A Wavelet‐Based Extension of Generalized Linear Models to Remove the Effect of Spatial Autocorrelation. 基于小波扩展广义线性模型消除空间自相关的影响

Biogeographical studies are often based on a statistical analysis of data sampled in a spatial context. However, in many cases standard analyses such as regression models violate the assumption of independently and identically distributed errors. In this article, we show that the theory of wavelets provides a method to remove autocorrelation in generalized linear models (GLMs). Autocorrelation can be described by smooth wavelet coefficients at small scales. Therefore, data can be decomposed into uncorrelated and correlated parts. Using an appropriate linear transformation, we are able to extend GLMs to autocorrelated data. We illustrate our new method, called the wavelet‐revised model (WRM), by applying it to multiple regression with response variables conforming to various distributions. Results are presented for simulated data and real biogeographical data (species counts of the plant genus Utricularia [bladderworts] in grid cells throughout Germany). The results of our WRM are compared with those of GLMs and models based on generalized estimating equations. We recommend WRMs, especially as a method that allows for spatial nonstationarity. The technique developed for lattice data is applicable without any prior knowledge of the real autocorrelation structure.     

A Geostatistical Linear Regression Model for Small Area Data. 一种适用于小区域数据的地统计线性回归模型

We present a new linear regression model for use with aggregated, small area data that are spatially autocorrelated. Because these data are aggregates of individual‐level data, we choose to model the spatial autocorrelation using a geostatistical model specified at the scale of the individual. The autocovariance of observed small area data is determined via the natural aggregation over the population. Unlike lattice‐based autoregressive approaches, the geostatistical approach is invariant to the scale of data aggregation. We establish that this geostatistical approach also is a valid autoregressive model; thus, we call this approach the geostatistical autoregressive (GAR) model. An asymptotically consistent and efficient maximum likelihood estimator is derived for the GAR model. Finite sample evidence from simulation experiments demonstrates the relative efficiency properties of the GAR model. Furthermore, while aggregation results in less efficient estimates than disaggregated data, the GAR model provides the most efficient estimates from the data that are available. These results suggest that the GAR model should be considered as part of a spatial analyst's toolbox when aggregated, small area data are analyzed. More important, we believe that the GAR model's attention to the individual‐level scale allows for a more flexible and theory‐informed specification than the existing autoregressive approaches based on an area‐level spatial weights matrix. Because many spatial process models, both in geography and in other disciplines, are specified at the individual level, we hope that the GAR covariance specification will provide a vehicle for a better informed and more interdisciplinary use of spatial regression models with area‐aggregated data.     

Aggregation of Regional Economic Time Series with Different Spatial Correlation Structures. 具有不同空间相关结构的区域经济时间序列集成

In this article, we compare the relative efficiency of different forecasting methods of space‐time series when variables are spatially and temporally correlated. We consider two cases: (1) univariate forecasting (i.e., a space‐time series aggregated into a single time series) and (2) the more general instance of multivariate forecasting (i.e., a space‐time series aggregated into a coarser spatial partition). We extend the results in the literature by including the consideration of larger datasets and the treatment of edge effects and of negative spatial correlation. We first introduce a statistical framework based on the space‐time autoregressive class of random field models, which constitutes the basis of our simulation study, and we present the various alternative forecasting methods considered in the simulation. We then present the results of a Monte Carlo study related to univariate forecasting. In order to allow a comparison with the findings of Giacomini and Granger (2004), we consider the same forecasting strategies and the same combinations of the parameter values used there, but with a larger parametric set. Finally, we extend our analysis to the case of multivariate forecasting. The outcomes obtained provide operational suggestions about how to choose between alternative forecasting methods in empirical circumstances.     

