SPATIAL HEDONIC MODELS OF AIRPORT NOISE,PROXIMITY, AND HOUSING PRICES*

ABSTRACT Despite the refrain that housing prices are determined by “location, location, and location,” few studies of airport noise and housing prices have incorporated spatial econometric techniques. We compare various spatial econometric models and estimation methods in a hedonic price framework to examine the impact of noise on 2003 housing prices near the Atlanta airport. Spatial effects are best captured by a model including both spatial autocorrelation and autoregressive parameters estimated by a generalized moments approach. In our preferred model, houses located in an area in which noise disrupts normal activities (defined by a day–night sound level of 70–75 decibels) sell for 20.8 percent less than houses located where noise does not disrupt normal activities (defined by a day–night sound level below 65 decibels). The inclusion of spatial effects magnifies the negative price impacts of airport noise. Finally, after controlling for noise, houses farther from the airport sell for less; the price elasticity with respect to distance is −0.15, implying that airport proximity is an amenity.     

SELECTION BIAS IN SPATIAL ECONOMETRIC MODELS

ABSTRACT. The problem of spatial autocorrelation has been ignored in selection-bias models estimated with spatial data. Spatial autocorrelation is a serious problem in these models because the heteroskedasticity with which it commonly is associated causes inconsistent parameter estimates in models with discrete dependent variables. This paper proposes estimators for commonly-employed spatial models with selection bias. A maximum-likelihood estimator is applied to data on land use and values in 1920s Chicago. Evidence of significant heteroskedasticity and selection bias is found.     

An Analysis of Arrow and String Bag Craft Variability in the Upper Sepik and Central New Guinea

ABSTRACT This paper describes an analysis of relationships between language, geography and material culture in the upper Sepik region of New Guinea. We used Mantel tests and principal coordinate analysis to assess and compare the associations between arrow and string bag crafts and language and geographical distance. The Mantel tests resulted in a significant association between each class’ craft variability and both geographical distance and language, however, after statistical control was applied to either of the independent variables only a significant association with geographical distance remained. We argue that these results indicate craft techniques were readily disseminated and that craft distributions are unlikely to reflect any deeper historical relationships between groups. The spatial autocorrelation of arrow crafts was particularly great. A comparison of the principal coordinate analysis plots indicates a greater degree of diffusion and synthesis had taken place between lowland and highland arrow craft traditions. We conclude that the more intensive learning regimes needed to master string bag craft techniques, as well as a greater degree of interdependence between string bag components, had ensured more abrupt differences between bag types resulting in a more moderate degree of spatial autocorrelation.     

PERSISTENCE OF REGIONAL UNEMPLOYMENT: APPLICATION OF A SPATIAL FILTERING APPROACH TO LOCAL LABOR MARKETS IN GERMANY*

ABSTRACT The geographical distribution and persistence of regional/local unemployment rates in heterogeneous economies (such as Germany) have been, in recent years, the subject of various theoretical and empirical studies. Several researchers have shown an interest in analyzing the dynamic adjustment processes of unemployment and the average degree of dependence of the current unemployment rates or gross domestic product from the ones observed in the past. In this paper, we present a new econometric approach to the study of regional unemployment persistence, in order to account for spatial heterogeneity and/or spatial autocorrelation in both the levels and the dynamics of unemployment. First, we propose an econometric procedure suggesting the use of spatial filtering techniques as a substitute for fixed effects in a panel estimation framework. The spatial filter computed here is a proxy for spatially distributed region‐specific information (e.g., the endowment of natural resources, or the size of the “home market”) that is usually incorporated in the fixed effects coefficients. The advantages of our proposed procedure are that the spatial filter, by incorporating region‐specific information that generates spatial autocorrelation, frees up degrees of freedom, simultaneously corrects for time‐stable spatial autocorrelation in the residuals, and provides insights about the spatial patterns in regional adjustment processes. We present several experiments in order to investigate the spatial pattern of the heterogeneous autoregressive coefficients estimated for unemployment data for German NUTS‐3 regions. We find widely heterogeneous but generally high persistence in regional unemployment rates.     

The Size-adjusted Critical Region of Moran's I Test Statistics for Spatial Autocorrelation and Its Application to Geographical Areas

In this article, we explore the expression of the asymptotic approximation of the distribution function of Moran's I test statistic for the check of spatial autocorrelation, and we derive a more accurate critical value, which gives the rejection region similar to significant level α to the order of N^?1 (N = sample size). We show that in some cases our procedure effectively finds the significance of positive spatial autocorrelation in the problem testing for the lack of the spatial autocorrelation. Compared with our method, the testing procedure of Cliff and Ord (1971) is clearly ad hoc and should not be applied blindly, as they pointed out. Our procedure is derived from the theory of asymptotic expansion. We numerically analyze four types of county systems with rectangular lattices and three regional areas with irregular lattices.     

