青岛城市道路邻近中心性及其应用方法

以青岛为实证研究区域,从理论角度探讨了道路全局和局部邻近中心性的地理含义及其各自优缺点,认为全局邻近中心性(GCC)反映的是网络上各点到网络“质心”的距离,可从全局角度反映区位重要性;局部邻近中心性(LCC)反映小范围空间区域的重心,与局部路网密度相关。进一步根据核密度估计(KDE)的机理证明,在城市地理空间结构的实际应用案例中适宜采用KDE法来进行空间分析。本文以手机话务量数据反映的城市居民活动空间结构为例讨论了邻近中心性核密度估计(CC-KDE)的应用,发现与居民活动相关性最高的是核密度全局邻近中心性(GCC-KDE),因为全局邻近中心性核密度估计(GCC-KDE)综合了到网络“质心”距离与道路网络局部密度双重因素,故反映城市人口等与道路密度相关的社会经济现象更有效。     

安徽省物流与经济时空耦合研究

以1994—2012年安徽省各地市货运总量和GDP数据为基础,采用重心模型、地理联系率、地理集中指数分析物流和经济空间演变规律,揭示物流增长与经济发展的时空耦合特征,并根据耦合指数识别不同区域的耦合差异。在省域尺度下,无论是重心变迁轨迹还是地理联系率,都表现出物流与经济在时空上存在明显的耦合关系。二者重心演变路径在空间上具有相似性,重心间距离呈现出扩大—减小—扩大的变化过程,且物流重心在时间上滞后于经济重心,移动距离和幅度大于经济重心,表明区域经济发展对物流增长具有明显的拉动作用;地理联系率都在99以上,这也表明全省物流与经济发展在时空上具有明显的一致性。在地级市尺度下,物流与经济的地理集中度呈现出空间相关性且趋于增强,区域的不平衡性趋于缓和;基于物流和经济耦合指数可将省域内17个地市划分为经济超前于物流型、经济物流协调型、经济滞后于物流型三大类型。     

"时空接近"空间自相关模型构建及其应用——以长三角区域经济差异分析为例

地理学第一定律是空间自相关统计学的核心和基石,传统的空间自相关以邻接性或空间距离函数确定空间权重矩阵。但随着快速交通网络变得具有渗透性,空间距离内涵已发生质的变化,"接近"概念有必要从"空间接近"拓展为"时空接近"。基于这一新的内涵,提出"时空接近"空间自相关概念并构建相应计算模型,结合表征时间距离的可达性指标将该模型应用于长三角区域经济差异分析。通过计算全局Moran's I和局域Moran's I,结果表明:在全局空间自相关指标上,"时空接近"空间自相关能更好地揭示地理事物间的关联动态变化。在局域空间自相关指标上,当时间距离截取在合理范围时,邻接性表现出较强的稳健性,但"时空接近"与传统"空间接近"的空间相关性存在较大差异,两者结合能更客观揭示地理事物空间关联的现象和本质。     

高校知识溢出对我国高技术产业创新的影响研究——基于省级区域的空间计量分析

基于2002~2011年中国30个省域（直辖市、自治区）的面板数据,以高技术产业为研究对象,应用空间计量模型扩展知识生产函数,选用地理邻接和反地理距离空间权重矩阵,从投入和产出两方面估计高校知识溢出对区域创新的影响。结果表明：高校的知识产出和研发人员投入均对区域创新有显著促进作用;企业规模、市场开放程度和政府支持力度也与区域创新呈显著正相关;地理距离较近地区间的创新存在显著负向溢出效应。因而,应重视高校在区域创新发展的作用,正确引导创新在省域间的流向。     

