Post COVID-19 pandemic recovery of intracity human mobility in Wuhan: Spatiotemporal characteristic and driving mechanism

Abstract

After successfully inhibiting the first wave of COVID-19 transmission through a city lockdown, Wuhan implemented a series of policies to gradually lift restrictions and restore daily activities. Existing studies mainly focus on the intercity recovery under a macroscopic view. How does the intracity mobility return to normal? Is the recovery process consistent among different subareas, and what factor affects the post-pandemic recovery? To answer these questions, we sorted out policies adopted during the Wuhan resumption, and collected the long-time mobility big data in 1105 traffic analysis zones (TAZs) to construct an observation matrix (A). We then used the nonnegative matrix factorization (NMF) method to approximate A as the product of two condensed matrices (WH). The column vectors of W matrix were visualized as five typical recovery curves to reveal the temporal change. The row vectors of H matrix were visualized to identify the spatial distribution of each recovery type, and were analyzed with variables of population, GDP, land use, and key facility to explain the recovery driving mechanisms. We found that the "staggered time" policies implemented in Wuhan effectively staggered the peak mobility of several recovery types ("staggered peak"). Besides, different TAZs had heterogeneous response intensities to these policies ("staggered area") which were closely related to land uses and key facilities. The creative policies taken by Wuhan highlight the wisdom of public health crisis management, and could provide an empirical reference for the adjustment of post-pandemic intervention measures in other cities.

Keywords: COVID-19; Intracity mobility; Nonnegative matrix factorization; Spatial lag regression; Spatiotemporal analysis.

Graphical abstract

Fig. 1

Conceptual framework of this study.

Fig. 2

Daily mobility counts and study area map.

Fig. 3

Baidu heat maps of key dates and observation matrix of human mobility indexes Notes. A unified grading standard was applied in part a to compare the human mobility index over multiple days.

Fig. 4

Selection of the best decomposition rank..$\mathit{k}$

Fig. 5

Nonnegative matrix factorization results.

Fig. 6

Comparison between the typical recovery types and observed time sequences.

Fig. 7

Spatial visualization of the occurrence intensity of each mobility recovery type.

Fig. 8

Local spatial autocorrelation of the intensity of each mobility recovery type (*** p < 0.001).