Spatial Non-stationarity Research Articles

Evaluation of Geological Hazard Susceptibility Based on the Regional Division Information Value Method

The traditional susceptibility evaluation of geological hazards usually comprises a global susceptibility evaluation of the entire study area but ignores the differences between the local areas caused by spatial non-stationarity. In view of this, the geographically weighted regression model (GWR) was used to divide the study area at regional scale. Seven local areas were obtained with low spatial auto-correlation of each evaluation factor. Additionally, 11 evaluation factors, including the aspect, elevation, curvature, ground roughness, relief amplitude, slope, lithology, distance from the fault, height of the cut slope, multiyear average rainfall and the normalized difference vegetation index (NDVI) were selected to establish the evaluation index system of the geological hazard susceptibility. The Pearson coefficient was used to remove the evaluation factors with high correlation. The global and seven local areas were evaluated for susceptibility using the information value model and the global and regional division susceptibility evaluation results were obtained. The results show that the regional division information value model had better prediction performance (AUC = 0.893) and better accuracy. This model adequately considers the influence of the geological hazard impact factors in the different local areas on geological hazard susceptibility and weakens the influence of some factors that have higher influence in the global model but lower influence in local areas on the evaluation results. Therefore, the use of the regional division information value model for susceptibility evaluation is more consistent with the actual situation in the study area and is more suitable for guiding risk management and hazard prevention and mitigation.

Spatially heterogeneous influence of street greenery on street-level PM2.5 pollution using mobile monitoring from a three-dimensional perspective

Greenery may be effective in mitigating particulate matter (PM) pollution. However, most previous studies have explored the effects of greenery on air quality from a two-dimensional rather than a three-dimensional (3D) perspective. In this study, a geographically weighted regression (GWR) model was constructed to explore the effect of street greenery on street-level PM2.5 concentrations using street view imagery from a 3D perspective. Mobile monitoring of PM2.5 concentrations was conducted in Wuhan. The results demonstrated that GWR is suitable for exploring street-level air quality. From a 3D perspective, street greenery strongly affects the air quality of the surrounding area within 300 m. Moreover, considering spatial nonstationarity, the negative impact of street greenery on air quality depends on the pollution and ventilation conditions. High pollution and canyon effects amplify the negative impact of street greenery on pollution dispersion. Thus, the negative effect of street greenery on air quality is sensitive to the density of second-level roads and of commercial areas, the number of intersections and the distance to rivers. Dense planting trees in these areas must be reconsidered based on the possibility of ventilation loss. These results provide insights into street-level PM2.5 pollution and can contribute to the evidence-based design of urban green infrastructure.

A regionally varying habitat model to inform management for greater sage-grouse persistence across their range

