Identifying Critical Locations in a Spatial Network with Graph Theory

Effective management of infrastructural networks in the case of a crisis requires a prior analysis of the vulnerability of spatial networks and identification of critical locations where an interdiction would cause damage and disruption. This article presents a mathematical method for modelling the vulnerability risk of network elements which can be used for identification of critical locations in a spatial network. The method combines dual graph modelling with connectivity analysis and topological measures and has been tested on the street network of the Helsinki Metropolitan Area in Finland. Based on the results of this test the vulnerability risk of the network elements was experimentally defined. Further developments are currently under consideration for eventually developing a risk model not only for one but for a group of co‐located spatial networks.     

基于地理加权中心节点距离的网络社区发现算法

提出一种基于地理加权中心节点距离的网络社区发现算法（geographical weighted central node distance based Louvain method，GND-Louvain）。该算法扩展了传统复杂网络领域的经典社区发现方法Louvain，利用地理加权中心节点来度量社区发现过程中的空间距离关系，并将此距离衰减效应加入到距离模块度模型中，以此来计算和评估空间网络社区划分结果的质量，并探究了空间社区发现结果不稳定的原因。通过定义节点计算顺序，保证了社区发现结果的质量和稳定性。利用中国铁路网线路数据，设计了5种不同空间约束的空间社区发现对比性实验。结果证明，GND-Louvain算法的准确性最高，并且算法结果最稳定。     

利用时空神经网络模型的长江经济带气温反演

传统基于遥感的气温反演方法往往使用全局模型,从而忽略了气温分布及其时空影响异质性,特别是在较大区域尺度的研究中存在不足。针对长江经济带区域,引入时空地理加权神经网络模型,建立一种高精度的气温估计方法。通过在广义回归网络模型中建立局部模型来顾及时空异质性的影响,融合遥感数据、同化数据、站点数据,获取面域分布的近地表气温信息。采用基于站点的十折交叉验证方法对模型性能进行评估,结果表明,时空地理加权神经网络有效提高了气温估计的精度（均方根误差为1.899℃,平均绝对误差（mean absolute error,MAE）为1.310℃,相关系数为0.976）,与多元线性回归和传统的全局神经网络方法相比,MAE值分别降低了1.112℃和0.378℃。气温空间分布制图结果显示,该方法结果能很好地反映长江经济带气温空间上的差异和不同季节的特征信息,具有实际应用价值。     

A GIS‐based multi‐criteria seismic vulnerability assessment using the integration of granular computing rule extraction and artificial neural networks

This study proposes multi‐criteria group decision‐making to address seismic physical vulnerability assessment. Granular computing rule extraction is combined with a feed forward artificial neural network to form a classifier capable of training a neural network on the basis of the rules provided by granular computing. It provides a transparent structure despite the traditional multi‐layer neural networks. It also allows the classifier to be applied on a set of rules for each incoming pattern. Drawbacks of original granular computing (GrC) are covered, where some input patterns remained unclassified. The study was applied to classify seismic vulnerability of the statistical units of the city of Tehran, Iran. Slope, seismic intensity, height and age of the buildings were effective parameters. Experts ranked 150 randomly selected sample statistical units with respect to their degree of seismic physical vulnerability. Inconsistency of the experts' judgments was investigated using the induced ordered weighted averaging (IOWA) operator. Fifty‐five classification rules were extracted on which a neural network was based. An overall accuracy of 88%, κ = 0.85 and R² = 0.89 was achieved. A comparison with previously implemented methodologies proved the proposed method to be the most accurate solution to the seismic physical vulnerability of Tehran.     

