Spatial Expansion Method Research Articles

Digital Integration of Temperature Field of Cable-Stayed Bridge Based on Finite Element Model Updating and Health Monitoring

A health monitoring system typically collects and processes data to observe the health status of a bridge. The cost limitations imply that only the measurement point data of a few key points can be obtained; however, the entire bridge monitoring information cannot be established, which significantly interferes with the data integrity of the structural monitoring system. In this study, a solution is proposed for reconstructing the monitoring data of the entire bridge. By updating the finite element (FE) model based on structural thermal analysis, numerical simulation technology, and other methods, the temperature field integration model of a cable-stayed bridge is realized. The temperature spatial expansion method of deep learning is established by using the complete simulated temperature field of the entire bridge based on limited measured temperature data; this data is verified and applied to the main beam and bridge tower, thereby establishing the complete measured temperature field of the whole bridge. This is of great significance in supplementing health monitoring information, allowing for the accurate monitoring and evaluation of the structural safety and service performance of long bridges.

Consistent spatial expansion functions on hexagonal surfaces for use in radiation transport methods

Some of the radiation transport solvers that are based on expansion function methods in hexagonal geometry generally use projection techniques that transform the spatial dependence of the angular flux in hexagonal geometry to Cartesian geometry by mapping and then expanding in terms of expansion functions (e.g., Legendre polynomials). However, this approach usually requires a relatively higher-order expansion in space for accurate representation of the spatial distribution of the flux. To overcome this limitation, a set of consistent spatial expansion functions have been developed in hexagonal geometry in this paper. These functions satisfy the orthogonality condition on hexagonal surfaces and guarantee the conservation of currents after expansion regardless of the truncation order. The latter is an important property for preservation of neutron balance.The new spatial expansion method has been implemented into the coarse mesh radiation transport (COMET) code and tested in both the single assembly and the full core Advanced High Temperature Reactor (AHTR) benchmark problems. The eigenvalues and assembly-averaged and stripe-wise fission density distributions predicted by the new method with 2nd order spatial expansion functions were found to exhibit similar accuracy as those computed by the current mapping method with 4th order spatial expansion functions. This indicates that the angular fluxes in AHTRs can be predominantly represented by a low order expansion if the new spatial expansion functions are used. As a result, to maintain the same accuracy, the computational efficiency of COMET is improved significantly (2–5 times) by using the new spatial expansion method.

Spatially varying coefficient models in real estate: Eigenvector spatial filtering and alternative approaches

Real estate policies in urban areas require the recognition of spatial heterogeneity in housing prices to account for local settings. In response to the growing number of spatially varying coefficient models in housing applications, this study evaluated four models in terms of their spatial patterns of local parameter estimates, multicollinearity between local coefficients, and their predictive accuracy, utilizing housing data for the metropolitan area of Vienna (Austria). The comparison covered the spatial expansion method (SEM), moving window regression (MWR), geographically weighted regression (GWR), and genetic algorithm-based eigenvector spatial filtering (ESF), an approach that had not previously been employed in real estate research. The results highlight the following strengths and limitations of each method: 1) In contrast to SEM, MWR, and GWR, ESF depicts more localized patterns of the parameter estimates and does not smooth local particularities. 2) ESF is less affected by multicollinearity between the local parameter estimates than MWR, GWR, and SEM. 3) Even though the in-sample explanatory power and prediction accuracy of ESF is superior compared to the competitors, repeated sampling indicates a limited out-of-sample fit and prediction accuracy, suggesting over-fitting tendencies. 4) The application of ESF is less intuitive than MWR and GWR, which are available off-the-shelf.

Local revenue response to service quality: spatial effects in seasonal ticket revenue data

Purpose – This paper aims to consider spatial effects in the analysis of the relationship of revenue and service quality. When firms’ customers are located in spatially dispersed areas, it can be difficult to manage service quality on a geographically small scale because the relative importance of service quality might vary spatially. Moreover, standard approaches discussed so far in the marketing science literature usually neglect spatial effects, such as spatial dependencies (e.g. spatial autocorrelation) and spatial drift (spatial non-stationarity). Design/methodology/approach – The authors propose a comprehensive but intelligible approach based on spatial econometric methods that cover spatial dependencies and spatial drift simultaneously. In particular, they incorporate the spatial expansion method (spatial drift) into spatial econometric models (e.g. spatial lag model). Findings – Using real company data on seasonal ticket revenue (dependent variable) and service quality (independent variables) of a regional public transport service provider, the authors find that the elasticity for the length of the public transport network is between 0.2 and 0.5, whereas the elasticity for the headway is between −0.2 and 0.6, for example. The authors control for several socio-economic, socio-demographic and land-use variables. Practical implications – Based on the empirical findings, the authors show that addressing spatial effects of service data can improve management’s ability to implement programs aimed at enhancing seasonal ticket revenue. Therefore, they derive a spatial revenue response function that enables managers to identify small-scale areas that are most efficient in terms of increasing revenue by service improvement. Originality/value – The paper addresses the need to account for spatial effects in revenue response functions of public transport companies.

