位移监测在滑坡时空运动研究中的应用

滑坡时空运动特征是滑坡地质体在内因和外因共同作用下，自一种状态向另一种状态转化的地质过程。滑坡位移监测是研究滑坡变形影响因素、动态规律及预测预报的主要途径，特别是滑坡深部位移监测，又为研究滑坡体的时空运动过程提供了重要信息。本文结合几个滑坡的深部位移监测实践，综合分析位移监测信息，研究滑坡体的时空运动特征及发展趋势，为滑坡时空运动系统研究及稳定性预测提供依据。     

面向对象的时空数据模型及其实现技术

在基于时间片的连续快照模型基础上,基于OpenGIS定义的几何类型扩充了时态类型,构建面向对象的时空数据模型,定义其抽象数据类型及相关操作;并采用对象关系数据库PostgreSQL作为实现平台,扩展其存储和查询能力。通过实例验证了上述模型及实现方法的有效性和实用性。     

深圳土地利用时空变化与地形因子的关系研究

引入GIS空间分析和数字地形模型(DEM)技术,利用土地利用数据和地形数据,定量分析土地利用时空变化与地形因子的关系,从土地利用空间分布、土地利用转移和土地利用变化速度3方面总结地形因子对深圳土地利用时空变化的影响特征及规律。结果表明:在深圳土地利用时空变化过程中,地形因子对土地利用变化的影响作用明显,在不同地形级别上土地利用分布、土地利用转移和土地利用变化速度等均表现出不同的时空变化特征。     

广州市零售商业中心的居民消费时空行为及其机制

居民消费行为的空间选择及其影响因素是地理学的重点关注领域,已有研究对行为中时间选择、时空关系及内在机制的研究相对缺乏。为揭示居民消费时空行为形成的影响因素以及各因素内在相互作用机制,基于2016年广州城市居民消费行为调查数据,分析消费者到大型商业中心内消费的时空特征,构建结构方程模型,探讨消费时空行为影响因素和作用机制。研究表明：居民在商业中心的消费行为存在明显的时空差异。在影响路径方面,消费者社会经济属性通过影响消费偏好导致消费时空行为的差异,商业空间属性既可以直接影响消费时空行为,也可以通过影响消费偏好而间接对其施加影响,消费偏好既可以直接影响消费时空行为,又是消费者社会经济属性和商业空间属性影响消费时空行为的中间变量。在显著性因素方面,年龄、家庭结构、在广州居住时间、就业状况、家庭月收入等消费者社会经济属性变量会显著影响消费时空行为,各个商业空间属性变量都在不同程度上影响消费行为在时间和空间上的差异,仅有出行交通方式、出行花费时间2个消费偏好变量会显著影响消费者消费时空间行为。本文结论可以加深对商业中心内消费时空行为影响因素及其作用机制的理解,并为商业网点规划调整、商业中心转型升级提供建议。     

The GRASS GIS temporal framework

The availability of continental and global-scale spatio-temporal geographical data sets and the requirement to efficiently process, analyse and manage them led to the development of the temporally enabled Geographic Resources Analysis Support System (GRASS GIS). We present the temporal framework that extends GRASS GIS with spatio-temporal capabilities. The framework provides comprehensive functionality to implement a full-featured temporal geographic information system (GIS) based on a combined field and object-based approach. A significantly improved snapshot approach is used to manage spatial fields of raster, three-dimensional raster and vector type in time. The resulting timestamped spatial fields are organised in spatio-temporal fields referred to as space-time data sets. Both types of fields are handled as objects in our framework. The spatio-temporal extent of the objects and related metadata is stored in relational databases, thus providing additional functionalities to perform SQL-based analysis. We present our combined field and object-based approach in detail and show the management, analysis and processing of spatio-temporal data sets with complex spatio-temporal topologies. A key feature is the hierarchical processing of spatio-temporal data ranging from topological analysis of spatio-temporal fields over boolean operations on spatio-temporal extents, to single pixel, voxel and vector feature access. The linear scalability of our approach is demonstrated by handling up to 1,000,000 raster layers in a single space-time data set. We provide several code examples to show the capabilities of the GRASS GIS Temporal Framework and present the spatio-temporal intersection of trajectory data which demonstrates the object-based ability of our framework.     