高光谱影像稀疏解混的空间同质分析法

本文针对高光谱遥感影像端元丰度的稀疏性和空间分布平滑性,提出一种基于空间同质分析的稀疏解混算法。该算法首先对高光谱影像进行空间同质分析来提取同质指数,然后根据同质指数对稀疏回归解混模型中的空间正则项赋予不同权重,使其能更好地反映高光谱影像端元丰度分布的空间复杂性,进而实现对高光谱混合像元的有效分解。模拟数据和真实数据的实验分析表明：本文提出的算法能更好地保持结果的稀疏性和丰度空间分布的平滑性并且具有一定的抗噪性,提高了整体的解混精度。     

Spatially constrained regionalization with multilayer perceptron

In this article, multilayer perceptron (MLP) network models with spatial constraints are proposed for regionalization of geostatistical point data based on multivariate homogeneity measures. The study focuses on non‐stationarity and autocorrelation in spatial data. Supervised MLP machine learning algorithms with spatial constraints have been implemented and tested on a point dataset. MLP spatially weighted classification models and an MLP contiguity‐constrained classification model are developed to conduct spatially constrained regionalization. The proposed methods have been tested with an attribute‐rich point dataset of geological surveys in Ukraine. The experiments show that consideration of the spatial effects, such as the use of spatial attributes and their respective whitening, improve the output of regionalization. It is also shown that spatial sorting used to preserve spatial contiguity leads to improved regionalization performance.     

基于GIS的物流配送中心选址模型研究

配送中心是物流网络中的重要节点,对于优化企业物流系统,合理配置库存资源,提高物流的共同化程度发挥着重要作用。本文应用G IS网络分析方法和改进P中心选址算法,建立了配送中心选址优化模型。该模型由几何网络确定配送中心与需求点间距离、并引入租金、坡度、库存量等因素参与模型计算,通过总费用最小化确定仓库的最佳位置。因采用多因素参与决策和算法的改进,提高了配送中心选址精度,降低了用户选择的盲目性。     

集成暴雨洪水管理模型的带权雨水网络构建

针对城市雨水网络传统规划手段落后导致城市地面雨水不能及时有效地收集和排除的问题,该文提出了基于GIS和暴雨洪水管理模型(SWMM)集成的带权雨水网络的构建方法。通过软件互操作模式将SWMM模型封装成.NET托管动态库,实现了其与GIS空间分析组件的无缝集成;以新城区竖向设计高程和规划路网为基础数据,构建顾及路网的格网DEM;基于新城区规划用地类型和研究区暴雨模型,运用GIS空间分析实现了水文参数的自动提取;进一步率定雨水管网权重因子,利用GIS几何网络分析实现了带权有向雨水网络自动构建及布局优化。实验结果表明:该方法较传统方法工作效率更高,且布网方案、雨水出口选择、管力计算等方面更加科学合理。     

Is your neighbor your friend? Scan methods for spatial social network hotspot detection

GIS analyses use moving window methods and hotspot detection to identify point patterns within a given area. Such methods can detect clusters of point events such as crime or disease incidences. Yet, these methods do not account for connections between entities, and thus, areas with relatively sparse event concentrations but high network connectivity may go undetected. We develop two scan methods (i.e., moving window or focal processes), EdgeScan and NDScan, for detecting local spatial-social connections. These methods capture edges and network density, respectively, for each node in a given focal area. We apply methods to a social network of Mafia members in New York City in the 1960s and to a 2019 spatial network of home-to-restaurant visits in Atlanta, Georgia. These methods successfully capture focal areas where Mafia members are highly connected and where restaurant visitors are highly local; these results differ from those derived using traditional spatial hotspot analysis using the Getis–Ord Gi* statistic. Finally, we describe how these methods can be adapted to weighted, directed, and bipartite networks and suggest future improvements.     

Approach to calculating spatial similarity degrees of the same river basin networks on multi-scale maps

Obtaining spatial similarity degrees among the same objects on multi-scale maps is of importance in map generalization. This paper firstly defines the concepts of ‘map scale change’ and ‘spatial similarity degree’; then it proposes a model for calculating the spatial similarity degree between a river basin network at one scale and its generalized version at another scale. After this, it validates the new model and gets 16 points in the model validation process. The x-coordinate and y-coordinate of each point are map scale change and spatial similarity degree, respectively. Last, a formula for calculating spatial similarity degree taking map scale change as the only variable is obtained by the curve fitting method. The formula along with the model can be used to automate the algorithms for simplifying river basin networks.     

