Spatial Error Research Articles

Assessing the impact of rural E-commerce on regional poverty alleviation: A spatial spillover analysis

China's innovative and sustainable e-commerce poverty alleviation model has not only generated significant social and economic value but also serves as an effective practical case for global poverty reduction. This study employed 611 counties in contiguous poverty-affected areas in China from 2011 to 2022 as valid research samples. It adopted the Spatial Autoregression Model, Spatial Error Model, and Spatial Durbin Model to examine the impact of rural e-commerce on the quality of poverty alleviation, spatial spillover effects, and regional heterogeneity in contiguous poverty-affected areas. The results indicate that rural e-commerce improves the quality of poverty alleviation in local regions and has a spatial spillover effect on the quality of poverty alleviation in adjacent regions. Moreover, significant regional heterogeneity exists in the direct promotion and spatial spillover effects of rural e-commerce on the quality of poverty alleviation in contiguously poor areas. Specifically, in the impoverished areas of eastern, central, and northeastern China, rural e-commerce not only enhances the quality of poverty alleviation in the local region but also has a positive spillover effect on the poverty alleviation quality of neighboring regions. The findings of this study provide valuable insights for rural e-commerce to consolidate the achievements of poverty alleviation and align them with rural revitalization efforts.

Effect of Social Vulnerability on Cocaine-Related Mortality Rates in U.S. Counties

ABSTRACT Cocaine-related mortality rates have risen sharply since 2013 and social vulnerability is a crucial indicator for drug-related mortality rates. Therefore, the purpose of this study was to investigate the relationship between social vulnerability and cocaine-related mortality rates in U.S. counties. The Data were collected from the CDC WONDER, CDC’s Social Vulnerability Index (CDC’s SVI), and American Community Survey (ACS). The Data were analyzed by spatial autoregression models. According to present results, first, counties with social vulnerability (socioeconomic) were positively related to higher rates of cocaine overdose death (spatial lag: B = 0.323, p < .05; spatial error: B = 0.513, p < .01). Second, counties with social vulnerability (minority status & language) were negatively related to higher rates of cocaine overdose death (spatial lag: B = -0.233, p < .05). Third, counties with social vulnerability (housing type & transportation) were positively related to higher rates of cocaine overdose death (spatial lag: B = 0.413, p < .001; spatial error: B = 0.378, p < .001). In conclusion, the spread of cocaine overdose on U.S. counties with social vulnerabilities demonstrated a disproportionate burden of cocaine-related mortality.

Telomere length in early childhood and its association with attention: a study in 4-6 year old children.

Telomere length (TL), a marker of cellular aging, has been studied in adults with regard to its connection to cognitive function. However, little is known about the association between TL and cognitive development in children. This study investigated the interplay between TL and cognitive functioning in 283 Belgian children aged four to six years of the Environmental Influence on Aging in Early Life (ENVIRONAGE) birth cohort. Child leukocyte TL was measured using qPCR, while cognitive functioning, including attention and memory, was assessed using the Cambridge Neuropsychological Test Automated Battery (CANTAB). Linear regression models were employed to examine the association between TL and cognitive outcomes, adjusting for potential confounders. We found an inverse association between TL and the spatial errors made during the Motor Screening task (p = 0.017), indicating a higher motor accuracy in children with longer telomeres. No significant associations were found between TL and other cognitive outcomes. Our results suggest a specific link between TL and motor accuracy but not with the other cognitive domains.

Addressing multicollinearity in spatial modelling: A district level spatial analysis of pandemic COVID-19 in India.