基于隐马尔可夫模型的时空轨迹语义匹配方法

时空轨迹数据关联的语义信息能更好地反映用户行为,对于POI密集分布的城市区域,轨迹的语义信息很难根据单一的距离或时间要素进行匹配,该文设计一种基于隐马尔可夫模型(HMM)的时空轨迹语义匹配方法。首先,利用时间阈值与距离阈值提取逗留点,并利用考虑时间的DBSCAN聚类方法对逗留点进行聚类,得到由抽象停留位置构成的轨迹;然后,结合POI数据获得停留位置的候选语义,再以停留位置序列为观测序列,以每个停留位置所关联的候选地点作为隐藏状态建立HMM,并用改进的加权距离的TF-IDF方法计算HMM的观测概率;最后,解算HMM得到最有可能的访问地点序列作为轨迹的语义匹配结果。该方法不依赖其他外部数据或训练数据,适用于POI密集分布的城市区域,基于真实时空轨迹数据集的实验结果验证了该方法的有效性。     

三亚市旅游休闲业态时空演变特征及其形成机制分析

以三亚市为案例地,通过爬取六类旅游休闲业态POI数据,综合利用最邻近距离指数、核密度估计、多空间聚类、地理探测器等空间分析技术,深入挖掘三亚市旅游休闲业态的时空演变特征及其形成机制。结果表明：（1）业态总体数量及特征空间尺度急剧上升,分布格局呈“极核-轴线-面域”分布形态;主要集聚于三亚湾度假区,散布周边临海区域,且集聚程度随空间尺度的增大呈先增后减态势（;2）各类旅游休闲业态空间集聚特征显著,其中购物、住宿、餐饮业态与总体集聚特征呈现高度一致性,各类业态集聚分布特征显著不同且尺度依赖性明显,其中旅游景点业态集聚程度随时间距离的增加不断减小,其余业态均先增后减（;3）三亚市旅游休闲业态空间格局的形成是由政策引导城市发展决定的,即在旅游休闲需求升级与城市发展的转型升级的双向推动下,从经济水平、人口特征、交通条件及资源禀赋四个方面影响旅游休闲业态空间演变格局。     

人群群组虚实活动轨迹的对比分析

人群在虚实场景中的时空行为特征与异同是虚拟地理实验的一个研究热点。该文以大型商场局部双向通道为例,在考虑人群群组活动的情况下,设计了基于PC与VR一体机两种终端设备的协同虚拟地理实验,并从群组轨迹间距离和群组运动偏向角差两方面,同现实场景下的人群活动轨迹采集数据进行可视化对比分析。分析结果表明:相比于现实人群活动轨迹采集数据,基于PC端和VR一体机端的协同虚拟地理实验数据,在人群群组的活动轨迹上存在明显的差异,降低了人群的群组性;同时,相比于PC端,基于VR一体机端的协同虚拟地理实验,在人群的群组性上有一定程度的提高。最后结合对比分析结果,分析了造成协同虚拟地理实验中人群群组性降低的原因,以及相比于PC端,使用VR一体机进行协同虚拟地理实验的优势。     

山区过渡性地理空间人文自然交互性识别与分异解析

山区因其人文自然交互过程具有显著的地域性、时空分异性与不确定性,成为典型的过渡性地理空间,其类型量化识别与分异特征的解析可为山区乡村振兴背景下国土空间高质量发展提供决策依据。本文基于地理不确定性的概念构建时空变率指数,识别出中国山区过渡性地理空间分布与分区,并运用地理探测器解析其地理时空变率的驱动力谱。结果发现：中国山区过渡性地理空间总面积为238.32×10⁴ km²（约占中国陆表面积的1/4）,其地理时空变率从第一阶梯到第三阶梯呈递减趋势;全国山区过渡性地理空间可分为12个分区,其中昆仑—祁连山分区面积最大;人文驱动因子对人口与土地利用的时空变率解析力最强,基础地形因子对植被覆盖时空变率与地理时空变率指数的解析力最为显著,各因子中海拔与夜间灯光的解析驱动力最强。整体而言,人文要素的时空动态均对东南山区过渡性地理空间具有显著的塑造性,而西北山区过渡性地理空间主要受到自然要素的时间变化和人文要素的空间变化所驱动。本文为山区过渡性地理空间的不确定性、多样性与人地关系地域性的定量研究提供了新的见解与启示。     

