Becoming coca: A materiality approach to a commodity chain analysis of hoja de coca in Colombia

Coca is a controversial plant, existing on the boundary between legality and illegality. This study aims at providing an analytical technique for discussing the problematic of coca in Colombia. Using new theoretical propositions in human geography, a more‐than‐human approach is adopted to encounter coca holistically. The results are a narrative account of coca's social life as experienced by the researcher following its network of non‐cocaine derivatives. An analytical section invokes the Foucauldian dispositif to the drug trade and utilizes concepts of informed materials and technological zones to describe coca outside a political economy discourse. The research finds that coca's dynamic materiality complicates it as a commodity and that these conventional approaches do not fully encapsulate this complexity. By grappling with the messiness of coca's materiality, this paper reveals the multiplicity and interplay of coca's definitions, which lie at the heart of many conflicts.     

基于Markov模型的安宁市土地利用预测

根据安宁市2003～2009年土地利用更新调查数据,利用Markov模型以3年为间隔预测安宁市2012～2021年内的土地利用结构变化。结果表明:2012～2021年安宁市的耕地、园地、林地等农用地大量转变为建设用地;为了保障基本社会生活需求,耕地和园地的农作物生产用地向建设用地转变较少,林地向建设用地转变较多。     

中国大陆地区强震的逆向追踪方法研究

强震发生前, 震源附近的地震活动往往会呈现出一些空间和时间的异常特征, 分析和提取这些异常特征的方法较多。 本文采用了与常规强震预测方法迥异的地震预测方法地震活动反向追踪法(Reverse Tracing of Precursors, RTP), 对中国大陆近30年来的几个M_S≥7.5大震(含2008年5月12日汶川M_S8.0地震)进行了探索性的回溯研究。 结果表明, 该方法在中国大陆大震预测方面有一定的适用性, 并利用该方法对中国大陆地区的未来强震活动做了一定的预测。     

南北地震带等地区强地震在华北中南部的“响应地震”研究

2008年5月12日T14:28汶川发生8.0级大地震,2008年5月13日T5:00江苏省发生滨海ML3.8地震。滨海地震是否是由汶川地震触发的、可指示滨海附近中短期内将发生中强地震的"诱发前震"?基于此,本文进行了如下研究:①统计南北地震带及中国台湾7.3级以上、日本7.5级以上大地震;②统计①中大震发生后3天时间(72 h)内,华北中南部地区发生的M_L 3以上地震;③统计大震发生后1年内在M_L 3地震附近地区(距震中350 km以内)发生的5级以上地震;④对上述3组地震是否存在链式关系进行统计研究,即研究特定条件下的A—F—B链式地震现象在华北中南部的预报效果。研究结果表明:根据目前所拥有的统计资料,无法确定2008年5月13日滨海M_L 3.8地震是由汶川8级地震诱发的"前兆地震"。     

有限m重非齐次马氏链随机变换的一个强极限定理

将赌博系统的随机变换概念推广到有限优重非齐次马氏链情形,采用鞅方法研究了有限m重非齐次马氏链函数的强极限定理,作为推论得到有限m重非齐次马氏链随机变换的若干强极限定理。     

Analysis of the 2012–2013 Torreperogil-Sabiote seismic swarm

This study analyses the temporal clustering, spatial clustering, and statistics of the 2012–2013 Torreperogil-Sabiote (southern Spain) seismic swarm. During the swarm, more than 2200 events were located, mostly at depths of 2–5 km, with magnitude event up to m_bLg 3.9 (M_w 3.7). On the basis of daily activity rate, three main temporal phases are identified and analysed. The analysis combines different seismological relationships to improve our understanding of the physical processes related to the swarm's occurrence. Each temporal phase is characterized by its cumulative seismic moment. Using several different approaches, we estimate a catalog completeness magnitude of m_c≅ 1.5. The maximum likelihood b-value estimates for each swarm phase are 1.11 ± 0.09, 1.04 ± 0.04, and 0.90 ± 0.04, respectively. To test the hypothesis that a b-value decrease is a precursor to a large event, we study temporal variations in b-value using overlapping moving windows. A relationship can be inferred between change in b-value and the regime style of the rupture. b-values are indicators of the stress regime, and influence the size of ruptures. The fractal dimension D₂ is used to perform spatial analysis. Cumulative gamma and beta functions are used to analyse the behaviour of inter-event distances during the earthquake sequence.     

