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The oscillation cycle and intensity in each part of the pelagic longline were investigated in Sagami Bay, Japan, to evaluate the effect on bait loss. The results of three longline operations were analyzed. The longline consisted of 25 ordinary and 15 midwater float baskets, with six branch lines per basket. Acceleration loggers were attached to the upper end (main float) and lower end of the float line, and the hook of the first and third branch lines. A depth logger was attached to the upper and lower ends of the branch line (hook). A video logger was attached, facing downward at a position 0.6 m above the hook. The periodicity of the oscillation was clarified by autocorrelation analysis, and the sum of the absolute values of the dynamic acceleration of the three axes (ODBA) was compared. From the video footage and autocorrelation analyses, the oscillation cycle of the hooks was approximately 3 s, which is the same as that of the main float. The ODBA of the first hook was 1.9–2.3 m/s2, which was significantly larger than the main float (1.1–1.4 m/s2) and the third hook (0.2–1.8 m/s2, p < 0.001). Therefore, the oscillation of the hook was derived from the vertical movement of the main float due to the wave, and it decreased as the distance from the main float increased. The third hook in the midwater float basket had an unclear cycle, and the ODBA was significantly smaller than that of the ordinary baskets. [ABSTRACT FROM AUTHOR]

Urban vibrancy describes the prosperity, diversity, and accessibility of urban areas, which is vital to a city's socio-economic development and sustainability. While many efforts have been made for statically measuring and evaluating urban vibrancy, there are few studies on the evolutionary process of urban vibrancy, yet we know little about the relationship between urban vibrancy evolution and sophisticated spatiotemporal dynamics. In this article, we make use of multi-sourced urban data to develop a data-driven framework, U-Evolve, to investigate urban vibrancy evolution. Specifically, we first exploit the spatiotemporal characteristics of urban areas to create multi-view time-dependent graphs. Then, we analyze the contextual features and graph patterns of multi-view time-dependent graphs in terms of informing future urban vibrancy variations. Our analysis validates the informativeness of multi-view time-dependent graphs for characterizing and informing future urban vibrancy evolution. After that, we construct a feature based model to forecast future urban vibrancy evolution and quantify each feature's importance. Moreover, to further enhance the forecasting effectiveness, we propose a graph learning based model to capture spatiotemporal autocorrelation of urban areas based on multi-view time-dependent graphs in an end-to-end manner. Finally, extensive experiments on two metropolises, Beijing and Shanghai, demonstrate the effectiveness of our forecasting models. The U-Evolve framework has also been deployed in the production environment to deliver real-world urban development and planning insights for various cities in China. [ABSTRACT FROM AUTHOR]

Purpose: The aim of the paper is to examine indebtedness factors in the field of the structure of own revenues and expenditure as well as spatial relationships on the example of municipalities of the Wielkopolska Region in Poland. Design/methodology/approach: The subject literature was studied to present miscellaneous predictors of the indebtedness of the local governments. Then, using the fiscal, socio-economic, and political variables for 222 municipalities from Wielkopolska Region in Poland, panel data models with fixed effects were estimated for the period 2010-2021, and the spatial autocorrelation statistics. Findings: There is a direct relationship between the share of PIT and CIT in own revenues and the indebtedness of the municipalities. Furthermore, the decrease of the unemployment and the growth of the business density contribute to the decrease in the debt to revenues ratio. In addition, the increase of the indebtednessresulted from an increase of the investment activity, the share of spendings on wages as well as the expenditures on education. The use of EU funds also stimulates the indebtedness of the municipalities. Moreover, in 2018-2021 the territorial proximity affected the weak, positive correlation of the debt to revenues ratio between the municipalities. Research limitations/implications: The research presents the predictive power of the applied variables, which contributes to the identification of the role of the predictor in the creation of debt. Practical implications: The research study is aimed at the estimation of the models, which could be applied to predict the level of the indebtedness in the municipalities. Social implications: The research contributes to the higher recognition of the relationships between the changing fiscal, economic and political circumstances on the indebtedness of the municipality, e.g., in the process of creating local strategies. Originality/value: The study examines the indebtedness factors concerning the composition of the own revenues and expenditures, as well as the socio-economic issues and spatial relationships, considering the predictive power of each factor. [ABSTRACT FROM AUTHOR]

Small area estimation methods have become a widely used tool to provide accurate estimates for regional indicators such as poverty measures. Recent research has provided evidence that spatial modelling still can improve the precision of regional and local estimates. In this paper, we provide an intrinsic spatial autocorrelation model and prove the propriety of the posterior under a flat p rior. Further, we show using the SAIPE poverty data that the gain in efficiency using a spatial model can be essentially important in the presence of a lack of strong auxiliary variables. [ABSTRACT FROM AUTHOR]

In different scientific fields, some parts of the collected dataset may include incomplete data with at least one missing value. To tackle this issue, statisticians and machine learning experts have introduced different missing value imputation (MVI) approaches. Hydrological data are mostly time series with stationary (or cyclic) trends that its observations have high autocorrelation (or cyclic auto-correlation). In addition, these time series frequently exhibit random and non-constant oscillations from their trends. Therefore, the selected MVI approach for hydrological time series should consider these serious aspects. One aspect of hydrological time series data that has not been closely examined in the MVI studies up to now is the existence of cyclic trend, named cyclostationarity. In this paper, a novel MVI approach for cyclostationary hydrological time series, called cyclic clustering, will be discussed. The performance of the proposed method is studied by employing numerous simulated datasets. The results indicate that the cyclic clustering approach is robust to impute missing values for both stationary and cyclostationary hydrological time series. The applicability of the introduced approach is also investigated in a real-world problem. [ABSTRACT FROM AUTHOR]

Apart from quick detection of abnormal changes in a multivariate process, it is also critical to accurately identify the factors of abnormal changes after an out-of-control signal in multivariate statistical process control. Some diagnostic methods for fault identification were proposed by scholars on which one of the basic assumptions is that the process data are independent. The independent assumption is reasonable for many applications. However, with the development of industrial automation, the process data usually meet the phenomenon of autocorrelation. As it is well known, the autocorrelation affects detection ability of control chart. It is a natural question that is whether the autocorrelation affects the diagnostic performance of the diagnostic procedures. In the article, we propose the method adopted the analogy residual sequences and compare it with the performance of existing diagnostic methods for different shift sizes and various autocorrelation and cross-correlation structures. To limit the complexity, our discussion employs a first-order vector autoregressive process and focuses mainly on bivariate data in this article. The results of the simulation show the performances of diagnostic procedures were affected by autocorrelation and the impacts become greater as autocorrelation increases. On the other hand, the proposed method in the article can be free from the impacts of autocorrelation. Finally, a real example is also presented to demonstrate the implementation of the proposed method. [ABSTRACT FROM AUTHOR]

