Bayesian Spatial Filtering for Hedonic Models: An Application for the Real Estate Market

This article presents a Bayesian method based on spatial filtering to estimate hedonic models for dwelling prices with geographically varying coefficients. A Bayesian Adaptive Sampling algorithm for variable selection is used, which makes it possible to select the most appropriate filters for each hedonic coefficient. This approach explores the model space more systematically and takes into account the uncertainty associated with model estimation and selection processes. The methodology is illustrated with an application for the real estate market in the Spanish city of Zaragoza and with simulated data. In addition, an exhaustive comparison study with a set of alternatives strategies used in the literature is carried out. Our results show that the proposed Bayesian procedures are competitive in terms of prediction; more accurate results are obtained in the estimation of the regression coefficients of the model, and the multicollinearity problems associated with the estimation of the regression coefficients are solved.     

Bayesian Updating of Seismic Demand Models and Fragility Estimates for Reinforced Concrete Bridges with Two-Column Bents

Probabilistic models have been developed in a previous study by the authors to estimate the seismic deformation demands on structural components of reinforced concrete (RC) bridges with two-column bents. However, such models should be updated to reflect the latest laboratory of field data. Using a Bayesian approach, this article updates a currently available probabilistic model for the deformation demands of columns in bridges with two-column RC bents. The updated model incorporates information from newly available experimental data from shake table tests conducted based on a record of the 1994 Northridge Earthquake for a structural system with three bents with two columns per bent. The updated model is more accurate than the previous one in predicting the deformation demand of bridges with two-column RC bents and reduces the statistical uncertainty due to the addition of new data. As an application, fragility estimates for an example bridge are computed using the updated model both at the component (column) and system (bridge) levels.     

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.     

A Simple(R) Model to Predict the Source of Dietary Carbon in Individual Consumers

Quantitative individual human diet reconstruction using isotopic data and a Bayesian approach typically requires the inclusion of several model parameters, such as individual isotopic data, isotopic and macronutrient composition of food groups, diet‐to‐tissue isotopic offsets and dietary routing. In an archaeological context, sparse data may hamper a widespread application of such models. However, simpler models may be proposed to address specific archaeological questions. As a consequence of the intake of marine foods, individuals from the first century ad Roman site of Herculaneum showed well‐defined bone collagen radiocarbon age offsets from the expected terrestrial value. Taking as reference these radiocarbon offsets and using as model input stable isotope data (δ¹³C and δ¹⁵N), the performance of two Bayesian mixing model instances (routed and concentration‐dependent model versus non‐routed and concentration‐independent) was compared to predict the carbon contribution of marine foods to bone collagen. Predictions generated by both models were in good agreement with observed values. The model with higher complexity showed only a slightly better performance in terms of accuracy and precision. This demonstrates that under similar circumstances, a simple Bayesian approach can be applied to quantify the carbon contribution of marine foods to human bone collagen.     

A Bayesian Approach to Modeling Binary Data: The Case of High‐Intensity Crime Areas

This paper reports the fitting of a number of Bayesian logistic models with spatially structured or/and unstructured random effects to binary data with the purpose of explaining the distribution of high‐intensity crime areas (HIAs) in the city of Sheffield, England. Bayesian approaches to spatial modeling are attracting considerable interest at the present time. This is because of the availability of rigorously tested software for fitting a certain class of spatial models. This paper considers issues associated with the specification, estimation, and validation, including sensitivity analysis, of spatial models using the WinBUGS software. It pays particular attention to the visualization of results. We discuss a map decomposition strategy and an approach that examines properties of the full posterior distribution. The Bayesian spatial model reported provides some interesting insights into the different factors underlying the existence of the three police‐defined HIAs in Sheffield.     

