Capturing the heterogeneity of urban growth in South Korea using a latent class regression model

This study aims to analyze the spatial patterns of urban growth in South Korea between 2000 and 2010. Fourteen suspected causative independent variables were selected and latent class regression (LCR) was used to analyze the relationship between dependent (urban growth) and independent (causative) variables. The goodness‐of‐fit was assessed by comparison to logistic regression (LR) analysis. The LR analysis produced consistent coefficients for each independent variable across the study area. In contrast, an LCR analysis, with a three‐class assumption, resulted in a different magnitude and directional effects of the coefficients for each class. The LCR analysis enabled the identification of both spatially homogeneous and heterogeneous areas. In addition, the LCR analysis performed better than the LR analysis with a lower Akaike information criterion and Bayesian information criterion value, and a higher receiver operating characteristic value. We conclude that LCR analysis should be used to establish causative “driving” factors for efficient urban growth planning and urban spatial policy.     

基于缓冲区分析的城市化与地表水质关系研究——以烟台沿海区县为例

将城市化过程中的人为土地定义为建设用地,利用2004年TM影像得到烟台沿海区县建设用地数据。用缓冲区分析提取乡镇水平上建设用地比率和人口密度数据,缓冲半径从100m到2000m,利用ArcView空间分析功能对建设用地比率、人口密度、河网等级和水质进行叠置分析,并结合逐步回归分析方法揭示烟台沿海区县28个水质监测站各水质参量的空间变化情况。结果显示,通过建设用地比率和人口密度共同作用可以解释85%以上的水质参量模型,反映城市化的建设用地比率比人口密度能更好地解释水质参量的空间变化,大多数水质参量的最佳模型出现在2000m的缓冲半径上,表明建设用地比率和人口密度是影响研究区域水质的首要因素。同时,运用缓冲区分析等GIS空间分析方法为区域城市化进程与水环境质量的研究提供了新的途径。     

Evaluation of environmental parameters in logistic regression models for landslide susceptibility mapping

Abstract

The aim of this study was to determine how well the landslide susceptibility parameters, obtained by data-dependent statistical models, matched with the parameters used in the literature. In order to achieve this goal, 20 different environmental parameters were mapped in a well-studied landslide-prone area, the Asarsuyu catchment in northwest Turkey. A total of 4400 seed cells were generated from 47 different landslides and merged with different attributes of 20 different environmental causative variables into a database. In order to run a series of logistic regression models, different random landslide-free sample sets were produced and combined with seed cells. Different susceptibility maps were created with an average success rate of nearly 80%. The coherence among the models showed spatial correlations greater than 90%. Models converged in the parameter selection peculiarly, in that the same nine of 20 were chosen by different logistic regression models. Among these nine parameters, lithology, geological structure (distance/density), landcover-landuse, and slope angle were common parameters selected by both the regression models and literature. Accuracy assessment of the logistic models was assessed by absolute methods. All models were field checked with the landslides resulting from the 12 November 1999, Kayna?li Earthquake (Ms = 7.2).     

城市公共交通覆盖度空间统计分析研究

为了合理分析城市公共交通发展现状并对其发展提供可参考的依据,本文利用地理信息系统空间分析和统计方法,在确立多个评价指标的基础上,对城市公共交通覆盖度情况进行不同角度、不同方面的评价。以沈阳市内部分区域为研究对象,提取研究区域基础地理数据,结合人口数据以及公共交通线网缓冲区,选取城市主次干道为边界划分分析单元,将人口密度与道路数据相结合,将评价体系与数据进行对比分析。根据评价指标,得到研究范围内城市公交资源的分布状况。实验结果表明,该方法能够较好地分析城市公共交通分布特征,为城市交通规划设计提供思路。     