Based on the theoretical knowledge of technological innovation, this paper designs a sensor data value incubation model and a sensor data collection model to collect the original data on green innovation efficiency. In order to explore the spatial and temporal differentiation characteristics of China's provincial green innovation efficiency and the spatial spillover effect of China's provincial green innovation efficiency, the entropy weight TOPSIS model is adopted to measure the green innovation efficiency of China's 30 provinces (cities) from 2005 to 2021 and analyze its temporal and spatial evolution characteristics. This paper uses an exploratory spatial data analysis method to prove the agglomeration phenomenon of China's provincial green innovation efficiency in the ground space. Finally, a spatial econometric model is introduced to study the impact of government R&D investment, green supervision, and green innovation efficiency on spatial spillover effects. The study found the following three conclusions: First, the efficiency of green innovation in China's provinces has been fluctuating in stages, and the overall trend has been increasing year by year. The overall efficiency of green innovation in China's provinces is low, and the overall development is uneven and uncoordinated. Second, from the results of the spatial autocorrelation test, there is a clear and positive spatial correlation between China's provincial green innovation efficiency and agglomeration in the geospatial space. Third, government R&D inputs and different types of green regulations have a significant impact on green innovation efficiency and have significant spatial spillover effects. Along from the eastern region to the central region, then to the northeast and western regions, the degree of effect on green innovation efficiency and the intensity of spatial spillover effects have gradually weakened. It is believed that the government should reduce R&D investment in the eastern region and increase green sewage charges. Instead, the government should raise R&D investment in the central, northeast, and western regions and offer enterprises green innovation subsidies. [ABSTRACT FROM AUTHOR]

Background: Millions of dollars have been spent in fighting malaria in Namibia. However, malaria remains a major public health concern in Namibia, mostly in Kavango West and East, Ohangwena and Zambezi region. The primary goal of this study was to fit a spatio-temporal model that profiles spatial variation in malaria risk areas and investigate possible associations between disease risk and environmental factors at the constituency level in highly risk northern regions of Namibia. Methods: Malaria data, climatic data, and population data were merged and Global spatial autocorrelation statistics (Moran's I) was used to detect the spatial autocorrelation of malaria cases while malaria occurrence clusters were identified using local Moran statistics. A hierarchical Bayesian CAR model (Besag, York and Mollie's model "BYM") known to be the best model for modelling the spatial and temporal effects was then fitted to examine climatic factors that might explain spatial/temporal variation of malaria infection in Namibia. Results: Average rainfall received on an annual basis and maximum temperature were found to have a significant spatial and temporal variation on malaria infection. Every mm increase in annual rainfall in a specific constituency in each year increases annual mean malaria cases by 0.6%, same to average maximum temperature. The posterior means of the time main effect (year t) showed a visible slightly increase in global trend from 2018 to 2020. Conclusion: The study discovered that the spatial temporal model with both random and fixed effects best fit the model, which demonstrated a strong spatial and temporal heterogeneity distribution of malaria cases (spatial pattern) with high risk in most of the Kavango West and East outskirt constituencies, posterior relative risk (RR: 1.57 to 1.78). [ABSTRACT FROM AUTHOR]

Urban green space takes a dominant role in alleviating the urban heat island (UHI) effect. Most investigations into the effects of cooling factors from urban green spaces on the UHI have evaluated the correlation between each factor and land surface temperature (LST) separately, and the contribution weights of various typical cooling factors in mitigating the thermal environment have rarely been analyzed. For this research, three periods of Landsat 8 data captured between 2014 and 2018 of Xuzhou during the summer and autumn seasons were selected along with corresponding meteorological and flux measurements. The mono-window method was employed to retrieve LST. Based on the characteristics of the vegetation and spatial features of the green space, eight factors related to green space were selected and computed, consisting of three indices that measure vegetation and five metrics that evaluate landscape patterns: vegetation density (VD), evapotranspiration (ET), green space shading degree (GSSD), patch area ratio (PLAND), largest patch index (LPI), patch natural connectivity (COHESION), patch aggregation (AI), and patch mean shape index distribution (SHPAE_MN). Linear regression and bivariate spatial autocorrelation analyses between each green space factor and LST showed that there were significant negative linear and spatial correlations between all factors and LST, which proved that the eight factors were all cooling factors. In addition, LST was strongly correlated with all factors (|r| > 0.5) except for SHPAE_MN, which was moderately correlated (0.3 < |r| < 0.5). Based on this, two principal components were extracted by applying principal component analysis with all standardized green space factors as the original variables. To determine the contribution weight of each green space factor in mitigating the urban heat island (UHI) effect, we multiplied the influence coefficient matrix of the initial variables with the standardized multiple linear regression coefficients between the two principal component variables and LST. The final results indicated that the vegetation indices of green space contribute more to the alleviation of the UHI than its landscape pattern metrics, and the contribution weights are ranked as VD ≥ ET > GSSD > PLAND ≈ LPI > COHESION > AI > SHAPE_MN. Our study suggests that increasing vegetation density is preferred in urban planning to mitigate urban thermal environment, and increasing broadleaf forests with high evapotranspiration and shade levels in urban greening is also an effective way to reduce ambient temperature. For urban green space planning, a priority is to multiply the regional green space proportion or the area of largest patches. Second, improving the connectivity or aggregation among patches of green space can enhance their ability to cool the surrounding environment. Altering the green space spatial shape is likely the least significant factor to consider. [ABSTRACT FROM AUTHOR]

The average S-wave velocity (VS) in the upper 30 m (V S 30) is a proxy for seismic wave amplification. Microtremor array exploration is one of the available methods for site characterization, but the recorded data require complicated processing that can lead to different estimations of V S 30 depending on the analyst and processing software. We propose a method of estimating V S 30 by using derivatives obtained in the early stages of microtremor array data processing. Statistical analysis with 2376 virtually generated subsurface VS structure models revealed that the frequencies at which the spatial-autocorrelation (SPAC) coefficients and centreless circular array (CCA) coefficients take specific values strongly correlate with V S 30, which we used to develop formulas for estimating V S 30. Numerical validations using actual VS profiles at 616 sites in Japan showed that the proposed method could estimate V S 30 with a root-mean-square deviation (RMSD) of 57–80 m/s with SPAC coefficients and 56m/s with CCA coefficients. Our proposed methods were applicable for 98–100 per cent of the VS profiles when we limited our estimation to sites with V S 30 < 760 m/s. The results indicated that SPAC coefficients from arrays with radii of 8–20 m can be used for V S 30 estimation and are less affected by incoherent noise. In contrast, CCA coefficients are much more sensitive to incoherent noise, which resulted in the overestimation of V S 30. The estimated V S 30 values from the recorded microtremor array data were in good agreement with the reference values from the actual VS profiles. The proposed method allows for robust and efficient V S 30 estimation without relying on the analyst's skills or software. [ABSTRACT FROM AUTHOR]

