HQ-Tree： A Distributed Spatial Index Based on Hadoop

In this paper,we propose a novel spatial data index based on Hadoop:HQ-Tree.In HQ-Tree,we use PR QuadTrec to solve the problem of poor efficiency in parallel processing,which is caused by data insertion order and space overlapping.For the problem that HDFS cannot support random write,we propose an updating mechanism,called "Copy Write",to support the index update.Additionally,HQ-Tree employs a two-level index caching mechanism to reduce the cost of network transferring and I/O operations.Finally,we develop MapReduce-based algorithms,which are able to significantly enhance the efficiency of index creation and query.Experimental results demonstrate the effectiveness of our methods.

HPSIN: a new hybrid P2P spatial indexing network

Geographic information system （GIS） is increasingly managing very large sets of data, hence a centralized data index may not always provide the most scalable solution. Recently, the peer to peer （P2P） networks have become very popular for sharing information in a totally decentralized manner. In this paper, a new hybrid P2P spatial indexing network （HPSIN） is proposed, which combines distributed quad-tree with distributed Hash table （DHT） based Chord network to maintain both query efficiency and system load balance. In addition, a simple theoretical model based on opened queueing network for HPSIN is established. Assuming each peer as M/M/1 queueing processor in the model, fundamental characteristics of the system is captured, and expression of average query delay is obtained in close form. The theoretical analysis and numerical computing results show that there exists an optimum point of tradeoff between efficiency and load balance. By setting a proper value of start index level ls for different network scale and query rate, HPSIN will achieve the minimum overall query delay, therefore, can adapt to different P2P application environments.

2-Level R-tree Index Based on Spatial Grids and Hilbert R-tree

Multi-level spatial index techniques are always used in large spatial databases. After a general survey of R-tree relevant techniques, this paper presents a novel 2-level index structure, which is based on the schemas of spatial grids, Hilbert R-tree and common R-tree. This structure is named H2R-tree, and it is specifically suitable for the indexing highly skewed, distributed, and large spatial database. Algorithms and a sample are given subsequently.

ANALYSIS OF HABITAT PATTERN CHANGE OF RED-CROWNED CRANES IN THE LIAO HE DELTA USING SPATIAL DIVERSITY INDEX

Habitat pattern change of red-crowned cranes (Grus japonensis) in t he Liaohe Delta between 1988 and 1998 was analyzed with the help of Spatial Dive rsity Index based on remote sensing data and field investigation. The result sho wed that the influence from human activities on the wetland habitat of red-crow ned cranes was prominent with the development of oil and agricultural exploitati on, and the habitat pattern of red-crowned cranes had been obviously changed by the human disturbance during the ten years. The areas with high Spatial Diversi ty values (SD≥0.65) and that with mid-high values (0.5≤SD< 0.65), which const ituted the main part of suitable habitat of red-crowned cranes,had reduced to 9142ha and 5576ha respectively, with the shrinking of natural land cover, such a s reed and Suaeda community. The habitat pattern became more fragmented, which w as caused by roads and wells during oil exploration. It was indicated that the s uitability and quality of habitat for red-crowned cranes in the Liaohe Delta we re degraded in the last decade. The results also showed that diversity index cou ld reflect the habitat suitability of red-crowned cranes quantitatively and des cribe the spatial pattern of the habitat explicitly. This study will provide a s cientific basis for habitat protection of red-crowned cranes and other rare spe cies in wetlands.

Spatial-temporal Analysis of Daily Air Quality Index in the Yangtze River Delta Region of China During 2014 and 2016

Urban air pollution is a prominent problem related to the urban development in China, especially in the densely populated urban agglomerations. Therefore, scientific examination of regional variation of air quality and its dominant factors is of great importance to regional environmental management. In contrast to traditional air pollution researches which only concentrate on a single year or a single pollutant, this paper analyses spatiotemporal patterns and determinants of air quality in disparate regions based on the air quality index(AQI) of the Yangtze River Delta region(YRD) of China from 2014 to 2016. Results show that the annual average value of the AQI in the YRD region decreases from 2014 to 2016 and exhibit a basic characteristic of ‘higher in winter, lower in summer and slightly high in spring and autumn'. The attainment rate of the AQI shows an apparently spatial stratified heterogeneity, Hefei metropolitan area and Nanjing metropolitan area keeping the worst air quality. The frequency of air pollution occurring in large regions was gradually decreasing during the study period. Drawing from entropy method analysis, industrialization and urbanization represented by per capita GDP and total energy consumption were the most important factors. Furthermore, population agglomeration is a factor that cannot be ignored especially in some mega-cities. Limited to data collection, more research is needed to gain insight into the spatiotemporal pattern and influence mechanism in the future.

