Abstract
Residents’ activity space reflects multiple aspects of human life related to space, time, and type of activity. How to measure the activity space at multiple geographic scales remains a problem to be solved. Recently, the emergence of big data such as mobile phone data and point of interest data has brought access to massive geo-tagged datasets to identify human activity at multiple geographic scales and to explore the relationship with built environment. In this research, we propose a new method to measure three types of urban residents’ activity spaces—i.e., maintenance activity space, commuting activity space, and recreational activity space—using mobile phone data. The proposed method identifies the range of three types of residents’ activity space at multiple geographic scales and analyzing the relationship between the built environment and activity space. The research takes Zhuhai City as its case study and discovers the spatial patterns for three activity space types. The proposed method enables us to achieve a better understanding of the human activities of different kinds, as well as their relationships with the built environment.
Highlights
Activity space is the term geographers usually use to describe the set of locations with which a person has direct contact during daily activities [1], mainly including residences, workplaces, and daily shopping sites
It is considered that the Catering, Shopping, Residence, Point of Interest (POI) diversity, road network density, and distance to the city center may have an impact on the radius
The model 1 shows that these independent variables are significantly related to the size of maintenance activity space, because these six variables are relevant to the basic needs of residents
Summary
Activity space is the term geographers usually use to describe the set of locations with which a person has direct contact during daily activities [1], mainly including residences, workplaces, and daily shopping sites It represents the space and time costs to fulfill the necessities of life and plays an important role in revealing the living quality of citizens. Yang et al believe that the fit scale for subsistence trips is a traffic analysis zone (TAZ) and a 600 m buffer, and the suitable scales for maintenance and recreation activities should be 250 m and 1500 m buffers [10] These studies indicate that, for studying different activities, the geographic scales should be set independently according to the activity purposes
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