河网汇水区域的层次化剖分与地图综合

对于具有网络状结构的河系数据的综合化简,判断河流分支在河网中的重要性需要考虑三个层次的结构信息:全局范围内的空间分布模式;局域环境下的分布密度;单条河流的几何特征。为提取这些结构化信息,本文基于网络分析运用Delaunay三角网模型建立了各级河流分支汇水区域的层次化剖分模型,其基本思想是将汇水区域划定当作“空间竞争”问题来求解,运用类似于Voronoi图的空间等剖分几何构造表达“袭水”过程,在各支流子系统内部及其环境之间通过Delaunay三角网骨架线确定汇水区域的分水岭。基于该层次剖分模型可计算河流分布密度、相邻河流间距、汇水范围及层次关系,进而推算出河系网中每一条河流的重要性系数,实现不同尺度下河流的综合选取。     

从航空数字影像提取北极海冰形态参数的方法研究

在 1999年 7月至 9月的中国首次北极科学考察中 ,对北极密集冰区进行了航空数字相机遥感观测 ,获得一批高分辨率的不同类型海冰数字影像。探讨了利用数字影像分析海冰形态参数的方法。利用这些方法不仅可以分析海冰边缘、密集度、冰水比例等常规参数 ,而且可以获得卫星遥感尚无法探测的一些海冰形态的新参数 ,这些参数将增加对不同类别海冰的认识 ,对于研究北极融冰过程和冰区的海气相互作用提供有价值的手段和分析方法。给出的方法可以应用到中国渤海海冰和其它海域海冰的研究。     

Mapping the Results of Geographically Weighted Regression

Abstract

Geographically weighted regression (GWR) is a local spatial statistical technique for exploring spatial nonstationarity. Previous approaches to mapping the results of GWR have primarily employed an equal step classification and sequential no-hue colour scheme for choropleth mapping of parameter estimates. This cartographic approach may hinder the exploration of spatial nonstationarity by inadequately illustrating the spatial distribution of the sign, magnitude, and significance of the influence of each explanatory variable on the dependent variable. Approaches for improving mapping of the results of GWR are illustrated using a case study analysis of population density–median home value relationships in Philadelphia, Pennsylvania, USA. These approaches employ data classification schemes informed by the (nonspatial) data distribution, diverging colour schemes, and bivariate choropleth mapping.     

An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: cases with different types of spatial data

ABSTRACT

Spatial heterogeneity represents a general characteristic of the inequitable distributions of spatial issues. The spatial stratified heterogeneity analysis investigates the heterogeneity among various strata of explanatory variables by comparing the spatial variance within strata and that between strata. The geographical detector model is a widely used technique for spatial stratified heterogeneity analysis. In the model, the spatial data discretization and spatial scale effects are fundamental issues, but they are generally determined by experience and lack accurate quantitative assessment in previous studies. To address this issue, an optimal parameters-based geographical detector (OPGD) model is developed for more accurate spatial analysis. The optimal parameters are explored as the best combination of spatial data discretization method, break number of spatial strata, and spatial scale parameter. In the study, the OPGD model is applied in three example cases with different types of spatial data, including spatial raster data, spatial point or areal statistical data, and spatial line segment data, and an R “GD” package is developed for computation. Results show that the parameter optimization process can further extract geographical characteristics and information contained in spatial explanatory variables in the geographical detector model. The improved model can be flexibly applied in both global and regional spatial analysis for various types of spatial data. Thus, the OPGD model can improve the overall capacity of spatial stratified heterogeneity analysis. The OPGD model and its diverse solutions can contribute to more accurate, flexible, and efficient spatial heterogeneity analysis, such as spatial patterns investigation and spatial factor explorations.     

Using Topological Relationships to Inform a Data Integration Process

When spatial datasets are overlaid, corresponding features do not always coincide. This may be a result of the datasets having differing quality characteristics, being captured at different scales or perhaps being in different projections or datums. Data integration methods have been developed to bring such datasets into alignment. Although these methods attempt to maintain topological relationships within each dataset, spatial relationships between features in different datasets are generally not considered. The preservation of inter‐dataset topology is a research area of considerable current interest. This research addresses the preservation of topology within a data integration process. It describes the functional models established to represent a number of spatial relationships as observation equations. These are used to provide additional information concerning the relative positions of features. Since many topological relationships are best modelled as inequalities, an algorithm is developed to accommodate such relationships. The method, based on least squares with inequalities (LSI), is tested on simulated and real datasets. Results are presented to illustrate the optimal positioning solutions determined using all of the available information. In addition, updated quality parameters are provided at the level of the individual coordinate, enabling communication of local variation in the resultant quality of the integrated datasets.     