Measuring the impacts of weather variability on home-based trip chaining behaviour: a focus on spatial heterogeneity

Using the 2011 Swedish national travel survey data, this paper explores the influence of weather characteristics on individuals’ home-based trip chaining complexity. A series of panel mixed ordered Probit models are estimated to examine the influence of individual/household social demographics, land use characteristics, and weather characteristics on individuals’ home-based trip chaining complexity. A thermal index, the universal thermal climate index (UTCI), is used in this study instead of using directly measured weather variables in order to better approximate the effects of the thermal environment. The effects of UTCI are segmented into different seasons to account for the seasonal difference of UTCI effects. Moreover, a spatial expansion method is applied to allow the impacts of UTCI to vary across geographical locations, as individuals in different regions have different weather/climate adaptions. The effects of weather are examined in subsistence, routine, and discretionary trip chains. The results reveal that the ‘ground covered with snow’ condition is the most influential factor on the number of trips chained per trip chain among all other weather factors. The variation of UTCI significantly influences trip chaining complexity in autumn but not in spring and winter. The routine trip chains are found to be most elastic towards the variation of UTCI. The marginal effects of UTCI on the expected number of trips per routine trip chain have considerable spatial variations, while these spatial trends of UTCI effects are found to be not consistent over seasons.

Developing a web-based accessibility calculator prototype for the Greater Montreal Area

A mixture of modes is considered a key element towards sustainable transportation. New technologies that provide information about various modes and environments can help to inform choices regarding travel and accessibility. In this paper we describe the development of a web-based accessibility calculator prototype for the Greater Montreal Area in Canada. The core of this tool is a statistical model of trip length developed using the spatial expansion method. The model is used to obtain estimates of trip length for a desired profile, based on attributes such as age, gender, family structure, and mode of travel, as well as geographical location. These estimates are used to calculate a cumulative opportunities accessibility measure to different types of essential destinations. Travel behavior information is drawn from Montreal’s 2008 Household Travel Surveys. Geocoded information about trip origins and destinations is used to calculate trip length. A broad array of covariates related to individual and household attributes, and urban form are used. A geocoded business point database is used for the calculation of cumulative opportunities. To simplify the use of the accessibility calculator, the model is implemented in a user-friendly way using Google Maps API v3 and a convenient interface. Different possible uses of the accessibility calculator are illustrated in the paper. The accessibility calculator can be used by members of the public or planners/policy makers to measure the level of accessibility for a specific address and personal profile by various modes of transportation.

Incorporating spatial variation in housing attribute prices: a comparison of geographically weighted regression and the spatial expansion method

Hedonic house price models typically impose a constant price structure on housing characteristics throughout an entire market area. However, there is increasing evidence that the marginal prices of many important attributes vary over space, especially within large markets. In this paper, we compare two approaches to examine spatial heterogeneity in housing attribute prices within the Tucson, Arizona housing market: the spatial expansion method and geographically weighted regression (GWR). Our results provide strong evidence that the marginal price of key housing characteristics varies over space. GWR outperforms the spatial expansion method in terms of explanatory power and predictive accuracy.

Local Forms of Spatial Analysis

Local forms of spatial analysis focus on exceptions to the general trends represented by more traditional global forms of spatial analysis. There is currently a rapid expansion in the development of such techniques but their history almost exactly parallels that of Geographical Analysis, with the first examples of local analysis appearing in the late 1960s. Indeed, Geographical Analysis has published many of the significant contributions in this field. This paper reviews the development of local forms of spatial analysis and assesses the current situation. Following a discussion on the nature and importance of local analysis, examples are given of local forms of point pattern analysis; local graphical approaches; local measures of spatial dependency; the spatial expansion method; adaptive filtering; multilevel modeling; geographically weighted regression; random coefficients models; autoregressive models; and local forms of spatial interaction models.

PROBIT WITH SPATIAL AUTOCORRELATION

ABSTRACT. Commonly‐employed spatial autocorrelation models imply heteroskedastic errors, but heteroskedasticity causes probit to be inconsistent. This paper proposes and illustrates the use of two categories of estimators for probit models with spatial autocorrelation. One category is based on the EM algorithm, and requires repeated application of a maximum‐likelihood estimator. The other category, which can be applied to models derived using the spatial expansion method, only requires weighted least squares.

Some robust approaches to testing and estimation in spatial econometrics

Some robust approaches are outlined that form a basis for a more realistic statistical inference in spatial econometric models. Three specific issues are addressed: significance tests on coefficients in the spatial expansion method that are robust to the presence of heteroskedasticity of unknown form; heteroskedasticity-robust specification tests for spatial dependence; and boot-strap estimation in spatial autoregressive models. The techniques are presented in formal terms and their application to spatial analysis is illustrated in a number of simple empirical examples.