语义与几何相结合的道路网融合研究与实现

道路网是最重要的地理空间要素之一,空间数据融合能够把不同来源道路空间数据或信息加以结合,以获得信息量更丰富或更适于处理、分析、决策的新的数据集。传统的方法受限于道路网数据模型、属性数据类型以及缺少唯一标识的属性信息,道路网融合方法多以各个弧段或道路的位置、形状、方向等几何特征进行匹配,而忽略了道路的语义匹配。本文在数据来源与技术分析的基础上,提出了一种在工程化应用中可行的语义与几何相结合的道路网匹配方法,并通过FME实现空间数据融合,旨在为两个或多个道路网数据融合、联动更新提供方法参考。     

基于道路网络分析的Voronoi面域图构建算法

提出一种基于网络分析的Voronoi面域图和加权Voronoi面域图构建算法。鉴于道路网络在城市中心地、公共设施引力传导与功能覆盖上的重要作用,采用网络最短路径距离分析和最短路径时间分析构建的Voronoi面域图可以模拟出中心功能的辐射影响范围空间划分的实际情形,进而为空间分析和空间优化提供有力支持。算法过程主要包括:设施邻近道路结点检索和分界结点计算;基于网络最短路径分析Dijkstra算法和分界结点计算的网络Voronoi划分;基于空间离散化、邻近道路分析的空间Voronoi划分及其矢量化处理算法。计算实验结果表明本文提出算法可靠和高效,能够模拟出具有预期精度和形态复杂的网络Voronoi面域图形。     

加权网页排序算法在道路网自动选取中的应用

针对现有算法在计算道路网节点重要度时忽略节点间的相互影响以及道路密度引起的重要度异常等问题,提出了一种基于加权网页排序算法的道路网自动提取方法。首先将道路连接成路段,以路段为网络节点,道路交叉作为节点连线,路段长度作为边的权重,将道路网抽象成有向有权图;然后利用加权网页排序算法计算有向有权图节点的重要度,并利用链接作弊检测的方法修正由道路密度引起的节点重要度异常,得到道路节点的最终重要度排序,从而完成道路网的提取。通过真实路网数据进行实验分析,结果表明,相对基于网络中心性的方法,该算法的提取结果能够更好地保留原始路网的密度差异和整体结构。     

Measures of spatio-temporal accuracy for time series land cover data

Remote sensing is a useful tool for monitoring changes in land cover over time. The accuracy of such time-series analyses has hitherto only been assessed using confusion matrices. The matrix allows global measures of user, producer and overall accuracies to be generated, but lacks consideration of any spatial aspects of accuracy. It is well known that land cover errors are typically spatially auto-correlated and can have a distinct spatial distribution. As yet little work has considered the temporal dimension and investigated the persistence or errors in both geographic and temporal dimensions. Spatio-temporal errors can have a profound impact on both change detection and on environmental monitoring and modelling activities using land cover data. This study investigated methods for describing the spatio-temporal characteristics of classification accuracy. Annual thematic maps were created using a random forest classification of MODIS data over the Jakarta metropolitan areas for the period of 2001–2013. A logistic geographically weighted model was used to estimate annual spatial measures of user, producer and overall accuracies. A principal component analysis was then used to extract summaries of the multi-temporal accuracy. The results showed how the spatial distribution of user and producer accuracy varied over space and time, and overall spatial variance was confirmed by the principal component analysis. The results indicated that areas of homogeneous land cover were mapped with relatively high accuracy and low variability, and areas of mixed land cover with the opposite characteristics. A multi-temporal spatial approach to accuracy is shown to provide more informative measures of accuracy, allowing map producers and users to evaluate time series thematic maps more comprehensively than a standard confusion matrix approach. The need to identify suitable properties for a temporal kernel are discussed.     

