Spatial Error Research Articles

Impact of topography and land cover on air temperature space-time variability in an urban environment with contrasted topography (Dijon, France, 2014–2021)

The influence of topography and land cover on air temperature space-time variability is examined in an urban environment with contrasted topography through simple and multiple linear regression (SLR and MLR) models, ran for each hour of the period 2014–2021, to explain spatial patterns of air temperature measured by a dense network. The SLR models reveal a complementary influence of topography and land cover, with the largest influence during daytime and nighttime, respectively. The MLR significantly improves upon the SLR models despite persistent intensity errors at night and spatial errors in the early morning. Topography influences air temperatures all year round, with temperature decreasing with height during the day and frequent thermal inversions at night (up to 30% of the time). Impervious surfaces are more influential in summer and early fall, especially during the late afternoon for the fraction covered by buildings and during the early night for the distance from the city centre. They contribute to increase air temperature close to the city centre and where the fraction covered by buildings is large. By contrast, vegetation contributes to cool air temperature during the night, especially in spring and early summer for field crops, summer and early fall for forests, and late fall and winter for low vegetation. Our framework proves to be a low-cost and efficient way to assess how strongly and how recurrently the static surface conditions influence air temperature along the annual and diurnal cycles. It is easily transposable to other areas and study fields.

Influence of Urban Railway Network Centrality on Residential Property Values in Bangkok

In recent decades, Bangkok has experienced substantial investments in its urban railway network, resulting in a profound transformation of the city’s landscape. This study examines the relationship between railway development and property value uplift, particularly focusing on network centrality, which is closely linked to urban structure. Our findings are based on two primary analyses: network centrality and spatial hedonic models. The network centrality analysis reveals that closeness centrality underscores the city’s prevailing monocentric structure, while the betweenness centrality measure envisions the potential emergence of urban subcenters. In our hedonic analysis of condominiums near railway stations, we formulated various regression models with different specifications, incorporating spatial effects and network centrality. With Bangkok’s predominant monocentric structure in mind, we found that the spatial regression model, including a spatial error specification and closeness centrality, outperforms the others. This suggests that the impact of railways on property values extends beyond station proximity and encompasses network centrality, intricately linked with the city’s urban structure. We applied our developed model to estimate the expected increase in property values at major interchange stations with high network centralities. These numerical values indicate a considerable potential for their evolution into urban subcenters. These insights offer valuable policy recommendations for effectively harnessing transit-related premiums and shaping the future development of both the railway system and the city.

Green spaces in highly urbanized tracts tied to lower prevalence of chronic respiratory diseases: A nationwide study across levels of urbanicity

Previous studies have found conflicting results between green space and respiratory diseases, possibly due to the use of generalized green space measures and the diverse urbanicity levels in which they were conducted. Few nationwide studies examined how urbanicity modifies the relationship between green spaces and respiratory diseases while considering the geospatial relation between green space and human population, population density, and accessibility.This study evaluates the relationships between population-weighted forest and open space exposure (inside and outside park), chronic obstructive pulmonary disease (COPD), and asthma in the contiguous United States. The COPD and asthma data were obtained from the Centers for Disease Control and Prevention. The green space exposure within an 800 m buffer was computed using National Land-cover Dataset and WorldPop Global Population Data. The associations were estimated using spatial error models and adjusted for important covariates. We also performed stratified analyses by four urbanicity levels.Green spaces inside but not outside park were associated with a lower prevalence of COPD and asthma. We found that a 1 m2 increase in forest inside park was associated with a decrease of 3.57 and 2.97 cases per 100,000 population in COPD and asthma, and a 1 m2 increase in open space inside park was associated with a decrease of 4.82 and 7.66 cases per 100,000 population in COPD and asthma. A trend of an inverse relationship was observed across urbanicity: the beneficial associations occurred in highly urbanized tracts but not in the three less-urbanized tracts.Our results provide further evidence that nearby forests and open spaces inside park, particularly in urban areas, were tied to a lower prevalence of respiratory diseases in the US. The beneficial association suggests the necessity of ensuring access to nearby green spaces, especially those in parks, to promote respiratory health.