SPATIAL AUTOCORRELATION IN BRITISH UNEMPLOYMENT

ABSTRACT. The recent literature on local unemployment in Britain and the U.S. is reviewed, and a model estimated for Britain which is consistent with the findings of that literature. Spatial econometric techniques are applied to test and elaborate on that model. The results indmte the presence of significant spillovers in adjustments to local demand shocks. These spillovers occur over a wide spatial field and are strongest after a lag, suggesting that they reflect migration behavior. In addition, highly localized spatial interactions are found, consistent with commuting adjustments in response to shocks. Incorporating these effects eliminates spatial autocorrelation from the residuals. A simulation exercise is undertaken to demonstrate the effects of supply- and demand-side shocks on the model solution.     

城市长租公寓的空间布局与影响机制实证研究——以深圳为例

长租公寓作为住房市场的新兴业态，是贯彻国家“租购并举”住房政策的重要保障。本文以深圳为例，基于迈点研究院的数据，通过GIS空间分析工具，探讨长租公寓的空间布局及影响机制。研究表明：(1)深圳长租公寓空间布局呈现空间集聚性、距离衰减性、类型分异性特征，具体表现为紧邻中心城区的空间集聚性，从中心城区边缘向外围递减的距离衰减性，酒店、公寓、房地产系长租公寓热点区呈中心城区向外扩展态势的类型分异性；(2)地铁、超市商城、常住人口是影响长租公寓空间布局的主要因素，其影响作用存在空间异质性；(3)运用地租和新消费者理论，从供求视角构建长租公寓空间布局的影响机制。本研究可丰富新兴住房业态空间集聚模式和中国住房研究。     

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.     

中国城市人口集聚分布——基于空间效应的研究

城市人口集聚分布以及城市带的形成是一个空间现象,空间力量对其形成和演化具有重要影响。本文应用空间计量经济学与趋势面分析相结合方法,以617个县级以上城市1998年和2011年的城镇人口和经纬度坐标数据来研究中国城市人口空间集聚分布与趋势。研究发现,中国城市人口分布与其周围相邻城市的人口分布关系密切,并且其6阶空间自相关效应是最强的;同时地理位置对中国城市人口体系的空间分布和纵向形态形成具有显著影响。在空间自相关和空间位置相关的共同作用下,城市人口规模聚集区已在东部沿海地区形成。     

Local Spatial Autocorrelation in a Biological Model

We review the recently developed local spatial autocorrelation statistics I_i, c_i, G_i, and G_i*. We discuss two alternative randomization assumptions, total and conditional, and then newly derive expectations and variances under conditional randomization for I_i and c_i, as well as under total randomization for c_i. The four statistics are tested by a biological simulation model from population genetics in which a population lives on a 21 × 21 lattice of stepping stones (sixty-four individuals per stone) and reproduces and disperses over a number of generations. Some designs model global spatial autocorrelation, others spatially random surfaces. We find that spatially random designs give reliable test results by permutational methods of testing significance. Globally autocorrelated designs do not fit expectations by any of the three tests we employed. Asymptotic methods of testing significance failed consistently, regardless of design. Because most biological data sets are autocorrelated, significance testing for local spatial autocorrelation is problematic. However, the statistics are informative when employed in an exploratory manner. We found that hotspots (positive local autocorrelation) and coldspots (negative local autocorrelation) are successfully distinguished in spatially autocorrelated, biologically plausible data sets.     

The Colocation Quotient: A New Measure of Spatial Association Between Categorical Subsets of Points. 协同区位商：点集分类子集间空间关联性的新度量标准

This article presents a new metric we label the colocation quotient (CLQ), a measurement designed to quantify (potentially asymmetrical) spatial association between categories of a population that may itself exhibit spatial autocorrelation. We begin by explaining why most metrics of categorical spatial association are inadequate for many common situations. Our focus is on where a single categorical data variable is measured at point locations that constitute a population of interest. We then develop our new metric, the CLQ, as a point‐based association metric most similar to the cross‐k‐function and join count statistic. However, it differs from the former in that it is based on distance ranks rather than on raw distances and differs from the latter in that it is asymmetric. After introducing the statistical calculation and underlying rationale, a random labeling technique is described to test for significance. The new metric is applied to economic and ecological point data to demonstrate its broad utility. The method expands upon explanatory powers present in current point‐based colocation statistics.     

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).     

Robustness of Spatial Autocorrelation Specifications: Some Monte Carlo Evidence

Abstract This paper examines the robustness of various models of spatial autocorrelation through a series of Monte Carlo experiments in which each model takes a turn at the data generator. The generated data are then used to estimate all of the models. The estimated models are evaluated primarily on their predictive power.     

