Residual Autocorrelation Research Articles

АНАЛІЗ ВЗАЄМОЗВ’ЯЗКУ БІТКОЇНА З ФОНДОВИМИ ІНДЕКСАМИ, ТОВАРАМИ ТА ЦІННИМИ ПАПЕРАМИ

The article analyzes the impact of 44 different factors on the change in the price of bitcoin, such as: yields on US one-year bonds, prices for futures on palladium, platinum, gold, silver, copper, Chinese stock indices, German stock index DAX, French index CAC 40, stock indices of South Africa, India, Mauritius, England, Switzerland, Sweden, South Korea, etc. The most influential factors were selected, with the help of which two regression models were built. The presence of insignificant factors in the first model led to the need to consider the second one, which is much better. The second model lacks multicollinearity, but there are autocorrelation of residues and heteroscedasticity. There was an attempt to eliminate autocorrelation of residues, but it led to a significant reduction in the coefficient of determination to 22%. An attempt to explain the causality of the Bitcoin exchange rate to the KOSPRI index has been made. South Korea is a country, which actively cooperates with two main leaders in Bitcoin mining, namely China, where 65 % of new bitcoins are created, and the United States – 23 % of new bitcoins. Modern economic leaders actively influence not only the economy of their country, but also other economies. Korean companies are actively penetrating both the Chinese economy and the US national economy, which determines their impact on Bitcoin mining, as a large amount of necessary electronics is produced by South Korean companies such as SK hynix Inc. As for the palladium futures rate, palladium is a metal which is actively used in production of the electronics needed to mine bitcoin. Prospects for further research depend on trends in the price of bitcoin. It is possible that the price of the cryptocurrency Bitcoin will gradually rise, as some corporations begin to recognize Bitcoin as a means of payment for goods and services. This will increase demand and, as a result, prices. The confidence of large companies in Bitcoin will indicate the reliability of the cryptocurrency, which in turn will give confidence to minority investors in the need to add cryptocurrency to investment portfolios. The rise in prices will indicate the need to study the impact of various factors on changes in the price of Bitcoin. If the popularity of cryptocurrency begins to wane, it will lead to a rapid loss of its price. This development option will indicate the inexpediency of continuing to study the relationship of this cryptocurrency with these factors and the need to find other ones.

MULTIVARIATE REGRESSION MODEL OF NATURAL GAS CONSUMPTION AS A FUNCTION OF POPULATION AND TEMPERATURE IN GERMANY

In this paper a mathematical multiple regression model has been constructed based on statistical data on natural gas consumption in the Federal Republic of Germany (FRG) as a function of temperature and population. A normal distribution of the raw data and residuals was analysed, and a regression equation with a possible economic treatment of the data was derived. Using mathematical methods, the autocorrelation of the model residuals was investigated. Based on the calculations, the importance of these indicators for gas consumption is shown. The importance of other variables, which are not yet reported in this study, is noted.

Detecting space\u2013time clusters of COVID-19 in Brazil: mortality, inequality, socioeconomic vulnerability, and the relative risk of the disease in Brazilian municipalities