利用景观格局指数的空间分辨率对水域面积提取影响的分析

对8个不同地区对应的同一时间的ETM+数据和MODIS数据,利用谱间关系法得到30 m和250 m分辨率具有不同景观格局分布的水体专题图,研究分辨率对不同景观格局分布的水体提取的影响。通过比较发现,区域内水体边缘密度很小时,ETM+和MODIS提取结果的误差很小;当区域内水体边缘密度很大时,ETM+和MODIS提取结果的误差相应就变大。通过引入景观格局指数与两种分辨率的提取结果进行回归分析发现,对于不破碎区域的水体,MODIS和ETM+可以得到相近的精度;而对于中度破碎的水体,引入景观格局指数信息能显著地提高中度破碎水体的精度;但对于高度破碎的水体,通过引入景观格局指数信息的多元回归几乎不能提高精度。     

Landslide Susceptibility Analysis of Shiv-Khola Watershed, Darjiling: A Remote Sensing & GIS Based Analytical Hierarchy Process (AHP)

In the present study, Remote Sensing Technique and GIS tools were used to prepare landslide susceptibility map of Shiv-khola watershed, one of the landslide prone part of Darjiling Himalaya, based on 9 landslide inducing parameters like lithology, slope gradient, slope aspect, slope curvature, drainage density, upslope contributing area, land use and land cover, road contributing area and settlement density applying Analytical Hierarchy Approach (AHA). In this approach, quantification of the factors was executed on priority basis by pair-wise comparison of the factors. Couple comparing matrix of the factors were being made with reasonable consistency for understanding relative dominance of the factors as well as for assigning weighted mean/prioritized factor rating value for each landslide triggering factors through arithmetic mean method using MATLAB Software. The factor maps/thematic data layers were generated with the help of SOI Topo-sheet, LIIS-III Satellite Image (IRS P6/Sensor-LISS-III, Path-107, Row-052, date-18/03/2010) by using Erdas Imagine 8.5, PCI Geomatica, Arc View and ARC GIS Software. Landslide frequency (%) for each class of all the thematic data layers was calculated to assign the class weight value/rank value. Then, weighted linear combination (WLC) model was implied to determine the landslide susceptibility coefficient value (LSCV or ??M??) integrating factors weight and assigned class weight on GIS platform. Greater the value of M, higher is the propensity of landslide susceptibility over the space. Then Shivkhola watershed was classified into seven landslide susceptibility zones and the result was verified by ground truth assessment of existing landslide location where the classification accuracy was 92.86 and overall Kappa statistics was 0.8919.     

Object based building footprint detection from high resolution multispectral satellite image using K-means clustering algorithm and shape parameters

Object-based image analysis (OBIA) has been a new area of research in satellite image processing applications, since it improves the quality of information acquisition about geospatial objects and also enables to add spatial and contextual information to the objects of interest. The extraction of buildings from High Resolution Satellite (HRS) image in an urban scenario has been an intricate problem due to their different size, shape, varying rooftop textures and low contrast between building and surrounding region. In this study, a new object-based automatic building extraction technique has been proposed to extract building footprints from HRS pan sharpened IKONOS multispectral image. The study is mainly emphasizing on obtaining optimal values for segmentation parameters, shape parameters, and defining rule set to extract buildings and eliminate misclassified other urban features. The suitability of the technique has been judged using different indicators, such as, completeness, correctness and quality.     

