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1.
Spatial interpolation has been widely used to improve the spatial granularity of data, or to mediate between inconsistent zoning schemes of spatial data. Traditional areal interpolation methods translate values of source zones to those of target zones. These methods have difficulty in dealing with flow data, as each instance is associated with a pair of zones. This study develops a new concept, flow line interpolation, to fill the abovementioned gap. We also develop a first flow line interpolation method to estimate commuting flow data between spatial units in a target zoning scheme based on such data in a source zoning scheme. Three models (i.e., areal‐weighted, intelligent, and gravity‐type flow line interpolation) are presented. To test the estimation accuracy and the application potential of these models, a case study of Fulton County in Georgia is conducted. The results reveal that both the areal‐weighted and intelligent models are very promising flow line interpolation methods. 相似文献
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人口统计数据的空间分布化研究 总被引：21，自引：0，他引：21
分析了传统的人口空间分布密度衰减函数－指数型和Gauss型，指出了其应用的局限性，对于有两个中心以上的城市，提出了将人口统计数据空间分布化的思路和方法。 相似文献
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张苏 《测绘与空间地理信息》2020,(4):148-150,153
空间插值通过采集少量的数据点,利用其中的空间关联,推求该区域内其他位置的属性值。本文以山东省阳谷县土壤重金属Cu采样数据为例进行空间探索性分析,分别采用了反距离权重插值和普通克里金插值两种方法进行空间分布插值模拟。结果表明,针对研究区采样数据,反距离权重插值方法生成的模型平均误差为1.97 mg/kg,总体精度为92%;普通克里金插值模型的平均误差为1.91 mg/kg,总体精度为92.35%,普通克里金插值方法更优。 相似文献
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针对传统基于空间插值和时间序列上的插值补全形变缺失数据的方法在空间点位分布稀疏、观测值连续缺失以及含有粗差的情况下插补效果不佳的问题,提出了一种基于抗差Kriged Kalman Filter的形变缺失数据插补方法。该方法是一种时空插值的算法,在空间点位分布稀疏时考虑时间上的相关性,在时间上出现连续缺失时考虑其他点位对插补点的影响,以提高插补缺失数据的精度。又将抗差估计融合到Kriged Kalman Filter中以抵抗形变数据中粗差对插补精度的影响。利用模拟数据及天津GPS地面沉降数据进行了实验分析。结果表明:由于该法考虑了监测点的时空相关性以及具有抗差性能,使得插补结果在空间点位稀疏、连续缺失或具有粗差的情况下都具有较高的插补精度。 相似文献
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A Spatial Conditioned Latin Hypercube Sampling Method for Mapping Using Ancillary Data 总被引：2，自引：0，他引：2 
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下载免费PDF全文 Bingbo Gao Yuchun Pan Ziyue Chen Fang Wu Xuhong Ren Maogui Hu 《Transactions in GIS》2016,20(5):735-754
For obtaining maps of good precision by the spatial inference method, the distribution of sampling sites in geographical and feature space is very important. For a regional variable with trends, the predicting error comes from trend estimation, variogram estimation and spatial interpolation. Based on the cLHS (conditioned Latin hypercube Sampling) method, a sampling method called scLHS (spatial cLHS) considering all these three aspects with the help of ancillary data is proposed in this article. Its advantage lies in simultaneously improving trend estimation, variogram estimation and spatial interpolation. MODIS data and simulated data were used as sampling fields to draw sample sets using scLHS, cLHS, cLHS with x and y coordinates as covariates, simple random and spatial even sampling methods, and the distribution and prediction errors of sample sets from different methods were evaluated. The results showed that scLHS performed well in balancing spreading in geographic and feature space, and can generate points pairs with small distances, and the sample sets drawn by scLHS produced smaller mapping error, especially when there were trends in the target variable. 相似文献
6.
