Abstract:

Urban population distribution and activities are always the hot research topics. Identifying the spatial-temporal variation and predicting future trends are of great significance for estimating population accurately, making policy effectively, and warning of population booming timely. With the availability of data and the development of data processing technique, multisource data with both spatial and temporal features, such as mobile signaling data, have been used in population studies. In this paper, q-statistic was firstly applied as an exploratory analysis, then Bayesian spatial-temporal models were used to evaluate patterns of urban population and make prediction of future trends. The Chaoyang, Beijing in 2017 was selected as empirical study of this model. The spatially stratified heterogeneity was detected by q-statistic in Geodetector firstly. Then we explored the overall spatial variation, overall time trend and the departures of the local trends from the overall trend of resident population in Chaoyang by use of Bayesian spatial-temporal hierarchical model. Secondly, we applied Bayesian Gaussian predictive process to predict the resident population in December of 2017 by incorporating other relevant influential factors. The results show the perfect spatial stratified heterogeneity for resident population in Chaoyang, and the overall spatial variation demonstrates an increasing trend of population from center to the outside along the main ring road in Beijing. The overall time trend is still growing all over Chaoyang district, while the local trends, which departure from the overall trend of resident population, are different between each sub-districts in Chaoyang. Moreover, the spatial distribution of predicted resident population shows a high consistency with the observed resident population, and the prediction accuracy can be well accepted on the scale of Chaoyang district. However, prediction accuracy shows obvious difference on scale of sub-districts, with the worst prediction accuracy in the capital airport area. These findings show that Bayesian hierarchical model and Bayesian Gaussian predictive process are reliable in empirical study of population evaluation and prediction by effective application of multisource spatial-temporal data. Researches in this paper can be an excellent theoretical and practical support for mining multisource spatial-temporal data and assisting multiscale analysis with Bayesian spatial-temporal model, and provide an important basis for population controlling and early warning in urban population management.

Key words: multisource data, q-statistic, Bayesian hierarchical model, gaussian predictive process, spatial-temporal variation, population prediction