Hierarchical spatial interaction among the Italian regions: a nonlinear relative dynamics approach

Regional interactions and spillover effects should be considered as important factors in growth analysis of regional economies. Using modified versions of the Dendrinos–Sonis model, this paper analyses the spatial hierarchical system of Italy. The interaction among Italian regions is considered at three different levels of spatial aggregation, the NUTS-1, NUTS-2 and NUTS-3 levels. Compared to more popular spatial econometric approaches, the Dendrinos–Sonis model and its extensions provide greater flexibility in the way interaction between regions is handled but the results strongly depend on the choice of a reference region.

Critical network infrastructure analysis: interdiction and system flow

Effective management of critical network infrastructure requires the assessment of potential interdiction scenarios. Optimization approaches have been essential for identifying and evaluating such scenarios in networked systems. Although a primary function of any network is the distribution of flow between origins and destinations, the complexity and difficulty of mathematically abstracting interdiction impacts on connectivity or flow has been a challenge for researchers. This paper presents an optimization approach for identifying interdiction bounds with respect to connectivity and/or flow associated with a system of origins and destinations. Application results for telecommunications flow are presented, illustrating the capabilities of this approach.

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A copula-based closed-form binary logit choice model for accommodating spatial correlation across observational units

This study focuses on accommodating spatial dependency in data indexed by geographic location. In particular, the emphasis is on accommodating spatial error correlation across observational units in binary discrete choice models. We propose a copula-based approach to spatial dependence modeling based on a spatial logit structure rather than a spatial probit structure. In this approach, the dependence between the logistic error terms of different observational units is directly accommodated using a multivariate logistic distribution based on the Farlie-Gumbel-Morgenstein (FGM) copula. The approach represents a simple and powerful technique that results in a closed-form analytic expression for the joint probability of choice across observational units, and is straightforward to apply using a standard and direct maximum likelihood inference procedure. There is no simulation machinery involved, leading to substantial computation gains relative to current methods to address spatial correlation. The approach is applied to teenagers’ physical activity participation levels, a subject of considerable interest in the public health, transportation, sociology, and adolescence development fields. The results indicate that failing to accommodate heteroscedasticity and spatial correlation can lead to inconsistent and inefficient parameter estimates, as well as incorrect conclusions regarding the elasticity effects of exogenous variables.

Latent lifestyle preferences and household location decisions

Lifestyle, indicating preferences towards a particular way of living, is a key driver of the decision of where to live. We employ latent class choice models to represent this behavior, where the latent classes are the lifestyles and the choice model is the choice of residential location. Thus, we simultaneously estimate lifestyle groups and how lifestyle impacts location decisions. Empirical results indicate three latent lifestyle segments: suburban dwellers, urban dwellers, and transit-riders. The suggested lifestyle segments have intriguing policy implications. Lifecycle characteristics are used to predict lifestyle preferences, although there remain significant aspects that cannot be explained by observable variables.

Modeling spatial autocorrelation in spatial interaction data: empirical evidence from 2002 Germany journey-to-work flows

Since before the inception of work by Okabe, the intermingling of spatial autocorrelation (i.e., local distance and configuration) and distance decay (i.e., global distance) effects has been suspected in spatial interaction data. This convolution was first treated conceptually because technology and methodology did not exist at the time to easily or fully address spatial autocorrelation effects within spatial interaction model specifications. Today, however, sufficient computer power coupled with eigenfunction-based spatial filtering offers a means for accommodating spatial autocorrelation effects within a spatial interaction model for modest-sized problems. In keeping with Okabe’s more recent efforts to dissemination spatial analysis tools, this paper summarizes how to implement the methodology utilized to analyze a particular empirical flows dataset.

A weighted difference barrier method in landscape genetics

Identifying barriers of species and characterize their effects on spatial distribution provide essential information to research in landscape genetics. We propose a weighted difference barrier (WDB) method as an alternative to maximum difference barriers (MDB), and to initiate and integrate more spatial modeling and methods into the problem solving process. Overall, WDB provides quick and straightforward improvements to the drawbacks of MDB. WDB integrates more sample location relationships into the barrier construction and reveals potential barriers that would otherwise go undetected. WDB incorporates both within group and between group genetic information, and delineates the barriers as a more complex pattern.

Hidden negative spatial autocorrelation

Mostly lip service treatments of negative spatial autocorrelation (NSA) appear in the literature, although spatial scientists confront it in practice. NSA was detected serendipitously in recalcitrant empirical analyses containing a sizeable amount of global positive spatial autocorrelation (PSA) unaccounted for by standard spatial statistical models, and labeled hidden because conventional spatial statistical tools detected only PSA while giving absolutely not hint of NSA existing. The meaning of this phenomenon is explored empirically, with findings including: a better understanding of NSA, spatial filter model construction guidelines, effective illustrations of NSA, and how hidden NSA furnishes a diagnostic for model misspecification.

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.