A Novel Hybrid Method for Measuring the Spatial Autocorrelation of Vehicular Crashes: Combining Moran’s Index and Getis-Ord G_i<sup style='margin-left:-7px;'>*</sup>Statistic

Spatial autocorrelation is a measure of the correlation of an observation with other observations through space. Most statistical analyses are based on the assumption that the values of observations are independent of one another. Spatial autocorrelation violates this assumption, because observations at near-by locations are related to each other, and hence, the consideration of spatial autocorrelations has been gaining attention in crash data modeling in recent years, and research have shown that ignoring this factor may lead to a biased estimation of the modeling parameters. This paper examines two spatial autocorrelation indices: Moran’s Index;and Getis-Ord Gi* statistic to measure the spatial autocorrelation of vehicle crashes occurred in Boone County roads in the state of Missouri, USA for the years 2013-2015. Since each index can identify different clustering patterns of crashes, therefore this paper introduces a new hybrid method to identify the crash clustering patterns by combining both Moran’s Index and Gi*?statistic. Results show that the new method can effectively improve the number, extent, and type of crash clustering along roadways.

A Methodology for Estimating Leaf Area Index by Assimilating Remote Sensing Data into Crop Model Based on Temporal and Spatial Knowledge

In this paper,a methodology for Leaf Area Index(LAI) estimating was proposed by assimilating remote sensed data into crop model based on temporal and spatial knowledge.Firstly,sensitive parameters of crop model were calibrated by Shuffled Complex Evolution method developed at the University of Arizona(SCE-UA) optimization method based on phenological information,which is called temporal knowledge.The calibrated crop model will be used as the forecast operator.Then,the Taylor′s mean value theorem was applied to extracting spatial information from the Moderate Resolution Imaging Spectroradiometer(MODIS) multi-scale data,which was used to calibrate the LAI inversion results by A two-layer Canopy Reflectance Model(ACRM) model.The calibrated LAI result was used as the observation operator.Finally,an Ensemble Kalman Filter(EnKF) was used to assimilate MODIS data into crop model.The results showed that the method could significantly improve the estimation accuracy of LAI and the simulated curves of LAI more conform to the crop growth situation closely comparing with MODIS LAI products.The root mean square error(RMSE) of LAI calculated by assimilation is 0.9185 which is reduced by 58.7% compared with that by simulation(0.3795),and before and after assimilation the mean error is reduced by 92.6% which is from 0.3563 to 0.0265.All these experiments indicated that the methodology proposed in this paper is reasonable and accurate for estimating crop LAI.

Integration of Geological, Geophysical and Seismological Data for Seismic Hazard Assessment Using Spatial Matching Index

Probabilistic seismic hazard assessment (PSHA) takes into account as much data as possible for defining the initial seismic source zone model. In response to this, an algorithm has been developed for integration of geological, geophysical and seismological data through a spatial index showing the presence or absence of a potential seismic source feature in the input data. The spatial matching index (SMI) is calculated to define the coincidence of independent data showing any indications for existence of a fault structure. It is applied for hazard assessment of Bulgaria through quantification of the seismic potential of 416 square blocks, 20 × 20 km in size covering the entire territory of Bulgaria and extended by 20 km outside of the country borders. All operations are carried out in GIS environment using its capabilities to work with different types of georeferenced spatial data. Results show that the highest seismic potential (largest SMI) is observed in 56 block elements (13% of the territory) clearly delineating cores of the source zones. Partial match is registered in 98 block elements when one of the features is missing. Not any evidence for earthquake occurrence is predicted by our calculation in 117 elements, comprising 28% of the examined area. The quantitative parameter for spatial data integration which is obtained in the present research may be used to analyze information regardless of its type and purpose.

