Understanding spatial and temporal change patterns of population in urban areas using mobile phone data

Abstract

The wide application of information computing technology has allowed for the emergence of big data on tracing human activities. Therefore, it provides an opportunity to explore temporal profile of population changes in geographical area subdivisions. In this paper, we present a multi-step method to characterize and approximate temporal changes of population in a geographical area subdivision using eigen decomposition. Datasets in weekday and weekend are decomposed to obtain the principal temporal change profiles in Xiamen, China. The Principal Components are common patterns of temporal population changes shared by most geographical area subdivisions. Its corresponding elements in eigenvectors could be regard as a coefficient to principal components. Then, a measure, which is the similarity of each eigenvector to a basis vector, that could characterize the temporal population change is established. Based on this, the coupling interaction between population changes and land use characteristics is explored using this measure. It shows that it is restricted by land use characteristics and also is a reflection of population changes over time. These results provided an insight on understanding temporal population change patterns and it would help to improve urban planning and establish a job-housing balance.