三个全球气候模式对中国气温季节变化模拟能力的空间差异特征分析

针对第五次国际耦合模式比较计划（CMIP5）中3个全球气候模式对中国气温季节变化模拟能力的空间差异特征进行具体分析。结果表明：BCC-CSM1.1（m）模式和GFDL-CM3模式能够再现中国气温的季节性变化,在中国东部地区模拟能力较强,平均绝对误差和均方根误差均较小,在中国西部地区模拟能力较弱,平均绝对误差和均方根误差较大。与BCC-CSM1.1（m）和GFDL-CM3模式相比,HADGEM2-ES模式再现中国地区气温季节变化的能力最弱,平均绝对误差和均方根误差在西部部分地区、内蒙古地区和东北地区较大,在华南地区南部较小。在相同模式下,日平均气温模拟效果最好,其次是日最低气温,日最高气温模拟效果最差。纬度、经度、海拔和坡度对气候模式模拟效果的影响存在模式间的差异,而坡向和地形遮蔽度对模式的模拟效果无明显影响。     

A lightweight ensemble spatiotemporal interpolation model for geospatial data

ABSTRACT

Missing data is a common problem in the analysis of geospatial information. Existing methods introduce spatiotemporal dependencies to reduce imputing errors yet ignore ease of use in practice. Classical interpolation models are easy to build and apply; however, their imputation accuracy is limited due to their inability to capture spatiotemporal characteristics of geospatial data. Consequently, a lightweight ensemble model was constructed by modelling the spatiotemporal dependencies in a classical interpolation model. Temporally, the average correlation coefficients were introduced into a simple exponential smoothing model to automatically select the time window which ensured that the sample data had the strongest correlation to missing data. Spatially, the Gaussian equivalent and correlation distances were introduced in an inverse distance-weighting model, to assign weights to each spatial neighbor and sufficiently reflect changes in the spatiotemporal pattern. Finally, estimations of the missing values from temporal and spatial were aggregated into the final results with an extreme learning machine. Compared to existing models, the proposed model achieves higher imputation accuracy by lowering the mean absolute error by 10.93 to 52.48% in the road network dataset and by 23.35 to 72.18% in the air quality station dataset and exhibits robust performance in spatiotemporal mutations.     

Editorial

Abstract

The analysis of geographical information is compared with other production processes in which a user can only accept an end-product if it meets certain quality requirements. Whereas users are responsible for defining the levels of quality they need to use the results of the analyses of geographical information systems in their work, database managers, experts and modellers could greatly assist users to achieve the quality of results they seek by formalizing information on: (1) data collection, level of resolution and quality; (2) the use of the basic analytical functions of the geographical information system; and (3) the data requirements, sensitivity and error propagation in models. These meta-data could be incorporated in a knowledge base alongside the geographical information system where, together with procedures for on-line error propagation, a user could be advised on the best way to achieve a desired aim. If the analysis showed that the original constellation of data, methods and models could not achieve the aim with the desired quality, the intelligent geographical information system would present a range of alternative strategies—better methods, more data, different data, better models, better model calibration, or better spatial resolution—and their costs by which the user's aims could reasonably be achieved.     

On the use of dimensioned measures of error to evaluate the performance of spatial interpolators

Spatial cross‐validation and average‐error statistics are examined with respect to their abilities to evaluate alternate spatial interpolation methods. A simple cross‐validation methodology is described, and the relative abilities of three, dimensioned error statistics—the root‐mean‐square error (RMSE), the mean absolute error (MAE), and the mean bias error (MBE)—to describe average interpolator performance are examined. To illustrate our points, climatologically averaged weather‐station temperatures were obtained from the Global Historical Climatology Network (GHCN), Version 2, and then alternately interpolated spatially (gridded) using two spatial‐interpolation procedures. Substantial differences in the performance of our two spatial interpolators are evident in maps of the cross‐validation error fields, in the average‐error statistics, as well as in estimated land‐surface‐average air temperatures that differ by more than 2°C. The RMSE and its square, the mean‐square error (MSE), are of particular interest, because they are the most widely reported average‐error measures, and they tend to be misleading. It (RMSE) is an inappropriate measure of average error because it is a function of three characteristics of a set of errors, rather than of one (the average error). Our findings indicate that MAE and MBE are natural measures of average error and that (unlike RMSE) they are unambiguous.     