Objective landslide detection and surface morphology mapping using high-resolution airborne laser altimetry

A map of extant slope failures is the most basic element of any landslide assessment. Without an accurate inventory of slope instability, it is not possible to analyze the controls on the spatial and temporal patterns of mass movement or the environmental, human, or geomorphic consequences of slides. Landslide inventory maps are tedious to compile, difficult to make in vegetated terrain using conventional techniques, and tend to be subjective. In addition, most landslide inventories simply outline landslide boundaries and do not offer information about landslide mechanics as manifested by internal deformation features. In an alternative approach, we constructed accurate, high-resolution DEMs from airborne laser altimetry (LIDAR) data to characterize a large landslide complex and surrounding terrain near Christchurch, New Zealand. One-dimensional, circular (2-D) and spherical (3-D) statistics are used to map the local topographic roughness in the DEMs over a spatial scale of 1.5 to 10 m. The bedrock landslide is rougher than adjacent unfailed terrain and any of the statistics can be employed to automatically detect and map the overall slide complex. Furthermore, statistics that include a measure of the local variability of aspect successfully delineate four kinematic units within the gently sloping lower half of the slide. Features with a minimum size of surface folds that have a wavelength of about 11 to 12 m and amplitude of about 1 m are readily mapped. Two adjacent earthflows within the landslide complex are distinguished by a contrast in median roughness, and texture and continuity of roughness elements. The less active of the earthflows has a surface morphology that presumably has been smoothed by surface processes. The Laplacian operator also accurately maps the kinematic units and the folds and longitudinal levees within and at the margins of the units. Finally, two-dimensional power spectra analyses are used to quantify how roughness varies with length scale. These results indicate that no dominant length scale of roughness exists for smooth, unfailed terrain. In contrast, zones with different styles of landslide deformation exhibit distinctive spectral peaks that correspond to the scale of deformation features, such as the compression folds. The topographic-based analyses described here may be used to objectively delineate landslide features, generate mechanical inferences about landslide behavior, and evaluate relatively the recent activity of slides.     

复杂网络视角下时空行为轨迹模式挖掘研究

针对时空行为轨迹大数据的序列性、时空交互性、多维度性等复杂特性,构建结合时间地理学与复杂网络的分析框架,建立时空行为路径与时空行为网络之间的转换关系,利用复杂网络社群发现算法对时空行为轨迹进行社群聚类、模式挖掘与可视化。基于北京郊区居民一周内活动出行GPS轨迹数据的案例分析发现:(1)复杂网络分析方法可以有效挖掘具有相似行为的群体特征和识别出典型的行为模式。(2)可以灵活处理多元异构与多维度的行为轨迹大数据以及满足不同叙事、不同空间相互作用、不同时序的应用需求。(3)北京郊区被调查居民的行为模式存在日间差异与空间分异。     

洞庭湖植被对降水的响应

We analyzed the Normalized Difference Vegetation Index (NDVI) from satellite images and precipitation data from meteorological stations from 1998 to 2007 in the Dongting Lake wetland watershed to better understand the eco-hydrological effect of atmospheric precipitation and its relationship with vegetation. First,we analyzed its general spatio-temporal distribution using its mean,standard deviation and linear trend. Then,we used the Empirical Orthogonal Functions (EOF) method to decompose the NDVI and precipitation data into spatial and temporal modes. We selected four leading modes based on North and Scree test rules and analyzed the synchronous seasonal and inter-annual variability between the vegetation index and precipitation,distinguishing time-lagged correlations between EOF modes with the correlative degree analysis method. According to our detailed analyses,the vegetation index and precipitation exhibit a prominent correlation in spatial distribution and seasonal variation. At the 90% confidence level,the time lag is around 110 to 140 days,which matches well with the seasonal variation.     

Analysis of vegetation response to rainfall with satellite images in Dongting Lake

We analyzed the Normalized Difference Vegetation Index (NDVI) from satellite images and precipitation data from meteorological stations from 1998 to 2007 in the Dongting Lake wetland watershed to better understand the eco-hydrological effect of atmospheric precipitation and its relationship with vegetation. First, we analyzed its general spatio-temporal distribution using its mean, standard deviation and linear trend. Then, we used the Empirical Orthogonal Functions (EOF) method to decompose the NDVI and precipitation data into spatial and temporal modes. We selected four leading modes based on North and Scree test rules and analyzed the synchronous seasonal and inter-annual variability between the vegetation index and precipitation, distinguishing time-lagged correlations between EOF modes with the correlative degree analysis method. According to our detailed analyses, the vegetation index and precipitation exhibit a prominent correlation in spatial distribution and seasonal variation. At the 90% confidence level, the time lag is around 110 to 140 days, which matches well with the seasonal variation.     