This study focuses on conducting spatial analysis of COVID-19 at the district level in India. Leveraging data from www.covidindia.org for confirmed cases and deaths, and integrating population characteristics from the National Family Health Survey 5 (2019-2021) and supplementary sources. The objective is to identify risk factors using spatial modelling techniques while addressing multicollinearity through principal component analysis (PCA). This study utilizes spatial analysis to identify COVID-19 hotspots and coldspots at the district level in India. It highlights highly affected districts such as Mumbai, Pune, Chennai, Kolkata, and Bengaluru, as well as low affected districts in central and north-eastern regions. The study utilized the spatial lag model (SLM), spatial error model (SEM), geographical weighted regression (GWR), and multiscale geographical weighted regression (MGWR) models to analyse the impact of demographic, socioeconomic, climatic, and comorbidity factors on COVID-19, accounting for spatial proximity. Among these models, MGWR exhibited superior performance. Key risk factors associated with the COVID-19 phenomenon identified, providing insights into the impact of household conditions, educational level of women, tobacco and alcohol consumption rates, number of health centres, and climatic factors. Moreover, the local coefficients estimated by MGWR model furnish detailed information regarding the strength and direction of the relationships between predictors and COVID-19 cases and deaths within each spatial unit. The findings emphasize the significance of addressing multicollinearity in spatial modelling. It is beneficial for accurate parameter estimation, proper interpretation of coefficients, improved spatial analysis, and providing reliable insights to support decision-making in spatial contexts.

Assessment of spatial variation in lung cancer incidence and air pollutants: spatial regression modeling approach

ABSTRACT A notable finding is that Kerala’s capital Thiruvananthapuram has shown an increasing trend in lung cancer (LC) incidence. Long-term exposure to air pollution is a significant environmental risk factor for LC. This study investigated the spatial association between LC and exposure to air pollutants in Thiruvananthapuram, using Spatial Lag Model (SLM), Spatial Error Model (SEM), and Geographically Weighted Regression (GWR). The results showed that overall LC incidence rate was 111 per 105 males (age >60 years), whereas spatial distribution map revealed that 48% of the area had an incidence rate greater than 150. The results revealed a significant association between PM2.5 and LC. SLM was identified as the best model that predicted 62% variation in LC. GWR model improved model performance and made better local predictions in the southeastern parts of the study area. This study explores the effectiveness of spatial regression techniques for dealing spatial effects and pinpointing high-risk areas.

The spatial spillover effect and its attenuation boundary of urban economy on port efficiency.

Cities are commonly recognized as the immediate hinterland of ports and play a crucial role in fostering the sustainable development of ports. Therefore, it is imperative to investigate the influence of cities on ports. By employing panel data from 2001 to 2021 for both ports and cities in the Bohai Rim region, this study examines the spatial spillover effect of urban economy on port efficiency using the spatial error model (SEM). The findings show that urban economies have a significant spatial spillover effect on port efficiency, but this effect diminishes across different spatial matrices. In particular, the geographical matrix demonstrates a stronger spatial spillover effect of the urban economy on port efficiency. These research findings help to establish a collaborative mechanism for port-city development and provide useful insights for government management decision-making.

Executive function and spatial abilities in physically active children: an explorative study

BackgroundRegular physical activity has consistently shown promise in improving cognitive functioning among children. However, there is a shortage of comprehensive studies that delve into these benefits across various cognitive domains. This preliminary investigation aimed to discern potential disparities in cognitive performance between active and sedentary children, with a specific focus on inhibitory control, cognitive flexibility, and visuo-spatial working memory abilities.MethodsThe study employed a cross-sectional design encompassing 26 children (mean age 9.53 ± 2.20 years), categorized into two groups: Active and Sedentary. Executive functions were assessed using the NEPSY-II, while visuo-spatial working memory abilities were evaluated through the table version of the Radial Arm Maze (table-RAM) task. All outputs were analyzed with One-way ANOVAS or Kruskal–Wallis Tests to assess differences between Active and Sedentary children in both executive functioning and visuo-spatial working memory processes.ResultsThe findings revealed that the Active group outperformed the sedentary group in inhibitory control (F1,23 = 4.99, p = 0.03*), cognitive flexibility (F1,23 = 5.77, p = 0.02*), spatial span (F1,23 = 4.40, p = 0.04*), and working memory errors (F1,23 = 8.59, p = 0.01**). Both spatial span and working memory errors are parameters closely associated with visuo-spatial working memory abilities.ConclusionsAlthough preliminary, these results offer evidence of a positive link between physical activity and cognitive functioning in children. This indicates the importance of promoting active behaviors, especially within educational environments.

