A Deep Learning Representation of Spatial Interaction Model for Resilient Spatial Planning of Community Business Clusters

• URL: http://arxiv.org/abs/2401.04849v1
• Date: Tue, 9 Jan 2024 23:42:21 GMT
• Title: A Deep Learning Representation of Spatial Interaction Model for Resilient Spatial Planning of Community Business Clusters
• Authors: Haiyan Hao and Yan Wang
• Abstract summary: We propose a SIM-GAT model to predict visitation flows between community business clusters and their trade areas. A graph-based deep learning model, i.e., Graph AttenTion network (GAT), is used to capture the clusters and interdependencies of business.
• Score: 4.8051028509814575
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing Spatial Interaction Models (SIMs) are limited in capturing the complex and context-aware interactions between business clusters and trade areas. To address the limitation, we propose a SIM-GAT model to predict spatiotemporal visitation flows between community business clusters and their trade areas. The model innovatively represents the integrated system of business clusters, trade areas, and transportation infrastructure within an urban region using a connected graph. Then, a graph-based deep learning model, i.e., Graph AttenTion network (GAT), is used to capture the complexity and interdependencies of business clusters. We developed this model with data collected from the Miami metropolitan area in Florida. We then demonstrated its effectiveness in capturing varying attractiveness of business clusters to different residential neighborhoods and across scenarios with an eXplainable AI approach. We contribute a novel method supplementing conventional SIMs to predict and analyze the dynamics of inter-connected community business clusters. The analysis results can inform data-evidenced and place-specific planning strategies helping community business clusters better accommodate their customers across scenarios, and hence improve the resilience of community businesses.

Related papers

• Meta-Transfer Learning Empowered Temporal Graph Networks for Cross-City Real Estate Appraisal [28.752306064729474]
  Deep learning has shown great promise for real estate appraisal by harnessing substantial online transaction data from web platforms. We propose Meta-Transfer Learning Empowered Temporal Graph Networks (MetaTransfer) to transfer valuable knowledge from multiple data-riches to the data-scarce city.
  arXiv  Detail & Related papers  (2024-10-11T16:16:38Z)
• FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction [47.336599393600046]
  textscFedNE is a novel approach that integrates the textscFedAvg framework with the contrastive NE technique. We conduct comprehensive experiments on both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2024-09-17T19:23:24Z)
• Adaptive Hierarchical SpatioTemporal Network for Traffic Forecasting [70.66710698485745]
  We propose an Adaptive Hierarchical SpatioTemporal Network (AHSTN) to promote traffic forecasting. AHSTN exploits the spatial hierarchy and modeling multi-scale spatial correlations. Experiments on two real-world datasets show that AHSTN achieves better performance over several strong baselines.
  arXiv  Detail & Related papers  (2023-06-15T14:50:27Z)
• Multi-Temporal Relationship Inference in Urban Areas [75.86026742632528]
  Finding temporal relationships among locations can benefit a bunch of urban applications, such as dynamic offline advertising and smart public transport planning. We propose a solution to Trial with a graph learning scheme, which includes a spatially evolving graph neural network (SEENet) SEConv performs the intra-time aggregation and inter-time propagation to capture the multifaceted spatially evolving contexts from the view of location message passing. SE-SSL designs time-aware self-supervised learning tasks in a global-local manner with additional evolving constraint to enhance the location representation learning and further handle the relationship sparsity.
  arXiv  Detail & Related papers  (2023-06-15T07:48:32Z)
• Look Around! A Neighbor Relation Graph Learning Framework for Real Estate Appraisal [6.14249607864916]
  We propose a novel Neighbor Relation Graph Learning Framework (ReGram) for real estate appraisal. ReGram incorporates the relation between target transaction and surrounding neighbors with the attention mechanism. Experiments on the real-world dataset with various scenarios demonstrate that ReGram robustly outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-12-23T08:20:19Z)
• A variational Bayesian spatial interaction model for estimating revenue and demand at business facilities [15.242014520266391]
  We study the problem of estimating potential revenue or demand at business facilities and understand its generating mechanism. We develop a Bayesian spatial interaction model, henceforth BSIM, which provides probabilistic predictions about revenues generated by a particular business location. We construct a real-world, large spatial dataset for pub activities in London, UK, which includes over 1,500 pubs and 150,000 customer regions.
  arXiv  Detail & Related papers  (2021-08-05T13:03:20Z)
• Modelling Urban Dynamics with Multi-Modal Graph Convolutional Networks [8.767281392253976]
  We propose a novel deep learning framework which aims to better model the popularity and growth of urban venues. We present our deep learning architecture which integrates both spatial and topological features into a temporal model which predicts the demand of a venue at the subsequent time-step. Relative to state-of-the-art deep learning models, our model reduces the RSME by 28% in London and 13% in Paris.
  arXiv  Detail & Related papers  (2021-04-29T20:00:47Z)
• Modelling Cooperation and Competition in Urban Retail Ecosystems with Complex Network Metrics [8.767281392253976]
  We first present a modeling framework which examines the role of new businesses in their respective local areas. Using a longitudinal dataset from location technology platform Foursquare, we model new venue impact across 26 major cities worldwide. We then devise a data-driven metric that captures the first-order correlation on the impact of a new venue on retailers within its vicinity. Lastly, we build a supervised machine learning model to predict the impact of a given new venue on its local retail ecosystem.
  arXiv  Detail & Related papers  (2021-04-28T19:18:23Z)
• Cooperative Policy Learning with Pre-trained Heterogeneous Observation Representations [51.8796674904734]
  We propose a new cooperative learning framework with pre-trained heterogeneous observation representations. We employ an encoder-decoder based graph attention to learn the intricate interactions and heterogeneous representations.
  arXiv  Detail & Related papers  (2020-12-24T04:52:29Z)
• Edge-assisted Democratized Learning Towards Federated Analytics [67.44078999945722]
  We show the hierarchical learning structure of the proposed edge-assisted democratized learning mechanism, namely Edge-DemLearn. We also validate Edge-DemLearn as a flexible model training mechanism to build a distributed control and aggregation methodology in regions.
  arXiv  Detail & Related papers  (2020-12-01T11:46:03Z)
• Topology-based Clusterwise Regression for User Segmentation and Demand Forecasting [63.78344280962136]
  Using a public and a novel proprietary data set of commercial data, this research shows that the proposed system enables analysts to both cluster their user base and plan demand at a granular level. This work seeks to introduce TDA-based clustering of time series and clusterwise regression with matrix factorization methods as viable tools for the practitioner.
  arXiv  Detail & Related papers  (2020-09-08T12:10:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.