Determinants of Variation in Population–Employment Interaction Findings: A Quasi‐Experimental Meta‐Analysis. 人口‐就业相互作用的决定因素研究：一种准实验元分析法

This article reports about a metaregression analysis of empirical results generated using data for the northern Netherlands (1988–2002) in order to investigate the ambiguity in results in the population–employment interaction literature. Specifically, the analysis deals with the issue whether “jobs follow people” or “people follow jobs.” The article starts with introducing the basics of quasi‐experimental meta‐analysis and with identifying some advantages of using quasi‐experimental meta‐analysis as compared with the standard meta‐analysis approach. Two subsequent sections document the selection of the population–employment interaction model and salient characteristics of the data set as well as the setup of the primary analyses. A total of 4,050 quasi‐experimental empirical results for the jobs–people direction of causality are generated using different specifications and estimators for a spatial econometric interaction model. The subsequent metaregression analysis reveals that the empirical results are largely shaped by the spatial, temporal, and employment characteristics of the data sampling. The results also appear much more sensitive to different measurements of the model's key variables when compared with alternative specifications of the spatial weights matrix. The main determinant driving empirical results about jobs–people causality are differences in model specification and estimation, as revealed by an inherent bias in parameter estimates and misguided inferences for some of the commonly used specifications. Finally, suggestions for future research are identified.     

Gibrat Revisited: An Urban Growth Model Incorporating Spatial Interaction and Innovation Cycles. 再评吉尔布瑞特：顾及空间相互作用和创新周期的城市增长模型

We designed a geographical model for simulating the distribution of urban growth in systems of cities. The model incorporates the hierarchical and spatial diffusion of innovation cycles through gravitational interactions within a set of cities. Using theoretical simulations, we demonstrate that this model is able to reproduce the observed properties of urban systems for the log‐normal distribution of city sizes as well as the observed distribution of growth rates. Our experimentation was performed on a large harmonized historical database that includes a few hundred French urban agglomerations between 1831 and 1999 (Pumain‐INED database). Both spatial interaction and innovation cycles are necessary ingredients to explain the evolution of urban hierarchies. We suggest that Gibrat's generic stochastic growth model based on independent entities should be replaced by a more relevant model of spatially and temporally interdependent geographical entities.     

A New Perspective about Moran's Coefficient: Spatial Autocorrelation as a Linear Regression Problem. Moran系数的新视角：空间自相关视为线性回归问题

The computation of Moran's index of spatial autocorrelation requires the definition of a spatial weighting matrix. The eigendecomposition of this doubly centered matrix (i.e., one that forces the sums of all rows and columns to equal zero) has interesting properties that have been exploited in various contexts: distribution properties of the Moran coefficient (MC), spatial filtering in linear models, generalized linear models, and multivariate analysis. In this article, this eigendecomposition is used to propose a new view of MC based on its interpretation in the simple context of linear regression. I use this interpretation to demonstrate the different properties of MC and also the inefficiency of this index in some situations involving simultaneous positive and negative spatial autocorrelation. I propose some new statistics and procedures for testing spatial autocorrelation, and conduct a simulation study to evaluate these new approaches.     

Minority–Majority Relations in the Schelling Model of Residential Dynamics. 住宅动态迁移Schelling模型中的少数‐多数关系

The Schelling model describing segregation between two groups of residential agents, reflects the most abstract, basic view of noneconomic forces motivating residential migrations: be close to people of “your own” kind. The model assumes that residential agents, located in neighborhoods where the fraction of “friends” is less than a predefined threshold value F, try to relocate to neighborhoods where this fraction is F or higher. For groups of equal size, Schelling's residential pattern converges either to complete integration (random pattern) or segregation, depending on F. We investigate Schelling model pattern dynamics as a function of F in addition to two other parameters—the ratio of groups' numbers, and neighborhood size. We demonstrate that the traditional integration–segregation pattern dichotomy should be extended. In the case of groups of different sizes, a wide interval of F‐values exists that entails a third persistent residential pattern, one in which a portion of the majority population segregates while the rest remains integrated with the minority. We also demonstrate that Schelling model dynamics essentially depend on the formalization of urban agents' residential behavior. To obtain realistic results, the agents should be satisficers, and the fraction of the agents relocating irrespective of the neighborhood's state should be nonzero. We discuss the relationship between our results and real‐world residential dynamics.     

Adaptive Cell Tower Location Using Geostatistics. 基于地统计学的适应性基站发射塔选址研究

In this article, we address the problem of allocating an additional cell tower (or a set of towers) to an existing cellular network, maximizing the call completion probability. Our approach is derived from the adaptive spatial sampling problem using kriging, capitalizing on spatial correlation between cell phone signal strength data points and accounting for terrain morphology. Cell phone demand is reflected by population counts in the form of weights. The objective function, which is the weighted call completion probability, is highly nonlinear and complex (nondifferentiable and discontinuous). Sequential and simultaneous discrete optimization techniques are presented, and heuristics such as simulated annealing and Nelder–Mead are suggested to solve our problem. The adaptive spatial sampling problem is defined and related to the additional facility location problem. The approach is illustrated using data on cell phone call completion probability in a rural region of Erie County in western New York, and accounts for terrain variation using a line‐of‐sight approach. Finally, the computational results of sequential and simultaneous approaches are compared. Our model is also applicable to other facility location problems that aim to minimize the uncertainty associated with a customer visiting a new facility that has been added to an existing set of facilities.     