A Spatial Error Model in Structural Equation for the Human Development Index Modeling

A Spatial Error Model in Structural Equation for the Human Development Index Modeling

A Novel Localized Fast Multipole Method for Computations With Spatially Correlated Observation Error Statistics in Data Assimilation

AbstractSeveral observation types (e.g., geostationary satellite and Doppler radar observations) have recently been found to exhibit strong spatial error correlations. Including these error statistics in data assimilation for numerical weather prediction can improve analysis quality and forecast skill. Moreover, it allows for increases in the spatial density of observations assimilated, which is needed for the provision of information on appropriate scales for high‐resolution forecasting. However, introducing correlated error statistics may increase the computational complexity and parallel communication costs of matrix‐vector products involving observation precision matrices (inverse observation error covariance matrices). Without new approaches, we cannot take full advantage of new observation uncertainty estimates. We develop a new numerical approximation method based on a particular type of fast multipole method and a domain localization approach. The basic idea is to divide the observation domain into boxes and then separate calculations of matrix‐vector products according to the partition. These calculations can be done in parallel with very low communication overheads. The new method is easy to implement and parallelize, and it is applicable to a wide variety of observation precision matrices. We applied the new method to a simple variational data assimilation problem and found that the computational cost of the variational minimization was dramatically reduced while preserving analysis accuracy across a range of scales. The new method has the potential to be used as an efficient technique for practical applications where a large number of observations with mutual error correlations need to be assimilated quickly.

Spatio-temporal pattern and associate meteorological factors of airborne diseases in Bangladesh using geospatial mapping and spatial regression model.

Airborne diseases due to climate change pose significant public health challenges in Bangladesh. Little was known about the spatio-temporal pattern of airborne diseases at the district level in the country. Therefore, this study aimed to investigate the spatio-temporal pattern and associated meteorological factors of airborne diseases in Bangladesh using exploratory analysis and spatial regression models. This study used district-level reported cases of airborne diseases (meningococcal, measles, mumps, influenza, tuberculosis, and encephalitis) and meteorological data (temperature, relative humidity, wind speed, and precipitation) from 2017 to 2020. Geospatial mapping and spatial error regression models were utilized to analyze the data. From 2017 to 2020, a total of 315 meningococcal, 5159 measles, 1341 mumps, 346 influenza, 4664 tuberculosis, and 229 encephalitis cases were reported in Bangladesh. Among airborne diseases, measles demonstrated the highest prevalence, featuring a higher incidence rate in the coastal Bangladeshi districts of Lakshmipur, Patuakhali, and Cox's Bazar, as well as in Maulvibazar and Bandarban districts from 2017 to 2020. In contrast, tuberculosis (TB) emerged as the second most prevalent disease, with a higher incidence rate observed in districts such as Khagrachhari, Rajshahi, Tangail, Bogra, and Sherpur. The spatial error regression model revealed that among climate variables, mean (β = 9.56, standard error [SE]: 3.48) and maximum temperature (β = 1.19, SE: 0.40) were significant risk factors for airborne diseases in Bangladesh. Maximum temperature positively influenced measles (β = 2.74, SE: 1.39), whereas mean temperature positively influenced both meningococcal (β = 5.57, SE: 2.50) and mumps (β = 11.99, SE: 3.13) diseases. The findings from the study provide insights for planning early warning, prevention, and control strategies to combat airborne diseases in Bangladesh and similar endemic countries. Preventive measures and enhanced monitoring should be taken in some high-risk districts for airborne diseases in the country.

