Configurations of community in flood risk management

ABSTRACT

Despite a notable increase in the literature on community resilience, the notion of ‘community’ remains underproblematised. This is evident within flood risk management (FRM) literature, in which the understanding and roles of communities may be acknowledged but seldom discussed in any detail. The purpose of the article is to demonstrate how community networks are configured by different actors, whose roles and responsibilities span spatial scales within the context of FRM. Accordingly, the authors analyse findings from semi-structured interviews, policy documents, and household surveys from two flood prone areas in Finnish Lapland. The analysis reveals that the ways in which authorities, civil society, and informal actors take on multiple roles are intertwined and form different types of networks. By implication, the configuration of community is fuzzy, elusive and situated, and not confined to a fixed spatiality. The authors discuss the implications of the complex nature of community for FRM specifically, and for community resilience more broadly. They conclude that an analysis of different actors across scales contributes to an understanding of the configuration of community, including community resilience, and how the meaning of community takes shape according to the differing aims of FRM in combination with differing geographical settings.     

Vulnerable groups and preliminary insights into intersecting categories of identity in Laamu Atoll,Maldives

The notion of vulnerability has acquired a significant role in discussions around reducing risk and identifying vulnerable groups, which is particularly important for targeting risk‐reducing policy and practice. Through ten semi‐structured interviews with 24 stakeholders, this study collected local perceptions on the following groups considered most vulnerable in Laamu Atoll, Maldives: youth and children, women, the elderly, people with disabilities, displaced persons and migrants, and farmers. In doing this, the significance of intersecting categories of identity for understanding vulnerability emerged and was explored; an approach that is often absent or scarcely applied in studies that identify vulnerable groups in the Maldives and elsewhere. Intersections between location, youth and gender or disability, as well as gender and marital status emerged as relevant for the Laamu Atoll context. Although this study acknowledges the incompleteness of its intersectional analysis, these findings provide important preliminary insights into the role of intersectionality in shaping vulnerability experience. To enhance scholarship on vulnerability, future studies could further explore intersectionality in Laamu Atoll and the Maldives using this study as a base and also improve its practical applicability for policy and practice.     

南极测绘中小像幅航片的区域网平差

本文介绍了用日产 Bronika SQ- A1 2 0型非量测相机对东南极拉斯曼丘陵 1 :40 0 0 0摄影比例尺小像幅航片进行加密的情况。通过用武汉测绘科技大学研制的联合平差程序 Wu CAPS软件的带附加参数的光束法区域网平差获得了实地上平面± 2 .82 9m,高程± 4.973 m的加密精度。文中还分析了若干精度问题 ,所得结论对今后的进一步研究将有所裨益     

Use of Fuzzy Membership Input Layers to Combine Subjective Geological Knowledge and Empirical Data in a Neural Network Method for Mineral-Potential Mapping

Use of GIS layers, in which the cell values represent fuzzy membership variables, is an effective method of combining subjective geological knowledge with empirical data in a neural network approach to mineral-prospectivity mapping. In this study, multilayer perceptron (MLP), neural networks are used to combine up to 17 regional exploration variables to predict the potential for orogenic gold deposits in the form of prospectivity maps in the Archean Kalgoorlie Terrane of Western Australia. Two types of fuzzy membership layers are used. In the first type of layer, the statistical relationships between known gold deposits and variables in the GIS thematic layer are used to determine fuzzy membership values. For example, GIS layers depicting solid geology and rock-type combinations of categorical data at the nearest lithological boundary for each cell are converted to fuzzy membership layers representing favorable lithologies and favorable lithological boundaries, respectively. This type of fuzzy-membership input is a useful alternative to the 1-of-N coding used for categorical inputs, particularly if there are a large number of classes. Rheological contrast at lithological boundaries is modeled using a second type of fuzzy membership layer, in which the assignment of fuzzy membership value, although based on geological field data, is subjective. The methods used here could be applied to a large range of subjective data (e.g., favorability of tectonic environment, host stratigraphy, or reactivation along major faults) currently used in regional exploration programs, but which normally would not be included as inputs in an empirical neural network approach.     

Monitoring qualitative spatiotemporal change for geosensor networks

Recent technological advances in geosensor networks demand new models of distributed computation with dynamic spatial information. This paper presents a computational model of spatial change in dynamic regions (such as may be derived from discretizations of continuous fields) founded on embeddings of graphs in orientable surfaces. Continuous change, connectedness and regularity of dynamic regions are defined and local transition rules are used to constrain region evolution and enable more efficient inference of a region's state. The model provides a framework for the detection of global high‐level events based on local low‐level ‘snapshot’ spatiotemporal data. The approach has particular relevance to environmental monitoring with geosensor networks, where technological constraints make the detection of global behaviour from local conditions highly advantageous.     

