Hypercube-Based Visualization Architecture for Web-Based Environmental Geospatial Information Systems

The geospatial field significantly influences the development of the environmental domain, including a wide range of geospatial and cartographic information systems, such as Geographic Information Systems (GIS), Multimedia Atlas Information Systems (MAIS) and more recently web-based GIS (Web-GIS). In this context, we introduce web-based environmental geospatial information systems (Web-EGIS) as a special case of Web-GIS, aiming to integrate the functionalities of geospatial information systems with the enormous quantity of specialized, distributed and highly heterogeneous environmental geo-referenced data and services. We define three main characteristics of the Web-EGIS. The first is a generic hypercube-based data organization and visualization. The next characteristic is a standard-based, three-tier service-oriented architecture. The third and last characteristic is the traceability of the architectural and design decisions, for which we introduce the new concept of an ‘Environmental Application Context’, arguing that not only the functional but also the non-functional requirements (NFRs) have an important role in defining the architecture, software components and data services of such systems. In a nutshell, a Web-EGIS is characterized by a coherent user experience through the hypercube-based visualization concept, technically supported by a service-oriented architecture that is structured according to an extensive analysis of NFRs. On this basis, we introduce the Geodata visualization and interactive training environment (GeoVITe) Platform for Interdisciplinary Environmental Research as a reference implementation of a Web-EGIS with its basic design requirements, integrative hypercube-based visualization for heterogeneous data sources, extended web cartography functionalities, and its de facto system architecture. The concept of the hypercube-based visualization is better transferred to the system architecture by understanding the ‘Environmental Application Context’ of this particular Web-EGIS, which also makes it easier to maintain and enhance. The resulting system is a support platform for research activities in the environmental domain.     

基于情境感知和序列模式挖掘的气象学习资源推荐算法

随着互联网的快速发展,气象部门职工作为学习者可以获得的学习资源得到极大丰富。信息超载导致检索合适的在线学习资源时遇到了困难;学习者在不同学习环境和序列访问模式上也有不同的学习需求。但是,现有的推荐系统,如基于内容的推荐和协同过滤,没有结合学习者的情境和序列访问模式,推荐结果准确度不高。本文提出了一种结合情境感知、序列模式挖掘和协同过滤算法的混合推荐算法来为学习者推荐学习资源。混合推荐算法中,情境感知被用来整合学习者的情境信息,如知识水平和学习目标;序列模式挖掘被用来对网络日志进行挖掘,发现学习者的序列访问模式;协同过滤被用来根据学习者的情境数据和序列访问模式为目标学习者计算预测并生成建议。实验和应用效果表明,该混合推荐算法推荐的质量和准确性方面优于其他推荐算法。     

Complexity theory, spatial simulation models, and land use dynamics in the Northern Ecuadorian Amazon

Our research questions and analytical approaches are used to examine coupled human-natural systems in the Northern Ecuadorian Amazon. They are based on complexity theory and extend from our earlier work in Cellular Automata (CA) in which land use/land cover (LULC) change patterns were spatially simulated to examine deforestation and agricultural extensification on household farms. The basic intent is to understand linkages between people and the environment by explicitly considering pattern-process relationships and the nature of feedback mechanisms among social, biophysical, and geographical factors that influence LULC dynamics within the study area. In this research, we describe how our CA modeling approach emphasizes the human dimensions of LULC change by including socio-economic and demographic characteristics at the household-level along with biophysical data that describe the resource endowments of farms, geographic accessibility of farms to roads and communities, and the evolving nature of human-environment interactions over time and space in response to exogenous and endogenous factors.A LULC change scenario is examined by comparing model outcomes generated for a base CA model and an alternative CA model to explore the effects of increases in household income on land use change patterns at the farm level, achieved as a consequence of improved geographic accessibility to roads and communities and increased off-farm employment as a household livelihood strategy. Growth or transitions rules in our CA model, as well as neighborhood associations are sensitive to socio-economic and demographic factors of households, resource endowments of farms, geographic accessibility, and the uncertainty associated with peasant farming in a frontier setting. Model outcomes indicate that increases in household income are associated with more land in pasture and more land being cultivated for crops as a result of greater access to agricultural markets. In addition, more land in secondary forest succession occurs as a consequence of greater access to roads and communities, thereby, affording a better opportunity for off-farm employment and greater levels of household income.     