Eryong Liu 《制图学和地理信息科学》2016,43(4):321-327
This article presents a new development in measuring the positional error of line features in Geographic Information Systems (GIS), in the form of a new measure for estimating the average error variance of line features, including line segment, polyline, polygon, and curved lines. This average error measure is represented in the form of a covariance matrix derived by an analytical approach. Corresponding error indicators are derived from this matrix. The error of line features mainly results from two factors: (1) an error propagated from the original component points of line features and (2) a model error of interpolation between these points. In this study, a method of average error estimation has been derived regarding the first type error of line features that are interpolated by either linear or cubic interpolation methods. The main contribution of the research is the provision of an error measure to assess the quality of spatial data in application settings. The proposed error models for estimating average error variance of line features in a GIS are illustrated by both simulated and practical experiments. The results show that the line accuracy from a linear interpolation is better than a line interpolated using a cubic model. 相似文献
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为了分析云南元谋干热河谷典型冲沟插值误差的空间分布特征,采用反距离加权（inverse distance weighting,IDW）、局部多项式（local polynomial interpolation,LPI）、张力样条（spline with tension,ST）、析取克里格（disjunctive Kriging,DK）以及不规则三角网（triangulated irregular network,TIN）模型方法对高程采样点进行插值,用交叉验证法、相对差系数及沟谷线差异衡量其插值精度。遴选高程误差大于1 m的误差点,用变异系数（coefficient of variation,CV）、全局Moran指数和Getis-Ord Gi*指数分析其空间格局特征。结果表明:TIN和DK精度较高,IDW精度最低;高程误差均呈聚集分布,聚集程度TIN > LPI > DK > ST > IDW;高程误差均呈空间正自相关,TIN模型插值误差的自相关程度最高;误差热点位于坡度大的区域。 相似文献
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针对多路径误差的空间分布特征,该文提出采用克里金插值法构建多路径误差的空间插值模型。通过采用ArcGIS中克里金插值工具构建的多路径误差的空间插值模型,较好地展现出多路径误差与测站位置、反射源距离等因素的空间分布特征。实验结果表明:对比泛克里金插值法、普通克里金插值法、简单克里金插值法3种方法构建的多路径误差插值模型,普通克里金插值法的效果最好。研究成果直观反映出在林区、多层建筑物等环境下多路径误差影响较大,还预测出未采样区域的多路径误差影响范围,该研究成果可应用于GPS观测站选址、GPS测量技术设计等领域。该文从空间分布特征的角度提出了一种新的研究多路径误差特性的思路,实现了对指定测区内的多路径误差的空间分布特征的探究。 相似文献
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《制图学和地理信息科学》2013,40(4):273-274
A number of areal interpolation methods have been developed to estimate population for overlapping, discontinuous, or fragmented areas. Previous studies examined the relative accuracy of various methods; this research advances those endeavors by comparing the effectiveness of seven different methods using a national random sample of census block groups and blocks. As the results show, the areal interpolation methods produce good population estimates for nested census blocks except in areas of heterogeneous land use or unusual contexts. In addition, estimation conducted in areas with small populations or low population density was vulnerable to high percentage error. Amongst the different methods, road network allocation and statistical regression (with area and roads as predictors) produced the best population estimates for the sample blocks. 相似文献
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在比较分析已有内插模型的基础上,以卫星高度角、参考站高程、距离为变量,以大气映射函数为基本模型,提出了一种新的适用于高程差异大的大气误差内插模型。利用江苏连续运行参考站系统部分参考站数据,采用不同的内插模型进行处理、比较与分析。结果表明,对于高程差异大、卫星高度角小的用户站,映射函数估计方法、低阶曲面模型能够准确地估计出其与主参考站之间双差对流层误差,其最大均方差不超过2cm;映射函数估计双差电离层误差方法与低阶曲面模型、线性内插模型的估计精度相当。在以上4种方法的比较中,距离线性内插模型的估计精度最差,将近5cm。 相似文献
11.