Copyright of Rozwój Regionalny & Polityka Regionalna is the property of Uniwersytetu im. Adama Mickiewicza (IH UAM) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper ought to determine and examine whether the Harrod-Domar model is applicable in explaining the economic growth in the Philippine setting from 1981 to 2021, whereas the variables are Gross Savings and Gross Capital Formation. Using the multivariate Ordinary Least Squares (OLS) regression, the results showed that all independent variables are shown to be positively significant parameters of GDP growth. Furthermore, several tests employed in the study, including the Variance Inflation Factor, the Breusch-Godfrey test, Breusch-Pagan-Godfrey, have no evidence of multicollinearity or autocorrelation and heteroscedasticity in the regression model. The Harrod-Domar Model economic growth that is focused on in this study was popular among developed countries, yet considering the Philippines is one of the developing countries in the world, the researchers aim to understand whether the Harrod-Domar Model is applicable in the Philippine settings. [ABSTRACT FROM AUTHOR]

Copyright of Environmental Science & Technology (10036504) is the property of Editorial Board of Environmental Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Unlike most studies on urban innovation, which focus on the effects of land-use policies, this study investigates the effect of urban innovation (UI) on land finance dependence (LFD). In China, innovation-related policies are intended to create a shared business environment where new entrepreneurs can contribute to achieving sustainable economic growth. This study analyzes the spatiotemporal dynamics of LFD and UI from 2005 to 2019 using the spatial autocorrelation model and dynamic spatial Durbin model. It finds a positive spatial association between LFD and UI throughout the studied regions during the study period. It also reveals a significant inhibitory effect of UI on LFD and a negative impact on the LFD of neighbouring cities. Moreover, the inhibitory effects will increase over time. Lastly, this study shows varied impacts of UI on LFD in different regions, especially in eastern China. Important policy recommendations for high-quality development in China are provided. [ABSTRACT FROM AUTHOR]

Investigating forest phenology prediction is a key parameter for assessing the relationship between climate and environmental changes. Traditional machine learning models are not good at capturing long-term dependencies due to the problem of vanishing gradients. In contrast, the Gated Recurrent Unit (GRU) can effectively address the problem of vanishing gradients and allow the neural network to capture longer-range dependencies. In this study, an optical camera was used as experimental equipment to obtain forest images. The absolute greenness index (GEI) data of the region of interest (ROI) in the images were calculated to fit the seasonal variation curve of forest phenology. The GRU neural network model was introduced to train and analyze the GEI data, and the performance of the GRU neural network was evaluated using the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) methods. Finally, the model was used to predict the trend of GEI data for the next 60 days. The results showed that: (1) In terms of training and predicting forest phenology, the GRU model was validated using histograms and autocorrelation graphs, which indicated that the distribution of predicted data was consistent with the trend of actual data, the GRU model data was feasible, and the model was stable. (2) The MSE values of the GRU model at rain-fed-CK (preset point 1), sufficient drip irrigation-DIFI (preset point 3), and sufficient furrow irrigation-BIFI (preset point 5) were 9.055 × 10−5, 12.91 × 10−5, and 8.241 × 10−5, respectively. The RMSE values were 9.516 × 10−3, 11.36 × 10−3, and 7.313 × 10−3, respectively. The MAE values were 7.174 × 10−3, 8.241 × 10−3, and 5.351 × 10−3, respectively. These results indicate that the overall performance of the GRU model was good. (3) The predicted GEI data for the next 60 days showed a trend consistent with actual changes in GEI data, as demonstrated by the GRU model. The GRU model has become the preferred method for phenological prediction due to its simple internal structure and relatively short training time. Results show that the GRU model can achieve forest phenological change prediction and can reveal in-depth insights into future forest growth and climate change, providing a theoretical basis for the application of forest phenological prediction. [ABSTRACT FROM AUTHOR]

Spatial regression models have recently received a lot of attention in a variety of fields to address the spatial autocorrelation effect. One important class of spatial models is the Conditional Autoregressive (CA). Theses models have been widely used to analyze spatial data in various areas, as geography, epidemiology, disease surveillance, civilian planning, mapping of poorness signals and others. In this article, we propose the Liu-type pretest, shrinkage and positive shrinkages estimators for the large-scale effect parameter vector of the CA regression model. The set of the proposed estimators are evaluated analytically via their asymptotic bias, quadratic bias, the asymptotic quadratic risks, and numerically via their relative mean squared errors. Our results demonstrate that the proposed estimators are more efficient than Liu-type estimator. To conclude this paper, we apply the proposed estimators to the Boston housing prices data, and applied a bootstrapping technique to evaluate the estimators based on their mean squared prediction error. [ABSTRACT FROM AUTHOR]

The current study investigates the effect of within‐profile and between‐profile autocorrelations on the performance of four monitoring methods of simple linear profiles in Phase II. To this end, a general correlation model between error terms is considered such that the correlation structure of within‐profiles errors and the error terms between consecutive profiles follow an autoregressive (AR) times series model of order one. Extensive simulations have been done to assess the effect of both autocorrelation types and the profile size on the estimations of the model parameters as well as the performance of control charts. The performance of the monitoring schemes is investigated and compared with respect to the average run length (ARL) metric. The simulation study results show that the autocorrelation within and between profiles has a negative impact on the monitoring technique's performance. It reduces the control chart's ability to detect process shifts compared to no or weak autocorrelation cases. Moreover, the performance of all the methods is improved by increasing the profile size. An illustrative example is also provided to demonstrate the use of the proposed methods for monitoring the stability of profiles in the chemical industry. [ABSTRACT FROM AUTHOR]

In this article, we propose a two-stage LASSO estimation approach for the estimation of a full spatial weight matrix of spatiotemporal autoregressive models. In addition, we allow for an unknown number of structural breaks in the local means of each spatial location. These locally varying mean levels, however, can easily be mistaken as spatial dependence and vice versa. Thus, the proposed approach jointly estimates the spatial dependence, all structural breaks, and the local mean levels. For selection of the penalty parameter, we propose a completely new selection criterion based on the distance between the empirical spatial autocorrelation and the spatial dependence estimated in the model. Through simulation studies, we will show the finite-sample performance of the estimators and provide practical guidance as to when the approach could be applied. Finally, the method will be illustrated by an empirical example of intra-city monthly real-estate prices in Berlin between 1995 and 2014. The spatial units will be defined by the respective postal codes. The new approach allows us to estimate local mean levels and quantify the deviation of the observed prices from these levels due to spatial spillover effects. In doing so, the entire spatial dependence structure is estimated on a data-driven basis. for this article are available online. [ABSTRACT FROM AUTHOR]

Passive seismic methods in highly populated urban areas have gained much attention from the geophysics and civil engineering communities. Linear arrays are usually deployed for passive surface wave investigations because of their high convenience, and passive surface wave imaging methods commonly used for linear arrays can be grouped as non-interferometric methods (e.g. passive multichannel analysis of surface wave, refraction microtremor) and interferometric methods (e.g. multichannel analysis of passive surface waves and spatial autocorrelation). It is well known that the seismic interferometry method is able to retrieve Green's function between inter-station pairs based on passive seismic data and that is how interferometric methods work. Although non-interferometric methods are also popular and effective in near-surface seismic imaging, particularly in the geotechnical industry, there is no theoretical proof to clarify the accuracy and/or the bias of these methods. In this study, we use numerical derivations and simulations to demonstrate the underlying physics for both non-interferometric and interferometric methods, under two common noise source environments including a homogeneous source distribution and a dominant in-line source distribution. We also prove the strength of interferometric methods for accurate dispersion imaging over the non-interferometric methods, and provide a way to estimate the biases in non-interferometric measurements. Finally, we present comprehensive comparisons between different passive surface wave methods with three typical field examples considering various observation systems. [ABSTRACT FROM AUTHOR]

