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Abstract Forest fires are among the major natural disasters that destroy the balance of forest ecosystems. The construction of a forest fire prediction model to investigate the driving mechanism of fire drivers on forest fires can help reveal the mechanism of forest fire occurrence and its risk, and thus contribute to the prevention and control of forest fires. However, previous studies on the mechanisms of forest fire drivers have not considered the effect of differences in spatial scale of action of forest fire drivers on the predicted effect. Therefore, the present study proposes a spatial prediction model of forest fires that considers the spatial scale effect of forest fire drivers to predict forest fire risk. First, based on historical forest fire data and geographic environmental data in the Yunnan Province, geographically weighted logistic regression (GWLR) was used to determine the forest fire drivers and to estimate the probability of forest fire occurrence at locations where fire observations are absent. Then, multi-scale geographically weighted regression (MGWR) was used to explore the spatial scales of action of different drivers on forest fires. The results show that meteorological factors such as relative humidity, air temperature, air pressure, sunshine hours, daily precipitation, wind speed, topographic factors such as elevation, slope, and aspect, anthropogenic factors such as population density and road network, as well as vegetation type, were significantly correlated with forest fires; thus, they are identified as important factors influencing occurrence of forest fires in the Yunnan Province. The MGWR model regression results show that the role of different forest fire drivers on forest fire occurrence has spatial scale differences. The spatial scale of drivers such as altitude, aspect, wind speed, temperature, slope, and distance from the road to the fire point was larger and their spatial influence was relatively stable, with spatial heterogeneity having less influence on the model evaluation results. The spatial scale of drivers such as relative humidity, sunshine, air pressure, precipitation, population density, and vegetation type were smaller, and spatial heterogeneity had a more obvious influence on the model evaluation results. This study provides a reference for selecting drivers and evaluating their spatial scale effects to construct predictive regional forest fire models.

IntroductionThe use of pseudo-absence data constrained by environmental conditions can facilitate potential distribution predictions of invasive species. However, pseudo-absence data generated by existing methods are usually not representative because the relationship between the presence and pseudo-absence points is either simplistic or neglected. This could under or overestimate the potential distribution of invasive species.MethodsTo address this deficiency, this study proposes a new method for obtaining pseudo-absence data based on geographic similarities. First, the reliability of pseudo-absences was quantified based on the geographic similarity to the occurrence of species. Subsequently, a representative pseudo-absence reliability threshold interval was determined. Finally, different pseudo-absence acquisition methods were assessed by combining virtual species with a real invasive species.ResultsThe analysis demonstrated that the geographic similarity method can improve model accuracy and achieve a more realistic distribution compared with the traditional method of sampling for pseudo-absence data.DiscussionThis result indicates that the pseudo-absence data obtained using the geographic similarity approach were more representative. Our study provides valuable insights into improving invasive plant distribution predictions by considering the geographical relationships between species occurrences and the surrounding environments.

Understanding the mechanisms and risks of forest fires by building a spatial prediction model is an important means of controlling forest fires. Non-fire point data are important training data for constructing a model, and their quality significantly impacts the prediction performance of the model. However, non-fire point data obtained using existing sampling methods generally suffer from low representativeness. Therefore, this study proposes a non-fire point data sampling method based on geographical similarity to improve the quality of non-fire point samples. The method is based on the idea that the less similar the geographical environment between a sample point and an already occurred fire point, the greater the confidence in being a non-fire point sample. Yunnan Province, China, with a high frequency of forest fires, was used as the study area. We compared the prediction performance of traditional sampling methods and the proposed method using three commonly used forest fire risk prediction models: logistic regression (LR), support vector machine (SVM), and random forest (RF). The results show that the modeling and prediction accuracies of the forest fire prediction models established based on the proposed sampling method are significantly improved compared with those of the traditional sampling method. Specifically, in 2010, the modeling and prediction accuracies improved by 19.1% and 32.8%, respectively, and in 2020, they improved by 13.1% and 24.3%, respectively. Therefore, we believe that collecting non-fire point samples based on the principle of geographical similarity is an effective way to improve the quality of forest fire samples, and thus enhance the prediction of forest fire risk.

Under conditions of rapid urbanization, quantitative measurement of the expansion characteristics of urban landscapes and evaluation of their spatial and temporal differentiation properties is an important scientific problem. The Multi-order Adjacency Index (MAI) is an effective indicator of the quantitative characteristics of urban landscape expansion; however, it cannot directly reflect the spatial heterogeneity of this expansion. Therefore, it is necessary to combine the MAI with a spatial heterogeneity detection method to measure and evaluate the spatiotemporal divergence features of urban landscape expansion. In this study, taking Chenggong District, Kunming City, Yunnan Province, China as the study area, we apply MAI to analyze the changing process of urban landscape pattern expansion, and use the Geodetector to analyze the drivers affecting the spatial divergence of this expansion. We compare the results with the Landscape Extension Index and geographically-weighted regression models. The results show that MAI can reveal urban landscape expansion well at the micro-level, and analysis of the urban landscape expansion process reveals that it is mainly socio-economic factors that lead to the spatial heterogeneity of urban landscape expansion. Combining MAI and Geodetector can solve the problem that existing methods cannot directly reflect the heterogeneity of urban expansion landscape patterns and their causes.

