一种面要素语义位置的GeoSOT网格定位方法

为解决传统地图查询方法中对复杂地址对象难以定位的问题,该文面向地理网格的时空大数据分析需求,结合面要素特点与语义分析,提出一种面要素语义位置的GeoSOT网格定位方法。首先通过最小外包矩形(Minimum Bounding Rectangle,MBR)将面要素以九宫格形式分割成9个区块,单独计算各区块的网格编码集合;然后提取位置语句中的名称与空间关系,根据具体情境进行直接查询,或利用网格编码计算目标位置;最后根据语义考虑是否对编码结果做差集运算,从而得到目标的GeoSOT网格定位范围。实验表明,对比现有的地图服务,该方法能够实现语境更加复杂的位置定位,具有可行性和有效性,其提供的未知要素推理定位方法能够有效补充、扩展原有的面要素数据集,可为后续针对面状区域的时空大数据分析提供网格化的数据资源。     

一种改进的DQG格网编码与经纬度高效转换算法

格网编码与经纬度间的转换效率是影响格网应用的关键因素。现有转换算法难以满足海量空间大数据的实时计算与分析需求,效率有待提升。为此,该文提出一种改进的DQG格网编码与经纬度高效转换算法:首先,引入二进制DQG格网编码代替四进制编码;然后,根据DQG格元的分布特征推导出格元行号和经度差的关系,以此改进格元列号的计算过程;最后,使用查找表进行Morton码的编码和解码,以提高转换效率。实验结果表明,该文提出的DQG格网编码与经纬度转换算法的平均效率分别是四进制DQG、单层二维SDZ算法的20.15倍和4.58倍,基本满足海量格网数据的实时转换与计算需求,为海量空间大数据的高效计算与分析奠定了基础。     

A discrete global grid system for earth system modeling

To support Earth system modeling, we propose a discrete global grid system that expresses multi-resolution spatial data. Specifically, a unified coding model that expresses a grid of nodes, edges, and cells is constructed for a triangular discrete global grid system. To fulfill the requirements of practical applications, we design a code-based topological query method for this grid system and an algorithm to transform between grid codes and geographic coordinates. We evaluate the Global Finite Volume Community Ocean Model (Global-FVCOM) on the triangular discrete global grid system in the proposed uniform coding model. The ocean tidal waves simulated by the Global-FVCOM running on the coded grid are then compared with results obtained using a traditional irregular spherical grid system, and the results display comparable accuracy. The uniform coding model proposed in this paper provides a triangular discrete global grid system that can represent multi-resolution spatial data and can be used in Earth system models. This unified coding model can also be applied to the geographic coordinate system made up of latitudes and longitudes, as well as diamond and hexagonal grids.     

An Extended Minimum Spanning Tree method for characterizing local urban patterns

Detailed and precise information on urban building patterns is essential for urban design, landscape evaluation, social analyses and urban environmental studies. Although a broad range of studies on the extraction of urban building patterns has been conducted, few studies simultaneously considered the spatial proximity relations and morphological properties at a building-unit level. In this study, we present a simple and novel graph-theoretic approach, Extended Minimum Spanning Tree (EMST), to describe and characterize local building patterns at building-unit level for large urban areas. Building objects with abundant two-dimensional and three-dimensional building characteristics are first delineated and derived from building footprint data and high-resolution Light Detection and Ranging data. Then, we propose the EMST approach to represent and describe both the spatial proximity relations and building characteristics. Furthermore, the EMST groups the building objects into different locally connected subsets by applying the Gestalt theory-based graph partition method. Based on the graph partition results, our EMST method then assesses the characteristics of each building to discover local patterns by employing the spatial autocorrelation analysis and homogeneity index. We apply the proposed method to the Staten Island in New York City and successfully extracted and differentiated various local building patterns in the study area. The results demonstrate that the EMST is an effective data structure for understanding local building patterns from both geographic and perceptual perspectives. Our method holds great potential for identifying local urban patterns and provides comprehensive and essential information for urban planning and management.     