Interpreting regional differences is crucial in promoting regional and cohesion policies. The different levels of development of the regions, their various historical and geographical factors, as well as diverse political attitudes, institutional conditions, and their interactions fundamentally determine the economic convergence processes in the regions. Recent research has emphasised a pluralistic approach in which composite measures of progress and well-being are prioritised. The primary goal of the research was to create a more complex region categorisation model than the traditional gross domestic product (GDP-) based categorisation. This study analyses the changes and correlations of the Europe 2020 Strategy's smart and inclusive growth indicators at the regional level between 2009 and 2019. To create the region categorisation model, the indicators of the strategy were concentrated and then expanded with other background variables into composite indicators, thus creating the main components: 'Relative deprivation' and 'Innovation environment'. The regions were categorised along the dimensions of the new latent variables to form the outstanding, catching-up, and lagging groups, which can be generally characterised by development, catching-up, and lagging, respectively. The outstanding group typically includes the capital of the member states and the regions of their agglomeration. The catching-up and lagging groups in chiefly consist of member states that joined after 2004. The lagging group are mostly external border regions and can be considered underdeveloped along both dimensions; in addition to their below-average innovation environment, their labour market position also lags behind. The model developed in this research provides a more nuanced picture of regional development differences than the traditional GDP-based categorisation. [ABSTRACT FROM AUTHOR]

The earliest case of Covid-19 was documented in Wuhan city of China and since then the virus has been spreading throughout the globe. The aim of this study is to evaluate the clusters of Covid-19 among the provinces in Turkey and to examine whether the clustering pattern has changed after the country's lockdown strategy. The spatial dependence of Covid-19 in 81 provinces of Turkey was examined by spatial analysis between February 8 and June 28, 2021. Global and Local Moran's I and Gi* were employed to measure the global and local spatial autocorrelation degrees. The geographical distribution of Covid-19 in the provinces of Turkey showed a strong spatial autocorrelation while the spatial structure of the clusters varied by weeks. The findings of the study show that the complete lockdown carried out in Turkey has been quite effective in mitigating Covid-19. The importance of spatial relations in preventing the spread of the disease in Turkey has also been demonstrated in this context. [ABSTRACT FROM AUTHOR]

Poverty is still a significant problem in Indonesian development. The poverty alleviation programs implemented have yet to pay attention to spatial aspects, so the policies are often not on target. This study aims to reveal the spatially varying relationships between the poverty level and its factors at the regional scale and compare three fixed kernels as a weighting matrix for GWR. The method used is geographically weighted regression (GWR) with poverty data for 2021. The study results show spatial autocorrelation and is grouped in 29 regencies/cities. The expenditure per Capita, life expectancy, percentage of houses and households with proper drinking water, open unemployment rate, labor force participation rate, and GDP at constant prices show different effects in each region. The results strengthen the argument that spatial aspects cannot be ignored in regional development, especially poverty alleviation. Therefore, areabased poverty alleviation can be used as a basis for determining/determining policies so that they can be more targeted. [ABSTRACT FROM AUTHOR]

Through spatial–temporal scanning statistics, the spatial–temporal dynamic distribution of pulmonary tuberculosis incidence in 31 provinces and autonomous regions of China from 2008 to 2018 is obtained, and the related factors of spatial–temporal aggregation of tuberculosis in China are analyzed to provide strong scientific basis and data support for the prevention and control of pulmonary tuberculosis. This is a retrospective study, using spatial epidemiological methods to reveal the spatial–temporal clustering distribution characteristics of China's tuberculosis epidemic from 2008 to 2018, in which cases data comes from the China Center for Disease Control and prevention. Office Excel is used for general statistical description, and the single factor correlation analysis adopts χ2 Test (or trend χ2 Inspection). Retrospective discrete Poisson distribution space time scanning statistics of SaTScan 9.6 software are used to analyze the space time dynamic distribution of tuberculosis incidence in 31 provinces, cities and autonomous regions in China from 2008 to 2018. ArcGIS 10.2 software is used to visualize the results. The global spatial autocorrelation analysis adopts Moran's I of ArcGIS Map(Monte Carlo randomization simulation times of 999) is used to analyze high-risk areas, low-risk areas and high-low risk areas. From 2008 to 2018, 10,295,212 cases of pulmonary tuberculosis were reported in China, with an average annual incidence rate of 69.29/100,000 (95% CI: (69.29 ± 9.16)/100,000). The annual GDP (gross domestic product) of each province and city showed an upward trend year by year, and the number of annual medical institutions in each province and city showed a sharp increase in 2009, and then tended to be stable; From 2008 to 2018, the national spatiotemporal scanning statistics scanned a total of 6 clusters, including 23 provinces and cities. The national high-low spatiotemporal scanning statistics of the number of pulmonary tuberculosis cases scanned a total of 2 high-risk and low-risk clusters. The high-risk cluster included 8 provinces and cities, and the low-risk cluster included 12 provinces and cities. The global autocorrelation Moran's I index of the incidence rate of pulmonary tuberculosis in all provinces and cities was greater than the expected value (E (I) = −0.0333); The correlation analysis between the average annual GDP and the number of pulmonary tuberculosis cases in each province and city from 2008 to 2018 was statistically significant. From 2008 to 2018, the spatial and temporal scanning and statistical scanning areas of tuberculosis incidence in China were mainly concentrated in the northwest and southern regions of China. There is an obvious positive spatial correlation between the annual GDP distribution of each province and city, and the aggregation degree of the development level of each province and city is increasing year by year. There is a correlation between the average annual GDP of each province and the number of tuberculosis cases in the cluster area. There is no correlation between the number of medical institutions set up in each province and city and the number of pulmonary tuberculosis cases. [ABSTRACT FROM AUTHOR]

Phase retrieval gives the problem of recovering some signal from phaseless measurements. This paper mainly studies the stability estimation of phase recovery from the measurement of discrete linear canonical Gabor transformation (DLCGT). We first show that we can recover the signal x$$ x $$ on the connected components of G$$ G $$ in global phase sense. Then, we give the stability estimates results from the DLCGT measurements and the autocorrelation relation. [ABSTRACT FROM AUTHOR]

