Spatial simulation using abstraction of virtual geographic environments

In this paper,we address two challenging issues underlying spatial simulation using software agents immersed in virtual geographic environments(VGE).First,the way to describe virtual VGE models using accurate spatial decomposition approaches structured using graph theory techniques.Second,the use of graph abstraction techniques to support realistic and advanced navigation and path planning capabilities for software agents considering the VGE’s characteristics.In order to illustrate our contributions to the growing field of spatial simulations,we present and discuss a case study involving an urban VGE model populated with agents who autonomously and differently interact with multiple abstractions of the same physical environment.

Using autologistic spatial models to simulate the distribution of land-use patterns in Zhangjiajie, Hunan Province

Nowadays, spatial simulation on land use patterns is one of the key contents of LUCC. Modeling is an important tool for simulating land use patterns due to its ability to integrate measurements of changes in land cover and the associated drivers. The conventional regression model can only analyze the correlation between land use types and driving factors but cannot depict the spatial autocorrelation characteristics. Land uses in Yongding County, which is located in the typical karst mountain areas in northwestern Hunan province, were investigated by means of modeling the spatial autocorrelation of land use types with the purpose of deriving better spatial land use patterns on the basis of terrain characteristics and infrastructural conditions. Through incorporating components describing the spatial autocorrelation into a conventional logistic model, we constructed a regression model （Autologistic model）, and used this model to simulate and analyze the spatial land use patterns in Yongding County. According to the comparison with the conventional logistic model without considering the spatial autocorrelation, this model showed better goodness and higher accuracy of fitting. The distribution of arable land, wood land, built-up land and unused land yielded areas under the ROC curves （AUC） was improved to 0.893, 0.940, 0.907 and 0.863 respectively with the autologistic model. It is argued that the improved model based on autologistic method was reasonable to a certain extent. Meanwhile, these analysis results could provide valuable information for modeling future land use change scenarios with actual conditions of local and regional land use, and the probability maps of land use types obtained from this study could also support government decision-making on land use management for Yongding County and other similar areas.

Optimization of Shanghai marine environment monitoring sites by integrating spatial correlation and stratified heterogeneity

The water quality grades of phosphate（PO4-P） and dissolved inorganic nitrogen（DIN） are integrated by spatial partitioning to fit the global and local semi-variograms of these nutrients. Leave-one-out cross validation is used to determine the statistical inference method. To minimize absolute average errors and error mean squares,stratified Kriging（SK） interpolation is applied to DIN and ordinary Kriging（OK） interpolation is applied to PO4-P.Ten percent of the sites is adjusted by considering their impact on the change in deviations in DIN and PO4-P interpolation and the resultant effect on areas with different water quality grades. Thus, seven redundant historical sites are removed. Seven historical sites are distributed in areas with water quality poorer than Grade IV at the north and south branches of the Changjiang（Yangtze River） Estuary and at the coastal region north of the Hangzhou Bay. Numerous sites are installed in these regions. The contents of various elements in the waters are not remarkably changed, and the waters are mixed well. Seven sites that have been optimized and removed are set to water with quality Grades III and IV. Optimization and adjustment of unrestricted areas show that the optimized and adjusted sites are mainly distributed in regions where the water quality grade undergoes transition.Therefore, key sites for adjustment and optimization are located at the boundaries of areas with different water quality grades and seawater.

Strategic Planning for Equitable RWIS Implementation:A Comprehensive Study Incorporating a Multi-variable Semivariogram Model

This paper extends the previously developed method of optimizing Road Weather Information Systems(RWIS)station placement by unveiling a sophisticated multi-variable semivariogram model that concurrently considers multiple vital road weather variables.Previous research primarily centered on single-variable analysis focusing on road surface temperature(RST).The study bridges this oversight by introducing a framework that integrates multiple critical weather variables into the RWIS location allocation framework.This novel approach ensures balanced and equitable RWIS distribution across zones and aligns the network with areas both prone to traffic accidents and areas of high uncertainty.To demonstrate the effectiveness of this refinement,the authors applied the framework to Maine’s existing RWIS network,conducted a gap analysis through varying planning scenarios and generated optimal solutions using a heuristic optimization algorithm.The analysis identified areas that would benefit most from additional RWIS stations and guided optimal resource utilization across different road types and priority locations.A sensitivity analysis was also performed to evaluate the effect of different weightings for weather and traffic factors on the selection of optimal locations.The location solutions generated have been adopted by MaineDOT for future implementations,attesting to the model’s practicality and signifying an important advancement for more effective management of road weather conditions.

