Spatial Model Research Articles

The Impact of Agglomeration on CO2 Emissions in China's Transport Sector: A Spatial Econometric Analysis

The long-term processes of urbanization and industrialization have led to the agglomeration of population and industry, fostering economic development while introducing opportunities and challenges for carbon reduction in transport. This paper integrates the Stochastic Impacts by Regression on Population, Affluence, and Technology Model with the Spatial Durbin Model to assess the effects of population agglomeration and industrial agglomeration on transport carbon dioxide emissions. The empirical results show that a 1% increase in population agglomeration decreases local transport carbon dioxide emissions by 1.7065% and generates a spillover effect of 1.0542% in surrounding areas. In contrast, industrial agglomeration increases regional transport carbon dioxide emissions by an average of 0.3309% without significant spillover effects. Furthermore, economic agglomeration exhibits an N-shaped relationship with transport carbon dioxide emissions, reflecting the dual influences of the "economic effect" and the "congestion effect". Mechanism analysis reveals that both types of agglomeration can modulate the impact of infrastructure development on transport carbon dioxide emissions, suggesting that effective infrastructure planning can help alleviate the negative environmental impacts. This study provides a spatial mode for understanding the synergistic effects of population planning, industrial development, and environmental improvement, offering significant reference value for policymakers in the decision-making related to low-carbon transport development.

Dynamic inversion and optimal tracking control on the ball-plate system based on a linearized nonholonomic multibody model

This paper addresses the optimal control of the ball-plate system, a well-known nonholonomic system in the context of nonprehensile manipulation, using a multibody dynamics approach. The trajectory tracking control of a steady-state circular motion of the ball on the plate, for any radius and potentially off-centric with respect to the plate’s pivoting point, is achieved by designing a Linear-Quadratic Regulator. A spatial multibody model of the ball-plate system is considered. A key contribution is the analytical computation of the circular steady motion of the ball by dynamic inversion, including the control actions to achieve this reference solution. This enables the analytical computation of the linearized equations along this reference motion, resulting in a periodic linear time-varying (LTV) system, and the application of linear controllability criteria for LTV systems. A controllable linear system, involving the Cartesian coordinates of the contact point and the yaw angle of the sphere, is obtained using a convenient coordinate partition in the linearization. Compared to existing results on the same problem, closed-loop stability about the desired trajectory is achieved for any radius of the circular trajectory.

Seam estimation based on dense matching for parallax-tolerant image stitching

Image stitching with large parallax poses a significant challenge in the field of computer vision. Existing seam-based approaches attempt to address parallax artifacts by stitching images along seams. However, issues such as object mismatches, disappearances, and duplications still arise occasionally, primarily due to inaccurate alignment of dense pixels or inappropriate seam estimation methods. In this paper, we propose a robust seam-based parallax-tolerant image stitching method that leverages dense flow estimation from state-of-the-art approaches. Firstly, we develop a seam estimation method that does not require pre-estimation of image warping model. Instead, it directly estimates the seam by measuring the local smoothness of the optical flow field and incorporating a penalty term for duplications. Subsequently, we design an iterative algorithm that utilizes the location of estimated seam to solve a spatial smooth warping model and eliminate outlier corresponding pairs. By employing this approach, we effectively address the intertwined challenges of estimating the warping model and seam. Experiment on real-world images shows that our proposed method achieves superior local alignment accuracy near the stitching seam and outperforms other state-of-the-art techniques on visual stitching result.

Trends in socioeconomic disparities in urban heat exposure and adaptation options in mid-size U.S. cities