Specifications and control of spatial frequency errors of components in two-beam laser static holographic exposure for pulse compression grating fabrication

Abstract The large-aperture pulse compression grating (PCG) is a critical component in generating an ultra-high-intensity, ultra-short-pulse laser; however, the size of the PCG manufactured by transmission holographic exposure is limited to large-scale high-quality materials. The reflective method is a potential way for solving the size limitation, but there is still no successful precedent due to the lack of scientific specifications and advanced processing technology of exposure mirrors. In this paper, an analytical model is developed to clarify the specifications of components, and advanced processing technology is adopted to control the spatial frequency errors. Hereafter, we have successfully fabricated a multilayer dielectric grating of 200 mm × 150 mm by using an off-axis reflective exposure system with Φ300 mm. This demonstration proves that PCGs can be manufactured by using the reflection holographic exposure method and shows the potential for manufacturing the meter-level gratings used in 100 petawatt class high-power lasers.

Poverty Modeling in Indonesia: a Spatial Regression Analysis

The government has made various efforts to reduce poverty in Indonesia. However, based on the World Population Review report, Indonesia is still ranked as the 73rd poorest country in the world in 2022 based on the value of gross national income. Therefore, it is necessary to identify the factors that affect poverty. This research was conducted by comparing classical, spatial lag, and spatial error regression, and the best model will be selected. The results show that the spatial error regression model is the best, based on the highest coefficient of determination and the lowest Akaike's information criterion value. Based on the best model, it is found that the expected years of schooling, the rate of gross regional domestic product, the percentage of households that have access to proper sanitation services, and the percentage of households with electric lighting sources have a significant effect on the percentage of poor people. The percentage of poor people in a province is also influenced by the percentage of poor people in the surrounding provinces. The results of this simulation can help the government take initiatives or policies aimed at reducing poverty in Indonesia based on variables that affect poverty.

Good Practices for estimating and using length-at-age in integrated stock assessments

Estimating growth (increase in size with age) is an integral component of fish population assessment. The use of integrated assessment models combined with the influence of misfitting size composition data on results have led to renewed interest in how growth is modeled in the assessment process. The types of data available to describe the growth process control how the length-at-age relationship will be estimated. Many factors contribute to the complexity of estimating length-at-age, including multiple sources of biological variability and difficulties in getting representative samples. The growth process in the population dynamics model is linked to all other processes and data but most directly influences the assessment model through 1) converting numbers into weight and vice versa, 2) productivity, and 3) modifying fits of size composition data. In some cases, an assessment may be insensitive to moderate levels of misspecification of the growth process, and therefore, relatively simple treatments may be adequate. However, in many cases, especially those where the fit of size composition is influential in estimating scale, a more thorough treatment of the growth process is needed. A complete treatment of growth will estimate the most important forms of biological variability, including individual, sex-specific, temporal, and spatial variability. Several types of sampling bias, including selectivity, length-stratified sampling, and spatial and measurement error, will likely also need to be addressed. When sufficient data are available, assessment authors should consider estimating the growth process as part of the integrated assessment model or consider empirical approaches for situations with high biological variability and sampling bias.

A novel algorithm for robust estimation of ants' speed on convoluted trajectories derived from their gait pattern

AbstractAccurate measurements of travel distance and the corresponding speed are crucial for the analysis of animal movements. Particularly, the trajectories of ants were used in numerous behavioral studies. However, measurements of travel distance involve the dilemma of setting the proper time window: Estimates from a short time window are vulnerable to spatial errors in observation, while estimates from a long time window lead to an underestimation of the travel distance. To overcome these difficulties, we propose a novel algorithm that successively interpolates two consecutive points of an ant's trajectory for a given time window by embracing the alternating tripod gait and other gait patterns of ants. We demonstrate that this algorithm is more reliable compared with the conventional method of travel distance estimation based on the sum of the consecutive straight‐line displacement (SLD). After obtaining speed estimates for a range of sampling time windows, we applied a fitting method that can estimate the actual speed without prior knowledge of spatial error distribution. We compared results from several methods of speed estimation extracted from the empirical and simulated data of ant trajectories. The accuracy of our algorithm was comparable with or much higher than the accuracy of the sum over the consecutive SLD with optimal window length. Hence, subjective selection of the sampling time window can be avoided by using the proposed algorithm. We provide software that enables empirical scientists to utilize the proposed methods rather than the conventional SLD method of distance and speed estimation.

