.png){kind=icon}
返回
Density-based multi-scale flow mapping and generalization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compenvurbsys.2019.101359.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Mapping large volume of origin-destination flow data (or spatial interactions) has long been a challenging problem because of the conflict between massive location-to-location connections and the limited map space. Current approaches for flow mapping only work with a small dataset or have to use data aggregation, which not only cause a significant loss of information but may also produce misleading maps. In this paper, we present a density-based flow map generalization approach that can extract flow patterns and facilitate the analysis and visualization of big origin-destination flow data at multiple scales. Unlike existing methods that generalize flow data by spatial unit-based aggregation, our new flow map generalization algorithm is based on flow density distribution. To demonstrate the approach and assess its effectiveness, a case study is carried out to map 829,039 taxi trips within the New York City. With parameter settings, the proposed method can discover inherent and abstract flow patterns at different map scales and generalization levels, which naturally supports interactive and multi-scale flow mapping.
Keyword:
OD flow
Spatial interaction
Flow map
Multi-scale
Cartographic generalization
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
8.3
论文数:
1.6K
被引数:
8.3K
机构
引用论文
Regulatory reform and managerial choice: an analysis of the cost savings from airline deregulation
PREAI
分享收藏
分享收藏Towards an automatic landslide mapping tool based on satellite imagery and geomorphological parameters. A study of the Itogon area (Philippines) after Typhoon Mangkhut基于卫星影像和地貌参数的自动滑坡制图工具：台风Mangkhut后菲律宾伊托贡（Itogon）地区的研究.png)
IF0
PREAI
分享收藏分享收藏
查看更多内容