Abstract
Abstract. There existing a significant social and spatial differentiation in the residential communities in urban city. People live in different places have different socioeconomic background, resulting in various geographically activity patterns. This paper aims to label the characteristics of residential communities in a city using collective activity patterns derived from taxi trip data. Specifically, we first present a method to allocate the O/D (Origin/Destination) points of taxi trips to the land use parcels where the activities taken place in. Then several indices are employed to describe the collective activity patterns, including both activity intensity, travel distance, travel time, and activity space of residents by taking account of the geographical distribution of all O/Ds of the taxi trip related to that residential community. Followed by that, an agglomerative hierarchical clustering algorithm is introduced to cluster the residential communities with similar activity patterns. In the case study of Wuhan, the residential communities are clearly divided into eight clusters, which could be labelled as ordinary communities, privileged communities, old isolated communities, suburban communities, and so on. In this paper, we provide a new perspective to label the land use under same type from people’s mobility patterns with the support of big trajectory data.
Highlights
The daily activity space of individuals directly shed a light on people’s lifestyle, the quality level of living, and as well as individual preferences
By grouping the residential communities with similar activity patterns at a collective level, we highlight the spatial distributions of residential communities of different types in different areas in Wuhan
We studied about the activity patterns in different residential communities derived from taxi trip data
Summary
The daily activity space of individuals directly shed a light on people’s lifestyle, the quality level of living, and as well as individual preferences. People live in different places may have different mobility patterns out of both geographical and socioeconomic reasons. The variation of activity space between people of different social groups or living in different residential communities reveals important aspects of social separation and isolation, has gained rising attention by city planner and policy makers in recent years(Wang et al 2012). Significant differences are found in the usage of time and space between individuals of different social groups. Zhang & Chai (2011) focused on the middle and low income groups in Beijing, and found that their daily activity patterns show a trend of fragmentation in time and disparity in space People from lower class have small activity space around the inner city or around their residential communities, while people from upper class have more time spending on outdoor activities and the travel space is much wider. Zhang & Chai (2011) focused on the middle and low income groups in Beijing, and found that their daily activity patterns show a trend of fragmentation in time and disparity in space
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