There is ample evidence that Environmental Justice communities experience high levels of extreme heat. However, it is unknown how disparities in urban heat exposure and adaptation options change over time. This study investigates socioeconomic disparities over time in urban heat exposure and adaptation options in eight mid-sized Northeastern cities. We ask: How were socioeconomic factors associated with heat exposure and adaptation options over time? We analyzed disparities at the census block group level and census block level, respectively. At the census block group level, we ran spatial regression models between socioeconomic variables, including race, income, gender, and age, and heat exposure and adaptation variables, including land surface temperature, normalized different vegetation index (NDVI), tree cover, and air conditioning ownership rate. We found that: Low median household income is always associated with high LST and low NDVI from 1990 to 2020; Low percentage of females is always associated with high LST and low NDVI from 1990 to 2020. High percentage of POC is associated with high LST in 2010 and 2020, but not in 1990 and 2000; Low median household income and low percentage of elderly are associated with lower tree cover; High percentage of POC, low percentage of elderly, and low median household income are associated with lower AC rates. In analysis at the census block level by city, we found that disparities in urban heat exposure between predominantly POC and predominantly white communities increased in most cities during 1990-2020. Predominantly POC communities consistently have lower vegetation cover over time in most cities. Disparities in vegetation cover per unit area increased in most cities, whereas disparities in vegetation cover per capita decreased in most cities. Our findings of the trends in disparities in heat exposure and adaptation are useful for forecasting disparities in the future. These findings also suggest that interventions should prioritize cities with increasing disparities in heat exposure and adaptation.

Green apple detection method based on multi-dimensional feature extraction network model and Transformer module

To enhance fast and accurate detection of pollution-free green apples for food safety, this paper uses the DETR network as a framework to propose a new method for pollution-free green apple detection based on a multi-dimensional feature extraction network and Transformer module. Firstly, an improved DETR network main feature extraction module adopts the ResNet18 network and replaces some residual layers with deformable convolutions (DCNv2), enabling the model to better adapt to pollution-free fruit changes at different scales and angles, while eliminating the impact of microbial contamination on fruit testing; Subsequently, the extended spatial pyramid pooling model (DSPP) and multi-scale residual aggregation module (FRAM) are integrated, which help reduce feature noise and minimize the loss of underlying features during the feature extraction process. The fusion of the two modules enhances the model’s ability to detect objects of different scales, thereby improving the accuracy of near-color fruit detection; At the same time, in order to solve the problems of slow convergence speed and large calculation amount of the basic network model, the convergence speed of the overall network model is improved by replacing the attention mechanism of Transformer. Experimental results show that compared with the original DETR model, the proposed algorithm has improved in AP, AP50 and AP75 indicators, especially in the AP50 indicator, which has the most obvious improvement reaching a detection accuracy of 97.12%. In the meantime, the trained network model is deployed on the picking robot. Compared with the original DETR network model, its average detection accuracy is as high as 96.58%, and the detection speed is increased by about 51%. Mixed sample detection tests were carried out before and after the model deployment, and the detection rate of the proposed method for non-polluted fruits reached more than 0.95. enabling the picking robot to efficiently complete the task of picking green apples. The test results show that the algorithm proposed in this article exhibits great potential in the task of detecting pollution-free near-color fruits by the picking robot. It ensures pollution-free fruit picking and the application of AI in food safety.

RingGesture: A Ring-Based Mid-Air Gesture Typing System Powered by a Deep-Learning Word Prediction Framework.

Text entry is a critical capability for any modern computing experience, with lightweight augmented reality (AR) glasses being no exception. Designed for all-day wearability, a limitation of lightweight AR glass is the restriction to the inclusion of multiple cameras for extensive field of view in hand tracking. This constraint underscores the need for an additional input device. We propose a system to address this gap: a ring-based mid-air gesture typing technique, RingGesture, utilizing electrodes to mark the start and end of gesture trajectories and inertial measurement units (IMU) sensors for hand tracking. This method offers an intuitive experience similar to raycast-based mid-air gesture typing found in VR headsets, allowing for a seamless translation of hand movements into cursor navigation. To enhance both accuracy and input speed, we propose a novel deep-learning word prediction framework, Score Fusion, comprised of three key components: a) a word-gesture decoding model, b) a spatial spelling correction model, and c) a lightweight contextual language model. In contrast, this framework fuses the scores from the three models to predict the most likely words with higher precision. We conduct comparative and longitudinal studies to demonstrate two key findings: firstly, the overall effectiveness of RingGesture, which achieves an average text entry speed of 27.3 words per minute (WPM) and a peak performance of 47.9 WPM. Secondly, we highlight the superior performance of the Score Fusion framework, which offers a 28.2% improvement in uncorrected Character Error Rate over a conventional word prediction framework, Naive Correction, leading to a 55.2% improvement in text entry speed for RingGesture. Additionally, RingGesture received a System Usability Score of 83 signifying its excellent usability.