Map-image registration accuracy using least-squares polynomials

Abstract

Results of a simulation study of map-image rectification accuracy are reported. Sample size, spatial distribution pattern and measurement errors in a set of ground control points, and the computational algorithm employed to derive the estimate of the parameters of a least-squares bivariate map-image transformation function, are varied in order to assess the sensitivity of the procedure. Standard errors and confidence limits are derived for each of 72 cases, and it is shown that the effects of all four factors are significant. Standard errors fall rapidly as sample size increases, and rise as the control point pattern becomes more linear. Measurement error is shown to have a significant effect on both accuracy and precision. The Gram-Schmidt orthogonal polynomial algorithm performs consistently better than the Gauss-Jordan matrix inversion procedure in all circumstances.     

Developing and testing of an error propagation model for GIS overlay operations

Abstract

This study examines the propagation of thematic error through GIS overlay operations. Existing error propagation models for these operations are shown to yield results that are inconsistent with actual levels of propagation error. An alternate model is described that yields more consistent results. This model is based on the frequency of errors of omission and commission in input data. Model output can be used to compute a variety of error indices for data derived from different overlay operations.     

Development and test of an error model for categorical data

Abstract

An error model for spatial databases is defined here as a stochastic process capable of generating a population of distorted versions of the same pattern of geographical variation. The differences between members of the population represent the uncertainties present in raw or interpreted data, or introduced during processing. Defined in this way, an error model can provide estimates of the uncertainty associated with the products of processing in geographical information systems. A new error model is defined in this paper for categorical data. Its application to soil and land cover maps is discussed in two examples: the measurement of area and the measurement of overlay. Specific details of implementation and use are reviewed. The model provides a powerful basis for visualizing error in area class maps, and for measuring the effects of its propagation through processes of geographical information systems.     

中国1995—2000年省间人口迁移模拟与迁移因素对迁移规模贡献的分解

尝试用泊松模拟方法建立人口迁移模型,并且与传统人口迁移模型的结果进行比较,说明泊松人口迁移模型的优点.本研究使用一种新的人口迁移因素分解方法,在人口迁移模型的基础上,估计空间因素、迁入地和迁出地因素的空间结构、迁入地迁出地因素本身对人口迁移规模的贡献.本研究使用的实例数据是中国2000年人口普查得到的1995—2000年省间人口迁移数据.     

Improving Coal Quality Estimations with Geostatistics and Geophysical Logs

In most modern coal mines, there are many coal quality parameters that are measured on samples taken from boreholes. These data are used to generate spatial models of the coal quality parameters, typically using inverse distance as an interpolation method. At the same time, downhole geophysical logging of numerous additional boreholes is used to measure various physical properties but no coal quality samples are taken. The work presented in this paper uses two of the most important coal quality variables—ash and volatile matter—and assesses the efficacy of using a number of geostatistical interpolation methods to improve the accuracy of the interpolated models, including the use of auxiliary variables from geophysical logs. A multivariate spatial statistical analysis of ash, volatile matter and several auxiliary variables is used to establish a co-regionalization model that relates all of the variables as manifestations of an underlying geological characteristic. A case study of a coal mine in Queensland, Australia, is used to compare the interpolation methods of inverse distance to ordinary kriging, universal kriging, co-kriging, regression kriging and kriging with an external drift. The relative merits of these six methods are compared using the mean error and the root mean square error as measures of bias and accuracy. The study demonstrates that there is significant opportunity to improve the estimations of coal quality when using kriging with an external drift. The results show that when using the depth of a sample as an external drift variable there is a significant improvement in the accuracy of estimation for volatile matter, and when using wireline density logs as the drift variable there is improvement in the estimation of the in situ ash. The economic benefit of these findings is that cheaper proxies for coal quality parameters can significantly increase data density and the quality of estimations.

     

