslides classified as rotational, active, and deeper than 5 m. Three different sets of data were used to produce nine explanatory variables (layers). The study area was divided into grids of 90 m × 90 m, and the ‘seed cell’ technique was applied to obtain statistically balanced population distribution over landslide inventory area. The constructed dataset was divided into two datasets as training and test. The initial assessment consisted of multicollinearity of explanatory variables. Empirical information entropy analysis was implemented to quantify the spatial distribution of the outcomes of these methods. Results of the analyses were validated by using success rate curve (SRC) and prediction rate curve (PRC) methods. Additionally, statistical and spatial comparisons of the results were performed to determine the most suitable susceptibility zonation method in this large-scale study area. In accordance with all these comparisons, it is concluded that ANN was the best method to represent landslide susceptibility throughout the study area with an acceptable processing time.     

GIS支持下三峡库区秭归县滑坡灾害空间预测

基于GIS空间分析和统计模型相结合进行区域评价与空间预测是滑坡灾害研究的重要方向之一。以三峡库区秭归县为研究区,选择坡度、坡向、边坡结构、工程岩组、排水系统、土地利用和公路开挖作为评价因子。为提高模型的预测精度、可信度和推广能力,利用窗口采样规则降低训练样本之间的空间相关性。建立Logistic回归模型,对滑坡灾害与评价因子进行定量相关性分析。计算研究区滑坡灾害易发性指数,对其进行聚类分析,绘制滑坡易发性分区图,其中高、中易发区占整个研究区面积的38.9%,主要分布在人类工程活动频繁和靠近排水系统的区域。经过验证,该模型的预测精度达到77.57%。     

Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong

Steep terrain and high a frequency of tropical rainstorms make landslide occurrence on natural terrain a common phenomenon in Hong Kong. This paper reports on the use of a Geographical Information Systems (GIS) database, compiled primarily from existing digital maps and aerial photographs, to describe the physical characteristics of landslides and the statistical relations of landslide frequency with the physical parameters contributing to the initiation of landslides on Lantau Island in Hong Kong. The horizontal travel length and the angle of reach, defined as the angle of the line connecting the head of the landslide source to the distal margin of the displaced mass, are used to describe runout behavior of landslide mass. For all landslides studied, the horizontal travel length of landslide mass ranges from 5 to 785 m, with a mean value of 43 m, and the average angle of reach is 27.7°. This GIS database is then used to obtain a logistic multiple regression model for predicting slope instability. It is indicated that slope gradient, lithology, elevation, slope aspect, and land-use are statistically significant in predicting slope instability, while slope morphology and proximity to drainage lines are not important and thus excluded from the model. This model is then imported back into the GIS to produce a map of predicted slope instability. The results of this study demonstrate that slope instability can be effectively modeled by using GIS technology and logistic multiple regression analysis.     

利用树轮重建九房山滑坡活动年份

理解地貌过程以及认识过去灾害发生的频率,是自然灾害风险评价的重要任务。树木年轮由于其具有定年准确、连续性强、分辨率高和易于复本等特点,近年来被用到恢复过去灾害发生的时间。随着滑坡、泥石流等山地地质灾害性事件发生频率的增加,地质灾害风险管理和评价受到了越来越多的关注。在样品量足够的前提下,树木年轮方法可以精确地确定滑坡事件发生的年份。以甘肃南部武都区外纳乡的九房山滑坡为研究对象,利用树木年轮分析方法对该滑坡的灾害事件发生的时间进行了初步的研究。结果显示:1.树木年轮样品在1980—1982年,1984—1987年以及1991-1993年都出现了年轮宽度迅速减小或者迅速增加的现象。本研究将1980—1982年,1984—1987年以及1991—1993年定为滑坡活动年份,其中1992年和1993年的滑坡与当地记载的滑坡年份一致。强降水,地震影响以及不合理的灌溉活动都可能导致滑坡的复活,但降雨仍是导致九房山滑坡复活的重要因素。2.树木年轮样品中出现的偏心木和伪轮现象也是对滑坡灾害发生的佐证,说明受到滑坡灾害的影响,树木的生长环境发生了改变。     

An integrated model for predicting rainfall-induced landslides

This study proposes a novel method that combines a deterministic slope stability model and a statistical model for predicting rainfall-induced landslides. The method first uses the deterministic model to derive the rainfall rate critical to induce slope failure for each land unit. Then it calculates the difference between the critical rainfall threshold and estimated rainfall intensity. Using the difference and estimated rainfall duration as explanatory variables, the method derives a logit (integrated) model to compute landslide occurrence probabilities. To demonstrate the effectiveness of this method, the study used radar rainfall estimates and landslides associated with a typhoon (tropical cyclone) to develop the integrated model and the same types of data associated with another typhoon to validate the model. The model had a modified success rate of 84.0% for predicting landslides and stable areas, and model validation yielded a modified success rate of 87.4%. Both rates were better than those from the critical rainfall model. The main advantage of the integrated model lies in its use of rainfall variables that are not included in calculating the critical rainfall. Also, as a probabilistic model, the integrated model is better suited for decision-making in watershed management. This study has advanced the method for predicting rainfall-triggered landslides.     

GIS支持下滑坡斜坡类型定量化及制图研究

斜坡类型描述岩层产状与斜坡的角度关系,很大程度上决定了斜坡岩土体变形的方式和强度,对地质灾害分布具有重要作用。斜坡的顺向坡、反向坡与地形的阳坡、阴坡概念相似,可以利用改进的太阳辐射地形因子计算模型(TOBIA指数)对斜坡类型进行定量化表达。计算TOBIA指数需要斜坡坡度、坡向、岩层倾角、倾向4个参数。以三峡库区顺向坡基岩滑坡多发地段青干河流域为例,通过区域地质图上产状点获取离散岩层倾角和倾向数值,经空间插值得到空间连续分布的倾角和倾向参数;通过数字高程模型获取坡度和坡向参数,得到区内TOBIA指数分布。在此基础上进一步研究指数和滑坡发育关系。结果表明,TOBIA指数值与区内斜坡类型密切相关,根据TOBIA指数值能很好地区分斜坡类型。以二分类变量逻辑回归模型对坡度和指数两个变量进行分析,发现引入TOBIA指数后,回归模型对已知滑坡拟合度由55%提高到71.5%,能有效提高区域滑坡灾害危险性区划结果精度。