Does advanced human capital structure improve carbon emission performance in China? Empirical research from a spatial spillover perspective

Facilitating coordination between economic growth and carbon mitigation is vital for the sustainability of human society. Identifying the driving factors of Carbon emission performance (CEP) is crucial for its improvement. Nevertheless, empirical research on the relationship between advanced human capital structure (AHCS) and CEP remains scarce. This study analyzes the impact of AHCS on CEP utilizing a dataset covering 30 Chinese provinces from 2006 to 2021. First, we construct a global epsilon-based measure (EBM) model with undesired output to accurately measure the multi-period CEP for Chinese provinces. Next, we evaluate the level of AHCS from the perspective of dynamic evolution. Subsequently, a spatial Durbin model (SDM) is employed to examine the spatial spillover effects of AHCS on CEP. The results illustrate that CEP exhibits significant positive spatial spillover effects across provinces. Additionally, AHCS has a direct and significant positive impact on local CEP and can indirectly enhance CEP in neighboring regions through spillover effects, although the impact varies across different regions. The impact mechanism analysis indicates that AHCS enhances CEP via technological advancement, industrial structure upgrading (ISU), and energy efficiency improvement, despite the asymmetric effects observed between local and neighboring provinces. Therefore, shaping a favorable educational environment to optimize the human capital structure is critical for the improvement of CEP. In addition, promoting resource sharing and technology exchange through regional cooperation is essential for China’s low-carbon transition development.

Regularization of the Ensemble Kalman Filter using a non-parametric, non-stationary spatial model

Regularization of the Ensemble Kalman Filter using a non-parametric, non-stationary spatial model

Design and experiment of a deviation information detection mechanism for sugar beet harvesters based on agricultural machinery and agronomy integration

To address large geometric shape differences, poor ridge distribution straightness and the uncertain spatial distribution of beet roots during the harvesting period, as well as damage caused by the inaccurate row correction detection mechanisms of combine harvesters, this study conducts a design analysis and performance tests of deviation information detection mechanisms based on the integration of agricultural machinery and agronomy. The agronomic process of mechanized sugar beet production was analyzed, the geometric dimensions of root tubers under typical planting patterns of typical varieties in main sugar beet production areas were measured, and a three-dimensional geometric model of beet root tubers was established. A device for measuring ridge shape and root tuber distribution was designed, and agronomic parameters during the harvesting period, such as plant spacing, ridge height, unearthed height, and deviation distance of beet root tubers, were measured and analyzed. A spatial model of ridge shape and beet root tuber growth distribution on the ridge during harvesting was established. The overall structure of the deviation information detection mechanism was analyzed and designed, and the structural forms and key parameters of key mechanisms, such as left‒right swing detection and up‒down floating profiling, were designed on the basis of agronomic parameter analysis. Using the missed detection rate as the evaluation index, field performance tests of the deviation information detection mechanism were conducted. The results revealed that when the average forward speed of the harvester was 0.84 m/s, the average missed detection rate of the deviation information detection mechanism was 0.56%. This method has high adaptability and detection accuracy and achieves a high level of integration for agricultural machinery (detection spatial region of the detection mechanism) and agronomy (beet root distribution spatial region). This study provides a technical basis and methodological reference for improving the quality and efficiency of automatic row correction combined with harvesting operations for crops such as sugar beets.

Spatio-temporal land use/land cover change analysis and assessment of urban heat island in Ghana: A focus on the Greater Accra Region

The expansion of cities coupled with economic development over the years has transformed many locations into hotspots for massive urban populace. The Greater Accra Region which is the capital city of Ghana is no exception. The urban population growth rate in the region has expanded extensively at the expense of increasingly vegetation cover. As a result, there is an increasing need to investigate urban resilience, land use/land cover (LULC) change, and urban heat islands (UHI) dynamics in the region. The aim of this study is to analyze multi-spectral Landsat images of 2000 and 2020 to examine the LULC change and the UHI trend across the Greater Accra Region of Ghana. The results from the analysis show a significant change in the spatial trend of land surface temperature (LST) and UHI between the years 2000 and 2020. Spatial distribution of LST from 0°C to 28°C and 17°C to 33°C in the years 2000 and 2020 respectively were observed. Urban areas dominated more than half of the study area in 2020, covering 85% (equivalent to 3160 sq. km), and reflecting a 20% increase from the year 2000 to 2020. During the same period, sparse and dense forested areas decreased by 10% (373 sq. km) and 4% (146 sq. km) with a 5% reduction in bare land (equivalent to 177 sq. km). The spatio-temporal analysis revealed a significant surge in the population of urban areas within the study area. The UHI areas also increased from 13.20% in 2000 to 18.20% in 2020. On the other hand, non-UHI areas decreased from 86.80% to 81.80% during the same period. The successful contribution of this research demonstrates the usefulness of spatial models as tools in generating LULC maps for assessing UHI to facilitate future sustainable city planning.