Prediction method for risks of coal and gas outbursts based on spatial chaos theory using gas desorption index of drill cuttings

Based on the evolution of geological dynamics and spatial chaos theory, we proposed the advanced prediction an advanced prediction method of a gas desorption index of drill cuttings to predict coal and gas outbursts. We investigated and verified the prediction method by a spatial series data of a gas desorption index of drill cuttings obtained from the II3112 coal roadway at the Shitai Mine. Our experimental results show that the spatial distribution of the gas desorption index of drill cuttings has some chaotic characteristics, which implies that the risk of coal and gas outbursts can be predicted by spatial chaos theory. We also found that a proper amount of sample data needs to be chosen in order to ensure the accuracy and practical maneuverability of prediction. The relative prediction error is small when the prediction pace is chosen carefully. In our experiments, it turned out that the optimum number of sample points is 80 and the optimum prediction pace 30. The corresponding advanced prediction pace basically meets the requirements of engineering applications.

黄河流域河南段水资源空间均衡研究

基于水资源负载指数、水土资源匹配系数和用水效益系数等测算模型,研究了黄河流域河南段2011—2020年水资源空间均衡性.结果表明,黄河流域河南段8市水资源负载指数大致呈波动增长趋势且远大于10,水资源利用程度很高,基尼系数为0.31;水土资源匹配系数大致呈波动下降趋势且平均值为0.33,水土匹配程度较低,基尼系数为0.28;用水效益系数呈稳步上升趋势,单方水GDP产值为208元,用水效益与经济发展水平相对合理,基尼系数为0.32;水资源空间均衡系数平均值为0.55,空间均衡等级为Ⅲ级,属于一般失衡.基于此,合理配置水资源、提高用水效率、提前规划引水调水等有利于促进黄河流域河南段社会经济和水资源利用空间均衡发展.

江南华南茶树高温热害等级指标及分布特征

利用1961—2022年江南和华南茶区510个气象站的日最高气温数据和历史茶树高温热害灾情数据,采用灾情反演和K-Means聚类分析方法,构建并验证江南和华南茶区茶树高温热害等级指标,分析茶树高温热害时空分布特征。结果表明:江南和华南茶区茶树轻度、中度、重度高温热害指标为连续14 d日最高气温的滑动平均值T14≥34.5℃的持续日数分别为1~17 d、18~38 d和超过38 d,验证样本完全符合的准确率为73.9%,基本符合的准确率为91.3%;江南和华南茶区茶树高温热害总次数呈波动变化,分别在1999年和1997年达到最低值,并在2021年达到最高值;华南茶区相对于江南茶区高温热害次数更多,尤其是轻度茶树高温热害,且近62年华南茶区茶树高温热害次数增加趋势显著。

中国农村碳排放强度时空演变趋势及影响因素分析

运用碳排放系数法测算中国农村碳排放量,结合核密度估计法和莫兰指数对农村碳排放强度时空演变趋势及空间关联特征进行分析,借助STIRPAT模型和灰色预测模型定量定性分析其影响因素并做出预测。结果显示:2010—2020年中国农村碳排放强度整体呈下降趋势,但农村人均碳排放量呈上升趋势;中国区域内部存在多极化现象,空间正相关性逐渐加强,具有集聚和依赖性特征。农业生产中畜牧业是农村碳排放主要影响因素,预计未来农村碳排放强度将持续下降,农村人均碳排放量将逐步上升。

中国创新型产业集群创新效率评价与空间格局分析

基于DEA-BCC模型和Malmquist指数模型,从静态和动态两方面对我国创新型产业集群创新效率进行综合评价,研究结果表明:我国创新型产业集群创新效率水平有所下降,仍有较大进步空间,且存在两极分化的现象.较多省(区、市)处于纯技术有效规模无效的DEA弱有效状态,存在投入产出不匹配问题.考察期内全要素生产率变化指数整体呈“N”型上升趋势,主要得益于技术进步的拉动作用,纯技术效率与规模效率共同起了阻碍作用.区域间及区域内创新型产业集群发展存在不平衡性,东部地区增长最快;其次为西部地区,中部、东北相对落后.从空间分布看,全国创新型产业集群创新效率表现出由南北向内陆逐步递减的分布格局,创新效率较高的省(区、市)成片分布在东南、西南、西北地区,空间非均衡性呈现出先缩小后扩大的趋势.

