Estimation of Anisotropic,Time-Varying Spatial Spillovers of Fine Particulate Matter Due to Wind Direction

This paper investigates the effect of daily wind direction and speed on the spatio-temporal distribution of particulate matter, . Interdependencies between the values of different monitoring sites are characterized by incorporating time-varying anisotropic spatial weighting matrices. These weights are parameterized with respect to wind direction, speed and a range that marks the bandwidth of admissible deviations between wind direction and bearing. The empirical analysis is based on daily values recorded by monitoring sites located across the eastern United States in 2015 as well as several meteorological regressors. More precisely, we propose a space-time dynamic panel data model with different spatial autoregressive, temporal and exogenous dependencies. All model parameters are estimated by the quasi-maximum likelihood approach. The estimation procedure, including the identification of the range and spatial parameters, is verified by Monte Carlo simulations. We show that part of the spatial dependency of values is explained by wind direction.     

A Comparison of Spatial and Nonspatial Methods in Statistical Modeling of NO2: Prediction Accuracy,Uncertainty Quantification,and Model Interpretation

${\text{NO}}_2$ is a traffic-related air pollutant. Ground ${\text{NO}}_2$ monitoring stations measure ${\text{NO}}_2$ concentrations at certain locations and statistical predictive methods have been developed to predict ${\text{NO}}_2$ as a continuous surface. Among them, ensemble tree-based methods have shown to be powerful in capturing nonlinear relationships between ${\text{NO}}_2$ measurements and geospatial predictors but it is unclear if the spatial structure of ${\text{NO}}_2$ is also captured in the response-covariates relationships. We dive into the comparison between spatial and nonspatial data models accounting for prediction accuracy, model interpretation and uncertainty quantification. Moreover, we implement two new spatial and a nonspatial methods that have not been applied to air pollution mapping. We implemented our study using national ground station measurements of ${\text{NO}}_2$ in Germany and the Netherlands of 2017. Our results indicate heterogeneous levels of importance of modeling the spatial process in different areas. The prediction intervals predicted with ensemble tree-based methods are more satisfactory than the geostatistical methods. The two new methods implemented each obtained better prediction accuracy compared to the original ensemble tree-based and stacking methods. The probabilistic distribution of the spatial random field estimated by the geostatistical methods could provide useful information for analyzing emission sources and the spatial process of observations.     

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.     

Boron isotope geochemistry of Na‐bicarbonate,Na‐chloride,and Ca‐chloride waters from the Northern Apennine Foredeep basin: other pieces of the sedimentary basin puzzle

The boron stable isotope ratio δ¹¹B of 12 water samples representative of three chemical facies (fresh Na‐bicarbonate, brackish Na‐chloride, saline, and brine Ca‐chloride) has been analyzed. Interpretation of the δ¹¹B data, along with the chemical compositions, reveals that Na‐carbonate waters from the Northern Apennine are of meteoric origin, with boron contributions from clay desorption and mixing with seawater‐derived fluids of Na‐chloride or Ca‐chloride compositions. The comparison of our new results with the literature data on other sedimentary basins of Mediterranean, and worldwide, confirms the contribution of Na‐bicarbonate waters to the genesis of mud volcano fluids. The Na‐chloride sample of Salvarola (SAL), which may represent the end‐member of the mud volcanoes, and the Ca‐chloride brine water from Salsomaggiore (SM) indicate boron release from clays compatible with the diagenetic process. The empirical equation: relating boron concentration and the stable isotope composition of the fluids observed in this study and the literature is proposed to trace the effect of diagenesis in sedimentary basins. A geothermometer associated to the diagenetic equation is also proposed: The application of this equation to obtain reservoir temperatures from δ¹¹B compositions of waters should be carefully evaluated against the results obtained from other chemical and isotopic geothermometers from other basins around the world.     

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.     

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.     

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.     