Weighted Bidimensional Regression. 加权二维回归

Shape analysis is useful for a wide variety of disciplines and has many applications. One of the many approaches to shape analysis focuses on shapes that are represented by predefined landmarks on an object. Some landmarks may be measured with greater precision, exhibit more natural variation, or be more important than others to an analysis. This article introduces a method for including this information when estimating mapping relations or assessing the degree of similarity between two objects that are represented by a set of two‐dimensional landmarks. Weighted bidimensional regression combines aspects of weighted least squares regression and bidimensional regression as a way to weight variables that are represented by a set of two‐dimensional spatial coordinates. One possible weighting scheme is suggested, and the effect of weighting is demonstrated through a face‐matching application. Results indicate that appropriate weighting increases the ability to correctly match two faces and that weighting has the largest effect when used with a projective transformation.     

The notion τὸ πέρας τῆς ἀνακομιδῆς and the location of Ptolemais of the Hunts in the Periplus of the Erythraean Sea

The Periplus of the Erythraean Sea describes the route of a trade ship in the ancient Erythraean Sea following certain reference points. One of these was called τ? π?ρα? τ?? ?νακομιδ??, from which the distance to Ptolemais of the Hunts was given. It was generally understood as the ‘endpoint of return’ and thought to be Berenice. In fact, the phrase is to be understood as ‘the endpoint of sailing/delivery from (Egypt)’ and the place appears to be identifiable with modern Anfile Bay, where the trade ships turned back. The port itself had to be visited on the way back to Egypt. The reason was connected with the primary goal of the establishment of the port: it was much more practical to take elephants aboard on the way back to Egypt. The information on Ptolemais of the Hunts seems to derive from a late Ptolemaic source. The exact route of the ship, referred to by the author of the Periplus, is unknown and even knowledge of the exact distance from the reference point does not allow us to identify a region, where Ptolemais of the Hunts is to be sought.     

Dealing with Timing and Synchronization in Opportunities for Joint Activity Participation. 公共活动参与机会中实时性与同步性处理

The ability of people to access opportunities offered by the built environment is circumscribed by various sets of space–time constraints, including the requirements to meet other persons at particular times and places to undertake activities together. While models of space–time accessibility recognize that joint activities may constrain the performance of activities in space and time, their specifications do not explicitly acknowledge the opportunities that individuals of a group have for joint activity participation. Therefore, this article focuses on joint activity participation and argues that collective activity decisions are the outcome of a complex process involving various aspects of timing, synchronization, and social hierarchy. The utility‐theoretic model proposed here quantifies the extent to which opportunities can be jointly accessed by a particular group of people within a specific time period. Central to the approach are three key variables: the attractiveness of an opportunity, the time available for activity participation, and the travel time to an activity location. Because of the multiperson character of joint activities, the determination of these variables is subject to individual preferences, privileges, and power differentials within a group. Specific attention is given to how time‐of‐day and synchronization effects influence the opportunities accessible to a group of individuals. The impact of these factors on joint accessibility is illustrated by a real‐world example of an everyday rendezvous scenario. The outcomes of a simulation exercise suggest that time‐of‐day and synchronization effects significantly affect the benefits that can be gained from opportunities for joint activities.     

Growth and Convergence in a Multiregional Model with Space–Time Dynamics. 多区域时空动态模型的增长与收敛

The goal of this article is to test four distinct hypotheses about whether the relative location of an economy affects economic growth and economic well‐being using an extended Solow–Swan neoclassical growth model that incorporates both space and time dynamics. We show that the econometric specification takes the form of an unconstrained spatial Durbin model, and we investigate whether the results depend on some methodological issues, such as the choice of the time span and the inclusion of fixed effects. To estimate the fixed effects spatial Solow–Swan model, we adjust the Arrelano and Bond (1991) generalized method‐of‐moments (GMM) estimator to deal with endogeneity not only arising from the initial income level, as in the basic model, but also from the initial income levels and economic growth rates observed in neighboring economies.     

