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This study establishes a method for predicting ground vibrations caused by railway tunnels in unsaturated soils with spatial variability.

First, an improved 2.5D finite-element-method-perfect-matching-layer (FEM-PML) model is proposed. The Galerkin method is used to derive the finite element expression in the ub-pl-pg format for unsaturated soil. Unlike the ub-v-w format, which has nine degrees of freedom per node, the ub-pl-pg format has only five degrees of freedom per node; this significantly enhances the calculation efficiency. The stretching function of the PML is adopted to handle the unlimited boundary domain. Additionally, the 2.5D FEM-PML model couples the tunnel, vehicle and track structures. Next, the spatial variability of the soil parameters is simulated by random fields using the Monte Carlo method. By incorporating random fields of soil parameters into the 2.5D FEM-PML model, the effect of soil spatial variability on ground vibrations is demonstrated using a case study.

The spatial variability of the soil parameters primarily affected the vibration acceleration amplitude but had a minor effect on its spatial distribution and attenuation over time. In addition, ground vibration acceleration was more affected by the spatial variability of the soil bulk modulus of compressibility than by that of saturation.

Using the 2.5D FEM-PML model in the ub-pl-pg format of unsaturated soil enhances the computational efficiency. On this basis, with the random fields established by Monte Carlo simulation, the model can calculate the reliability of soil dynamics, which was rarely considered by previous models.

The purpose of this paper is twofold: first is to examine the changing spatio-temporal patterns and regional trends in residential fires; and second is to investigate the likely association of fire risk with seasons, calendar events and socio-economic disadvantage.

Using spatial analytic and predictive techniques, 11 years of fire incident data supplied by the Queensland Fire and Emergency Services are mapped and analysed.

The results show significant spatial and temporal variability in the distribution of residential fires. Residential fire incidents are more likely to occur in the inner city and across more disadvantaged areas. Mapped outputs show some areas in Brisbane at a higher risk of fire than others and that the risk of fire escalates at specific times of the year, in neighbourhoods with a higher disadvantage, during major sporting events and school holidays. The residential fires showed strong seasonal periodicity. There is a continuous yet gradual increase in the number of fire incidents recorded for all five sub-regions within SEQ. Sunshine Coast experienced the highest upward trend whereas Toowoomba and West Moreton show the lowest increase.

This study provides an empirical basis to guide future operational strategies through targeting high fire risk areas at particular times. This, in turn, will help utilise finite resources in areas where and when they need and thus enable minimise emergency management costs.

Tourism has become a sustainable and viable tool in place making or community revitalization process. Residents’ perceptions of tourism impacts are critical to the sustainability of the tourism industry. This study follows a quantitative research approach to examine how variation in patronage pattern impact its host community using Olumo Rock in Abeokuta, Nigeria, as a case study.

Primary data from 324 residents are analysed using mean scores, chi-square and one-way ANOVA analysis. Secondary data such as the number of monthly patronage and precipitation and temperature were also analysed.

The findings revealed that the majority of residents do not patronize the tourism destination and that patronage patterns were seasonal and varied within and between seasons in Olumo. The perception of the residents living adjacent to the tourism destination established that they experienced positive and sometimes negative regardless of the season of the year or the proximity residential neighbourhood to tourism destination.

The findings of this study are sufficiently valuable to merit further investigation. It also provides an important spatial–temporal platform for future tourism impacts variability research in Nigeria and other countries in the tropic region. Furthermore, it is apparent from this study that temporal analyses in a given tourism destination may not translate effectively into another. In this respect, tourism managers in Olumo Rock should be aware of fluctuation in patronage pattern so as to introduction other attraction components at the right season.

Models of race-based segregation establish that individual characteristics or housing market attributes are complementary causes of the observed level of races’ concentration inside an urban space. The goal of this work is to establish which variables, and in which order of magnitude, among individual characteristics, housing features, and local amenities correlate with immigrants’ segregation, in the case of consistent within-city immigrants’ mobility. We capture the degree of segregation for different immigration groups by a local concentration statistics that is directly obtained from segregation curves, and we use data on the Verona Municipality as a case study. We find strong evidence in favor of the role of the housing market and housing ownership distribution across city areas.

Investigations of urban public services remain confined to western settings while research on urban public services in non-western cities focuses mainly on the availability and delivery of basic services. Using the case study of Calcutta, this study is an empirical investigation of the evolution, spatial distribution, and changes in spatial patterns of public libraries for the period 1850–1991. It seeks to demonstrate the provision and accessibility to public libraries at the intraurban scale thereby extending research of urban service delivery to a non-western city. Within the context of urban service delivery – who benefits and why, the location of libraries in three time periods are analyzed. The study finds that the urban morphology of the colonial city continues to exert a strong influence on the growth and spatial distribution of public libraries. Empirical evidence suggests that there is no locational bias based on physical accessibility in the distribution of public libraries. No progressive or regressive spatial arrangement based on socioeconomic variables is indicated.

