Abstract
ABSTRACTNovel digital data sources allow us to attain enhanced knowledge about locations and mobilities of people in space and time. Already a fast-growing body of literature demonstrates the applicability and feasibility of mobile phone-based data in social sciences for considering mobile devices as proxies for people. However, the implementation of such data imposes many theoretical and methodological challenges. One major issue is the uneven spatial resolution of mobile phone data due to the spatial configuration of mobile network base stations and its spatial interpolation. To date, different interpolation techniques are applied to transform mobile phone data into other spatial divisions. However, these do not consider the temporality and societal context that shapes the human presence and mobility in space and time. The paper aims, first, to contribute to mobile phone-based research by addressing the need to give more attention to the spatial interpolation of given data, and further by proposing a dasymetric interpolation approach to enhance the spatial accuracy of mobile phone data. Second, it contributes to population modelling research by combining spatial, temporal and volumetric dasymetric mapping and integrating it with mobile phone data. In doing so, the paper presents a generic conceptual framework of a multi-temporal function-based dasymetric (MFD) interpolation method for mobile phone data. Empirical results demonstrate how the proposed interpolation method can improve the spatial accuracy of both night-time and daytime population distributions derived from different mobile phone data sets by taking advantage of ancillary data sources. The proposed interpolation method can be applied for both location- and person-based research, and is a fruitful starting point for improving the spatial interpolation methods for mobile phone data. We share the implementation of our method in GitHub as open access Python code.
Highlights
To better understand how societies function and to improve social justice and environmental sustainability, we need to attain enhanced spatio-temporal knowledge about the locations and mobilities of people (Hägerstrand 1970, Sheller and Urry 2006, Cresswell and Merriman 2011, Kwan 2013)
The proposed multi-temporal function-based dasymetric (MFD) interpolation method refines a night-time population distribution derived from mobile phone data by activity function type significantly better than areal weighting (AW) when compared to reference data (Figure 4)
This paper gives attention to one of the important challenges that has not been widely discussed to date – the uneven spatial resolution of call detail records (CDRs) data due to the uneven spatial configuration of mobile network base stations, and its spatial interpolation
Summary
To better understand how societies function and to improve social justice and environmental sustainability, we need to attain enhanced spatio-temporal knowledge about the locations and mobilities of people Authority (legislation) and environmental (physical extent of mobile network) domains may limit where and when mobile phones can be used These aspects may influence research findings derived from CDR data while creating spatial, temporal and socio-economic biases at both the individual and aggregated levels (Yuan et al 2012, Wesolowski et al 2013, Järv et al 2014, Zhao et al 2016). With this paper we aim: (1) to put forward a generic conceptual framework for a multi-temporal function-based dasymetric (MFD) interpolation method for mobile phone data; and (2) to empirically investigate how and to what extent the proposed method improves a night-time and daytime population distribution derived from mobile phone data. We discuss the method, obtained outcomes and future steps
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