Geostatistical Prediction and Simulation of Point Values from Areal Data

The spatial prediction and simulation of point values from areal data are addressed within the general geostatistical framework of change of support (the term support referring to the domain informed by each measurement or unknown value). It is shown that the geostatistical framework (i) can explicitly and consistently account for the support differences between the available areal data and the sought-after point predictions, (ii) yields coherent (mass-preserving or pycnophylactic) predictions, and (iii) provides a measure of reliability (standard error) associated with each prediction. In the case of stochastic simulation, alternative point-support simulated realizations of a spatial attribute reproduce (i) a point-support histogram (Gaussian in this work), (ii) a point-support semivariogram model (possibly including anisotropic nested structures), and (iii) when upscaled, the available areal data. Such point-support-simulated realizations can be used in a Monte Carlo framework to assess the uncertainty in spatially distributed model outputs operating at a fine spatial resolution because of uncertain input parameters inferred from coarser spatial resolution data. Alternatively, such simulated realizations can be used in a model-based hypothesis-testing context to approximate the sampling distribution of, say, the correlation coefficient between two spatial data sets, when one is available at a point support and the other at an areal support. A case study using synthetic data illustrates the application of the proposed methodology in a remote sensing context, whereby areal data are available on a regular pixel support. It is demonstrated that point-support (sub-pixel scale) predictions and simulated realizations can be readily obtained, and that such predictions and realizations are consistent with the available information at the coarser (pixel-level) spatial resolution.     

Area‐to‐Point Prediction Under Boundary Conditions

This article proposes a geostatistical solution for area‐to‐point spatial prediction (downscaling) taking into account boundary effects. Such effects are often poorly considered in downscaling, even though they often have significant impact on the results. The geostatistical approach proposed in this article considers two types of boundary conditions (BC), that is, a Dirichlet‐type condition and a Neumann‐type condition, while satisfying several critical issues in downscaling: the coherence of predictions, the explicit consideration of support differences, and the assessment of uncertainty regarding the point predictions. An updating algorithm is used to reduce the computational cost of area‐to‐point prediction under a given BC. In a case study, area‐to‐point prediction under a Dirichlet‐type BC and a Neumann‐type BC is illustrated using simulated data, and the resulting predictions and error variances are compared with those obtained without considering such conditions.     

Reconstructing Population Density Surfaces from Areal Data: A Comparison of Tobler's Pycnophylactic Interpolation Method and Area-to-Point Kriging

We compare Tobler's pycnophylactic interpolation method with the geostatistical approach of area-to-point kriging for distributing population data collected by areal unit in 18 census tracts in Ann Arbor for 1970 to reconstruct a population density surface. In both methods, (1) the areal data are reproduced when the predicted population density is upscaled; (2) physical boundary conditions are accounted for, if they exist; and (3) inequality constraints, such as the requirement of non-negative point predictions, are satisfied. The results show that when a certain variogram model, that is, the de Wijsian model corresponding to the free-space Green's function of Laplace's equation, is used in the geostatistical approach under the same boundary condition and constraints with Tobler's approach, the predicted population density surfaces are almost identical (up to numerical errors and discretization discrepancies). The implications of these findings are twofold: (1) multiple attribute surfaces can be constructed from areal data using the geostatistical approach, depending on the particular point variogram model adopted—that variogram model need not be the one associated with Tobler's solution and (2) it is the analyst's responsibility to justify whether the smoothness criterion employed in Tobler's approach is relevant to the particular application at hand. A notable advantage of the geostatistical approach over Tobler's is that it allows reporting the uncertainty or reliability of the interpolated values, with critical implications for uncertainty propagation in spatial analysis operations.     

The China factor in the US–South Korea alliance: the perceived usefulness of China in the Korean Peninsula

Over the past 10 years, South Korea has chosen inconsistent strategies with respect to the US–South Korea alliance. On the one hand, Seoul disagreed with Washington about the extended role of United States Forces Korea and the deployment of US missile defence systems in East Asia. On the other hand, these problems ironically coincided with South Korea's strong support for the USA in operations in Afghanistan and Iraq. What explains the inconsistency of South Korea's alliance policies? Major schools of thought in international relations have offered explanations, but their analyses are deficient and indeterminate. This article looks at the South Korea–China–North Korea triangle as a new approach to explaining the puzzling behaviour of South Korea. The model shows that South Korea's alliance policies are driven by two causal variables. First, North Korea is an impelling force for South Korea to remain as a strong US alliance partner. This encourages Seoul to maintain cooperation with Washington in wide-ranging alliance tasks. Second, South Korea's policies are likely to reflect the way the nation perceives how useful China is in taming North Korea. The perceived usefulness of China causes Seoul to accommodate China and decrease cooperation with the USA. This might strain the relationship with the USA should South Korea evade alliance missions that might run contrary to China's security interests.     

