Spatial Error Autocorrelation Research Articles

Creating a Soil Map Using Digital Soil Mapping on the Example of the Diurtiulinsky Municipal District

The study describes the use of machine learning methods, geostatistics, etc. in establishing soil properties depending on various classes of soil. The most commonly used data are information from spectral reflectance bands of satellite images and terrain models. Besides, there is also great potential for creating new data tiers. The study relies on a method known as SCORPAN-SSPFe, which assumes spatial error autocorrelation as a standalone function. This method is actively used in places where there is not enough information about soil data. Besides, four types of interpolation were compared using the SCORPAN method: multiple linear regression, cubistic model, cubistic model with kriging and random forest model, which use extensive but common values of soil properties associated with soil classes. The research result is obtained by applying the method to conduct large-scale soil surveys, which determines the purpose and relevance of our study.

Spatial dependence model with feature difference

AbstractThis paper presents a new spatial dependence model with an adjustment of feature difference. The model accounts for the spatial autocorrelation in both the outcome variables and residuals. The feature difference adjustment in the model helps to emphasize feature changes across neighboring units, while suppressing unobserved covariates that are present in the same neighborhood. The prediction at a given unit incorporates components that depend on the differences between the values of its main features and those of its neighboring units. In contrast to conventional spatial regression models, our model does not require a comprehensive list of global covariates necessary to estimate the outcome variable at the unit, as common macro‐level covariates are differenced away in the regression analysis. Using the real estate market data in Hong Kong, we applied Gibbs sampling to determine the posterior distribution of each model parameter. The result of our empirical analysis confirms that the adjustment of feature difference with an inclusion of the spatial error autocorrelation produces better out‐of‐sample prediction performance than other conventional spatial dependence models. In addition, our empirical analysis can identify components with more significant contributions.

The Hedonic Modeling of Property Prices: Case Study on Cape Town, South Africa

The real estate and valuation discipline has embraced the recent developments in spatial data analysis as a way of remediating obvious limitations of the ordinary least squares (OLS) approach in handling spatial effects. However, despite the development, the South African property market has yet to embrace spatial analysis when estimating property prices. The objective of this study is to understand the rationale behind the use of spatial hedonic modeling on the Cape Town property market by first testing the data against the existence of spatial effects and using the appropriate techniques to correct the glitches. A spatial error autocorrelation model and geographically weighted regression (GWR) were employed to correct spatial dependence (autocorrelation) and spatial heterogeneity on 3,232 observations. The relative performance of the two spatial modeling techniques as revealed by their goodness-of-fit are quite impressive but the spatial error model marginally outperforms the GWR. Thus, it is recommended that any of these techniques can be used in modeling property prices in the Cape Town market.

Does Airbnb Disrupt the Private Rental Market? An Empirical Analysis for French Cities

This article evaluates whether Airbnb rentals affect the rents in the private rental sector in eight cities in France. We estimate a hedonic equation for each city on individual data for apartments, allowing for heteroscedasticity and spatial error autocorrelation of unknown forms and using a large variety of structural and contextual characteristics of the apartments. We show that the density of Airbnb rentals puts upward pressure on rents in Lyon, Montpellier, and Paris, whereas it has no significant effect in other cities. If we restrict the analysis to the professional business of Airbnb rentals, which we define as the lodgings owned by an investor who rents either several “entire home” dwellings (regardless of the number of days) or an “entire home” dwelling for more than 120 days a year, we find a greater effect, which concerns only the two largest cities of France, that is, Marseille and Paris. When we focus on new tenancy agreements, the impact is even higher and concerns Paris, Marseille, and Montpellier. The impact of the Airbnb activity on rents is shown to increase with the proportion of owner-occupiers and decrease with hotel density, both in Montpellier and Paris. However, the share of second homes leads to contrasting effects.