一种改进的空间上下文点云分类方法

考虑到点云数据具有线性分布和密度不均匀的特点,以及现有复杂场景点云分类方法中缺少对非局部空间上下文信息的有效利用,提出了一种改进的空间上下文点云分类方法。该方法在提取点云数据顾及曲率的自适应邻域的基础上,首先估算点云局部特征与依赖性空间上下文,并基于超级体素提取分布性空间上下文,最后采用高阶条件随机场模型,实现对点云数据的自动分类,避免了利用单一点云局部特征分类的局限性。试验结果表明,本文方法能够有效提高点云数据地物分类精度。     

A context-sensitive correlated random walk: a new simulation model for movement

Computational Movement Analysis focuses on the characterization of the trajectory of individuals across space and time. Various analytic techniques, including but not limited to random walks, Brownian motion models, and step selection functions have been used for modeling movement. These fall under the rubric of signal models which are divided into deterministic and stochastic models. The difficulty of applying these models to the movement of dynamic objects (e.g. animals, humans, vehicles) is that the spatiotemporal signal produced by their trajectories a complex composite that is influenced by the Geography through which they move (i.e. the network or the physiography of the terrain), their behavioral state (i.e. hungry, going to work, shopping, tourism, etc.), and their interactions with other individuals. This signal reflects multiple scales of behavior from the local choices to the global objectives that drive movement. In this research, we propose a stochastic simulation model that incorporates contextual factors (i.e. environmental conditions) that affect local choices along its movement trajectory. We show how actual global positioning systems observations can be used to parameterize movement and validate movement models and argue that incorporating context is essential in modeling movement.     

长江经济带区域合作的格局与演变

梳理区域合作的政治经济环境演变,试图对长江经济带区域合作的主体、主题、形式和发展水平等进行综合分析。流域管治事务复杂,部门管理与行政区管理交叉,造就了区域合作模式多样与发展水平差异。经济带在长江防洪、大通关和检疫等方面的合作发展迅速,而在水资源环境、港口岸线、基础设施和生态补偿等方面的信息共享和合作平台建设仍发展缓慢。长三角已经建立了覆盖全、层次多、主题丰富的区域合作机制;由于国家长期对成渝经济区的推崇,两地在区域性项目建设上合作密切;中游四省合作发展水平相对最低,缺乏强有力的中心城市引领,地区竞争大于合作激励。     

Assessing the social context of wildfire-affected areas. The case of mainland Portugal

Wildfires cause different impacts, depending on the conditions and resilience level of the exposed communities. Wildfire occurrence in mainland Portugal was assessed with regard to socioeconomic and demographic parameters, to identify the most distinctive conditions of fire-affected areas, without implying the existence of causal relationships. The latest population and agriculture census data were used to retrieve conditions at the civil parish level, regarding demographic patterns, social and labor conditions, physical structures and agricultural activities. To identify differences between parishes, two groups were created with the communities that showed the highest and lowest 20% of wildfire incidence between 2007 and 2014, separately for density of fire events and for burned area. A stepwise approach based on classification trees and random Forest methods was applied to identify the best discriminant variables between the groups. First, irrelevant variables were removed by an interactive process based on misclassification rates. The second step used random Forest analysis to the remaining variables to evaluate their importance in distinguishing the groups. In the final step, cluster analysis was applied to test the correspondence between the clusters created with the selected variables and the initial groups. Results showed that parishes with higher fire density have higher population density, higher proportion of young and educated people, larger families and more overcrowded buildings. On the contrary, parishes with larger burned area are less populated, less attractive to foreigners, have a higher proportion of elderly people, more degraded housing conditions and agricultural activities, visible in the density of sheep and goat and pastures, are still relevant. The cluster analysis demonstrated a better performance of the model for wildfire density, revealing a strong association with socioeconomic dynamics with an agreement above 0.85, much higher than for burned areas which is 0.29. Overall, the spatial distribution of wildfire impacts is framed by societal settings and particular conditions must be further understood to improve the coping capacity of affected communities.     

移动地图制图的句法模型

采用形式语言的方法,研究了移动地图制图的句法模型。剖析了移动地图制图的过程,将句法模型定位在制图的逻辑演算层面;结合地图表达的层次性和递归性特征,将移动地图制图的句法结构抽象为"制图词汇+句法规则"的最简形式。在制图词汇方面,建立了制图词汇的分类体系,并重点对空间关系谓词系统进行了建模;在句法规则方面,描述了句法操作规则和不同层次的句法单位描述规则。探讨了句法模型的编译流程和关键技术,并通过试验对移动地图制图句法模型的可行性进行了验证。句法模型的本质是一种使用有限规则和词汇动态生成不同地图表达形式的形式语言语法,可作为一种高阶的人机交互制图接口。     

基于模板的位置地图表达运行机制研究

对位置地图表达模板及表达约束条件进行分析研究,初步设计基于模板的位置地图表达框架。在顾及用户背景信息、地图显示载体信息、情境信息的综合作用下,通过对情境信息进行解析和推理,建立不同地图叠加要素与位置地图要素表达模板的映射关系,并对位置地图表达的触发原理、触发判断和运行机制进行分析研究。     

结合全局信息描述子的局部特征匹配算法

针对影像间存在相似性场景时容易出现误匹配的问题,该文提出了局部DAISY描述子与全局纹理信息相结合的算法。详细论述了DAISY描述子和全局纹理的生成过程。该算法首先提取SIFT特征;然后计算特征点的DAISY描述子与全局描述子,获得结合全局信息的DAISY描述子;最后采用卡方统计量作为度量函数进行特征描述子的匹配。实验结果表明,该文算法能得到较好的匹配结果。但是该算法在大角度旋转情况下误匹配率较大。