磨万铁路站前广场尺度研究

铁路站前广场作为重要的集散空间,在交通组织及站城融合发展中具有重要作用,合理确定站前广场设计尺度,有利于节约土地资源,提高交通接驳效率并创造宜人的城市开放空间。结合磨万铁路10个站前广场工程实例,运用数据统计、对比分析、相关性分析等方法,开展了站前广场布局模式、面积指标、空间尺度等研究。研究结果表明:(1)站前广场的布局和尺度受站房及场地条件影响较大,站区规划时宜综合考虑站前广场的布局;(2)站前广场各部分用地中,车行道、停车场、集散广场3项占比最大,表明交通疏解是一般中小型站前广场的主要功能;(3)磨万铁路站前广场旅客专用场地面积指标普遍大于规范规定的最小面积指标,且呈现出随车站规模增大而逐步减小的趋势;(4)站前广场宽度与长度比一般趋向于黄金分割比,站房场坪长度与站前广场长度比趋向于1∶1,站房高度与站前广场宽度的高宽比在1∶4~1∶2之间。

基于景观指数的关中地区耕地细碎化程度的时空变迁及其驱动因素

[目的]优化陕西耕地细碎化治理,协调粮食需求增长与环境约束之间的矛盾,维护地区粮食安全和促进农业现代化发展。[方法]以关中地区为研究区,以土地利用数据和其他自然经济数据为基础,运用洛伦兹曲线、变异系数法、景观指数、空间自相关等方法构建耕地细碎化评价模型以探索关中地区耕地细碎化(Cultivated Land Fragmentation,简称CLF)的时空变迁,并利用地理探测器对区内CLF进行了驱动力分析。[结果](1)关中地区耕地资源空间分布相对均衡,耕地专业化程度保持较高水平。(2)关中地区的CLF程度变化时空差异显著,CLF程度整体呈现下降态势,但部分地区CLF程度呈增减并存趋势,整体呈现东低西高的态势。(3)关中地区CLF时空分化受多种因素影响,社会经济因素的综合影响明显强于自然因素,其中海拔高度、人口密度、人均耕地面积和地区总产值GDP为主要影响因素;因子间的交互作用效应强于单因子,以双因子增强和非线性增强为主。[结论]关中地区耕地细碎化问题在20年间得到了一定程度的改善,但呈现出较大的地区差异,应进一步在社会经济因素方面对耕地细碎化现象加强综合治理,以推动关中地区农业机械化、现代化发展。

全国能源消费结构低碳化的空间非均衡性及动态演进

剖析中国能源消费结构低碳化趋势、衡量其空间非均衡状态是推进能源革命及绿色发展的关键环节。文章根据2003—2020年我国各省市能源消费的面板数据,利用泰尔指数和空间自相关法,探究全国能源消费结构低碳化的空间非均衡性及动态演进特征。结果显示:1)我国能源消费结构低碳化指数总体呈稳步上升的趋势,由2003年的5.298上升至2020年的5.862;2)全国能源消费结构低碳化分布的空间非均衡性较弱,呈现先升后降态势,即泰尔指数总体呈现先上升后下降发展态势,区域间能源消费结构低碳化的差异对总体差异的影响越来越大;3)全国能源消费结构低碳化存在明显的空间正相关,空间集聚效应现象,且近年来总体空间格局分布变化较小。江浙沪地区始终呈现高高聚集的特征,而西南和华中地区则处于弱弱聚集。根据分析结果,提出构建区域利益共同体,通过技术交流等多种方式互惠互助、合作共赢,共同促进能源低碳化发展;推动产业结构高级化,激发社会创造力和市场活力,破解落后高耗能产业的模式锁定和路径依赖;优化能源产业结构,积极推进能源消费转型等政策建议。

