Model Uncertainty in Matrix Exponential Spatial Growth Regression Models

This article considers the most important aspects of model uncertainty for spatial regression models, namely, the appropriate spatial weight matrix to be employed and the appropriate explanatory variables. We focus on the spatial Durbin model (SDM) specification in this study that nests most models used in the regional growth literature, and develop a simple Bayesian model‐averaging approach that provides a unified and formal treatment of these aspects of model uncertainty for SDM growth models. The approach expands on previous work by reducing the computational costs through the use of Bayesian information criterion model weights and a matrix exponential specification of the SDM model. The spatial Durbin matrix exponential model has theoretical and computational advantages over the spatial autoregressive specification due to the ease of inversion, differentiation, and integration of the matrix exponential. In particular, the matrix exponential has a simple matrix determinant that vanishes for the case of a spatial weight matrix with a trace of zero. This allows for a larger domain of spatial growth regression models to be analyzed with this approach, including models based on different classes of spatial weight matrices. The working of the approach is illustrated for the case of 32 potential determinants and three classes of spatial weight matrices (contiguity‐based, k‐nearest neighbor, and distance‐based spatial weight matrices), using a data set of income per capita growth for 273 European regions.     

Employment Dynamics,Spatial Restructuring,and the Business Cycle

The multinodal metropolis is an evolving system, affected by both long-term restructuring and by business cycle dynamics. This study of the Dallas-Fort Worth Metroplex points, first, to the multiplicity and wide dispersion of employment centers within the region and, second, to the importance of vintage. During business cycle upswings new centers in new and more dispersed locations receive the preponderant share of new growth. During business cycle downswings, old activities are pruned most heavily from old locations. The result is an increasingly complex array of specialized agglomerations.     

Gaussian Process Regression and Bayesian Model Averaging: An Alternative Approach to Modeling Spatial Phenomena

Gaussian Process Regression (GPR) is a nonparametric technique that is capable of yielding reliable out‐of‐sample predictions in the presence of highly nonlinear unknown relationships between dependent and explanatory variables. But in terms of identifying relevant explanatory variables, this method is far less explicit about questions of statistical significance. In contrast, more traditional spatial econometric models, such as spatial autoregressive models or spatial error models, place rather strong prior restrictions on the functional form of relationships, but allow direct inference with respect to explanatory variables. In this article, we attempt to combine the best of both techniques by augmenting GPR with a Bayesian Model Averaging (BMA) component that allows for the identification of statistically relevant explanatory variables while retaining the predictive performance of GPR. In particular, GPR‐BMA yields a posterior probability interpretation of model‐inclusion frequencies that provides a natural measure of the statistical relevance of each variable. Moreover, while such frequencies offer no direct information about the signs of local marginal effects, it is shown that partial derivatives based on the mean GPR predictions do provide such information. We illustrate the additional insights made possible by this approach by applying GPR‐BMA to a benchmark BMA data set involving potential determinants of cross‐country economic growth. It is shown that localized marginal effects based on partial derivatives of mean GPR predictions yield additional insights into comparative growth effects across countries.     

Employment Densities, Spatial Autocorrelation, and Subcenters in Large Metropolitan Areas

Employment density functions are estimated for 62 large metropolitan areas. Estimated gradients are statistically significant for distance from the nearest subcenter as well as for distance from the traditional central business district. Lagrange Multiplier (LM) tests imply significant spatial autocorrelation under highly restrictive ordinary least squares (OLS) specifications. The LM test statistics fall dramatically when the models are estimated using flexible parametric and nonparametric methods. The results serve as a warning that functional form misspecification causes spatial autocorrelation.     

A General Misspecification Test for Spatial Regression Models: Dependence, Heterogeneity, and Nonlinearity

There is an increasing awareness of the potentials of nonlinear modeling in regional science. This can be explained partly by the recognition of the limitations of conventional equilibrium models in complex situations, and also by the easy availability and accessibility of sophisticated computational techniques. Among the class of nonlinear models, dynamic variants based on, for example, chaos theory stand out as an interesting approach. However, the operational significance of such approaches is still rather limited and a rigorous statistical-econometric treatment of nonlinear dynamic modeling experiments is lacking. Against this background this paper is concerned with a methodological and empirical analysis of a general misspecification test for spatial regression models that is expected to have power against nonlinearity, spatial dependence, and heteroskedasticity. The paper seeks to break new research ground by linking the classical diagnostic tools developed in spatial econometrics to a misspecification test derived directly from chaos theory—the BDS test, developed by Brock, Dechert, and Scheinkman (1987). A spatial variant of the BDS test is introduced and applied in the context of two examples of spatial process models, one of which is concerned with the spatial distribution of regional investments in The Netherlands, the other with spatial crime patterns in Columbus, Ohio.     

