Monitoring Chinese Population Migration in Consecutive Weekly Basis from Intra-city scale to Inter-province scale by Didi's Bigdata

TL;DR

This paper presents a novel system using Didi's big data to monitor population migration across China weekly, from community to provincial levels, leveraging WiFi hotspot movement as a proxy for migration.

Contribution

It introduces a new method for real-time migration monitoring using WiFi AP data, with a comprehensive processing pipeline to improve accuracy.

Findings

Migration can be detected within a week.

The system provides nationwide migration insights.

WiFi AP movement correlates with population migration.

Abstract

Population migration is valuable information which leads to proper decision in urban-planning strategy, massive investment, and many other fields. For instance, inter-city migration is a posterior evidence to see if the government's constrain of population works, and inter-community immigration might be a prior evidence of real estate price hike. With timely data, it is also impossible to compare which city is more favorable for the people, suppose the cities release different new regulations, we could also compare the customers of different real estate development groups, where they come from, where they probably will go. Unfortunately these data was not available. In this paper, leveraging the data generated by positioning team in Didi, we propose a novel approach that timely monitoring population migration from community scale to provincial scale. Migration can be detected as soon as in a week. It could be faster, the setting of a week is for statistical purpose. A monitoring system is developed, then applied nation wide in China, some observations derived from the system will be presented in this paper. This new method of migration perception is origin from the insight that nowadays people mostly moving with their personal Access Point (AP), also known as WiFi hotspot. Assume that the ratio of AP moving to the migration of population is constant, analysis of comparative population migration would be feasible. More exact quantitative research would also be done with few sample research and model regression. The procedures of processing data includes many steps: eliminating the impact of pseudo-migration AP, for instance pocket WiFi, and second-hand traded router; distinguishing moving of population with moving of companies; identifying shifting of AP by the finger print clusters, etc..