中国丘陵山区农村贫困时空格局及其演变机制研究——以江西省于都县为例

解决区域性整体贫困问题是全面建成小康社会的前提。丘陵山区是中国农村贫困人口的聚集区,开展丘陵山区贫困时空格局及其演变机制研究对实现贫困地区转型发展与乡村振兴具有重要的理论意义。论文以江西省于都县为例,以村域为研究单元,采用空间自相关、核密度估计、地理探测器等模型方法刻画了于都县农村贫困地理格局及其分异特征,识别了丘陵山区驱动农村贫困化的主导因子,揭示了农村贫困化的驱动机制。结果表明：① 丘陵山区农村贫困化存在明显的地域分异规律和空间集聚性特征,且聚集效应随减贫力度加大有所减弱;② 坡度、行政村到县城中心的可达度、路网密度和公共服务点数量是丘陵山区农村贫困化分异的主导因子,且随时间推移,资源禀赋不足对农村贫困化的驱动作用有所减弱,区位条件对农村贫困化的驱动作用逐渐增强;③ 不同时期丘陵山区农村贫困化地域分异的主导因素有所不同。农村贫困化有其自然和人文根源,科学减贫政策的制定与实施需以深化贫困发生机制理论认知为基础,协同推进区域性贫困和个体性贫困的减轻,从而实现贫困地区转型发展和乡村振兴。     

上海市风险投资企业的空间分布与“技术—资本”地理邻近性

风险投资企业与“标的”产业、金融行业存在紧密关联,表现为“技术—资本”的耦合性联系。该特征如何映射在空间上,尤其是经济密度较高的城市内部空间,对于理解区位的重要性及其空间尺度一致性具有启示。以上海市街区为基本研究单元,选取2005年和2015年两个时间截面,运用核密度估计、时序稳定性“热点”和空间Tobit模型分析方法,从中外资比较视角探讨风险投资企业的空间分布与“技术—资本”地理邻近性。研究表明：① 中外资企业分别呈现单中心和多中心集聚格局,以及“面域扩散”和“点域扩散”趋势,并均表现出邻近“技术”与“资本”的分布特征;② 风险投资企业的“资本”地理邻近性超越“技术”地理邻近性,并在“技术”地理邻近性中,风险投资企业与高科技产业的地理邻近关系超越新创企业。同时,中外资企业的“技术—资本”地理邻近性差异主要体现在,中资企业的“资本”地理邻近性强于外资企业,外资企业的“技术”地理邻近性偏重于“技术”中的创业层面,而中资企业则偏重于“技术”中的高科技产业层面。③ 随时间的变化,中外资企业具有不同的区位模式偏好及时间演变特征,但“资本”地理邻近型、“技术”地理邻近型、“技术—资本”地理邻近型和自身集聚型这四类典型区位模式始终稳定存在。研究认为促进区位内部企业间的多维邻近性和互动联系,对基于“知识和风险资本”的创新集群和创新空间发展具有重要的实践意义。     

Using kernel density estimation to assess the spatial pattern of road density and its impact on landscape fragmentation

Road density (i.e., km/km²) is a useful broad index of the road network in a landscape and has been linked to several ecological effects of roads. However, previous studies have shown that road density, estimated by grid computing, has weak correlation with landscape fragmentation. In this article, we propose a new measure of road density, namely, kernel density estimation function (KDE) and quantify the relation between road density and landscape fragmentation. The results show that road density estimated by KDE (km/km²) elucidates the spatial pattern of the road network in the region. Areas with higher road density are dominated by a larger proportion of built-up landscape and less possession of forest and vice versa. Road networks segregated the landscape into smaller pieces and a greater number of patches. Furthermore, Spearman rank correlation model indicates that road density (km/km²) is positively related to landscape fragmentation. Our results suggest that road density, estimated by KDE, may be a better correlate with effects of the road on landscape fragmentation. Through KDE, the regional spatial pattern of road density and the prediction of the impact of the road on landscape fragmentation could be effectively acquired.     

