A density-based approach for detecting network-constrained clusters in spatial point events

Existing spatial clustering methods primarily focus on points distributed in planar space. However, occurrence locations and background processes of most human mobility events within cities are constrained by the road network space. Here we describe a density-based clustering approach for objectively detecting clusters in network-constrained point events. First, the network-constrained Delaunay triangulation is constructed to facilitate the measurement of network distances between points. Then, a combination of network kernel density estimation and potential entropy is executed to determine the optimal neighbourhood size. Furthermore, all network-constrained events are tested under a null hypothesis to statistically identify core points with significantly high densities. Finally, spatial clusters can be formed by expanding from the identified core points. Experimental comparisons performed on the origin and destination points of taxis in Beijing demonstrate that the proposed method can ascertain network-constrained clusters precisely and significantly. The resulting time-dependent patterns of clusters will be informative for taxi route selections in the future.     

Two-stage permutation tests for determining homogeneity within a spatial cluster

The discovery of spatial clusters formed by proximal spatial units with similar non-spatial attribute values plays an important role in spatial data analysis. Although several spatial contiguity-constrained clustering methods are currently available, almost all of them discover clusters in a geographical dataset, even though the dataset has no natural clustering structure. Statistically evaluating the significance of the degree of homogeneity within a single spatial cluster is difficult. To overcome this limitation, this study develops a permutation test approach Specifically, the homogeneity of a spatial cluster is measured based on the local variance and cluster member permutation, and two-stage permutation tests are developed to determine the significance of the degree of homogeneity within each spatial cluster. The proposed permutation tests can be integrated into the existing spatial clustering algorithms to detect homogeneous spatial clusters. The proposed tests are compared with four existing tests (i.e., Park’s test, the contiguity-constrained nonparametric analysis of variance (COCOPAN) method, spatial scan statistic, and q-statistic) using two simulated and two meteorological datasets. The comparison shows that the proposed two-stage permutation tests are more effective to identify homogeneous spatial clusters and to determine homogeneous clustering structures in practical applications.     

A comparative analysis of gridding systems for point-based land cover/use analysis

For spatial analyses, raster land cover/use maps are converted into points, where each point holds attribute of its corresponding land cover/use. However, these are not identical in terms of areas or shapes; thus assigning a point to each isolated shape is not an adequate solution and for that gridding is suggested. Square, hexagon and triangle are among the basic land use gridding systems where each of them has its own advantages in such process. This research aims to compare the systems in providing accurate representations of the original land cover/use maps, assess the data loss while increasing resolution and suggest suitable gridding system. The research finds the errors in area and feature numbers as criteria for selected classes. Modules that find out errors in each scale considering each criterion and class alone are proposed. The modules suggest both the best system for each criterion alone and for combined criteria.     

大规模真实感三维地形技术综述

计算机虚拟仿真领域的地形构建及显示方法的研究动态,描述三维地形建模、基于视点相关的细分简化、基于块的格网简化、模型的纹理映射和显示控制、误差度量中,分析和总结几个主要方法的特点及使用场合．     

数字城市:智能城市的初级阶段

针对国内外数字城市研究和建设中的主要问题,介绍了全球化进程中正在兴起和汇聚的新技术———物联网与传感器网络、空间信息网格等为数字城市的演进提供的可行性。提出数字城市的发展必然导向智能城市,并将数字城市作为智能城市的初级阶段进行重新诠释;提出为了适应数字城市向智能城市进化,数字城市系统的构建必须满足开放性、易拓展性、可重用性、可动态适应的智能性等特征。     

空间视图在图形数据更新中的应用研究

在研究Geodatabase数据模型的基础上,给出了空间视图的定义,探讨了基于空间视图的图形数据更新过程,设计了更新的结构图和活动图。     

福建省基础空间数据管理技术研究

福建省基础地理信息中心作为福建省基础空间数据的管理和分发服务单位,管理和分发福建省多种类、多比例尺、多数据源、分布式、多时态的基础空间信息,在基础空间数据管理上存在多种形式,本文通过几种管理模式的技术分析,结合中心的管理实际,总结了福建省空间数据管理的多样性和技术特点,对比分析出各种管理模式的优势和缺点。     