CO2 emissions have become a topical issue worldwide, but few studies have considered the spatial effect of income on carbon emissions and explored the relationship between CO2 emissions and income by establishing direct, indirect, and total environmental Kuznets curves (EKCs). Using an annual panel dataset collected over the 1997–2017 period in China, this study first analyzed the spatiotemporal evolutionary process of CO2 emissions and subsequently developed direct, indirect, and total EKC-based spatial Durbin model (SDM) and partial derivative approach. These results indicate that, first, CO2 emissions have characteristic positive spatial autocorrelation, with gravity centers that have shifted westward. Second, the direct EKC forms a line, while the total EKC resembles a lying-S shape as well as the total EKC, which indicates that compared to local economic growth, neighboring growth plays a very different role in impacting local CO2 emissions. Furthermore, neighboring economic growth seems to have stronger impacts on local emissions, and the turning point of the total EKC comes much earlier than that of the conventional EKC due to the spillover effects of economic growth. Finally, the growth of the population, as well as the rise of energy intensity, can stimulate CO2 emissions in both local and neighboring regions. Industrialization seems to have a nonsignificant impact on emission changes due to the offsetting effects of the positive direct and negative indirect impacts of the share of secondary industry. Improvements in local urbanization may lead to an increase in emissions, while neighboring improvements may have stronger restricting effects; thus, urbanization improvement is beneficial to emissions reduction. This study provides more scientific information from both local and neighboring perspectives, which may differ from conventional results but still be beneficial for emissions reduction policy-makers to introduce corresponding measures. [ABSTRACT FROM AUTHOR]

A piecewise function can sometimes provide the best fit to a time series. The breaks in this function are called change points, which represent the point at which the statistical properties of the model change. Often, the exact placement of the change points is unknown, so an efficient algorithm is required to combat the combinatorial explosion in the number of potential solutions to the multiple change point problem. Bayesian solutions to the multiple change point problem can provide uncertainty estimates on both the number and location of change points in a dataset, but there has not yet been a systematic study to determine how the choice of hyperparameters or the presence of autocorrelation affects the inference made by the model. Here, we propose Bayesian model averaging as a way to address the uncertainty in the choice of hyperparameters and show how this approach highlights the most probable solution to the problem. Autocorrelation is addressed through a pre-whitening technique, which is shown to eliminate spurious change points that emerge due to a red noise process. However, pre-whitening a dataset tends to make true change points harder to detect. After an extensive simulation study, the model is applied to two climate applications: the Pacific Decadal Oscillation and a global surface temperature anomalies dataset. [ABSTRACT FROM AUTHOR]

Context and Background: Land is an important resource for human survival as well as that of other living organisms. Despite its importance, there are many problems that come with its management. There are increasing controversies and disputes over the ownership, access, and use of land. This is due to the increase in land demand, owing to factors such as population growth, urbanization, and food security. Land disputes in the Ashanti Region have drastically escalated recently as a result of these factors. Many land parcels have been left underdeveloped as a result of these disputes. Goal and Objectives: This study investigates land dispute cases from 2016 to 2021 in the Ashanti Region using survey records of land dispute cases from the Ashanti Regional Lands Commission. Methodology: The study reviewed 203 land dispute case files from the Lands Commission to perform spatial analysis in identifying the hotspot zones of land disputes in the area of study. Ripley's K-function was used to test the null hypothesis of complete spatial randomisation and Moran's I was used to measure the spatial autocorrelation of the land dispute incidences. A kernel density map was used to show the intensity of recorded disputes in the hotspot zones. Results: It was evident from the study that most of the land dispute cases were recorded in and around the central part of the study area. A map depicting the spatial extent and hotspot zones of the land dispute cases was developed to provide contextual background for policy makers, and researchers. Based on the Moran Scatterplots, Local Indicators of Spatial Association (LISA) cluster maps were used to identify hotspot zones in the study area. A total of 12 localities were identified as hotspot zones in the study area. The incidence of land disputes was mostly in the peri-urban areas with proximity to the Central Business District. Further research is recommended to probe into factors that influence a locality or town's vulnerability to land. [ABSTRACT FROM AUTHOR]

Purpose: The major goal was to investigate effect of profitability, leverage, and efficiency on financial distress in Kenya's State Corporations in the commercial and manufacturing sector. The study also attempted to determine moderating effect of size on relationship between profitability, leverage, efficiency, and financial distress in Kenyan Commercial and Manufacturing Corporations. Methodology: Positivist philosophy and explanatory non-experimental research approach were used in this investigation. The study's population consisted of 25 State Corporations in Kenya in Commercial and manufacturing category. For the purposes of this study, a census of all 25 Commercial and Manufacturing Corporations was employed in study. Secondary data from audited accounts of state corporations for period 2015-2020 was used in analysis. Binary logistic regression was used in analysis. Diagnostics tests included multicollinearity, heteroscedasticity, likelihood ratio and autocorrelation tests. STATA statistical software was used to analyse data. Findings were presented using tables. Findings: The research outcomes indicated that profitability had significant effect on financial distress of commercial and manufacturing state corporations. Results also indicated that leverage had insignificant effect on financial distress. Efficiency also had significant effect on financial distress. The study adopted the product term interaction model approach in testing moderating effect of firm size on relationship between profitability, leverage and efficiency on financial distress. There was evidence showing that firm size significantly moderated the relationship between efficiency and financial distress in commercial and manufacturing state corporations in Kenya. Unique Contribution to Theory, Practice and Policy: This study relied on Agency, Stewardship, Efficiency, Peckingorder and Trade-off theories.The results indicated that profitability and efficiency variables are useful to management, those charged with governance and users of financial statement information in detection and mitigation of financial distress. The management and users of financial statements information should pay attention particularly to profitability and efficiency ratios. Findings are also useful to the government by providing an insight of distressed firms so that the exchequer can know and make prudence decision on the distressed state corporations that require financial bailouts. Lastly, this study adds a contribution to the limited literatures on financial distress in commercial and manufacturing state corporations in Kenya. [ABSTRACT FROM AUTHOR]

The price of market products is the result of the interaction of supply and demand. However, within the same country, prices can vary significantly, especially during crisis periods. The purpose of this study is to identify patterns in the changing spatial dependence of the prices of certain product categories, namely pasta, potatoes, sugar, candies, poultry and butter. We used daily data from 1 January 2019, to 31 March 2022, and analyzed two important indicators: spatial variation and spatial autocorrelation of average daily prices. The analysis showed that spatial dependency changes over time and follows its own pattern for each product category. We recognized cyclic changes in spatial autocorrelation and noticed the effect of legislative restrictions on spatial correlations. It has been shown that the spatial variation of prices and spatial autocorrelation can change in different directions. [ABSTRACT FROM AUTHOR]

