珠三角中心镇城市化对区域城市空间结构的影响——基于CA的模拟和分析

在珠三角的城市化过程中,中心镇的城市化对区域城市空间结构的形成和发展具有重要的影响,定量地研究中心镇的城市化对揭示珠三角地区城市化过程与城市空间结构的变化具有重要意义。本研究以东莞市为例,运用城市元胞自动机模型,模拟市域内1988-2004年的城市扩张过程,分析中心镇城市化对整个城市空间结构的影响。主要结论:1988-1993年,中心镇的城市化对区域城市空间结构影响较小,城市主要沿道路扩张,城市在空间布局上相对松散;1993-1997年,中心镇对区域城市空间结构影响增强,城市以中心镇为中心的扩张非常明显,城市空间沿道路的扩张,基本形成了渐进扩散式的"点-轴"空间发展模式,形成沿道路和中心镇扩展的格局;1997年以后,中心镇的辐射作用进一步增强,形成了"点-轴-集聚区"的城市空间扩展模式,城市空间扩展区在空间上更集中于中心镇。     

Entropy in Spatial Aggregation

This paper introduces an approach to the measurement of locational phenomena in a spatial hierarchy using entropy statistics. A number of such statistics suitable for the study of spatial aggregation are derived, and each of these statistics is decomposed at different levels of the spatial hierarchy using principles of decomposition first applied by Theil. These decomposition statistics are compared with the variance analysis applied by Moellering and Tobler and with the spatial entropy measure suggested by Curry. The use of these statistics is then illustrated by data from the Reading subregion and New York City, and the paper is concluded with an analysis of a possible role for entropy and information in problems involving equal-area zoning.     

Local Indicators of Spatial Association—LISA

The capabilities for visualization, rapid data retrieval, and manipulation in geographic information systems (GIS) have created the need for new techniques of exploratory data analysis that focus on the “spatial” aspects of the data. The identification of local patterns of spatial association is an important concern in this respect. In this paper, I outline a new general class of local indicators of spatial association (LISA) and show how they allow for the decomposition of global indicators, such as Moran's I, into the contribution of each observation. The LISA statistics serve two purposes. On one hand, they may be interpreted as indicators of local pockets of nonstationarity, or hot spots, similar to the G_i and G*_i statistics of Getis and Ord (1992). On the other hand, they may be used to assess the influence of individual locations on the magnitude of the global statistic and to identify “outliers,” as in Anselin's Moran scatterplot (1993a). An initial evaluation of the properties of a LISA statistic is carried out for the local Moran, which is applied in a study of the spatial pattern of conflict for African countries and in a number of Monte Carlo simulations.     

Maximum Getis–Ord Statistic Adjusted for Spatially Autocorrelated Data

Local statistics test the null hypothesis of no spatial association or clustering around the vicinity of a location. To carry out statistical tests, it is assumed that the observations are independent and that they exhibit no global spatial autocorrelation. In this article, approaches to account for global spatial autocorrelation are described and illustrated for the case of the Getis–Ord statistic with binary weights. Although the majority of current applications of local statistics assume that the spatial scale of the local spatial association (as specified via weights) is known, it is more often the case that it is unknown. The approaches described here cover the cases of testing local statistics for the cases of both known and unknown weights, and they are based upon methods that have been used with aspatial data, where the objective is to find changepoints in temporal data. After a review of the Getis–Ord statistic, the article provides a review of its extension to the case where the objective is to choose the best set of binary weights to estimate the spatial scale of the local association and assess statistical significance. Modified approaches that account for spatially autocorrelated data are then introduced and discussed. Finally, the method is illustrated using data on leukemia in central New York, and some concluding comments are made.     

"The Problem of Spatial Autocorrelation" and Local Spatial Statistics

This article examines the relationship between spatial dependency and spatial heterogeneity, two properties unique to spatial data. The property of spatial dependence has led to a large body of research into spatial autocorrelation and also, largely independently, into geostatistics. The property of spatial heterogeneity has led to a growing awareness of the limitation of global statistics and the value of local statistics and local statistical models. The article concludes with a discussion of how the two properties can be accommodated within the same modelling framework.     

