Journal article
Density-based multi-scale flow mapping and generalization
Computers, Environment and Urban Systems, Vol.77, p.101359
09/2019
DOI: 10.1016/j.compenvurbsys.2019.101359
Abstract
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. •We present a flow map generalization approach to visualize big origin-destination flow data.•We design a framework for multi-scale flow mapping at multiple generalization levels.•This is a data driven method to discover inherent patterns based on the density distribution of data.•The method is useful to understand different kinds of OD data, such as commuting, migration, spatial social networks.
Details
- Title: Subtitle
- Density-based multi-scale flow mapping and generalization
- Creators
- Xi Zhu - Fuzhou UniversityDiansheng Guo - Key Laboratory of Spatial Data Mining and Information Sharing of MOE, Fuzhou University, Fuzhou, ChinaCaglar Koylu - Department of Geographical and Sustainability, University of Iowa, Iowa City, USAChongcheng Chen - Key Laboratory of Spatial Data Mining and Information Sharing of MOE, Fuzhou University, Fuzhou, China
- Resource Type
- Journal article
- Publication Details
- Computers, Environment and Urban Systems, Vol.77, p.101359
- Publisher
- Elsevier Ltd
- DOI
- 10.1016/j.compenvurbsys.2019.101359
- ISSN
- 0198-9715
- eISSN
- 1873-7587
- Grant note
- DOI: 10.13039/501100012166, name: national key research and development program of China, award: 2017YFB0504202
- Language
- English
- Date published
- 09/2019
- Academic Unit
- Geographical and Sustainability Sciences; Public Policy Center (Archive); Center for Social Science Innovation
- Record Identifier
- 9983983663902771
Metrics
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