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Annuals of GIS 年度最佳论文(2020)

已有 952 次阅读 2020-10-24 22:33 |系统分类:论文交流

近日Annuals of GIS对2019年全年发表在本刊的论文进行了统计,评选出了以下最佳论文:


最佳论文The best paper awards (papers published in 2019):

1st place:

Maja Kalinic & Jukka M. Krisp (2019) Fuzzy inference approach in traffic congestion detection, Annals of GIS, 25:4, 329-336, DOI: 10.1080/19475683.2019.1675760

中文摘要交通故障的实时监测是智能交通系统的主要任务之一。传统方法通过对交通流量和车辆平均速度进行分类以评估交通状况。一般来说,只要分析的数据能够正确地代表观察到的情况,这种数学方法是可以接受的,并且会产生良好的结果。但是,这种方式无法预知交通状况和驾驶员的个人行为。在这种情况下,如此“酥脆”的计算模型无法有效处理伴随的歧义和不确定性问题。一种替代方法是应用模糊逻辑系统,该系统能够进行基于知识的分析,以进行有效且高效的交通拥堵检测。在本文中,交通流量和密度可以作为所提出的模糊推理模型的输入数据,而输出结果则以检测到的拥塞程度(从“无拥塞”到“极端拥塞”状况不等)形式呈现。结果表明,用于拥塞检测的模糊逻辑推理模型可能非常适合交通规划、管理和安全评估。

关键词:交通流理论,交通,拥堵,模糊逻辑,模糊推理模型

 

2nd place:

Bardia Mashhoodi, Dominic Stead & Arjan van Timmeren (2019) Spatial homogeneity and heterogeneity of energy poverty: a neglected dimension, Annals of GIS, 25:1, 19-31, DOI: 10.1080/19475683.2018.1557253

中文摘要:自20世纪70年代以来,众多研究已经从社会人口、住房和经济三个方面探索了能源贫困的决定因素。然而,还有一个核心问题尚未得到解决:国家层面上能源贫困的决定因素是什么?即是哪些决定因素导致了一个国家的所有地区的严重的能源贫困?特定社区内能源贫困的决定因素是什么?哪些因素导致了一个国家内部社区的能源贫困? 本研究旨在通过分析2014年荷兰的2473个社区的能源贫困水平和能源支出占家庭可支配收入的百分比来回答这些问题。研究采用半参数地理加权回归分析法对实验数据进行了分析。研究发现,有两个决定因素对能源贫困的影响具有空间同质性:(i)低收入家庭所占的百分比;(ii)养老金领取者所占的百分比。另外六个决定因素对能源贫困的影响具有空间异质性:(i)家庭规模;(ii)失业率;(iii)始建年代;(iv)私人出租住宅的比例;(v)夏季天数;(vi)霜冻天数。最后,本研究对空间同质性和异质性的决定因素的影响进行了估计和映射,对结果进行了讨论并提出了一些政策上的建议。

关键词:家庭能源支出,能源贫困,家庭能源消耗,半参数地理加权回归法,荷兰

 

3rd place:

Yunqiang Zhu & Jie Yang (2019) Automatic data matching for geospatial models: a new paradigm for geospatial data and models sharing, Annals of GIS, 25:4, 283-298, DOI: 10.1080/19475683.2019.1670735


中文摘要:随着全球气候和环境研究的发展,地理空间模型的综合性和复杂度越来越高,因此需要更多的输入数据。为了提高输入数据准备的效率并节省时间、劳动力和金钱,在讨论了其他相关研究之后,本文提出了一种新的框架,以将现有的开放式Web数据与地理空间模型进行匹配。本文首先介绍了其基本思想和总体框架,然后详细研究了四个关键问题。新框架可以根据基于统一描述因子的数据相似度值,自动判断开放数据是否与地理空间模型的输入数据一致;如果它们不完全一致,可以使用相应的Web处理服务智能地处理差异。最后,得到完全匹配的数据以供地理空间模型使用。因此,可能会由此框架发展出一个新范式用于促进地理空间数据和模型的链接与共享。这不仅会使得地理空间模型得到更多应用,还将极大地增加开放数据的价值和可用性。

关键词:自动配对, 地理空间数据, 模型, 数据相似度, 智能数据处理

 

下载最多论文The most downloaded awards (papers published in 2019):