Soil water availability strongly drives the differentiation of acquisitive and conservative strategies for coexisting woody species in a Chinese subtropical evergreen forest

AbstractQuestionsTrait–environment relationships hold great promise for elucidating the mechanisms that drive community assembly. We asked to what extent does functional trait coordination underlie the differentiation of acquisitive–conservative strategies along environmental gradients? Also, to what extent does soil water availability determine trait combinations and subsequently shape microhabitat preferences for species with distinct ecological strategies?LocationYunnan Province, southwest China.MethodsPearson correlation and trait network analyses were used to quantify trait associations. A combined RLQ–fourth‐corner analysis and a spatial autoregressive error model were employed to examine trait–environment relationships and explain species distribution along environmental gradients.ResultsLeaf and stem traits were tightly coordinated along the first RLQ axis. Herein, traits representing construction cost exhibited trade‐offs against traits representing resource acquisition rate, underlying an acquisitive–conservative strategy differentiation along the environmental gradients. Significant overall and bivariate trait–environment relationships were found. In addition, the topographic wetness index contributed substantially to the environmental variation, and significantly influenced most traits alone. These results supported the hypothesis that soil water availability was the key environmental factor in selecting trait combinations and driving the differentiation of acquisitive and conservative strategies for coexisting species compared with other environmental factors. Finally, deciduous and a proportion of the evergreen species were acquisitive, whereas the remaining evergreen species were conservative, preferring wetter and drier microhabitats, respectively.ConclusionsOur findings indicate that soil water availability plays a central role in shaping both the trait combinations and microhabitat preferences of species with different ecological strategies rather than different leaf habits. These findings also highlight the fundamental significance of functional traits in facilitating the differentiation between acquisitive and conservative strategies along environmental gradients.

STEPSBI: Quick spatiotemporal fusion with coarse- and fine-resolution scale transformation errors and pixel-based synthesis base image pair

Spatiotemporal fusion (STF) is crucial for reconciling the conflict between temporal and spatial resolutions of remote sensing observations. However, fusing images in heterogeneous areas remains challenging under continuous missing values. Moreover, most current STF methods only consider temporal errors and disregard the spatial scale error in time variation. Therefore, we proposed a Quick Spatiotemporal Fusion with Coarse- and Fine-Resolution Scale Transformation Errors and Pixel-Based Synthesis Base Image Pair (STEPSBI). First, the optimal pixel-based image synthesis strategy was designed using all available fine- and coarse-resolution images. Then, the scale transformation error (STE) of coarse-resolution downscaling to fine-resolution in temporal variation was quantified. And a residual term was introduced to reduce the prediction error from the temporal variation. Finally, the spatial scale and temporal errors were corrected using the results of super-pixel segmentation as spatial weights. This model has two strengths: (1) Pixel-based image synthesis alleviates the absence of base images under continuous missing values; and (2) STE correction restores spatial details of heterogeneous areas in rapid land cover change. In scenarios of continuous missing images, abrupt land cover changes, and high-resolution heterogeneity, we evaluated STEPSBI, the Gap Filling and Savitzky–Golay filtering method (GF-SG), the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), the Flexible Spatiotemporal DAta Fusion (FSDAF), the cross-attention-based adaptive weighting fusion network (CAFE), and the Multi-scene Spatiotemporal Fusion Network (MUSTFN). The results indicate that STEPSBI yields better overall performance than other models in cropland, woodland, grassland, and other land cover types. Furthermore, ablation experiments demonstrated that each component improved the model’s performance. In addition, STEPSBI had higher fusion efficiency because it was developed on the Google Earth Engine cloud computing platform. Therefore, STEPSBI was feasible for advancing fine monitoring of spatiotemporal image fusion under continuous missing values and heterogeneous surfaces. The program of STEPBSI is freely available at: https://code.earthengine.google.com/684844aa42f64fa6b4eebe3bc0dd6483.

