Spatial Measurement Error Research Articles

Measurement error models for spatial network lattice data: Analysis of car crashes in Leeds

Abstract Road casualties represent an alarming concern for modern societies. During the last years, several authors proposed sophisticated approaches to help authorities implement new policies. These models were usually developed considering a set of socio-economic variables and ignoring the measurement error, which can bias the statistical inference. This paper presents a Bayesian model to analyse car crash occurrences at the network-lattice level, taking into account measurement error in the spatial covariate. The suggested methodology is exemplified by considering the collisions in the road network of Leeds (UK) during 2011–2019. Traffic volumes are approximated using an extensive set of counts obtained from mobile devices and the estimates are adjusted using a spatial measurement error correction.

Assessment of binocular fixational eye movements including cyclotorsion with split-field binocular scanning laser ophthalmoscopy

Fixational eye movements are a hallmark of human gaze behavior, yet little is known about how they interact between fellow eyes. Here, we designed, built and validated a split-field binocular scanning laser ophthalmoscope to record high-resolution eye motion traces from both eyes of six observers during fixation in different binocular vergence conditions. In addition to microsaccades and drift, torsional eye motion could be extracted, with a spatial measurement error of less than 1 arcmin. Microsaccades were strongly coupled between fellow eyes under all conditions. No monocular microsaccade occurred and no significant delay between microsaccade onsets across fellow eyes could be detected. Cyclotorsion was also firmly coupled between both eyes, occurring typically in conjugacy, with gradual changes during drift and abrupt changes during saccades.

Novel Algorithm for Mutual Alignment of Railway Track Geometry Measurements

Track geometry measurements are regularly collected to monitor the condition of a railway network. To detect deterioration patterns and enable predictive maintenance, sequential measurement runs must be mutually aligned which has been proven a serious challenge. This paper presents a novel algorithm for mutual alignment of track geometry signal data. It resolves several previously intractable alignment problems: highly segmented data with variable sample rate, spatially correlated and uncorrelated measurement errors, convergence to true locations, and consistency over time. The algorithm adjusts spatial measurement errors by splitting signals in continuous segments. Re-sampled, error-corrected signals are mutually aligned using cross correlation, and this process is repeated until the mutual alignment meets a pre-defined precision threshold. Missing measurement values are handled by imputing an interpolated offset from nearby segments, ensuring that the signals remain continuous. By using weighted average offsets over all aligned signals, the law of large numbers guarantees convergence and consistency. The practical feasibility of the algorithm is demonstrated on empirical track geometry measurement data from the British railway network, owned and operated by Network Rail.

Offline map matching using time-expanded graph for low-frequency data

Map matching is an essential preprocessing step for most trajectory-based intelligent transport system services. Due to device capability constraints and the lack of a high-performance model, map matching for low-sampling-rate trajectories is of particular interest. Therefore, we developed a time-expanded graph matching (TEG-matching) that has three advantages (1) high speed and accuracy, as it is robust for spatial measurement error and a pause such as at traffic lights; (2) being parameter-free, that is, our algorithm has no predetermined hyperparameters; and (3) only requiring ordered locations for map matching. Given a set of low-frequency GPS data, we construct a time-expanded graph (TEG) whose path from source to sink represents a candidate route. We find the shortest path on TEG to obtain the matching route with a small area between the vehicle trajectory. Additionally, we introduce two general speedup techniques (most map matching methods can apply) bottom-up segmentation and fractional cascading. Numerical experiments with worldwide vehicle trajectories in a public dataset show that TEG-matching outperforms state-of-the-art algorithms in terms of accuracy and speed, and we verify the effectiveness of the two general speedup techniques.