Spatial model of groundwater contamination risks from pit-latrines in a low-income country

Spatial model of groundwater contamination risks from pit-latrines in a low-income country

Exploring the spatial effects influencing the EGFR/ERK pathway dynamics with machine learning surrogate models

The fate of cells is regulated by biochemical reactions taking place inside of them, known as intracellular pathways. Cells display a variety of characteristics related to their shape, structure and contained fluid, which influences the diffusion of proteins and their interactions. To gain insights into the spatial effects shaping intracellular regulation, we apply machine learning (ML) to explore a previously developed spatial model of the epidermal growth factor receptor (EGFR) signaling. The model describes the reactions between molecular species inside of cells following the transient activation of EGF receptors. To train our ML models, we conduct 10,000 numerical simulations in parallel where we calculate the cumulative activation of molecules and transcription factors under various conditions such as different diffusion speeds, inactivation rates, and cell structures. We take advantage of the low computational cost of ML algorithms to investigate the effects of cell and nucleus sizes, the diffusion speed of proteins, and the inactivation rate of the Ras molecules on the activation strength of transcription factors. Our results suggest that the predictions by both neural networks and random forests yielded minimal mean square error (MSEs), while linear generalized models displayed a significant larger MSE. The exploration of the surrogate models has shown that smaller cell and nucleus radii as well, larger diffusion coefficients, and reduced inactivation rates increase the activation of transcription factors. These results are confirmed by numerical simulations. Our ML algorithms can be readily incorporated within multiscale models of tumor growth to embed the spatial effects regulating intracellular pathways, enabling the use of complex cell models within multiscale models while reducing the computational cost.

Spatiotemporal Relationship Between Land Subsidence and Ecological Environmental Quality in Shenfu Mining Area, Loess Plateau, China

The exploitation of coal resources has caused problems such as ground deformation, affecting the ecological environment. Spatiotemporal varying characteristics between land subsidence and ecological environmental quality (EEQ) are an important research hotspot. Using the SBAS-InSAR method, 64 Sentinel-1 images were utilized to monitor land subsidence in the Shenfu mining area, one of China’s largest coal source regions. And the remote sensing ecological index (RSEI) was used to monitor and evaluate EEQ of the Shenfu mining area. Global and local spatial autocorrelation methods were used to assess the spatial aggregation degree and change patterns over time. Spatial Econometric Models were employed to explore the impacts of land subsidence on EEQ. The results showed the following: (1) The average RSEI values in the Shenfu mining area were 0.531, 0.488, and 0.523 in 2016, 2018, and 2020, respectively; there was a slight downward trend in EEQ. The permanent scatter (PS) point deformation rate ranged from −353.40 mm/year to +246.24 mm/year, with average deformation rates of 0.1642, 0.2181, and 0.2490 mm/year, respectively. (2) There was a significant correlation and spatial agglomeration effect between land surface subsidence and EEQ. Low–high, high–low, and low–low clusters were the main types of relationships, indicating that land subsidence primarily has a negative spatial impact on the ecological environment. (3) The relationship between land subsidence and EEQ varied spatially in the Shenfu mining area at 500 × 500 grid units. This research can provide scientific guidance for disaster prevention and sustainable development in mining areas by considering long-term differences in ecological environmental quality and its correlation with land subsidence.

