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.     

Impacts of Multiple-Period Lags in Dynamic Logit Models

This paper will provide an introduction to a new field of research, viz. the sensitivity of the solution trajectory of a dynamic logit model (belonging to the class of discrete choice models) in the light of a multiperiod lag structure. It is well known from recent advances in the area of chaos and turbulence theory that the stability of a dynamic system is critically dependent on various factors, such as threshold values of parameters, initial conditions, and also the lag structure. This paper aims to identify the consequences of different lag structures in dynamic logit models (including also dynamic spatial interaction models). Various simulation experiments will be used to show that the onset of instability of the solution trajectory tends to decrease as the number of time lags increases (depending also on the growth rate of the system).     

基于区域视角的中国工业旅游发展的思考

本文以全国工业旅游示范点为样本,对工业旅游点的区域分布特征及空间密度差异、工业旅游点所属行业的区域分布特征与空间密度差异进行初步分析,并探索这些特征与各区域工业发展特征的相关性,在此基础上,从制定区域工业旅游发展规划、选择工业旅游空间开发模式、构筑"无边界"的区域工业旅游合作方式等方面对各级区域范围的工业旅游发展进行探讨.     

An information theoretic approach to the estimation of migration flows

This study is concerned with the application of different types of models to the study of migration flows. Two traditional approaches to migration modeling, the demographic fixed-rate and the gravity/entropy families of models, are first described, and their adequacy is assessed. The author next discusses the derivation of minimum information models. Consideration is given to destination-weighted models and to net-constrained models. A unified approach that incorporates both a priori micro and estimated macro parameters is then considered. The author concludes that the extended minimum information approach is the most suitable for building dynamic spatial interaction models, given the current state of U.S. internal migration data.     

MODERN MACROECONOMICS AND REGIONAL ECONOMIC MODELING*

ABSTRACT Despite a history of regional economic models being patterned after their national counterparts, modern macroeconomic methodology has yet to be fully embraced by regional scientists. In this paper, I argue that modern macroeconomic methodology has much to offer the field of regional economic modeling. For one, the empirical strategies used in implementing dynamic stochastic general equilibrium models provide insights into how regional computable general equilibrium models could be better parameterized and empirically verified. In addition, the structural macroeconomic approach more generally could be used to construct structural regional policy analysis models for use as alternatives to traditional regional models.     

Fiber-Based Modeling of Circular Reinforced Concrete Bridge Columns

This article presents the application of fiber-based analysis to predict the nonlinear response of reinforced concrete bridge columns. Specifically considered are predictions of overall force-deformation hysteretic response and strain gradients in plastic hinge regions. This article discusses the relative merits of force-based and displacement-based fiber elements, and proposes a technique for prediction of nonlinear strain distribution based on the modified compression field theory. The models are compared with static and dynamic test data and recommendations are made for fiber-based modeling of RC bridge columns.     

SOLVING A THREE-DIMENSIONAL URBAN ACTIVITY MODEL OF LAND USE INTENSITY AND TRANSPORT CONGESTION*

ABSTRACT Recent developments in combining input-output and transportation planning models have made it possible to construct realistic comprehensive urban and regional activity models of land use intensity. These models form the basis for a rigorous approach to studying the interactions among urban activities. However, efficient computational solution methods for implementing such comprehensive models are still not available. In this paper, an efficient solution method for a nonlinear programming urban systems model is developed by combining Evans's partial linearization technique with Powell's hybrid method. The solution algorithm is applied to a small but realistic urban area with a detailed transportation network.     

Lagrange Multiplier Test Diagnostics for Spatial Dependence and Spatial Heterogeneity

Several diagnostics for the assessment of model misspecification due to spatial dependence and spatial heterogeneity are developed as an application of the Lagrange Multiplier principle. The starting point is a general model which incorporates spatially lagged dependent variables, spatial residual autocorrelation and heteroskedasticity. Particular attention is given to tests for spatial residual autocorrelation in the presence of spatially lagged dependent variables and in the presence of heteroskedasticity. The tests are formally derived and illustrated in a number of simple empirical examples.     