Finding shortest paths on real road networks: the case for A*

The problem of identifying the shortest path along a road network is a fundamental problem in network analysis, ranging from route guidance in a navigation system to solving spatial allocation problems. Since this type of problem is solved so frequently, it is important to craft an approach that is as efficient as possible. Based upon past research, it is generally accepted that several efficient implementations of the Dijkstra algorithm are the fastest at optimally solving the ‘one‐to‐one’ shortest path problem (Cherkassky et al. 1996 Cherkassky, B. V., Goldberg, A. V. and Radzik, T. 1996. Shortest paths algorithms: theory and experimental evaluation.. Mathematical Programming: Series A and B, 73: 129–174. [Crossref], [Web of Science ®] , [Google Scholar]). We show that the most efficient state‐of‐the‐art implementations of Dijkstra can be improved by taking advantage of network properties associated with GIS‐sourced data. The results of this paper, derived from tests of different algorithmic approaches on real road networks, will be extremely valuable for application developers and researchers in the GIS community.     

Constructing personalized transportation networks in multi-state supernetworks: a heuristic approach

An integrated view encompassing the networks for public and private transport modes as well as the activity programs of travelers is essential for accessibility analysis. In earlier research, the multi-state supernetwork has been put forward by the authors as a suitable technique to model the system in such an integrated fashion. An essential part of a supernetwork involving multi-modal and multi-activity is the personalized transportation network, which is an under-researched topic in the academic community. This article attempts to develop a heuristic approach to construct personalized transportation networks for an individual's activity program. In this approach, the personalized network consists of two types of network extractions from the original transportation system: public transport network and private vehicle network. Three examples are presented to illustrate that the public transport network and private vehicle network can represent an individual's attributes and be applied in large-scale applications for analyzing the synchronization of land-use and transportation systems.     

Statistics-based outlier detection for wireless sensor networks

Wireless sensor network (WSN) applications require efficient, accurate and timely data analysis in order to facilitate (near) real-time critical decision-making and situation awareness. Accurate analysis and decision-making relies on the quality of WSN data as well as on the additional information and context. Raw observations collected from sensor nodes, however, may have low data quality and reliability due to limited WSN resources and harsh deployment environments. This article addresses the quality of WSN data focusing on outlier detection. These are defined as observations that do not conform to the expected behaviour of the data. The developed methodology is based on time-series analysis and geostatistics. Experiments with a real data set from the Swiss Alps showed that the developed methodology accurately detected outliers in WSN data taking advantage of their spatial and temporal correlations. It is concluded that the incorporation of tools for outlier detection in WSNs can be based on current statistical methodology. This provides a usable and important tool in a novel scientific field.     

Stochastic model-based methods for handling uncertainty in areal interpolation

Handling of uncertainty in the estimation of values from source areas to target areas poses a challenge in areal interpolation research. Stochastic model-based methods offer a basis for incorporating such uncertainty, but to date they have not been widely adopted by the GIS community. In this article, we propose one use of such methods based in the problem of interpolating count data from a source set of zones (parishes) to a more widely used target zone geography (postcode sectors). The model developed also uses ancillary statistical count data for a third set of areas nested within both source and target zones. The interpolation procedure was implemented within a Bayesian statistical framework using Markov chain Monte Carlo methods, enabling us to take account of all sources of uncertainty included in the model. Distributions of estimated values at the target zone level are presented using both summary statistics and as individual realisations selected to illustrate the degree of uncertainty in the interpolation results. We aim to describe the use of such stochastic approaches in an accessible way and to highlight the need for quantifying estimation uncertainty arising in areal interpolation, especially given the implications arising when interpolated values are used in subsequent analyses of relationships.     

深圳市道路网络结构特征的成因及其景观格局效应

本文以我国典型快速城市化地区深圳市为例,综合使用GIS技术、道路网络结构特征分析、景观格局分析和相关分析方法研究其道路网络结构特征的成因及其景观生态效应。在确定了24个独立的空间研究单元的基础上,重点分析了深圳市道路网络结构特征的相关关系、城市化水平差异对道路网络结构特征的影响和道路格局特征的景观整体及重要组分的格局效应。结果表明:城市建设用地密度的增加导致交通用地密度、节点和廊道储量增加,道路网络结构复杂程度、格局指数降低;资源条件、环境和生态保护约束是导致道路网络复杂性增加、结构发育水平下降、网络格局指数不断降低的主要原因;深圳市的道路网络格局特征对全市景观整体格局没有表现显著的约束性影响,对建设用地显示出环境保护约束和空间吸引两个方面的综合效应,对于林地则表现出生态保护约束、空间排斥和物理分割三个方面的综合效应。