Exploring spatiotemporal clusters based on extended kernel estimation methods

We examined three different ways to integrate spatial and temporal data in kernel density estimation methods (KDE) to identify space–time clusters of geographic events. Spatial data and time data are typically measured in different units along respective dimensions. Therefore, spatial KDE methods require special extensions when incorporating temporal data to detect spatiotemporal clusters of geographical event. In addition to a real-world data set, we applied the proposed methods to simulated data that were generated through random and normal processes to compare results of different kernel functions. The comparison is based on hit rates and values of a compactness index with considerations of both spatial and temporal attributes of the data. The results show that the spatiotemporal KDE (STKDE) can reach higher hit rates while keeping identified hotspots compact. The implementation of these STKDE methods is tested using the 2012 crime event data in Akron, Ohio, as an example. The results show that STKDE methods reveal new perspectives from the data that go beyond what can be extracted by using the conventional spatial KDE.     

基于随机森林和时空核密度方法的不同周期犯罪热点预测对比

犯罪预测对于制定警务策略、实施犯罪防控具有重要意义。机器学习和核密度是2类主流犯罪热点预测方法,然而目前还鲜有研究对这2类方法在不同时间周期下的犯罪预测效果进行系统比较,本文试图对此进行补充。本文以2013-2016年5月的公共盗窃犯罪历史数据作为输入,分别对比了在接下来2周、1个月、2个月、3个月4个不同时间周期随机森林方法与基于时空邻近性的核密度方法的犯罪热点预测效果,结果发现：在各时间周期上,随机森林分类热点预测方法的面积和案件量命中率均比时空核密度方法准确性高;并且2种方法均能有效地识别犯罪热点中的高发区域,其中在较小范围较短时间内随机森林识别热点中的高发区效率更高,而在较大范围较长时间周期上时空核密度方法识别高发区更优。     

基于时空行为视角的犯罪地理创新研究框架

犯罪地理研究与时空行为领域的研究有着共同的理论基础,二者都强调时空间环境与人类行为的相互作用。犯罪行为是人类特殊行为的一种,相关实证前沿已开始重视不同犯罪相关主体（犯罪者、潜在受害者、警察等）时空行为规律的挖掘、日常活动的动态衡量及不同主体行为的交互等方面对犯罪发生的影响;时空行为研究中的活动空间、环境暴露、群体分异、活动交互等维度的成果可拓展至犯罪地理研究。近年来,两大领域的前沿成果不断涌现,但目前仍缺乏从时空行为视角对犯罪地理学研究进行系统梳理,并对两者的融合创新进行深入探讨。为此,本研究在梳理犯罪地理中时空行为的理论基础和评述犯罪者、潜在受害者、警察时空活动对犯罪时空格局的影响研究基础上,基于犯罪地理和时空行为领域的交叉维度构建犯罪时空行为创新研究的框架,尝试推进该交叉领域在理论、方法与应用等方面的发展。     

Simulating visit probability distributions within planar space-time prisms

The space-time prism is key concept in time geography and moving objects databases; it demarcates all locations that a mobile object can occupy given anchor locations and times and a maximum velocity for travel. Although the prism’s spatial and temporal extent is widely applied as a measure of accessibility and object locational uncertainty, until recently little attention has been paid to the properties of the prism interior such as the probabilities of the object visiting different locations within the prism. Better understanding of the visit probability distribution within the prism can improve theoretical understanding as well as refine the prism as a practical measure of space-time accessibility and object uncertainty. This paper presents two methods for modeling the distribution of visit probabilities within planar space-time prisms: (1) a directed Random Walk method for discrete space and time, and (2) a truncated Brownian Bridges method for continuous space and time. We illustrate these methods and demonstrate the effect of prism and mobility parameters on the visit probability distributions within the prism.     

A GPU-accelerated adaptive kernel density estimation approach for efficient point pattern analysis on spatial big data