An Empirically Based Implementation and Evaluation of a Hierarchical Model for Commuting Flows. 一种基于经验的层状通勤流模型实现与评估

This article provides an empirical evaluation of a hierarchical approach to modeling commuting flows. As the gravity family of spatial interaction models represents a benchmark for empirical evaluation, we begin by reviewing basic aspects of these models. The hierarchical modeling framework is the same that Thorsen, Ubøe, and Nævdal (1999) used. However, because some modifications are required to construct a more workable model, we undertake a relatively detailed presentation of the model, rather than merely referring to the presentation in Thorsen, Ubøe, and Nævdal (1999) . The model uses a hierarchical specification of a transportation network and the individual search procedure. Journeys to work are determined by the effects of distance deterrence and of intervening opportunities, and by the location of potential destinations relative to alternatives at subsequent levels in a transportation network. The model calibration uses commuting data from a region in western Norway. The estimated parameter values are reasonable, and the explanatory power is very satisfactory when compared with the results of a competing destinations approach.     

A Hybrid Framework for Space–Time Modeling of Environmental Data. 环境数据时空建模的混合框架

The space–time autoregressive integrated moving average (STARIMA) model family provides useful tools for modeling space–time processes that exhibit stationarity (or near stationarity) in space and time. However, a more general method for routine use and efficient computation is needed to model the nonlinearities and nonstationarities of environmental space–time series. This article presents a hybrid framework combining machine learning and statistical methods to address this issue. It uses an artificial neural network (ANN) to extract global deterministic (nonlinear) space–time trends and a STARIMA model to extract local stochastic space–time variations in data. A four‐stage procedure is proposed for analyzing and modeling space–time series. The proposed framework and procedures are applied to forecast annual average temperature at 137 national meteorological stations in China. The results demonstrate that the hybrid framework achieves better forecasting accuracy than the STARIMA model alone. This finding suggests that the combination of machine learning and statistical methods provides a very powerful tool for analyzing and modeling space–time series of environmental data that have strong spatial nonlinear and nonstationary components.     

A Fractal Approach to Identifying Urban Boundaries. 城市边界识别的分形方法

Fractal geometry can be used for determining the morphological boundaries of metropolitan areas. A two‐step method is proposed here: (1) Minkowski's dilation is applied to detect any multiscale spatial discontinuity and (2) a distance threshold is located on the dilation curve corresponding to a major change in its behavior. We therefore measure the maximum curvature of the dilation curve. The method is tested on theoretical urban patterns and on several European cities to identify their morphological boundaries and to track boundary changes over space and time. Results obtained show that cities characterized by comparable global densities may exhibit different distance thresholds. The less the distances separating buildings differ between an urban agglomeration and its surrounding built landscape, the greater the distance threshold. The fewer the buildings that are connected across scales, the greater the distance threshold.     

A Framework for Exploring Urban Retail Discontinuities. 探索城市零售业不连续性的框架

A key area in the analysis of urban structural evolution is identifying discontinuities. Effective analysis could improve long‐term forecasting and provide a better understanding of how to steer an urban system toward a desirable future state. We use a simple aggregate retail model to demonstrate an algorithm for identifying discontinuities in model parameter space. Explorations of retailing in both Greater London and South Yorkshire in the United Kingdom illustrate how understanding a system's potential for discontinuity can provide insights for both policy makers and retail businesses. The Harris and Wilson model, described in the section so‐named, is used as a simple archetype to illustrate the new framework. This model can be developed in a straightforward way to incorporate further refinement. In “ Executing the model and visualizing the results ,” we describe a single model run and in “ Investigating discontinuities ,” we explain our framework for detecting and analyzing discontinuities. “ Identifying discontinuities in the London retail system ” shows the results of applying this methodology to the Greater London retail system, and in “ Practical applications ,” we explore the policy applications for this technique as related to the decline of town centers in the South Yorkshire retail system. Some concluding comments are offered in “ Conclusions .”     