A framework to enhance semantic flexibility for analysis of distributed phenomena

While some geographic phenomena hold uniform properties, such as land‐use zones, many geographic phenomena are distributed such that their properties vary across an extended area. While such distributed phenomena are best represented as continuous surfaces, individual objects (or features) often emerge among clusters of high or low values in a field. For example, areas of relatively high elevation may be viewed as hills, while flat low‐lying areas are perceived as plains in a terrain. A comprehensive spatial analysis of distributed phenomena should examine both the spatial variance of its attribute surfaces and the characteristics of individual objects embedded in the field. An immediate research challenge to meet such spatial analysis needs is that these emerging features often have vague boundaries that vary according to the use and the user. The nature, and even existence, of these objects depend upon the range of values, or thresholds, used to define them. We propose a representation framework that takes a dual raster‐vector approach to capture both field‐ and object‐like characteristics of distributed phenomena and maintain multiple representations of embedded features delineated by boundaries that are likely to be relevant for the expected uses of the data. We demonstrate how boundaries influence the analysis and understanding of spatiotemporal characteristics of distributed phenomena. Using precipitation as a proof of concept, we show how the proposed framework enhances semantic flexibility in spatiotemporal query and analysis of distributed phenomena in geographic information systems.     

地理表达研究进展

地理表达是地理学的一个基本问题,也是地理信息科学研究面临的一个重大挑战.从地理表达机理、内容和形式的角度,对近期地理表达研究进行了总结.本质上,地理表达是一个空间认知、信息转换与信息传输的交互过程.地理表达内容涉及地理实体及其空间关系、不确定性、地理动态及地理本体等方面.地理表达形式经历了从自然语言、地图到GIS的演变...     

基于球面DQG的矢量与地形数据无缝集成

现有的全球大规模空间数据可视化系统主要侧重于影像和地形数据的综合表达,针对矢量与地形的集成可视化能力相对较弱。该文以球面退化四叉树格网(Degenerate Quad-tree Grids,DQG)为基础,通过DQG格网的三角化过程构建了地表DEM模型,并提出了从矢量线对象到地形格网表面的映射方法。采用GTOPO30数据集和国界矢量数据进行了相关实验,结果表明:该方法能实现矢量数据与多分辨率DEM的无缝集成,并能有效地避免矢量对象"悬浮"和"入地"等现象。     

Object-based spatial cluster analysis of urban landscape pattern using nighttime light satellite images: a case study of China

Previous studies have demonstrated urban built-up areas can be derived from nighttime light satellite (DMSP-OLS) images at the national or continent scale. This paper presents a novel object-based method for detecting and characterizing urban spatial clusters from nighttime light satellite images automatically. First, urban built-up areas, derived from the regionally adaptive thresholding of DMSP-OLS nighttime light data, are represented as discrete urban objects. These urban objects are treated as basic spatial units and quantified in terms of geometric and shape attributes and their spatial relationships. Next, a spatial cluster analysis is applied to these basic urban objects to form a higher level of spatial units – urban spatial clusters. The Minimum Spanning Tree (MST) is used to represent spatial proximity relationships among urban objects. An algorithm based on competing propagation of objects is proposed to construct the MST of urban objects. Unlike previous studies, the distance between urban objects (i.e., the boundaries of urban built-up areas) is adopted to quantify the edge weight in MST. A Gestalt Theory-based method is employed to partition the MST of urban objects into urban spatial clusters. The derived urban spatial clusters are geographically delineated through mathematical morphology operation and construction of minimum convex hull. A series of landscape ecologic and statistical attributes are defined and calculated to characterize these clusters. Our method has been successfully applied to the analysis of urban landscape of China at the national level, and a series of urban clusters have been delimited and quantified.     

