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.

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.

In both geographic information system and terrain analysis, drainage systems are important components. Owing to local topography and subsurface geology, a drainage system achieves a particular drainage pattern based on the form and texture of its network of stream channels and tributaries. Although research has been done on the description of drainage patterns in geography and hydrology, automatic drainage pattern recognition in river networks is not well developed. This article introduces a new method for automatic classification of drainage systems in different patterns. The method applies to river networks, and the terrain model is not required in the process. A series of geometric indicators describing each pattern are introduced. Network classification is based on fuzzy set theory. For each pattern, the level of membership of the network is given by the different indicator values. The method was implemented, and the experimental results are presented and discussed.

The weighted K-nearest neighbor algorithm (WKNN) is easily implemented, and it has been widely applied. In the large-scale positioning regions, using all fingerprint data in matching calculations would lead to high computation expenses, which is not conducive to real-time positioning. Due to signal instability, irrelevant fingerprints reduce the positioning accuracy when performing the matching calculation process. Therefore, selecting the appropriate fingerprint data from the database more quickly and accurately is an urgent problem for improving WKNN. This paper proposes an improved Bluetooth indoor positioning method using a dynamic fingerprint window (DFW-WKNN). The dynamic fingerprint window is a space range for local fingerprint data searching instead of universal searching, and it can be dynamically adjusted according to the indoor pedestrian movement and always covers the maximum possible range of the next positioning. This method was tested and evaluated in two typical scenarios, comparing two existing algorithms, the traditional WKNN and the improved WKNN based on local clustering (LC-WKNN). The experimental results show that the proposed DFW-WKNN algorithm enormously improved both the positioning accuracy and positioning efficiency, significantly, when the fingerprint data increased.

Data-driven urban human activity mining has become a hot topic of urban dynamic modeling and analysis. Semantic activity chain modeling with activity purpose provides scientific methodological support for the analysis and decision-making of human behavior, urban planning, traffic management, green sustainable development, etc. However, the spatial and temporal uncertainty of the ubiquitous mobile sensing data brings a huge challenge for modeling and analyzing human activities. Existing approaches for modeling and identifying human activities based on massive social sensing data rely on a large number of valid supervised samples or limited prior knowledge. This paper proposes an effective methodology for building human activity chains based on mobile phone signaling data and labeling activity purpose semantics to analyze human activity patterns, spatiotemporal behavior, and urban dynamics. We fully verified the effectiveness and accuracy of the proposed method in human daily activity process construction and activity purpose identification through accuracy comparison and spatial-temporal distribution exploration. This study further confirms the possibility of using big data to observe urban human spatiotemporal behavior.

Studying the spatiotemporal pattern of urban leisure activities helps us to understand the development and utilization of urban public space, people’s quality of life, and the happiness index. It has outstanding value for improving rational resource allocation, stimulating urban vitality, and promoting sustainable urban development. This study aims at discovering the spatiotemporal distribution patterns and people’s behavioral preferences of urban leisure activities using quantitative technology merging ubiquitous sensing big data. On the basis of modeling individual activity traces using mobile signaling data (MSD), we developed a space-time constrained dasymetric interpolation method to refine the urban leisure activity spatiotemporal distribution. We conducted an empirical study in Nanjing, China. The results indicate that leisure plays an essential role in daily human life, both on weekdays and weekends. Significant differences exist in spatiotemporal and type selection in urban leisure. The weekend afternoon is the breakout period of leisure, and entertainment is the most popular leisure activity. Furthermore, the correlation between leisure resource allocation and leisure activity participation was argued. Our findings confirm that data-driven approaches would be a promising method for analyzing human behavior patterns; therefore, assisting in land planning decisions and promoting social justice and sustainability.