基于信噪比检验的双截断奇异值估计

将复共线性对参数估计危害的度量结果与截断奇异值估计相结合,提出了基于信噪比检验的双截断奇异值估计。利用信噪比检验,根据每个参数最小二乘估计信噪比估值的大小将待估参数分为受复共线性危害较大和较小的两部分,并对这两部分参数的截断奇异值估计进行不同强度的截断。对受复共线性危害较大的部分参数,使其截断参数相对较小,对受复共线性危害较小的部分参数,使其截断参数较大。这种精细化的处理在有效降低参数估计方差的同时减少了偏差的引入。将基于信噪比检验的双截断奇异值估计应用于GEO卫星定轨仿真算例中,实验结果表明,新方法的解算精度较高。     

自DEM由不同算法提取坡度的对比分析

以陕北韭园沟流域1:10 000水平分辨率为5 m的DEM为研究对象,运用方差分析、回归分析、排序分析等分析方法,采用6种不同算法分别提取坡度。通过比较不同算法所提取坡度的平均值、最大值、标准差、坡度的中误差,定量分析中误差与地形变化的关系后,提出在黄土丘陵沟壑区提取坡度时选用三阶反距离平方权差分算法和三阶反距离权差分较为合理。     

罗德里格矩阵在空间后方交会直接解法中的应用

经典的空间后方交会直接解法是先解算出空间距离,求出各摄影光束的方向角,解算一个6阶方程,然后解算出外方位元素.6阶方程中有18个三角函数,计算工作量较大,对此法进行改进,避免了三角函数计算,方程由6阶降为3阶,计算方便快速.     

Using Landsat Imagery and Census Data for Urban Population Density Modeling in Port-au-Prince,Haiti

This research uses the most recent (2003) census data and a Landsat ETM+ image to build a population estimation model for Port-au-Prince, Haiti. The purpose of the study is to establish the linkage of population density with remotely sensed surface reflectance signals of an urban area, and use that to estimate population when census data are not available in a timely fashion. The research begins with deriving subpixel vegetation-impervious surface-soil (VIS) fractions derived from the Landsat ETM+ multispectral bands, and then uses the geographically weighted regression (GWR) model to examine how the variation of population density can be explained by the VIS variables and their derivatives. With comparison to the ordinary least square (OLS) model, the GWR model accounts for spatial non-stationarity in the relationship between population patterns and land characteristics in the study area. The study reveals that three VIS variables are significant in explaining population density: the mean value of houses fraction image, the mean value of vegetation fraction image, and the standard deviation of vegetation fraction image.     

统计数据总量约束下全局优化阈值的冬小麦分布制图

大范围、长时间和高精度农作物空间分布基础农业科学数据的准确获取对资源、环境、生态、气候变化和国家粮食安全等问题研究具有重要现实意义和科学意义。本文针对传统阈值法农作物识别过程中阈值设置存在灵巧性差和自动化程度低等弱点,以中国粮食主产区黄淮海平原内河北省衡水市景县为典型实验区,首次将全局优化算法应用于阈值模型中阈值优化选取,开展了利用全局优化算法改进基于阈值检测的农作物分布制图方法创新研究。以冬小麦为研究对象,国产高分一号(GF-1)为主要遥感数据源,在作物面积统计数据为总量控制参考标准和全局参数优化的复合型混合演化算法SCE-UA (Shuffled Complex Evolution-University of Arizona)支持下,提出利用时序NDVI数据开展阈值模型阈值参数自动优化的冬小麦空间分布制图方法。最终,获得实验区冬小麦阈值模型最优参数,并利用优化后的阈值参数对冬小麦空间分布进行提取。通过地面验证表明,利用本研究所提方法获取的冬小麦识别结果分类精度均达到较高水平。其中冬小麦识别结果总量精度达到了99.99%,证明本研究所提阈值模型参数优化方法冬小麦提取分类结果总量控制效果良好;同时,与传统的阈值法、最大似然和支持向量机等分类方法相比,本研究所提阈值模型参数优化法区域冬小麦作物分类总体精度和Kappa系数分别都有所提高,其中,总体精度分别提高4.55%、2.43%和0.15%,Kappa系数分别提高0.12、0.06和0.01,这体现出SCE-UA全局优化算法对提高阈值模型冬小麦空间分布识别精度具有一定优势。以上研究结果证明了利用本研究所提基于作物面积统计数据总量控制以及SCE-UA全局优化算法支持下阈值模型参数优化作物分布制图方法的有效性和可行性,可获得高精度冬小麦作物空间分布制图结果,这对提高中国冬小麦空间分布制图精度和自动化水平具有一定意义,也可为农作物面积农业统计数据降尺度恢复重建和大范围区域作物空间分布制图研究提供一定技术参考。     

