面插值的研究进展

空间插值从广义上讲包括点插值和面插值 ,本文从有无辅助数据的角度介绍了还不为多数人熟知的面插值 ,在无辅助数据的方法中介绍了基于点的面插值法、面域比重插值法、使用控制区的面域比重法、Pycnophylactic面插值法等的算法和特点 ,在有辅助数据的方法中介绍了 EM算法和遥感作为辅助数据的面插值法 ,并介绍了面插值中的特例 -统计数据的空间化方法。     

一种改进的生成区域日降水场的方法及精度分析

利用全国687个气象站点11年的日降水数据,对基于地理特征和统计回归的函数拟合类模型DAYMET生成中国区域日降水场的能力进行了验证。交叉验证表明,DAYMET模型估计日降水累计得到的年降水量的绝对偏差11年平均为29.8%,年降水总量估计偏差低于20％的站点占48.3％。鉴于中国陆地区域降水深受季风的影响,不同方位气象站点对插值点的影响也有所不同,引入了站点不同方位对插值的影响权重,对DAYMET模型进行了改进,改进后年降水量的绝对偏差降为27%。与梯度距离平方反比法相比,该方法具有较高的区域降水插值精度。还以无定河流域降水插值为例,说明降水插值精度的高低与区域内雨量站点的多寡紧密相联。     

基于贝叶斯最大熵的甘肃省多年平均降水空间化研究

 贝叶斯最大熵方法可以对具有一定不确定性的“软数据”和认为没有误差的“硬数据”进行插值。对甘肃省1961—1990年52个气象站点的多年平均降水数据进行空间化研究。通过比较普通克里格、共协克里格、三元回归建模后残差插值以及基于贝叶斯最大熵的3种不同软硬数据参与情况下的插值结果,发现考虑降水30 a时间序列不完整性以及辅助变量经验模型不确定性的插值结果的MAE和RMSE,比直接使用多年平均降水数据直接插值的MAE和RMSE小,表明贝叶斯最大熵方法通过对不确定性的考虑可以有效降低预测结果的绝对误差。从降水的空间分布来看,考虑辅助变量DEM的插值结果能相对较好的体现高程对降水的地形影响,尤其分区将辅助变量转换为软数据可以有效体现不同区域高程对降水的不同影响问题。综合误差评价以及降水插值结果的空间分布,认为BME插值过程中可以考虑数据本身以及辅助数据利用的不确定性,使降水空间化的结果更加真实客观,同时为合理利用辅助信息提供了一个新思路。     

资源型城镇空间扩展遥感监测分析——以鄂尔多斯市为例

以RS和GIS作为技术支撑,基于多期Landsat TM期遥感影像数据,采用全数字化人机交互式解译方法提取鄂尔多斯市城镇空间信息特征,结合GIS空间分析功能,以地学统计方法定量研究了城镇扩展的强度和紧凑度,同时以评价指标与社会经济统计数据相结合方式,对鄂尔多斯市城镇扩展驱动力进行分析,结果表明资源的开发导致经济发展和人口增长是城镇扩展的重要驱动因子,政府政策性要素在宏观上及区域地理环境作用着城镇的扩展。     

人口数据空间化的处理方法

在人口空间分布区划的基础上,利用基于LANDSAT TM信息获取的1:10万比例尺的土地利用/覆盖数据,建立与统计人口数据的多元相关关系模型,计算各种士地利用类型中的居住人口系数,在GIS支持下计算出全国1km格网人口空间分布数据,然后结合DEM数据、居民点分布数据对空间化处理结果进行修正,并在各大区内随机抽样若干县采集乡镇行政边界和统计人口数据对模型计算结果进行了验证.     

空间插值方法的适用性分析初探

空间插值是地学研究中的基本内容,也是GIS空间分析中的主要方法之一。如何科学、有效地选择适合于特定问题的插值模型显得尤为重要。为解决此问题,该文以ArcGIS提供的常用空间插值模型为研究对象,采用理论模型研究和试验对比的方式,按照各种插值模型的预测能力、先决条件、复杂程度、输出精度和处理速度等进行分类,对每种内插类型的适用性应用场景进行详细阐述。研究发现,针对同一应用场景,采用不同的内插方法产生的结果具有明显的差异,各类插值模型所关注问题的侧重点也各不相同。这进一步表明,按照插值的各种特征对常用内插方法进行分类,有助于在具体的问题中选用更为适合的内插方法,可为插值模型的科学应用提供参考。     