SPATIALLY INFORMED INSIGHTS: MODELING PERCENTAGE POVERTY IN EAST JAVA PROVINCE USING SEM WITH SPATIAL WEIGHT VARIATIONS

The East Java Province stands as one of Indonesia's regions grappling with a notably elevated poverty rate, accounting for 11.32% of the populace. A strategic approach employed to comprehend and redress this issue involves the application of spatial analysis, wherein spatial factors are intricately integrated into the modeling and cartographic representation of poverty data. The primary objective of this research is to discern the principal determinants influencing the incidence of poverty in East Java Province, employing data reflective of the population's poverty percentages within the province for the year 2021. The study incorporates six pivotal variables, namely: the population poverty rate, open unemployment rate, labor force participation rate, average years of schooling, adjusted per capita expenditure, and the gross regional domestic product (GRDP), predicated on adjusted expenditure. Diverse weighting schemes are applied based on both distance (1) and contiguity (2). The optimal predictive model utilized is the Spatial Error Model (SEM) incorporating a Distance Band Weighing (DBW) mechanism with a designated maximum distance ( ) of 75000 meters. Outcomes indicate that the variable wielding the most substantial influence on the poverty percentage in East Java Province is the average years of schooling. Specifically, an increase in the pursuit of formal education manifests as a negative correlate to the poverty percentage, implying an inverse relationship. Moreover, the SEM model adheres to the requisite assumptions, encompassing (1) the normality of residuals, (2) homogeneity of residuals, and (3) non-spatial autocorrelation of residuals. Comparative analyses reveal that the SEM model utilizing DBW yields diminished values for MAE, MSE, RMSE, AIC, and MAPE in comparison to its linear regression counterpart. Furthermore, the pseudo- values obtained from the SEM surpass those derived from the linear regression model. Rigorous likelihood ratio tests underscore substantial disparities between the SEM and linear regression models, with the former proving more efficient and markedly enhancing the model's explanatory prowess concerning variations in the dataset.

SPATIAL MODELING OF POVERTY IN BENGKULU PROVINCE WITH MIXED GEOGRAPHICALLY WEIGHTED REGRESSION

The percentage of poor people in Bengkulu Province is high from year to year. The poverty rate in Bengkulu Province also tends to fluctuate. If there is a decrease in the poverty rate, the decrease is relatively small. Poverty in the regions of Bengkulu Province also varies from district to district, subdistrict, and village to village, because poverty data is spatial data that varies regionally. The diversity of poverty data in Bengkulu Province is influenced by spatial effects, namely spatial dependency and spatial heterogeneity. Spatial dependency occurs due to spatial error correlation in cross section data, while spatial heterogeneity occurs due to random area effects, which is the difference between one region and another. Therefore, classical methods are not qualified enough to analyze the resulting diversity. This research will model the poverty of each district/city in Bengkulu Province using Mixed Geographically Weighted Regression (MGWR), because this method is quite complex in modeling data that contains spatial heterogeneity and variations in geospatial data. This modeling aims to identify and analyze poverty indicators in Bengkulu Province spatially, namely based on poverty data in each district/city in Bengkulu Province. The results showed that by using the MGWR method, the variables that locally influence the percentage of extreme poor people in each district/city in Bengkulu Province are Female Household Head Gender and not having a waterheater . Meanwhile, the variable that has a global effect on the percentage of the extreme poor in each district/city in Bengkulu Province is not having a flat screen television ().

ERegPose: An explicit regression based 6D pose estimation for snake-like wrist-type surgical instruments.

Accurately estimating the 6D pose of snake-like wrist-type surgical instruments is challenging due to their complex kinematics and flexible design. We propose ERegPose, a comprehensive strategy for precise 6D pose estimation. The strategy consists of two components: ERegPoseNet, an original deep neural network model designed for explicit regression of the instrument's 6D pose, and an annotated in-house dataset of simulated surgical operations. To capture rotational features, we employ an Single Shot multibox Detector (SSD)-like detector to generate bounding boxes of the instrument tip. ERegPoseNet achieves an error of 1.056mm in 3D translation, 0.073rad in 3D rotation, and an average distance (ADD) metric of 3.974mm, indicating an overall spatial transformation error. The necessity of the SSD-like detector and L1 loss is validated through experiments. ERegPose outperforms existing approaches, providing accurate 6D pose estimation for snake-like wrist-type surgical instruments. Its practical applications in various surgical tasks hold great promise.

