Exploring the spatial dimension of community-level flood risk perception: a cognitive mapping approach

Environmental perceptions are central to individuals’ behavioural interactions with the environment. Cognitive maps, portraying a spatial representation of an individual’s environmental perception, can be aggregated to gain insight into the collective environmental perception of groups and populations. This paper uses cognitive mapping techniques to examine one aspect of environmental perception, flood risk perception, within a residential population (n?=?305). Flood risk perception was examined for the whole sample and six subgroup pairs. Using subgroups allowed examination of how factors previously shown to influence flood risk perception influence the cognitive map production in this population. We use a novel technique (slope analysis) to examine how the population’s perception of flood risk compares with expert assessments of flood risk, and compare the results of this novel technique with a commonly used cognitive map analysis technique (majority threshold method). Both methods identify areas where there is consensus within the population as to which areas are at risk of flooding. However, slope analysis usefully identifies areas where the population’s perception of flood risk lacks consensus, and is at odds with expert assessments of flood risk, without the loss of information inherent in the majority threshold method. Thus, this technique provides a novel approach to studies of environmental perception that can be widely applied within many fields.     

Developing the interaction matrix technique as a tool assessing the impact of traffic on air quality

This paper develops a technique which can be used as a preliminary tool for assessing air quality related to urban traffic. It combines a Geographic Information System (GIS) with an interaction matrix-type methodology based on a system analysis approach. The matrix identifies and quantifies interactions between all selected variables involved in a system as well as their interaction with the system as a whole. This matrix is used to determine the weightings to apply to spatial datasets within a GIS to develop a pollution vulnerability map. The focus of the paper is to introduce and assess a more versatile coding of the interaction matrix with respect to previously used coding. A case study is presented in which the modified interaction methodology is applied to data for a busy urban location. The resulting vulnerability map, in terms of pollution vulnerable hot spots, was compared to a pollution map derived from an advanced dispersion model. The interaction matrix technique with GIS can be used as a tool complementary to sophisticated numerical modelling and has potential as an analytical tool to evaluate multidisciplinary systems.     

基于SOM和多元分析的滇池沉积物污染特征空间模式研究

联合运用层次聚类分析(HCA)、判别分析(DA)和自组织映射神经网络(SOM)3种数值方法,对滇池2008—2010年17个底泥监测点位(10个常规点位及7个新增点位)9种污染物进行空间差异性和相似性分析,并评价各指标在空间的分布特征及监测点代表性.结果表明:滇池沉积物整体污染程度为草海>外海中部及南部>外海北部.As、Hg、Pb、Cd、Cu和Zn污染最严重的点位是断桥;草海中心的凯氏氮污染最严重,其他指标污染水平仅次于断桥;Cr和TP污染最严重的点位是新增点位盘龙江2;海埂是全局污染最轻的点位.现有的常规监测点位对整体污染特征的代表性较弱,建议取消异质性最低的白鱼口、观音山西、观音山东和罗家营站点;考虑污染特征和监测点位空间分布的均匀性,建议将盘龙江2、海埂和马料河设为常规监测点位.     

应急管理地理信息系统设计及实现

介绍了面向石油化工企业的应急管理地理信息系统的功能设计及实现,利用遥感影像配合矢量图实现地理信息支撑平台,满足企业日常安全管理和应急响应过程中地图浏览、查询定位、空间分析、事故影响范围模拟计算及可视化展示、专题图及属性浏览、动态信息集成等功能需要。     

Developing shared qualitative models for complex systems

Understanding complex systems is essential to ensure their conservation and effective management. Models commonly support understanding of complex ecological systems and, by extension, their conservation. Modeling, however, is largely a social process constrained by individuals’ mental models (i.e., a small-scale internal model of how a part of the world works based on knowledge, experience, values, beliefs, and assumptions) and system complexity. To account for both system complexity and the diversity of knowledge of complex systems, we devised a novel way to develop a shared qualitative complex system model. We disaggregated a system (carbonate coral reefs) into smaller subsystem modules that each represented a functioning unit, about which an individual is likely to have more comprehensive knowledge. This modular approach allowed us to elicit an individual mental model of a defined subsystem for which the individuals had a higher level of confidence in their knowledge of the relationships between variables. The challenge then was to bring these subsystem models together to form a complete, shared model of the entire system, which we attempted through 4 phases: develop the system framework and subsystem modules; develop the individual mental model elicitation methods; elicit the mental models; and identify and isolate differences for exploration and identify similarities to cocreate a shared qualitative model. The shared qualitative model provides opportunities to develop a quantitative model to understand and predict complex system change.     