A spatial heterogeneity-based rough set extension for spatial data

When classical rough set (CRS) theory is used to analyze spatial data, there is an underlying assumption that objects in the universe are completely randomly distributed over space. However, this assumption conflicts with the actual situation of spatial data. Generally, spatial heterogeneity and spatial autocorrelation are two important characteristics of spatial data. These two characteristics are important information sources for improving the modeling accuracy of spatial data. This paper extends CRS theory by introducing spatial heterogeneity and spatial autocorrelation. This new extension adds spatial adjacency information into the information table. Many fundamental concepts in CRS theory, such as the indiscernibility relation, equivalent classes, and lower and upper approximations, are improved by adding spatial adjacency information into these concepts. Based on these fundamental concepts, a new reduct and an improved rule matching method are proposed. The new reduct incorporates spatial heterogeneity in selecting the feature subset which can preserve the local discriminant power of all features, and the new rule matching method uses spatial autocorrelation to improve the classification ability of rough set-based classifiers. Experimental results show that the proposed extension significantly increased classification or segmentation accuracy, and the spatial reduct required much less time than classical reduct.     

Sampling and mapping of heterogeneous surfaces: multi-resolution tiling adjusted to spatial variability

Abstract

Mapping by sampling and prediction of local and regional values of two-dimensional surfaces is a frequent, complex task in geographical information systems. This article describes a method for the approximation of two-dimensional surfaces by optimizing sample size, arrangement and prediction accuracy simultaneously. First, a grid of an ancillary data set is approximated by a quadtree to determine a predefined number of homogeneous mapping units. This approximation is optimal in the sense of minimizing Kullback-divergence between the quadtree and the grid of ancillary data. Then, samples are taken from each mapping unit. The performance of this sampling has been tested against other sampling strategies (regular and random) and found to be superior in reconstructing the grid using three interpolation techniques (inverse squared Euclidean distance, kriging, and Thiessen-polygonization). Finally, the discrepancy between the ancillary grid and the surface to be mapped is modelled by different levels and spatial structures of noise. Conceptually this method is advantageous in cases when sampling strata cannot be well defined a priori and the spatial structure of the phenomenon to be mapped is not known, but ancillary information (e.g., remotely-sensed data), corresponding to its spatial pattern, is available.     

Quadtree storage of vector data†

Abstract

Vector data storage has various advantages in a cartographic or geographical information system (GIS) environment, but lacks internal spatial relationships between individual features. Quadtree structures have been extensively used to store and access raster data. This paper shows how quadtree methods may be adapted for use in spatially indexing vector data. It demonstrates that a vector quadtree stored in floating point representation overcomes the classical problem with raster quadtrees of data approximation. Examples of vector quadtrees applied to realistic size data sets are given     

A linear tessellation model to identify spatial pattern in urban street networks

Street patterns reflect the distribution characteristics of a street network and affect the urban structure and human behavior. The recognition of street patterns has been a topic of interest for decades. In this study, a linear tessellation model is proposed to identify the spatial patterns in street networks. The street segments are broken into consecutive linear units with equal length. We define five focal operations using neighborhood analysis to extract the geometric and topological characteristics of each linear unit for the purpose of grid-pattern recognition. These are then classified by Support Vector Machine, and the result is optimized based on Gestalt principles. The experimental results demonstrate that our method is effective for mining grid patterns in a street network.     

资源枯竭型城市转型期产业用地变化图谱

运用地理信息图谱的理论和GIS空间分析方法,构建了资源枯竭城市——大庆市的网格信息图谱模型,分析了研究区1996-2010年产业用地的演变特征和规律。结果表明：第一产业用地面积最广,主要分布在矿产资源不丰富的城市外围地区,并呈现聚集和增加趋势;主要工矿区的第二产业用地变化最为剧烈,第二产业用地正逐步从资源地的传统矿区向规划新建的工业园区迁移;第三产业用地主要分布在城市中心区域,呈点状分散于核心居民区周围,第三产业用地仍表现为粗放模式发展。图谱研究说明资源枯竭型城市在转型发展过程中,土地规划在土地类型转化中发挥了主体作用。     

虚拟地理实验概念框架与应用初探

从实验科学与实验地理学的角度,结合空间信息与虚拟地理环境技术,讨论"虚拟地理实验"概念、特征及研究理论与框架.以辽宁浑河沈阳城市段防洪为案例,介绍虚拟地理实验在城市洪水模拟和防洪工程规划中的应用.     