Isotopic Evidence for Diet Among Historic Bedouin of Khirbat al‐Mudayna,Jordan

Interpreting the behaviours of nomadic groups from the archaeological record is particularly challenging owing to the ephemeral remains produced by their lifestyle. As such, human burials – even from isolated contexts – provide a wealth of information about these transient groups. Stable carbon and nitrogen isotope analysis of a series of human skeletal remains (n = 22) from isolated burials were utilized to reconstruct the diet of historic (ca. 13^th–19^th century CE) Bedouin from the Khirbat al‐Mudayna vicinity in modern‐day Jordan. Carbon isotope values ranging from −17.7 to −12.5‰ point to variable dietary contributions from both C₃‐ and C₄‐based foods, while corresponding faunal δ¹³C ratios ( = −19.0 ± 1.7‰, 1σ) exhibit depletion in ¹³C characteristic of a predominantly C₃ diet. While mean human δ¹⁵N values ( = 12.7 ± 1.9‰, 1σ) may suggest that the majority of protein consumed by these nomads came from animal and not plant sources, elevated δ¹⁵N ratios in some fauna may be indicative of ¹⁵N‐enriched soils as reported in some arid desert environments. Considerable variability in both δ¹³C and δ¹⁵N values also points to differences in individual life histories and subsistence economies over time. Despite a lack of temporal cohesion, these data illustrate the information that can be gleaned from the isolated interments of nomadic or semi‐nomadic individuals. Copyright © 2015 John Wiley & Sons, Ltd.     

Evidence for SiO2‐NaCl complexing in H2O‐NaCl solutions at high pressure and temperature

Experimental studies reveal complex dissolution behavior of quartz in aqueous NaCl solutions at high temperature and pressure, involving variation from salting‐in to salting‐out that changes with temperature, pressure, and salt concentration. The behavior is not explainable by traditional electrostatic theory. An alternative hypothesis appeals to complexing of SiO₂ with NaCl and can explain the observations. However, the hypothesis of complexing, as previously applied, is inadequate in several respects: it neglects polymerization of solute silica, regards the SiO₂‐NaCl hybrid complex(es) as anhydrous, which seems unlikely, and invokes an incorrect stoichiometry of the hydrated silica monomer, now known to be Si(OH)₄?2H₂O. These neglected features can be incorporated into the complexing model in a revised formulation based on a simple thermodynamic analysis using existing quartz solubility data. The analysis leads to a quasi‐ideal solution model with silica monomers, dimers, and two distinct hydrous SiO₂‐NaCl hybrid complexes with overall NaCl:H₂O = 1:6, one Na‐bearing and one Cl‐bearing. Their (equal) molar concentrations (X_hc) are governed by a pressure‐ and temperature‐dependent equilibrium constant, , where a_Nacl and are the respective activities of the solvent components. The stability of the hybrid complexes (i.e., their concentration) is very sensitive to H₂O activity. The entire set of experimental quartz‐solubility data at 700°C, 1–15 kbar, is reproduced with high fidelity by the expression (P is pressure in kbar), including the transition from low‐pressure salting‐in to high pressure salting‐out. The results indicate that hybrid SiO₂‐NaCl complexes are the main hosts for dissolved silica at NaCl concentrations greater than 6 wt%, which are likely common in crustal fluids. At higher temperatures, approaching the critical end point in the system SiO₂‐H₂O, the model becomes progressively inaccurate, probably because polymers higher than the dimer become significant as SiO₂ concentration increases.     

