Distinguishing extensive and intensive properties for meaningful geocomputation and mapping

A most fundamental and far-reaching trait of geographic information is the distinction between extensive and intensive properties. In common understanding, originating in Physics and Chemistry, extensive properties increase with the size of their supporting objects, while intensive properties are independent of this size. It has long been recognized that the decision whether analytical and cartographic measures can be meaningfully applied depends on whether an attribute is considered intensive or extensive. For example, the choice of a map type as well as the application of basic geocomputational operations, such as spatial intersections, aggregations or algebraic operations such as sums and weighted averages, strongly depend on this semantic distinction. So far, however, the distinction can only be drawn in the head of an analyst. We still lack practical ways of automation for composing GIS workflows and to scale up mapping and geocomputation over many data sources, e.g. in statistical portals. In this article, we test a machine-learning model that is capable of labeling extensive/intensive region attributes with high accuracy based on simple characteristics extractable from geodata files. Furthermore, we propose an ontology pattern that captures central applicability constraints for automating data conversion and mapping using Semantic Web technology.     

Spatiotemporal heterogeneity and influencing mechanism of ecosystem services in the Pearl River Delta from the perspective of LUCC

Journal of Geographical Sciences - Land use/land cover change (LUCC) has considerable impact on ecosystem services. It is essential to quantify the spatiotemporal heterogeneity of ecosystem...     

海州湾4种鱼类生长特征的空间异质性

传统的渔业资源评估均假设鱼类的生长参数是匀质的,然而近年来越来越多的研究表明海洋鱼类生长存在空间异质性。为探究海州湾鱼类生长参数的空间异质性现象,本研究分析了2013–2018年海州湾及其邻近海域方氏云鳚(Pholis fangi)、尖海龙(Syngnatus acus)、小黄鱼(Larimichthys polyactis)和赤鼻棱鳀(Thryssa kammalensis)的空间分布,使用电子体长频率分析方法结合Bootstrap重抽样方法估算了这4种鱼类的生长参数及其在深、浅水区域中的差异。结果显示,这4种鱼类生长参数均表现出一定的空间异质性,其中尖海龙和小黄鱼生长参数的空间异质性表现较为明显。这种差异可能是由于空间上的理化条件、群落结构以及物种本身洄游分布的差异而产生的。     

北部湾典型海岛生态系统服务价值空间异质性对比研究

基于北部湾海域典型海岛高分1号数据,借助于CART决策树方法,获取了广西北部湾两个典型海岛的景观格局数据,并采用生态系统服务价值计量模型、空间自相关模型及地统计学中的变异函数模型对其生态系统服务价值进行了空间异质性分析。结果表明:团和岛单位面积生态系统服务价值是七星岛的1.08倍,但其变异系数低于七星岛;七星岛由于其地质背景为泥沙岛,景观格局受到人类活动以及自然环境的强烈干扰,趋向于复杂化和破碎化;而团和岛由于所处的外部环境以及自身的地质特征,景观格局呈现出整体斑块化分布状态;团和岛的景观格局主要受到结构性因素的控制,而七星岛则以随机性因素控制为主,选用变程和Moran’s指数来表征其复杂化和破碎化,来决定其优先开发时序,可对前者进行适当开发,而对后者适当进行生态保护;对受到结构性因素影响的岛屿在进行开发时,时序的识别选用变程和Moran’s指数来表征,若变程和Moran’s指数越小,说明海岛整体的景观分布格局趋向于复杂化和破碎化,反之生态系统较好,可进行优先开发。同时本文构建了北部湾海岛生态系统异质性评估与自然资源管理框架,并提出了海岛管理的建议与对策。     

京津唐地区HFMD时空变异分析与影响因子探测

手足口病（HFMD）是一种多发于儿童的常见传染病。近年来,中国HFMD发病人数逐年上升,疾病疫情也日益受到社会广泛关注。虽已有不少相关研究,但对于探测其时空异质性及量化潜在影响因子解释力的研究仍然较少。本文采用地理探测器及贝叶斯时空层次模型,对2009-2013年京津唐地区HFMD发病率的时空异质性进行分析,并量化各影响因子及其两两交互作用对HFMD发病率的解释力。结果表明：① HFMD的相对风险存在时间异质性,其发病风险在春夏季（5-7月）达到峰值,而冬季（12-次年2月）发病风险最低;② HFMD的相对风险存在空间异质性,在经济发达的地区HFMD发病率较高;③ 影响HFMD发病率时间异质性的主要气象因子为平均温度、累积降水、相对湿度,解释力分别为0.38,0.27,0.13,且交互作用都大于独自影响的作用,如平均温度和相对湿度、平均温度和降水,平均温度和风速两两交互的解释力分别为0.43, 0.40, 0.42。通过研究京津唐地区HFMD发病率的时空异质性以及影响因子的量化状况,为本地区HFMD的预防和控制提供理论依据。     