The first case of COVID-19 in South America occurred in Brazil on February 25, 2020. By July 20, 2020, there were 2,118,646 confirmed cases and 80,120 confirmed deaths. To assist with the development of preventive measures and targeted interventions to combat the pandemic in Brazil, we present a geographic study to detect “active” and “emerging” space–time clusters of COVID-19. We document the relationship between relative risk of COVID-19 and mortality, inequality, socioeconomic vulnerability variables. We used the prospective space–time scan statistic to detect daily COVID-19 clusters and examine the relative risk between February 25–June 7, 2020, and February 25–July 20, 2020, in 5570 Brazilian municipalities. We apply a Generalized Linear Model (GLM) to assess whether mortality rate, GINI index, and social inequality are predictors for the relative risk of each cluster. We detected 7 “active” clusters in the first time period, being one in the north, two in the northeast, two in the southeast, one in the south, and one in the capital of Brazil. In the second period, we found 9 clusters with RR > 1 located in all Brazilian regions. The results obtained through the GLM showed that there is a significant positive correlation between the predictor variables in relation to the relative risk of COVID-19. Given the presence of spatial autocorrelation in the GLM residuals, a spatial lag model was conducted that revealed that spatial effects, and both GINI index and mortality rate were strong predictors in the increase in COVID-19 relative risk in Brazil. Our research can be utilized to improve COVID-19 response and planning in all Brazilian states. The results from this study are particularly salient to public health, as they can guide targeted intervention measures, lowering the magnitude and spread of COVID-19. They can also improve resource allocation such as tests and vaccines (when available) by informing key public health officials about the highest risk areas of COVID-19.

FITTING AND CALIBRATING A MIXED-EFFECTS SEGMENTED TAPER MODEL FOR BRUTIAN PINE

Taper models are one of several necessary tools in modern forest inventory, giving information on diameter at any point along the tree stem and this information can also be used to estimate stem volume. In this study, we used nonlinear mixed-effects (NLME) modeling approach to minimize existing statistical problems in constructing taper equations. A segmented taper model of Max and Burkhart (1976) was fitted using this approach to consider for within- and between-tree variation in brutian pine (Pinus brutia Ten.) stem taper. Based on evaluation statistics, the model including random-effects parameters β 1, β 3 and β 4 were found to be the best. Inclusion of random parameters were not completely eliminated heterogenous variance and autocorrelation in residuals. Incorporating variance function and a continuous autoregressive error structure (CAR(1)) to NLME Max and Burkhart model removed the heteroscedasticity and autocorrelation in residuals. Upper stem diameters were used to localized stem taper model to individual tree. For this, two different measurement scenarios were evaluated as one and two upper stem diameter measurements. Inclusion of random parameters were improved the predictive capability of taper model in particularly the middle and lower sections of stem based on upper stem diameter measurements. The calibration using upper stem diameter measurements can improve the tree-level accuracy of stem taper model is therefore recommended.

Dengue in Queensland, Australia

BackgroundDengue is a wide-spread mosquito-borne disease globally with a likelihood of becoming endemic in tropical Queensland, Australia. The aim of this study was to analyse the spatial variation of dengue notifications in relation to climate variability and socio-ecological factors in the tropical climate zone of Queensland, Australia.Methods Data on the number of dengue cases and climate variables including minimum temperature, maximum temperature and rainfall for the period of 1st January 2010 to 31st December 2015 were obtained for each statistical local area (SLA) from Queensland Health and Australian Bureau of Meteorology, respectively. Socio-ecological data including estimated resident population, percentage of Indigenous population, housing structure (specifically terrace house), socio-economic index and land use types for each SLA were obtained from Australian Bureau of Statistics, and Australian Bureau of Agricultural and Resource Economics and Sciences, respectively. To quantify the relationship between dengue, climate and socio-ecological factors, multivariate Poisson regression models in a Bayesian framework were developed with a conditional autoregressive prior structure. Posterior parameters were estimated using Bayesian Markov Chain Monte Carlo simulation with Gibbs sampling.Results In the tropical climate zone of Queensland, the estimated number of dengue cases was predicted to increase by 85% [95% Credible Interval (CrI): 25%, 186%] and 7% (95% CrI: 0.1%, 14%) for a 1-mm increase in annual rainfall and 1% increase in the proportion of terrace houses, respectively. The estimated spatial variation (structured random effects) was small compared to the remaining unstructured variation, suggesting that the inclusion of covariates resulted in no residual spatial autocorrelation in dengue data.ConclusionsClimate and socio-ecological factors explained much of the heterogeneity of dengue transmission dynamics in the tropical climate zone of Queensland. Results will help to focus mosquito control activities in the most needed areas and will help in designing effective local dengue control programs.