This article is concerned with a generative approach to supervised classification of spatio-temporal data collected at fixed areal units and modeled by Gaussian Markov random field. We focused on the classifiers based on Bayes discriminant functions formed by the log-ratio of the class conditional likelihoods. As a novel modeling contribution, we propose to use decision threshold values induced by three popular spatial autocorrelation indexes, i.e., Moran's I, Geary's C and Getis-Ord G. The goal of this study is to extend the recent investigations in the context of geostatistical and hidden Markov Gaussian models to one in the context of areal Gaussian Markov models. The classifiers performance measures are chosen to be the average accuracy rate, which shows the percentage of correctly classified test data, balanced accuracy rate specified by the average of sensitivity and specificity and the geometric mean of sensitivity and specificity. The proposed methodology is illustrated using annual death rate data collected by the Institute of Hygiene of the Republic of Lithuania from the 60 municipalities in the period from 2001 to 2019. Classification model selection procedure is illustrated on three data sets with class labels specified by the threshold to mortality index due to acute cardiovascular event, malignant neoplasms and diseases of the circulatory system. Presented critical comparison among proposed approach classifiers with various spatial autocorrelation indexes (decision threshold values) and classifier based hidden Markov model can aid in the selection of proper classification techniques for the spatio-temporal areal data. [ABSTRACT FROM AUTHOR]

AGIS method based on spatial autocorrelation analysis used to identification and ranking of thoron (220Rn)concentration. Spatial radiation patterns are analyzed using Moran's I statistic. Getis-Ord Gi* is utilized to locate clusters of high and low measurements and create a map of thoron hot spots. One hundred schools in the center of Najaf City were examined for thoron using CR-39 detectors (produced from Track Analysis Systems Ltd., UK) for this research. Average thoron levels were 2.99 Bq/m3, with a range of 9.00 Bq/m3 to 0.22 Bq/m3. The radiation levels found in this investigation were significantly lower than the UNSCEAR 2000 safety standards of 40 Bq/m3. Moran's, I have used it to analyze the clustering of districts across a research region and to measure the spatial distribution of data. Getis-Ord Gi* statistics were used to identify cold and hot spots within the research area. Thoron concentrations were shown to have insignificant spatially random distribution patterns, as demonstrated by Global Moran's I. (Moran's I =0.28, p-value=0.24). [ABSTRACT FROM AUTHOR]

Geographic range size is the most commonly implemented criterion of species' extinction risk used in IUCN Red List assessments, especially for poorly-recorded species. IUCN applies two contrasting range size measures to capture different facets of a species' distribution: Extent of Occurrence (EOO; Criterion B1) is the area bounding all known occurrences and is a proxy for the spatial autocorrelation of risk, while the Area of Occupancy (AOO; Criterion B2) is the area occupied within this boundary and is related to population size at finer grains. Various methods have been proposed to measure both EOO and AOO. We evaluate the impact of applying four methods for each of Criterion B1 and of B2, as well as key parameter choices, on the Red List status of 227 poorly-recorded neotropical pteridophyte species. Between 2 and 100% of species would be considered threatened depending on methodology. The minimum convex polygon method of estimating EOO was relatively robust to sampling effort for all but the least-recorded species. The IUCN-recommended method for estimating AOO of summing occupied 2 × 2 km grid cells was very strongly correlated with the total number of records. It is likely that only a small fraction of species can be adequately assessed using this method, and we recommend caution applying the method to poorly-recorded species in particular, where models predicting occupancy in unsampled areas (e.g. species distribution models) may provide more accurate assessments. It is vital that methodological information is retained with assessments, and comparisons should only be made between assessments utilising equivalent methods. [ABSTRACT FROM AUTHOR]

In this article we first revisit some earlier work on fractionally differenced white noise and correct some issues with previously published formulae. We then look at vector processes and derive formula for the Autocorrelation function, which is extended in this work to a larger range of parameter values than considered elsewhere, and compare this with previously published work. [ABSTRACT FROM AUTHOR]

Target recovery through scattering media is an important aspect of optical imaging. Although various algorithms combining deep-learning methods for target recovery through scattering media exist, they have limitations in terms of robustness and generalization. To address these issues, this study proposes a data-decoupled scattering imaging method based on autocorrelation enhancement. This method constructs basic-element datasets, acquires the speckle images corresponding to these elements, and trains a deep-learning model using the autocorrelation images generated from the elements using speckle autocorrelation as prior physical knowledge to achieve the scattering recovery imaging of targets across data domains. To remove noise terms and enhance the signal-to-noise ratio, a deep-learning model based on the encoder–decoder structure was used to recover a speckle autocorrelation image with a high signal-to-noise ratio. Finally, clarity reconstruction of the target is achieved by applying the traditional phase-recovery algorithm. The results demonstrate that this process improves the peak signal-to-noise ratio of the data from 15 to 37.28 dB and the structural similarity from 0.38 to 0.99, allowing a clear target image to be reconstructed. Meanwhile, supplementary experiments on the robustness and generalization of the method were conducted, and the results prove that it performs well on frosted glass plates with different scattering characteristics. [ABSTRACT FROM AUTHOR]

This work focuses on functional data presenting spatial dependence. The spatial autocorrelation of stock exchange returns for 71 stock exchanges from 69 countries was investigated using the functional Moran's I statistic, classical principal component analysis (PCA) and functional areal spatial principal component analysis (FASPCA). This work focuses on the period where the 2015–2016 global market sell-off occurred and proved the existence of spatial autocorrelation among the stock exchanges studied. The stock exchange return data were converted into functional data before performing the classical PCA and FASPCA. Results from the Monte Carlo test of the functional Moran's I statistics show that the 2015–2016 global market sell-off had a great impact on the spatial autocorrelation of stock exchanges. Principal components from FASPCA show positive spatial autocorrelation in the stock exchanges. Regional clusters were formed before, after and during the 2015–2016 global market sell-off period. This work explored the existence of positive spatial autocorrelation in global stock exchanges and showed that FASPCA is a useful tool in exploring spatial dependency in complex spatial data. [ABSTRACT FROM AUTHOR]

The rolling bearing is an important part of mechanical equipment, and its performance significantly affects the quality and life of the mechanical equipment. This article uses the integrated fiber Bragg grating resonant structure sensor excited by periodic micro-shocks caused by micro faults to realize the extraction of information relating to potential faults. Because the fault signal is weak and can easily be interfered with by ambient noise, in order to extract the effective signal, this article determines the autoregressive model of bearing vibration by the final prediction error criterion and the recursive least squares estimation algorithm. The augmented state space model is established based on the autoregressive model. A Kalman filter is used to reduce the noise interference, and then the reduction noisy signal is analyzed by power spectrum and improved autocorrelation envelope spectrum to realize the detection of bearing faults. Through data analysis and method comparison, the proposed improved autocorrelation envelope spectrum analysis can directly extract the bearing fault frequency, which is superior to other methods such as cepstral analysis. [ABSTRACT FROM AUTHOR]