1st place:

Chia-Yu Wu, Joann Mossa, Liang Mao & Mohammad Almulla (2019) Comparison of different spatial interpolation methods for historical hydrographic data of the lowermost Mississippi River, Annals of GIS, 25:2, 133-151, DOI: 10.1080/19475683.2019.1588781

Abstract: The lowermost Mississippi River (LMR) is important to the environment and economy of continental United States. Although bathymetric data have been collected over many decades at numerous cross-sectional sounding points, there has been no consensus on appropriate interpolator for generating bathymetry. Such interpolation is critical to reliable assessments of channel morphology and channel change, which serve for dredging, engineering projects, and mapping of navigation hazards. This study aimed to identify an optimal spatial interpolation for mapping the river bathymetry from cross-sectional sounding measurements. We evaluated a variety of spatial interpolation methods including Inverse Distance Weighting (IDW), Ordinary Kriging (OK), Radial Basis Function (RBF), and Local Polynomial Interpolation (LPI). In addition, we also considered the anisotropic form of IDW (Elliptical IDW as EIDW), that of OK (OKA), and Universal Kriging (UK). Two reaches in the LMR, located between approximately RM (River Miles) 170–140 and RM 60–35, were chosen as the study area. Those interpolators were compared in terms of root-mean-square error (RMSE), mean absolute error (MAE), bias, and coefficient of determination (r2). Our results demonstrate that both of RBF and OKA performed the best in mapping the bathymetry of study reaches. Furthermore, our results also indicate that the addition of anisotropy can significantly reduce RMSE by 5–20%, as compared to isotropic methods. The findings better inform other researchers on selecting a proper interpolation technique for mapping river bathymetry, particularly for other reaches of the Mississippi River.

KEYWORDS: Bathymetryspatial interpolation methodshydrographical cross-sectionsMississippi River

 

2nd place:

Harjeet Kaur, Srimanta Gupta, Surya Parkash, Raju Thapa, Arindam Gupta & G. C. Khanal (2019) Evaluation of landslide susceptibility in a hill city of Sikkim Himalaya with the perspective of hybrid modelling techniques, Annals of GIS, 25:2, 113-132, DOI: 10.1080/19475683.2019.1575906a

中文摘要:山体滑坡是丘陵地带季风季节常见且频繁发生的自然灾害。本研究以印度锡金邦甘托克市为实验样区,通过随机森林(RF)与概率似然比(RF-PLR),模糊逻辑(FL)和熵指数综合使用的方法来掉滑坡敏感性进行量化评估,并以此为基础进行综合分析。本文研究的滑坡清单是依据LISS-IV(MX)卫星图像,谷歌地球数据和印度地质调查局的报告数据进行编绘。研究考虑了斜坡、高程、曲率、坡面、土地利用/土地覆盖、地质、地貌、降雨、土壤类型、土壤厚度、水分状况、距道路距离等12个滑坡条件因子作为滑坡易感性地质空间模型的输入参数。模型导出的滑坡易感性图被依据标准划分为低、中、高、特高四个危险区。研究在ArcGIS环境中对模型进行比较以评估分析模型的兼容性,并通过混淆矩阵评估模型性能。结果表明,概率似然比(RF-PLR)方法的准确率为69.36%,高于其他两种模型(分别为9.68%和19.35%)。采用成功率和预测率法对输出结果进行验证,其中概率似然比、概率模糊逻辑和概率熵指数曲线下面积(AUC)的成功率和预测率分别为76%、67%、83%、78%和85%、80%。此外,采用Wilcoxon符号秩检验分析模型性能差异,发现概率似然比与概率模糊逻辑,概率似然比与概率熵指数的结果差异具有统计学意义。

关键词:山体滑坡敏感性,甘托克,锡金喜马拉雅山

 

3rd place:

L. Gavériaux, G. Laverrière, T. Wang, N. Maslov & C. Claramunt (2019) GIS-based multi-criteria analysis for offshore wind turbine deployment in Hong Kong, Annals of GIS, 25:3, 207-218, DOI: 10.1080/19475683.2019.1618393