Urban construction land demand prediction and spatial pattern simulation under carbon peak and neutrality goals:A case study of Guangzhou,China

Urban construction land has relatively high human activity and high carbon emissions.Research on urban construction land prediction under carbon peak and neutrality goals(hereafter“dual carbon”goals)is important for territorial spatial planning.This study analyzed quantitative relationships between carbon emissions and urban construction land,and then modified the construction land demand prediction model.Thereafter,an integrated model for urban construction land demand prediction and spatial pattern simulation under“dual carbon”goals was developed,where urban construction land suitability was modified based on carbon source and sink capacity of different land-use types.Using Guangzhou as a case study,the integrated model was validated and applied to simulate the spatiotemporal dynamics of its urban construction land during 2030–2060 under baseline development and“dual carbon”goals scenarios.The simulation results showed that Guangzhou’s urban construction land expanded rapidly until 2030,with the spatial pattern not showing an intensive development trend.Guangzhou’s urban construction land expansion slowed during 2030–2060,with an average annual growth rate of 0.2%,and a centralized spatial pattern trend.Under the“dual carbon”goal scenario,Guangzhou’s urban construction land evolved into a polycentric development pattern in 2030.Compared with the baseline development scenario,urban construction land expansion in Guangzhou during 2030–2060 is slower,with an average annual growth rate of only 0.1%,and the polycentric development pattern of urban construction land was more prominent.Furthermore,land maintenance and growth,that is,a carbon sink,is more obvious under the“dual carbon”goals scenario,with the forest land area nearly 10.6%higher than that under the baseline development scenario.The study of urban construction land demand prediction and spatial pattern simulation under“dual carbon”goals provides a scientific decision-making support tool for territorial spatial planning,aiding in quantifying territorial spatial planning.

SIMULATION OF THE SPATIAL DISTRIBUTION OF CLIMATIC ELEMENTS IN MOUNTAINOUS REGIONS

In this paper,an effective method of simulating the spatial distribution of climatic elements in mountainous areas by using the semi-empirical theory is presented.As an example,the spatial distributions of temperature, vapor pressure,relative humidity,wind speed and precipitation in the Jianyang region and the Shaxi basin of Fujian Province are computed with this method,and the simulated results are in good agreement with the observations.

Spatial Resilience to Wildfires through the Optimal Deployment of Firefighting Resources:Impact of Topography on Initial Attack Effectiveness

Strongly affected by the escalating impacts of climate change,wildfires have been increasing in frequency and severity around the world.The primary aim of this study was the development of specific territorial measures—estimating the optimal locations of firefighting resources—to enhance the spatial resilience to wildfires in the fire-prone region of Chalkidiki Prefecture in northern Greece.These measures focus on the resistance to wildfires and the adaptation of strategies to wildfire management,based on the estimation of burn probability,including the effect of anthropogenic factors on fire ignition.The proposed location schemes of firefighting resources such as vehicles consider both the susceptibility to fire and the influence of the topography on travel simulation,highlighting the impact of road slope on the initial firefighting attack.The spatial scheme,as well as the number of required firefighting forces is totally differentiated due to slope impact.When we ignore the topography effect,a minimum number of fire vehicles is required to achieve the maximization of coverage(99.2%of the entire study area)giving priority to the most susceptible regions(that is,employing 18 of 24 available fire vehicles).But when we adopt more realistic conditions that integrate the slope effect with travel time,the model finds an optimal solution that requires more resources(that is,employing all 24 available fire vehicles)to maximize the coverage of the most vulnerable regions within 27 min.This process achieves 80%of total coverage.The proposed methodology is characterized by a high degree of flexibility,and provides optimized solutions to decision makers,while considering key factors that greatly affect the effectiveness of the initial firefighting attack.

Spatial modeling of the Ulmus pumila growing season in China's temperate zone

To reveal the ecological mechanism of spatial patterns of plant phenology and spatial sensitivity of plant phenology responses to climate change,we used Ulmus pumila leaf unfolding and leaf fall data at 46 stations of China's temperate zone during the period 1986-2005 to simulate 20-year mean and yearly spatial patterns of the beginning and end dates of the Ulmus pumila growing season by establishing air temperature-based spatial phenology models,and validate these models by extensive spatial extrapolation.Results show that the spatial patterns of 20-year mean and yearly February-April or September-November temperatures control the spatial patterns of 20-year mean and yearly beginning or end dates of the growing season.Spatial series of mean beginning dates shows a significantly negative correlation with spatial series of mean February-April temperatures at the 46 stations.The mean spring spatial phenology model explained 90% of beginning date variance(p<0.001) with a Root Mean Square Error(RMSE) of 4.7 days.In contrast,spatial series of mean end dates displays a significantly positive correlation with spatial series of mean September-November temperatures at the 46 stations.The mean autumn spatial phenology model explained 79% of end date variance(p<0.001) with a RMSE of 6 days.Similarly,spatial series of yearly beginning dates correlates negatively with spatial series of yearly February-April temperatures and the explained variances of yearly spring spatial phenology models to beginning date are between 72%-87%(p<0.001),whereas spatial series of yearly end dates correlates positively with spatial series of yearly September-November temperatures and the explained variances of yearly autumn spatial phenology models to end date are between 48%-76%(p<0.001).The overall RMSEs of yearly models in simulating beginning and end dates at all modeling stations are 7.3 days and 9 days,respectively.The spatial prediction accuracies of growing season's beginning and end dates based on both 20-year mean and yearly models are close to the spatial simulation accuracies of these models,indicating that the models have a strong spatial extrapolation capability.Further analysis displays that the negative spatial response rate of growing season's beginning date to air temperature was larger in warmer years with higher regional mean February-April temperatures than in colder years with lower regional mean February-April temperatures.This finding implies that climate warming in winter and spring may enhance sensitivity of the spatial response of growing season's beginning date to air temperature.