The Impact of Neutral and Nonneutral Productivity Growth on Creative Capital in a Trading Regional Economy. 区域经济贸易中中性和非中性生产力增长对创新资本的影响

We analyze a two‐sector model of a trading creative regional economy (TCRE) of the sort studied by Florida. Our analysis focuses on two cases. The first case concerns the impact of faster neutral, whereas the second case concerns the impact of faster nonneutral, productivity growth in the tradable sector on the employment of creative people in each of a TCRE's two sectors. In both cases, the sign of the percentage change in the steady‐state use of creative capital in the nontradable sector is ambiguous. In other words, the potential departure of creative people from the tradable to the nontradable sector does not depend upon whether the faster productivity growth being studied is neutral or nonneutral. In addition, because the effects of faster productivity growth in the tradable sector are not necessarily innocuous, a potential role exists for activist policy designed to countermand the possibly negative effects of faster productivity growth on either the tradable or the nontradable sector. This finding of possible “uneven development” is consistent with Florida's view that TCREs can give rise to externalities such as uneven regional development.     

Degenerative Joint Disease and Social Status in the Terminal Late Archaic Period (1000–500 b.c.) of Ohio

This research presents an analysis of the inferred Late Archaic social structure in Ohio based on degenerative joint disease (DJD, also known as osteoarthritis) and mortuary practices. We tested the hypothesis that mechanical loading involving physical activities is differentially distributed in a population along levels or types of social stratification. This hypothesis was investigated via statistical treatment of DJD as a skeletal stress marker of activity, its occurrence by age and sex, an association with grave goods, and spatial distribution in terminal Late Archaic cemeteries. The skeletal samples used in this study came from three cemeteries, the Boose, Kirian‐Treglia (KT), and Duff sites, dating to the Ohio terminal Late Archaic period. In general, the high overall prevalence of DJD in these people indicates that this population led a rigorous life. This study hypothesized that the burials in the Late Archaic period in Ohio might be socially patterned as evidenced from the unequal distribution of grave goods and skeletal variability in DJD. Nevertheless, the analyses suggest that there is no statistical association between DJD and mortuary practices including grave goods and burial location in a cemetery. As observed in numerous hunter–gatherer populations, the societies in our sample were also characterized by the absence of a marked social stratification. The results suggest that there were only ‘natural inequalities’ in Late Archaic societies due to biological factors, such as age and sex. Copyright © 2011 John Wiley & Sons, Ltd.     

Analysis of a Distribution of Point Events Using the Network‐Based Quadrat Method

This study proposes a new quadrat method that can be applied to the study of point distributions in a network space. While the conventional planar quadrat method remains one of the most fundamental spatial analytical methods on a two‐dimensional plane, its quadrats are usually identified by regular, square grids. However, assuming that they are observed along a network, points in a single quadrat are not necessarily close to each other in terms of their network distance. Using planar quadrats in such cases may distort the representation of the distribution pattern of points on a network. The network‐based units used in this article, on the other hand, consist of subsets of the actual network, providing more accurate aggregation of the data points along the network. The performance of the network‐based quadrat method is compared with that of the conventional quadrat method through a case study on a point distribution on a network. The χ² statistic and Moran's I statistic of the two quadrat types indicate that (1) the conventional planar quadrat method tends to overestimate the overall degree of dispersion and (2) the network‐based quadrat method derives a more accurate estimate on the local similarity. The article also performs sensitivity analysis on network and planar quadrats across different scales and with different spatial arrangements, in which the abovementioned statistical tendencies are also confirmed.     

The p‐Regions Problem. p‐区域问题

The p‐regions problem involves the aggregation or clustering of n small areas into p spatially contiguous regions while optimizing some criteria. The main objective of this article is to explore possible avenues for formulating this problem as a mixed integer‐programming (MIP) problem. The critical issue in formulating this problem is to ensure that each region is a spatially contiguous cluster of small areas. We introduce three MIP models for solving the p regions problem. Each model minimizes the sum of dissimilarities between all pairs of areas within each region while guaranteeing contiguity. Three strategies designed to ensure contiguity are presented: (1) an adaptation of the Miller, Tucker, and Zemlin tour‐breaking constraints developed for the traveling salesman problem; (2) the use of ordered‐area assignment variables based upon an extension of an approach by Cova and Church for the geographical site design problem; and (3) the use of flow constraints based upon an extension of work by Shirabe. We test the efficacy of each formulation as well as specify a strategy to reduce overall problem size.