Identifying habitat needs for species with large distributions is challenging because species-habitat associations may vary across scales and regions (spatial nonstationarity). Furthermore, management efforts often cross jurisdictional boundaries, complicating the development of cohesive conservation strategies among management entities. The greater sage-grouse (Centrocercus urophasianus) is a rapidly declining species that spans 11 U.S. states and responds to habitat conditions across a wide range of spatial scales and regions. Allowing for regional variance in species-habitat associations and suitability predictions could systematically identify important habitats at levels relevant to management. We collaboratively developed a model with Bureau of Land Management (BLM) biologists that: (1) evaluated the scale of effect for different environmental covariates; (2) accounted for regional differences in population-level responses; and (3) predicted probabilities of persistence across the U.S. occupied range. We modeled range-wide lek persistence data (6615 communal breeding sites classified as active or inactive) as a function of environmental covariates. Environmental covariates included sagebrush cover, pinyon-juniper cover, topography, precipitation, point and line disturbance densities, and landscape configuration metrics. Our model treated habitat assessment areas – regionally delineated by BLM biologists – as random intercepts and slopes that allowed for geographic variation in species-habitat associations and predicted probabilities of lek persistence. Our final model indicated support for 12 environmental covariates predicting lek persistence at scales extending between 1- to 15-km radii from lek centers, and a covariate measuring distance to the occupied range boundary. Five of these covariates showed significant regionally varying responses: sagebrush clumpiness (a measure of habitat aggregation), pinyon-juniper cover, point disturbance of anthropogenic features such as energy infrastructure and communication towers, elevation, and a topographic index associated with mesic habitats. This spatial nonstationarity indicates unitary range-wide recommendations, or rules-of-thumb with respect to their effects on lek persistence, may be problematic for these environmental conditions. For covariates that did not include random slopes, and which were potentially amenable to management actions, we found that leks were predicted to become extirpated when sagebrush cover fell below 9.6 % (summarized at the 3.2-km radius extent), and the proportion of classified sagebrush habitat fell below 0.7 (1-km). We produced a continuous predictive probability surface of lek persistence which we binned based on model sensitivity thresholds to produce habitat quality categories. The highest quality habitat (capturing 50 % of active leks) covered 25.5 % of the occupied range, while the combined lowest through highest quality habitats (capturing 95 % of active leks) covered 65.0 %. Accommodating regional environmental differences in models that are relevant to habitat management planning will help ensure their applicability to targeted goals. Continuous collaboration between modelers and land managers early in the modeling process increases the likelihood of this outcome.

A Novel 3D Non-Stationary Massive MIMO Channel Model for Shortwave Communication Systems

In this paper, a novel three-dimensional (3D) non-stationary massive multiple-input multiple-output (MIMO) channel model for shortwave communication systems is proposed. Three transmission modes, i.e., groundwave, near vertical incident skywave (NVIS), and long-distance skywave are considered to eliminate the blind area and realize the full-coverage for shortwave communication. The ionospheric absorption loss and surface reflection loss during multi-hop transmissions are explored in the proposed channel model. In addition, new massive MIMO channel characteristics including the near-field spherical wavefront effect and spatial non-stationarity are considered. Temporal and frequency non-stationarities are also modeled due to the receiver (Rx) mobility and large relative bandwidth, respectively. The analytical and simulated space cross-correlation function (SCCF), time autocorrelation function (TACF), and frequency correlation function (FCF) of the proposed model are compared. The simulated path loss and singular value spread (SVS) are compared with those of the corresponding channel measurements, illustrating good fittings. In addition, the delay power spectral density (PSD) and Doppler PSD, and channel capacity are also simulated and analyzed. The proposed model can be used as a basis for the design and construction of shortwave communication systems.

Spatiotemporal correlation and multivariate analysis between vegetation health, terrestrial water storage and precipitation

Vegetation health is an essential indicator in the global hydrologic cycle as it is interrelated with the hydrological components. In tropical areas where vegetation dominates, analysing their correlation at a regional scale helps forecast the hydrologic cycle and understand vegetation’s response to climate change. However, the interactions between vegetation, terrestrial water storage and climate factors such as precipitation remain poorly understood in this region. Therefore, using Landsat and Gravity Recovery and Climate Experiment (GRACE) remote sensing and observed precipitation data, this study analysed the spatiotemporal correlation of Normalized Difference Vegetation Index (NDVI), Terrestrial Water Storage Anomaly (TWSA) and precipitation for the whole Peninsular Malaysia. The correlation coefficient (R) was used to assess the temporal variability of NDVI with TWSA and precipitation separately. Furthermore, a Geographically Weighted Regression (GWR) model was constructed to evaluate the spatial non-stationarity and heterogeneity relationships between the multi variables. The findings revealed complex interactions between the variables, where the strength of the correlations varied depending on the localised region and study period. The results suggest that downscaled GRACE-derived TWSA data would be helpful for detailed vegetation modelling and water resources management.