Race, profit, and algorithms: Neighborhood-level analysis of iBuyers’ profit margin

ABSTRACT iBuyers are firms that use automated valuation models (AVMs), streamline home buying processes, and provide all-cash offers to purchase homes. Although the previous literature has explored the roles and limitations of iBuyers in the housing market, empirical research on the racial implications of these algorithmic home buying processes remains understudied. Using a spatial lag model, this study shows the spatial clustering of iBuyer profit margins, that iBuyers gain more profits when they resell to individuals than institutions, and that some iBuyers have a statistically significant correlation between their profit margins and the proportion of marginalized racial groups within a census tract, while controlling for individual housing characteristics, neighborhood housing quality and demand, and neighborhood amenities and socioeconomic factors. These findings suggest that the more adeptly iBuyers can forecast housing values, the greater the potential to maximize profits from homeowners in communities of color. Consequently, this research contributes to the understanding of how technological mechanisms operate within a purportedly race-neutral framework and advocates for the development and deployment of algorithmic systems guided by the principles of antisubordination, rather than relying solely on notions of “fairness” and anticlassification.

Innovative talent agglomeration, spatial spillover effects and regional innovation performance-Analyzing the threshold effect of government support.

As the primary source of innovation in science and technology, the gathering of creative talent plays a significant role in fostering regional innovation and providing the impetus needed to realize the Chinese people's aspiration of a great national rejuvenation. Using panel data from 31 provinces over a 12-year period between 2009 and 2020, the spatial Durbin model is constructed to examine the spatial spillover impact of talent agglomeration on the advancement of regional innovation performance, and the panel threshold model is identified and set up to consider whether the nonlinear effect between talent agglomeration and regional achievements in innovation is significant. The analysis demonstrates that: talent pooling has a non-linear effect on the level of innovation performance development, within a certain scale, talent pooling produces an increasing marginal contribution to innovation performance, but after exceeding the limit, it produces a diminishing marginal contribution; the double threshold effect of talent pooling on regional innovation performance is more significant, and government support as a moderating variable confirms that there is a structural mutation between talent pooling and innovation capability. The structural mutation, within a certain threshold range, plays the role of efficient promotion, providing reference for improving regional innovation level.

Legislative configuration and strength through the idea of Ends Independence

ABSTRACT Prior approaches to evaluating legislative strength cross-nationally have focused on the idea of Means Independence, defined as the procedural autonomy of a legislature. However, expanded Means Independence might not necessarily effect policy effects outside the chief executive’s preference, referred to as Ends Independence. Spatial models estimate the influence of different configurations of legislative processes on Ends Independence. Findings show that an increase in Means Independence does not simultaneously increase Ends Independence. Ends Independence can be guaranteed only when the opposition has the right to propose first, and results are minimally affected by new information updated through the exercise of Means Independence. These findings demonstrate the importance of understanding the overall structure of the legislative process to assess legislative power vis-à-vis an authoritarian executive.

Site selection for biomass-solar hybrid renewable energy facilities: Spatial modelling based on fuzzy logic-geographic information systems

The aim of this study is to propose a fuzzy logic-geographic information systems (GIS) based spatial model within the context of hybrid renewable energy, rural areas, and spatial planning approach in the site selection process of renewable energy facilities. In this context, potential suitability index and site selection suitability index for biomass-solar hybrid renewable energy facilities are determined within the spatial model, through a case study conducted in Türkiye. The study involves a two-stage process: the first stage focuses on establishing the national-level potential, and the second stage involves site selection in areas with high potential. Suitable locations are determined based on the optimal suitability index values, which are derived from fuzzy logic operators including AND, OR, SUM, PRODUCT, and GAMMA, applied in the GIS. The study results indicate that by assigning different γ values to the fuzzy GAMMA operator, results for the fuzzy AND (γ = 0.45–0.60), fuzzy OR (γ = 1.00), fuzzy SUM (γ = 1.00), and fuzzy PRODUCT (γ = 0.00–0.15) operators can be obtained. Furthermore, as the γ value increases, the suitability index also rises, and the optimal site selection occurs at γ = 0.90. The performance of the fuzzy GAMMA operator in big data sets and two-stage processes demonstrates its applicability in site selection.

