Spatial Effects Research Articles

The effect of spatial lag on modeling geomatic covariates using analysis of variance

In recent years, statistical methods have been developed that include spatial considerations, for example, those that incorporate data with georeferencing. The descriptive part of geographical information systems currently provides many visualization and analysis tools; however, in terms of analysis, these systems are still quite limited, therefore, ignorance of these limitations may result in data with spatial effects being treated with conventional statistical methods for non-spatial use, which can certainly invalidate the excellent work of data capture with advanced tools such as those that are used daily in the geomatic context. This prompted the current document, drawing attention to how geomatic information analyzed with statistical methods that imply independence in modeled observations can be invalid. The Moran index is compared with a proposal for a spatial lag coefficient in the context of experimental design so that users of variance analysis do not apply this well-known procedure in a ritualistic way, perhaps revising some assumptions and perhaps ignoring more important ones. The distortion of the p value generated from the analysis of variance is clear in the presence of spatial dependence. In this case, it is associated with the lag or spatial overlap. The methodology is easy to apply in other designs with the development of the design matrix, its reparameterization and the choice of the respective weight matrix. This may cause users to reconsider the traditional method of analysis and incorporate some appropriate analysis methodology to address spatial effects present in data or in outputs from the modeling process.

Quantifying massively parallel microbial growth with spatially mediated interactions.

Quantitative understanding of microbial growth is an essential prerequisite for successful control of pathogens as well as various biotechnology applications. Even though the growth of cell populations has been extensively studied, microbial growth remains poorly characterised at the spatial level. Indeed, even isogenic populations growing at different locations on solid growth medium typically show significant location-dependent variability in growth. Here we show that this variability can be attributed to the initial physiological states of the populations, the interplay between populations interacting with their local environment and the diffusion of nutrients and energy sources coupling the environments. We further show how the causes of this variability change throughout the growth of a population. We use a dual approach, first applying machine learning regression models to discover that location dominates growth variability at specific times, and, in parallel, developing explicit population growth models to describe this spatial effect. In particular, treating nutrient and energy source concentration as a latent variable allows us to develop a mechanistic resource consumer model that captures growth variability across the shared environment. As a consequence, we are able to determine intrinsic growth parameters for each local population, removing confounders common to location-dependent variability in growth. Importantly, our explicit low-parametric model for the environment paves the way for massively parallel experimentation with configurable spatial niches for testing specific eco-evolutionary hypotheses.

Spatial Averaging Effects in Adverse Pressure Gradient Turbulent Boundary Layers

Spatial Averaging Effects in Adverse Pressure Gradient Turbulent Boundary Layers

Spillover Effects of Urban Expansion on Land Green Use Efficiency: An Empirical Study Based on Multi-Source Remote Sensing Data in China

Land is an important carrier of resource elements, and improving land green use efficiency (LGUE) is the key to achieving “smart growth” in cities. This study takes 269 cities in China from 2008 to 2020 as the research object and integrates multisource remote sensing data, GIS spatial analysis, and spatial econometric models to explore the evolutionary characteristics of LGUE and the spatiotemporal effects of urban expansion on LGUE. The results show that (1) urban LGUE increases over time and has certain gradient distribution and spatial dependence characteristics; (2) urban expansion has a significant inverted U-shaped relationship with LGUE, indicating that maintaining reasonable urban expansion is the key to improving LGUE, but excessive urban expansion has a strong negative spatial effect on LGUE. In terms of spillover effect, for every 1% increase in the square term of urban expansion, the LGUE of the local city decreases by 0.0673%, but the neighboring city increases by 0.2283%; and (3) urban expansion has significant spatiotemporal heterogeneous effects on LGUE, and spatial development boundaries must be reasonably determined to achieve “smart growth”. Currently, some cities in China are expanding rapidly, and we hope to provide key support for promoting the “smart growth” of cities and improving LGUE.

Analysis of the dynamic contact behaviour of high-speed train transmission gear excited by wheel defects

Studies on wheel flatness and polygonal wear have demonstrated that wheel defects cause severe impact forces at the wheel–rail interfaces. This study investigated the dynamic contact mechanical behaviour of high–speed train transmission gears when subjected to wheel defects. First, a train-track coupling dynamics model was established, considering the localized contact characteristics of transmission gears. This model was developed using the slice method and the Hertz contact model, exploring the spatial coupling effects between the gear contact interface and the train–track interface. Subsequently, the dynamic model was employed to extract essential contact mechanical parameters of the transmission gear under the influence of wheel defects, including gear meshing load, gear contact stress, and sliding velocity. Furthermore, an analysis was conducted to uncover and elucidate the mechanism by which wheel flatness and polygonal wear impact the dynamic contact behaviour of the transmission gear. This study underscores the importance of considering wheel defects when assessing the contact fatigue and wear performance of high-speed train transmission gears.

