Table 14.

Applications of deep learning for urban, rural, regional, real estate, and transportation economics

Application sub field	Article	Aim of study	Data set	Data size	Time span	Model
Slums’ degree of deprivation	(Ajami et al., 2019)	Predict data-driven index of multiple deprivation	1114 households living in 37 notified slums	1,114 households living	2010	CNN
Transportation system	(He, 2021)	Predict investment benefits and national economic attributes	Railway transportation industry from the National Bureau of Statistics	-	2013–2019	EEMD-LSTM
	(Ding et al., 2019)	Estimate socioeconomic status	Smart card: the dataset contains all the subway records in Shanghai; POI dataset of Shanghai is crawled based on GaoDe Map API Service2; Housing price dataset is crawled from Lianjia.com 3 website	-	2015	S2S models containing DNN and LSTM
	(Markou et al., 2020)	Time series forecasting of taxi demand	Taxi data are available by the NYC TLC.	around 600 million taxi trips after data filtering	2013–2016	A neural network architecture based on FC dense layers and a Deep Gaussian Processes architecture
Real estate market	(Bazan-Krzywoszanska & Bereta, 2018)	Forecast real estate value	the city center of Zielona Gora	163 sale and purchase transactions	2016–2017	DNN
	(Yao et al., 2018)	Map fine-scale urban housing prices	Fang.com, Tianditu.cn, several basic geographic and social media datasets	-	-	UMCNN, RF
	(Rafiei & Adeli, 2016)	Estimate the sale prices of real estate units	Tehran, Iran	360 residential condominiums (3–9 stories)	1993–2008	Deep RBM, nonmating genetic algorithm

Note: EEMD (Ensemble Empirical Mode Decomposition), FC (Fully-Connected), UMCNN (Convolutional Neural Network for United Mining), NYC TLC (New York City Taxi and Limousine Commission)