Predicting Model Improvement by Accounting for Spatial Autocorrelation: A Socioeconomic Perspective

In geographical literature, numerous studies have demonstrated the differences that arise if spatial autocorrelation (SAC) is incorporated into a conventional nonspatial modeling procedure, but little is known about when these differences might be magnified. This study addressed this query by conducting two sets of regression modeling for 561 variables representing housing prices, metropolitan industry, health, crime, education, and (un)employment across various parts of the United States: (1) nonspatial ordinary least squares (OLS) using a set of selected independent variables and (2) spatial regression incorporating spatial filters into the nonspatial OLS as additional independent variables. This incorporation generally improved the model outcomes through decreases in residual autocorrelation and Akaike’s information criterion (AIC). The degree of improvement correlated positively with the level of SAC inherent in the dependent variables. That is, strongly autocorrelated socioeconomic variables underwent greater decreases in residual autocorrelation and AIC than those variables with weaker SAC. The results imply that spatial modeling outcomes are sensitive to and potentially predictable by the level of SAC possessed by dependent variables. Therefore, the degree of SAC present in a socioeconomic variable can serve as a direct indicator of how much improvement a nonspatial OLS will experience if that SAC is properly accounted for.

Climate variability, socio-ecological factors and dengue transmission in tropical Queensland, Australia: A Bayesian spatial analysis

BackgroundDengue is a wide-spread mosquito-borne disease globally with a likelihood of becoming endemic in tropical Queensland, Australia. The aim of this study was to analyse the spatial variation of dengue notifications in relation to climate variability and socio-ecological factors in the tropical climate zone of Queensland, Australia. MethodsData on the number of dengue cases and climate variables including minimum temperature, maximum temperature and rainfall for the period of January 1st, 2010 to December 31st, 2015 were obtained for each Statistical Local Area (SLA) from Queensland Health and Australian Bureau of Meteorology, respectively. Socio-ecological data including estimated resident population, percentage of Indigenous population, housing structure (specifically terrace house), socio-economic index and land use types for each SLA were obtained from Australian Bureau of Statistics, and Australian Bureau of Agricultural and Resource Economics and Sciences, respectively. To quantify the relationship between dengue, climate and socio-ecological factors, multivariate Poisson regression models in a Bayesian framework were developed with a conditional autoregressive prior structure. Posterior parameters were estimated using Bayesian Markov Chain Monte Carlo simulation with Gibbs sampling. ResultsIn the tropical climate zone of Queensland, the estimated number of dengue cases was predicted to increase by 85% [95% Credible Interval (CrI): 25%, 186%] and 7% (95% CrI: 0.1%, 14%) for a 1-mm increase in average annual rainfall and 1% increase in the proportion of terrace houses, respectively. The estimated spatial variation (structured random effects) was small compared to the remaining unstructured variation, suggesting that the inclusion of covariates resulted in no residual spatial autocorrelation in dengue data. ConclusionsClimate and socio-ecological factors explained much of the heterogeneity of dengue transmission dynamics in the tropical climate zone of Queensland. Results will help to further understand the risk factors of dengue transmission and will provide scientific evidence in designing effective local dengue control programs in the most needed areas.