Using multi-scale interaction mechanisms in yolk-shell structured C/Co composite materials for electromagnetic wave absorption

Advanced chemical engineering for simultaneous modulation of nanomaterial morphology, defects, interfaces, and structure to enhance electromagnetic and microwave absorption (MA) performance. However, accurately distinguishing the MA contributions of different scale factors and tuning the optimal combined effects remains a formidable challenge. This study employs a synergistic approach combining template protection etching and vacuum annealing to construct a controlled system of micrometer-sized cavities and amorphous carbon matrices in metal-organic framework (MOF) derivatives. The results demonstrate that the spatial effects introduced by the hollow structure enhance dielectric loss but significantly weaken impedance matching. By increasing the proportion of amorphous carbon, the balance between electromagnetic loss and impedance matching can be effectively maintained. Importantly, in a suitable graphitization environment, the presence of oxygen vacancies in amorphous carbon can induce significant polarization to compensate for the reduced conductivity loss due to the absence of sp2 carbon. Through the synergistic effects of morphology and composition, the samples exhibit a broader absorption bandwidth (6.28 GHz) and stronger reflection loss (−61.64 dB) compared to the original MOF. In conclusion, this study aims to elucidate the multiscale impacts of macroscopic micro-nano structure and microscopic defect engineering, providing valuable insights for future research in this field.

Assessing the role of public, media, and government attention on air pollution governance in China

Attention from social subjects, such as the government, public, and media, serves as a vital diver for urban environmental improvement. While most empirical studies about environmental attention have discussed only one or two of these subjects so far, the literature overlooks the potential collective influence. Drawing on the concept of attention and stakeholder theory, this paper integrates government, public, and media into a unified framework. Utilizing data from Chinese prefecture-level cities spanning from 2012 to 2020, this paper reveals how multi-subject attention influences air quality, including its spatial spillover effects and specific mechanisms. This study finds that 1) multi-subject attention could effectively inhibit air pollution, the direct effects of government, public, and media attention are -7.278, -0.077, and -0.502, respectively. 2) Only the public and media attention have significant cross-city suppressing effects on air pollution. Specifically, the spillover effects of the public and media attention's proxy variables are -4.003 and -1.850. 3) Overall, multi-subject attention can effectively reduce air pollution by establishing environmental laws and regulations, strengthening environmental enforcement, and increasing environmental protection investment. However, there are significant differences in the ways government, public, and media attention improve air quality in local and surrounding cities through these three mechanisms.

Analysis on the spatial correlation network and driving factors of carbon emissions in China's logistics industry

Promoting the green development of the logistics industry has become a focal point of attention in China. This study combines an improved gravity model and social network analysis method, focusing on the nineteen provinces of the Yangtze Economic Belt and Yellow River Basin. It constructs a spatial correlation network of carbon emissions in the logistics industry from 2010 to 2021, exploring its formation mechanism and spatial evolution characteristics. The study utilizes a quadratic assignment procedure model to investigate internal driving factors. Building upon this, the study employs an improved STIRPAT model to predict the emission reduction path of the future logistics industry. Results are as follows: (1) Carbon consumption is most pronounced in Shandong, Jiangsu, and Shanghai. The carbon emission shows the characteristics of larger downstream; (2) The bridges of carbon emission-related networks gradually shift from Shandong to Shanxi, Anhui, and Sichuan. Carbon emissions in each sector and spatial spillover effects exhibit dynamic correlations and interactive influences; (3) Energy intensity, freight volume, and the spatial correlation network of the logistics industry are highly correlated; (4) The overall carbon emissions from the logistics industry show a decreasing trend in the future. Anhui and Shaanxi provinces will have high carbon emissions in 2035. The conclusions aim to provide policy suggestions for the region's low-carbon transformation.