Spatial and Attribute Neural Network Weighted Regression for the Accurate Estimation of Spatial Non-Stationarity

Geographically neural network weighted regression is an improved model of GWR combined with a neural network. It has a stronger ability to fit nonlinear functions, and complex geographical processes can be modeled more fully. GNNWR uses the distance metric of Euclidean space to express the relationship between sample points. However, except for spatial location features, geographic entities also have many diverse attribute features. Incorporating attribute features into the modeling process can make the model more suitable for the real geographical process. Therefore, we proposed a spatial-attribute proximities deep neural network to aggregate data from the spatial feature and attribute feature, so that one unified distance metric can be used to express the spatial and attribute relationships between sample points at the same time. Based on GNNWR, we designed a spatial and attribute neural network weighted regression (SANNWR) model to adapt to this new unified distance metric. We developed one case study to examine the effectiveness of SANNWR. We used PM2.5 concentration data in China as the research object and compared the prediction accuracy between GWR, GNNWR and SANNWR. The results showed that the “spatial-attribute” unified distance metric is useful, and that the SANNWR model showed the best performance.

Detecting Spatially Non-Stationary between Vegetation and Related Factors in the Yellow River Basin from 1986 to 2021 Using Multiscale Geographically Weighted Regression Based on Landsat

As an important ecological barrier in northern China, the ecological environment of the Yellow River Basin (YRB) has been greatly improved in recent decades. However, due to spatially non-stationarity, the contribution of human activities and natural factors to vegetation restoration may exhibit different coupling effects in various areas. In this paper, the Normalized Difference Vegetation Index (NDVI) of the YRB from 1986 to 2021 was used as the dependent variable, and terrain, meteorological, and socioeconomic factors were used as independent variables. With the help of Multiscale Geographically Weighted Regression (MGWR), which could handle the scale difference well, combined with Ordinary Least Squares (OLS) and traditional Geographically Weighted Regression (GWR), the spatial non-stationary relationship between vegetation and related factors was discussed. The results showed that: (1) The vegetation was subject to fluctuating changes from 1986 to 2021, mainly improving, with a growth rate of 0.0018/year; the spatial distribution pattern of vegetation in the basin was high in the southeast and low in the northwest. (2) Compared with the OLS and GWR, the MGWR could better explain the relationship between vegetation and various factors. (3) The response scale of vegetation and related factors was significantly variant, and this scale changed with time. The effect scale of terrain factor is lower than climate and social factors. (4) There was obvious spatial heterogeneity in the effects of various influencing factors on vegetation. The vegetation of the upstream was mainly positively affected by mean annual temperature (coefficients ∈ [1.507, 1.784]); while potential evapotranspiration was the dominant factor of vegetation in the middle and lower reaches of the basin (coefficients ∈ [−1.724, −1.704]); it was worth noting that the influence of social factors on vegetation was relatively small. This study deeply explores the spatial non-stationarity of vegetation and various related factors, thereby revealing the evolution law of vegetation pattern and providing scientific support for monitoring and improving the ecological environment quality of the YRB.

Nativist with(out) a cause: a geographical analysis of the populist radical right in the 2017 and 2021 Czech parliamentary elections

ABSTRACT Although there has been a considerable amount of new research examining the recent upsurge of populist radical right parties, Central and Eastern European countries remain understudied, and the importance of geographical context and spatial scales are often overlooked in the field of political science. This leads to limited understanding of contemporary nativist manifestations across diverse European environments. Our study partially fills these gaps by analysing the main Czech populist radical right party, Freedom and Direct Democracy. To further elaborate upon various contextual effects, spatial processes and spatial non-stationarity of data, we compare the results of four statistical methods (OLS, LISA, GWR, MGWR). Accordingly, we demonstrate the limitations of global models and the added value of employing multiscale and geographically weighted models. Although various relationships between analysed variables that drive nativist manifestations across Czech regions are identified and discussed, we conclude that the most vital factor regarding populist radical right voting in Czechia is education followed by religiosity.