Show me the code: spatial analysis and open source

This paper considers the intersection of academic spatial analysis with the open source revolution. Its basic premise is that the potential for cross-fertilization between the two is rich, yet some misperceptions about these two communities pose challenges to realizing these opportunities. The paper provides a primer on the open source movement for academicians with an eye towards correcting these misperceptions. It identifies a number of ways in which increased adoption of open source practices in spatial analysis can enhance the development of the next generation of tools and the wider practice of scientific research and education.

Analyzing the shear strength parameters of the Chiu-fen-erh-shan landslide: integrating strong-motion and GPS data to determine the best-fit accelerogram

This study adopts the Chiu-fen-erh-shan landslide as a case study for incorporating comprehensive accelerograph and global positioning system (GPS) data to determine the best-fit acceleration data for analyzing a rock avalanche. Previous investigations indicate that the distance from an accelerograph to a landslide site is crucial to determining the best-fit acceleration data to use in conducting a seismic analysis. Unfortunately, the Chiu-fen-erh-shan landslide and its nearest accelerograph station are located in different geological zones. Thus, GPS data were compared to the displacements derived from the accelerograms of nearby monitoring stations to help select the best accelerograph data. This emphasizes that a high density distribution of accelerographs and GPS installations are essential to acquire the best data for the seismic analysis, especially in complex geological zones. After applying the best-fit accelerogram to Newmark’s sliding model and an empirical displacement attenuation formula to back-calculate the shear strength parameters of the sliding surface, a cohesion of 0 kPa and friction angle of the sliding surface of 24.8° were found for this landslide.

Using fuzzy logic for modeling aquifer architecture

Modeling the geologic architecture of an aquifer and visualizing its three-dimensional structure require lithologic data recorded during well drilling. Uncertainties in layer boundaries arise due to questionable quality of drilling records, mixing during the drilling process, which results in blurred contacts, and natural heterogeneity of the geologic materials. An approach for modeling and visualizing the spatial distribution of aquifer units three-dimensionally based on fuzzy set theory is developed. An indicator is defined for evaluating the possibility of aquifer existence based on fuzzy set theory and probability principles. A specific interpolation method for aquifer 3D spatial distribution requiring only very basic borehole log data is proposed. A 3D modeling and visualization system for aquifers is also developed, which can implement basic GIS functions, like borehole identification and cross-section creation. The methodology developed is tested using real borehole lithology data available for an aquifer in British Columbia, Canada.

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Income distribution dynamics and cross-region convergence in Europe

This paper presents a continuous version of the model of distribution dynamics to analyse the transition dynamics and implied long-run behaviour of the EU-27 NUTS-2 regions over the period 1995–2003. It departs from previous research in two respects: first, by introducing kernel estimation and three-dimensional stacked conditional density plots as well as highest density regions plots for the visualisation of the transition function, based on Hyndman et al. (J Comput Graph Stat 5(4):315–336, 1996), and second, by combining Getis’ spatial filtering view with kernel estimation to explicitly account for the spatial dimension of the growth process. The results of the analysis indicate a very slow catching-up of the poorest regions with the richer ones, a process of shifting away of a small group of very rich regions, and highlight the importance of geography in understanding regional income distribution dynamics.

A spatiotemporal analysis of aggregate labour force behaviour by sex and age across the European Union

This study investigates the causes of variation in age-specific male and female labour force participation rates using annual data from 154 regions across ten European Union member states for the period 1983–1997. Regional participation rates appear to be strongly correlated in time, weakly correlated in space and to parallel their national counterparts. An econometric model is designed consistent with these empirical findings. To control for potential endogeneity of the explanatory variables, we use an instrumental variables estimation scheme based on a matrix exponential spatial specification of the error terms. Many empirical studies of aggregate labour force behaviour have ignored population distribution effects, relying instead on the representative-agent paradigm. In order for representative-agent models to accurately describe aggregate behaviour, all marginal reactions of individuals to changes in aggregate variables must be identical. It turns out that this condition cannot apply to individuals across different sex/age groups.

Modeling network autocorrelation within migration flows by eigenvector spatial filtering

Although the assumption of independence among interaction flows frequently is engaged in spatial interaction modeling, in many circumstances it leads to misspecified models and incorrect inferences. An informed approach is to explicitly incorporate an assumed relationship structure among the interaction flows, and to explicitly model the network autocorrelation. This paper illustrates such an approach in the context of U.S. interstate migration flows. Behavioral assumptions, similar to those of the intervening opportunities or the competing destinations concepts, exemplify how to specify network flows that are related to particular origin–destination combinations. The stepwise incorporation of eigenvectors, which are extracted from a network link matrix, captures the network autocorrelation in a Poisson regression model specification context. Spatial autocorrelation in Poisson regression is measured by the test statistic of Jacqmin-Gadda et al. (Stat Med 16(11):1283–1297, 1997). Results show that estimated regression parameters in the spatial filtering interaction model become more intuitively interpretable.

