Mapping the porosity of international border to pedestrian traffic: a comparative data classification approach to a study of the border region in Austria,Italy, and Slovenia

National borders play an important role in everyday life. Interest in border studies has increased with recent changes in geographical locations of the border or the fluctuation of the permeability of the border between some countries, such as in the European Union. Whether the nations are trying to increase traffic flow of the border or to implement stricter border control, having appropriate information of the border is crucial for effective policymaking.

The objective of this research was to identify areas of high porosity, or high permeability, for pedestrians along the southern national border region in Carinthia, Austria using terrain, land use, and road data along with geocomputational methods. Two unsupervised classification methods, the fuzzy K-means clustering and the Self-Organizing Map, were applied to segment the border into homogeneous zones according to topographic and infrastructural attributes. The fuzzy K-means clustering method was chosen for its ability to allow for a continuous approach to classification. With this method, an object can belong, with different degrees of membership, to multiple classes, which is a more realistic reflection of the natural world than discrete clustering, where each object can only belong to one class. However, the fuzzy K-means clustering method does have disadvantages, i.e. the user must determine the number of classes and the input parameters are required to be in continuous format. The second classification method, the Self-Organizing Map, is a type of artificial neural network and was chosen for its ability to automatically determine the number of classes and handle categorical data. The Self-Organizing Map is unique because it can transform high dimensional data into low dimensional display while preserving the topology and spatial distribution of the input parameters. The results of the two classification methods suggest that the fuzzy K-means classification is more effective than the Self-Organizing Map for this situation. However, more research is needed to determine the fit of these algorithms for particular spatial data classification tasks.

The results obtained from this research provide an insight into the permeability of the border region of Carinthia, Slovenia, and Italy to pedestrian traffic and can be potentially useful for decision making processes for tourism development and road transportation management in that region. Furthermore, the approach presented in this article can be applied to other national borders to identify zones permeable to pedestrian traffic.     

An enhanced supervised spatial decision support system of image classification: consideration on the ancillary information of paddy rice area

The analysis, measurement, and computation of remote sensing images often require an enhanced supervised classification technique to develop an efficient spatial decision support system. Rice is a crop of global importance, which has drawn a great interest in using remote sensing techniques for evaluating its production. Ancillary information is widely used to improve the classification accuracy of satellite images. However, few of these studies questioned the importance and strategies of using this ancillary information. The enhanced decision support system in our study has two stages. In the first stage, the images are obtained from the remote sensing technique and the ancillary information is employed to increase the accuracy of classification. In the second stage, it is decided to construct an efficiently supervised classifier, which is used to evaluate the ancillary information. Back-propagation neural network (BPN) with extended delta bar delta (EDBD) algorithm is incorporated into our decision support classifier system. This classifier renders two crucial contributions: (1) the EDBD algorithm accelerates the convergence speed of the learning process and (2) the relative importance (RI) on each band of ancillary information is evaluated rationally.     

Predicting potential distributions of geographic events using one-class data: concepts and methods

One common problem with geographic data is that, for a specific geographic event, only occurrence information is available; information about the absence of the event is not available. We refer to these specific types of geospatial data as geographic one-class data (GOCD). Predicting the potential spatial distributions that a particular geographic event may occur from GOCD is difficult because traditional binary classification methods that require availability of both positive and negative training samples cannot be used. The objective of this research is to define GOCD and propose novel approaches for modelling potential spatial distributions of geographic events using GOCD. We investigate the effectiveness of one-class support vector machine (OCSVM), maximum entropy (MAXENT) and the newly proposed positive and unlabelled learning (PUL) algorithm for solving GOCD problems using a case study: species distribution modelling from synthetic data. Our experimental results indicate that generally OCSVM, MAXENT and PUL are effective in modelling the GOCD. Each method has advantages and disadvantages, but PUL seems to be the most promising method.     

Evaluating walkability: a capability-wise planning and design support system

We present a methodology and a planning and design support software tool for evaluating walkability and pedestrian accessibility of places which are relevant for people’s capabilities, and thus an important component of quality of life in cities. A multicriteria evaluation model, at the core of the decision support system, is used to assign walkability scores to points in urban space. Walkability scores are obtained through algorithms which process spatial data and run the evaluation model in order to derive potential pedestrian routes along the street network, taking into account the quality of urban space on several attributes relevant for walkability. One of its notable characteristics is a certain reversal of perspective in evaluating walkability: the walkability score of a place does not reflect how that place is per se walkable, but instead how and where to can one walk from there, that is to say, what is the walkability the place is endowed with. This evaluation incorporates three intertwined elements: the number of destinations/opportunities reachable by foot, their walking distances, and the quality of the paths to these destinations. In this article, we furthermore demonstrate possible uses of the support system by reporting and discussing the results of a case-study assessment of a project for the Lisbon’s Segunda Circular (Second Ring Road). The software tool is made freely available for download.     