Large Sample-Size Distribution of Statistics Used In Testing for Spatial Correlation

Test statistics for testing for spatial correlation in continuous variables have been given by both Moran and Geary and have subsequently been generalized. It has been conjectured for a long time that under the hypothesis of no spatial correlations all these statistics are normally distributed when the sample size is large. This paper proves a very general theorem on the large sample normality of quadratic forms. As corollaries to the theorem the asymptotic normality, under the hypothesis, of all the above-mentioned statistics is established. The necessary conditions are quite unrestrictive. It is also shown, by means of a counter example, that the conditions given in a similar theorem (Cliff and Ord) are inadequate to ensure normality.     

ACCOUNTING FOR NEIGHBORING EFFECTS IN MEASURES OF SPATIAL CONCENTRATION*

ABSTRACT A common problem with spatial economic concentration measures based on areal data (e.g., Gini, Herfindhal, entropy, and Ellison‐Glaeser indices) is accounting for the position of regions in space. While they purport to measure spatial clustering, these statistics are confined to calculations within individual areal units. They are insensitive to the proximity of regions or to neighboring effects. Clearly, since spillovers do not recognize areal units, economic clusters may cross regional boundaries. Yet with current measures, any industrial agglomeration that traverses boundaries will be chopped into two or more pieces. Activity in adjacent spatial units is treated in exactly the same way as activity in far‐flung, nonadjacent areas. This paper shows how popular measures of spatial concentration relying on areal data can be modified to account for neighboring effects. With a U.S. application, we also demonstrate that the new instruments we propose are easy to implement and can be valuable in regional analysis.     

Empirical Evaluation of Alternative Model Specifications to Predict Commuting Flows

This study is devoted to empirical and modeling aspects on how characteristics of spatial structure influence commuting flows. Within a doubly-constrained framework, results from a competing-destinations formulation are evaluated and compared to results from the traditional gravity model. The evaluation depends critically upon the specification of within-zone journeys-to-work. Specific labor-market characteristics are found to be significant to explain how workers are absorbed in diagonal elements of the trip-distribution matrix. We also find that the parametric specification of the accessibility measure is important, and that the competing-destinations formulation is superior to the traditional gravity model.     

What Were We Thinking?

This article outlines the context in geography and statistics in the mid 1960s, at the height of geography's so-called "quantitative revolution," that led us into a long-term collaboration about spatial statistics, which has continued in surges and lulls for some 40 years. We focus upon problems in spatial autocorrelation, including the measurement of autocorrelation, distribution theory, and variable geographical lattices. This narrative may not describe how it was, but it does describe how we remember the events of the time.     

A Simulation Study to Explore Inference about Global Moran's I with Random Spatial Indexes

Inference procedures for spatial autocorrelation statistics assume that the underlying configurations of spatial units are fixed. However, sometimes this assumption can be disadvantageous, for example, when analyzing social media posts or moving objects. This article examines for the case of point geometries how a change from fixed to random spatial indexes affects inferences about global Moran's I, a popular spatial autocorrelation measure. Homogeneous and inhomogeneous Matérn and Thomas cluster processes are studied and for each of these processes, 10,000 random point patterns are simulated for investigating three aspects that are key in an inferential context: the null distributions of I when the underlying geometries are varied; the effect of the latter on critical values used to reject null hypotheses; and how the presence of point processes affects the statistical power of Moran's I. The results show that point processes affect all three characteristics. Inferences about spatial structure in relevant application contexts may therefore be different from conventional inferences when this additional source of randomness is taken into account.     

Toward a Theory of Spatial Statistics: Another Step Forward

An update is presented to an earlier piece outlining some steps that needed to be taken toward the establishment of a theory of spatial statistics. Findings that have appeared since this first paper are summarized and interpreted, and extensions and suggestions are offered for the further establishment of a basis for a theory of spatial statistics. Topics include boundary considerations, the role of latent spatial dependencies, and small-sample-size issues. These topics embrace the problems of data transformations, edge effect bias, reference sampling distributions, multivariate autocorrelation models, conditional expectations, and higher-order autoregressive structures. In part, a course is charted for the next step to be taken.     