Private Domestic Investment and Manufacturing Sector Output in Nigeria

This study examined the impact of private domestic investment on manufacturing sector output in Nigeria from 1970 to 2017. The study specifically looked at the impact of private domestic investment on manufacturing sector’s output in a static and dynamic model. Six variables were employed in the study and were sourced from CBN statistical bulletin and World Development Indicators for the period covering from 1970 to 2017. The analysis of the variables undergoes three approaches, the pre-analysis of data, model estimation and the diagnostic analysis of the model. The first approach employed tables and graphs to explain the behaviour of the data, equally the univariate analysis of the data were examine with the Augmented Dickey-Fuller equations and the possibility of long-term relationship. The models were estimated with the ARDL estimator and model selected with the Akaike Information Criteria, and finally the models estimated were tested using the Jaque-Bera statistics, Ramsey RESET Test, Breusch-Godfrey and Harvey test for residual normality, specification bias, autocorrelation and heteroskedasticity respectively. The results from the analytical methods shows that there is over 82 percent increase in the output of Manufacturing sector in the late 1970s and early 1980s and over 98 percent increase the output of the manufacturing sector in the late within 2010 and 2015. Also, the study observed that the responses of output of the manufacturing sector to private domestic investment are positive and significant in the static and dynamic models. The study found that the impact of private domestic investment on manufacturing sector output were fairly elastic in the static model and fairly inelastic in the dynamic model. Finally, the study found that the model have a weak adjustment mechanism. The adjustment of disequilibrium between static and dynamic equilibrium is weak or just 24.9 percent. Since private domestic investment is significant and positively impacted on the performance of the manufacturing sector irrespective of the time zone, the study recommended for increase in the credit to private sector by the apex monetary authority.

Predicting spatiotemporal abundance of breeding waterfowl across Canada: A Bayesian hierarchical modelling approach

AbstractAimOur aim was to develop predictive statistical models for mapping the abundance of 18 waterfowl species at a pan‐Canadian level. We refined the previous generation of national waterfowl models by (a) developing new, more interpretable statistical models that (b) explicitly account for spatiotemporal variations in waterfowl abundance, while (c) testing for associations with an updated suite of habitat covariates.LocationAll of Canada, excluding the Northern Arctic ecozone.MethodsOur response variables were annual species counts on 2,227 aerial‐survey segments over a period of 25 years (1990–2015). Combining machine‐learning and hierarchical regression modelling, we devised an innovative covariate selection strategy to select for each species the best subset of a panel of 232 candidate habitat covariates. With the selected covariates, we implemented hierarchical generalized linear models in a Bayesian framework, using the integrated nested Laplace approximation and stochastic partial differential equation approaches.ResultsOn average, our models explained 47% of the observed variance for spatiotemporal predictions and 74% for temporally averaged spatial predictions. The 18 species models included 94 significant waterfowl‐habitat associations involving 42 distinct habitat covariates, with an average of 5.3 covariates per model. Covariates for forest attributes were the most represented in our models. The proportional biomass of Populus tremuloides was the most frequently selected covariate (10/94 associations in 10/18 species). Model predictions generated spatial and spatiotemporal maps of species abundances over almost all of Canada.Main conclusionsWe showed that it is possible to efficiently combine machine‐learning, variable selection and hierarchical Bayesian methods that exploit high‐dimensional covariate spaces. Our approach yielded powerful and easily interpretable species distribution models with very few covariates, while accounting for residual autocorrelation. Possible applications of the resulting models and maps include the development of biodiversity indicators, the evaluation and execution of conservation planning strategies, and ecosystem services monitoring.

Estimating neighbourhood-level prevalence of adult obesity by socio-economic, behavioural and built environment factors in New York City

ObjectivesSome studies reveal that socio-economic status, behavioural factors, accessibility to supermarket or food store, are associated with the prevalence of obesity and overweight. In this study, we aimed to examine to what extent socio-economic, behavioural and built environment characteristics can contribute to spatial disparities in adult obesity. Study designThe spatial analysis was undertaken to understand the association of spatial disparities in adult obesity and spatial disparities in socio-economic, behavioural and built environment characteristics. MethodsA spatial regression model which can remove the impact of auto-correlation in the residuals of conventionally regression models was applied to modelling local-scale rate of adult obesity (N = 59). ResultsOwing to the presence of residual spatial auto-correlation in the non-spatial regression model estimated, a spatial regression model was set up successfully to model local-scale rate of adult obesity across New York City (R2 = 0.8353, N = 59). Compared with socio-economic and built environment factors, behavioural factors make statistically significant contributions to spatial disparities in the prevalence of adult obesity (POAO). Particularly, two behavioural factors (‘sugary drinks consumption’ and ‘fruits and vegetable consumption’) can explain more than 70% of the variance of POAO (adjusted R2 = 0.7323, N = 59). Surprisingly, physical activity prevalence (percent of physically active adults) makes no statistically significant contributions. ConclusionsThe results further suggest that the reduction of adult obesity prevalence could benefit more from decreasing intake of sugary drinks than increasing physical activity. The local government and policy are advised to prioritise decreasing exposure of residents to sugary drinks through restricting advertising or increasing taxes rather than increasing neighbourhoods’ walkability through urban planning.