面向对象的区域规划空间信息时空数据模型

区域规划的空间信息具有动态性和复杂性,面向对象技术作为一种有效的空间数据建模技术,为区域规划空间信息整合提供了思路。本文以面向对象的基本理论和方法为基础,提出采用面向对象技术建立区域规划空间信息时空数据模型,并对所建立的主要地理特征类进行定义与描述。     

2011—2020年黄淮海平原冬小麦低温冷害与气象要素变化的时空分析（英文）

黄淮海平原是我国典型的农田生态系统之一,频发的低温冷害对该区域的冬小麦种植业造成了巨大的经济损失。为了有效、大范围地监测冷害的时空变化情况及其气象成因,利用2005–2015年来源于气象站点的逐日最低气温和MODIS的NDVI和FPAR数据,结合逻辑回归模型构建了黄淮海平原冬小麦低温冷害监测模型,并分析了2011–2020年黄淮海平原冬小麦冷害的时空变化。将其与气象要素的时空变化进行相关性分析,了解气象要素对冬小麦低温冷害发生情况的影响。结果表明,2011–2020年冬小麦冷害的危害性逐渐降低,且冷害发生高概率、高频区域由北向南移动。同时,气象要素对冬小麦受害程度的影响从高到低分别为热量、降水和日照时长,但这些要素的影响具有一定的空间异质性。     

Generic query tool for spatio-temporal data

Geographical information systems are more and more based on a DBMS with spatial extensions, which is also the case for the system described in this paper. The design and implementation of a generic geographical query tool, a platform for querying multiple spatio-temporal data sets and associated thematic data, is presented. The system is designed to be generic, that is without one specific application in mind. It supports ad-hoc queries covering both the spatial and the thematic part of the data. The generic geographic query tool will be illustrated with spatial and thematic Cadastral data. Special attention will be given to the temporal aspects: a spatio-temporal data model will be described together with a set of views for easy querying. DBMS views play an important role in the architecture of the system: integration of models, aggregation of information, presentation of temporal data, and so on. The current production version of the geographic query tool within the Dutch Cadastre is based on GeoICT products with a relatively small market share (Ingres and GEO++). A new prototype version is being developed using mainstream Geo-ICT products (Oracle and MapInfo). First results and open issues with respect to this prototype are presented.     

基于网络时空核密度的交通事故多发点鉴别方法

交通事故多发点是道路交通安全管理的重要治理对象,如何利用空间统计方法对其进行高效鉴别是研究热点。以华东某地为研究区域,以2013~2015年该研究区域的道路交通事故数据为研究对象,以时空道路网络为视角,通过路网匹配和路网裁剪形成事故时空子路段,提出一种基于交通事故场景的网络时空核密度估计值作为鉴别指标,用累计频率法和零膨胀的负二项回归模型确定鉴别阈值的事故多发点鉴别方法。     

基于贝叶斯层次时空模型的甘肃省土地利用程度演变分析

以甘肃省为例,在基于Google Earth Engine (GEE)平台实现1995年、2000年、2005年、2010年、2015年和2020年土地变化监测的基础上,利用贝叶斯层次时空模型(BHM)分析土地利用程度的时空变化特征。结果表明：① 研究期间内甘肃省土地利用程度呈增长趋势,其中1995―2000年和2010―2015年增长速度较明显;② 土地利用程度空间格局“东高西低”,热点区域主要分布在陇中、陇东和陇南地区;③ 土地利用程度局部变化呈现明显区域差异,整体表现为“东弱西强”,局部变化热点区域主要分布在河西地区;④ 影响土地利用程度变化的主要因素是经济规模和产业结构,其中经济因素影响程度最高。     

中国COVID-19疫情扩散的时空模式及影响因素

在2020年全球暴发新型冠状病毒肺炎（COVID-19）疫情的背景下,揭示中国疫情扩散时空模式及影响因素对于科学制定防疫策略具有重要作用。针对2020年1月24日—3月18日期间中国COVID-19疫情从快速扩散到逐步控制的完整过程,基于累计确诊病例数据,以317个地级市为对象,建立疫情扩散时空模式判别模型,结合峰位置、半峰间距、峰度、偏度等参数,解析时空模式的基本特征;基于交通可达性、城市关联程度和人口流动构建多元Logistic回归模型,揭示时空模式的关键影响因素。结果显示：① 距武汉市直线距离588 km为判别疫情扩散4种空间模式的有效边界,综合同一空间模式下的时间过程类别,得到13类疫情扩散时空模式。② 蛙跳型的疫情扩散相对严重;除近距离蛙跳型以外,其余空间模式的疫情扩散时间过程差异明显;各种时空模式的新增确诊病例峰值大多为2020年2月3日;所有普通类城市的平均半峰间距约为14 d,与COVID-19病毒的潜伏期一致。③ 与武汉市的人口关联度主要影响蔓延型和近距离蛙跳型空间模式,与武汉市的通航状况对远距离蛙跳型空间模式具有正向影响,迁出人口数量对蛙跳型空间模式有显著作用,综合型空间模式受初级和次级疫情暴发地的双重影响。不同城市应根据自身的疫情扩散时空模式,在疫情期间高度重视交通管控,从关键环节遏制疫情扩散。     