顾及多级空间索引结构的矢量地图局部加密方法

随着地理信息产业的蓬勃发展,地图与各类生活服务深度融合,地图从专业化应用逐步走向大众化应用。大数据时代的到来,为地图数据的分发、共享、获取与动态更新等提供了极大便利,但伴随而来的是地图数据传输过程中面临的信息泄露、窃取等安全隐患问题也日益严峻。现有的矢量地图加密方法大多是在经典密码学算法基础上扩展而来,虽然已有加密方法考虑到矢量地图的特征,但是现有方法大多仍以整个地图或图层为加密单元,在用户需要访问某个局部的感兴趣区域时需要对整个地图文件或图层进行解密,对于矢量地图的按需局部加密仍未提出有效方案,还难以满足大数据时代地图按需用图、众包地图数据采集与云端更新等应用场景对矢量地图局部加密的应用需求。为此,本文从矢量地图的数据结构特点出发,提出了一种顾及多级空间索引的矢量地图局部加密方法。通过点、线、面不同矢量地图数据的加密试验分析,结果表明:①本文方法可以有效实现矢量地图的局部加解密;②相比于现有加密方法,本文方法采用局部加密策略,且支持多级分块并行加密,具有更高的加密效率;③本文针对不同加密单元采用不同加密参数,显著提高了加密算法的抗攻击能力。

喀斯特山地城市景观格局对城市内涝的影响研究——以贵阳市为例

全球气候变化背景下,随着城镇化高速发展,城市内涝灾害愈加明显,已成为造成喀斯特地区损失较为严重的灾害之一。选择西南喀斯特山地城市——贵阳市为研究区,基于2016—2020年实测和调查的内涝灾害数据,运用空间自相关和相关分析方法,探究景观格局对城市内涝的影响。结果表明:1)2016—2020年贵阳市主城区内涝频发,且空间分布差异性显著,呈现显著的空间自相关和集聚分布特征。2)不透水面、绿地与内涝频次的相关性显著,分别呈正、负相关。3)内涝周边500 m缓冲区内景观由少数聚集程度大的斑块构成,优势度较高,景观中的优势斑块类型仅有一种或少量几种;不同土地利用类型的景观格局空间聚集情况差异显著,其中铁路与道路、房屋建筑的景观指数空间聚类较为显著。4)内涝与土地利用类型面积的关系不显著,但与土地利用类型的景观指数有显著相关性,且不同土地利用景观格局对内涝的影响差异较大。研究结果为喀斯特城市内涝防治和景观格局规划管理提供重要参考和借鉴意义。

滨海城市灾害韧性的时空演化特征研究--以深圳为例

近年来,我国自然灾害频发,特别是广东沿海一带的滨海城市频繁遭遇自然灾害的袭击,给滨海地区人民带来巨大的生命威胁和财产损失。该文以深圳市为研究对象,依托深圳市1980年—2019年的面板数据,从经济韧性指数、社会韧性指数、生态韧性指数和工程韧性指数四个维度,构建深圳市灾害韧性评价指标体系,运用极值熵值法等评价方法,研究深圳市近四十年灾害韧性的时空演变特征,包括不同维度、不同指标的韧性高低情况、韧性趋势变化。研究结果表明:①深圳市整体灾害韧性呈现出上升趋势。②深圳市灾害韧性的空间分布不均衡,中等韧性区的数量最多,低韧性和较低韧性区的数量基本维持不变,较高韧性和高韧性区的数量较少。③深圳市各区的LISA集聚情况较明显。

省域共同富裕水平测度、差异特征与空间收敛分析——基于PROMETHEE评价法的广东省城市数据研究

共同富裕是社会主义的本质要求,对于整体发展水平高且内部板块发展差距大的省市进行共同富裕水平评估与分析具有重要的示范意义.在基于三生空间理论重新解构共同富裕指标体系的基础上,利用PROMETHEE评价法、泰尔指数、收敛模型对广东省共同富裕水平进行实证分析.结果表明:广东省共同富裕水平处于上升态势,整体上呈现由珠三角向外围降低的半圆形空间格局,粤东、粤西、粤北的多数城市处于低低集聚区;广东省共同富裕水平的提升具有区域不均衡性,差距主要来源于区域间,但差距呈逐渐减小趋势,同时粤东、粤西组内差距明显;广东省共同富裕水平存在σ收敛、绝对与条件β收敛,4个区域的绝对β收敛速度与条件β收敛速度的位序格局具有迥异性,省域内部板块间追赶效应显著;广东省城市间共同富裕水平存在显著的正空间相关性,产业结构与基础设施对周边城市共同富裕水平具有提升作用,而对外开放程度抑制邻域城市共同富裕水平提升.基于上述结论,本文提出培育不同层级增长极+强化区域网络关联度,抑制负向作用+强化正向作用(外生变量),以此促进广东省尽快实现共同富裕的目标.