Assessment of an Experimental Version of fvGFS for TC Genesis Forecasting Ability in the Western North Pacific

Abstract We evaluated the ability of the fvGFS with a 13-km resolution in simulating tropical cyclone genesis (TCG) by conducting hindcast experiments for 42 TCG events over 2018–19 in the western North Pacific (WNP). We observed an improved hit rate with a lead time of between 5 and 4 days; however, from 4- to 3-day lead time, no consistent improvement in the temporal and spatial errors of TCG was obtained. More “Fail” cases occurred when and where a low-level easterly background flow prevailed: from mid-August to September 2018 and after October 2019 and mainly in the eastern WNP. In “Hit” cases, 850-hPa streamfunction and divergence, 200-hPa divergence, and genesis potential index (GPI) provided favorable TCG conditions. However, the Hit–Fail case differences in other suggested factors (vertical wind shear, 700-hPa moisture, and SST) were nonsignificant. By contrast, the reanalysis used for validation showed only significant difference in 850-hPa streamfunction. We stratified the background flow of TCG into four types. The monsoon trough type (82%) provided the most favorable environmental conditions for successful hindcasts, followed by the subtropical high (45%), easterly (17%), and others (0%) types. These results indicated that fvGFS is more capable of enhancing monsoon trough circulation and provides a much better environment for TCG development but is less skillful in other types of background flow that provides weaker large-scale forcing. The results suggest that the most advanced high-resolution weather forecast models such as the fvGFS warrant further improvement to properly simulate the subtle circulation features (e.g., mesoscale convection system) that might provide seeds for TCG. Significance Statement This study provides an evaluation of tropical cyclone genesis prediction skill of fvGFS. Favorable large-scale environmental factors for successful prediction are identified. Skill dependence on environmental factors provides guidance for evaluating the reliability of a genesis forecast in advance.

A calibration method for spatial error of industrial robot under variable load conditions based on double-ball rotary structure and three contact displacement sensors

A new calibration device using a double-ball rotary structure and three contact displacement sensors is proposed to measure the spatial errors of six-axis serial robot. The error model is divided into kinematics part and static stiffness part, established by Modified DH (MDH) model and virtual joint method. The MDH parameter errors are compensated by modifying the angles of each axis with Newton Raphson compensation method and ridge estimation. The stiffness parameters are also identified by the least square method. The self-designed double-ball rotary device is calibrated by coordinate measuring machine (CMM), while the three-point measuring device is calibrated by gauge block. Then, the kinematic and static stiffness calibration and compensation experiments are carried out on EPSON C4 A901 robot in turn. The spatial error under none load and 1.0 kg, 1.5 kg, 2.0 kg gravity load are measured separately. The experiment results show that the motion accuracy of the robot is improved to the range from 0.0163 mm to 0.0236 mm under the gravity load conditions between weights of 1.0 kg and 2.0 kg. Finally, by comparing the measuring results from the laser displacement sensor and laser interferometer, the measurement accuracy of each axis of the self-designed device is equivalent to the laser displacement sensor and the laser interferometer in micron range.

A novel sampling approach for prediction of post-earthquake damage to buildings

Predicting damage to buildings using field-based samples is important for crisis management after an earthquake. Because building damage in seismic regions is spatially correlated, neighboring samples will contain similar information. However, if they are treated as independent observations, the total sample set will not provide sufficient information. To prevent this, sample locations must be dispersed throughout the region and the spatial correlation of observations must be taken into account. The current study proposes a two-stage sampling approach to select well-dispersed samples of small size containing substantial information. The sample locations have been chosen using the Halton iterative partitioning method and individual buildings were randomly selected for each location. The selected sample buildings were employed in a kriging regression model to predict the damage ratio. Multiple factors were used in the model as predictor variables to increase the prediction accuracy. The proposed sampling approach was compared to other sampling methods in terms of spatial balance and prediction error using both simulated and real datasets from the 2017 Sarpol-e Zahab earthquake in Iran. The proposed approach provided better results than the other approaches. Although some factors affected the building damage in the actual data, a combination of fault strike angles and seismic intensity measures provided more accurate predictions than other combinations.