Kernel density estimation (KDE) is a classic approach for spatial point pattern analysis. In many applications, KDE with spatially adaptive bandwidths (adaptive KDE) is preferred over KDE with an invariant bandwidth (fixed KDE). However, bandwidths determination for adaptive KDE is extremely computationally intensive, particularly for point pattern analysis tasks of large problem sizes. This computational challenge impedes the application of adaptive KDE to analyze large point data sets, which are common in this big data era. This article presents a graphics processing units (GPUs)-accelerated adaptive KDE algorithm for efficient spatial point pattern analysis on spatial big data. First, optimizations were designed to reduce the algorithmic complexity of the bandwidth determination algorithm for adaptive KDE. The massively parallel computing resources on GPU were then exploited to further speed up the optimized algorithm. Experimental results demonstrated that the proposed optimizations effectively improved the performance by a factor of tens. Compared to the sequential algorithm and an Open Multiprocessing (OpenMP)-based algorithm leveraging multiple central processing unit cores for adaptive KDE, the GPU-enabled algorithm accelerated point pattern analysis tasks by a factor of hundreds and tens, respectively. Additionally, the GPU-accelerated adaptive KDE algorithm scales reasonably well while increasing the size of data sets. Given the significant acceleration brought by the GPU-enabled adaptive KDE algorithm, point pattern analysis with the adaptive KDE approach on large point data sets can be performed efficiently. Point pattern analysis on spatial big data, computationally prohibitive with the sequential algorithm, can be conducted routinely with the GPU-accelerated algorithm. The GPU-accelerated adaptive KDE approach contributes to the geospatial computational toolbox that facilitates geographic knowledge discovery from spatial big data.     

Uncertainty analysis of space–time prisms based on the moment-design method

Space–time prism (STP) is an important concept for the modeling of object movements in space and time. An STP can be conceptualized as the result of the potential path of a moving object revolving around in the three-dimensional space. Though the concept has found applications in time geography, research on the analysis and propagation of uncertainty in STPs, particularly under high degree of nonlinearity, is scanty. Based on the efficiency and effectiveness of the moment-design (M-D) method, this paper proposes an approach to deal with nonlinear error propagation problems in the potential path areas (PPAs) of STPs and their intersections. Propagation of errors to the PPA and its boundary, and to the intersection of two PPAs is investigated. Performance of the proposed method is evaluated via a series of experimental studies. In comparison with the Monte Carlo method and the implicit function method, simulation results show the advantages of the M-D method in the analysis of error propagation in STPs.     

Modelling potential movement in constrained travel environments using rough space–time prisms

The widespread adoption of location-aware technologies (LATs) has afforded analysts new opportunities for efficiently collecting trajectory data of moving individuals. These technologies enable measuring trajectories as a finite sample set of time-stamped locations. The uncertainty related to both finite sampling and measurement errors makes it often difficult to reconstruct and represent a trajectory followed by an individual in space–time. Time geography offers an interesting framework to deal with the potential path of an individual in between two sample locations. Although this potential path may be easily delineated for travels along networks, this will be less straightforward for more nonnetwork-constrained environments. Current models, however, have mostly concentrated on network environments on the one hand and do not account for the spatiotemporal uncertainties of input data on the other hand. This article simultaneously addresses both issues by developing a novel methodology to capture potential movement between uncertain space–time points in obstacle-constrained travel environments.     

日常生活数字化转向的时间地理学应对

信息和通信技术(ICTs)对日常生活产生重要影响,而时间地理学及时应对了日常生活的数字化转向。论文梳理了日常生活数字化转向的基本特征,包括破碎化、多任务与时空替代等方面。相关实证研究试图回答ICTs是否以及如何改变日常生活,但是忽略了对其内在机制的阐释。时间地理学应对日常生活数字化转向的最新研究包括理论扩展与可视化及分析工具发展等方面,这为理解与刻画日常生活的数字化转向提供了新的理论框架与有效工具。研究发现,首先,经典时间地理学的理论和概念可扩展应用于虚拟空间中的行为。时空间去耦合是日常生活数字化转向的内在机制。其次,基于GIS开发了物质—虚拟混合空间行为的可视化和测度方法。最后,新时间地理学对日常活动复杂情境的分析直观揭示了日常生活数字化转向的时空去耦合机制与破碎化、多任务、时空替代等基本特征,并提出前后台活动、ICT介导的活动束、ICT介导的秩序口袋等理解移动ICTs影响下日常活动转型的创新概念。最后,以时间地理学为基础,提出以实虚活动系统为核心的日常生活数字化转向研究框架,并从数字生活、智慧社会和安全城市等方面讨论未来研究议题。