This paper aims to make a preliminary estimate of the degree of integration in the US product market (widely acknowledged to be the most integrated among geographically large economies) as an upper bound of spatial integration that is practically achievable in markets covering fairly large territories.

The approach takes the form of an econometric model derived from the fact that local price of a tradable good should not be dependent on local demand under the law of “one price is a tool to measure market integration”. It is applied to data on the cost of a grocery basket and prices for three individual goods in 2000 across 29 US cities.

The regression results suggest that the US market is not perfectly integrated. Thus, the estimated degree of its integration can be deemed, indeed, as a feasible maximum. Applying this benchmark to the European part of Russia in 2000, its degree of market integration turns out to be comparable – by the order of magnitude – with the feasible one. The roles of a few factors that could potentially cause segmentation of the US market are estimated.

The estimated degree of US market integration is crude because of the relatively small spatial sample. Further research has to substantially widen the spatial sample and estimate integration of the US market across a number of points in time.

The paper suggests a realistic benchmark standard for judging the extent of market integration in various (geographically large) economies.

The present study aims to demonstrate the performance assessment of flexible pavement structure in probabilistic framework with due consideration of spatial variability modeling of input parameter.

The analysis incorporates mechanistic–empirical approach in which numerical analysis with spatial variability modeling of input parameters, Monte Carlo simulations (MCS) and First Order Reliability Method (FORM) are combined together for the reliability analysis of the flexible pavement. Random field concept along with Cholesky decomposition technique is used for the spatial variability modeling of the input parameter and implemented in commercially available finite difference code FLAC for the numerical analysis of pavement structure.

Results of the reliability analysis, with spatial variability modeling of input parameter, are compared with the corresponding results obtained without considering spatial variability of parameters. Analyzing a particular three-layered flexible pavement structure, it is demonstrated that spatial variability modeling of input parameter provides more realistic treatment to property variations in space and influences the response of the pavement structure, as well as its performance assessment.

Research is based on reliability analysis approach, which can also be used in decision-making for quality control and flexible pavement design in a given environment of uncertainty and extent of spatially varying input parameters in a space.

The purpose of this study is to develop a stochastic finite element method (FEM) to solve the calculation precision deficiency caused by spatial variability of dam compaction quality.

The Choleski decomposition method was applied to generate constraint random field of porosity. Large-scale laboratory triaxial tests were conducted to determine the quantitative relationship between the dam compaction quality and Duncan–Chang constitutive model parameters. Based on this developed relationship, the constraint random fields of the mechanical parameters were generated. The stochastic FEM could be conducted.

When the fully random field was simulated without the restriction effect of experimental data on test pits, the spatial variabilities of both displacement and stress results were all overestimated; however, when the stochastic FEM was performed disregarding the correlation between mechanical parameters, the variabilities of vertical displacement and stress results were underestimated and variation pattern for horizontal displacement also changed. In addition, the method could produce results that are closer to the actual situation.

Although only concrete-faced rockfill dam was tested in the numerical examples, the proposed method is applicable for arbitrary types of rockfill dams.

The value of this study is that the proposed method allowed for the spatial variability of constitutive model parameters and that the applicability was confirmed by the actual project.

The purpose of this paper is to examine the spatial components of producer heterogeneity in crop insurance product selection among US corn producers and identifies neighborhood spillover or agent marketing effects in these decisions.

County‐level insurance and yield data are used to demonstrate that a gradual shift from yield‐based insurance to revenue‐based insurance has spatial patterns. Conventional risk variables such as yield variability, price variability, prevalence of irrigation, other crops, and yield‐price relationships play an important role in this shift and are consistently estimated only when spatial components are included. A spatial random effects model is used to also identify the impact of spatial lag effects, which include neighborhood spillover and agent marketing effects, on the share of corn acres insured with revenue‐based plans vs yield‐based plans.

Theoretically consistent variables associated with risk are found to significantly influence the choice between crop revenue and yield insurance. Non‐linear parameters identify the region‐specific effects from changes in irrigation, yield price correlation, and the prevalence of corn production on insurance decisions. In addition, spatial components such as the decisions made by nearby producers and marketing drives are also found to influence decisions. These results may demonstrate the relative influence of trusted sources, such as nearby producers and insurance agents, on insurance decisions.

Traditional risk variables are consistently estimated by controlling for spatial heterogeneity. This study also reveals the propensity of producers to rely on the opinions of other producers or agents that they know.