The spatial spillover effects of regional integration on carbon emissions

The Yangtze River Delta (YRD) region serves as the leading economic force of the Yangtze River Economic Belt (YREB) in China. It is crucial for the region to take a leadership role in regional integration to facilitate carbon emission reduction. This paper employs a spatial econometric model to examine the impact, spatial spillover effect, and mechanisms of regional integration on carbon emissions in the Yangtze River Delta, using data from 41 cities in the region from 2008 to 2019. The results indicate that there is an inverted U-shaped relationship between the level of regional integration and carbon emissions, with emissions initially increasing before decreasing. Additionally, the impact of regional integration on carbon emissions exhibits a positive spatial spillover effect and threshold characteristics, with carbon emissions inhibited and a positive spatial spillover observed once the threshold is exceeded. Moreover, regional integration reduces carbon emissions through the upgrading of industrial structures and the innovation of green technology. Implications for carbon emission reduction include: actively integrating into the Yangtze River Delta regional integration development; strengthening cross-regional carbon emission synergistic governance; and promoting carbon emission reduction through mechanisms such as promoting industrial upgrading and low-carbonization development, as well as incentivizing technological innovation.

Research on the Impact of Digital Green Finance on Agricultural Green Total Factor Productivity: Evidence from China

Green development has become one of the important concepts leading China’s economic developments, and it is extremely meaningful to boost the continuous growth of agricultural green total factor productivity (AGTFP) to achieve the construction of a powerful agricultural country. Using China’s provincial data from 2011 to 2020, this manuscript calculates AGTFP through the SBM–GML model, and the digital green finance (DGF) through a comprehensive indicator system. The double fixed-effect model, quantile model and spatial Durbin model are used for in-depth study of the benchmark influence, the nonlinear effect and spatial spillover effect of DGF on AGTFP. The main research conclusions of the article are as follows: (1) DGF is significantly conducive to the improvement of AGTFP. Along with the continuous growth of AGTFP, the promoting effect of DGF has gradually increased. (2) In terms of impact path, green finance can properly promote the growth of AGTFP, while the role of the degree of digitization is not very significant. Meanwhile, the main channel for DGF to promote AGTFP is through green technology efficiency. (3) The impact of DGF on AGTFP varies spatially, while the role is more effective in regions with a higher degree of economic development and well-developed modernization. (4) There is a spatial spillover effect of DGF’s impact on AGTFP, which means that DGF can simultaneously promote the growth of AGTFP in local regions and neighboring regions.

Study on the Spatial Effects of Grain Change on Food Security of Feed from the Perspective of Big Food

Using panel data from 30 provinces in China from 2005 to 2020, this paper uses a spatial double difference model to evaluate the policy impact of the “grain-to-feed” policy on feed grain production in pilot areas and adjacent spatial areas. Research has found that the “grain-to-feed” policy has a significant impact on the feed grain production in pilot areas and can significantly increase the feed grain production in pilot areas by about 2.71 million tons. The “grain-to-feed” policy has strengthened the positive connection between pilot areas and adjacent pilot areas, increased feed grain production, and has a significant spatial spillover effect. Robustness analysis shows that whether using different methods to measure spatial adjacency or using different standards to distribute subsidies, the “grain-to-feed” policy can significantly increase feed grain production, narrow the supply and demand gap of feed grain, and ensure feed grain security. Further analysis shows that the “grain-to-feed” policy can not only ensure the security of feed grain for the current and next periods but also promote the increase in farmers’ income, which is long-term and sustainable. Compared with non-pilot areas, the “grain-to-feed” policy can mitigate the negative impact of wage–price signals on feed grain production in pilot areas. It is recommended that government departments accelerate the transformation of food security concepts, establish a “Big Food Perspective”, gradually promote the pilot of the “grain-to-feed” policy nationwide, increase the subsidy amount of the “grain-to-feed” policy, increase financial support for scientific and technological research and achievement transformation in the field of feed grain, prevent the impact of economic price signal fluctuations on feed grain production, and effectively ensure the security of feed grain in China.

The Impact of Green Innovation on CO2 Emissions in China: Evidence from Spatial Regression Model

Using data from USPTO, CEADs, the Chinese Statistical Yearbook, and spatial econometric models for the panel of the 30 Chinese provinces for the period 2000∼2019, this study explores and compares the impact of green and non-green innovation on CO2 emissions. The findings reveal a significant spatial correlation in CO2 emissions across provinces, validating the appropriateness of spatial econometric models for this analysis. The results indicate that both green and non-green innovations significantly reduce CO2 emissions, with green innovation having a more pronounced effect despite its lower prevalence. Additionally, patents and their citations notably enhance environmental quality in neighboring provinces through indirect effects. The empirical results highlight that China should focus on: (1) spatial spillover effects of technological, especially green, innovation; (2) removing barriers to green patent invention and citation; (3) transitioning from an energy-driven to a green innovation-driven economy; and (4) enhancing environmental quality by mitigating CO2 emissions.