MmWave extra-large-scale MIMO based active user detection and channel estimation for high-speed railway communications

The current High-Speed Railway (HSR) communications increasingly fail to satisfy the massive access services of numerous user equipment brought by the increasing number of people traveling by HSRs. To this end, this paper investigates millimeter-Wave (mmWave) extra-large scale (XL)-MIMO-based massive Internet-of-Things (IoT) access in near-field HSR communications, and proposes a block simultaneous orthogonal matching pursuit (B-SOMP)-based Active User Detection (AUD) and Channel Estimation (CE) scheme by exploiting the spatial block sparsity of the XL-MIMO-based massive access channels. Specifically, we first model the uplink mmWave XL-MIMO channels, which exhibit the near-field propagation characteristics of electromagnetic signals and the spatial non-stationarity of mmWave XL-MIMO arrays. By exploiting the spatial block sparsity and common frequency-domain sparsity pattern of massive access channels, the joint AUD and CE problem can be then formulated as a Multiple Measurement Vectors Compressive Sensing (MMV-CS) problem. Based on the designed sensing matrix, a B-SOMP algorithm is proposed to achieve joint AUD and CE. Finally, simulation results show that the proposed solution can obtain a better AUD and CE performance than the conventional CS-based scheme for massive IoT access in near-field HSR communications.

Hierarchical Bayesian modeling of ocean heat content and its uncertainty

The accurate quantification of changes in the heat content of the world’s oceans is crucial for our understanding of the effects of increasing greenhouse gas concentrations. The Argo program, consisting of Lagrangian floats that measure vertical temperature profiles throughout the global ocean, has provided a wealth of data from which to estimate ocean heat content. However, creating a globally consistent statistical model for ocean heat content remains challenging due to the need for a globally valid covariance model that can capture complex nonstationarity. In this paper, we develop a hierarchical Bayesian Gaussian process model that uses kernel convolutions with cylindrical distances to allow for spatial nonstationarity in all model parameters while using a Vecchia process to remain computationally feasible for large spatial datasets. Our approach can produce valid credible intervals for globally integrated quantities that would not be possible using previous approaches. These advantages are demonstrated through the application of the model to Argo data, yielding credible intervals for the spatially varying trend in ocean heat content that accounts for both the uncertainty induced from interpolation and from estimating the mean field and other parameters. Through cross-validation, we show that our model outperforms an out-of-the-box approach as well as other simpler models. The code for performing this analysis is provided as the R package BayesianOHC.

Spatial non-stationarity in the distribution of fish species richness of tropical streams

Spatial non-stationarity in the distribution of fish species richness of tropical streams

Spatial and temporal dynamics of Mexican spotted owl habitat in the southwestern US

ContextUnderstanding habitat dynamics is essential for effective conservation as landscapes rapidly change. In a companion paper in this issue, Shirk et al. (2023) introduced an automated habitat monitoring system using Google Earth Engine and applied this framework to develop a dynamic model of Mexican spotted owl (Strix occidentalis lucida) habitat across the southwestern US from 1986 to 2020.ObjectivesWe explored the application of this dynamic model of Mexican spotted owl habitat in the context of the species’ ecology.MethodsWe evaluated environmental correlates of Mexican spotted owl habitat, assessed potential spatial non-stationarity in habitat selection, estimated long-term trends in habitat by quantifying changes in habitat amount and quality between 1986 and 2020, and evaluated the extent to which habitat changes over the past 35 years have been driven by wildfire.ResultsTopography and climate appeared to outweigh reflectance-based (vegetation) metrics in describing Mexican spotted owl habitat and habitat selection was non-stationary across modeling sub-regions. Total habitat area for Mexican spotted owls declined by ~ 21% since 1986 (0.6% annually), but trends varied spatially and some even reversed over the past decade. Wildfire was responsible for between 8 and 35% of total habitat loss, depending on the sub-region considered.ConclusionsThe automated habitat monitoring system allowed trend estimation and accurate assessment of current habitat status for Mexican spotted owls; maps were accurate, spatially detailed, and current. The ability to continually produce accurate maps for large land areas for threatened species such as the Mexican spotted owl facilitates science-based land management on public lands in the southwestern US.

