Paleofire reconstruction for high-elevation forests in the Sierra Nevada, California, with implications for wildfire synchrony and climate variability in the late Holocene

Here, we present two high-resolution records of macroscopic charcoal from high-elevation lake sites in the Sierra Nevada, California, and evaluate the synchroneity of fire response for east- and west-side subalpine forests during the past 9200 yr. Charcoal influx was low between 11,200 and 8000 cal yr BP when vegetation consisted of sparse Pinus-dominated forest and montane chaparral shrubs. High charcoal influx after ∼ 8000 cal yr BP marks the arrival of Tsuga mertensiana and Abies magnifica, and a higher-than-present treeline that persisted into the mid-Holocene. Coeval decreases in fire episode frequency coincide with neoglacial advances and lower treeline in the Sierra Nevada after 3800 cal yr BP. Independent fire response occurs between 9200 and 5000 cal yr BP, and significant synchrony at 100- to 1000-yr timescales emerges between 5000 cal yr BP and the present, especially during the last 2500 yr. Indistinguishable fire-return interval distributions and synchronous fires show that climatic control of fire became increasingly important during the late Holocene. Fires after 1200 cal yr BP are often synchronous and corroborate with inferred droughts. Holocene fire activity in the high Sierra Nevada is driven by changes in climate linked to insolation and appears to be sensitive to the dynamics of the El Niño-Southern Oscillation.     

基于Ripley’s K函数浙江城市空间格局及其影响分析

以Ripley′s K函数为核心,通过城市区位与规模联立估计、地理权重引入、全局函数分解等手段,研究2001~2010年浙江省已有县级及以上城市空间分布的总体及局部估计问题,探讨城市空间格局与特征,以期揭示边界效应和市场潜力对浙江城市空间格局的影响机理。结果表明:1浙江省城市区位与规模的空间格局不一致,即前者是分散分布而后者是集聚分布,且随着空间尺度的增加,其分散或集聚程度逐步加强。2无论是行政边界还是海岸线,对浙江省城市区位与规模的空间分布演化有一定的负向作用。空间尺度越大,边界负效应越强。3在城市化发展过程中,浙江形成了3个各具产业特色的块状组团式城市聚集区,但边界负效应会抑制它们的发展,此时杭州、宁波将承担起服务全省经济的重要职责。4浙江城市规模因受市场潜力因素的影响会出现空间分布不均衡性。城市规模与市场潜力显著正相关,其程度却随着空间尺度的增加而减小。     

基于Ripley’s K函数的南京市ATM网点空间分布模式研究

运用Ripley’s K函数的相关理论,以南京市ATM网点为研究对象,分别从平面与网络空间两种视角,在中心城区范围与主城区范围两种空间尺度上,通过单变量\begin{array}{c}K\end{array}函数法分析ATM网点的分布模式,通过双变量\begin{array}{c}K\end{array}函数法分析ATM网点与地铁站点的空间关联情况,最后对计算结果进行评价与分析。研究表明,ATM网点在南京主城区与中心城区均呈现出较强的集聚状态;在一定的距离范围内,ATM网点与地铁站点之间也有较强的依赖关系。同时,对于沿着路网分布的地理空间点状对象而言,利用网络\begin{array}{c}K\end{array}函数法进行空间点模式分析比用平面\begin{array}{c}K\end{array}函数法更加符合实际情况。     

Massively parallel spatial point pattern analysis: Ripley’s K function accelerated using graphics processing units

This study presents a massively parallel spatial computing approach that uses general-purpose graphics processing units (GPUs) to accelerate Ripley’s K function for univariate spatial point pattern analysis. Ripley’s K function is a representative spatial point pattern analysis approach that allows for quantitatively evaluating the spatial dispersion characteristics of point patterns. However, considerable computation is often required when analyzing large spatial data using Ripley’s K function. In this study, we developed a massively parallel approach of Ripley’s K function for accelerating spatial point pattern analysis. GPUs serve as a massively parallel platform that is built on many-core architecture for speeding up Ripley’s K function. Variable-grained domain decomposition and thread-level synchronization based on shared memory are parallel strategies designed to exploit concurrency in the spatial algorithm of Ripley’s K function for efficient parallelization. Experimental results demonstrate that substantial acceleration is obtained for Ripley’s K function parallelized within GPU environments.     