Income inequality and household debt: A U.S. state-level spatial analysis

While most agree that the 2007–2008 financial bust stemmed from an excessive credit boom, researchers disagree about the causes of the boom amid income inequality, monetary excesses, and an unregulated credit market. Empirical findings on inequality's impact on credit booms are contradictory. This paper examines the impact of income inequality on household debt, utilizing U.S. state-level data from 1999 to 2015 within macroeconomic and credit market contexts. Diverging from prior research, it provides consistent and robust estimates by using the control function approach to identify exogenous variations in inequality and employing a spatial error panel model to remove significant cross-sectional dependence. The results indicate that inequality indices have a negative or insignificant effect on household debt, while monetary policy and credit market uncertainty also affect household debt. Thus, higher inequality is linked with credit contraction rather than expansion; rising inequality does not trigger a credit boom.

The Effects of Motionless Interventions Based on Visual-Auditory Instructions With Sonification on Learning a Rhythmic Motor Skill.

Our aim in this study was to investigate the effects of motionless interventions, based on visual-auditory integration with a sonification technique, on the learning a complex rhythmic motor skill. We recruited 22 male participants with high physical fitness and provided them four acquisition sessions in which to practice hurdle running, based on a visual-auditory instructional pattern. Next, we divided participants into three groups: visual-auditory, auditory, and control. In six sessions of motionless interventions, with no physical practice, participants in the visual-auditory group received a visual-auditory pattern similar to their experience during the acquisition period. The auditory group only listened to the sound of sonified movements of an expert hurdler, and the control group received no instructional interventions. Finally, participants in all three groups underwent post-intervention and transfer tests to determine their errors in the spatial and relative timing of their leading leg's knee angular displacement. Both visual-auditory and auditory groups had significantly less spatial error than the control group. However, there were no significant group differences in relative timing in any test phase. These results indicate that the use of the sonification technique in the form of visual-auditory instruction adapted to the athletes' needs benefitted perception-sensory capacities to improve motor skill learning.

Estimating the uncertainty of sea-ice area and sea-ice extent from satellite retrievals

Abstract. The net Arctic sea-ice area (SIA) can be estimated from the sea-ice concentration (SIC) by passive microwave measurements from satellites. To be a truly useful metric, for example of the sensitivity of the Arctic sea-ice cover to global warming, we need, however, reliable estimates of its uncertainty. Here we retrieve this uncertainty by taking into account the spatial and temporal error correlations of the underlying local sea-ice concentration products. As 1 example year, we find that in 2015 the average observational uncertainties of the SIA are 306 000 km2 for daily estimates, 275 000 km2 for weekly estimates, and 164 000 km2 for monthly estimates. The sea-ice extent (SIE) uncertainty for that year is slightly smaller, with 296 000 km2 for daily estimates, 261 000 km2 for weekly estimates, and 156 000 km2 for monthly estimates. These daily uncertainties correspond to about 7 % of the 2015 sea-ice minimum and are about half of the spread in estimated SIA and SIE from different passive microwave SIC products. This shows that random SIC errors play a role in SIA uncertainties comparable to inter-SIC-product biases. We further show that the September SIA, which is traditionally the month with the least amount of Arctic sea ice, declined by 105 000±9000 km2 a−1 for the period from 2002 to 2017. This is the first estimate of a SIA trend with an explicit representation of temporal error correlations.

Indoor AR Navigation Framework Based on Geofencing and Image-Tracking with Accumulated Error Correction

This study presents a novel framework for improving indoor augmented reality (AR) navigation with modern smartphone technology, which is achieved by addressing two major challenges: managing large absolute coordinate spaces and reducing error accumulation in camera-based spatial tracking. Our contribution is significant in two ways. First, we integrate geofencing with indoor navigation by considering spatial tracking errors, timing for audio guidance, and dynamic 3D arrow visualization for effective local-to-global spatial coordinate transformation. This method achieves precise local positioning and seamlessly integrates with larger spatial contexts, overcoming the limitations of current AR systems. Second, we introduce a periodic image-based calibration approach to minimize the inherent error accumulation in camera-based tracking, enhancing accuracy over longer distances. Unlike prior studies focusing on individual technologies, our work explores the software architecture of indoor AR navigation by providing a comprehensive framework for its design and practical use. The practicality of our approach is validated through the implementation of a smartphone application at the Mineral Industry Museum of Akita University, highlighting the limitations of component technologies and demonstrating our framework’s effectiveness.