Spatio-temporal forecasting for the US Drought Monitor

Abstract The US Drought Monitor is the leading drought monitoring tool in the United States. Updated weekly and freely distributed, it records the drought conditions as geo-referenced polygons showing one of six ordered levels. These levels are determined by a mixture of quantitative environmental measurements and local expert opinion across the entire United States. At present, forecasts of the Drought Monitor only convey the expected direction of drought development (i.e. worsen, persist, subside) and do not communicate any uncertainty. This limits the utility of forecasts. In this paper, we describe a Bayesian spatio-temporal ordinal hierarchical model for use in modelling and projecting drought conditions. The model is flexible, scalable, and interpretable. By viewing drought data as areal rather than point-referenced, we reduce the cost of sampling from the posterior by avoiding dense matrix inversion. Draws from the posterior predictive distribution produce future forecasts of actual drought levels—rather than only the direction of drought development—and all sources of uncertainty are propagated into the posterior. Spatial random effects and an autoregressive model structure capture spatial and temporal dependence, and help ensure smoothness in forecasts over space and time. The result is a framework for modelling and forecasting drought levels and capturing forecast uncertainty.

Assessing green production efficiency and spatial characteristics of China’s real estate industry based on the undesirable super-SBM model

As China strives to balance rapid urbanization with environmental conservation, increasing attention is being paid to the pursuit of green production efficiency (GPE) in the real estate industry. The undesirable super-SBM model was used to calculate the GPE of China's real estate industry from 2001 to 2020. Additionally, GPE spatial distribution characteristics in China's real estate industry were analyzed using the standard deviation ellipse (SDE), Moran’s index, Theil index, random kernel density estimation (RKDA), and spatial Markov chain (SMC) methods. The GPE exhibited a U-shaped trend, with 2008 as the inflection point, first decreasing and then increasing. It reached a maximum value of 0.747 in 2020. The Theil index increased from 0.043 to 0.121 nationwide, indicating the overall characteristics of low-level slow growth, and imbalance. Discrepancies in input–output scales, the southward shift of economic centers, and population movements contribute significantly to the disparities between the east and west, north and south, and regions divided by the Hu Huanyong Line (Hu Line). The GPE exhibited club convergence characteristics; however, polarization phenomena exist in local areas. Spatial spillover effects were also observed in GPE. Finally, we provide recommendations for promoting green development in the real estate industry, including green building technology, fiscal subsidy investment, and population migration management.

Spatial Evolution and Driving Factors of Ecological Well-Being Performance in the Yellow River Basin

Ecological well-being performance (EWP) is a key indicator of sustainable development and has garnered significant research attention. This study measures the overall and stage-by-stage efficiency of the urban agglomerations in the Yellow River Basin based on the ends–means framework of steady-state economics. This study then delves into the spatiotemporal transfer characteristics of EWP through Moran’s I, and spatial Markov chains. Additionally, this research investigates the factors influencing EWP using a random forest model. The findings indicate a notable enhancement in EWP in the urban agglomerations in the YRB from 2006 to 2021, showing clear spatial agglomeration patterns. The shift in EWP types displays a “path dependence” effect, with distinct evolutionary paths influenced by spatial lag effects. Ecological input emerges as a key internal driver of EWP, while urbanization and technological advancements are highlighted as significant external factors. Industrial agglomeration and industrial structure also contribute to improving EWP. The findings of this study help to clarify the spatial and temporal characteristics of ecological welfare performance and its driving mechanisms in the urban agglomerations of the Yellow River Basin. This is conducive to the achievement of high-quality urban transformation and regional green development, and it provides a reference for the construction of an ecological civilization.

Can urbanization improve carbon performance?

Cities are the core carriers and key positions to achieve the dual carbon goals. It is of great significance to explore whether promoting urbanization can improve carbon emission performance, which is of great significance to comprehensively promote the goal of carbon neutrality. Based on the panel data from 2006 to 2021, this paper analyzes the spatial autocorrelation of carbon emission performance per unit space and the impact mechanism of urbanization process on it. The fixed-effect model was further used to identify the influencing factors of spatial carbon emission performance. The results show that: 1) China’s carbon emission performance per unit space is declining year by year. 2) There is a strong positive spatial correlation and stable path dependence on the performance of carbon emissions per unit space in each region. 3) To a certain extent, increasing the level of urbanization will reduce the carbon emission performance per unit space. 4) The urbanization process has a spatial spillover effect on the carbon emission performance per unit space of surrounding provinces, and the spatial spillover effect of industrial structure and energy consumption structure is more obvious than that of economic level, population density and urbanization rate. Based on the conclusions, this paper puts forward specific policy suggestions to reduce the carbon emission performance per unit space to help the low-carbon development of cities.

