Urban vitality provides an important basis for evaluating urban development and spatial balance. In the era of big data, the quantitative analysis of urban vitality has become a research hotspot in the field of urban sustainability and planning research. However, time variation characteristics are often neglected, which leads to one-sidedness in the pattern analysis of urban vitality. In this paper, a method for extracting vitality areas and integrating spatiotemporal features clustering is proposed. The method is used to divide urban space into multiple vitality areas scientifically. The spatial and temporal distribution patterns of urban vitality areas are found, and the driving factors of various vitality patterns are analyzed by combining points of interest (POI)-based land use characteristics. To illustrate this method, this paper takes Nanjing city as an example. One week's worth of mobile phone data indicated that Nanjing has 10 and 8 vitality areas on weekdays and weekends, respectively. The spatial and temporal distribution patterns of the vitality areas and their correlation with land use were analyzed, which proved that POI density and entropy have strong correlations with urban vitality.

A quantitative study of urban vitality brings new insights for evaluating the external construction environment and internal development power of cities. However, it still has limited knowledge of the relations between people’s diverse urban life and urban vitality, although urban activities are often used as the proxy for urban vitality. This paper aims to deeply mine the content of urban social life and reveal the driving mechanism of urban vitality after inspecting human activities. We propose a general framework for exploring the spatial pattern and driving mechanism of urban vitality using multi-source big data. It builds a mapping relationship between various urban activities and urban vitality aspects, including economic and social. In addition, the physical environment (static) and human–land interaction (dynamic) indicators are designed to analyze the driving mechanism of urban vitality using the Geographically Weighted Regression model. The results show that the spatial pattern and driving factors of urban vitality are heterogeneous over space regarding both the economic and social aspects of our experimental study. This work provides us with multiple perspectives to understand the connotation of urban vitality and urges us to develop rational strategies to make the city more vital, coordinated, and sustainable.

As Hainan Island belonged to tropical monsoon influenced region, vegetation coverage was high. It is accessible to acquire the vegetation index information from remote sensing images, but predicting the average vegetation index in spatial distributing trend is not available. Under the condition that the average vegetation index values of observed stations in different seasons were known, it was possible to qualify the vegetation index values in study area and predict the NDVI (Normal Different Vegetation Index) change trend. In order to learn the variance trend of NDVI and the relationships between NDVI and temperature, precipitation, and land cover in Hainan Island, in this paper, the average seasonal NDVI values of 18 representative stations in Hainan Island were derived by a standard 10-day composite NDVI generated from MODIS imagery. ArcGIS Geostatistical Analyst was applied to predict the seasonal NDVI change trend by the Kriging method in Hainan Island. The correlation of temperature, precipitation, and land cover with NDVI change was analyzed by correlation analysis method. The results showed that the Kriging method of ARCGIS Geostatistical Analyst was a good way to predict the NDVI change trend. Temperature has the primary influence on NDVI, followed by precipitation and land-cover in Hainan Island.