The Determinants of Regional Freight Transport: A Spatial,Semiparametric Approach

In the context of modeling regional freight the four‐stage model is a popular choice. The first stage of the model, freight generation and attraction, however, suffers from three shortcomings: first of all, it does not take spatial dependencies among regions into account, thus potentially yielding biased estimates. Second, there is no clear consensus in the literature as to the choice of explanatory variables. Second, sectoral employment and gross value added are used to explain freight generation, whereas some recent publications emphasize the importance of variables which measure the amount of logistical activity in a region. Third, there is a lack of consensus regarding the functional form of the explanatory variables. Multiple recent studies emphasize nonlinear influences of selected variables. This article addresses these shortcomings by using a spatial variant of the classic freight generation and attraction models combined with a penalized spline framework to model the explanatory variables in a semiparametric fashion. Moreover, a Bayesian estimation approach is used, coupled with a penalized Normal inverse‐Gamma prior structure, to introduce uncertainty regarding the choice and functional form of explanatory variables. The performance of the model is assessed on a real‐world example of freight generation and attraction of 258 European NUTS‐2 level regions, covering 25 European countries.     

Local Government Portfolios and Regional Growth: Some Combined Dynamic CGE/Optimal Control Results

A theoretical policy model is presented that combines regional dynamic CGE modeling and optimal control to explore the role of local government taxation and expenditure in enhancing regional growth. It contributes to the regional CGE literature by explicitly solving for an optimal policy and augments earlier regional optimal control models by adding endogenous optimization of producer and consumer agents in response to endogenously determined prices. Results of three policy regimes are analyzed in terms of gains in the objective function, impacts on income inequality, and sensitivity to model parameterization.     

TESTING THE SPATIAL SCALE AND THE DYNAMIC STRUCTURE IN REGIONAL MODELS (A CONTRIBUTION TO SPATIAL ECONOMETRIC SPECIFICATION ANALYSIS)*

This article addresses the problem of specification uncertainty in modeling spatial economic theories in stochastic form. It is ascertained that the traditional approach to spatial econometric modeling does not adequately deal with the type and extent of specification uncertainty commonly encountered in spatial economic analyses. Two alternative spatial econometric modeling procedures proposed in the literature are reviewed and shown to be suitable for analyzing systematically two sources of specification uncertainty, viz., the level of aggregation and the spatio-temporal dynamic structure in multiregional econometric models. The usefulness of one of these specification procedures is illustrated by the construction of a simple multiregional model for The Netherlands.     

Nonlinear Multivariate Spatial Modeling Using NLPCA and Pair‐Copulas

A novel geostatistical modeling approach is developed to model nonlinear multivariate spatial dependence using nonlinear principal component analysis (NLPCA) and pair‐copulas. In spatial studies, multivariate measurements are frequently collected at each location. The dependence between such measurements can be complex. In this article, a multivariate geostatistical model is developed that can capture both nonlinear spatial dependence across locations and nonlinear dependence between measurements at a particular location. Nonlinear multivariate dependence between spatial variables is removed using NLPCA. Subsequently, a pair‐copula based model is fitted to each transformed variable to model the univariate nonlinear spatial dependencies. NLPCA and pair‐copulas, within the proposed model, are compared with stepwise conditional transformation (SCT) and conventional kriging. The results show that, for the two case studies presented, the proposed model that utilizes NLPCA and pair‐copulas reproduces nonlinear multivariate structures and univariate distributions better than existing methods based on SCT and kriging.     

Spatial Analysis in Physical Geography and the Challenge of Deterministic Uncertainty

The application of complex and nonlinear dynamical systems (NDS) theory in physical geography and geosciences has proceeded through several stages, and has recently entered a phase where field-testable hypotheses and historical or mechanistic explanations are being generated. However, there are some fundamental challenges. It seems clear that chaos and dynamical self-organization are present, and may be common in earth surface systems, and that these phenomena have spatial manifestations in the landscape. However, NDS theory and methods have been formulated primarily in the temporal domain and are typically ill-suited to real-world spatial data. Spatial analytical methods are not generally capable of distinguishing deterministic complexity and uncertainty from noise. Thus, the detection of the signals of complex deterministic dynamics in real landscapes and spatial data is a major challenge. Entropy-based methods of spatial analysis can be directly linked to nonlinear dynamics, and are at present the best available method to approach this problem. However, there is evidence in the spatial analysis literature suggesting that development of techniques to detect deterministic uncertainty is possible. Pending such a break-through, three general approaches are described, based on spatial analysis of chronosequences, the characteriziation of changes in spatial structure over time, and the spatial-domain testing of specific hypotheses relevant to deterministic uncertainty. Current trends generally suggest a shift in mathematical modeling and spatial analysis in physical geography away from traditional determinism toward approaches that incorporate locational, historical, and scale contingency.     

