Some further clarification on Markov chain random fields and transiograms

The Markov chain random field (MCRF) theory and the transiogram spatial measure were proposed several years ago. Basic sequential simulation algorithms based on simple MCRF models such as the Markov chain sequential simulation algorithm and the Markov chain sequential co-simulation algorithm have been developed and used in a series of application studies. However, misunderstanding of these two ideas and the geostatistical approach built on them arose recently among some researchers in geostatistics. The purpose of this article is to further clarify some issues related to these two ideas, so as to avoid further misunderstanding. For those issues already clarified, trivial, or obviously irrelevant, we do not talk about them here.     

Examining the sensitivity of spatial scale in cellular automata Markov chain simulation of land use change

Understanding the spatial scale sensitivity of cellular automata is crucial for improving the accuracy of land use change simulation. We propose a framework based on a response surface method to comprehensively explore spatial scale sensitivity of the cellular automata Markov chain (CA-Markov) model, and present a hybrid evaluation model for expressing simulation accuracy that merges the strengths of the Kappa coefficient and of Contagion index. Three Landsat-Thematic Mapper remote sensing images of Wuhan in 1987, 1996, and 2005 were used to extract land use information. The results demonstrate that the spatial scale sensitivity of the CA-Markov model resulting from individual components and their combinations are both worthy of attention. The utility of our proposed hybrid evaluation model and response surface method to investigate the sensitivity has proven to be more accurate than the single Kappa coefficient method and more efficient than traditional methods. The findings also show that the CA-Markov model is more sensitive to neighborhood size than to cell size or neighborhood type considering individual component effects. Particularly, the bilateral and trilateral interactions between neighborhood and cell size result in a more remarkable scale effect than that of a single cell size.     

我国人均水资源量分布的俱乐部趋同研究——基于扩展的马尔科夫链模型

研究区域水资源不平衡背后的“俱乐部趋同”现象,对于水资源调节以及水资源均衡配置具有重要的理论和实践意义。基于2004-2015年我国31个省（市、区）的人均水资源数据,利用扩展的马尔科夫链模型检测了不同时长下我国省际水资源量分布的俱乐部趋同现象,并分析了其时间特征。结果表明：即使在5 a的时间积累下,我国人均水资源量仍存在着明显的俱乐部趋同现象,高水平和低水平地区水资源量分布高度固化,且俱乐部趋同程度在2008年开始向恶化的趋势发展,可见当前我国区域水资源调节力度有待进一步加强。最后论文根据各地区人均水资源在考察期各年所属俱乐部类型、类型转移频率等因素对各地区进行了分类,以对区域水资源调节提供支持和参考。     

北京电子信息产业及其价值链空间分布特征研究

作为主导产业部门之一的电子信息产业在北京具有较强的产业实力及较好的发展前景。本研究通过分析电子信息产业和大型电子信息企业主要价值链环节在北京的分布情况, 总结了电子信息产业及其价值链的空间分布特征。研究发现:北京市电子信息产业郊区化趋势明显且呈集聚分布, 大型电子信息企业主要价值链环节也呈集聚分布, 生产环节的远郊化明显, 呈现多中心空间结构;北京市主要电子信息产业集聚区呈现明显的功能分工、大型电子信息企业主要价值链环节呈现明显的区域分工。     

多尺度视角下的产业价值链与空间升级研究框架与展望

改革开放以来,在出口导向的产业发展模式驱动下,中国积极加入全球化进程并快速实现产业升级。进入21世纪后,在面临成本上升和制度环境变化等诸多危机和挑战的情况下,中国制造业开始了新一轮的产业升级。这从根本上重塑了产业组织和空间结构,改变着中国制造业在地方、区域乃至全球出口市场中的角色功能和影响力。基于文献分析,论文将产业升级划分为价值链升级和空间升级2种模式,并分别从全球、国家、区域、地方、企业等多尺度视角下探讨了中国产业升级的机制及其影响,得出以下主要结论：① 价值链升级和空间升级现象相互依赖、相互作用,是产业升级的2个侧面,综合两者的分析框架能更全面地反映中国产业升级的现状;② 中国产业在升级模式上体现了复杂多样性,已有研究关注的产业升级模式较为单一,未来应更加关注中国产业升级路径和方式的非线性、复杂性和多元性特征;③ 中国产业升级是由全球、国家、区域、地方、企业等多重尺度力量相互耦合下共同驱动的,透过多元地理尺度融合的分析框架解读产业价值链升级和空间升级机制,才能更好地理解中国产业的发展和赶超之路。     

我国中部六省产业集聚与扩散的空间计量

＂中部崛起＂战略给中部六省各地区的大发展带来了新的机遇。进入十二五时期,中部地区有必要总结之前发展过程中的经验,从产业结构调整和升级着手,掌握产业更新规律,尽可能规避经济发展中的不利因素,实现产业结构升级承托的区域经济跨越式可持续发展。本文收集整理了我国中部六省共88个地市2000年至2008年第二产业数据,采用LIS...     