Geometric Error Identification of Gantry-Type CNC Machine Tool Based on Multi-Station Synchronization Laser Tracers

Laser tracers are a three-dimensional coordinate measurement system that are widely used in industrial measurement. We propose a geometric error identification method based on multi-station synchronization laser tracers to enable the rapid and high-precision measurement of geometric errors for gantry-type computer numerical control (CNC) machine tools. This method also improves on the existing measurement efficiency issues in the single-base station measurement method and multi-base station time-sharing measurement method. We consider a three-axis gantry-type CNC machine tool, and the geometric error mathematical model is derived and established based on the combination of screw theory and a topological analysis of the machine kinematic chain. The four-station laser tracers position and measurement points are realized based on the multi-point positioning principle. A self-calibration algorithm is proposed for the coordinate calibration process of a laser tracer using the Levenberg–Marquardt nonlinear least squares method, and the geometric error is solved using Taylor’s first-order linearization iteration. The experimental results show that the geometric error calculated based on this modeling method is comparable to the results from the Etalon laser tracer. For a volume of 800 mm × 1000 mm × 350 mm, the maximum differences of the linear, angular, and spatial position errors were 2.0 μm, 2.7 μrad, and 12.0 μm, respectively, which verifies the accuracy of the proposed algorithm. This research proposes a modeling method for the precise measurement of errors in machine tools, and the applied nature of this study also makes it relevant both to researchers and those in the industrial sector.

Calibration for Improving the Medium-Range Soil Temperature Forecast of a Semiarid Region over Tibet: A Case Study

The high complexity of the parameter–simulation problem in land surface models over semiarid areas makes it difficult to reasonably estimate the surface simulation conditions that are important for both weather and climate in different regions. In this study, using the dense site datasets of a typical semiarid region over Tibet and the Noah land surface model with the constrained land parameters of multiple sites, an enhanced Kling–Gupta efficiency criterion comprising multiple objectives, including variable and layer dimensions, was obtained, which was then applied to calibration schemes based on two global search algorithms (particle swarm optimization and shuffled complex evaluation) to investigate the site-scale spatial complexities in soil temperature simulations. The calibrations were then compared and further validated. The results show that the Noah land surface model obtained reasonable simulations of soil moisture against the observations with fine consistency, but the negative fit and huge spatial errors compared with the observations indicated its weak ability to simulate the soil temperature over regional semiarid land. Both calibration schemes significantly improved the soil moisture and temperature simulations, but particle swarm optimization generally converged to a better objective than shuffled complex evaluation, although with more parameter uncertainties and less heterogeneity. Moreover, simulations initialized with the optimal parameter tables for the calibrations obtained similarly sustainable improvements for soil moisture and temperature, as well as good consistency with the existing soil reanalysis. In particular, the soil temperature simulation errors for particle swarm optimization were unbiased, while those for the other method were found to be biased around −3 K. Overall, particle swarm optimization was preferable when conducting soil temperature simulations, and it may help mitigate the efforts in surface forecast improvement over semiarid regions.

How planning intervention changes the “price tags” of property rights? Towards a dynamic understanding of China's small property right housing

While studies of property rights have long gone beyond the legal-illegal dichotomy, how to quantify the varying degree of property rights remains a challenging task. Focusing on a special type of informal housing—Small Property Right Housing (SPRH) in China, this study examines how planning intervention affects the pricing mechanism of SPRH through the theoretical lens of graded property rights. A previous study has unravelled the pricing mechanism of SPRH by quantifying the impact of informal institutions inherent to the SPRH market that give different “price tags” to varying degree of property rights, while not being able to capture the impact of external interventions. Drawing on two SPRH datasets before and after a Master Plan of urban villages conservation announced in 2019, we employ difference in difference (DID) estimations to examine how planning intervention affects SPRH prices by way of altering the strength of informal property rights. We also develop spatial error models (SEM) to account for spatial autocorrelation. This research extends and enriches the robust conceptualisation of graded degree of informal property rights by capturing the changes caused by external intervention to offer a dynamic and more accurate understanding of SPRH pricing mechanism. It also demonstrates that planning can go beyond sanctioning and redlining to tolerate and steer informal development.