INTRODUCTION: WHITHER SPATIAL ECONOMETRICS?

ABSTRACT Spatial econometrics has become a mainstay for regional scientists aiming to estimate geographic spillovers in regional outcomes. Yet, many remain skeptical, especially urban economists who prefer natural experimental approaches. Their concerns revolve around identification and a general lack of a theoretical foundation in the estimation of spatial econometric models. This theme issue includes three papers from leading regional scientists to appraise the status of spatial econometrics. The outcome is sweeping proposals from (1) abandoning standard spatial econometrics because it cannot identify causality, (2) using nonparametric approaches, and (3) implementing more nuanced changes revolving around better theoretical and empirical modeling.     

Forecasting Structural Change With a Regional Econometric Input-Output Model

The sophistication of regional economic models has been demonstrated in numerous ways, most recently in the form of linking several modeling systems or in the expansion in the number of equations that can be manipulated successfully to produce impact analyses or forecasts. In this paper, an alternative perspective is employed. What do regional macro-level forecasts indicate about the process of structural change? A new methodology is illustrated that enables analysts to make forecasts of detailed structural change in the interindustry relations in an economy. Using a regional econometric input-output model developed for the Chicago metropolitan region, derived input-output tables are extracted for the period 1975–2016. These tables are then analyzed to determine the forecasted direction of structural changes for the region. The innovation illustrated here is based on a model that exploits the general equilibrium spirit of computable general equilibrium models through the adjustment of input coefficients to clear markets.     

皖江城市带空间经济联系的网络特征及优化方向研究

基于社会网络分析的视角,分析了皖江城市带城市空间经济联系的网络特征,提出了其经济联系的优化方向。结果显示:从网络密度看,空间经济联系网络还处于弱联结状态;就点度中心度来说,芜湖的点出度最大,合肥的点入度最大;由接近中心度与中间中心度可知,合肥、芜湖、马鞍山等城市的经济联系最紧密,但网络中还存在不均衡。通过凝聚子群分析得出,网络中存在四个子群,其中芜湖、马鞍山联系最紧密,边缘区城市间经济联系较少。结合皖江城市带空间经济联系的网络特征,从内、外两方面,抽象出其经济联系的优化方向。     

Spatial Autoregressive Models for Geographically Hierarchical Data Structures

This article discusses how standard spatial autoregressive models and their estimation can be extended to accommodate geographically hierarchical data structures. Whereas standard spatial econometric models normally operate at a single geographical scale, many geographical data sets are hierarchical in nature—for example, information about houses nested into data about the census tracts in which those houses are found. Here we outline four model specifications by combining different formulations of the spatial weight matrix W and of ways of modeling regional effects. These are (1) groupwise W and fixed regional effects; (2) groupwise W and random regional effects; (3) proximity‐based W and fixed regional effects; and (4) proximity‐based W and random regional effects. We discuss each of these model specifications and their associated estimation methods, giving particular attention to the fourth. We describe this as a hierarchical spatial autoregressive model. We view it as having the most potential to extend spatial econometrics to accommodate geographically hierarchical data structures and as offering the greatest coming together of spatial econometric and multilevel modeling approaches. Subsequently, we provide Bayesian Markov Chain Monte Carlo algorithms for implementing the model. We demonstrate its application using a two‐level land price data set where land parcels nest into districts in Beijing, China, finding significant spatial dependence at both the land parcel level and the district level.     

Spatial Analysis of Regional Inequalities in Turkey

In this paper, we examine regional inequalities in Turkey not only at the interprovincial level but for three different regional definitions as well. It has raised questions about inequalities not only between regions (interregional) but inequalities within each region. Hence, one contribution of this paper is to test the effects of aggregation and scale on the identification of regional inequalities using currently accepted spatial analytic methods. The results indicate that overall inequalities are decreasing; however spatial dependence is becoming more dominant. The Theil Index indicates that interregional inequalities are increasing while intraregional inequalities are declining for all spatial partitions from 1980 to 1997. Most developed provinces are enhancing overall inequalities, although there is some evidence of a spread effect on their neighbours.     

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.