Investigating the Impacts of Urbanization on Vegetation Net Primary Productivity: A Case Study of Chengdu–Chongqing Urban Agglomeration from the Perspective of Townships

As an emerging national strategic urban agglomeration in China, the changing trend of vegetation net primary productivity (NPP) and the impact of the urbanization level (UL) on carbon cycle functions in the Chengdu–Chongqing urban agglomeration (CUA) have received increasing attention. Previous studies have largely overlooked externalities and the heterogeneity of urbanization effects, and urbanization has also been analyzed in isolation (with focus being on land and population urbanization). In this study, the spatial evolution of NPP was evaluated from 2000 to 2020 at the township level (3859) using multivariate remote sensing data and a comprehensive index (UL) that included population urbanization, land urbanization, and economic urbanization. Bivariate spatial autocorrelation, spatial Durbin models, and geographically weighted regression models were used to analyze the spatial externalities of urbanization impacts and assess the global and local effects. The results show that the region’s mean NPP increased by 177.25 g*c/m2 (annual growth of 1.59%), exhibiting a distribution of “low in the middle and high in the periphery” and low-value clustering along major traffic arteries and rivers. Low-value-NPP areas were mainly located in urban centers, while the high-level areas were in the mountainous region (in the southwest and southeast) and significantly expanded over time. Negative correlation clusters were the main clustering types between the UL and NPP; the “High-Low” negative correlation clusters accelerated outward from the urban centers of Chengdu and Chongqing. Overall, urbanization had negative direct and spillover effects on NPP, exhibiting spatial non-stationarity of the negative driving effect within the urban agglomeration. The results indicate the need to strengthen regional ecological joint governance and adopt more place-based urbanization optimization strategies. This study offers new insights to help to reduce the constraining effects of urbanization on vegetation productivity and ecological functions from the perspectives of population agglomeration, land expansion, and industrial construction.

Landsat-8 and Sentinel-2 Based Prediction of Forest Plantation C Stock Using Spatially Varying Coefficient Bayesian Hierarchical Models

This study sought to establish the performance of Spatially Varying Coefficient (SVC) Bayesian Hierarchical models using Landsat-8, and Sentinel-2 derived auxiliary information in predicting plantation forest carbon (C) stock in the eastern highlands of Zimbabwe. The development and implementation of Zimbabwe’s land reform program undertaken in the year 2000 and the subsequent redistribution and resizing of large-scale land holdings are hypothesized to have created heterogeneity in aboveground forest biomass in plantation ecosystems. The Bayesian hierarchical framework, accommodating residual spatial dependence and non-stationarity of model predictors, was evaluated. Firstly, SVC models utilizing Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Enhanced Vegetation Index (EVI), derived from Landsat-8 and Sentinel-2 data and 191 sampled C stock observations were constructed. The SVC models built for each of the two multispectral remote sensing data sets were assessed based on the goodness of fit criterion as well as the predictive performance using a 10-fold cross-validation technique. The introduction of spatial random effects in the form of Landsat-8 and Sentinel-2 derived covariates to the model intercept improved the model fit and predictive performance where residual spatial dependence was dominant. For the Landsat-8 C stock predictive model, the RMSPE for the non-spatial, Spatially Varying Intercept (SVI) and SVC models were 8 MgCha−1, 7.77 MgCha−1, and 6.42 MgCha−1 whilst it was 7.85 MgCha−1, 7.69 MgCha−1 and 6.23 MgCha−1 for the Sentinel-2 C stock predictive models, respectively. Overall, the Sentinel-2-based SVC model was preferred for predicting C stock in plantation forest ecosystems as its model provided marginally tighter credible intervals, [1.17–1.60] MgCha−1 when compared to the Landsat-8 based SVC model with 95% credible intervals of [1.13–1.62] Mg Cha−1. The built SVC models provided an understanding of the performance of the multispectral remote sensing derived predictors for modeling C stock and thus provided an essential foundation required for updating the current carbon forest plantation databases.