中文摘要在任何海上可再生能源系统项目的早期阶段,找到合适的海域都是项目展开的先决条件。本文介绍了一种方法框架,以确定在香港海湾建造海上风力发电场的最合适海域。该框架依托多标准决策分析(MCDA)和地理信息系统(GIS)。在分析的第一阶段,首先确定了不适合部署海上风电机的区域。在第二阶段,根据MCDA和不同情景,对符合条件的海洋区域进行排名。最后,我们进行了成本分析以便改善整个方法框架,并将其与香港湾的先前研究进行了比较。

关键词:
GIS,海上风电场,MCDA,现场评估,辅助决策系统


 

引用最多论文The most cited papers (papers published in 2018):

1st place:

Michael F. Goodchild (2018) Reimagining the history of GIS, Annals of GIS, 24:1, 1-8DOI: 10.1080/19475683.2018.1424737

ABSTRACT: Fifty years after the initial efforts that coalesced as geographic information system (GIS), it is possible to look back and ask whether the decisions made then are still viable. Those decisions were constrained by the computing environment of the time, which was extremely primitive compared to today’s. The Canada Geographic Information System is used to illustrate those decisions and their consequences. Today it is possible to imagine a very different birth of GIS, based on globes rather than maps, and with positional uncertainty and spatial resolution addressed at the outset using hierarchical data structures for the globe. These structures also have the advantages of a congruent geography operating at multiple scales. GIS today still reflects in part the constraints of computing in the mid 1960s.

KEYWORDS: GIS history, digital globe, discrete global grid, hierarchical data structure

 

2nd place:

AXing Zhu, Guonian Lu, Jing Liu, ChengZhi Qin & Chenghu Zhou (2018) Spatial prediction based on Third Law of Geography, Annals of GIS, 24:4, 225-240, DOI: 10.1080/19475683.2018.1534890

ABSTRACTCurrent methods of spatial prediction are based on either the First Law of Geography or the statistical principle or the combination of these two. The Second Law of Geography contributes to the revision of these methods so they are adaptive to local conditions but at the cost of increasing demand for samples. This paper presents a new thinking about spatial prediction based on the Third Law of Geography which focuses on the similarity of geographic configuration of locations. Under the Third Law of Geography, spatial prediction can be made on the basis of the similarity of geographic configurations between a sample and a prediction point. This allows the representativeness of a single sample to be used in prediction. A case study in predicting spatial variation of soil organic matter content was used to compare the spatial prediction based the Third Law of Geography with those based on the First Law and the statistical principle. It is concluded that spatial prediction based on the Third Law of Geography does not require samples to be over certain size nor to be of a particular spatial distribution to achieve a high quality prediction. The prediction uncertainty associated with spatial prediction based on the Third Law of Geography is more indicative to quality of the prediction, thus more effective in allocating error reduction efforts. These properties make spatial prediction based on the Third Law of Geography more suitable for prediction over large and complex geographic areas.

KEYWORDS: First Law of Geography, Second Law of Geography, Third Law of Geography, spatial prediction, spatial interpolation, Kriging, soil mapping, geographic configuration

 

3rd place:

Xiao Huang, Cuizhen Wang & Zhenlong Li (2018) A near real-time flood-mapping approach by integrating social media and post-event satellite imagery, Annals of GIS, 24:2, 113-123, DOI: 10.1080/19475683.2018.1450787

ABSTRACT: Rapid flood mapping is critical for timely damage assessment and post-event recovery support. Remote sensing provides spatially explicit information for the mapping process, but its real-time imagery is often not available due to bad weather conditions during the event. Using the 2015 South Carolina Flood in downtown Columbia as a case study, this article proposes a novel approach to retrieve near real-time flood probability map by integrating the post-event remote sensing data with the real-time volunteered geographic information (VGI). Relying on each VGI point, an inverse distance weighted height filter was introduced to build a probability index distribution (PID) layer from the high-resolution digital elevation model (DEM) data. For each PID layer, a Gaussian kernel was developed to extract its moisture weight from the normalized difference water index (NDWI) of an EO-1 Advanced Land Imager (ALI) image. Finally, a normalized flood probability map was produced by chaining the moisture weighted PIDs in a Python environment. Results indicate that, by adding the wetness information from post-event satellite observations, the proposed model could provide near real-time flood probability distribution with real-time social media, which is of great importance for emergency responders to quickly identify areas in need of immediate attention.

 

KEYWORDS: NDWI, rapid flood mapping, remote sensing, tweets, volunteered geographic information




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