Combining Geostatistical Methods and Hierarchical Scene Models for Analysis of Multiscale Variation in Spatial Data

Concepts from Hierarchical Analysis of Variance (ANOVA) can be combined with ideas from geostatistics to describe the multiscale structure of spatial data. Hierarchical ANOVA involves modeling spatial data as the sum of effects associated with processes acting at different spatial scales. These effects can be modeled as stationary regionalized variables, whose spatial structure can be described using the variogram. According to this model, the variogram of the spatial data is the sum of variograms and cross‐variograms of the effects. Whereas hierarchical ANOVA reveals the relationship between scale and variability, the hierarchical decomposition of the variogram relates scale with spatial structure. This analysis method can reveal otherwise undetected features of spatial data, and can guide further analysis.     

GeoDa : An Introduction to Spatial Data Analysis

This article presents an overview of GeoDa™, a free software program intended to serve as a user-friendly and graphical introduction to spatial analysis for non-geographic information systems (GIS) specialists. It includes functionality ranging from simple mapping to exploratory data analysis, the visualization of global and local spatial autocorrelation, and spatial regression. A key feature of GeoDa is an interactive environment that combines maps with statistical graphics, using the technology of dynamically linked windows. A brief review of the software design is given, as well as some illustrative examples that highlight distinctive features of the program in applications dealing with public health, economic development, real estate analysis, and criminology.     

中国旅游创新与旅游经济增长关系研究——基于空间面板数据模型

本文运用空间面板数据模型,选用1998～2009年数据研究了中国省域旅游创新与旅游经济增长的关系,发现：中国省域旅游创新与旅游经济增长呈现显著空间集聚性;旅游创新不仅推动当地旅游经济的增长,还通过空间传导机制对邻近区域的旅游经济产生正向的溢出效应。说明应充分重视创新在旅游经济增长中的积极作用,通过旅游创新成果的溢出效应扩大旅游创新对旅游经济增长的作用程度和范围。     

Implementing Spatial Data Analysis Software Tools in R

This article reports on work in progress on the implementation of functions for spatial statistical analysis, in particular of lattice/area data in the R language environment. The underlying spatial weights matrix classes, as well as methods for deriving them from data from commonly used geographical information systems are presented, handled using other contributed R packages. Since the initial release of some functions in 2001, and the release of the spdep package in 2002, experience has been gained in the use of various functions. The topics covered are the ingestion of positional data, exploratory data analysis of positional, attribute, and neighborhood data, and hypothesis testing of autocorrelation for univariate data. It also provides information about community building in using R for analyzing spatial data.     

Variations in the Spatial Pattern of Settlement Locations: An Analysis Based on Proportional Hazards Models

Models based on hazard functions are used to analyze spatial trends in the distance intervals separating point locations. The proportional hazards model, which has been widely applied to analyze intervals of time, is used to investigate variation in the spacing of settlements in Nebraska. This model allows spatial trends in the intervals between settlements to be investigated under very general conditions regarding the interdependence of settlement locations and permits the coordinate locations of the intervals to be treated as spatially varying covariates. An empirical analysis reveals an East-West trend in the spacing of settlements.     

Beyond Replication: the Quantification of Route Models in the North Jazira,Iraq

The primary aim of this paper is to present a solution to the issue of the statistical validation of route models. In addition, it introduces a body of theory taken from the broader field of route studies, isolates individual physical variables commonly used to predict route locations and quantifies them against the preserved hollow ways in the North Jazira Survey area, ending with a discussion of the complexity of human travel and the paramount importance of cultural variables.     

Geography, Spatial Data Analysis, and Geostatistics: An Overview

Geostatistics is a distinctive methodology within the field of spatial statistics. In the past, it has been linked to particular problems (e.g., spatial interpolation by kriging) and types of spatial data (attributes defined on continuous space). It has been used more by physical than human geographers because of the nature of their types of data. The approach taken by geostatisticians has several features that distinguish it from the methods typically used by human geographers for analyzing spatial variation associated with regional data, and we discuss these. Geostatisticians attach much importance to estimating and modeling the variogram to explore and analyze spatial variation because of the insight it provides. This article identifies the benefits of geostatistics, reviews its uses, and examines some of the recent developments that make it valuable for the analysis of data on areal supports across a wide range of problems.     