多元统计回归及地理加权回归方法在多尺度人口空间化研究中的应用

对统计型人口数据进行格网形式的空间化可更直观地展示人口的空间分布,但不同的人口空间化建模方法和不同的格网尺度在表达人口空间化结果方面存在差异。本文在人口特征分区的基础上,引入DMSP/OLS夜间灯光对城镇用地进行再分类,采用多元统计回归和地理加权回归方法(GWR),开展人口统计数据空间化多尺度模型研究,生成1 km、5 km和10 km等3个尺度的2010年安徽省人口空间数据,并对3个尺度下2个模型结果进行精度评价与比较。结果表明：人口空间数据精度不仅与建模所用方法关系密切,还受到建模格网尺度大小的影响。基于多元统计回归方法的模型估计人口数与实际人口的平均相对误差值随着尺度的增加而降低,而基于GWR方法获得的人口空间数据误差值随着尺度的增加而升高。整体来看,基于GWR方法的1 km研究尺度的人口空间数据平均相对误差最低(22.31%)。区域地形地貌条件与人口空间数据误差有较强的关联,地貌类型复杂的山区人口空间数据误差较大。     

基于空间异质分区的残差IDW插值方法

空间插值可以利用已有观测数据修补缺失的观测数据,也可以利用离散数据构建连续的表面数据,但现有的空间插值方法没有充分考虑空间数据的异质性。该文提出一种基于空间异质分区的残差反距离加权插值方法(RRIDW)。首先根据采样点属性值对研究区域进行空间异质分区;为了进一步去除不同子区域内的空间趋势,对每个子区域计算趋势面,进而计算得到采样点属性值的异质分区残差,利用属性值残差进行反距离加权插值;最后结合趋势计算得到待求点处的空间插值结果。实验采用两组实际PM2.5浓度数据和降雨量数据,运用交叉验证方法对RRIDW方法与其他常用空间插值方法进行对比分析,验证了该方法的优越性和可行性。     

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。     

人口统计数据的空间转换

在经济和社会研究中,所要研究的区域之上经常没有数据,而这些数据需要由已知区域的数据求得,即统计数据需要空间转换,这就通常涉及到面积内插。本文从GIS的角度研究如何解决人口内插问题,认为面积内插和GIS中的叠加分析是一致的。在传统的面积内插方法的基础上是提出了基于人口真实分布的面积内插方法,并推导出了公式。同时提出了人口密度的递归算法,即把居住区分为人口稀疏地区和人口稠密地区,估计出人口稀疏地区的人口密度,就可以求出人口密集地区的人口密度;再把人口密集区分为新的人口稀疏区和密集区,此过程反复直至求出接近于人口真实分布的人口模型。     

Fine-resolution population mapping using OpenStreetMap points-of-interest

Data on population at building level is required for various purposes. However, to protect privacy, government population data is aggregated. Population estimates at finer scales can be obtained through areal interpolation, a process where data from a first spatial unit system is transferred to another system. Areal interpolation can be conducted with ancillary data that guide the redistribution of population. For population estimation at the building level, common ancillary data include three-dimensional data on buildings, obtained through costly processes such as LiDAR. Meanwhile, volunteered geographic information (VGI) is emerging as a new category of data and is already used for purposes related to urban management. The objective of this paper is to present an alternative approach for building level areal interpolation that uses VGI as ancillary data. The proposed method integrates existing interpolation techniques, i.e., multi-class dasymetric mapping and interpolation by surface volume integration; data on building footprints and points-of-interest (POIs) extracted from OpenStreetMap (OSM) are used to refine population estimates at building level. A case study was conducted for the city of Hamburg and the results were compared using different types of POIs. The results suggest that VGI can be used to accurately estimate population distribution, but that further research is needed to understand how POIs can reveal population distribution patterns.     

Evaluating geo-located Twitter data as a control layer for areal interpolation of population

Control data are critical for improving areal interpolation results. Remotely sensed imagery, road network, and parcels are the three most commonly used ancillary data for areal interpolation of population. Meanwhile, the open access geographic data generated by social networks is emerging as an alternative control data that can be related to the distribution of population. This study evaluates the effectiveness of geo-located night-time tweets data as ancillary information and its combination with the three commonly used ancillary datasets in intelligent areal interpolation. Due to the skewed Twitter user age, the other purpose of this study is to test the effect of age bias control data on estimation of different age group populations. Results suggest that geo-located tweets as single control data does not perform as well as the three other control layers for total population and all age-specific population groups. However, the noticeable enhancement effect of Twitter data on other control data, especially for age groups with a high percentage of Twitter users, suggests that it helps to better reflect population distribution by increasing variation in densities within a residential area delineated by other control data.     

A Point-Based Intelligent Approach to Areal Interpolation

Areal interpolation is the data transfer from one zonal system to another. A survey of previous literature on this subject points out that the most effective methods for areal interpolation are the intelligent approaches, which often take two-dimensional (2-D) land use or one-dimensional (1-D) road network information as ancillary data to give insight on the underlying distribution of a variable. However, the 2-D or 1-D ancillary information is not always applicable for the variable of interest in a specific study area. This article introduces a point-based intelligent approach to the areal interpolation problem by using zero-dimensional (0-D) points as ancillary data that are locationally associated with the variable of interest. The connection between zonal variables and point locations can be modeled with a linear or a nonlinear exponential function, which incorporates the distribution of the variables in the transferring of the information from the source zone to the target zone. An experimental study interpolating the population data at a suburbanized area suggests that the proposed method is an attractive alternative to other areal interpolation solutions based on the evaluation of its resulting accuracy and efficiency.     