Predicting Probabilistic-Based Strong Ground Motion Time Series for Citadel of Arg-E-Bam (South-East of Iran)

The main objective of this article is to present a probabilistic-based strong motion compatible with the source-path and site soil condition given the probability of exceedence for citadel of Arg-e-Bam site bed rock (South-East of Iran). A Fourier amplitude spectral attenuation relation for bed rock beneath the site is proposed which permits the estimation of time-histories through a probabilistic seismic hazard analysis procedure. Due to lack of data, the two well-known simulation techniques, point source and finite fault models have been used for generating hundreds of strong motion as input data. Tens of model parameter values such as stress-drop nucleation points were used, in each specified magnitude-distance, to reduce the uncertainty effects inherently existing in seismological/geological parameters. The proposed attenuation relation is validated by comparing the estimated strong motion, in the form of Fourier amplitude spectral, using the proposed attenuation relation with those of recorded ground motion data at three stations far away from the assumed source so that the results would not be influenced by the near source problems such as directivity and fling step. The results of proposed technique is assessed by comparing the estimated response spectra, with 10% probability of exceedence and 5% damping ratio, with those of traditional uniform hazard spectra. The proposed technique is supposed to be used in retrofitting procedure of international historical adobe structures in Arg-e-Bam site, which have been damaged during the destructive Bam earthquake 2003, Iran     

Climate and the Chronology of Iranian History

This article offers a chronology of climate events in Iran over the last millennium to challenge traditional chronologies of the Iranian past based on politics, war, and economics. Using insights gleaned from the historiography of climate in Iran, and from neighboring regions, especially the Ottoman Empire, four episodes of climate cooling show how climate-induced environmental processes affected more people for longer in Iran than did the policies of the empires ostensibly ruling over them. Thus, the article aims to put climate more squarely on the agenda of historians of Iran so that the conjectures advanced here can be confirmed, revised, or discarded through future empirical research. Moreover, the article seeks to bring Iran into wider conversations in environmental and global history.     

BAYSIAN INFERENCE FOR ORDERED RESPONSE DATA WITH A DYNAMIC SPATIAL‐ORDERED PROBIT MODEL

ABSTRACT Many databases involve ordered discrete responses in a temporal and spatial context, including, for example, land development intensity levels, vehicle ownership, and pavement conditions. An appreciation of such behaviors requires rigorous statistical methods, recognizing spatial effects and dynamic processes. This study develops a dynamic spatial‐ordered probit (DSOP) model in order to capture patterns of spatial and temporal autocorrelation in ordered categorical response data. This model is estimated in a Bayesian framework using Gibbs sampling and data augmentation, in order to generate all autocorrelated latent variables. It incorporates spatial effects in an ordered probit model by allowing for interregional spatial interactions and heteroskedasticity, along with random effects across regions or any clusters of observational units. The model assumes an autoregressive, AR(1), process across latent response values, thereby recognizing time‐series dynamics in panel data sets. The model code and estimation approach is tested on simulated data sets, in order to reproduce known parameter values and provide insights into estimation performance, yielding much more accurate estimates than standard, nonspatial techniques. The proposed and tested DSOP model is felt to be a significant contribution to the field of spatial econometrics, where binary applications (for discrete response data) have been seen as the cutting edge. The Bayesian framework and Gibbs sampling techniques used here permit such complexity, in world of two‐dimensional autocorrelation.     

Knowledge-Based Performance Assessment of Existing RC Buildings

One of the most challenging aspects of the seismic assessment of existing buildings is the characterization of structural modeling uncertainties. Recent codes, such as Eurocode 8, seem to synthesize the effect of structural modeling uncertainties in the so-called confidence factors that are applied to mean material property estimates. The confidence factors are classified and tabulated as a function of discrete knowledge levels acquired based on the results of specific in-situ tests and inspections. In this approach, the effect of the application of the confidence factors on structural assessment is not explicitly stated. This work presents probabilistic performance-based proposals for seismic assessments of RC buildings based on the knowledge levels. These proposals take advantage of the Bayesian framework for updating the probability distributions for structural modeling parameters based on the results of tests and inspections. As structural modeling parameters, both the mechanical material properties and also the structural detailing parameters are considered. These proposals can be categorized based both on the amount of structural analysis effort required and on the type of structural analysis performed. An efficient Bayesian method is presented which relies on simplified assumptions and employs a small sample of structural model realizations and ground motion records in order to provide an estimate of structural reliability. As an alternative proposal suitable for code implementation, the simplified approach implemented in the SAC-FEMA guidelines is adapted to existing structures by employing the efficient Bayesian method. This method takes into account the effect of both ground motion uncertainty and the structural modeling uncertainties on the global performance of the structure, in a closed-form analytical safety-checking format. These alternative proposals are demonstrated for the case study structure which is an existing RC frame. In particular, it is shown how the parameters for the safety-checking format can be estimated and tabulated as a function of knowledge level, outcome of tests, and the type of structural analysis adopted.     