Comparison of 2c- and 3cLIF droplet temperature imaging

This work presents “pulsed 2D-3cLIF-EET” as a measurement setup for micro-droplet internal temperature imaging. The setup relies on a third color channel that allows correcting spatially changing energy transfer rates between the two applied fluorescent dyes. First measurement results are compared with results of two slightly different versions of the recent “pulsed 2D-2cLIF-EET” method. Results reveal a higher temperature measurement accuracy of the recent 2cLIF setup. Average droplet temperature is determined by the 2cLIF setup with an uncertainty of less than 1 K and a spatial deviation of about 3.7 K. The new 3cLIF approach would become competitive, if the existing droplet size dependency is anticipated by an additional calibration and if the processing algorithm includes spatial measurement errors more appropriately.

Spatial measurement errors in the field of spatial epidemiology.

BackgroundSpatial epidemiology has been aided by advances in geographic information systems, remote sensing, global positioning systems and the development of new statistical methodologies specifically designed for such data. Given the growing popularity of these studies, we sought to review and analyze the types of spatial measurement errors commonly encountered during spatial epidemiological analysis of spatial data. MethodsGoogle Scholar, Medline, and Scopus databases were searched using a broad set of terms for papers indexed by a term indicating location (space or geography or location or position) and measurement error (measurement error or measurement inaccuracy or misclassification or uncertainty): we reviewed all papers appearing before December 20, 2014. These papers and their citations were reviewed to identify the relevance to our review.ResultsWe were able to define and classify spatial measurement errors into four groups: (1) pure spatial location measurement errors, including both non-instrumental errors (multiple addresses, geocoding errors, outcome aggregations, and covariate aggregation) and instrumental errors; (2) location-based outcome measurement error (purely outcome measurement errors and missing outcome measurements); (3) location-based covariate measurement errors (address proxies); and (4) Covariate-Outcome spatial misaligned measurement errors. We propose how these four classes of errors can be unified within an integrated theoretical model and possible solutions were discussed.ConclusionSpatial measurement errors are ubiquitous threat to the validity of spatial epidemiological studies. We propose a systematic framework for understanding the various mechanisms which generate spatial measurement errors and present practical examples of such errors.

Spatial measurement error and correction by spatial SIMEX in linear regression models when using predicted air pollution exposures.

Spatial modeling of air pollution exposures is widespread in air pollution epidemiology research as a way to improve exposure assessment. However, there are key sources of exposure model uncertainty when air pollution is modeled, including estimation error and model misspecification. We examine the use of predicted air pollution levels in linear health effect models under a measurement error framework. For the prediction of air pollution exposures, we consider a universal Kriging framework, which may include land-use regression terms in the mean function and a spatial covariance structure for the residuals. We derive the bias induced by estimation error and by model misspecification in the exposure model, and we find that a misspecified exposure model can induce asymptotic bias in the effect estimate of air pollution on health. We propose a new spatial simulation extrapolation (SIMEX) procedure, and we demonstrate that the procedure has good performance in correcting this asymptotic bias. We illustrate spatial SIMEX in a study of air pollution and birthweight in Massachusetts.

Correcting for Spatial Measurement Error in Air Pollution Cohort Studies

In epidemiologic analyses of long-term health effects of air pollution, exposures for study subjects are often not directly measured. A common strategy is to employ a first-stage model to estimate ...

Spatial measurement error in infectious disease models

Individual-level models (ILMs) for infectious disease can be used to model disease spread between individuals while taking into account important covariates. One important covariate in determining the risk of infection transfer can be spatial location. At the same time, measurement error is a concern in many areas of statistical analysis, and infectious disease modelling is no exception. In this paper, we are concerned with the issue of measurement error in the recorded location of individuals when using a simple spatial ILM to model the spread of disease within a population. An ILM that incorporates spatial location random effects is introduced within a hierarchical Bayesian framework. This model is tested upon both simulated data and data from the UK 2001 foot-and-mouth disease epidemic. The ability of the model to successfully identify both the spatial infection kernel and the basic reproduction number (R 0) of the disease is tested.