Intervening Opportunities, Competing Searchers, and the Intrametropolitan Flow of Male Youth Labor

This paper analyzes the determinants of the journey-to-work commuting patterns for male teenage workers within a single local labor market: the Oakland Primary Metropolitan Statistical Area. Controlling for the intervening opportunities and the intervening labor supply between a given origin and destination reduces the estimated negative effect of distance on the interzonal flow of labor by nearly 90 percent. Nonetheless, physical distance has a significant and substantial negative effect on intrametropolitan youth labor flows. Despite the high correlation between intervening opportunities and intervening competing workers, both spatial variables have sizable, and independent, effects on labor flows.     

我国装备制造业的创新、知识溢出和产学研合作——基于一个扩展的知识生产函数方法

知识溢出的本地化现象揭示了地理边界的限制机理,然而越来越多的研究表明知识溢出也可发生在远距离的空间范围内。本文以我国装备制造业为例,采用扩展的知识生产函数方法探讨大学和科研机构的知识溢出对区域创新的影响。结果表明,大学和科研机构能够产生强烈的本地和跨区域的知识溢出效应,地理空间和创新网络在跨区域知识溢出中扮演重要角色。其中,产学研合作产生的知识溢出不仅局限于区域尺度,也发生在更高的国家尺度;在地理空间的制约影响下,高素质劳动力的流动对区域创新的影响十分显著且稳定,而大学和科研机构的衍生溢出机制则不那么明显,其原因可能与空间尺度选择有关。     

A spatial analysis of crime in Vancouver, British Columbia: a synthesis of social disorganization and routine activity theory

This article investigates the spatial dimension of automotive theft, break and enter, and violent crime in Vancouver, British Columbia in 1996. The article uses and synthesizes social disorganization theory and routine activity theory as a theoretical backcloth and employs a spatial autoregressive regression procedure that accounts for spatial autocorrelation between crime rates and socio-economic characteristics at the census tract level. Strong support is found for synthesizing these two most common spatial theories of crime. In particular, high unemployment (social disorganization theory) and the presence of young populations (routine activity theory) are the strongest predictors of criminal activity.     

Exploring Spatial Patterns Using an Expanded Spatial Autocorrelation Framework

Spatial autocorrelation (SA) is regarded as an important dimension of spatial pattern. SA measures usually consist of two components: measuring the similarity of attribute values and defining the spatial relationships among observations. The latter component is often represented by a spatial weights matrix that predefines spatial relationship between observations in most measures. Therefore, SA measures, in essence, are measures of attribute similarity, conditioned by spatial relationship. Another dimension of spatial pattern can be explored by controlling observations to be compared based upon the degree of attribute similarity. The resulting measures are spatial proximity measures of observations, meeting predefined attribute similarity criteria. Proposed measures reflect degrees of clustering or dispersion for observations meeting certain levels of attribute similarity. An existing spatial autocorrelation framework is expanded to a general framework to evaluate spatial patterns and can accommodate the proposed approach measuring proximity. Analogous to the concept of variogram, clustergram is proposed to show the levels of spatial clustering over a range of attribute similarity, or attribute lags. Specific measures based on the proposed approach are formulated and applied to a hypothetical landscape and an empirical example, showing that these new measures capture spatial pattern information not reflected by traditional spatial autocorrelation measures.     

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.     

Evaluating the Temporal and Spatial Heterogeneity of the European Convergence Process, 1980–1999*

Abstract. In this paper, we suggest a framework that allows testing simultaneously for temporal heterogeneity, spatial heterogeneity, and spatial autocorrelation in β‐convergence models. Based on a sample of 145 European regions over the 1980–1999 period, we estimate a Seemingly Unrelated Regression Model with spatial regimes and spatial autocorrelation for two sub‐periods: 1980–1989 and 1989–1999. The assumption of temporal independence between the two periods is rejected, and the estimation results point to the presence of spatial error autocorrelation in both sub‐periods and spatial instability in the second sub‐period, indicating the formation of a convergence club between the peripheral regions of the European Union.     

Gravity Parameter Estimation

The question of whether ordinary least-squares (OLS) regression provides unbiased estimates for parameters of the gravity model is investigated. Holding attractiveness constant and looking only at the location of destinations, OLS is shown to give unbiased estimates if underlying behavior conforms to the gravity hypothesis. Johnston's conjectures on this are examined and found to explain only partially observed variations in distance exponents. Allowing attractiveness to vary, spatial autocorrelation in attractiveness is shown to lead to bias in OLS estimation due to misspecification, even where behavior conforms to the gravity hypothesis. Deterministic and stochastic illustrations are provided.