Abstract
Estimating and mapping population distributions dynamically at a city-wide spatial scale, including those covering suburban areas, has profound, practical, applications such as urban and transportation planning, public safety warning, disaster impact assessment and epidemiological modelling, which benefits governments, merchants and citizens. More recently, call detail record (CDR) of mobile phone data has been used to estimate human population distributions. However, there is a key challenge that the accuracy of such a method is difficult to validate because there is no ground truth data for the dynamic population density distribution in time scales such as hourly. In this study, we present a simple and accurate method to generate more finely grained temporal-spatial population density distributions based upon CDR data. We designed an experiment to test our method based upon the use of a deep convolutional generative adversarial network (DCGAN). In this experiment, the highest spatial resolution of every grid cell is 125125 square metre, while the temporal resolution can vary from minutes to hours with varying accuracy. To demonstrate our method, we present an application of how to map the estimated population density distribution dynamically for CDR big data from Beijing, choosing a half hour as the temporal resolution. Finally, in order to cross-check previous studies that claim the population distribution at nighttime (from 8 p.m. to 8 a.m. on the next day) mapped by Beijing census data are similar to the ground truth data, we estimated the baseline distribution, first, based upon records in CDRs. Second, we estimate a baseline distribution based upon Global Navigation Satellite System (GNSS) data. The results also show the Root Mean Square Error (RMSE) is about 5000 while the two baseline distributions mentioned above have an RMSE of over 13,500. Our estimation method provides a fast and simple process to map people’s actual density distributions at a more finely grained, i.e., hourly, temporal resolution.
Highlights
The dynamic nature of temporal and spatial population distributions has a profound impact on urban and transportation planning [1,2,3], safety for human crowds [4,5,6], disaster impact assessment [7,8,9], human activity-travel behaviour [10,11] and epidemiological modelling [12,13,14]
Because there is no real ground truth available to match the date of our call detail record (CDR) data, we devised a process whereby an artificial people’s distribution is generated, and use our estimation method to map the distribution from the artificial CDR that is sampled from artificial people points
In order to meet the requirements to get human activity at a finely-grained spatial and temporal scales over a large urban area with a low computation cost, we presented an estimation method to map dynamic population density distribution using mobile phone data
Summary
The dynamic nature of temporal and spatial population distributions has a profound impact on urban and transportation planning [1,2,3], safety for human crowds [4,5,6], disaster impact assessment [7,8,9], human activity-travel behaviour [10,11] and epidemiological modelling [12,13,14]. More detailed population distribution models with a higher spatial and temporal resolution have many applications. Especially in Chinese cities, there are plenty of shared bicycles to serve citizens which can be improved by dynamically provisioning the resources according to people’s distributions [17]. Another case concerns take-away food outlets and markets, if these are aware of where and when the population is distributed at a higher or lower density, they can arrange for the goods and labour force resources to be distributed, dynamically and more effectively, to generate a higher profit [18]
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