Siamese-Like Convolutional Neural Network for Fine-Grained Income Estimation of Developed Economies

Estimating the per-capita income and the household income at a fine-grained geographical scale is critical but challenging, even across the developed economies. In this article, a novel Siamese-like Convolutional Neural Network, integrating Ridge Regression and Gaussian Process Regression, has been developed for fine-grained estimation of income across different parts of New York City. Our model (the GP-Mixed-Siamese-like-Double-Ridge model) makes good use of the pairwise comparison of location-based house price information, daytime satellite image, street view and spatial location information as the inputs. Taking the per-capita income and the median household income in New York City as the ground truths, our model outperforms (R-2 = 0.72-0.86 for five-fold validation) other state-of-the-art income estimation models and achieves good performance in cross-district and cross-scale validation. We also find that models which partially share our model architecture, including the Spatial-Information-GP and the Mixed-Siamese-like model, perform well under certain spatial granularity and data availability. Since such models rely on less data input types and simpler architectures, they can be used to save resources on data collection and model training. Hence, using our model for fine-grained income estimation does not mean excluding these models that share similar architectures. Our fine-grained income estimation model can allow the per-capita and the household income data generated in fine-grained resolution to couple with other types of data, such as the air pollution or the epidemic data, of the same scale, to ensure that any location-specific socio-economic-related study and evidence-based decision-making at the fine-grained resolution can be conducted. Future research will focus on extending our model for fine-grained income estimation in developing metropolises, and for developing other socio-economic indicators.