Using geographically weighted models to explore how crowdsourced landscape perceptions relate to landscape physical characteristics

This study explores how formal measures of landscape wildness (i.e. absence of human artefacts, perceived naturalness of land cover, remoteness from mechanised access, and ruggedness of the terrain) correlate with crowdsourced measures of landscape aesthetic quality as captured in Scenic-Or-Not data for Great Britain. It evaluates multiple linear regression (MLR) and two spatially varying coefficients models: geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR). The MLR provided a baseline model in an analysis of national data, exhibiting the presence of spatially autocorrelated residuals and suggesting that geographically weighted models may be appropriate. A standard GWR was found to exacerbate local collinearity between covariates, both overfitting and underfitting the model with highly varied and localised results. This was due to its single one-size-fits-all bandwidth and the assumption that all relationships between the target and predictor variables operate over the same spatial scale. MGWR relaxes this assumption by determining parameter-specific bandwidths, mitigating the local collinearity issues found in a standard GWR and resulting in more spatially stable and consistent coefficient estimates. The findings also indicated that the relationship between some covariates (such as remoteness) and perceived landscape quality varied little spatially, while clear gradients were found for other covariates. For example, naturalness was stronger in the north and west, ruggedness was stronger in the south and east, and the absence of human artefacts was weaker in Scotland and the north than in England and the south. Overall, the study showed that MGWR is more sensitive than GWR to the spatial heterogeneity in the statistical relationships between landscape factors and public perceptions. These findings provide nuanced understandings of how these relationships vary spatially, underscoring the value of such approaches in landscape scale analyses to support policy and planning. The discussion section of this paper considers the MGWR as the default geographically weighted model, assessing the potential for the use of crowdsourced data in landscape studies. In so doing, it illustrates how such approaches could be used to explore both subjective and objective landscape evaluations.

Effect of a free healthcare policy on health services utilisation for non‐malarial febrile illness by children under five years in Burkina Faso: an interrupted time series analysis

To assess the effect of a free healthcare policy for children under five years old implemented in Burkina Faso since April 2016, on the use of health care of non-malarial febrile illnesses (NMFI). To assess the immediate and long-term effect of the free healthcare policy in place, we conducted an interrupted time series analysis of routinely collected data on febrile illnesses from three urban primary health centres of Ouagadougou between 1 January 2015 and 31 December 2016. Of the 39046 febrile cases reported in the study period, 17017 NMFI were included in the study. Compared to the period before the intervention, we observed an immediate, non-statistically significant increase of 7% in the number of NMFI (IRR=1.07; 95% CI=0.75, 1.51). Compared to the trend that would have been expected in absence of the intervention, the results showed a small but sustained increase of 6% in the trend of monthly number of NMFI during the intervention period (IRR=1.06; 95%CI=1.01, 1.12). Our study highlighted an increase in the uptake of healthcare services, specifically for NMFI by children under five years of age, after the implementation of a free care policy. This analysis contributes to informing decision makers on the need to strengthen the capacities of healthcare centres and to anticipate the challenges of the sustainability of this policy.

