RNN Predict Street Commercial Vitality

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Deep Learning RNN-Predict-Street-Commercial-Vitality
Overview

RNN-for-Predicting-Street-Vitality

Code and dataset for Predicting the Vitality of Stores along the Street based on Business Type Sequence via Recurrent Neural Network (CAADRIA 2022)

In this study, we use a sequence-based neural network to explore the relationship between the sequence of store types along a street and its commercial vitality. We use customer review data from OTO platforms to represent the store vitality degree. After selection, the data of stores in 50 streets were collected in order.

input: the sequence of store businesse types along a street

output: the corresponding sequence of business vitality indexes

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Owner
Zidong LIU
Zidong Liu graduated from Bartlett, UCL. His research interests focus on generative design and machine learning-based urban feature prediction.
Zidong LIU
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