Zhejiang Province is a "demonstration area for high-quality development and construction of common prosperity" in China. Moreover, the county is the basic unit and power source for the economic development of Zhejiang Province. Therefore, the research on the spatial–temporal characteristics and influencing factors of county-level carbon emissions is of great significance for Zhejiang Province to achieve the strategic goal of carbon peak and carbon neutrality. Based on the carbon emissions and socio-economic data of 62 counties in Zhejiang Province from 2014 to 2020, the spatial dependence and agglomeration of county-level carbon emissions are analyzed through the spatial autocorrelation test and local spatial autocorrelation test respectively. According to the spatial–temporal characteristics of county-level carbon emissions revealed by the index of Moran's I and local Moran's I, the spatial error STIRPAT model is used to study the influencing factors of county-level carbon emissions in Zhejiang Province, China. The main results are as follows: (1) The total amount of county-level carbon emissions of 62 counties fluctuates from 259.69 to 326.28 million tons and shows a growth trend. (2) Moran's I index is between 0.369 and 0.399. The county-level carbon emissions have a significant spatial correlation, and the spatial agglomeration trend is relatively stable, which is consistent with the hypothesis of the geographical polarization effect. (3) High-high agglomeration counties are concentrated in the northeast of Zhejiang Province, while low-low agglomeration counties are mainly in the southwest. (4) The relationship between county per capita GDP and carbon emissions has not been "decoupled," because when other variables remain unchanged, the county's total carbon emissions will increase by 2.866% for every 1% increase in the county's per capita GDP; the increase of the proportion of secondary industry contributes to the decline of carbon emissions, and the low-carbon effect brought by large-scale industrial development as well as scientific and technological innovation has not yet appeared. (5) The estimate of the spatial coefficient λ was 0.324, which illustrates that the carbon emission of a single county is positively affected by the carbon emission of the neighboring counties, and other socio-economic factors affecting carbon emission among counties also have a spatial correlation. Therefore, the policy of realizing regional coordinated development as well as the carbon peaking and carbon neutrality goals should not only focus on industrial layout, but also take a dynamic and comprehensive consideration from a spatial perspective. [ABSTRACT FROM AUTHOR]

Imaging through scattering medium is challenging but important for different applications. Most advances rely on computational image reconstruction from scattering signals. In these conventional investigations, speckles were always treated as scrambled grainy patterns. Directly seeing through scattering diffusers has never been realized. Here, we report a new strategy to see through random diffusers directly using self-imaging of speckles. By analyzing the physics, a direct observation strategy through scattering media is reported with improved image quality. Using this method, we experimentally demonstrated reconstruction-free real-time imaging of static and moving objects with their actual orientation information under single-wavelength and white light illumination. We also proposed a modified speckle autocorrelation imaging (SAI) method inspired by the self-imaging results. Importantly, our strategy requires no pre-calibration or acquisition of point-spread-function, no active control of wavefronts or complicated equipment, nor iterations or carefully adjusted parameters, paving the way towards rapid and high-quality imaging through scattering diffusers. [ABSTRACT FROM AUTHOR]

Except for about a half dozen papers, virtually all (co)authored by Griffith, the existing literature lacks much content about the interface between spatial optimization, a popular form of geographic analysis, and spatial autocorrelation, a fundamental property of georeferenced data. The popular p‐median location‐allocation problem highlights this situation: the empirical geographic distribution of demand virtually always exhibits positive spatial autocorrelation. This property of geospatial data offers additional overlooked information for solving such spatial optimization problems when it actually relates to their solutions. With a proof‐of‐concept outlook, this paper articulates connections between the well‐known Majority Theorem of the 1‐median minisum problem and local indices of spatial autocorrelation; the LISA statistics appear to be the more useful of these later statistics because they better embrace negative spatial autocorrelation. The relationship articulation outlined here results in the positing of a new proposition labeled the egalitarian theorem. [ABSTRACT FROM AUTHOR]

We propose a novel efficient model-fitting algorithm for state space models. State space models are an intuitive and flexible class of models, frequently used due to the combination of their natural separation of the different mechanisms acting on the system of interest: the latent underlying system process; and the observation process. This flexibility, however, often comes at the price of more complicated model-fitting algorithms due to the associated analytically intractable likelihood. For the general case a Bayesian data augmentation approach is often employed, where the true unknown states are treated as auxiliary variables and imputed within the MCMC algorithm. However, standard "vanilla" MCMC algorithms may perform very poorly due to high correlation between the imputed states and/or parameters, often leading to model-specific bespoke algorithms being developed that are nontransferable to alternative models. The proposed method addresses the inefficiencies of traditional approaches by combining data augmentation with numerical integration in a Bayesian hybrid approach. This approach permits the use of standard "vanilla" updating algorithms that perform considerably better than the traditional approach in terms of improved mixing and lower autocorrelation, and has the potential to be incorporated into bespoke model-specific algorithms. To demonstrate the ideas, we apply our semi-complete data augmentation algorithm to different application areas and models, leading to distinct implementation schemes and improved mixing and demonstrating improved mixing of the model parameters. for this article are available online. [ABSTRACT FROM AUTHOR]

Slow feature analysis (SFA) has shown great advantages in process monitoring. However, there is no limitation imposed on the serial correlation of feature except the first-order autocorrelation, so the residual feature may not exhibit the white noise-like behaviors and may include useful information. In this paper, according to the properties of white noise, we propose a serial correlation analysis (SCA) method to improve the performance of feature extraction, where the residual feature is guaranteed to conform to the characteristics of white noise as much as possible by minimizing the sum of the squares of the multi-order autocorrelation coefficients, and the principal features are those features with a large sum of the squares. In addition, the determination of parameters is discussed, and monitoring indices are constructed for the principal feature and the residual feature to evaluate the static and dynamic status of the process operation. Finally, the proposed SCA method is applied in two numerical cases and the Tennessee Eastman (TE) process, and the results verify the effectiveness of the proposed method. • The statistical properties of residual features extracted by SFA are analyzed. • A serial correlation analysis method is proposed to fully extract data information. • SCA is used to evaluate the process steady operation status and dynamics change. • The proposed method is applied in two numerical cases and the TE process. [ABSTRACT FROM AUTHOR]

Many widely used observational data sets are comprised of several overlapping instrument records. While data inter-calibration techniques often yield continuous and reliable data for trend analysis, less attention is generally paid to maintaining higher-order statistics such as variance and autocorrelation. A growing body of work uses these metrics to quantify the stability or resilience of a system under study and potentially to anticipate an approaching critical transition in the system. Exploring the degree to which changes in resilience indicators such as the variance or autocorrelation can be attributed to non-stationary characteristics of the measurement process – rather than actual changes in the dynamical properties of the system – is important in this context. In this work we use both synthetic and empirical data to explore how changes in the noise structure of a data set are propagated into the commonly used resilience metrics lag-one autocorrelation and variance. We focus on examples from remotely sensed vegetation indicators such as vegetation optical depth and the normalized difference vegetation index from different satellite sources. We find that time series resulting from mixing signals from sensors with varied uncertainties and covering overlapping time spans can lead to biases in inferred resilience changes. These biases are typically more pronounced when resilience metrics are aggregated (for example, by land-cover type or region), whereas estimates for individual time series remain reliable at reasonable sensor signal-to-noise ratios. Our work provides guidelines for the treatment and aggregation of multi-instrument data in studies of critical transitions and resilience. [ABSTRACT FROM AUTHOR]