Anomaly Kriging Helps to Remove Bias in Spatial Model Runoff Estimates

AbstractThe low spatial density of streamflow gauging stations limits the accuracy of spatial streamflow estimates in many parts of the world. Strategies to improve runoff estimates in the absence of dense measurements have tended to focus on estimating parameters of runoff models in ungauged regions, through so‐called parameter regionalization methods. However, parameter regionalization can be affected by overdependence on calibration at gauged sites, model parameter equifinality, and ensuing estimation errors. As a result, spatial model runoff estimates typically exhibit spatially correlated biases. This analysis attempts to enhance the use of observations in spatial runoff estimation. Specifically, we assessed the potential to reduce systematic errors by spatially interpolating residuals (i.e., errors) between prior grid‐based streamflow estimates for Australia at 0.05° × 0.05° grid from the Australian Bureau of Meteorology's calibrated, operational Australian Water Resources Assessment Landscape model (AWRA‐L) and streamflow gauging records from 780 unimpeded, relatively small catchments. We analyzed spatial autocorrelation in residuals and tested an efficient two‐step correction approach involving a uniform correction and subsequent kriging of residuals. The approach removed an average of 41% of systematic bias in the model estimates and also improved other model performance measures. Further reduction in errors at shorter timescales may be achievable through a temporally hierarchical correction scheme.

Pengaruh Jumlah Industri dan Tenaga Kerja Terhadap Nilai Produksi Industri Formal Kecil

The purpose of this study to analyze the effect of the number of industries and labor on the value of production in small industries in Lamongan Regency using the Durbin-Watson test. The Durbin-Watson test is an autocorrelation test that assesses the presence of autocorrelation in residuals. Data taken from the Central Bureau of Statistics of Lamongan Regency. Furthermore, it was analyzed using SPSS assistance. Based on the results of the analysis, it is known that there is no autocorrelation between the number of industries and workers on the value of production in small industries in Lamongan Regency.

벡터자기회귀모형의 잔차 검정을 위한 와일드 붓스트랩 Ljung-Box 검정

본 논문에서는 벡터자기회귀모형 적합 후 잔차 검정을 위한 Ljung-Box(LB) 검정통계량에 와일드 붓스트랩을 적용하는 연구를 진행하였다. 일반적인 시계열 자료와는 달리 조건부 이분산성이 존재하는 다변량 금융시계열 자료에서는 LB 검정통계량의 제 1종의 오류가 주어진 유의수준보다 매우 크게 나타난다. 이를 극복하기 위해 벡터자기회귀모형에 와일드 붓스트랩을 이용한 LB 검정법을 제안하였다. 모의실험을 실행한 결과 와일드 붓스트랩 LB 검정법의 제 1종의 오류가 주어진 유의수준을 잘 만족하고 있음을 확인하였다.

Evaluating the effectiveness of national measles elimination action in mainland China during 2004–2016: A multi-site interrupted time-series study

BackgroundTo block transmission of measles, the China Ministry of Health endorsed the “2006–2012 National Measles Elimination Action Plan (NMEAP)” on November 11, 2006, while no published studies used mathematical models to assess the impact of this action. Also, the problem of residuals autocorrelation due to auto-correlated infectious disease data is often ignored in practice. MethodsBased on monthly counts of measles in 31 Chinese provinces during 2004–2016, a spatiotemporal auto-correlated mixed model was developed to assess the intervention effects over 10 years after the NMEAP, after controlling for seasonality and time-varying socioeconomic and meteorological factors. The first-order autoregression dynamic relationship was used to adjust the bias of conditional covariance in Quasi-likelihood function due to temporal autocorrelation and the spatial autoregressive term was used to capture the neighbor effects of adjacent provinces. ResultsThere were a similar seasonal pattern and spatial differences of measles incidence among provinces, yet the spatiotemporal pattern was diminished after the intervention. The introduction of the NMEAP was associated with a reduction of 51.03% [excess risk (ER%) = −51.03%, 95% CI: −62.32% to −36.37%] in measles morbidity on average during the 10-year intervention period. The intervention effect varied with time. The significant effect was observed 15 months after the intervention onset, and then gradually increased to a stable level, but was weakened slightly after 2012. The effect of birth rate and meteorological factors on measles morbidity reduced after the NMEAP. ConclusionsThere was clear evidence that prominent progress towards a goal of measles elimination had been achieved after the intervention, while the resurgence after 2012 indicated the goal for measles elimination by 2012 had not been reached.