Investigating the system dynamics technique for the modeling and simulation of the development of spatial data infrastructures

Spatial data infrastructure (SDI) is a complex system for which huge investments are being made worldwide. These large-scale investments in the development of SDIs incontrovertibly require reliable design and planning that guarantee a successful outcome. One approach to deal with such an expectation is to model the development process of the SDI system over time. If the model can be translated into the computer-based environment to be used as a virtual world, then the real situation can also be simulated. Such a simulation will enable the SDI coordinators/managers to gain knowledge about the behavior of the system under different decisions and situations and eventually help them to better develop the SDI through the informed decision making. However, a limited number of tools and techniques are currently available in the SDI modeling history in terms of the modeling and simulation of such a complex system. The system dynamics technique based on systems theory is a method for modeling and managing the feedback systems that are complex, dynamic and nonlinear over time. This article addresses the applicability of the system dynamics technique for modeling and simulating the development process of SDIs. It is argued that the system dynamics technique is capable of modeling the interactions among the factors affecting the SDI, the feedback loops and the delays. It is also highlighted that an SDI model based on the system dynamics technique enables the SDI coordinators/managers to simulate the effect of different factors or decisions on various aspects of SDI and evaluate alternative decisions and/or policies prior to making any commitment.     

基于超效率SBM模型的中国城市碳排放绩效时空演变格局及预测

由CO₂排放所引起的气候变化是当今社会所关注的热点话题,提高碳排放绩效是碳减排的重要途径。目前关于碳排放绩效的研究多从国家尺度和行业尺度进行探讨,由于能源消耗统计数据有限,缺乏城市尺度的研究。基于遥感模拟反演的1992—2013年中国各城市碳排放数据,采用超效率SBM模型对城市碳排放绩效进行测定,构建马尔可夫和空间马尔可夫概率转移矩阵,首次从城市尺度探讨了中国碳排放绩效的时空动态演变特征,并预测其长期演变的趋势。研究表明,中国城市碳排放绩效均值呈现波动中稳定上升的趋势,但整体仍处于较低的水平,未来城市碳排放绩效仍具有较大的提升空间,节能减排潜力大;全国城市碳排放绩效空间格局呈现“南高北低”特征,城市间碳排放绩效水平的差异性显著;空间马尔科夫概率转移矩阵结果显示,中国城市碳排放绩效类型转移具有稳定性,且存在“俱乐部收敛”现象,地理背景在中国城市碳排放绩效类型转移过程中发挥重要作用;从长期演变的趋势预测来看,中国碳排放绩效未来演变较为乐观,碳排放绩效随时间的推移而逐步提升,碳排放绩效分布呈现向高值集中的趋势。因此未来中国应继续加大节能减排力度以提高城市碳排放绩效,实现国家节能减排目标;同时不同地理背景的邻域城市之间应建立完善的经济合作联动机制,以此提升城市碳排放绩效水平并追求经济增长与节能减排之间协调发展,从而实现低碳城市建设和可持续发展。     

Evaluation,simulation, and optimization of land use spatial patterns for high-quality development: A case study of Zhengzhou city,China

Journal of Geographical Sciences - High-intensity land use and resource overloaded-induced regional land use spatial pattern (LUSP) are essential and challenging for high-quality development. The...     

上海迪士尼在建景区客源市场空间结构预测————旅游引力模型的修正及应用

客源市场预测是旅游科学决策的关键,由于缺乏历史数据,对尚未开业的旅游景区客源市场预测成为研究的难点.上海迪士尼乐园投资巨大,影响深远,广受关注,开园在即,迫切需要学界从理论层面对其客源市场展开研究.遵循"要素选取--系统分析--模型构建--市场预测--模型验证"的研究思路,对已有旅游引力模型进行修正:① 引入出游意愿,用百度指数进行测度,以明确旅游目的地偏好;② 引入出游率,用旅游人口取代总人口,以界定客源规模基数.而且修正模型中解释变量之间没有显著相关.基于2009-2013年香港迪士尼乐园内地客源市场数据,中国各省市区以及重点城市社会经济数据,运用修正引力模型,预测上海迪士尼乐园国内客源市场空间结构.研究表明:① 迪士尼乐园的强大品牌效应,在一定程度上弱化了空间阻尼的影响,进而导致内地游客对迪士尼乐园的旅游需求整体上缺乏弹性;② 上海迪士尼乐园国内客源市场在空间分布格局上,存在明显的近域指向,东部指向和大中城市指向特征;③ 经过交互验证,用修正引力模型推算的理论值与实际值的吻合程度更高,预测效果更好.