Spatial resolution optimization in a THGEM-based UV photon detector

Introduction THick Gas Electron Multiplier(THGEM)is considered in many UV photon detector applications.It has the capability of detecting single photon and imaging with high sensitivity.Operating parameters such as choice of gas mixture,pressure,drift field,drift gap,multiplication voltage,induction field and induction gap play an important role in deciding the spatial resolution of the detector.Detailed simulation study enables to optimize the above-mentioned parameters for a given THGEM-based imaging detector and hence to achieve improved performance for the same.Materials and methods Simulation,using ANSYS and Garfield++,starts with the release of primary electrons at random coordinates on the photocathode plane.They are tracked as they pass through the drift gap and THGEM hole till the electron cloud reaches anode plane.Distribution of electron cloud on the anode plane along X and Y axis is plotted in histogram and fitted with Gaussian function to determine spatial resolution.Ar/CO_(2)(70:30)mixture,which shows higher ETE and lower transverse diffusion,is chosen for this simulation study.Conclusion Transverse diffusion has a major impact on both ETE and the spatial resolution.Lower transverse diffusion coefficient is always desired for having better resolution as well as for ETE.It is found from the simulation study that higher gas pressure,lower drift field and induction field,smaller drift and induction gap can provide optimum detection efficiency with the best spatial resolution.The simulation method proposed here can also be extended to X-ray imaging detectors.

Changes of annual accumulated temperature over Southern China during 1960-2011

The spatial and temporal variations of ≥10℃ annual accumulated temperature （AAT10） were analyzed by using the linear trend line method, cumulative anomaly method and the multiple linear regression model （MLRM） interpolation method based on the daily meteorological observation data from 104 meteorological stations in Southern China and surrounding 39 meteorological stations from 1960 to 2011. The results show that： （1） From time scale point of view, the climatic trend of the AAT10 increased with an average of 7.54℃/decade in Southern China since 1960. The area of AAT10〈6000℃ decreased from 1960 to 2011, and the area of 6000℃〈AAT10〈8000℃ decreased from 1960 to 1979 and increased from 1980 to 2011, and the area of AAT10〉8000℃ increased from 1960 to 2011. （2） From spatial scale point of view, the AAT10 in Southern China reduced with increasing latitude and reduced with increasing altitude. The proportion of the area with 5000℃〈 AAT10〈8000℃ accounted for 70% of the study area, followed by the area of 4000℃〈AAT10 〈5000℃; and the area of AAT10〈4000℃ and AAT10〉8000℃ was the least. Climate trend rate of the AAT10 at 99% of the meteorological stations was greater than zero, which indicated that the AAT10 increased significantly in the central Yunnan province, southern Guangdong province as well as Hainan Island. （3） Comparison of period A （1960-1989） and period B （1980-2011） with the change of temperature zones shows that the boundaries of cool temperate zone, mid-temperate zone and warm temperate zone shifted northward and shrank westward. The northern boundary of north subtropical zone and mid-subtropical zone shifted northward gradually by over 0.5° and 0.5° latitude, respectively. The western part of northern boundary of south subtropical zone and marginal tropical zone shifted northward by 0.2° and 0.4° latitude, respectively. The change of temperature zones was expanded to high altitude and latitude. （4） The increase of the AAT10 is conducive to the production of tropical crops planted, which will increase the planting area that was suitable for tropical crops, and expand the planting boundaries to high latitude and high altitude.

Simulating soil erodibility in southeastern China using a sequential Gaussian algorithm

Soil erodibility(K)is a key factor of soil erosion,and its appropriate quantification and interpolation are vitally important for soil and water conservation.The traditional point-represent-polygon approaches and common kriging method for the estimation of K,however,do not sufficiently represent the original data.The objectives of this study were to simulate the spatial distribution of K using a sequential Gaussian algorithm and analyze the uncertainty in evaluating the risk of soil erodibility in southeastern China.We determined 101 sampling points in the area and collected disturbed soil samples from the 0-20 cm layer at each point.Soil properties were determined,and K was calculated using five common models:the EPIC(Erosion/Productivity Impact Calculator),approximate nomograph,Torri,Shirazi,and Wang models.Among the chosen models,the EPIC model performed the best at estimating K(KEPIC),which ranged from 0.019 to 0.060 t ha h(ha MJ mm)^(-1),with a mean of 0.043 t ha h(ha MJ mm)^(-1).The KEPIC was moderately spatially variable and had a limited spatial structure,increasing from south to north in our study area,and all spatial simulations using the cooperative kriging(CK)interpolation and the sequential Gaussian simulation(SGS)with 10,25,50,100,200,and 500 realizations had acceptable accuracies.The CK interpolation narrowed the range,and the SGS maintained the original characteristics of the calculated data.The proportions of the risk area were 38.0% and 10.1%,when the risk probability for K was 60% and 80%,respectively,and high risk areas were mostly located in the north.The results provide scientific guidance for managing the risk of soil erodibility in southeastern China.