国土空间规划的海洋分区研究

国土空间分区作为国土空间规划编制和自然资源统一管理的基础,对整个规划体系具有重要的支撑作用。在统一的国土空间规划体系下,用海分区既有其独立性与特殊性,也面临着陆海统筹、生态文明建设、海洋高质量发展等政策提出的 新要求。文章系统梳理了 1983 年以来海洋功能分区体系 4 次演变历程,结合国土空间规划改革对分区体系的内在需求,指出海洋功能分区体系存在未覆盖海域全域,分区层级的尺度、分工、内容和结构与国土空间规划的要求不一致,分区方案未考虑用途管制的需求,以及陆海统筹不足等问题。在剖析现有各类规划政策分区、管制分区和功能分区在国土空间规划体系中的定位和作用的基础上,提出以海洋主体功能区为基础,海洋生态红线为底线,以原海洋功能区划分区为主体,将无居民海岛纳入规划范围,并在生态、农业、城镇空间的大框架下统筹协调海陆分区,形成国土空间规划中的海洋分区体系。全国和省级国土空间规划统筹海陆主体功能,划定主体功能区,并建立海洋产业保障区名录、无居民海岛保护与利用名录和海洋自然保护地名录。市县级国土空间规划划定“功能+管制”的利用分区,共划分出海洋保护区、海洋发展区和海洋保留3个一级分区以及核心保护区、一般控制区、海水增养殖区等 12 个二级分区。     

浙江省县域旅游经济增长的空间溢出效应

论文以浙江省65个县域单元为例,综合运用探索性空间数据分析和空间杜宾模型,实证了2004—2016年浙江省县域旅游经济增长及其影响因素的空间溢出效应。结果表明:① 研究期内浙江省县域旅游经济整体表现出较强的空间依赖性,局部空间关联类型以高—高和低—低集聚为主,高—高集聚区主要位于中心市区,低—低集聚区主要位于浙西和浙南等地。旅游经济增长的空间自相关性表明考察其演化路径时不可忽视空间效应的存在。② 浙江省县域入境和国内旅游经济增长均存在显著的空间溢出效应,本地入境和国内旅游经济每增长1%,将会带动邻近县域入境和国内旅游经济分别增长0.192%和0.249%。经济发展水平、旅游资源禀赋、旅游基础设施和区位交通条件等均具有显著的空间溢出效应,忽视旅游经济增长的空间效应,将会高估这些因素的作用强度。③ 驱动旅游经济增长的各影响因素的作用强度和影响路径存在显著差异,未来应充分关注入境和国内旅游经济增长的空间依赖特征,将驱动旅游经济增长的各因素齐抓并举,方可实现县域旅游经济的高质量发展。     

Wall-to-wall spatial prediction of growing stock volume based on Italian National Forest Inventory plots and remotely sensed data

Spatial predictions of forest variables are required for supporting modern national and sub-national forest planning strategies, especially in the framework of a climate change scenario. Nowadays methods for constructing wall-to-wall maps and calculating small-area estimates of forest parameters are becoming essential components of most advanced National Forest Inventory (NFI) programs. Such methods are based on the assumption of a relationship between the forest variables and predictor variables that are available for the entire forest area. Many commonly used predictors are based on data obtained from active or passive remote sensing technologies. Italy has almost 40% of its land area covered by forests. Because of the great diversity of Italian forests with respect to composition, structure and management and underlying climatic, morphological and soil conditions, a relevant question is whether methods successfully used in less complex temperate and boreal forests may be applied successfully at country level in Italy.For a study area of more than 48,657 km² in central Italy of which 43% is covered by forest, the study presents the results of a test regarding wall-to-wall, spatially explicit estimation of forest growing stock volume (GSV) based on field measurement of 1350 plots during the last Italian NFI. For the same area, we used potential predictor variables that are available across the whole of Italy: cloud-free mosaics of multispectral optical satellite imagery (Landsat 5 TM), microwave sensor data (JAXA PALSAR), a canopy height model (CHM) from satellite LiDAR, and auxiliary variables from climate, temperature and precipitation maps, soil maps, and a digital terrain model.Two non-parametric (random forests and k-NN) and two parametric (multiple linear regression and geographically weighted regression) prediction methods were tested to produce wall-to-wall map of growing stock volume at 23-m resolution. Pixel level predictions were used to produce small-area, province-level model-assisted estimates. The performances of all the methods were compared in terms of percent root mean-square error using a leave-one-out procedure and an independent dataset was used for validation. Results were comparable to those available for other ecological regions using similar predictors, but random forests produced the most accurate results with a pixel level R² = 0.69 and RMSE_% = 37.2% against the independent validation dataset. Model-assisted estimates were more precise than the original design-based estimates provided by the NFI.