A tile-based method for geodesic buffer generation in a virtual globe

Geometric buffers are important for spatial analysis in many applications of geographic information systems (GISs), such as environmental measurement and management, human health, urban planning, etc. Geometric buffer generation algorithms are well studied in the Euclidean space where the buffer distance is measured by Euclidean metrics; however, very few algorithms are available for generating geometric buffers on the terrain surface in a virtual globe where the buffer distance is measured by geodesic metrics. This paper proposes a tile-based method for geodesic buffer generation according to the characteristics of a virtual globe. It extends the vector tile model (VTM) to organize terrain and vector data, and the XYH algorithm is improved to build geodesic distance fields for terrain meshes. Based on the data organization and the improved XYH algorithm, a geodesic buffer is generated via three main steps: selecting and assembling tiles, updating geodesic distance fields and tracing the boundaries of buffer zones. This method is implemented with multi-scale terrain and vector data, and the experimental results show that it is valid and exact and can be applied in practical applications.     

基于GIS的新疆气温数据栅格化方法研究

以新疆99个气象台站1971-2010年年平均气温为数据源,采用多元回归结合空间插值的方法对新疆区域气温数据进行栅格化研究。建立了年平均气温与台站经纬度和海拔高度的多元回归模型,对于残差数据的插值采用了反距离权重法(IDW) 、普通克立格法 (Kriging)和样条函数法(Spline)3种目前应用广泛的空间插值方法,针对于这3种方法进行了基于MAE和RMSIE的交叉验证和对比分析,结果表明在新疆的年平均气温的GIS插值方案中,IDW方法精度总体要高于其他两种插值方法。     

中国城市网络关联与经济增长溢出效应——基于大数据与网络分析方法的研究

深化城市网络空间特征的认识,科学测度经济增长的溢出效应,对推进新时期区域协调发展具有重要意义。本文结合城市网络和网络外部性两种研究视角,基于网络分析和空间计量方法,使用互联网大数据构建信息网络、企业网络、人口网络三种流要素城市网络,对中国336个地级以上行政单元的网络空间异质性、经济增长溢出效应进行实证分析。研究发现：中国城市网络联系具有显著的空间异质性,呈现出“中心-外围”特点,形成全国范围和省域、城市群范围两种尺度下的空间结构,表现出兼具择优选择与地理邻近的复杂特征;网络关联下城市经济增长具有显著的正向和负向溢出效应,反映出网络外部性对区域经济增长的重要影响,对比实体空间溢出效应表现出地理邻近关系下的相似性,以及跨越省级边界的差异性;对于全国范围下的网络关联矩阵,使用对称化和标准化处理更为合适,表明省域和城市群是推进区域经济协调发展的重要载体。     

Address databases for national SDI: Comparing the novel data grid approach to data harvesting and federated databases

The original purpose of addresses was to enable the correct and unambiguous delivery of postal mail. The advent of computers and more specifically geographic information systems (GIS) opened up a whole new range of possibilities for the use of addresses, such as routing and vehicle navigation, spatial demographic analysis, geo‐marketing, and service placement and delivery. Such functionality requires a database which can store and access spatial data effectively. In this paper we present address databases and justify the need for national address databases. We describe models used for national address databases, and present our evaluation framework for an address database at a national level within the context of a spatial data infrastructure (SDI). The models of data harvesting, federated databases and data grids are analyzed and evaluated according to our novel framework, and we show that the data grid model has some unique features that make it attractive for a national address database in an environment where centralized control and/or coordination is difficult or undesirable.