Revisiting the FDI–Growth Nexus: ARDL Bound Test for BRICS Standalone Economies

In this paper the author tests for the short-run dynamics and long-run cointegration relationship between foreign direct investment (FDI) inflows and economic growth for the BRICS (Brazil, Russia, India, China, and South Africa) standalone economies controlling for real exchange rate, trade openness, and domestic investment. The autoregressive distributed lag (ARDL) bounds testing method of cointegration is used to test for the long-run relationship of our FDI time series model by investigating annual macroeconomic datasets for the years 1981 to 2018 (inclusive). Coupled with the ARDL, the error correction model is applied to test for the short-run dynamics, while the Toda Yamamoto test is used to examine the causality direction between the constructs of interest. The Breusch-Godfrey and Ljung-Box are used as diagnostic tests for the ARDL assumptions of normality, independency, and autocorrelation in residuals, while the Breusch-Pagan-Godfrey test is used to test for heteroscedasticity. According to the short-run estimates, all variables have a significant lagged impact on FDI inflows with slight differences among countries. As for the long run, estimates reveal a positive and significant impact of GDP on FDI inflows for Russia, India, China, and South Africa but a positive and insignificant relationship for Brazil. The long-run estimates for the controlling variables evidence varied results among the BRICS countries. In contrast to Brazil and Russia, the Toda Yamamoto causality test discloses a significant and unidirectional flow between the GDP growth and FDI inflows for India, China, and South Africa. The results have meaningful implications for policy reform structures, economic integration among economies, multinational firms, and portfolio managers.

Good Practices for Species Distribution Modeling of Deep-Sea Corals and Sponges for Resource Management: Data Collection, Analysis, Validation, and Communication

Resource managers in the United States and worldwide are tasked with identifying and mitigating trade-offs between human activities in the deep sea (e.g., fishing, energy development, and mining) and their impacts on habitat-forming invertebrates, including deep-sea corals and sponges (DSCS). Related management decisions require information about where DSCS occur and in what densities. Species distribution modeling (SDM) provides a cost-effective means of identifying potential DSCS habitat over large areas to inform these management decisions and data collection. Here we describe good practices for DSCS SDM, especially in the context of data collection and management applications. Managers typically need information regarding DSCS encounter probabilities, densities, and sizes, defined at sub-regional to basin-wide scales and validated using subsequent, targeted data collections. To realistically achieve these goals, analysts should integrate available data sources in SDMs including fine-scale visual sampling and broad-scale resource surveys (e.g., fisheries trawl surveys), include environmental predictor variables representing multiple spatial scales, model residual spatial autocorrelation, and quantify prediction uncertainty. When possible, models fitted to presence-absence and density data are preferred over models fitted only to presence data, which are difficult to validate and can confound estimated probability of occurrence or density with sampling effort. Ensembles of models can provide robust predictions, while multi-species models leverage information across taxa and facilitate community inference. To facilitate the use of models by managers, predictions should be expressed in units that are widely understood and validated at an appropriate spatial scale using a sampling design that provides strong statistical inference. We present three case studies for the Pacific Ocean that illustrate good practices with respect to data collection, modeling, and validation; these case studies demonstrate it is possible to implement our good practices in real-world settings.