Exploring a Relationship Between Aggregate and Individual Levels Spatial Data Through Semivariogram Models

Analysis of social data is frequently done using aggregate-level data. There may not be a direct interest in spatial relationships in the data, but the presence of spatial interdependence may still need to be taken into account. This article explores the aggregation effect from a spatial perspective by assuming nonzero covariance for individual data from two different groups. We investigate the bias associated with aggregate-level data for semivariogram analysis. We show that the bias mainly arises from the average of the semivariogram within the groups. It is also shown how aggregated-level data may be used to estimate parameters of an individual-level semivariogram model. A nonlinear regression method is proposed to carry out this estimation procedure and a simulation is done to clarify the results.     

Models for Spatial Weights: A Systematic Look

General properties of spatial weights models, in particular Markovian properties, are systematically investigated. The role of stationary spatial distribution, interpretable as an importance-centrality or prominence index, is emphasized. Spatial interaction models, and among them the gravity model, are classified with respect to the time reversal and aggregation invariance properties obeyed by the associated spatial weights. Nine examples, involving connectivity, flows and distance decay analysis, integral geometry, and Dirichlet-Voronoi tessellations illustrate the main concepts, with a particular geometrical emphasis, and show how traditional, heuristic ingredients aimed at defining spatial weights can be recovered from general models.     

Analysis of Dynamic Spatial Interaction Models by Means of Optimal Control

This paper deals with the design of general classes of dynamic spatial interaction models. On the basis of a general (well-behaved) multiperiod objective function and of a dynamic model representing the evolution of a spatial interaction system, an optimal control model is constructed. Particular attention is given to the equilibrium and stability conditions. It turns out that it is possible to identify steady-state solutions for a dynamic spatial interaction model. Furthermore, it can also be demonstrated that the entropy model is a specific case of this spatial interaction system. A simple illustration for urban dynamics is given as well.     

Assessing Spatial Dependence in Count Data: Winsorized and Spatial Filter Specification Alternatives to the Auto-Poisson Model

The auto-Poisson probability model furnishes an obvious tool for modeling counts of geographically distributed rare events. Unfortunately, its original specification can accommodate only negative spatial autocorrelation, which itself is a rare event. More recent alternative reformulations, namely, the Winsorized and spatial filter specifications, circumvent this drawback. A comparison of their performances presented in this article reveals some of their relative advantages and disadvantages.     

Symptoms of Instability in Models of Spatial Dependence

In recent years, there has been a growing interest in the problems caused by the existence of instability in cross-sectional regressions. The results about local autocorrelation measures are part of this debate, as are the proposals concerning the concept of geographically weighted regressions. This article also deals with the problem of stability (or the lack thereof), but focusing the discussion on the supposition of constancy in the parameter of spatial dependence. In most cases, this assumption is treated, with the risks that this involves, as a maintained hypothesis, which should be ascertained before continuing with the modeling exercise. In the article, we present a simple heterogeneity test for this type of parameters, based on the Lagrange Multiplier principle. To illustrate its use, we take the distribution of per capita income among the European regions as our discussion case. According to our results, there are clear signs of structural breaks in the spatial distribution of this variable and the scale factor and the autocorrelation coefficient appear to be principal actors.     

Equilibrium and Economic Growth: Spatial Econometric Models and Simulations

Neoclassical theory assumes diminishing returns to capital and spatially constant exogenously-determined technological progress, although it is questionable whether these are realistic assumptions for modeling manufacturing productivity growth variations across European Union (E.U.) regions. In contrast, the model developed in this paper assumes increasing returns and spatially varying technical progress, and is linked to endogenous growth theory and particularly to 'new economic geography' theory. Simulations, involving 178 E.U.regions, show that productivity levels and growth rates are higher in all E.U. regions when the financially assisted (Objective 1) regions have faster output growth. This also reduces inequalities in levels of technology. Allowing the core regions to grow faster has a similar effect of raising productivity growth rates across the E.U., although inequality increases. Thus, the simulations are seen as an attempt to develop a type of 'computable geographical equilibrium' model which, as suggested by Fujita, Krugman, and Venables (1999), is the way theoretical economic geography needs to evolve in order to become a predictive discipline.