Improving population mapping using Luojia 1-01 nighttime light image and location-based social media data

Abstract

Fine-resolution population mapping, which is vital to urban planning, public health, and disaster management, has gained considerable attention in socioeconomic and environmental studies. Although population distribution has been considered highly correlated with urban facilities, the quantitative relationship between the two has yet to be revealed when considering huge heterogeneity. To address this problem, the present study proposed a novel population mapping method by adopting Luojia 1-01 nighttime light imagery, points of interest (POI), and social media check-in data. A grid-based attraction degree (AD) model was built to quantify the possibility of population concentration in each geographic unit with a comprehensive consideration of the distribution and the popularity of facilities. On the basis of kernel density estimation, 16 attraction indexes were extracted by matching POI and check-in data. Multiple variables were used to train a random forest model, through which fine-scale population mapping was conducted in Zhejiang, China. The comparison between demographic and WorldPop data proved the high accuracy of our approach (R2 = 0.75 and 0.58). To explore the characteristics of the model further, the most appropriate search distance (650 m) and acquisition time (19:00–08:00) of the check-in data were discussed. The contrast experiment revealed that the model could outperform those from previous studies on rural and suburban areas with a few check-in points and low AD; thus, the mapping error caused by heterogeneity considerably decreased. The results indicated the proposed method has great potential in fine-scale population mapping.