数学形态学和模式识别在建筑物多边形化简中的应用

针对居民地图形化简的一个方面--建筑物多边形的化简,提出一种在与地图比例尺相关的动态栅格和矢量数据相结合的数据模型支持下,综合利用数学形态学和神经网络支持下的模式识别的化简方法.在Visual C 环境下实现基于此方法的系统并进行实验,实验结果说明此方法在保持街区的形态特征上效果明显.这种方法将制图综合知识融入图形化简操作之中,是自动制图综合智能化的一次新的尝试.     

论山地灾害链

山地灾害链是具有灾变条件的山地环境,在致灾因素的作用下,一种山地灾害发生后,引起其他种类山地灾害也相继或滞后发生的灾变现象,通常由泥石流、山洪、滑坡、崩塌、冰崩、雪崩、堰塞湖和水土流失等灾种及其相关灾变现象构成,种类繁多,结构复杂,危害严重.根据山地灾害链的致灾因素不同将其划分成地球内营力作用、外营力作用和人为作用致灾3种类型,并进一步将其划分成8个亚类和128种灾害链形式.分析了山地灾害链的成因,认为山地灾害链是山地灾害的物质、能量和信息在特定条件下相互作用、相互渗透、相互传递和相互转化的结果.通过对山地害链的致灾因素、活动地域与结构特征分析和综合分析显示:山地灾害链的防治难度虽然很大,但除分布在高山和极高山区域、由冰雪崩塌和消融水引发的山地灾害链仅可预防,尚难治理外,其他山地灾害链都是可防、可治的.     

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 land-use-based modelling chain to assess the impacts of Natural Water Retention Measures on Europe’s Green Infrastructure

This article details the process of integrating models to answer a specific policy-driven question: ‘What could be the impact of proposed Natural Water Retention Measures (NWRMs) on Europe’s Green Infrastructure (GI)?’ It describes the new Land Use Modelling Platform (LUMP), now enabling a high spatial scale (100-m) and large coverage (pan-European), whereby several sector-specific models contribute to assessing the impact of regional-level policy on a given spatial topic of concern. The configuration (land claims and land allocations modules) and calibration (accessibility and biophysical suitability) of the LUMP are explained. Four NWRM scenarios (riparian areas, afforestation, grassland and baseline scenario) are configured to run the simulations. For the reference: year 2006, the spatial representation of GI is based on land-use features of a refined version of the CORINE Land Cover (CLC), and resumed as connected components made of nodes and links.

Mathematical morphological image processing and network graph theory model, available from the free software package GUIDOS (the Joint Research Center of the European Commission), enabled the measurement of the GI connectivity and identified most critical links. Results show that the competition for land claimed by different economic sectors, combined with policy-driven rule-sets for the implementation of different NWRMs, yields very different results for the 2030 land-use projections, and subsequently for the morphology of GI. Three indicators associated with the morphology of GI are computed in order to assess the model outputs for 2030. The indicators are computed to answer the following questions: (1) How is the quantity of GI affected by each of the NWRM, and what proportion of that GI is most valuable? (2) What is the location of the most critical nodes and connectors of GI, and what land-use conversions occur under these? (3) Are the average components getting larger or smaller?

Whereas the grassland measure results in the largest net increase of GI, the afforestation measure results in the overall largest number of hectares of key nodes and links within the network. Land conversions occur under the critical GI nodes and links, with a large increase in agricultural areas, especially for the riparian measure under critical nodes and the grassland measure under critical links. Also predominant is the swapping of land from pasture to forest under critical links with the afforestation measure. The riparian measure most increases the average size of GI components, and all three measures contribute to bridging two large components which were divided in the 2006 land-use map, thus increasing the size of the largest component by more than 50%.