Predicting intersection crash frequency using connected vehicle data: A framework for geographical random forest

Accurate crash frequency prediction is critical for proactive safety management. The emerging connected vehicles technology provides us with a wealth of vehicular motion data, which enables a better connection between crash frequency and driving behaviors. However, appropriately dealing with the spatial dependence of crash frequency and multitudinous driving features has been a difficult but critical challenge in the prediction process. To this end, this study aims to investigate a new Artificial Intelligence technique called Geographical Random Forest (GRF) that can address spatial heterogeneity and retain all potential predictors. By harnessing more than 2.2 billion high-resolution connected vehicle Basic Safety Message (BSM) observations from the Safety Pilot Model Deployment in Ann Arbor, MI, 30 indicators of driving volatility are extracted, including speed, longitudinal and lateral acceleration, and yaw rate. The developed GRF was implemented to predict rear-end crash frequency at intersections. The results show that: 1) rear-end crashes are more likely to happen at intersections connecting minor roads compared to major roads; 2) a higher number of hard acceleration and deceleration events beyond two standard deviations in the longitudinal direction is a leading indicator of rear-end crashes; 3) the optimal GRF significantly outperforms Global Random Forest, with a 9% lower test error and a substantially better fit; and 4) geographical visualization of variable importance highlights the presence of spatial non-stationarity. The proposed framework can proactively identify at-risk intersections and alert drivers when leading indicators of driving volatility tend to worsen.

Estimation of the soil arsenic concentration using a geographically weighted XGBoost model based on hyperspectral data

Considering the high toxicity of arsenic (As), its contamination of soil represents an alarming environmental and public health issue. Existing soil heavy metal concentration estimation models based on hyperspectral data ignore the spatial nonstationarity of the relationship between the soil spectrum and heavy metal concentration. A novel model (geographically weighted eXtreme gradient boosting or GW-XGBoost model) combining geographically weighted regression (GWR) method with XGBoost algorithm was proposed. The northeast district of Beijing, China, was chosen as a case study area to assess the effectiveness of the proposed model. The GW-XGBoost model was established to estimate the As concentration based on the typical spectrum of As and the spatial correlation between the spectrum and As concentration obtained using the GWR method, and the result was compared to that obtained with the XGBoost and GWR models. The accuracy of the GW-XGBoost model was obviously better than that of the other models (R2GW-XGBoost = 0.90, R2XGBoost = 0.48, and R2GWR = 0.74). Therefore, the proposed model is reliable, as it considers the spatial correlation between the spectrum and As concentration.

ELAA Channel Characterization with Parameter Estimation Based on a Generalized Array Manifold Model

The extremely large antenna arrays (ELAAs) and millimeter-wave systems have become key techniques for obtaining higher frequency spectrum efficiency in sixth-generation (6G) communication systems. It is necessary to determine appropriate statistical models to describe the channel characteristics for the ELAAs, such as spatial non-stationarity, dispersion in angular domain, and spatial consistency. Thus, a signal model based on the generalized array manifold (GAM) that describes the dispersion in direction using the definition of the slightly distributed scatterer (SDS) is proposed in this work. An estimator for the parameters of the GAM model, namely GAM Space-Alternating Generalized Expectation-maximization (GAM-SAGE), is also designed. Moreover, a method to obtain a stochastic SDS-based channel model (SBCM) that is capable of reproducing the spatial consistency is proposed. The method is then used to establish measurement-based models for line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios using a 40×40 receiver (Rx) planar antenna array at a carrier frequency of 40 GHz. The results demonstrate that the SBCM is capable of achieving spatially consistent results and outperforms the specular-path (SP) models in completely characterizing the ELAA channels at millimeter-wave bands, which are fundamental for the design of 6G.