Exploring the benefits of images with frequency visual content in predicting human ocular scanpaths using Artificial Neural Networks

We present a study of an artificial neural architecture that predict human ocular scanpaths while they are free-viewing different images types. This analysis is made by comparing different metrics that encompass scanpath patterns, these metrics aim to measure spatial and temporal errors; such as the MSE, ScanMatch, cross-correlogram peaks, and MultiMatch. Our methodology begin by choosing one architecture and training different parametric models per subject and image type, this allows to adjust the models to each person and a given set of images. We find out that there is a clear difference in prediction when people free-view images with high visual content (high-frequency contents) and low visual content (no-frequency contents). The input features selected for predicting the scanpath are saliency maps calculated from foveated images together with the past of the ocular scanpath of subjects, modeled by our architecture called FovSOS-FSD (Foveated Saliency and Ocular Scanpath with Feature Selection and Direct Prediction).The results of this study could be used to improve the design of gaze-controlled interfaces, virtual reality, as well as to better understand how humans visually explore their surroundings and pave a way to make future research.

Evaluating carbon emissions from the operation of historic dwellings in cities based on an intelligent management platform

Historic dwellings in cities are considered to be an important resource on the way to carbon neutrality because of their great potential for low-carbon renewal. However, in addition to obligatory heritage conservation requirements, there is no uniformity in historic dwellings’ operation mode and conditioning equipment, which means detailed carbon emissions accounting is critical but challenging. To estimate the spatio-temporal distribution of carbon emissions from historic dwellings and thus support the development of decarbonization retrofit strategies, this research proposed a low-interventional carbon footprint accounting system based on a digital twin management platform. Multiple digital technologies are applied to monitor and evaluate the occupants’ lifestyles, indoor environment, user location, and equipment energy consumption. Based on backend algorithms, the spatio-temporal distribution of carbon emissions is inserted into the building plan to graphically present the evaluation results. Compared with traditional simulation methods, the proposed system enables the automatic updating of energy accounting parameters by fusing multi-source data into one platform, improving accounting efficiency and accuracy simultaneously. A case study in China proves the outstanding performance of the accuracy of accounting results, with an approximate daily spatial error of 20 % and a total accounting error of less than 15 %.

A robot-aided visuomotor wrist training induces motor and proprioceptive learning that transfers to the untrained ipsilateral elbow

BackgroundLearning of a visuomotor task not only leads to changes in motor performance but also improves proprioceptive function of the trained joint/limb system. Such sensorimotor learning may show intra-joint transfer that is observable at a previously untrained degrees of freedom of the trained joint.ObjectiveHere, we examined if and to what extent such learning transfers to neighboring joints of the same limb and whether such transfer is observable in the motor as well as in the proprioceptive domain. Documenting such intra-limb transfer of sensorimotor learning holds promise for the neurorehabilitation of an impaired joint by training the neighboring joints.MethodsUsing a robotic exoskeleton, 15 healthy young adults (18–35 years) underwent a visuomotor training that required them to make continuous, increasingly precise, small amplitude wrist movements. Wrist and elbow position sense just-noticeable‐difference (JND) thresholds and spatial movement accuracy error (MAE) at wrist and elbow in an untrained pointing task were assessed before and immediately after, as well as 24 h after training.ResultsFirst, all participants showed evidence of proprioceptive and motor learning in both trained and untrained joints. The mean JND threshold decreased significantly by 30% in trained wrist (M: 1.26° to 0.88°) and by 35% in untrained elbow (M: 1.96° to 1.28°). Second, mean MAE in untrained pointing task reduced by 20% in trained wrist and the untrained elbow. Third, after 24 h the gains in proprioceptive learning persisted at both joints, while transferred motor learning gains had decayed to such extent that they were no longer significant at the group level.ConclusionOur findings document that a one-time sensorimotor training induces rapid learning gains in proprioceptive acuity and untrained sensorimotor performance at the practiced joint. Importantly, these gains transfer almost fully to the neighboring, proximal joint/limb system.

