Computer-Based Land Use Suitability Map

Kiefer, R. W.; Robbins, Michael L.

doi:10.1061/jsueax.0000441

Cited by 10 publications

(1 citation statement)

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“…Both phases involve subjective judgments following industrial guidelines such as [2,20], or empirically derived mathematical formulations, such as the gravity model [9,21]. This process is greatly aided by the development and application of geographic information systems (GIS) since the 1970s [22,23]. The GIS can provide detailed spatial data and classify the map based on several chosen criteria, such as proximity to roads and population density.…”

Section: Related Work 21 Traditional Approaches To Site Selectionmentioning

confidence: 99%

Site Selection via Learning Graph Convolutional Neural Networks: A Case Study of Singapore

Lan

Cheng

Wang³

et al. 2022

Remote Sensing

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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.

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Section: Related Work 21 Traditional Approaches To Site Selectionmentioning

confidence: 99%

Site Selection via Learning Graph Convolutional Neural Networks: A Case Study of Singapore

Lan

Cheng

Wang³

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

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Ecological Principles for Physical Planning

Maarel

1978

The Breakdown and Restoration of Ecosystems

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Project portfolio selection for construction contractors by MCDM–GIS approach

Hashemizadeh

2019

Int. J. Environ. Sci. Technol.

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In this paper, project portfolio selection (PPS) is considered as one of the main steps in project portfolio management (PPM). PPS which depends on different criteria such as strategic, financial, as well as project specifications is also suggested for construction contractors. Although it is difficult to consider all these criteria, the decision will be inaccurate when some of them are ignored. The most common multi-criteria decision-making (MCDM) methodologies and geographic information system (GIS) are combined as a method, by which the potential projects obtain a strategic score by the analytical hierarchy process (AHP). Then, they are ranked according to technical criteria by the technique for order preference by similarity to ideal solution (TOPSIS). The proposed approach includes three ordinal phases. First, scoring potential projects due to the company strategy is conducted by the AHP to determine strategic-aligned projects (SAPs). Then, SAPs are ranked by TOP-SIS. Finally, the final GIS sheet is prepared by the obtained technical score and communal criteria. This paper provides a clear and comprehensive insight to accept or reject SAPs by form a weighted sheet in GIS. It suggests that CCs select project portfolio according to all the financial and non-financial criteria. This approach can satisfy the PPM principals and resolve some of the shortcomings related to MCDM methods by GIS capacities to reduce complexity so that the decision-makers form project portfolios easily and effectively. This approach is described in a case study.

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Computer-Based Land Use Suitability Map

Cited by 10 publications

References 0 publications

Site Selection via Learning Graph Convolutional Neural Networks: A Case Study of Singapore

Site Selection via Learning Graph Convolutional Neural Networks: A Case Study of Singapore

Ecological Principles for Physical Planning

Project portfolio selection for construction contractors by MCDM–GIS approach

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