《制图学和地理信息科学》2013,40(4):411-423
Over the years many approaches to areal interpolation have been developed and utilized. They range from the simple 2-D areal weighing method which uses only the spatial Z variable being processed, to more sophisticated approaches which use auxiliary variable(s) to provide more specificity to the results. In the research reported here, four promising approaches are implemented and comparatively tested. These approaches have widely varying conceptual foundations, and different auxiliary variables, if used. The areal weighing reflects many earlier methods which assumes uniform distributions of the spatial Z variable, and does not use any auxiliary variable. Tobler's pycnophylactic method uses a volumetric preservation approach, which assumes spatial Z variable is heterogeneously distributed, but does not use any auxiliary variable. The traditional dasymetric method of Wright is used with remote sensing spectral data of land use. Xie's road network hierarchically weighted interpolation uses the road network as the auxiliary variable, and assumes that population density is related to the class of the road, and to the density of the road network. The research design implemented here uses Census population distributions at different levels in the hierarchy as the source and target variables analyzed. The source zone population is taken at the Census Tract level, and the target zones are specified at the Census Block Group level in the hierarchy. The first two tests use only the Census population Z data, and no auxiliary variables, whereas the next uses remotely sensed land use data as the auxiliary data variable, and the fourth test utilizes the road network hierarchy as the auxiliary variable. The paper reports on the findings from these tests, and then interprets them in a spatial setting in terms of accuracy and effectiveness. This research points to the network method as the most accurate of the areal interpolation methods tested in this research. 相似文献
12.
高精度降水场是水文、气象以及环境分析的重要数据支撑,直接影响相关服务的准确性。传统降水分布模拟大多依赖站点空间维的驱动因素,而忽略了降水时序变化特征对其空间分布的影响。使用2015—2017年中国湖北省83个国家气象观测站点和28个省级观测站点近3 a月平均累积降水资料,通过相关性分析,引入站点降水时序理论变差函数模型的拱高值(C)和块金值(C0)作为影响因素,使用地理加权回归(geographically weighted regression, GWR)建立湖北省月平均降水分布模型。结果表明:（1）各站点降水的时序变差函数曲线与降水的季节性基本吻合。站点时序理论变差函数模型中,有25.3%能够在4个月内达到平稳,36.14%在6个月内达到平稳。（2）站点降水时序理论变差函数模型的C和C0与逐年12月平均累积降水在0.01水平（双侧）上显著相关,平均相关系数分别为0.745和0.526,大于地理位置和高程对降水的影响。（3）引入C和C0 有助于提升GWR模型的整体拟合优度和插值精度。对比仅使用经纬度的GWR模型和引入时序理论变差函数特征的GWR模型,3 a平均整体拟合优度从0.852提升至0.912。验证集站点插值精度评价显示,3 a绝对误差、均方根误差和平均绝对百分误差下降幅度均大于60%。因此,引入时序理论变差函数特征的时空GWR模型能够获得较高精度的降水模拟结果,更适合具有丰富历史降水资料地区的降水空间分布估算。 相似文献
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基于最小二乘配置误差估计公式,建立了重力异常格网数据的分辨率和精度与重力异常内插值精度的关系,提出了在给定插值精度时反推已知格网数据的分辨率和精度的方法。以EGM2008重力场模型为例,在不同分辨率和精度条件下进行重力异常插值实验。实验结果与本文方法的计算结果基本一致,表明该方法具有一定的可行性。 相似文献
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This research evaluates the performance of areal interpolation coupled with dasymetric refinement to estimate different demographic attributes, namely population sub-groups based on race, age structure and urban residence, within consistent census tract boundaries from 1990 to 2010 in Massachusetts. The creation of such consistent estimates facilitates the study of the nuanced micro-scale evolution of different aspects of population, which is impossible using temporally incompatible small-area census geographies from different points in time. Various unexplored ancillary variables, including the Global Human Settlement Layer (GHSL), the National Land-Cover Database (NLCD), parcels, building footprints and the proprietary ZTRAX® dataset are utilized for dasymetric refinement prior to areal interpolation to examine their effectiveness in improving the accuracy of multi-temporal population estimates. Different areal interpolation methods including Areal Weighting (AW), Target Density Weighting (TDW), Expectation Maximization (EM) and its data-extended approach are coupled with different dasymetric refinement scenarios based on these ancillary variables. The resulting consistent small area estimates of white and black subpopulations, people of age 18–65 and urban population show that dasymetrically refined areal interpolation is particularly effective when the analysis spans a longer time period (1990–2010 instead of 2000–2010) and the enumerated population is sufficiently large (e.g., counts of white vs. black). The results also demonstrate that current census-defined urban areas overestimate the spatial distribution