Public Policy and Intergovernmental Relations: Measuring Perceived Change(s) in National Influence-The Effects of the Federalism Decade

Federal grants-in-aid have been a major instrument for the exercise of national influence on the states. This research investigates empirically the degree of perceived national influence (PNI) exerted through the grant process during the 1970s and 1980s. Respondents were state administrators heading agencies that received federal grants. Surveys at four points in time across the two decades produced a unidimensional measure of PNI. PNI levels were notably higher in the 1970s than in the 1980s. Two competing explanations were offered to account for the decline: (a) intergovernmental institutional policy changes promoted by the Ronald Reagan administration from 1981 through 1988, and (b) symbolic and rhetorical advocacy of an altered (reduced) national role in relation to the states. Both factors appear to have contributed to sharp decline in PNI between the mid-1970s and the mid-1980s.     

Spatial Distribution of Trees and Landscapes of the Past: A Mixed Spatially Correlated Multinomial Logit Model Approach for the Analysis of the Public Land Survey Data

Public Land Survey (PLS) data have been widely used in landscape studies of forest and woodlands in the pre‐ and early‐European‐settled Midwestern and Western United States. We aim to reconstruct presettlement forest vegetation at a finer spatial resolution than available from the PLS data using environmental covariates (slope, aspect, geology, and soil type) and the spatially correlated structure of witness tree data. To accommodate various data obtained from multiple sources while explicitly taking into account their spatial structures, we adopt a mixed spatially correlated multinomial logit model within the framework of a generalized linear mixed model. The application of the proposed model is illustrated using the three most abundant tree taxa from PLS data in the Arbuckle Mountains of south‐central Oklahoma. To assess the influence of each source of information on the spatial prediction, we considered four variant multinomial/spatial models and evaluated their relative predictive power using a validation technique. The probabilistic information about the spatial distribution of tree species obtained from different models reveals the need to integrate information about witness tree data as well as environmental covariates, and the nature of tree species; that is, a tendency to cluster in space to share environmental conditions in the reconstruction of the presettlement forest vegetation surface. Los datos sobre el uso y cobertura de tierras del Public Land Survey (PLS) han sido utilizados ampliamente en estudios de paisaje de bosques y de bosques históricos para periodo previo al asentamiento de migrantes europeos en el medio oeste y oeste de los Estados Unidos. Nuestro objetivo es reconstruir la vegetación forestal previa al asentamiento europeo a una resolución espacial más fina que la disponible actualmente en base a datos del PLS, usando covariables ambientales (pendiente, orientación, geología y tipo de suelo) y la estructura de correlación espacial de los datos de los árboles testigos. Para dar cabida a los diversos datos obtenidos de fuentes múltiples, y a la vez teniendo en cuenta explícitamente sus estructuras espaciales, adoptamos un modelo logit multinomial espacial mixto dentro del marco de los modelos mixtos lineales generalizados (GLMM). La aplicación del modelo propuesto es ilustrada con los tres tipos más abundantes de árboles según los datos del PLS para las montañas de Arbuckle en el centro‐sur de Oklahoma, EEUU. Para evaluar la influencia de cada fuente de información sobre la predicción espacial, se consideraron cuatro variantes de los modelos multinomial y espaciales. El poder predictivo de dichos modelos fue evaluado en relación con una técnica de validación. La información probabilística acerca de la distribución espacial de las especies de árboles obtenidos a partir de los diferentes modelos revela que para la reconstrucción de la superficie de la vegetación forestal histórica, es necesario integrar la información sobre los datos de árboles testigos así como las covariables ambientales y la naturaleza de las especies de árboles: es decir, la tendencia de los arboles a agruparse en el espacio para compartir las mismas condiciones ambientales. 公共土地调查(PLS)数据在欧洲人定居美国中西部和西部地区之前以及早期的森林和林地景观研究中得到广泛应用。本文旨在利用环境协变量（坡度、坡向、地貌和土地类型）证据树数据的空间关联结构,重建比PLS数据中更有效的更精细空间分辨率的前殖民期森林植被。为集成多种来源的各类数据,并明确地考虑数据间的空间结构,本文在广义线性混合模型(GLMM)框架下提出了混合空间关联多项Logit模型。以俄克拉荷马州中南部的阿尔布克尔山脉为研究区,提取PLS数据中三种最丰富的树种对模型进行验证。为估计每种信息来源对模型空间预测准确性的影响,本文考虑了4种变异的多项/空间模型并运用验证技术评估它们的相对预测能力。从不同模型获得的树种空间分布的概率信息表明,需要对证据树数据、环境协变量和树种自然属性信息进行集成,也就是说,在重建前殖民期森林植被曲面时,空间上的集聚趋势共享了环境条件。     

The ‘New Woman’ and the Politics of Love,Marriage and Divorce in Colonial Korea

This study seeks to explore the changing discursive forces that competed to define Korean women's identity and roles within the context of the new spaces created by colonialism and modernity. It argues that a small coterie of literate women seized the initiative to enhance their education, define the politics of physical aesthetics and con‐tribute to the debate about the changing gender roles and expectations in Korean society all under the guise of 'Westernisation' and progress. The emergence of these 'new women' challenged traditional notions of Korean womanhood and brought the 'woman question' to the forefront of public discourse.