Diffusion and kinetic control of weathering layer development

The formation of porous weathering rinds (layers of chemical alteration) on the exterior of rocks is a consequence of dissolution and precipitation of minerals occurring at the mineral–fluid interface within the pores. The speed at which the developed rind advances is controlled by both kinetic reaction rates and the transport of reaction products away from the pore spaces into the outside fluid. We show, using both reaction‐diffusion theory and numerics, that under diffusion limitations, the weathering rate depends on the size and curvature of the sample. This leads to a relationship between rind thickness, δ, and age, t. As the rind thickens, the result in three dimensions differs substantially from the one‐dimensional result of . We describe the conditions under which the one‐dimensional and diffusion‐limited approximations apply and how they evolve as the rock weathers. Under chemical kinetic limitations, the rind advances at a constant rate, dδ/dt = v. We defend the application of a spherical approximation to irregular non‐spherical rocks and apply our results to field observations reported in the literature to show consistency with established methods. Finally, we argue that the variability in size, as well as in mineralogy, over ensembles of grains contributes to heterogeneous weathering rates. We demonstrate that this heterogeneity can contribute to the aging, or gradual decrease with time, of weathering rates previously observed in laboratory and field measurements.     

Knowledge Spillovers and Total Factor Productivity: Evidence Using a Spatial Panel Data Model

This article investigates the impact of knowledge capital stocks on total factor productivity (TFP) through the lens of the knowledge capital model proposed by Griliches (1979) , augmented with a spatially discounted cross-region knowledge spillover pool variable. The objective is to shift attention from firms and industries to regions and to estimate the impact of cross-region knowledge spillovers on TFP in Europe. The dependent variable is the region-level TFP, measured in terms of the superlative TFP index suggested by Caves, Christensen, and Diewert (1982) . This index describes how efficiently each region transforms physical capital and labor into output. The explanatory variables are internal and out-of-region stocks of knowledge, the latter capturing the contribution of cross-region knowledge spillovers. We construct patent stocks to proxy annual regional knowledge capital stocks for N =203 regions during 1997–2002. In estimating the effects, we implement a spatial panel data model that controls for spatial autocorrelation as well as individual heterogeneity across regions. The findings provide a fairly remarkable confirmation of the role of knowledge capital contributing to productivity differences among regions and add an important spatial dimension to discussions in the literature by showing that productivity effects of knowledge spillovers increase with geographic proximity.     

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).     

Spatial‐temporal patterns of snow cover in western Canada

Snow cover is often measured as snow‐water equivalent (SWE), which refers to the amount of water stored in a snow pack that would be available upon melting. Snow cover and SWE represent a source of local snow‐melt release, and are sensitive to regional and global atmospheric circulation, and changes in climate. Monitoring SWE using satellite‐based passive microwave radiometry has provided nearly three decades of continuous data for North America. The availability of spatially and temporally extensive SWE data enables a better understanding of the nature of space‐time trends in snow cover, changes in these trends and linking these trends to underlying landscape and terrain characteristics. To address these interests, we quantify the spatial pattern of SWE by applying a local measure of spatial autocorrelation to 25 years of mean February SWE derived from passive microwave retrievals. Using a method for characterizing the temporal trends in the spatial pattern of SWE, temporal trends and variability in spatial autocorrelation are quantified. Results indicate that within the Canadian Prairies, extreme values of SWE are becoming more spatially coherent, with potential impacts on water availability, and hazards such as flooding. These results also highlight the need for Canadian ecological management units that consider winter conditions.     

A Comparison of Spatially Varying Regression Coefficient Estimates Using Geographically Weighted and Spatial‐Filter‐Based Techniques

Geographically weighted regression (GWR) is a technique that explores spatial nonstationarity in data‐generating processes by allowing regression coefficients to vary spatially. It is a widely applied technique across domains because it is intuitive and conforms to the well‐understood framework of regression. An alternative method to GWR that has been suggested is spatial filtering, which it has been argued provides a superior alternative to GWR by producing spatially varying regression coefficients that are not correlated with each other and which display less spatial autocorrelation. It is, therefore, worthwhile to examine these claims by comparing the output from both methods. We do this by using simulated data that represent two sets of spatially varying processes and examining how well both techniques replicate the known local parameter values. The article finds no support that spatial filtering produces local parameter estimates with superior properties. The results indicate that the original spatial filtering specification is prone to overfitting and is generally inferior to GWR, while an alternative specification that minimizes the mean square error (MSE) of coefficient estimates produces results that are similar to GWR. However, since we generally do not know the true coefficients, the MSE minimizing specification is impractical for applied research.     