Enabling point pattern analysis on spatial big data using cloud computing: optimizing and accelerating Ripley’s K function

Performing point pattern analysis using Ripley’s K function on point events of large size is computationally intensive as it involves massive point-wise comparisons, time-consuming edge effect correction weights calculation, and a large number of simulations. This article presented two strategies to optimize the algorithm for point pattern analysis using Ripley’s K function and utilized cloud computing to further accelerate the optimized algorithm. The first optimization sorted the points on their x and y coordinates and thus narrowed the scope of searching for neighboring points down to a rectangular area around each point in estimating K function. Using the actual study area in computing edge effect correction weights is essential to estimate an unbiased K function, but is very computationally intensive if the study area is of complex shape. The second optimization reused the previously computed weights to avoid repeating expensive weights calculation. The optimized algorithm was then parallelized using Open Multi-Processing (OpenMP) and hybrid Message Passing Interface (MPI)/OpenMP on the cloud computing platform. Performance testing showed that the optimizations effectively accelerated point pattern analysis using K function by a factor of 8 using both the sequential version and the OpenMP-parallel version of the optimized algorithm. While the OpenMP-based parallelization achieved good scalability with respect to the number of CPU cores utilized and the problem size, the hybrid MPI/OpenMP-based parallelization significantly shortened the time for estimating K function and performing simulations by utilizing computing resources on multiple computing nodes. Computational challenge imposed by point pattern analysis tasks on point events of large size involving a large number of simulations can be addressed by utilizing elastic, distributed cloud resources.     

基于Ripley'K函数的延吉市区崩塌地质灾害空间格局研究

崩塌是较为严重的一种地质灾害,研究崩塌体的空间格局有利于政府部门对其做出防治决策。本文基于最新时相的延吉市区高分1号遥感影像,从崩塌地质灾害成灾机理的角度,选取了如植被指数、土壤亮度指数、坡度等主要指标,并结合了前人对崩塌影像光谱特征的研究成果,建立了本次的自动化解译模型,在成功提取崩塌体后,经过栅矢数据转换,选用ArcGIS 10软件的K函数分析模块,进行了崩塌灾点的空间格局分析。     

Mapping seagrass coverage and spatial patterns with high spatial resolution IKONOS imagery

Seagrass habitats in subtidal coastal waters provide a variety of ecosystem functions and services and there is an increasing need to acquire information on spatial and temporal dynamics of this resource. Here, we explored the capability of IKONOS (IKO) data of high resolution (4 m) for mapping seagrass cover [submerged aquatic vegetation (%SAV) cover] along the mid-western coast of Florida, USA. We also compared seagrass maps produced with IKO data with that obtained using the Landsat TM sensor with lower resolution (30 m). Both IKO and TM data, collected in October 2009, were preprocessed to calculate water depth invariant bands to normalize the effect of varying depth on bottom spectra recorded by the two satellite sensors and further the textural information was extracted from IKO data. Our results demonstrate that the high resolution IKO sensor produced a higher accuracy than the TM sensor in a three-class % SAV cover classification. Of note is that the OA of %SAV cover mapping at our study area created with IKO data was 5–20% higher than that from other studies published. We also examined the spatial distribution of seagrass over a spatial range of 4–240 m using the Ripley’s K function [L(d)] and IKO data that represented four different grain sizes [4 m (one IKO pixel), 8 m (2 × 2 IKO pixels), 12 m (3 × 3 IKO pixels), and 16 m (4 × 4 IKO pixels)] from moderate-dense seagrass cover along a set of six transects. The Ripley’s K metric repeatedly indicated that seagrass cover representing 4 m × 4 m pixels displayed a dispersed (or slightly dispersed) pattern over distances of <4–8 m, and a random or slightly clustered pattern of cover over 9–240 m. The spatial pattern of seagrass cover created with the three additional grain sizes (i.e., 2 × 24 m IKO pixels, 3 × 34 m IKO pixels, and 4 × 4 m IKO pixels) show a dispersed (or slightly dispersed) pattern across 4–32 m and a random or slightly clustered pattern across 33–240 m. Given the first report on using satellite observations to quantify seagrass spatial patterns at a spatial scale from 4 m to 240 m, our novel analyses of moderate-dense SAV cover utilizing Ripley’s K function illustrate how data obtained from the IKO sensor revealed seagrass spatial information that would be undetected by the TM sensor with a 30 m pixel size. Use of the seagrass classification scheme here, along with data from the IKO sensor with enhanced resolution, offers an opportunity to synoptically record seagrass cover dynamics at both small and large spatial scales.     