A Simulation Study on Specifying a Regression Model for Spatial Data: Choosing between Autocorrelation and Heterogeneity Effects

In this simulation study, regressions specified with autocorrelation effects are compared against those with relationship heterogeneity effects, and in doing so, provides guidance on their use. Regressions investigated are: (1) multiple linear regression, (2) a simultaneous autoregressive error model, and (3) geographically weighted regression. The first is nonspatial and acts as a control, the second accounts for stationary spatial autocorrelation via the error term, while the third captures spatial heterogeneity through the modeling of nonstationary relationships between the response and predictor variables. The geostatistical‐based simulation experiment generates data and coefficients with known multivariate spatial properties, all within an area‐unit spatial setting. Spatial autocorrelation and spatial heterogeneity effects are varied and accounted for. On fitting the regressions, that each have different assumptions and objectives, to very different geographical processes, valuable insights to their likely performance are uncovered. Results objectively confirm an inherent interrelationship between autocorrelation and heterogeneity, that results in an identification problem when choosing one regression over another. Given this, recommendations on the use and implementation of these spatial regressions are suggested, where knowledge of the properties of real study data and the analytical questions being posed are paramount.     

Provisioning an Early City: Spatial Equilibrium in the Agricultural Economy at Angkor,Cambodia

Journal of Archaeological Method and Theory - A dominant view in economic anthropology is that farmers must overcome decreasing marginal returns in the process of intensification. However, it is...     

Properties of Tests for Spatial Dependence in Linear Regression Models

Based on a large number of Monte Carlo simulation experiments on a regular lattice, we compare the properties of Moran's I and Lagrange multiplier tests for spatial dependence, that is, for both spatial error autocorrelation and for a spatially lagged dependent variable. We consider both bias and power of the tests for six sample sizes, ranging from twenty-five to 225 observations, for different structures of the spatial weights matrix, for several underlying error distributions, for misspecified weights matrices, and for the situation where boundary effects are present. The results provide an indication of the sample sizes for which the asymptotic properties of the tests can be considered to hold. They also illustrate the power of the Lagrange multiplier tests to distinguish between substantive spatial dependence (spatial lag) and spatial dependence as a nuisance (error autocorrelation).     

The Geography of Need: Spatial Distribution of Barriers to Employment in Metropolitan Detroit

Although several studies examine the barriers to employment that limit the employability of welfare recipients, they have not analyzed how these barriers might be different for residents of central cities than for residents in surrounding suburban areas. We consider how the prevalence of barriers to employment varies by race and place in metropolitan Detroit. We find that the prevalence of mental health, substance abuse, and domestic violence barriers is higher in suburban areas, and that structural barriers are higher in the central city.     

A Tale of One City: Creative Destruction,Spatial Fixes,and Ideology in Binghamton,New York

Revitalization of the City of Binghamton has been seen as a key element for economic recovery of the central New York region. Recently, civic leaders have favored neoliberal strategies. Supporters justify these strategies based on notions of community well being. They also assume that government sponsored creative destruction will counter-act the inherent tendency within capitalism to seek spatial fixes. Archaeological research on the history of landscape transformations in Binghamton demonstrates the depth of capitalist spatial fixes that have contributed to Binghamton’s current economic crisis. The resulting exploitation of labor and the working class was often obscured by ideologies associated with individual achievement or social reform. Thus, the history of capitalist social relations as it was written in the city scape of Binghamton echoes contemporary conditions and has the potential to inform critiques of these conditions.     

Spatial Analysis of Employment and Population Density: The Case of the Agglomeration of Dijon 1999

The aim of this paper is to analyze the intraurban spatial distributions of population and employment in the agglomeration of Dijon (regional capital of Burgundy, France). We study whether this agglomeration has followed the general tendency of job decentralization observed in most urban areas or whether it is still characterized by a monocentric pattern. To that purpose, we use a sample of 136 observations at the communal and at the IRIS (infraurban statistical area) levels with 1999 census data and the employment database SIRENE (INSEE). First, we study the spatial pattern of total employment and employment density using exploratory spatial data analysis. Apart from the CBD, few IRIS are found to be statistically significant, a result contrasting with those found using standard methods of subcenter identification with employment cut‐offs. Next, in order to examine the spatial distribution of residential population density, we estimate and compare different specifications: exponential negative, spline‐exponential, and multicentric density functions. Moreover, spatial autocorrelation, spatial heterogeneity, and outliers are controlled for by using the appropriate maximum likelihood, generalized method of moments, and Bayesian spatial econometric techniques. Our results highlight again the monocentric character of the agglomeration of Dijon.     

