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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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.

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.

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.

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.

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.

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 constrained LAMBDA method for GPS attitude determination

An improved method to obtain fixed integer ambiguity in GPS attitude determination is presented. Known conditions are utilized as constraints to acquire attitude information when the float solution and its variance–covariance matrix are not accurate enough. The searching ellipsoidal region is first expanded to compensate for errors caused by the inaccurate float solution. Then the constraints are used to shrink the region to a proper size, which maintains the true integer ambiguity. Experimental results demonstrate that this scheme gives a fast search time and a higher success rate in determining the fixed integer ambiguity than the unconstrained method. The accuracy of attitude angles is also improved.

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.

Forecasting the impact of transport improvements on commuting and residential choice

This paper develops a probabilistic, competing-destinations, assignment model that predicts changes in the spatial pattern of the working population as a result of transport improvements. The choice of residence is explained by a new non-parametric model, which represents an alternative to the popular multinominal logit model. Travel times between zones are approximated by a normal distribution function with different mean and variance for each pair of zones, whereas previous models only use average travel times. The model’s forecast error of the spatial distribution of the Dutch working population is 7% when tested on 1998 base-year data. To incorporate endogenous changes in its causal variables, an almost ideal demand system is estimated to explain the choice of transport mode, and a new economic geography inter-industry model (RAEM) is estimated to explain the spatial distribution of employment. In the application, the model is used to forecast the impact of six mutually exclusive Dutch core-periphery railway proposals in the projection year 2020.

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.

Reliability checking for GNSS baseline and network processing

Reliability analysis is inseparably connected with the formulation of failure scenarios, and common test statistics are based on specific assumptions. This is easily overlooked when processing observation differences. Poor failure identification performance and misleading pre-analysis results, mainly meaningless minimum detectable biases and external reliability measures, are the consequence. A reasonable failure scenario for use with differenced GNSS observations is formulated which takes into account that individual outliers in the original data affect more than one processed observation. The proper test statistics and reliability indicators are given for use with correlated observations and both batch processing and Kalman filtering. It is also shown that standardized residuals and redundancy numbers fail completely when used with double differenced observations.

Analysis of high-frequency multipath in 1-Hz GPS kinematic solutions

High-frequency multipath would be problematic for studies at seismic or antenna dynamical frequencies as one could mistakenly interpret them as signals. A simple procedure to identify high-frequency multipath from global positioning system (GPS) time series records is presented. For this purpose, data from four GPS base stations are analyzed using spectral analyses techniques. Additional data, such as TEQC report files of L1 pseudorange multipath, are also used to analyze the high-frequency multipath and confirmation of the high-frequency multipath inferred from the phase records. Results show that this simple procedure is effective in identification of high-frequency multipath. The inferred information can aid interpretation of multipath at the GPS site, and is important for a number of reasons. For example, the information can be used to study GPS site selections and/or installations.

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.

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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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.

Artificial neural networks for predicting DGPS carrier phase and pseudorange correction

Artificial neural networks (ANNs) were used to predict the differential global positioning system (DGPS) pseudorange and carrier phase correction information. Autoregressive moving average (ARMA) and autoregressive (AR) models were bounded with neural networks to provide predictions of the correction. The neural network was employed to realize time-varying implementation. Online training for real-time prediction of the carrier phase enhances the continuity of service of the differential correction signals and, therefore, improves the positioning accuracy. When the correction signal from the DGPS was lost, the artificial neural networks predicted the correction data with good accuracy for the navigation system during a limited period. Comparisons of the prediction results using the two models are given.

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.

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.