Spatial Autocorrelation Statistics of Areal Prevalence Rates under High Uncertainty in Denominator Data

We propose a new estimator of spatial autocorrelation of areal incidence or prevalence rates in small areas, such as crime and health indicators, for correcting spatially heterogeneous sampling errors in denominator data. The approach is dubbed the heteroscedasticity‐consistent empirical Bayes (HC‐EB) method. As American Community Survey (ACS) data have been released to the public for small census geographies, small‐area estimates now form the demographic landscape of neighborhoods. Meanwhile, there is growing awareness of the diminished statistical validity of global and local Moran’s I when such small‐area estimates are used in denominator data. Using teen birth rates by census tracts in Mecklenburg County, North Carolina, we present comparisons of conventional and new HC‐EB estimates of Global and Local Moran’s I statistics created on ACS data, along with estimates on ground truth values from the 2010 decennial census. Results show that the new adjustment method dramatically enhances the statistical validity of global and local spatial autocorrelation statistics.     

Effect of salinity on mass and energy transport by hydrothermal fluids based on the physical and thermodynamic properties of H2O‐NaCl

Various thermodynamic properties of H₂O that are defined as pressure or temperature derivatives of some other variable, such as isothermal compressibility (β, pressure derivative of density), isobaric thermal expansion (α, temperature derivative of density), and specific isobaric heat capacity (c_f, temperature derivative of enthalpy), all show large magnitudes near the critical point, reflecting large variations in fluid density and specific enthalpy with small changes in temperature and pressure. As a result, mass (related to fluid density) and energy (related to fluid enthalpy) transport in this PT region are sensitive to changing PT conditions. Addition of NaCl to H₂O causes the region of anomalous behavior, here defined as the critical region, to migrate to higher temperatures and pressures. The critical region is defined as that region of PT space in which the dimensionless reduced susceptibility  ≥ 0.5. When NaCl is added to H₂O, the critical region migrates to higher temperature and pressure. However, the absolute magnitudes of thermodynamic properties that are defined as temperature and/or pressure derivatives (α, β, and c_f) all decrease with increasing salinity. Thus, the mass and energy transporting capacities of hydrothermal fluids in the critical region become less sensitive to changing PT conditions as the salinity increases. For example, quartz solubility can be described as a function of fluid density, and because density becomes less sensitive to changing PT conditions as salinity increases, quartz solubility also becomes less sensitive to changing PT conditions as fluid salinity increases. Similarly, fluxibility describes the ability of a fluid to transport heat by buoyancy‐driven convection, and fluxibility decreases with increasing salinity. Results of this study show that the mass and energy transport capacity of fluids in the Earth's crust are maximized in the critical region and that the sensitivity to changing PT conditions decreases with increasing salinity.     

What Were We Thinking?

This article outlines the context in geography and statistics in the mid 1960s, at the height of geography's so-called "quantitative revolution," that led us into a long-term collaboration about spatial statistics, which has continued in surges and lulls for some 40 years. We focus upon problems in spatial autocorrelation, including the measurement of autocorrelation, distribution theory, and variable geographical lattices. This narrative may not describe how it was, but it does describe how we remember the events of the time.     

Testing for Local Spatial Autocorrelation in the Presence of Global Autocorrelation

A fundamental concern of spatial analysts is to find patterns in spatial data that lead to the identification of spatial autocorrelation or association. Further, they seek to identify peculiarities in the data set that signify that something out of the ordinary has occurred in one or more regions. In this paper we provide a statistic that tests for local spatial autocorrelation in the presence of the global autocorrelation that is characteristic of heterogeneous spatial data. After identifying the structure of global autocorrelation, we introduce a new measure that may be used to test for local structure. This new statistic Oi is asymptotically normally distributed and allows for straightforward tests of hypotheses. We provide several numerical examples that illustrate the performance of this statistic and compare it with another measure that does not account for global structure.     

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.