京津冀地区植被时空动态及定量归因

作为气候变化的敏感指示器,植被的物候、生长、空间分布格局等特征及其动态变化主要取决于气候环境中的水热条件,因此在气候变化背景下,气候-植被关系成为了全球变化研究的前沿和热点问题。本文综合平均温度、降水、水汽压、湿度、日照时数、SPEI等气候因子,坡度、坡向海拔等地形因子及人为活动因子,应用地理探测器方法针对2006-2015年京津冀地区不同季节NDVI、不同地貌类型区、不同植被类型区生长季NDVI的定量归因研究,揭示了过去10年间植被时空分布格局,及植被对气候、非气候因素响应的季节差异与区域差异,以期为生态工程的建设与修复提供参考意义。趋势分析表明：①2006-2015年京津冀地区NDVI呈现增加趋势,但存在显著的空间差异,如山地生长季NDVI的增长速率大于平原、台地、丘陵等地;②基于地理探测器的定量归因结果表明,降水是年尺度上NDVI空间分布的主导因子（解释力39.4%）,土地利用与降水的交互作用对NDVI的影响最为明显（q=58.2%）;③NDVI对气候因子的响应存在季节性及区域性差异,水汽压是春季NDVI空间分布的主导因子,湿度是夏、秋两季的主导因子,土地利用是冬季的主导因子;④影响因子对生长季NDVI的解释力因不同地貌类型区、不同植被类型区而差异显著。     

语义因子支持的多源POI分类信息一致化处理

为了解决网络POI分类异构导致多源POI数据匹配与整合困难的问题,该文利用分类特征词提取、特征词去重与优化等关键技术,构建POI分类的语义概念格,然后通过概念格之间的语义关联关系,实现多源异构POI分类体系的映射与转换。最后通过实验验证了该文方法的有效性,并将其成功应用于省级地理信息公共服务平台(政务版)数据更新。     

Finding and sharing GIS methods based on the questions they answer

Geographic information has become central for data scientists of many disciplines to put their analyzes into a spatio-temporal perspective. However, just as the volume and variety of data sources on the Web grow, it becomes increasingly harder for analysts to be familiar with all the available geospatial tools, including toolboxes in Geographic Information Systems (GIS), R packages, and Python modules. Even though the semantics of the questions answered by these tools can be broadly shared, tools and data sources are still divided by syntax and platform-specific technicalities. It would, therefore, be hugely beneficial for information science if analysts could simply ask questions in generic and familiar terms to obtain the tools and data necessary to answer them. In this article, we systematically investigate the analytic questions that lie behind a range of common GIS tools, and we propose a semantic framework to match analytic questions and tools that are capable of answering them. To support the matching process, we define a tractable subset of SPARQL, the query language of the Semantic Web, and we propose and test an algorithm for computing query containment. We illustrate the identification of tools to answer user questions on a set of common user requests.     

Re-examining urban region and inferring regional function based on spatial–temporal interaction

Urban system is shaped by the interactions between different regions and regions planned by the government, then reshaped by human activities and residents’ needs. Understanding the changes of regional structure and dynamics of city function based on the residents’ movement demand are important to evaluate and adjust the planning and management of urban services and internal structures. This paper constructed a probabilistic factor model on the basis of probabilistic latent semantic analysis and tensor decomposition, for purpose of understanding the higher order interactive population mobility and its impact on urban structure changes. First, a four-dimensional tensor of time (T)?×?week (W)?×?origin (O)?×?destination (D) was constructed to identify the day-to-day activities in three time modes and weekly regularity of weekday/weekend pattern. Then we reclassified the urban regions based on the space clustering formed by the space factor matrix and core tensor. Finally, we further analysed the space–time interaction on different time scales to deduce the actual function and connection strength of each region. Our research shows that the application of individual-based spatial–temporal data in human mobility and space–time interaction study can help to analyse urban spatial structure and understand the actual regional function from a new perspective.     

地幔不均一性与地幔循环的多维表征和原因思考

地幔早先经核- 幔- 壳分异形成,后受不同尺度对流和循环的影响,因而具有不均一性特征。近三十年来,地幔化学通过研究大洋玄武岩发现了多样地幔端元和非放射性同位素证据并证明了地幔不均一性,逐渐建全了地幔地球化学体系。然而,地幔不均一性如何对应于时空尺度的地幔循环,以及地球演化如何影响地幔不均一性等,仍不清楚。此外,地球物理研究显示,岩石圈厚度差异、中下地幔的波速异常体以及俯冲板片形态的观测为纵横向对流系统提供了空间不均一性证据支持。联合地球化学和地球物理手段对研究地幔不均一性至关重要,用好透视地幔成分与结构的“双目镜”已成为共识。本文从地幔不均一性结合地球化学场、地球物理的不同表现形式,以及现今及历史时期的洋陆格局的对比,多维度联系地幔循环和演化,思考了超大陆旋回与地幔不均一化的内在逻辑。强调了从全球演化角度看地幔不均一性的重要性和提出多手段联合建立地幔循环驱动模型的展望。