Effects of crop residue management on winter durum wheat productivity in a long term experiment in Southern Italy

A long-term experiment comparing different crop residue (CR) managements was established in 1977 in Foggia (Apulia region, southern Italy). The objective of this study was to investigate the long-term effects of different types of crop residue management on main yield response parameters in a continuous cropping system of winter durum wheat. In order to correctly interpret the results, models accounting for spatial error autocorrelation were used and compared with ordinary least square models. Eight crop residue management treatments, based on burning of wheat straw and stubble or their incorporation with or without N fertilization and irrigation, were compared. The experimental design was a complete randomized block with five replicates. Results indicated that the dynamics of yield, grain protein content and hectolitric weight of winter durum wheat did not show any decline as usually expected when a monoculture is carried out for a long time. In addition, the temporal variability of productivity was more affected by meteorological factors, such as air temperature and rainfall, than CR management treatments. Higher wheat grain yields and hectolitric weights quite frequently occurred after burning of wheat straw compared with straw incorporation without nitrogen fertilization and autumn irrigation and this was attributed to temporary mineral N immobilization in the soil. The rate of 50 kg ha−1 of N seemed to counterbalance this negative effect when good condition of soil moisture occurred in the autumn period, so yielding the same productive level of straw burning treatment.

The potential of 2D Kalman filtering for soil moisture data assimilation

Abstract We examine the potential for parameterizing a two-dimensional (2D) land data assimilation system using spatial error auto-correlation statistics gleaned from a triple collocation analysis and the triplet of: (1) active microwave-, (2) passive microwave- and (3) land surface model-based surface soil moisture products. Results demonstrate that, while considerable spatial error auto-correlation exists in the errors for all three products, the inclusion of this information into a 2D assimilation system does not significantly improve the performance of the system relative to a one-dimensional (1D) baseline. This result is explained via an analytical evaluation of the impact of spatial error auto-correlation on the steady-state Kalman gain, which reveals that 2D filtering requires the existence of large auto-correlation differences (between the assimilation model and the assimilated observations) in order to enhance the analysis relative to a 1D filtering baseline. As a result, large error auto-correlations alone (in both the model or observations) are not sufficient to motivate the application of a 2D land assimilation system. These results have important consequences for the development of land data assimilation systems designed to ingest satellite derived surface soil moisture products for water resource and climate applications.

A simple spatial dependence test robust to local and distributional misspecifications

In this paper, we derive a class of modified score tests robust to local and distributional misspecifications for testing spatial error autocorrelation and spatial lag dependence. The proposed tests are general enough to include several popular tests for the spatial dependence as special cases. Moreover, we show that the popular test statistics proposed by Burridge (1980) and Anselin et al. (1996) are robust to distributional misspecification although they are derived under normality assumption.

Testing for spatial error dependence in probit models

In this note, we compare three test statistics that have been suggested to assess the presence of spatial error autocorrelation in probit models. We highlight the differences between the tests proposed by Pinkse and Slade (J Econom 85(1):125–254, 1998), Pinkse (Asymptotics of the Moran test and a test for spatial correlation in Probit models, 1999; Advances in Spatial Econometrics, 2004) and Kelejian and Prucha (J Econom 104(2):219–257, 2001), and compare their properties in a extensive set of Monte Carlo simulation experiments both under the null and under the alternative. We also assess the conjecture by Pinkse (Asymptotics of the Moran test and a test for spatial correlation in Probit models, 1999) that the usefulness of these test statistics is limited when the explanatory variables are spatially correlated. The Kelejian and Prucha (J Econom 104(2):219–257, 2001) generalized Moran’s I statistic turns out to perform best, even in medium sized samples of several hundreds of observations. The other two tests are acceptable in very large samples.

Applying Directed Acyclic Graphs to Assist Specification of a Hedonic Model

This research empirically tests the hypothesis that utilizing directed acyclic graphs (DAGs) as an ex-ante process to select variables for a hedonic model improves the model's performance. The results for both new and existing house submarkets indicated that DAG analysis mitigated the multicollinearity issue commonly observed in hedonic models. Using DAG analysis also improved the goodness-of-fit of the hedonic model for the new submarket. However, model specification through DAG analysis does not offer clear implications for improving forecasting accuracy, efficiency, and spatial error autocorrelation. The findings imply that DAG analysis for model specification can be a complementary step in the process of estimating hedonic models, especially when reducing standard error bias by alleviating potential multicollinearity is important in determining the attributes that affect housing prices.