Dual-mode capacitance and gamma-ray tomography using the Landweber reconstruction algorithm

A dual-mode tomography system based on electrical capacitance and gamma-ray tomography has been developed at the Department of Physics and Technology, University of Bergen. The objective of the dual-mode tomograph is to acquire cross-sectional images, i.e. tomograms, of hydrocarbon flow comprising oil, water and gas constituents. The capacitance tomograph utilizes an eight-electrode sensor set-up mounted around a PVC pipe structure which is sensitive to the electrical permittivity εr of the fluid. By using the capacitance tomograph, the produced water constituent can be separated from the gas and crude oil constituents, assuming that the liquid phase is oil continuous. The high-speed gamma-ray tomograph comprises five 500 mCi 241Am gamma-ray sources, each at a principal energy of 59.5 keV, which corresponds to five detector modules, each consisting of 17 CdZnTe detectors mounted around the same pipe section as the capacitance sensor. The gamma-ray tomograph discriminates between the gas and the liquid phase, since these have different photon attenuation properties. As a result, the gamma-ray tomograph is able to distinguish the gas phase from the liquid phase of the hydrocarbon flow. Consequently, the dual-mode capacitance and gamma-ray tomography set-up is able to distinguish the oil, water and gas constituents of hydrocarbon flow. This paper presents the work that has been performed related to static characterization of the dual-mode tomograph using the Landweber reconstruction algorithm on polypropylene phantoms. The objective of the work has been to quantitatively evaluate the static imaging performance of the dual-mode tomograph with respect to relative spatial measurement errors, i.e. root mean square errors of the reconstructed tomograms compared to that of the phantom. The work shows that dual-mode tomography using electrical capacitance and gamma-ray sensors is feasible on hydrocarbon flow components using a pixel-to-pixel fusion procedure on separately reconstructed tomograms based on the Landweber reconstruction algorithm.

Measurement error caused by spatial misalignment in environmental epidemiology

In many environmental epidemiology studies, the locations and/or times of exposure measurements and health assessments do not match. In such settings, health effects analyses often use the predictions from an exposure model as a covariate in a regression model. Such exposure predictions contain some measurement error as the predicted values do not equal the true exposures. We provide a framework for spatial measurement error modeling, showing that smoothing induces a Berkson-type measurement error with nondiagonal error structure. From this viewpoint, we review the existing approaches to estimation in a linear regression health model, including direct use of the spatial predictions and exposure simulation, and explore some modified approaches, including Bayesian models and out-of-sample regression calibration, motivated by measurement error principles. We then extend this work to the generalized linear model framework for health outcomes. Based on analytical considerations and simulation results, we compare the performance of all these approaches under several spatial models for exposure. Our comparisons underscore several important points. First, exposure simulation can perform very poorly under certain realistic scenarios. Second, the relative performance of the different methods depends on the nature of the underlying exposure surface. Third, traditional measurement error concepts can help to explain the relative practical performance of the different methods. We apply the methods to data on the association between levels of particulate matter and birth weight in the greater Boston area.

Static characterization of a dual sensor flow imaging system

A dual sensor flow imaging system based on electrical capacitance and gamma-ray tomography has been developed and tested at the Department of Physics, University of Bergen, Norway. The capacitance tomograph utilizes an eight-electrode sensor set-up, whilst the gamma-ray tomograph is configured in a setting of five radiation sources versus a total of 85 compact gamma-ray semiconductor detectors. The data acquisition system is based on transputer technology, whereas the reconstruction and image presentation processing system can be either a transputer-based network or a conventional state of the art processing engine. This paper presents the work that has been performed in relation to the static phantom characterization of the imaging system. The objective of the work has been to quantitatively evaluate the static imaging performance of the tomography system with respect to relative parameter and relative spatial measurement errors. The work shows that the dual sensor imaging system is feasible for acquisition of two and three phase tomograms, and accurate acquisition of process parameters. The difference in spatial accuracy of the soft field sensor tomograms (capacitance) from that of the hard field sensor tomograms (gamma-ray) is clearly demonstrated.