2009年甲型H1N1流感大流行时空分布特征分析

 本文利用球面距离的Ripley'K函数,分析了全球2009年甲型H1N1流感大流行早期疫情的点空间分布模式。同时,通过对比2000-2008年甲型流感病例数据,分析不同纬度国家2009年甲型H1N1流感新增病例数的时间序列特征及其与国家入境人数的相关性。结果表明,2009年甲型H1N1流感大流行早期疫情呈聚类分布,其L函数值最大值区间与65个全球城市的最大值区间相同。78.5%的病例分布在全球城市周围600km半径内。时间序列特征总体上类似于历年甲型流感,但是北回归线以北部分国家在6、7月非甲型流感流行季节仍有大量病例出现。并且北回归线以北国家冬季暴发集中在第45周到第48周之间,早于历年甲型流感流行时间。进一步分析认为,全球城市是本次流感国际传播网络的关键节点。国际旅行是流感传播的重要途径,并在本次流感大流行前期主导着流感跨国传播方向。同时不同纬度的环境条件对2009年甲型H1N1流感大流行有重要影响。     

国有商业银行与股份制商业银行的空间布局特征分析——以南京市江南8区为例

以南京市江南8区380家国有商业银行网点,211家股份制商业银行网点为研究样本数据,综合运用缓冲区分析、Ripley’s K函数和空间热点探测方法,分析了国有商业银行和股份制商业银行网点的空间布局特征。结果表明:两类商业银行均呈现出一定的中心集聚态势,但"向心"程度、集聚的广度与强度存在差异。缓冲区分析表明,股份制商业银行由市中心向外呈现明显递减的特点,但国有商业银行在前3个缓冲圈层内分布比较均匀;Ripley’s K函数方法表明,两类商业银行均呈现先增后减的倒"U"型空间集聚特征,但股份制商业银行集聚范围小于国有商业银行;空间热点探测方法表明,两类商业银行热点区域集聚强度相异,国有商业银行低于股份制商业银行。分析空间热点重叠部位,将国有商业银行与股份制商业银行空间布局导向分为商业中心导向、高新技术-商业中心导向和人力资本-政府机构-商业中心导向。     

基于Hilbert空间分区和Geohash索引的并行Ripley's K函数

作为二阶点模式分析方法,Ripley’s K函数(简称K函数)以距离为自变量探测不同尺度下点事件的分布模式及演变规律,在生态学、经济学、地理学等诸多领域得到广泛应用。然而,随着点规模的增加,估计与模拟阶段点对距离遍历计算时间开销激增,严重制约了K函数的应用,算法流程优化与并行加速成为应对海量点数据下K函数性能瓶颈及可计算性问题的关键技术手段。针对默认数据分区未考虑点事件空间邻近性导致跨节点通讯成本高昂且K函数距离阈值较大时索引优化失效的现象,本文提出一种基于空间填充曲线的K函数优化加速方法。该方法采用Hilbert曲线构建空间分区,在顾及数据空间邻近性的前提下减少分区间数据倾斜和通讯开销;在分区基础上,利用Geohash编码改进各分区内本地空间索引策略加速点对距离计算。本文以湖北省工商企业注册数据为例,通过对比实验分析了默认分区无索引、KDB分区组合R树索引、本文Hilbert分区组合Geohash索引算法在不同数据规模、距离阈值、集群规模下的计算耗时。结果表明,300 000点数据规模下本文方法的时间开销约为默认分区无索引方法的1/4,9台节点下加速比超过3.6倍。因此,该方法能有效...