Using ESDA with social weights to analyze spatial and social patterns of preschool children's behavior

To study the development of spatial and social behavior of preschool children, micro-level spatiotemporal data were collected for the first time in both spatial and social context using a novel behavioral coding system. These unique behavioral data enable us to explore the group-level, dynamic, spatial, and social patterns of preschool children's playing behavior from a hybrid geographic and social perspective. In this research, GIS and exploratory spatial data analysis (ESDA) techniques are employed together to study group-level spatial and social behavior emerging from children's everyday activities and interactions. ESDA with social weights is proposed to explore spatial and social patterns of preschool children's behavior at the same time. The results highlight the utility of this approach for studying the relationships between preschool children's playing behavior and preschool's environmental settings and the relationships between preschool children's personal activities and the formation of their social network space.     

利用SPAC法估算地壳S波速度结构

S波速度结构能够反映地球介质的物性差异,是地壳内低速区结构特征判别的重要依据.本文尝试利用空间自相关法(SPAC法)从地震台站微动信号的垂直分量中提取瑞利波相速度频散曲线,通过对频散曲线的反演获得地下介质的S波速度结构.以国家数字测震台网8个宽频带地震台站的实测微动数据为例,采用SPAC方法获得了首都圈地区北京附近约30 km 深度范围内的一维S波速度结构.结果表明,该区结晶基底埋深较浅约2 km;分别在5~8 km 和12~16 km 深处发育S波低速层;8 km 和 20 km 处是S波速度差异较大的速度分界面.这一结果与以往地震学及人工地震探测结果较为吻合,表明SPAC法估算地壳S波速度结构是可行、有效的.     

我国东部沿海三大经济区区域经济的空间自相关分析

本文以我国东部沿海三大经济区域——环渤海、长三角、珠三角为研究对象,依据空间统计学的空间自相关方法,采用以高速公路里程数为影响因子改进的空间权重矩阵,以及全局和局部Moran I指数模型,通过ArcToolbox空间分析工具分析三大区域经济空间格局。研究结果表明,长三角与珠三角区域发展呈显著的空间正相关,长三角地区较之其他两地区发展最为成熟,发展极辐射大。同时地理环境对于区域经济发展作用明显,发展核心城市集中于沿江地区或平原地区。     

基于时间序列分析的变形预报

研究了利用时间序列分析方法进行变形预报。首先叙述了变形观测数据预处理、时间序列平稳性检验、模型的选用和检验;然后针对一组实测数据,利用多项式提取趋势项,分析回归残差,建立了AR(2)预报模型,并利用模型进行了预报;最后将预报结果与实测数据比较,证明了预报模型的有效性。     

半方差函数与Moran’s I在土壤微量元素空间分布研究中的应用——以寿光市为例

利用半方差函数与Moran’s I两种空间分析方法,研究了山东省寿光市土壤中微量元素的空间分布特征,以探讨不同空间分析方法的差异性与准确性。结果表明,无论表层土壤还是深层土壤,半方差函数分析得到的土壤微量元素的空间相关距离大约为20~60 km,而空间自相关图获得的正自相关距离为20~25km,负自相关距离为25~55 km。除深层土壤中的Zn元素外,其他元素均达到了显著的自相关水平。空间自相关方法对土壤微量元素空间相关性的判断要优于半方差函数法。     

中国区域经济发展的地区差异GIS分析

本文主要针对我国改革开放以来地区间经济发展的差距究竟是扩大(发散)还是缩小(收敛)的问题开展了研究.首先,对全国各省(市)1980 - 2008年的人均名义GDP数据进行了空间自相关分析,探讨了全国区域经济发展的地区差异变化,揭示了1984年以后中国各地区之间的经济发展的正空间自相关关系,即全国区域经济发展差异在整体上...     

中国经济增长与制度变迁的空间面板数据分析

以North的体制经济学为理论依据,将新古典经济学的Solow增长模型扩展为一个包含制度变量的计量经济学模型,并结合空间面板数据分析方法,使用1997—2005年中国省区的社会经济数据实证检验了经济制度环境对经济增长的影响。研究结果表明:该时期的制度变迁对中国的经济增长具有显著贡献力,证实了North的制度经济学理论假设。此外,空间面板数据的分析方法深入地揭示了省域经济活动之间存在的空间自相关性,即地区之间通过技术扩散、要素转移以及社会文化交流等在地理空间上发生相互影响。     

基于GIS的区域经济统计分析模型研究及应用——以江西省为例

针对当前GIS软件不能有效满足区域经济统计分析的问题,提出了利用GIS技术重新构建区域经济统计分析模型的新思路。对该模型进行了具体建模和编程实现,并将该模型集成到区域经济统计分析系统中,最后利用该系统对江西省各地区经济进行了统计分析的应用实践。研究表明,该区域经济统计分析模型能够有效地应用于区域的经济统计分析,方便政府部门制定与经济相关的各种决策,具有广泛的应用推广价值。     

Cobble cam: grain‐size measurements of sand to boulder from digital photographs and autocorrelation analyses

A new application of the autocorrelation grain size analysis technique for mixed to coarse sediment settings has been investigated. Photographs of sand‐ to boulder‐sized sediment along the Elwha River delta beach were taken from approximately 1·2 m above the ground surface, and detailed grain size measurements were made from 32 of these sites for calibration and validation. Digital photographs were found to provide accurate estimates of the long and intermediate axes of the surface sediment (r² > 0·98), but poor estimates of the short axes (r² = 0·68), suggesting that these short axes were naturally oriented in the vertical dimension. The autocorrelation method was successfully applied resulting in total irreducible error of 14% over a range of mean grain sizes of 1 to 200 mm. Compared with reported edge and object‐detection results, it is noted that the autocorrelation method presented here has lower error and can be applied to a much broader range of mean grain sizes without altering the physical set‐up of the camera (～200‐fold versus ～6‐fold). The approach is considerably less sensitive to lighting conditions than object‐detection methods, although autocorrelation estimates do improve when measures are taken to shade sediments from direct sunlight. The effects of wet and dry conditions are also evaluated and discussed. The technique provides an estimate of grain size sorting from the easily calculated autocorrelation standard error, which is correlated with the graphical standard deviation at an r² of 0·69. The technique is transferable to other sites when calibrated with linear corrections based on photo‐based measurements, as shown by excellent grain‐size analysis results (r² = 0·97, irreducible error = 16%) from samples from the mixed grain size beaches of Kachemak Bay, Alaska. Thus, a method has been developed to measure mean grain size and sorting properties of coarse sediments. Copyright © 2009 John Wiley & Sons, Ltd.     

Analyzing spatial ecological data using linear regression and wavelet analysis

Spatial (two-dimensional) distributions in ecology are often influenced by spatial autocorrelation. In standard regression models, however, observations are assumed to be statistically independent. In this paper we present an alternative to other methods that allow for autocorrelation. We show that the theory of wavelets provides an efficient method to remove autocorrelations in regression models using data sampled on a regular grid. Wavelets are particularly suitable for data analysis without any prior knowledge of the underlying correlation structure. We illustrate our new method, called wavelet-revised model, by applying it to multiple regression for both normal linear models and logistic regression. Results are presented for computationally simulated data and real ecological data (distribution of species richness and distribution of the plant species Dianthus carthusianorum throughout Germany). These results are compared to those of generalized linear models and models based on generalized estimating equations. We recommend wavelet-revised models, in particular, as a method for logistic regression using large datasets.