高分辨率遥感影像建筑区域局部几何特征提取

及时准确地获取城市建筑区域的空间分布及其变化信息对于城市规划、空间地理数据库建设及区域社会经济分析具有重要意义。本文提出一种基于多尺度Gabor变换和感知聚类方法即张量投票TV (Tensor Voting)相结合的自适应局部几何不变特征检测方法,并将其应用于高空间分辨率遥感影像建筑区域提取。首先,考虑到高分辨率遥感影像复杂的几何结构特征,使用Gabor滤波器组对影像进行多尺度多方向变换检测奇异性特征。然后,在感知聚类框架下,根据张量投票理论将不同方向子带系数位置编码为相应的二阶对称方向张量,为了突出影像几何特征,对不同尺度、不同方向子带中任意像素位置方向张量使用滤波器响应系数加权并求和完成多尺度特征融合。再次,对张量特征分解得到点结构与线结构显著性图并使用非极大抑制提取相应角点和曲线等局部几何特征,同时生成约束准则筛选角点以确定建筑物坐标。最后,利用概率密度估计结合局部角点特征生成全局概率密度场描述影像中像素从属于建筑目标的概率,并使用最大类间方差法(Otsu)阈值分割自动提取居民地多边形区域。使用分辨率分别为0.49 m、0.98 m的Google Earth及0.8 m的高分二号等影像数据集进行实验,实验结果表明本文方法相对于已有的Harris和HSCD点检测算法,在建筑区域提取质量上(Quality)上分别提高了4.79%,5.96%;1.47%,3.76%和1.91%,4.08%。     

特征驱动下区实体匹配指标自适应融合技术

空间实体匹配过程中多个指标的融合问题是影响匹配效果的关键问题之一。本文针对这一问题,以区实体为例提出了一套基于范例库的解决方案。首先提取出影响实体匹配的数据特征因子并确定了量化方法,其次选取典型的匹配指标,接下来通过建立指标权值范例库确定各指标权值,最后根据权值和数据特征因子调整匹配过程。该方法使得数据具有学习能力,达到了指标权值的自适应性的目标。实验表明该方法可行,并且可以提升空间实体匹配算法的效率、准确度和智能化程度。     

Hypsometric analysis of watersheds developed on actively deforming Mohand anticlinal ridge,NW Himalaya

Hypsometric analysis describes the elevation distribution across an area of land surface. It is an important tool to assess and compare the geomorphic evolution of various landforms irrespective of the factor that may be responsible for it. The major factors governing the evolution of landscape are tectonics and/or climate and the variation in lithology. The present study takes into consideration the watersheds developed over actively deforming Mohand anticlinal ridge in the frontal part of NW Himalaya. The hypsometric analysis has been used as a morphometric parameter, i.e. hypsometric integral, to deduce its relationship with the area of watersheds. Statistical analysis of these parameters has been carried out by classifying them into different classes based on the natural breaks method. This brings out strong relationships for hypsometric integral classes and area classes with the number of watersheds in respective classes and the total area occupied by respective hypsometric and area classes. It has also been found that stronger relationships exist for watersheds on the southern flank as compared to watersheds of the northern flank. It also highlights the presence of an anomalous watershed on the northern flank that is possibly responsible for the weak statistical relationships on the northern flank. Removal of this anomalous watershed always brings out much stronger relationships for the northern flank. The anomalous watershed has been directly attributed to the difference in geologic structure as it is spatially related to the presence of the Bhimgoda Back Thrust (BBT) present in the area. The results are awe inspiring and very promising as they indicate some statistically strong relationships among the hypsometric integral and area of watersheds that are not apparent in the spatial distribution of these parameters, especially in actively deforming areas.