Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/165411
Title: Site selection via learning graph convolutional neural networks: a case study of Singapore
Authors: Lan, Tian
Cheng, Hao
Wang, Yi
Wen, Bihan
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: Lan, T., Cheng, H., Wang, Y. & Wen, B. (2022). Site selection via learning graph convolutional neural networks: a case study of Singapore. Remote Sensing, 14(15), 3579-. https://dx.doi.org/10.3390/rs14153579
Journal: Remote Sensing 
Abstract: Selection of store sites is a common but challenging task in business practices. Picking the most desirable location for a future store is crucial for attracting customers and becoming profitable. The classic multi-criteria decision-making framework for store site selection oversimplifies the local characteristics that are both high dimensional and unstructured. Recent advances in deep learning enable more powerful data-driven approaches for site selection, many of which, however, overlook the interaction between different locations on the map. To better incorporate the spatial interaction patterns in understanding neighborhood characteristics and their impact on store placement, we propose to learn a graph convolutional network (GCN) for highly effective site selection tasks. Furthermore, we present a novel dataset that encompasses land use information as well as public transport networks in Singapore as a case study to benchmark site selection algorithms. It allows us to construct a geospatial GCN based on the public transport system to predict the attractiveness of different store sites within neighborhoods. We show that the proposed GCN model outperforms the competing methods that are learning from local geographical characteristics only. The proposed case study corroborates the geospatial interactions and offers new insights for solving various geographic and transport problems using graph neural networks.
URI: https://hdl.handle.net/10356/165411
ISSN: 2072-4292
DOI: 10.3390/rs14153579
Schools: School of Electrical and Electronic Engineering 
Rights: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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