Multiscale Impact of Environmental and Socio-Economic Factors on Low Physical Fitness among Chinese Adolescents and Regionalized Coping Strategies.

As low physical fitness in adolescents increases their risk of all-cause mortality in future adulthood as well as regional public health budgets, many scholars have studied the factors influencing physical fitness in adolescents. However, the spatial non-stationarity and scale between physical fitness and influencing factors in adolescents are often neglected. To rectify this situation, this study constructed a multi-scale geographically weighted regression model based on data from the China National Student Fitness Survey and the China Statistical Yearbook in 2018 to investigate the spatial patterns of factors influencing low physical fitness among adolescents. The results showed that the influencing factors for measuring the physical fitness of Chinese adolescents had significant spatial heterogeneity and multi-scale effects. The local R2 values were relatively low in the western region of China. Consideration should be given to increasing the lifestyle and ethnic and cultural characteristics of local residents when selecting influencing factors in the future. The physical fitness of men was mainly influenced by socio-economic factors, while that of women was influenced by natural environmental factors. According to the different spatial distribution patterns of MGWR, this study suggests that each region should develop regionalized strategies to cope with the low physical fitness of adolescents, including taking advantage of the natural environment to develop physical fitness promotion projects, accelerating the upgrading of industrial structures in the north-eastern and western regions, and the need to remain cautious of rapid urbanization in the east.

Segment-Level Spatial Heterogeneity of Arterial Crash Frequency Using Locally Weighted Generalized Linear Models

Decades of literature in traffic crash modeling show the popularity of generalized linear models (GLMs). However, because of the failure to accommodate spatial heterogeneity, parameters estimated with these models are inconsistent and inefficient. In light of that, this study aims to investigate the spatial heterogeneity of crashes aggregated at roadway segment levels using geographically weighted Poisson regression (GWPR) and two variants of the geographically weighted negative binomial regression (GWNBR) model. The results indicate: (i) the GWNBR model with global dispersion parameter outperforms conventional Poisson, GWPR, and negative binomial (NB) models; (ii) the performance of the GWNBR model further enhances as the dispersion parameter becomes spatially non-stationary; (iii) tests of spatial heterogeneity and autocorrelation reveal the existence of non-stationarity and less than 1% likelihood of randomness; and (iv) median of parameter estimates reveal a positive association between crashes and posted speed limit, number of lanes, number of three-leg intersections, number of access points, and vehicle miles traveled (VMT). The study concludes that, when spatial heterogeneity is evident, conventional GLMs should be avoided to circumvent the futile estimation of parameters on the arterial segments. The findings are expected to contribute to the small pool of literature on spatial non-stationarity of parameters in segment-based aggregation, identification and selection of segments with similar influence factors in corridor-level studies, and spatial analysis of arterial crashes.

The Fractional Vegetation Cover (FVC) and Associated Driving Factors of Modeling in Mining Areas

To determine the fractional vegetation cover (FVC ) and associated driving factors of modeling in mining areas, six types of data were used as driving factors and three methods—multi-linear regression (MLR ), geographically weighted regression (GWR ), and geographically weighted artificial neural network (GWANN )—were adopted in the modeling. The experiments, conducted in Shengli mining areas located in Xilinhot city, China, show that the MLR model without consideration of spatial heterogeneity and spatial non-stationarity performs the worst and that the GWR model presents obvious location differences, since it predefines a linear relationship which is unable to describe FVC for some locations. The GWANN model, improving on these defects, is the most suitable model for the FVC driving process in mining areas; it outperforms the other two models, with root-mean-square error (RMSE ) and mean absolute percentage error (MAPE ) reaching 0.16 and 0.20. It has improvements of approximately 24% in RMSE and 33% in MAPE compared to the MLR model, and those values grow to 59% and 71% when compared with the GWR model.