Analysis of wall-modelled particle/mesh PDF methods for turbulent parietal flows

Abstract Lagrangian stochastic methods are widely used to model turbulent flows. Scarce consideration has, however, been devoted to the treatment of the near-wall region and to the formulation of a proper wall-boundary condition. With respect to this issue, the main purpose of this paper is to present an in-depth analysis of such flows when relying on particle/mesh formulations of the probability density function (PDF) model. This is translated into three objectives. The first objective is to assess the existing an-elastic wall-boundary condition and present new validation results. The second objective is to analyse the impact of the interpolation of the mean fields at particle positions on their dynamics. The third objective is to investigate the spatial error affecting covariance estimators when they are extracted on coarse volumes. All these developments allow to ascertain that the key dynamical statistics of wall-bounded flows are properly captured even for coarse spatial resolutions.

A formal model of Checked C1

We present a formal model of Checked C, a dialect of C that aims to enforce spatial memory safety. Our model pays particular attention to the semantics of dynamically sized, potentially null-terminated arrays. We formalize this model in Coq, and prove that any spatial memory safety errors can be blamed on portions of the program labeled unchecked; this is a Checked C feature that supports incremental porting and backward compatibility. While our model’s operational semantics uses annotated (“fat”) pointers to enforce spatial safety, we show that such annotations can be safely erased. Using PLT Redex we formalize an executable version of our model and a compilation procedure to an untyped C-like language, as well as use randomized testing to validate that generated code faithfully simulates the original. Finally, we develop a custom random generator for well-typed and almost-well-typed terms in our Redex model, and use it to search for inconsistencies between our model and the Clang Checked C implementation. We find these steps to be a useful way to co-develop a language (Checked C is still in development) and a core model of it.

Receiver architecture for on-off-keying free-space optical communications with channel estimation in the presence of residual spatial tracking error

Receiver architecture for on-off-keying free-space optical communications with channel estimation in the presence of residual spatial tracking error

Implicit Runge‐Kutta with spectral Galerkin methods for the fractional diffusion equation with spectral fractional Laplacian

AbstractAn efficient numerical method with high accuracy both in time and in space is proposed for solving ‐dimensional fractional diffusion equation with spectral fractional Laplacian. The main idea is discretizing the time by an ‐stage implicit Runge‐Kutta method and approximating the space by a spectral Galerkin method with Fourier‐like basis functions. In view of the orthogonality, the mass matrix of the spectral Galerkin method is an identity and the stiffness matrix is diagonal, which makes the proposed method is efficient for high‐dimensional problems. The proposed method is showed to be stable and convergent with at least order in time, when the implicit Runge‐Kutta method with classical order () is algebraically stable. As another important contribution of this paper, we derive the spatial error estimate with optimal convergence order which depends on the regularity of the exact solution but not on the fractional parameter . This improves the previous result which depends on the fractional parameter . Numerical experiments verify and complement our theoretical results.

Intra-urban differences underlying leprosy spatial distribution in central Brazil: geospatial techniques as potential tools for surveillance.

This ecological study identified an aggregation of urban neighbourhoods spatial patterns in the cumulative new case detection rate (NCDR) of leprosy in the municipality of Rondonópolis, central Brazil, as well as intra-urban socioeconomic differences underlying this distribution. Scan statistics of all leprosy cases reported in the area from 2011 to 2017 were used to investigate spatial and spatiotemporal clusters of the disease at the neighbourhood level. The associations between the log of the smoothed NCDR and demographic, socioeconomic, and structural characteristics were explored by comparing multivariate models based on ordinary least squares (OLS) regression, spatial lag, spatial error, and geographically weighted regression (GWR). Leprosy cases were observed in 84.1% of the neighbourhoods of Rondonópolis, where 848 new cases of leprosy were reported corresponding to a cumulative NCDR of 57.9 cases/100,000 inhabitants. Spatial and spatiotemporal high-risk clusters were identified in western and northern neighbourhoods, whereas central and southern areas comprised low-risk areas. The GWR model was selected as the most appropriate modelling strategy (adjusted R²: 0.305; AIC: 242.85). By mapping the GWR coefficients, we identified that low literacy rate and low mean monthly nominal income per household were associated with a high NCDR of leprosy, especially in the neighbourhoods located within high-risk areas. In conclusion, leprosy presented a heterogeneous and peripheral spatial distribution at the neighbourhood level, which seems to have been shaped by intra-urban differences related to deprivation and poor living conditions. This information should be considered by decision-makers while implementing surveillance measures aimed at leprosy control.