Prediction of Energy-Related Carbon Emissions in East China Using a Spatial Reverse-Accumulation Discrete Grey Model

In order to better analyze and predict energy-related carbon emissions in East China to address climate change, this paper enhances the predictive capabilities of grey models in spatial joint prediction by creating the reverse-accumulation spatial discrete grey model RSDGM (1,1,m) and accumulation spatial discrete grey breakpoint model RSDGBM (1,1,m,t), which took the impact of system shocks into consideration. The efficiency of the models is confirmed by calculating the energy-related carbon emissions in East China from 2010 to 2022. Future emissions are predicted, and the spatial spillover effect of emissions in East China is discussed. The conclusions are as follows: (1) The RSDGM (1,1,m) theoretically avoids errors in background values and parameter calculations, reducing computational complexity. Empirically, the model exhibits high performance and reflects the priority of new information in spatial joint analysis. (2) The RSDGBM (1,1,m,t) captures the impact of shocks on system development, improving the reliability of carbon emissions prediction. (3) Jiangsu and Shandong are positively affected by spatial factors in terms of carbon emissions, while Shanghai and Zhejiang are negatively affected. (4) It is estimated that carbon emissions in East China will increase by approximately 23.8% in 2030 compared to the level in 2022, with the levels in Zhejiang and Fujian expected to increase by 45.2% and 39.7%, respectively; additionally, the level in Shanghai is projected to decrease. Overall, East China still faces significant pressure to reduce emissions.

A bayesian shared component spatial modeling approach for identifying the geographic pattern of local associations: a case study of young offenders and violent crimes in Greater Toronto Area

Background settingTraditional spatial or non-spatial regression techniques require individual variables to be defined as dependent and independent variables, often assuming a unidirectional and (global) linear relationship between the variables under study. This research studies the Bayesian shared component spatial (BSCS) modeling as an alternative approach to identifying local associations between two or more variables and their spatial patterns.MethodsThe variables to be studied, young offenders (YO) and violent crimes (VC), are treated as (multiple) outcomes in the BSCS model. Separate non-BSCS models that treat YO as the outcome variable and VC as the independent variable have also been developed. Results are compared in terms of model fit, risk estimates, and identification of hotspot areas.ResultsCompared to the traditional non-BSCS models, the BSCS models fitted the data better and identified a strong spatial association between YO and VC. Using the BSCS technique allowed both the YO and VC to be modeled as outcome variables, assuming common data-generating processes that are influenced by a set of socioeconomic covariates. The BSCS technique offered smooth and easy mapping of the identified association, with the maps displaying the common (shared) and separate (individual) hotspots of YO and VC.ConclusionsThe proposed method can transform existing association analyses from methods requiring inputs as dependent and independent variables to outcome variables only and shift the reliance on regression coefficients to probability risk maps for characterizing (local) associations between the outcomes.

Mortality from type 2 diabetes mellitus across municipalities in Mexico

BackgroundOne in six Mexican adults’ lives with type 2 diabetes mellitus (T2DM), which is the third leading cause of death in the country. Analyzing the geographic distribution of T2DM mortality helps identify regions with higher mortality rates. This study aimed to examine the spatial patterns of mortality from type 2 diabetes mellitus (T2DM) across municipalities in Mexico and to analyze the main contextual factors linked to this cause of death in 2020.MethodsWe employed a spatial Bayesian hierarchical regression model to estimate the risk and probability of death from type 2 diabetes mellitus (T2DM) across Mexico’s municipalities.ResultsThe SMR results revealed geographic and age-specific patterns. Central Mexico and the Yucatán Peninsula exhibited the highest excess mortality rates. For the population under 50 years of age, municipalities in Oaxaca had the highest T2DM mortality rates, whereas those aged 50 years old and older had the highest rates in Tlaxcala and Puebla. Socioeconomic factors such as low levels of educational attainment, lack of health services, dietary deficiency, and marginalization were positively associated with increased T2DM mortality risk. By contrast, GDP per capita showed a negative association. High-risk areas for T2DM mortality were prominent along the south of the Pacific Coast, the Bajío, Central Mexico, and southern Yucatán for those under 50, and along a central strip extending to the Yucatán Peninsula for the older population. Significant uncertainties in mortality risk were identified, with Central Mexico, Oaxaca, Chiapas, and Tabasco showing high probabilities of excess risk for those under 50 years of age and extended risk areas along the Gulf of Mexico for those 50 years old and older.ConclusionsThe assessment and identification of spatial distribution patterns associated with T2DM mortality, and its main contextual factors, are crucial for informing effective public health policies aimed at reducing the impact of this chronic disease in Mexico.