(Re)constructing borders through the governance of tourism and trade in Ladakh,India

This paper analyses how everyday life, the state and nationhood are regulated and organised in a conflict-affected borderland space through economic activities. It focuses on two elements that are often overlooked when scholars discuss spatial governmentality: tourism and trade. Both are commonly declared to be elements of peace, peacebuilding and cosmopolitism. However, the spatial governance of tourism and trade can also profoundly shape how national belonging and the limits of territory are perceived and experienced by borderland populations and visitors. These dynamics can be acute in conflict-affected border zones, where state sovereignty may be under existential or territorial threat. This paper exposes such dynamics in the Indian conflict borderland area of Ladakh, a part of Jammu and Kashmir State until October 2019. Building on scholarship that has analysed cultural and social dynamics of “bordering” in the region, this paper argues that it is possible to read (socio-)economic boundary-making in Ladakh through the state's influence in the organisation and experience of trade and tourism for Ladakhis and visitors. The paper highlights how their spatial organisation, in part, underwrites difference and separation, and aids in framing the contested territory as ‘Indian’.     

Representing the Shape and Orientation of Destinations in Spatial Choice Models

This paper illustrates the importance of spatial unit shape and orientation in spatial choice analysis. It also examines the implications of assuming spatial units to be dimensionless points in this context. Through theory and simulation experiments, it is shown how these aspects cannot be ignored and how the ordinary multinomial logit model applied at the spatially aggregate level is particularly vulnerable to such oversights. The aggregated spatial logit model in the end is recommended as a general formulation which addresses many of these fundamental issues of interest to geographers and regional scientists.     

Nonparametric Significance Test for Discovery of Network‐Constrained Spatial Co‐Location Patterns

Spatial co‐location patterns are useful for understanding positive spatial interactions among different geographical phenomena. Existing methods for detecting spatial co‐location patterns are mostly developed based on planar space assumption; however, geographical phenomena related to human activities are strongly constrained by road networks. Although these methods can be simply modified to consider the constraints of networks by using the network distance or network partitioning scheme, user‐specified parameters or priori assumptions for determining prevalent co‐location patterns are still subjective. As a result, some co‐location patterns may be wrongly reported or omitted. Therefore, a nonparametric significance test without priori assumptions about the distributions of the spatial features is proposed in this article. Both point‐dependent and location‐dependent network‐constrained summary statistics are first utilized to model the distribution characteristics of the spatial features. Then, by using these summary statistics, a network‐constrained pattern reconstruction method is developed to construct the null model of the test, and the prevalence degree of co‐location patterns is modeled as the significance level. The significance test is evaluated using the facility points‐of‐interest data sets. Experiments and comparisons show that the significance test can effectively detect network‐constrained spatial co‐location patterns with less priori knowledge and outperforms two state‐of‐the‐art methods in excluding spurious patterns.     

农用地集约利用与农产品消费市场可达性关系研究——以江苏兴化市为例

基于农用地集约利用和市场可达性评价方法,计算了兴化市各乡镇农用地集约利用程度和农产品消费市场可达性并通过统计方法初步分析了两者的关系。结果表明:(1)农用地集约利用程度与农产品消费市场可达性具有明显的空间差异;(2)农用地集约利用程度与农产品消费市场可达性之间的秩相关系数为0.23,在α=0.05的置信水平上相关关系不显著,这说明县域尺度下农业用地集约利用与农产品消费市场可达性之间的空间模式已经突破了传统的"农产品消费市场可达性-农业生产方式-农用地集约利用程度"农业区位论模式。     

Spatial Governance across Borders Revisited: Organizational Forms and Spatial Planning in Metropolitan Cross-border Regions