Beyond Moran's I: Testing for Spatial Dependence Based on the Spatial Autoregressive Model

The statistic known as Moran's I is widely used to test for the presence of spatial dependence in observations taken on a lattice. Under the null hypothesis that the data are independent and identically distributed normal random variates, the distribution of Moran's I is known, and hypothesis tests based on this statistic have been shown in the literature to have various optimality properties. Given its simplicity, Moran's I is also frequently used outside of the formal hypothesis-testing setting in exploratory analyses of spatially referenced data; however, its limitations are not very well understood. To illustrate these limitations, we show that, for data generated according to the spatial autoregressive (SAR) model, Moran's I is only a good estimator of the SAR model's spatial-dependence parameter when the parameter is close to 0. In this research, we develop an alternative closed-form measure of spatial autocorrelation, which we call APLE , because it is an approximate profile-likelihood estimator (APLE) of the SAR model's spatial-dependence parameter. We show that APLE can be used as a test statistic for, and an estimator of, the strength of spatial autocorrelation. We include both theoretical and simulation-based motivations (including comparison with the maximum-likelihood estimator), for using APLE as an estimator. In conjunction, we propose the APLE scatterplot, an exploratory graphical tool that is analogous to the Moran scatterplot, and we demonstrate that the APLE scatterplot is a better visual tool for assessing the strength of spatial autocorrelation in the data than the Moran scatterplot. In addition, Monte Carlo tests based on both APLE and Moran's I are introduced and compared. Finally, we include an analysis of the well-known Mercer and Hall wheat-yield data to illustrate the difference between APLE and Moran's I when they are used in exploratory spatial data analysis.     

A Nonparametric Analysis of Employment Density in a Polycentric City

Nonparametric estimation procedures offer distinct advantages in modeling polycentric cities because they are flexible enough to account for functional form misspecification and incorrect subcenter sites. This paper presents locally weighted (LW) regression estimates of employment density in suburban Chicago. LW regression estimates are more accurate than OLS regression and capture the effects of missing variables. The results demonstrate that Chicago is indeed a polycentric city: although the traditional city center continues to affect employment density patterns in the suburbs, local peaks have developed around secondary employment centers.     

The Spatial Extent of Criminogenic Places: A Changepoint Regression of Violence around Bars

Crime scientists have long known that crime clusters near certain places such as drinking establishments, although the spatial parameters of that clustering are less established. This article proposes a methodology to estimate a distance beyond which there is significantly less evidence of a correlation between locations and concentrations of crime. The technique uses changepoints derived from a segmented regression applied to spatial buffers emanating from around particular crime‐generating land uses. Geographic information system techniques are used to create a series of buffers to determine the density of crime around sites. A changepoint Poisson regression of the buffer midpoints is used to estimate the distance beyond which crime densities do not appear to decline significantly with increasing distance. A case study of violent crime around 1,282 bars in Philadelphia, Pennsylvania, for 2008 reveals that violence is highly clustered within 25.9 m (85 feet) then dissipates rapidly, a pattern that is not replicated using control sites (fire stations). This is an estimate of the spatial extent of violence around bars, and the technique could be used to estimate the extent of other crimes around a variety of crime‐generating locations. Expertos en el estudio del crimen saben desde hace tiempo que los delitos violentos se concentran cerca de algunos lugares tales como establecimientos de bebidas, aunque los parámetros espaciales de dichas aglomeraciones son menos conocidos. Este artículo propone una metodología para estimar la distancia máxima a partir de la cual hay significativamente menos evidencia de una correlación entre puntos de interés y las aglomeraciones de crimen. La técnica empleada utiliza puntos de cambio (changepoints) derivados de una regresión segmentada (segmented regression) aplicada a las zonas de amortiguamiento (buffers) generadas en torno a usos del suelo particulares asociados a delincuencia. Técnicas SIG (Sistema de Información Geográfica) son utilizadas para crear una serie de buffers y determinar la densidad de delitos en torno a la ubicación de cada establecimiento (bar). Una regresión Poisson de tipo changepoint de los puntos medios de los buffers es empleada para estimar la distancia a partir de la cual las densidades del crimen no disminuyen significativamente con la distancia. Un estudio de caso de los delitos violentos en torno a 1.282 bares en Filadelfia, Pennsylvania en 2008 revela que la violencia está muy concentrada dentro de un radio de 25.9 m (85 pies) y luego se disipa rápidamente, un patrón que no se replica cuando el análisis es aplicado a sitios de control (estaciones de bomberos). El resultado es una estimación de la extensión espacial de la violencia alrededor de bares y la conclusión que la técnica podría ser utilizada para estimar la extensión de otros delitos en torno a una gran variedad de lugares asociados con la generación de la delincuencia. 犯罪学家早已明晰犯罪集聚于某些特定区域（如酒吧）的周围,尽管较少地构建这类聚集的空间参数。本文提出了一种方法可估算在一定距离之外,区位与犯罪集聚程度间相关性呈显著减少的证据。将从分段回归中获得的变异点应用于犯罪发生地的空间缓冲区。地理信息系统（GIS）技术用于产生一系列缓冲区以确定地点周围的犯罪密度。缓冲区中点的变异点泊松回归用于估算超出犯罪密度区不呈现随距离增加而显著衰退的距离。本文以宾夕法尼亚州费城1282个酒吧周围暴力犯罪为案例进行研究,揭示出2008年暴力犯罪集聚于25.9m的范围内,并在该距离之外的迅速消失,而当控制点选为消防站时该格局不再出现。实验表明,这是一种估算酒吧暴力犯罪空间范围的方法,并且该技术可用于估算不同类型犯罪产生地的距离范围。     

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.