Infant mortality in Brazil, 1980-2000: a spatial panel data analysis.

BackgroundInfant mortality is an important measure of human development, related to the level of welfare of a society. In order to inform public policy, various studies have tried to identify the factors that influence, at an aggregated level, infant mortality. The objective of this paper is to analyze the regional pattern of infant mortality in Brazil, evaluating the effect of infrastructure, socio-economic, and demographic variables to understand its distribution across the country.MethodsRegressions including socio-economic and living conditions variables are conducted in a structure of panel data. More specifically, a spatial panel data model with fixed effects and a spatial error autocorrelation structure is used to help to solve spatial dependence problems. The use of a spatial modeling approach takes into account the potential presence of spillovers between neighboring spatial units. The spatial units considered are Minimum Comparable Areas, defined to provide a consistent definition across Census years. Data are drawn from the 1980, 1991 and 2000 Census of Brazil, and from data collected by the Ministry of Health (DATASUS). In order to identify the influence of health care infrastructure, variables related to the number of public and private hospitals are included.ResultsThe results indicate that the panel model with spatial effects provides the best fit to the data. The analysis confirms that the provision of health care infrastructure and social policy measures (e.g. improving education attainment) are linked to reduced rates of infant mortality. An original finding concerns the role of spatial effects in the analysis of IMR. Spillover effects associated with health infrastructure and water and sanitation facilities imply that there are regional benefits beyond the unit of analysis.ConclusionsA spatial modeling approach is important to produce reliable estimates in the analysis of panel IMR data. Substantively, this paper contributes to our understanding of the physical and social factors that influence IMR in the case of a developing country.

Dynamic spatial panels: models, methods, and inferences

This paper provides a survey of the existing literature on the specification and estimation of dynamic spatial panel data models, a collection of models for spatial panels extended to include one or more of the following variables and/or error terms: a dependent variable lagged in time, a dependent variable lagged in space, a dependent variable lagged in both space and time, independent variables lagged in time, independent variables lagged in space, serial error autocorrelation, spatial error autocorrelation, spatial-specific and time-period-specific effects. The survey also examines the reasoning behind different model specifications and the purposes for which they can be used, which should be useful for practitioners.

Simple regression‐based tests for spatial dependence

Summary We propose simple and robust diagnostic tests for spatial error autocorrelation and spatial lag dependence. The idea is to reformulate the testing problem such that the outer product of gradients (OPG) variant of the LM test can be employed. Our versions of the tests are based on simple auxiliary regressions, where ordinary regression t- and F-statistics can be used to test for spatial autocorrelation and lag dependence. An important advantage of the proposed test statistics is that they are robust against heteroscedastic errors. Therefore, our approach gives practitioners an easy to implement and robust alternative to existing tests.

Analyzing the dynamics of homicide patterns in Chicago: ESDA and spatial panel approaches

This paper studies the relationship between homicide rate and socioeconomic factors at community area level in Chicago from 1960 to 1995. Most of prior studies of social disorganization theory are based on cross-sectional spatial regression or longitudinal studies. This research integrates space and time in testing social disorganization theory. First, exploratory spatial data analysis (ESDA) is used to examine dynamic spatial patterns of these indicators. This investigation justifies the estimation of homicide rates across community areas through panel-data models that extend to include spatial lag and spatial error autocorrelation.

Geographically weighted regression bandwidth selection and spatial autocorrelation: an empirical example using Chinese agriculture data

This research note examined the performance of Geographically Weighted Regression (GWR) using two calibration methods. The first method, Cross Validation (CV), has been commonly used in the applied literature using GWR. A second criterion selected an optimal bandwidth that corresponded with the smallest spatial error Lagrange Multiplier (LM) test statistic. We find that there is a tradeoff between addressing spatial autocorrelation and reducing degree of extreme coefficients in GWR. Although spatial autocorrelation can be controlled for by using the LM criterion, a substantial degree of extreme coefficients may remain. However, while the CV approach appears to be less prone to producing extreme coefficients, it may not always attend to the problems that arise in the presence of spatial error autocorrelation.