【Objective】There is a large population and strong demand for vegetables in Guangdong-Hong KongMacao Greater Bay Area (hereinafter called “the Greater Bay Area”). The evolution of vegetable production pattern and its influencing factors are studied in order to provide references for the spatial layout optimization and high-quality development of vegetable industry in the Greater Bay Area.【Method】The global autocorrelation indexes of vegetable gross outputs of each year were calculated to judge the regional correlation of vegetable production by using the statistical data at counties of the Greater Bay Area from 2010 to 2020 and the administrative map of the same year. The aggregation area of vegetable production and its spatial pattern evolution in counties were analyzed by local autocorrelation index. Spatial regression model was used to analyze the socio-economic factors affecting vegetable production under spatial correlation.【Result】From 2012 to 2020, the global autocorrelation index passes the test and the value is greater than 0, indicating that the vegetable production at county level forms a positive spatial correlation pattern, and the vegetable production has a certain degree of aggregation in space and increases with inter-annual changes. Through local spatial autocorrelation test and analysis, it is found that vegetable production areas such as Longmen county, Boluo county and Huicheng district shows a high-high aggregation pattern, and gradually forms an aggregation pattern in the northeast of the Greater Bay Area over time. The high-low and low-high spatial patterns of vegetable production are gradually affected by the positive correlation of adjacent areas, and finally forms the same pattern with the surrounding areas. Through the analysis of spatial regression model, it is found that the main factors affecting vegetable production are land, economic level, labor force and agricultural technology input in 2012. After 2016, the main influencing factor is land, while the influence of other factors is not obvious or significant, indicating that the expansion of vegetable planting scale is the first factor of yield increase.【Conclusion】There is a positive spatial correlation in vegetable production in Guangdong-Hong Kong-Macao Greater Bay Area, and gradually forms an industrial core area. In the future, it is necessary to increase investment in agricultural science and technology in the core areas of vegetable production such as Boluo, Longmen, Huicheng and Conghua, to further improve the modernization level of production, continuously drive the surrounding areas to improve the comprehensive industrial benefits, and promote the high-quality development of the vegetable industry. [ABSTRACT FROM AUTHOR]

Inter-state economic and social convergence was analyzed in Mexico’s 32 states during the period 2005-2019. Absolute economic convergence was tested through real income and average real labor income and average hourly real labor income. The variables of life expectancy, survival rate, child survival rate and educational level of workers were used to test for absolute social convergence. In addition, using the spatial dependence technique, Moran’s I, it was found that economic variables related to labor income present a high level of spatial correlation. In contrast, only social variables such as life expectancy and the educational level of workers are spatially correlated. [ABSTRACT FROM AUTHOR]

Copyright of Revista Ciência & Saúde Coletiva is the property of Associacao Brasileira de Pos-Graduacao em Saude Coletiva and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cocoa growing is one of the main activities in humid West Africa, which is mainly grown in pure stands. It is the main driver of deforestation and encroachment in protected areas. Cocoa agroforestry systems which have been promoted to mitigate deforestation, needs to be accurately delineated to support a valid monitoring system. Therefore, the aim of this research is to model the spatial distribution of uncertainties in the classification cocoa agroforestry. The study was carried out in Côte d'Ivoire, close to the Taï National Park. The analysis followed three steps (i) image classification based on texture parameters and vegetation indices from Sentinel-1 and -2 data respectively, to train a random forest algorithm. A classified map with the associated probability maps was generated. (ii) Shannon entropy was calculated from the probability maps, to get the error maps at different thresholds (0.2, 0.3, 0.4 and 0.5). Then, (iii) the generated error maps were analysed using a Geographically Weighted Regression model to check for spatial autocorrelation. From the results, a producer accuracy (0.88) and a user's accuracy (0.91) were obtained. A small threshold value overestimates the classification error, while a larger threshold will underestimate it. The optimal value was found to be between 0.3 and 0.4. There was no evidence of spatial autocorrelation except for a smaller threshold (0.2). The approach differentiated cocoa from other landcover and detected encroachment in forest. Even though some information was lost in the process, the method is effective for mapping cocoa plantations in Côte d'Ivoire. [ABSTRACT FROM AUTHOR]

Spline smoothing is a technique used to filter out noise in time series observations when predicting nonparametric regression models. Its performance depends on the choice of the smoothing parameter. Most of the existing smoothing methods applied to time series data tend to overfit in the presence of autocorrelated errors. This study aims to determine the optimum performance value, goodness of fit and model overfitting properties of the proposed Smoothing Method (PSM), Generalized Maximum Likelihood (GML), Generalized Cross-Validation (GCV), and Unbiased Risk (UBR) smoothing parameter selection methods. A Monte Carlo experiment of 1,000 trials was carried out at three different sample sizes (20, 60, and 100) and three levels of autocorrelation (0.2, 05, and 0.8). The four smoothing methods' performances were estimated and compared using the Predictive Mean Squared Error (PMSE) criterion. The findings of the study revealed that: for a time series observation with autocorrelated errors, Adj. R²(PSM λ = 0.04)provides the best-fit smoothing method for the model, the PSM does not over-fit data at all the autocorrelation levels considered (ρ = 0.2, 0.5 and 0.8); the optimum value of the PSM was at the weighted value of 0.04 when there is autocorrelation in the error term, PSM performed better than the GCV, GML, and UBR smoothing methods were considered at all-time series sizes (T = 20, 60 and 100). For the real-life data employed in the study, PSM proved to be the most efficient among the GCV, GML, PSM, and UBR smoothing methods compared. The study concluded that the PSM method provides the best fit as a smoothing method, works well at autocorrelation levels (ρ=0.2, 0.5, and 0.8), and does not over fit timeseries observations. The study recommended that the proposed smoothing is appropriate for time series observations with autocorrelation in the error term and econometrics real-life data. This study can be applied to; non -- parametric regression, non -- parametric forecasting, spatial, survival, and econometrics observations. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Zhejiang University (Science Edition) is the property of Journal of Zhejiang University (Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Levered exchange-traded products have been criticized as offering inferior long-term returns for two reasons: their higher volatility and, apparently, the belief that volatility diminishes long-term expected returns. Following the approach of Pessina and Whaley (2021) we analyze simulated returns of levered and inverse exchange traded funds (ETFs) and conclude that their expected long-run values are not diminished by volatility. We identity natural counterparties to the rebalancing trades of levered exchange traded funds (LETFs) and analyze the impact of autocorrelation on daily-rebalanced LETF returns, theoretically and empirically, for both LETFs and their counterparties. The results indicate that negative (positive) autocorrelation in daily returns will cause LETFs to perform relatively poorly (well) and their counterparties to perform relatively well (poorly). Accordingly, the extent to which levered and inverse ETFs have legitimate hedging applications and reasonable risk-adjusted returns (setting aside trading costs and fund expenses) depends on the extent to which the underlying returns are consistent with weak-form market efficiency. [ABSTRACT FROM AUTHOR]