Characterization of biological responses under different environmental conditions: A hierarchical modeling approach

A natural river system is organized as a nested hierarchy of interconnected habitats with specific environmental conditions to which the biological community has adapted. Due to this hierarchical structure, identifying the role of different stressors on the biological community is a formidable task. Efforts trying to link stressors to biological integrity have always been bound to the geographic scale of the selected study area, leading to scale-specific results. In this research, an attempt is made to lift this limitation and develop a hierarchical, scale-sensitive methodology that can identify the significant environmental stressors to the biological community at different scales. Sites with similar background environmental conditions are clustered using self-organizing maps (SOM). This is used to identify stressors which affect the biological community throughout the area of study - called environmental gradients or large-scale stressors. Subsequently, these clusters of similar observations (sampling sites) are progressively sub-divided using environmental variables with a significant but localized effect on the biological community - called small-scale stressors. A parent group of sites is split only when the resulting sub-groups have significantly different biological responses. At the end of this recursive sites decomposition procedure, the original set of observations is organized as a tree of environmentally homogeneous groups of observations characterized by unique biological responses to multiple stressors with different geographic extents. The developed hierarchical analysis methodology has been validated using a large-size dataset of environmental observations from the State of Ohio. Our results show that habitat degradation and increased nutrient loading are the large-scale stressors with a widespread impact in Ohio. Other stressors, such as heavy metals, pH or nitrate concentrations have significant albeit localized effects on biological integrity.     

Distance-based eigenvector maps (DBEM) to analyse metapopulation structure with irregular sampling

Characterizing spatial patterns due to ecological processes is a major issue for analysing and predicting species distributions. Grasping the non-linear nature of population dynamics over networks of discrete suitable sites is here central, as very specific signatures are expected. In the line of promising results from Fourier analysis of metapopulation maps, we found distance-based eigenvector maps (DBEM) to help disentangle the respective signatures of habitat and metapopulation structuring, with the great advantage of being applicable to irregular sampling schemes, a common feature of ecological surveys. A smoothing procedure was required to obtain the distinguishable signatures, and this may be a critical issue for investigating non-contingent and reliable patterns in spatial ecology.     

乐山市区大气硫氧化物污染状况及相关因子分析

本文研究了乐山市区较大面积范围内的大气硫氧化物相对污染水平。通过分析绘出了较详细的污染图谱;在得出SO_x时间分布规律基础上,了解了与它相关的因子;用模糊聚类方法,对乐山城区大气SO_2监测点提出了优化方案;并针对乐山市的大气状况,提出了对策。     

Monitoring of Biological Diversity: a Common-Ground Approach

Abstract:  Practical approaches to monitoring biological diversity vary widely among countries, and the accumulating data are frequently not generalizable at the international scale. Although many present monitoring schemes, especially in developed countries, produce highly complex data, there is often a lack of basic data about the level and spatial distribution of biodiversity. We augmented the general framework for improving biomonitoring, proposed by Green et al. (2005) , and identified its core tasks and attributes. The first priority for a more unified biodiversity monitoring is to agree on a minimum set of core tasks and attributes, which will make it possible to build a standardized biomonitoring system even in countries with few resources. Our scheme has two main organizational levels—taxa and ecosystems. The basic elements of the biomonitoring system proposed are recording of presence and absence of taxa and ecosystems in a target area, mapping of their distribution in space, and assessment of their status. All the elements have to be repeated over time. Although these tasks are fundamental, they are frequently not considered in currently functioning biomonitoring programs. The whole system has to be hierarchical and additive: if more resources are available, new activities may be added to the basic routine. Agreeing on a common standard will facilitate aggregating measures of biodiversity status and trends into regional and global indices. This information will relate directly to several Convention on Biological Diversity indicators for assessing progress toward the 2010 Biodiversity Target.     

Using Niche-Based Models to Improve the Sampling of Rare Species

Abstract:  Because data on rare species usually are sparse, it is important to have efficient ways to sample additional data. Traditional sampling approaches are of limited value for rare species because a very large proportion of randomly chosen sampling sites are unlikely to shelter the species. For these species, spatial predictions from niche-based distribution models can be used to stratify the sampling and increase sampling efficiency. New data sampled are then used to improve the initial model. Applying this approach repeatedly is an adaptive process that may allow increasing the number of new occurrences found. We illustrate the approach with a case study of a rare and endangered plant species in Switzerland and a simulation experiment. Our field survey confirmed that the method helps in the discovery of new populations of the target species in remote areas where the predicted habitat suitability is high. In our simulations the model-based approach provided a significant improvement (by a factor of 1.8 to 4 times, depending on the measure) over simple random sampling. In terms of cost this approach may save up to 70% of the time spent in the field.