时态GIS的面向过程语义与HAS表达框架

分析认为时空过程语义兼具连续体和偶发体的双重特性,是对现实世界地理过程的简化与抽象表达。针对对象建模视图和基于事件模型不足以描述过程语义的缺陷,提出了一种显式建模地理过程的HAS表达框架,将过程视图剖分为发生(Happenings)、动作(Actions)与状态(States)三域,分别描述时空过程的起因、行为过程和结果状态。在土地利用管理系统中的应用表明,其能较好地对土地利用变迁过程进行表达,能够通过时空查询实现对土地利用变迁过程的整体复原。     

中国城市群PM2.5空间分布规律（英文）

Haze pollution has become a severe environmental problem in the daily life of the people in China. PM_(2.5) makes a significant contribution to poor air quality. The spatio-temporal features of China's PM_(2.5) concentrations should be investigated. This paper, based on observed data from 945 newly located monitoring sites in 2014 and industrial working population data obtained from International Standard Industrial Classification(ISIC), reveals the spatio-temporal variations of PM_(2.5) concentrations in China and the correlations among different industries. We tested the spatial autocorrelation of PM_(2.5) concentrations in the cities of China with the spatial autocorrelation model. A correlation coefficient to examine the correlativity of PM_(2.5) concentrations and 23 characteristic variables for 190 cities in China in 2014, from which the most important ones were chosen, and then a regression model was built to further reveal the social and economic factors affecting PM_(2.5) concentrations. Results:(1) The Hu Huanyong Line and the Yangtze River were the E-W divide and S-N divide between high and low values of China.(2) The PM_(2.5) concentrations shows great seasonal variation, which is high in autumn and winter but low in spring and summer. The monthly average shows a U-shaped pattern, and daily average presents a periodic and impulse-shaped change.(3) PM_(2.5) concentrations had a distinct characteristic of spatial agglomeration. The North China Plain was the predominant region of agglomeration, and the southeastern coastal area had stable good air quality.     

中国畜禽产品市场空间关联及其影响因素

为探究中国畜禽产品市场空间关联特征及其原因,以猪肉和鸡肉为例,运用Diebold和Yilmaz提出的关联测度新方法测度了畜禽产品市场空间关联水平及其时变特征,在此基础上运用社会网络分析法刻画畜禽产品市场空间关联网络特征,并通过QAP分析考察影响畜禽产品市场空间关联的主要因素。结果表明：① 中国畜禽产品市场总体空间关联水平较高,且近年来整体呈上升趋势,其中,相比于鸡肉市场,猪肉市场总体空间关联水平更高,但鸡肉市场总体空间关联水平上升幅度更大。② 中国畜禽产品市场空间关联呈现出多线程、复杂的网络结构,空间关联网络较为紧密,与鸡肉市场相比,猪肉市场空间关联网络更为紧密,不同省份在畜禽产品市场的中角色和地位不同。其中,中东部畜禽产品主销区和主产区在空间关联网络中处于中心地位,扮演着中心行动者角色,而西部偏远地区省份,在网络中影响力较小,处于从属和边缘地位。③ 市场邻接、市场距离以及产区势力是影响畜禽产品市场空间关联的主要因素。此外,市场化程度对猪肉市场空间关联同样有显著影响。     

Co-clustering geo-referenced time series: exploring spatio-temporal patterns in Dutch temperature data

Clustering allows considering groups of similar data elements at a higher level of abstraction. This facilitates the extraction of patterns and useful information from large amounts of spatio-temporal data. Till now, most studies have focused on the extraction of patterns from a spatial or a temporal aspect. Here we use the Bregman block average co-clustering algorithm with I-divergence (BBAC_I) to enable the simultaneous analysis of spatial and temporal patterns in geo-referenced time series (time evolving values of a property observed at fixed geographical locations). In addition, we present three geovisualization techniques to fully explore the co-clustering results: heatmaps offer a straightforward overview of the results; small multiples display the spatial and temporal patterns in geographic maps; ringmaps illustrate the temporal patterns associated to cyclic timestamps. To illustrate this study, we used Dutch daily average temperature data collected at 28 weather stations from 1992 to 2011. The co-clustering algorithm was applied hierarchically to understand the spatio-temporal patterns found in the data at the yearly, monthly and daily resolutions. Results pointed out that there is a transition in temperature patterns from northeast to southwest and from ‘cold’ to ‘hot’ years/months/days with only 3 years belonging to ‘cool’ or ‘cold’ years. Because of its characteristics, this newly introduced algorithm can concurrently analyse spatial and temporal patterns by identifying location-timestamp co-clusters that contain values that are similar along both the spatial and the temporal dimensions.