Abstract Background A large number (n = 460) of A(H7N9) human infections have been reported in China from March 2013 through December 2014, and H7N9 outbreaks in humans became an emerging issue for China health, which have caused numerous disease outbreaks in domestic poultry and wild bird populations, and threatened human health severely. The aims of this study were to investigate the directional trend of the epidemic and to identify the significant presence of spatial-temporal clustering of influenza A(H7N9) human cases between March 2013 and December 2014. Methods Three distinct epidemic phases of A(H7N9) human infections were identified in this study. In each phase, standard deviational ellipse analysis was conducted to examine the directional trend of disease spreading, and retrospective space-time permutation scan statistic was then used to identify the spatio-temporal cluster patterns of H7N9 outbreaks in humans. Results The ever-changing location and the increasing size of the three identified standard deviational ellipses showed that the epidemic moved from east to southeast coast, and hence to some central regions, with a future epidemiological trend of continue dispersing to more central regions of China, and a few new human cases might also appear in parts of the western China. Furthermore, A(H7N9) human infections were clustering in space and time in the first two phases with five significant spatio-temporal clusters (p

Land-use change is a typical geographic evolutionary process characterized by spatial heterogeneity. As such, the driving factors, conversion rules, and rate of change vary for different regions around the world. However, most cellular automata (CA) models use the same transition rules for all cells in the model space when simulating land-use change. Thus, spatial heterogeneity change is ignored in the model, which means that these models are prone to over- or under simulation, resulting in a large deviation from reality. An effective means of accounting for the influence of spatial heterogeneity on the quality of the CA model is to establish a partitioned model based on cellular space partitioning. This study established a partitioned, dual-constrained CA model using the area-weighted frequency of land-use change (AWFLUC) to capture its spatial heterogeneity. This model was used to simulate the land-use evolution of the Dianchi Lake watershed. First, the CA space was divided into subzones using a dual-constrained spatial clustering method. Second, an artificial neural network (ANN) was used to automatically acquire conversion rules to construct an ANN-CA model of land-use change. Finally, land-use changes were simulated using the ANN-CA model based on data from 2006 to 2016, and model reliability was validated. The experimental results showed that compared with the non-partitioned CA model, the partitioned counterpart was able to improve the accuracy of land-use change simulation significantly. Furthermore, AWFLUC is an important indicator of the spatial heterogeneity of land-use change. The shapes of the division spaces were more similar to reality and the simulation accuracy was higher when AWFLUC was considered as a land-use change characteristic.

Taking the maximum vehicle driving distance, the distances from gas stations, the route length, and the number of refueling gas stations as the decision conditions, recommendation rules and an early refueling service warning mechanism for gas stations along a self-driving travel route were constructed by using the algorithm presented in this research, based on the spatial clustering characteristics of gas stations and the urgency of refueling. Meanwhile, by combining ArcEngine and Matlab capabilities, a scenario simulation system of refueling for self-driving travel was developed by using c#.net in order to validate and test the accuracy and applicability of the algorithm. A total of nine testing schemes with four simulation scenarios were designed and executed using this algorithm, and all of the simulation results were consistent with expectations. The refueling recommendation algorithm proposed in this study can automatically adapt to changes in the route length of self-driving travel, the maximum driving distance of the vehicle, and the distance from gas stations, which could provide variable refueling recommendation strategies according to differing gas station layouts along the route. Therefore, the results of this study could provide a scientific reference for the reasonable planning and timely supply of vehicle refueling during self-driving travel.

Change in urban construction land use is an important factor when studying urban expansion. Many scholars have combined cellular automata (CA) with data mining algorithms to perform relevant simulation studies. However, the parameters for rule extraction are difficult to determine and the rules are simplex, and together, these factors tend to introduce excessive fitting problems and low modeling accuracy. In this paper, we propose a method to extract the transformation rules for a CA model based on the Classification and Regression Tree (CART). In this method, CART is used to extract the transformation rules for the CA. This method first adopts the CART decision tree using the bootstrap algorithm to mine the rules from the urban land use while considering the factors that impact the geographic spatial variables in the CART regression procedure. The weights of individual impact factors are calculated to generate a logistic regression function that reflects the change in urban construction land use. Finally, a CA model is constructed to simulate and predict urban construction land expansion. The urban area of Xinyang City in China is used as an example for this experimental research. After removing the spatial invariant region, the overall simulation accuracy is 81.38% and the kappa coefficient is 0.73. The results indicate that by using the CART decision tree to train the impact factor weights and extract the rules, it can effectively increase the simulation accuracy of the CA model. From convenience and accuracy perspectives for rule extraction, the structure of the CART decision tree is clear, and it is very suitable for obtaining the cellular rules. The CART-CA model has a relatively high simulation accuracy in modeling urban construction land use expansion, it provides reliable results, and is suitable for use as a scientific reference for urban construction land use expansion.