Statistical modeling experiment of land precipitation variations since the start of the 20th century with external forcing factors

Fitting of the historic climate change with multiple linear regression (MLR) models often encounters the problem of residual autocorrelation and multiple correlation among the dependent factors, which leads the model not to be the Best Linear Unbiased Estimator (BLUE). To solve these problems, a set of sophisticated statistical modeling procedures are developed in this paper to detect the contributions of the external forcing to the global and regional land precipitation changes during the recent century. The radiative forcing (effective radiative forcing) data of multiple natural and anthropogenic factors are taken as independent variables and a combined model of MLR and Autoregressive Integrated Moving Average (ARIMA) is first used to separate the impacts of natural forcing and human activities (signals) and internal variability (noise) due to land precipitation changes since the start of the 20th century. Based on the results, a partial least squares regression (PLSR) model is further adopted to quantify the respective contributions of the each components of anthropogenic forcing. Some preliminary conclusions were drawn: (1) The combined model of MLR and ARIMA has clearly separated the influence of anthropogenic forcing signals in the global and regional (middle and high latitude in Northern Hemisphere) land precipitation anomaly at 5% significance level, and the explained variance is relatively large. (2) The PLSR model overcomes the morbidity of ordinary linear regression equations to a certain extent, and better separated the respective impact (contribution) of various natural and anthropogenic forcing factors on global and regional (middle and high latitude in Northern Hemisphere) land precipitation anomaly; and the relative strength of each factor’s contribution are determined. It has good application value for modeling and impact studies with multiple correlations among different independent variables. (3) The above combined model explains about 40% of the total variance of land precipitation anomalies change over global and middle latitude regions in Northern Hemisphere, and nearly 60% of the total variance in high latitude regions. Based on the fitting results, both the human activities and natural forcing are the deterministic impact factors of global land precipitation variations. But the natural forcing’s contribution is not significant, while human activities still explain a large part of the variance and thus have high significance for the regional land precipitation variations. In addition, the autocorrelation should also be considered as one of the important impact factors for global and regional land precipitation anomaly changes. (4) The anthropogenic aerosol and contrails have clear and significant positive contributions to the global and regional land precipitation anomalies, while the contributions of the remaining factors show obvious positive and negative differentiation, indicating that there is certain degree of uncertainty. Therefore, it is necessary to further optimize and improve the factors and models based on more accurate factor datasets for each region. (5) The modeling approach described above is primarily based on the idea to decompose and model the historical climate series. On one hand, it may put forward certain requirements for the “integrity” of the observational data; while on the other hand, it is not restricted by the study domains, climate variables and the independent variables. Moreover, it may even model/separate the anthropogenic/natural effects of climate changes in local scales. In addition, it can independently detect and analyze the relative contribution and significance of human activities or natural forcing to climate change in the historical observational series without using the climate system model. Therefore, it is proved to be a convenient approach for detecting the causes of climate change and a powerful supplement to model attribution.

The heterogeneous effect of socioeconomic driving factors on PM2.5 in China’s 30 province-level administrative regions: Evidence from Bayesian hierarchical spatial quantile regression

China has become one of the most serious PM2.5-dominated air pollution country. Despite a great deal of research has focused on analysing the influence of social and economic driving forces of PM2.5 pollution in China, most research in existence either applying mean regression or failing to consider the spatial autocorrelation. Motivated by this, this paper utilizes a Bayesian hierarchical spatial quantile regression method to explore the effect of socioeconomic activity on PM2.5 air pollution. By introducing spatial random effects into the model, the spatial autocorrelations of residuals are significantly reduced. The empirical study demonstrated that the PM2.5 concentration levels were strongly correlated with total population, urbanization rate, industrialization level and energy efficiency at all quantiles. For upper quantiles, the impact of urbanization rate on the haze is the greatest among all the predictors, then followed by the total population; while for lower quantiles, industrialization has the greatest impact on the PM2.5 concentration. The impacts of energy efficiency in the lower 15% and upper 15% quantiles are higher compared to any of the other quantiles.