Development of operating rules for a complex multi-reservoir system by coupling genetic algorithms and network optimization

Abstract

An alternative procedure for assessment of reservoir Operation Rules (ORs) under drought situations is proposed. The definition of ORs for multi-reservoir water resources systems (WRSs) is a topic that has been widely studied by means of optimization and simulation techniques. A traditional approach is to link optimization methods with simulation models. Thus the objective here is to obtain drought ORs for a real and complex WRS: the Júcar River basin in Spain, in which one of the main issues is the resource allocation among agricultural demands in periods of drought. To deal with this problem, a method based on the combined use of genetic algorithms (GA) and network flow optimization (NFO) is presented. The GA used was PIKAIA, which has previously been used in other water resources related fields. This algorithm was linked to the SIMGES simulation model, a part of the AQUATOOL decision support system (DSS). Several tests were developed for defining the parameters of the GA. The optimization of various ORs was analysed with the objective of minimizing short-term and long-term water deficits. The results show that simple ORs produce similar results to more sophisticated ones. The usefulness of this approach in the assessment of ORs for complex multi-reservoir systems is demonstrated.

Citation Lerma, N., Paredes-Arquiola, J., Andreu, J., and Solera, A., 2013. Development of operating rules for a complex multi-reservoir system by coupling genetic algorithms and network optimization. Hydrological Sciences Journal, 58 (4), 797–812.     

地市级气象综合观测自动化监控报警系统的设计与应用

以全国综合气象信息共享平台（China Integrated Meteorological Information Service System,简称CIMISS）内气象资料为数据源,设计一个气象综合观测自动化监控报警系统,实时监控观测数据的传输情况,同时通过收集历年来各地区观测到的历史极值、故障和数据异常情况,建立要素阈值算法判断气象观测数据是否异常,并将异常告警信息分别推送至电脑端和手机微信端,方便气象工作人员查看,及时进行故障处理。该系统集多个监控平台为一体,对气象观测数据监控和告警效果良好,大大提高台站维护维修工作效率,进一步提升了气象装备保障能力,对推进气象现代化、智能化、自动化具有重要意义。     

深基坑桩锚支护结构锚索轴力监测分析

为了解深基坑桩锚支护结构锚索的工作状态,对石家庄市鸣鹿大厦深基坑桩锚支护结构中预应力锚索轴力进行了锁定及基坑开挖回填过程中的监测。监测成果的分析表明：锚索锁定时穿心千斤顶油压表读数与锚索实际轴力存在较大差别,究其原因是张拉设备与锚具的不匹配;下道锚索张拉锁定会使上道锚索轴力减小,但影响不大;基坑回填后锚索轴力有所下降,但降幅不大;实测最大锚索轴力为锚索设计值的17.9%～50.7%。     

Social,Institutional, and Psychological Factors Affecting Wildfire Incident Decision Making

Managing wildland fire incidents can be fraught with complexity and uncertainty. Myriad human factors can exert significant influence on incident decision making, and can contribute additional uncertainty regarding programmatic evaluations of wildfire management and attainment of policy goals. This article develops a framework within which human sources of uncertainty in wildfire management can be classified and managed, specifically identifying social, institutional, and psychological factors that can affect wildland fire incident decision making. These factors are reviewed in the context of wildland fire incident management and the literature regarding fire manager decision making. I then provide specific recommendations for addressing these issues, with a focus on improving incident decision processes. Extending this framework to consider a broader set of human factors and to consider how human factors affect the broader wildfire management spectrum could lead to improved fire management outcomes.     

湛江市海洋产业发展的资金支持研究

湛江是海洋经济总量连续多年稳居广东省第三的海洋大市,其海洋产业迎来了快速发展的良机,亟需资金支持。基于金融创新理论、信贷配给理论和融资约束理论等金融发展理论,在分析湛江海洋产业资金支持实践和存在问题的基础上,提出信贷政策引导、多层次资本市场融资、海洋投资信托基金、信用担保、海洋保险联动和海洋金融服务平台等方面的建议,为探索促进湛江市海洋产业的发展提供参考。     

清单式管理在地震应急技术保障中的应用研究

地震是一种突发性强和破坏性大的自然灾害,重特大地震的发生往往致使较重的人员伤亡和财产损失。科学制作并合理利用应急技术产品是提升抢险救援效率的有效措施,可以有力支撑指挥层级快速决策与应急指挥。以四川长宁"6·17"地震中的应急技术保障工作为参鉴,结合当前常用应急监测技术手段,全面梳理应急技术保障产品在实践应用中的常态问题。并在此基础上,引入"清单制"管理思维,从何种阶段制作哪类产品,哪类产品需要哪些要素上进行细化构思,建立地震灾害应急技术保障产品清单,从管理模式上规范技术保障机制,促使地震应急技术支撑有力并且有序,切实提升震后应急处置效率。