Cardinal directions: a comparison of direction relation matrix and objects interaction matrix

How to express and reason with cardinal directions between extended objects such as lines and regions is an important problem in qualitative spatial reasoning (QSR), a common subfield of geographical information science and Artificial Intelligence (AI). The direction relation matrix (DRM) model, proposed by Goyal and Egenhofer in 1997, is one very expressive relation model for this purpose. Unlike many other relation models in QSR, the set-theoretic converse of a DRM relation is not necessarily representable in DRM. Schneider et al. regard this as a serious shortcoming and propose, in their work published in ACM TODS (2012), the objects interaction matrix (OIM) model for modelling cardinal directions between complex regions. OIM is also a tiling-based model that consists of two phases: the tiling phase and the interpretation phase. Although it was claimed that OIM is a novel concept, we show that it is not so different from DRM if we represent the cardinal direction of two regions a and b by both the DRM of a to b and that of b to a. Under this natural assumption, we give methods for computing DRMs from OIMs and vice versa, and show that OIM is almost the same as DRM in the tiling phase, and becomes less precise after interpretation. Furthermore, exploiting the similarity between the two models, we prove that the consistency of a complete basic OIM network can be decided in cubic time. This answers an open problem raised by Schneider et al. regarding efficient algorithms for reasoning with OIM.     

2003-2017年北京市地表热力景观时空分异特征及演变规律

利用2003-2017年MODIS地表温度数据,分别从数量、形状和结构角度揭示北京市不同季相和昼夜间地表热力景观时空分异特征,并进一步通过热力等级变化图谱及质心迁移轨迹揭示城市热力景观空间演变规律,探究热力景观等级转换生态过程。结论如下：① 城市地表热力景观季节和昼夜空间特征差异显著;② 中温区在城市热环境中占主导地位。白天中温区是最不稳定的热力景观等级;夜间次低温区和次高温区不稳定性增加;③ 地表热力景观等级变化以稳定型占主,反复变化型和前期变化型次之。地表热力景观等级通常呈现逐级递增或递减规律,山区呈现逐级降温趋势,北部城郊—山地交错地带表现出次低温和中温的反复转向,南部地区有一定的升温趋势;④ 2003-2017年高温区面积增大且质心向城市中心集中,低温区质心向城市外围扩散。生态涵养发展区对北京市地表热力景观质心迁移贡献率最高。热力景观时空分异特征及演变规律可为有效缓解城市热岛效应提供管理决策支持。     

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.     

Rangeland production: use of models incorporating aggregated knowledge and fuzzy construction

This work reports on a method using fuzzy membership functions to construct an aggregated interaction matrix in which the summation of variables is scaled according to the way rainfall and soil variables affect water availability to plants and hence influence rangeland productivity. Aggregation of the variables gives a comprehensive value which can be used to predict production. The model increases the predicability of production to 81% compared to models using rainfall alone and a multiplicative parametric one which give predictibilities of 61 and 76% respectively. The results showed that (1) the importance of rainfall in determining production was most important at lower rainfalls i.e. <350 mm; (2) soil texture and particularly slope were important through out the rainfall range (149–700 mm) investigated; and (3) soil depth was only important at the higher >350 mm rainfalls. The aggregated interaction matrix gives a measure of land productive capability.     

武汉市碳排放的测算及影响因素分解研究

研究基于14个主要方面碳源,测算了武汉市1996—2009年碳排放量。发现自1996年以来武汉市碳排放呈现"平稳—上升—平稳"的三阶段特征。进一步基于LMDI模型分解碳排放的影响因素,结果表明,能源结构、能源效率因素对碳排放量具有一定抑制作用,但效果不够显著且波动性较强,1997—2009年与基期相比,能源结构、能源效率因素分别累计实现了0.95%,11.30%的碳减排;而经济因素与人口规模因素则对碳排放具有较强推动作用,分别累计产生了64.65%,19.65%的碳增量。最后,据此提出推进武汉市碳减排的对策建议。     

毛乌素沙地臭柏、油蒿细根分解过程中养分与能量的变化

应用埋袋法对毛乌素沙地天然分布的优势种臭柏、油蒿细根分解进行研究。对两年的实验数据进行分析,结果显示,臭柏、油蒿细根分解过程中C、P、K、Mg均表现为释放,释放率油蒿＞臭柏,元素重量损失率K＞P＞C＞Mg;N在臭柏分解中表现为积累,在油蒿分解中表现为释放;Ca在臭柏、油蒿细根分解中都表现为积累的趋势,积累率为油蒿＞臭柏;能量在臭柏、油蒿细根分解过程中表现为释放。该研究为沙区土壤库中的能量转化和流动过程提供了理论依据。     

REVIEWS OF GEOGRAPHIC SOFTWARE

Geographic Software Reviewed in this article: LIFE TABLES and the LESLIE MATRIX. CONDUIT MENTMAP2 . Lawrence W. Carstensen Jr . PC-MATLAB , Version 1.0. The Math Works, Inc. QUICKMAP , version 1.0. Sammamish Data Systems, Inc. SYSTAT: THE SYSTEM FOR STATISTICS , Version 2.1, Systat Inc. USA DISPLAY . Instant Tecall     

不同水淹环境下河口感潮湿地枯落物分解及营养动态

采用分解袋法,以我国东南沿海典型的感潮河口--闽江河口最大的鳝鱼滩湿地为研究区域,选取2个潮水水淹环境差异显著的地段(一个为近潮沟地段,另一个为远潮沟地段)为研究样地,对闽江河口主要的本地种芦苇、短叶茳芏及外来人侵种互花米草枯落物分解过程及影响因素进行研究,结果表明:(1)经常性的潮汐水淹对3种植物枯落物分解速率的影响不显著;(2)经常性的潮汐水淹促进了短叶茳芏和互花米草枯落物N、P元素的损失,但对芦苇的影响不大;(3)在2种不同的水淹环境,3种植物枯落物分解速率由快到慢的顺序均是短叶茳芏>互花米草>和芦苇,分解速率在0.003166～0.005280 d-1之间,95%干物质分解需要的时间在1.56～2.61 a之间;(4)C浓度基本稳定,N浓度呈上升趋势,总N在研究末期表现为净损失,P浓度呈较大的波动,总P在研究末期也表现为净损失;(5)具有最低C/P的短叶茳芏枯落物分解速率最快.     

基于不同窗口纹理特征的SVM土壤盐渍化信息提取方法与精度分析研究

以塔里木盆地北缘绿洲--渭干河-库车河三角洲绿洲为例,借助ENVI遥感软件,利用ETM+数据,探讨了该绿洲土壤盐渍化信息提取的方法.传统的遥感图像分类方法多数在解决问题上存在精度不高、分类效率较低、不确定性强的缺陷,所以,选择好的分类方法对于提取盐渍化信息是至关重要的.近年来,将SVM应用于遥感图像分类已成为新的发展趋势.文章提出了基于纹理特征的支持向量机(Support Vector Machine,SVM)的分类方法,得出以下结论:分别结合3×3,5×5,7×7,9×9,11×11,13×13窗口纹理特征和光谱的SVM分类精度都很高,达到93%以上.并且在验证分类精度时,发现结合光谱和9×9窗口纹理信息的SVM分类的结果更符合实际情况.所以说加入纹理特征后使得光谱信息比较接近的3类地物(重度、中度、轻度盐渍地)的区分性增大,从而使精度提高.因此,基于纹理特征的SVM分类方法更有利于遥感图像分类和盐渍化信息监测,是地物遥感信息提取的有效途径.