Specifying a joint space- and time-lag using a bivariate Poisson distribution

Separate space- or time-lags have been considered regularly in data analyses; as space–time models are more recently being studied extensively in data analytic fashion, joint estimation of both lags has to be considered explicitly. This paper addresses this issue, taking into special consideration parametric parsimony together with specification richness; use of the bivariate Poisson frequency distribution is advocated and applied to an empirical case. The relation of this approach to random effects specifications is investigated. Data for Belgian regional products constitute the empirical case study.

A sampling approach to estimate the log determinant used in spatial likelihood problems

Likelihood-based methods for modeling multivariate Gaussian spatial data have desirable statistical characteristics, but the practicality of these methods for massive georeferenced data sets is often questioned. A sampling algorithm is proposed that exploits a relationship involving log-pivots arising from matrix decompositions used to compute the log determinant term that appears in the model likelihood. We demonstrate that the method can be used to successfully estimate log-determinants for large numbers of observations. Specifically, we produce an log-determinant estimate for a 3,954,400 by 3,954,400 matrix in less than two minutes on a desktop computer. The proposed method involves computations that are independent, making it amenable to out-of-core computation as well as to coarse-grained parallel or distributed processing. The proposed technique yields an estimated log-determinant and associated confidence interval.

An exploratory analysis of hierarchical spatial interaction: the case of regional income shares in Indonesia

Regional interaction is generally understood as implying movement between regions at the same level of an hierarchy. This paper extends the notion to include an hierarchical system, thus facilitating the consideration of vertical interaction in the analysis of regional interaction. Obviously, vertical interaction is not altogether a new concept. One could find this concept in many analyses related to national-local or federal-state relationships. This paper treats hierarchy in a strict sense; spatial units are interacting one with another within, but not between, presumed super regions. A particular example drawn in this paper is the province-region relationships for the case of Indonesia. In this framework, provinces form a region, and regions form the nation. The Dendrinos–Sonis model is then used as the basis for measuring the hierarchical spatial interaction in Indonesia. The analysis will explore the degree to which complementarity and competitive interaction revealed at one level in the hierarchy persist at lower or higher levels.

Ultra-tight GPS/INS/PL integration: a system concept and performance analysis

The architecture of the ultra-tight GPS/INS/PL integration is the key to its successful performance; the main feature of this architecture is the Doppler feedback to the GPS receiver tracking loops. This Doppler derived from INS, when integrated with the carrier tracking loops, removes the Doppler due to vehicle dynamics from the GPS/PL signal thereby achieving a significant reduction in the carrier tracking loop bandwidth. The bandwidth reduction provides several advantages such as: improvement in anti-jamming performance, and increase in post correlated signal strength which in turn increases the dynamic range and accuracy of measurements. Therefore, any degradation in the derived Doppler estimates will directly affect the tracking loop bandwidth and hence its performance. The quadrature signals from the receiver correlator, I (in-phase) and Q (quadrature), form the measurements, whereas the inertial sensor errors, position, velocity and attitude errors form the states of the complementary Kalman filter. To specify a reliable measurement model of the filter for this type of integrated system, a good understanding of GPS/PL signal characteristics is essential. It is shown in this paper that phase and frequency errors are the variables that relate the measurements and the states in the Kalman filter. The main focus of this paper is to establish the fundamental mathematical relationships that form the measurement model, and to show explicitly how the system error states are related to the GPS/PL signals. The derived mathematical relationships encapsulated in a Kalman filter, are tested by simulation and shown to be valid.

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A MATLAB toolbox for attitude determination with GPS multi-antenna systems

In this paper a MATLAB toolbox for determining the attitude of a rigid platform by means of multiple non-dedicated antennas using global positioning system is presented. The programs embedded in this toolbox cover the RINEX data analysis, single point positioning, differential positioning, coordinate conversion, attitude determination, and other auxiliary functions. After forming the baselines through double-differenced (carrier phase smoothed) code observables, the attitude parameters are obtained by applying the direct attitude computation and the least squares attitude estimation. The theoretical background is summarized, and some hints regarding the software implementation are given in the paper. Moreover, improvements yielding an expanded functionality are proposed.

A systematic investigation of cross-validation in GWR model estimation: empirical analysis and Monte Carlo simulations

In geographically weighted regression, one must determine a window size which will be used to subset the data locally. Typically, a cross-validation procedure is used to determine a globally optimal window size. Preliminary investigations indicate that the global cross-validation score is heavily influenced by a small number of observations in the dataset. At present, the ramifications of this behaviour in cross-validation are unknown. The research reported here explores the extent to which individual and groups of observations impact optimal window size determination, and whether one can explain why some points are more influential than others. In addition, we strive to examine the impact neighbourhood specification has on model quality in terms of predictive capabilities and the ability of the method to retrieve spatially varying processes. The analysis is based on several datasets and using simulated data in order to compare and validate results. The results provide some practical guidelines for the use of cross-validation.

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