基于复杂网络的城市道路重要度评价及路网自动综合方法

利用复杂网络理论,构建城市复杂路网模型,基于该模型从道路的结构和功能特征角度,以连接度、介中心和接近度为度量指标定义道路重要度评价模型,并顾及路网的整体形态及路网的拓扑连通性,提出基于复杂网络理论的路网综合算法。为检验方法有效性,针对成都市道路网络进行实验分析。实验结果表明,该道路重要度评价模型较好地反映道路在整个路网结构和功能上的重要程度,复杂路网综合算法能较好地保持原始路网的整体形态结构特征。     

贝叶斯网络下的GIS空间数据不确定性管理模型

通过分析GIS空间数据各种不确定性模型,提出了基于贝叶斯网络的GIS空间数据误差分析模型,论述了贝叶斯网络的基础理论及贝叶斯网络建模方法,为使用GIS空间数据库的用户提供了更可靠、更快捷的分析方法。     

显著性权重RX高光谱异常点检测

高光谱图像异常点检测中,传统RX异常点检测算法忽略了空间相关性,背景估计不准确。本文提出了一种基于图像局部邻域光谱显著性分析的加权RX算法。该算法通过引入图像显著性分析,对基于概率密度为权重的图像背景建模进行改进,建立光谱显著性权重图,重新定义RX算法中的均值向量和协方差矩阵,并给不同的目标赋予不同的权值,达到优化背景估计的目的。利用合成高光谱数据和真实高光谱数据进行异常点检测实验,结果表明,对于同一组数据,本文算法检测到的异常点数比传统算法多,虚警率较低,有效地提高了检测率。     

GIS-based cost distribution analysis of new consumer connections to an urban power grid

In this paper, we propose a method of cost distribution analysis of new consumer connections to a city power grid by accounting for spatial restrictions and characteristics of existing networks. In practice, the calculation of connection costs for each new consumer includes the network design and financial expenditure. We suggest that connection costs should be calculated for the whole city based on the normative parameters at the stage when the object location is selected by investors and when power grid development is planned by power companies. The proposed method enables the modeling of new power line connection routes from every parcel of city land to possible points of connection to the operating networks based on the raster design of the area. The optimal path is chosen by one criterion consisting of two components: the costs of both laying new power lines and providing sufficient power reserve in the chosen network connection point. Realized as a computer program, the method has been used to calculate the costs of connections to low-voltage power lines.     

犯罪网络构建及其时空分析——以入室盗窃为例

提出了一种基于时空影响范围的网络构造方法,构造了一种基于节点影响强度的犯罪传输网络,并引入复杂网络的度、平均度、聚集系数等特征参数分析犯罪传输网络。提取了犯罪预测过程中需要关注的重要节点,分析了其时间分布和空间分布特性,研究结果表明:（1）近邻的时空单元的犯罪率具有一定的关联关系。其中,节点的出度与入度具有正相关性,因此可以引入邻居时空单元的犯罪密度以量化和分析犯罪规律。（2）节点的度分布具有无标度特性,犯罪较少的小区也可能出现度较大的节点,而节点的度与未来犯罪率具有较大的关联性。因此,即便犯罪率较低的小区也要关注节点的度变化情况。（3）犯罪聚集系数大小与未来犯罪率的变化具有一定的关联性,较高的聚集系数意味着未来犯罪状态的变化。     

北斗／WSN网络的融合定位算法

通过对传统的无线传感器网络RSSI定位算法的研究,分析了其定位精度差,响应速度慢等特点,结合北斗定位系统的优点,提出了一种传统无线传感器定位与北斗导航定位相融合的算法,通过卡尔曼滤波,将两种算法的数据进行融合,得到了新的融合定位算法。通过仿真分析可得,新的融合算法在定位精度和收敛速度上,相较于传统单一的无线传感器网络节点定位算法都有了相应的提高,通过对这一算法的仿真,仿真结果说明该算法切实可行。