Forthcoming events

Spatial data uncertainty models (SDUM) are necessary tools that quantify the reliability of results from geographical information system (GIS) applications. One technique used by SDUM is Monte Carlo simulation, a technique that quantifies spatial data and application uncertainty by determining the possible range of application results. A complete Monte Carlo SDUM for generalized continuous surfaces typically has three components: an error magnitude model, a spatial statistical model defining error shapes, and a heuristic that creates multiple realizations of error fields added to the generalized elevation map. This paper introduces a spatial statistical model that represents multiple statistics simultaneously and weighted against each other. This paper's case study builds a SDUM for a digital elevation model (DEM). The case study accounts for relevant shape patterns in elevation errors by reintroducing specific topological shapes, such as ridges and valleys, in appropriate localized positions. The spatial statistical model also minimizes topological artefacts, such as cells without outward drainage and inappropriate gradient distributions, which are frequent problems with random field-based SDUM. Multiple weighted spatial statistics enable two conflicting SDUM philosophies to co-exist. The two philosophies are ‘errors are only measured from higher quality data’ and ‘SDUM need to model reality’. This article uses an automatic parameter fitting random field model to initialize Monte Carlo input realizations followed by an inter-map cell-swapping heuristic to adjust the realizations to fit multiple spatial statistics. The inter-map cell-swapping heuristic allows spatial data uncertainty modelers to choose the appropriate probability model and weighted multiple spatial statistics which best represent errors caused by map generalization. This article also presents a lag-based measure to better represent gradient within a SDUM. This article covers the inter-map cell-swapping heuristic as well as both probability and spatial statistical models in detail.     

Representing and reducing error in natural‐resource classification using model combination

Artificial Intelligence (AI) models such as Artificial Neural Networks (ANNs), Decision Trees and Dempster–Shafer's Theory of Evidence have long claimed to be more error‐tolerant than conventional statistical models, but the way error is propagated through these models is unclear. Two sources of error have been identified in this study: sampling error and attribute error. The results show that these errors propagate differently through the three AI models. The Decision Tree was the most affected by error, the Artificial Neural Network was less affected by error, and the Theory of Evidence model was not affected by the errors at all. The study indicates that AI models have very different modes of handling errors. In this case, the machine‐learning models, including ANNs and Decision Trees, are more sensitive to input errors. Dempster–Shafer's Theory of Evidence has demonstrated better potential in dealing with input errors when multisource data sets are involved. The study suggests a strategy of combining AI models to improve classification accuracy. Several combination approaches have been applied, based on a ‘majority voting system’, a simple average, Dempster–Shafer's Theory of Evidence, and fuzzy‐set theory. These approaches all increased classification accuracy to some extent. Two of them also demonstrated good performance in handling input errors. Second‐stage combination approaches which use statistical evaluation of the initial combinations are able to further improve classification results. One of these second‐stage combination approaches increased the overall classification accuracy on forest types to 54% from the original 46.5% of the Decision Tree model, and its visual appearance is also much closer to the ground data. By combining models, it becomes possible to calculate quantitative confidence measurements for the classification results, which can then serve as a better error representation. Final classification products include not only the predicted hard classes for individual cells, but also estimates of the probability and the confidence measurements of the prediction.     

Incorporating spatial association into statistical classifiers: local pattern-based prior tuning

ABSTRACT

This paper proposes a new classification method for spatial data by adjusting prior class probabilities according to local spatial patterns. First, the proposed method uses a classical statistical classifier to model training data. Second, the prior class probabilities are estimated according to the local spatial pattern and the classifier for each unseen object is adapted using the estimated prior probability. Finally, each unseen object is classified using its adapted classifier. Because the new method can be coupled with both generative and discriminant statistical classifiers, it performs generally more accurately than other methods for a variety of different spatial datasets. Experimental results show that this method has a lower prediction error than statistical classifiers that take no spatial information into account. Moreover, in the experiments, the new method also outperforms spatial auto-logistic regression and Markov random field-based methods when an appropriate estimate of local prior class distribution is used.     

Measuring Global Spatial Autocorrelation with Data Reliability Information

Assessing spatial autocorrelation (SA) of statistical estimates such as means is a common practice in spatial analysis and statistics. Popular SA statistics implicitly assume that the reliability of the estimates is irrelevant. Users of these SA statistics also ignore the reliability of the estimates. Using empirical and simulated data, we demonstrate that current SA statistics tend to overestimate SA when errors of the estimates are not considered. We argue that when assessing SA of estimates with error, one is essentially comparing distributions in terms of their means and standard errors. Using the concept of the Bhattacharyya coefficient, we proposed the spatial Bhattacharyya coefficient (SBC) and suggested that it should be used to evaluate the SA of estimates together with their errors. A permutation test is proposed to evaluate its significance. We concluded that the SBC more accurately and robustly reflects the magnitude of SA than traditional SA measures by incorporating errors of estimates in the evaluation. Key Words: American Community Survey, Geary ratio, Moran’s I, permutation test, spatial Bhattacharyya coefficient.     