Identifying spatial interactions in the presence of spatial error autocorrelation: An application to land use spillovers

Spatial spillovers—interaction effects among neighboring agents in space—are a common characteristic of a variety of processes that are of interest to environmental and resource economists. Empirical identification of these interactions is challenging, however, due to the endogenous nature of the interactions and the inevitable unobserved spatial correlation that, if uncontrolled, can result in spurious estimates of the interaction parameters. Traditional spatial econometric models rely on maintained assumptions that impose separate structures for the spatial error and interaction processes and thus are insufficient for solving this identification problem. To identify spatial land use spillovers in a hedonic model of residential housing values, we pursue an alternative approach by exploiting a natural experiment in the data. We use exogenous physical land features that impose a direct constraint on residential development on some, but not all, of the land that falls within our study region and use this to construct a “partial population identifier.” We find that this estimation strategy solves the endogeneity problem and reduces spatial error autocorrelation, but does not fully eliminate it. Estimation of the model using a more restricted sample in combination with the partial population identification strategy is successful in eliminating the remaining spatial error autocorrelation. We conclude that less restrictive approaches to controlling for unobserved spatial correlation, such as the natural experiment pursued here, may provide a superior alternative to identifying spatial spillovers.

Accounting for Spatial Error Correlation in the 2004 Presidential Popular Vote

One problem with describing election vote shares using ordinary least squares (OLS) is that it ignores the possible presence of spatial error correlation, whereby the errors are correlated in a systematic manner over space. This omission can bias OLS standard errors. We examine the 2004 presidential county vote outcome using OLS and a spatial error model (SEM) that accounts for spatial autocorrelation in the error structure. We find that spatial error correlation is present, that the SEM is superior to OLS for making inferences, and that several factors deemed important to the 2004 election outcome are not significant once the spatial error autocorrelation is taken into account.

A Spatial Econometric Analysis of Regional Specialization Patterns across EU Regions

In this paper, I perform a spatial econometric analysis of the determinants of regional specialization patterns across EU regions. Spatial correlation is evident, but this is due mostly to spatial error autocorrelation. Spatial interaction caused by positive economic interdependencies might be present for a very few labor-intensive sectors, yet it is inconsistent across different spatial weights matrices. No clear, disadvantageous spatial interdependencies of specialization in the periphery or increasing core-periphery tendencies were identified.

Convergence in Per-capita GDP across European Regions using Panel Data Models Extended to Spatial Autocorrelation Effects

This paper studies the convergence of per-capita GDP across European regions over a fairly long period. Most of the works in the field are based on either cross-sectional or fixed-effects estimates. We propose the estimation of convergence in per-capita GDP across European regions by making use of panel-data models extended to include spatial error autocorrelation (Anselin, 1988; Elhorst, 2003). This will allow us to extend the traditional beta-convergence model to include a rigorous treatment of the spatial correlation among the intercept terms. A spatial analysis of such intercept terms will also be performed in order to shed light on the concept of spatially conditional convergence.

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.

Unconditional Maximum Likelihood Estimation of Linear and Log‐Linear Dynamic Models for Spatial Panels

This article hammers out the estimation of a fixed effects dynamic panel data model extended to include either spatial error autocorrelation or a spatially lagged dependent variable. To overcome the inconsistencies associated with the traditional least‐squares dummy estimator, the models are first‐differenced to eliminate the fixed effects and then the unconditional likelihood function is derived taking into account the density function of the first‐differenced observations on each spatial unit. When exogenous variables are omitted, the exact likelihood function is found to exist. When exogenous variables are included, the pre‐sample values of these variables and thus the likelihood function must be approximated. Two leading cases are considered: the Bhargava and Sargan approximation and the Nerlove and Balestra approximation. As an application, a dynamic demand model for cigarettes is estimated based on panel data from 46 U.S. states over the period from 1963 to 1992.