In Europe, cross-border cooperation in spatial planning has intensified in recent years. Organizations with varying institutional characteristics have emerged in urban areas that cross a national border. They deal with problems stemming from the cross-border situation. The institutional and spatial aspects of organizations on the sub-national level in nine European cross-border metropolitan regions are compared in this article. The analytical concept of metropolitan governance applied here takes into account the simultaneous existence of different cross-border organizations as well as their spatial and functional relationships. Subsequently, this article assesses the impact of governance on spatial planning in two cases, Basel and Lille, where institutional changes occurred on the sub-national level between 2000 and 2010. The way organizational and spatial attributes of new forms of cross-border metropolitan governance influence the coordination of spatial policies is discussed here. The comparison of organizational characteristics and capacity to coordinate reveals important differences. Furthermore, comparing spatial scales shows how new cross-border perimeters are drawn along existing national territories. Organizations' competences and interests are decisive for the coordination and implementation of spatial policies.     

Spatial Empirics for Economic Growth and Convergence

This paper suggests some new empirical strategies for analyzing the evolution of regional income distributions over time and space. These approaches are based on extensions to the classical Markov transition matrices that allow for a more comprehensive analysis of the geographical dimensions of the transitional dynamics. This is achieved by integrating some recently developed local spatial statistics within a Markov framework. Insights to not only the frequency with which one economy may transition across different classes in the income distribution, but also how those transitions may or may not be spatially dependent are provided by these new measures. A number of indices are suggested as ways to characterize the space‐time dynamics and are illustrated in a case study of U. S. regional income dynamics over the 1929–1994 period.     

The Saddlepoint Approximation of Moran's I's and Local Moran's Ii's Reference Distributions and Their Numerical Evaluation

Global Moran's I and local Moran's I_i are the most commonly used test statistics for spatial autocorrelation in univariate map patterns or in regression residuals. They belong to the general class of ratios of quadratic forms for whom a whole array of approximation techniques has been proposed in the statistical literature, such as the prominent saddlepoint approximation by Offer Lieberman (1994). The saddlepoint approximation outperforms other approximation methods with respect to its accuracy and computational costs. In addition, only the saddlepoint approximation is capable of handling, in analytical terms, reference distributions of Moran's I that are subject to significant underlying spatial processes. The accuracy and computational benefits of the saddlepoint approximation are demonstrated for a set of local Moran's Ii statistics under either the assumption of global spatial independence or subject to an underlying global spatial process. Local Moran's I_i is known to have an excessive kurtosis and thus void the use of the simple approximation methods of its reference distribution. The results demonstrate how well the saddlepoint approximation fits the reference distribution of local Moran's I_i. Furthermore, for local Moran's I_i under the assumption of global spatial independence several algebraic simplifications lead to substantial gains in numerical efficiency. This makes it possible to evaluate local Moran's I_i's significance in large spatial tessellations.     

现代服务业集聚形成机理空间计量分析

在纳入空间效应前提下,构建现代服务业集聚形成机理空间面板计量模型,对我国28个省域相关数据实证研究表明:我国现代服务业集聚在省域之间有较强的空间依赖性和正的空间溢出效应。技术差异在时间维度上对现代服务业集聚促进作用显著,在空间维度上并不显著;交易费用与现代服务业集聚有显著的负相关性;知识溢出、规模经济、政府行为对现代服务业集聚促进作用显著。     

基于GIS的农村医疗设施空间可达性分析——以河南省兰考县为例

针对农村医疗设施空间分布的公平性问题,提出了使用GIS技术和空间可达性指标评估医疗设施的区域分布特征。以兰考县乡级以上卫生院为例,建立人口分布、医疗设施位置、行政区域等地理数据库,选择人均医疗资源分配、就医的最近距离、选择医院的机会、重力模型及改进的重力模型5个空间可达性模型,计算了各乡镇、各行政村的医疗设施可达性指标,并制作了相应的专题地图;在此基础上对医疗设施的空间布局进行了分析。空间可达性指标全面地反映医疗设施的空间分布特征,鉴别出资源分配较薄弱的区位,是农村医疗改革中设施规划和资源分配的重要依据。