A network model of stress contagion: evidence from the vocational classroom

PurposeOur study focuses on stress contagion in vocational school classes, examining how students’ stress experiences affect their spatial classmates. For this purpose, we apply a novel formal network model that allows us to differentiate between endogenous and exogenous peer effects in the stress contagion process. Using the network model, we investigate effects on students’ stress levels, considering the stress and coping experiences of spatial peers as well as didactic-methodological context factors.MethodsWe test our statistical model using secondary data collected in a study involving 53 students from two classes at a public German vocational training school. The students’ experiences of stress (time pressure, pressure to succeed) and coping strategies (understanding of the subject matter, self-confidence) were captured using the experience sampling method, while classroom characteristics (e.g., teacher instruction, cooperative work) were recorded through video-based analysis of lessons. Utilizing the panel data, we employ maximum likelihood estimation to assess the spatial peer effects model for both classrooms.ResultsAmong other findings, all model specifications revealed significant peer effects for both stress measures, indicating that the higher the stress experience of immediate peers in the classroom, the higher the individual stress experienced by the students. Concerning the considered context factors, we found, for example, that increased cooperative work leads to higher levels of stress experience.ConclusionFrom a substantive perspective, our results underscore the role of peer-to-peer contagion in the vocational classroom and thus suggest a nuanced examination of cooperative practices. From a research methodology perspective, our approach illustrates how various methods (such as experience sampling, video-based classroom observation, and spatial network information) complement and enrich each other, highlighting the added value of our network analytical approach

The impact of innovative human capital on green growth convergence in China's regions from the perspective of space

The convergence of green growth is not only very important for the sustainable development of mankind, but also plays a key role in the coordinated development of eastern, Midwest regions of China. The effect of innovative human capital (IHC) on the convergence of regional green growth in China needs to be explored. This paper empirically explores the impact of IHC on the convergence of regional green growth in China from spatial perspective for the first time. Through the regression analysis of the convergence models, three main conclusions are obtained: (1) there are obvious conditional β space convergence characteristics of green growth among inter-provincial units in China. Under the spatial correlation expressed by distance weights and adjacent weights, the conditional β space convergence characteristics of green growth are quite different. (2) Compared with the adjacency weight, under the distance weight, the spatial diffusion effect of innovative knowledge and technologies created by IHC is stronger. (3) There is club convergence of green growth in the East and the Midwest regions, and the quantity and quality of IHC in the Midwest region play a stronger role in the convergence of green growth than in the East region. Based on the above conclusions, this paper proposes some policies for the East and Midwest regions.

The Impact of Factor Price Change on China’s Cotton Production Pattern Evolution: Mediation and Spillover Effects

Cotton is an important agricultural crop and strategic resource. China is currently the country with the largest global cotton production and consumption, but few studies have systematically analyzed the dynamic relationship between factor price change (FPC) and cotton production pattern evolution (CPPE). Based on provincial panel data from the main cotton planting areas from 1985 to 2021, this paper used spatial econometric models to empirically analyze the impact of FPC including labor price, production material cost, and mechanical cost on CPPE from the perspective of mechanical substitution difficulty. The findings are that (1) FPC significantly affected CPPE, specifically the rise of labor price induced the demand for mechanical substitution, resulting in a significant cotton agglomeration effect; (2) spatial econometric analysis found that FPC had a significant spatial spillover effect on CPPE in adjacent regions; and (3) the transfer analysis found that higher mechanical substitution difficulty exerted an inhibitory effect on cotton planting, leading to a gradual shift of the main cotton planting areas from the Yangtze River Basin and Yellow River Basin with high labor prices to the Northwest Inland region with lower labor prices. Updating the production technology and improving the efficiency of cotton specialization are effective strategies. The results are valuable for policy making related to the development of China’s cotton industry.

Spatial analysis of the digital economy’s influence on urban sustainable development: A decade-long study of Chinese prefecture-level cities

Urban Sustainable Development (USD) is crucial for social advancement, and the rise of the Digital Economy (DE) offers potential pathways to achieve it. This study has employed the DPSIR framework and an entropy-based approach to evaluate the DE and USD across 284 Chinese prefecture-level cities from 2011 to 2020. Using the Spatial Durbin Model, the results have revealed that DE significantly enhances USD and has a spatial spillover effect of up to 300 km. The eastern region, as well as the Pearl River Delta and Yangtze River Delta regions, show stronger spillover effects than the central and western regions and the Beijing-Tianjin-Hebei region. These findings have provided insights for policymakers to leverage digital advancements to promote urban sustainability.