Enhancing areal interpolation frameworks through dasymetric refinement to create consistent population estimates across censuses

To assess micro-scale population dynamics effectively, demographic variables should be available over temporally consistent small area units. However, fine-resolution census boundaries often change between survey years. This research advances areal interpolation methods with dasymetric refinement to create accurate consistent population estimates in 1990 and 2000 (source zones) within tract boundaries of the 2010 census (target zones) for five demographically distinct counties in the US. Three levels of dasymetric refinement of source and target zones are evaluated. First, residential parcels are used as a binary ancillary variable prior to regular areal interpolation methods. Second, Expectation Maximization (EM) and its data-extended version leverage housing types of residential parcels as a related ancillary variable. Finally, a third refinement strategy to mitigate the overestimation effect of large residential parcels in rural areas uses road buffers and developed land cover classes. Results suggest the effectiveness of all three levels of dasymetric refinement in reducing estimation errors. They provide a first insight into the potential accuracy improvement achievable in varying geographic and demographic settings but also through the combination of different refinement strategies in parts of a study area. Such improved consistent population estimates are the basis for advanced spatio-temporal demographic research.     

Stochastic model-based methods for handling uncertainty in areal interpolation

Handling of uncertainty in the estimation of values from source areas to target areas poses a challenge in areal interpolation research. Stochastic model-based methods offer a basis for incorporating such uncertainty, but to date they have not been widely adopted by the GIS community. In this article, we propose one use of such methods based in the problem of interpolating count data from a source set of zones (parishes) to a more widely used target zone geography (postcode sectors). The model developed also uses ancillary statistical count data for a third set of areas nested within both source and target zones. The interpolation procedure was implemented within a Bayesian statistical framework using Markov chain Monte Carlo methods, enabling us to take account of all sources of uncertainty included in the model. Distributions of estimated values at the target zone level are presented using both summary statistics and as individual realisations selected to illustrate the degree of uncertainty in the interpolation results. We aim to describe the use of such stochastic approaches in an accessible way and to highlight the need for quantifying estimation uncertainty arising in areal interpolation, especially given the implications arising when interpolated values are used in subsequent analyses of relationships.     

SWAT分布式流域水文物理模型的改进及应用研究

SWAT (Soil and Water Assessment Tool) 模型是一个集成遥感 (RS)、地理信息系统 (GIS) 和数字高程模型(DEM)技术的先进的分布式流域水文物理模型。为了推动该模型在中国的适应性研究及应用,并改进模型以提高水文模拟的精度,针对模型在中国西北寒旱区的黑河流域和中西部温润的汉江流域的水文模拟中发现的问题进行了扩充和改进,增加了土壤粒径转换模块和天气发生器(WGEN)数据预处理模块,改进了模型中的WGEN算法、潜在蒸散量模拟算法以及气象参数的空间离散方法。利用扩充和改进后的模型对汉江褒河上游江口流域的降雨-径流过程进行了系统的研究。结果表明,不仅模型的使用效率有明显提高,而且改进后模型的效率系数和相关系数也比改进前有较大改善。     

Research Note—A New Method for Mapping Population and Understanding the Spatial Dynamics of Disease in Urban Areas: Asthma in the Bronx,New York

Determining an accurate depiction of population distribution for urban areas in order to develop an improved "denominator" is important for the calculation of higher-precision rates in GIS analyses, particularly when exploring the spatial dynamics of disease. Rather than using data aggregated by arbitrary administrative boundaries such as census tracts, we developed the Cadastral-Based Expert Dasymetric System (CEDS), an interpolation method using ancillary information to delineate areas of homogeneous values. This method uses cadastral data, land-use filters, modeling by expert system routines, and validation against various census enumeration units and other data. The CEDS method is presented through a case study of asthma hospitalizations in the borough of the Bronx in New York City, in relation to proximity buffers constructed around major sources of air pollution. The analysis using CEDS shows that asthma hospitalization risk due to proximity to pollution sources is greater than previously calculated using traditional disaggregation methods.     

多源信息的集成与融合及其在遥感制图中的优化利用

多源信息的集成和融合是地球信息科学领域的一大热点问题。它的意义和必要性与地球信息本身的特征、采集信息的手段特征及信息处理平台或系统的特点三方面紧密相联系。本文从不同传感器信息的集成和融合、遥感信息与非遥感地学信息的集成和融合、不同格式的 GIS数据的集成和复合三个方面研究了多源信息集成和融合的方法、前沿技术和应用领域,进而以黄土高原土壤侵蚀遥感调查和制图任务为例,介绍了多源信息集成和融合技术在该项目中的优化应用实例,包括技术流程分析、信息源分析、多源信息在土壤侵蚀遥感调查和制图中的融合方法、从遥感图像解译信息到 GIS数据库的转换技术等。