Abstract
The introduction of the Huff model is of critical significance in many fields, including urban transport, optimal location planning, economics and business analysis. Moreover, parameters calibration is a crucial procedure before using the model. Previous studies have paid much attention to calibrating the spatial interaction model for human mobility research. However, are whole sampling locations always the better solution for model calibration? We use active tracking data of over 16 million cell phones in Shenzhen, a metropolitan city in China, to evaluate the calibration accuracy of Huff model. Specifically, we choose five business areas in this city as destinations and then randomly select a fixed number of cell phone towers to calibrate the parameters in this spatial interaction model. We vary the selected number of cell phone towers by multipliers of 30 until we reach the total number of towers with flows to the five destinations. We apply the least square methods for model calibration. The distribution of the final sum of squared error between the observed flows and the estimated flows indicates that whole sampling locations are not always better for the outcomes of this spatial interaction model. Instead, fewer sampling locations with higher volume of trips could improve the calibration results. Finally, we discuss implications of this finding and suggest an approach to address the high-accuracy model calibration solution.
Highlights
The introduction of the Huff model is of critical significance in urban transport, economics and business areas, which can help us understand the accessibility, business opportunities, source and distribution of customers, and give suggestions to the optimal location planning of new trading areas [1,2,3,4,5]
Zhou et al [25] investigates how many samples are needed for a good performance of road selection and finds that only a small number (e.g., 50–100) of training samples is needed, while Zhao et al [26] indicates that sparse sampled call detail records data introduce some biases to human mobility research
We investigate the effects of sampling locations by calibrating a spatial interaction model as a case study, using mobile phone location data from the big data era, and attempt to answer the following questions: (1) Does using all sampling locations always perform better than small volume of sampling locations to calibrate the Huff model?
Summary
The introduction of the Huff model is of critical significance in urban transport, economics and business areas, which can help us understand the accessibility, business opportunities, source and distribution of customers, and give suggestions to the optimal location planning of new trading areas [1,2,3,4,5]. Whether the models were well calibrated to “best fit” the observed data needs to be answered before comparison and application If so, another question is how to derive the more valuable sampling locations to get high-accuracy calibration results. We investigate the effects of sampling locations by calibrating a spatial interaction model as a case study, using mobile phone location data from the big data era, and attempt to answer the following questions:. The results of this paper show that small volume of sampling location dataset may perform more effective for the calibration of the Huff model than large sampling locations, which could help utilize big data better for human mobility modeling; Secondly, we propose a method to select the more effective locations from massive mobile phone towers to improve the model calibration, which could be used to guide surveys or questionnaire for Sustainability 2017, 9, 159. Mobile phone location data can be a reasonable data source to describe human mobility [49]
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