The Emergence of Complex Metallurgy on the Iranian Plateau: Escaping the Levantine Paradigm

Models for the development of metallurgy in Southwest Asia have for a long time been focussed on research carried out in the lowland regions of the Levant and Mesopotamia. These models do not take into account the different developmental trajectories witnessed in the resource-rich highlands of Anatolia, the Caucasus, and Iran. In this paper, the beginnings of the use and production of metals in Iran will be juxtaposed with a cursory overview of the lowland model (the ‘Levantine Paradigm’) in order to highlight these differences. By synthesizing data from a number of current research projects exploring the early metallurgy of the Iranian Plateau, this paper demonstrates how at least one of the highland regions of Southwest Asia was at the very forefront of technological innovation from the seventh through the second millennium BC.      

On-line Model Updating in Hybrid Simulation Tests

Hybrid simulation has emerged as a relatively accurate and efficient tool for the evaluation of structural response under earthquake loading. In conventional hybrid simulation the response of a few critical components is obtained by testing while the numerical module is assumed to follow an analytical idealization. Where there is a much larger number of analytical components compared to the experimental parts, the overall response may be dominated by the idealized parts hence the value of hybrid simulation is diminished. It is proposed to modify the material constitutive relationship of the numerical model during the test, based on the data obtained from the physically tested component. An approach based on genetic algorithms is utilized as an optimization tool to identify the constitutive relationship parameters used in updating the numerical model. The proposed model updating approach is verified through two analytical examples of steel and reinforced concrete frames. The results show the effectiveness of the updating process in minimizing the errors, compared to the assumed exact solution.     

Attenuation Relationships for Iran

The purpose of this study is to derive the attenuation relationships for PGA, PGV, and EPA parameters for areas within the seismic zones of Zagros, Alborz and Central Iran with rock and soil substructures. In order to do so, at first the available scientific data including the methods used for deriving attenuation relationships and the parameters involved have been gathered. Afterwards, all the efforts have been focused on gathering a thorough catalogue of earthquakes occurred in Iran. In this regard, a majority of the available catalogs in Iran have been gathered and corrected through different methods and finally a set of 89 earthquake events including 307 earthquake records with reliable data was chosen.

Since in order to derive the attenuation relationships it is essential to extract the parameters from the acceleration records, a great effort was placed on gathering the earthquake acceleration records of Iran. This resulted in building a database of a majority of the earthquake records up to the year of 2004. Afterwards, correction methods applicable to earthquakes records of Iran considering the type of machines used and the ground type were examined which resulted in certain guidelines for correction of earthquake acceleration record data related to Iran.

In the next step the needed parameters were extracted from the earthquake acceleration record data which were consequently divided into two seismic zones of Zagros, and Alborz and Central Iran according to tectonic conditions. After examination of the parameters and choosing the most appropriate among them, the attenuation relationships were derived for such parameters.     

Quantitative basin modeling: present state and future developments towards predictability

A critique review of the state of quantitative basin modeling is presented. Over the last 15 years, a number of models are proposed to advance our understanding of basin evolution. However, as of present, most basin models are two dimensional (2‐D) and subject to significant simplifications such as depth‐ or effective stress‐dependent porosity, no stress calculations, isotropic fracture permeability, etc. In this paper, promising areas for future development are identified. The use of extensive data sets to calibrate basin models requires a comprehensive reaction, transport, mechanical (RTM) model in order to generate the synthetic response. An automated approach to integrate comprehensive basin modeling and seismic, well‐log and other type of data is suggested. The approach takes advantage of comprehensive RTM basin modeling to complete an algorithm based on information theory that places basin modeling on a rigorous foundation. Incompleteness in a model can self‐consistently be compensated for by an increase in the amount of observed data used. The method can be used to calibrate the transport, mechanical, or other laws underlying the model. As the procedure is fully automated, the predictions can be continuously updated as new observed data become available. Finally, the procedure makes it possible to augment the model itself as new processes are added in a way that is dictated by the available data. In summary, the automated data/model integration places basin simulation in a novel context of informatics that allows for data to be used to minimize and assess risk in the prediction of reservoir location and characteristics.     