福建省区域尺度太阳总辐射模拟估算研究

比较了3种以常规地面气象资料为基础估算太阳总辐射的气候学估算模型(晴天辐射模型、天文辐射模型和MTCLIM气候模型),并以2004年福建省仅有的福州站和建瓯站太阳总辐射实测值进行验证.研究结果表明:以晴天总辐射为基值计算太阳总辐射估算模型的精度最高,运用该模型在ARCGIS支持下对福建省太阳总辐射进行空间插值,拟合了福建省区域尺度太阳总辐射月总量和年总量的空间分布,为省区域尺度生态系统碳循环模型,水文模型及植被净第一性生产力的估算研究提供了重要的空间表达参数.     

福建省区域尺度太阳总辐射模拟估算研究

比较了3种以常规地面气象资料为基础估算太阳总辐射的气候学估算模型(晴天辐射模型、天文辐射模型和MTCLIM气候模型),并以2004年福建省仅有的福州站和建瓯站太阳总辐射实测值进行验证.研究结果表明:以晴天总辐射为基值计算太阳总辐射估算模型的精度最高,运用该模型在ARCGIS支持下对福建省太阳总辐射进行空间插值,拟合了福建省区域尺度太阳总辐射月总量和年总量的空间分布,为省区域尺度生态系统碳循环模型,水文模型及植被净第一性生产力的估算研究提供了重要的空间表达参数.     

Assessment of the importance of moderate spatial resolution cloud climatology data in solar radiation modeling

Improving solar radiation models is critical for supporting the increase in solar energy usage and modeling ecosystem dynamics. However, coarse spatial resolutions of solar radiation models overlook the impacts resulting from spatial variability of clouds at meso- and micro-scales. To address this problem, Moderate Resolution Imaging Spectroradiometer (MODIS) cloud climatology developed by the National Severe Storms Laboratory was used to relate cloudiness to surface solar radiation observations. We developed a linear regression model between the surface solar radiation and MODIS cloud climatology and used the model to estimate average radiation across Oklahoma. Furthermore, the study compared the average error and coefficient of determination to measured ground radiation. Error analysis of the regression model showed that the differences between observed radiation and estimated radiation were spatially autocorrelated for the Aqua MODIS satellite scan. This suggests cloudiness alone is not sufficient to predict surface solar radiation. This study found that simple cloud datasets alone can account for approximately 50% of the variation in observed solar radiation at 250-m spatial resolution, but additional datasets such as optical depth, elevation, and slope are needed to accurately explain spatial distributions of incoming shortwave radiation.     

基于IBIS模型的1955~2006年中国土壤温度模拟及时空演变分析

基于IBIS模型对中国1955~2006年的土壤上层1m的年平均与月平均土壤温度进行模拟,并利用全国气象站点土壤温度观测数据对模拟结果进行验证,结果显示中国南方区的模拟效果优于北方及青藏高原区,春、夏、秋三季模拟效果优于冬季,总体而言取得了较满意的效果。基于模拟结果,利用Mann-Kendall方法对中国1955~2006年年平均和月平均土壤温度进行趋势分析的结果表明,年平均土壤温度,中国北方呈显著上升趋势,南方呈非显著上升趋势,四川盆地、贵州中部、藏东南及天山地区等小部分区域呈现显著或非显著下降趋势;月平均土壤温度,北方基本保持显著上升趋势,南方地区7~9月份总体呈现出下降的趋势,8月份最为显著。     

A review of assessment methods for cellular automata models of land-use change and urban growth

ABSTRACT

Cellular automata (CA) models are in growing use for land-use change simulation and future scenario prediction. It is necessary to conduct model assessment that reports the quality of simulation results and how well the models reproduce reliable spatial patterns. Here, we review 347 CA articles published during 1999–2018 identified by a Scholar Google search using ‘cellular automata’, ‘land’ and ‘urban’ as keywords. Our review demonstrates that, during the past two decades, 89% of the publications include model assessment related to dataset, procedure and result using more than ten different methods. Among all methods, cell-by-cell comparison and landscape analysis were most frequently applied in the CA model assessment; specifically, overall accuracy and standard Kappa coefficient respectively rank first and second among all metrics. The end-state assessment is often criticized by modelers because it cannot adequately reflect the modeling ability of CA models. We provide five suggestions to the method selection, aiming to offer a background framework for future method choices as well as urging to focus on the assessment of input data and error propagation, procedure, quantitative and spatial change, and the impact of driving factors.