The impact of scale on extracting urban mobility patterns using texture analysis

Abstract

The development of high-precision location tracking devices and advancements in data collection, storage, transmission technologies, and data mining algorithms have led to the availability of large datasets with high spatiotemporal resolution. These geospatial big data can be used to identify human movement patterns in urban areas. However, identifying human movement patterns may yield different results depending on the scale size used. In this paper, we employed first and second order texture analysis algorithms to identify spatial patterns of human movement for various scale sizes based on taxi trajectory data from Nanjing, China. The results demonstrated that texture analysis can quantify changes in human movement patterns for different scale sizes in an urban area. Furthermore, the results may differ based on the location of the study area. This study contributed both methodologically and empirically. Methodologically, we used texture analysis to examine the impact of different scale sizes on the extraction of aggregated human travel patterns. Empirically, we quantified the effects of different scale sizes on extracting aggregated travel patterns of an urban area. Overall, the findings of this study can have significant implications for urban planning and policy-making, as understanding human movement patterns at different scales can provide valuable insights for optimizing transportation systems and enhancing overall urban mobility.

The Impact of Scale on Extracting Individual Mobility Patterns from Location-Based Social Media

“…These categories are cartographic, observational, measurement, and operational. The cartographic scale is used to demonstrate the relationship between the size of map features and their actual dimensions on the ground [ 52 , 55 ]. The observational scale delineates the extent of the area under investigation [ 52 ].…”

“…The measurement scale, synonymous with resolution, signifies the smallest discernible unit within a study [ 56 ]. The choice of the measurement scale significantly impacts both the input data and the outcomes of analysis because they change depending on the size and shape of the area unit of analysis [ 52 , 55 , 56 ]. Lastly, the operational scale typically denotes the spatial scope within which specific environmental processes actively occur [ 52 ].…”

“…It represents the level at which the greatest range of pattern variability can be effectively observed and analyzed. The operational scale plays a decisive role in determining the dimensions of spatial coverage (observational scale) and the level of detail (measurement scale) [ 52 , 55 ].…”

Analysis of Urban Residents’ Travelling Characteristics and Hotspots Based on Taxi Trajectory Data

“…(3) Trajectory data mining and personalized travel data collection: Traditionally, when mining trajectory data for transportation analysis, researchers relied on data from questionnaires and fixed-route buses, which failed to efficiently capture personalized travel information of residents. However, with the rapid advancement of network technology and the public transportation industry in recent years, it has become more convenient to collect personalized travel data from sources such as taxi global navigation satellite system (GNSS) trajectory data, residents' mobile signaling data, and social media data [8,9]. In one study, Zhu et al used shared bicycle travel data of residents and introduced the time series weighted regression (TSWR) model to address the challenge of sparse statistical data when making long-term predictions.…”