Analyzing Cross-classified Data with Inherent Spatial Dependence

Despite considerable recent progress in the methods available for the log-linear analysis of categorical data arising from complex sampling schemes, only a few papers have been published that deal with the parallel phenomenon of dependence induced by spatial sampling. This paper aims to add to the general awareness of this topic and suggests some new ideas for tackling the problems raised. In the paper it is shown that the method that has been proposed for the valid selection of log-linear models given spatially dependent data and some derivative methods are somewhat conservative when compared to an approach based on a model of spatial dependence outlined in section 4. The method also serves as a data exploratory technique to enhance the use of the more robust conservative approach.     

Reassessing the chronology of the Mississippian Central Illinois River Valley using Bayesian analysis

Chronology building has long served as a major focus of archaeological interest in the Central Illinois River valley (CIRV) of west-central Illinois. Previous methods have relied primarily upon relative dating techniques (e.g., ceramic seriation) as a means of sorting out temporal relationships between sites. This study represents the first investigation into the utility of Bayesian techniques (which consider radiocarbon dates in context with archaeological information) in the CIRV. We present the results of a detailed ceramic seriation of the region, data that we use as a priori information in our Bayesian models. We then offer contiguous, overlapping, and sequential models of site occupations in the Mississippian CIRV, review the output and appropriateness of each model, and consider their implications for the pace of sociopolitical change in the region.     

Winds in the ionosphere—A review

Winds in the upper atmosphere, and their effect on the ionosphere, are reviewed with an emphasis on information useful to ionospheric studies. The winds are driven by pressure gradients from solar and auroral heating, with some forcing by tidal energy from below. Simple calculations which balance the pressure gradient by ion drag and Coriolis forces are generally unreliable, so large-scale numerical models of the coupled atmosphere and ionosphere are required. The accuracy of these global models is limited by uncertainties in the energy inputs at high latitudes and at the lower boundary (about 90 km). The best current wind data come from incoherent scatter radar or airglow installations, at a few sites and for only a few nights per month. Satellite data are also available for several years, and results to 1989 are incorporated in the global HWM90 model. This seems acceptable for determining mean winds at night, less good during the day, and least good in the southern hemisphere where few data were available. Plots are given to show the mean winds at different latitudes and longitudes, for use in ionospheric calculations.Meridional winds alter the height of the mid-latitude F layer, causing large changes in the effective loss rate. This is the major cause of observed seasonal changes, of differences between the hemispheres, and of changes at different longitudes. An increased knowledge of the winds is essential for further progress in F region studies. Ionospheric data provide the most promising route, using routinely scaled parameters. The simplest calculations compare observed peak heights, obtained from M (3000)F2, with the value h_o predicted by simplified “servo” equations. Errors occurring for some hours after sunrise can be overcome using model results to define h_o this allows rapid and accurate wind calculations at dip latitudes of 23–62°. Winds can also be obtained from full model calculations, designed to match observed values of peak height or density.     

Bayesian Parameter Identification and Model Selection for Normalized Modulus Reduction Curves of Soils

This work develops a procedure that involves the use of Bayesian approach to quantify data scatterness, estimates the optimal values of model parameters, and selects the most appropriate model for the construction of normalized modulus reduction curves of soils. The proposed procedure is then demonstrated using real observation data based on a set of comprehensive resonant column tests on coarse-grained soils conducted in the study.     

CAN SEARCH‐MATCHING MODELS EXPLAIN MIGRATION AND WAGE AND UNEMPLOYMENT GAPS IN DEVELOPING ECONOMIES? A CALIBRATION APPROACH*

ABSTRACT A recent theoretical literature tries to understand the migration pattern in developing economies through the lens of search‐matching (SM) models, but there has been little empirical support for these models. This paper tries to fill this gap by examining whether the models are consistent with the data from developing economies. Combined with some suggestive statistics, our calibration exercise reveals that when augmented with additional frictions (fixed costs of starting up new businesses and mobility costs), our SM model can account for the data and is at least more consistent with the data than traditional Harris–Todaro models.