The appraisal of seven economic zones in China

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Linear interpolation and joint model fitting of experimental transiograms for Markov chain simulation of categorical spatial variables

In Markov chain random field (MCRF) simulation of categorical spatial variables with multiple classes, joint modeling of a large number of experimental auto and cross-transiograms is needed. This can be tedious when mathematical models are used to fit the complex features of experimental transiograms. Linear interpolation can be used to perform the joint modeling quickly regardless of the number and the complexity of experimental transiograms. In this paper, we demonstrated the mathematical validity of linear interpolation as a joint transiogram-modeling method, explored its applicability and limitations, and tested its effect on simulated results by case studies with comparison to the joint model-fitting method. Simulations of a five-class variable showed little difference in patterns for interpolated and fitted transiogram models when samples were sufficient and experimental transiograms were in regular shapes; however, they neither showed large difference between these two kinds of transiogram models when samples were relatively sparse, which might indicate that MCRFs were not much sensitive to the difference in the detail of the two kinds of transiogram models as long as their change trends were identical. If available, expert knowledge might play an important role in transiogram modeling when experimental transiograms could not reflect the real spatial variation of the categorical variable under study. An extra finding was that class enclosure feature (i.e., a class always appears within another class) was captured by the asymmetrical property of transiograms and further generated in simulated patterns, whereas this might not be achieved in conventional geostatistics. We conclude that (i) when samples are sufficient and experimental transiograms are reliable, linear interpolation is satisfactory and more efficient than model fitting; (ii) when samples are relatively sparse, choosing a suitable lag tolerance is necessary to obtain reliable experimental transiograms for linear interpolation; (iii) when samples are very sparse (or few) and experimental transiograms are erratic, coarse model fitting based on expert knowledge is recommended as a better choice whereas both linear interpolation and precise model fitting do not make sense anymore.     

A geospatial approach to assessing land change in the built-up landscape of Wa Municipality of Ghana

ABSTRACT

Urban landscapes are changing in response to changes in socio-economic conditions. Land change scientists seek to understand these land dynamics in the coupled human-environment system of urban landscapes. This study assessed land change in the built-up area of Wa Municipality between 1986 and 2016 using Landsat images. We used the Support Vector Machine algorithm for classifying the images. We recorded image classification accuracies of 97%, 95%, 92% and 96% for the 1986, 1996, 2006 and 2016 classified images, respectively. Our study finds that over the 1986–2016 period, agricultural land and bare land transitioned to build-up land by 9.23% and 3.79%, respectively, as compared to 2.79% for vegetation and 0.05% for water. Our in-municipal level analysis thus shows that urban landscapes could expand more sustainably by targeting other dominant land categories instead of the vegetation cover. The findings in this paper could serve as a spatial model for planning and reducing the unintended socio-ecological impacts of expansion in the built-up area.     

用非线性分类筛选模式做短期气候预测的尝试

在相关普查的基础上,使用最优分割挑选因子,逐步对预报量进行分类,按照业务预报的需求,形成分类预报模式,取得了较好的试报、预报效果。     

中国城市PM2.5污染对居民主观幸福感的影响及支付意愿研究

利用2015年中国40个城市大规模问卷调查数据,采用贝叶斯多层级有序分类响应模型探讨了PM_2.5污染对居民主观幸福感的影响,进而核算居民为减轻PM_2.5污染的支付意愿。研究发现：PM_2.5污染对主观幸福感具有显著的负向影响,PM_2.5污染每增加1个单位,主观幸福感将下降0.040个百分点;家庭月收入对主观幸福感具有显著的正向影响,家庭月收入每增加1个单位,主观幸福感将提升0.026个百分点。支付意愿不仅受到家庭收入水平的影响,还受到居民所处城市空气质量的影响;在承受PM_2.5污染的最初阶段,居民为改善空气质量的支付意愿最高。居民为改善空气质量愿意支付的金额平均为226元,占家庭月总收入的2.274%。     

基于本体的地理空间实体类型语义相似度计算模型的研究

地理空间实体类型语义相似度对于类型数据库综合具有重要理论意义。本文以本体作为揭示实体深层次语义信息的方法论,提出了符合认知特性的地理空间实体类型语义相似度计算模型,并通过计算土地利用类型语义相似度对模型进行了验证.     

Decrease in Danish semi-natural grassland – a social construct or a real-world change?

During the past century, the western hemisphere has seen a general trend of agricultural expansion on the behalf of semi-natural habitat types, such as heathlands and meadows. This has been documented in numerous studies of land use change. This trend is reflected in today’s European rural landscapes, which are dominated by intensive agriculture. However, many of these studies are based on cartographical sources, such as topographical and thematic maps, and thus prone to uncertainties regarding classification systems over time, variations in definitions of land use categories and lacking documentations of land use definitions. For this study, we conducted two change detection studies, covering the same four study areas in Denmark. The first study was based on topographic maps, and indicates a strong decline in the amount of semi-natural grassland (SNG). This was contrasted by the second study, which was based on an interpretation of aerial photos, and which indicated a much less pronounced reduction of SNGs. Year to year comparisons of these two analyses also revealed large discrepancies in the coverage of SNGs. Our results indicate that change detection studies must account for uncertainties in classifications and results should be interpreted with caution.     

Objective quantitative spatial verification of distributed snow cover simulations—an experiment for the whole of Switzerland / Vérification quantitative spatiale objective de simulations distribuées de la couche de neige—une étude pour l'ensemble de la Suisse

Abstract

Skill measures based on 2 × 2 contingency tables were adopted for the quantitative internal verification of snow cover simulations with the distributed hydrological model PREVAH, which provided a high resolution simulation of the hydrological cycle for Switzerland for the 1981–2000 period. Simulated snow cover has been compared to data monitored at 103 stations. The skill measures provide valuable quantitative indications on the correspondence of the modelled and observed values. The analysis with objective scores reveals better model estimates of snow cover presence and distribution in locations above 1000 m a.s.l., relative to lower areas. For explicit spatial verification, 20 NOAA-AVHRR snow cover scenes were compared to the model results. The spatial and temporal differences in the agreement between observed and simulated snow cover patterns were assessed. PREVAH provides reliable snow cover simulations. The results also reveal that scores from 2 × 2 contingency tables provide objective methodological support in the quantitative estimation of the agreement between observed and simulated spatial patterns.     

Combining spatial transition probabilities for stochastic simulation of categorical fields

Categorical spatial data, such as land use classes and socioeconomic statistics data, are important data sources in geographical information science (GIS). The investigation of spatial patterns implied in these data can benefit many aspects of GIS research, such as classification of spatial data, spatial data mining, and spatial uncertainty modeling. However, the discrete nature of categorical data limits the application of traditional kriging methods widely used in Gaussian random fields. In this article, we present a new probabilistic method for modeling the posterior probability of class occurrence at any target location in space-given known class labels at source data locations within a neighborhood around that prediction location. In the proposed method, transition probabilities rather than indicator covariances or variograms are used as measures of spatial structure and the conditional or posterior (multi-point) probability is approximated by a weighted combination of preposterior (two-point) transition probabilities, while accounting for spatial interdependencies often ignored by existing approaches. In addition, the connections of the proposed method with probabilistic graphical models (Bayesian networks) and weights of evidence method are also discussed. The advantages of this new proposed approach are analyzed and highlighted through a case study involving the generation of spatial patterns via sequential indicator simulation.