of urban population and dasymetrically refined areal interpolation improves estimates of urban population. Refined TDW using building footprints or the ZTRAX® dataset outperforms all other methods. The implementation of areal interpolation enriched by dasymetric refinement represents a promising strategy to create more reliable multi-temporal and consistent estimates of different population subgroups and thus demographic compositions. This methodological foundation has the potential to advance micro-scale modeling of various subpopulations, particularly urban population to inform studies of urbanization and population change over time as well as future population projections. 相似文献
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Traditionally, areal interpolation has referred to techniques for transferring attribute values from one partitioning of space to a different partition of space but this is only one of several situations that create the need for estimating unknown data values for areal units. This paper presents a categorization of four areal interpolation problems that includes the "missing" data problem, the traditional "alternative geography" problem, the overlay of a choropelth and an area-class data layer, and the overlay of two choropleth data layers and demonstrates the relationship between the last three problems and general spatial interaction modelling. The "alternative geography" and overlay of choropleth and area-class data layers mirrors a singly constrained spatial interaction model while the overlay of two choropleth layers is analogous to a doubly constrained interaction model. Iterative proportional fitting techniques with and without ancillary data are developed to solve these three classes of problems. 相似文献
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Jaroslav Hofierka Juraj Parajka Helena Mitasova & Lubos Mitas 《Transactions in GIS》2002,6(2):135-150
Regularized Spline with Tension (RST) is an accurate, flexible and efficient method for multivariate interpolation of scattered data. This study evaluates its capabilities to interpolate daily and annual mean precipitation in regions with complex terrain. Tension, smoothing and anisotropy parameters are optimized using the cross-validation technique. In addition, smoothing and rescaling of the third variable (elevation) is used to minimize the predictive error. The approach is applied to data sets from Switzerland and Slovakia and interpolation accuracy is compared to the results obtained by several other methods, expert-drawn maps and measured runoff. The results demonstrate that RST performs as well or better than the methods tested in the literature. The incorporation of terrain improves the spatial model of precipitation in terms of its predictive error, spatial pattern and water balance. 相似文献
19.
Population at risk of crime varies due to the characteristics of a population as well as the crime generator and attractor places where crime is located. This establishes different crime opportunities for different crimes. However, there are very few efforts of modeling structures that derive spatiotemporal population models to allow accurate assessment of population exposure to crime. This study develops population models to depict the spatial distribution of people who have a heightened crime risk for burglaries and robberies. The data used in the study include: Census data as source data for the existing population, Twitter geo-located data, and locations of schools as ancillary data to redistribute the source data more accurately in the space, and finally gridded population and crime data to evaluate the derived population models. To create the models, a density-weighted areal interpolation technique was used that disaggregates the source data in smaller spatial units considering the spatial distribution of the ancillary data. The models were evaluated with validation data that assess the interpolation error and spatial statistics that examine their relationship with the crime types. Our approach derived population models of a finer resolution that can assist in more precise spatial crime analyses and also provide accurate information about crime rates to the public. 相似文献
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针对目前DEM（Digital Elevation Model, DEM）数字地形分析的精度评价多数不考虑DEM误差的空间自相关性或仅仅采用经验的自相关性模型问题,本文从DEM插值入手,从理论上推导了插值条件下格网DEM邻域窗口内坡度噪声误差的空间自相关性模型以及坡度精度模型,并选取典型的插值方法和坡度差分算法,从实验角度分析了在顾及和不顾及空间自相关性两种情况下的格网DEM坡度计算模型的噪声精度,实验结果表明：坡度精度受DEM噪声误差的空间自相关性影响较大,并与DEM插值方法和坡度计算模型中的差分算法有关。 相似文献