Heavy rains with intense precipitation have become more frequent in the context of global climate changes (Myhre et al., 2019; Ogie et al., 2018) and pose significant threats to urban residents, mainly due to uncoordinated watershed management and undersized infrastructures (Chan et al., 2018; Dewan, 2015; Nahiduzzaman et al., 2015; Song et al., 2019). China is frequently affected by urban flooding, particularly in summer when the Asian monsoon brings heavy rains to inland China. It is estimated that 55.15 million people were affected by floods in China in 2017 alone and the direct economic loss was approximately CNY 214 billion, which significantly exceeds the impacts of the 2017 typhoon disasters (5.879 million people, CNY 34.62 billion). In addition to threatening human daily activities and cities' normal operation (Aerts et al., 2014; Grinberger and Felsenstein, 2016; Kasmalkar et al., 2020; Owrangi et al., 2014), the ever-increasing rainstorms endlessly challenge cities' flood resistance capacity and relevant authorities' real-time decisions in response to such adverse events. Urban decision-makers have learned that city management and planning would significantly benefit from a better understanding of urban resilience (O'Sullivan et al., 2012; Bertilsson et al., 2019; de Bruijn, 2004).

Urban resilience refers to the ability of an urban system to prepare for, respond to, and recover from adverse events (Ambelu et al., 2017; Hong et al., 2021; Liao, 2012; Meerow et al., 2016). Biologists, psychologists, engineers, and geographers have all made their own contributions to urban resilience studies (Adger et al., 2005; Brusberg and Shively, 2015; Olsson et al., 2015; Ouyang et al., 2012; Poulin and Kane, 2021; Shiferaw et al., 2014). Over the past 20 years, geographers have heavily relied on satellite imagery to assess disaster-related resilience as satellites have been providing ever-increasing information about the Earth at a relatively low cost (Mpandeli et al., 2019; Stefan et al., 2016; Tellman et al., 2021). For example, satellite-based emergency mapping systems have been developed to monitor the inundation and recovery processes of the 2005 Switzerland flood (Buehler et al., 2006); assess the damage, restoration, and reconstruction induced by the 2010 Haiti earthquake (Honey et al., 2010); and evaluate the changes in power supply before and after Hurricane Maria in 2017 (Román et al., 2019). Emergency rescuers can use high-resolution images to closely monitor ongoing natural disasters and coordinate disaster relief. However, it is almost impossible to extract near-real-time human dynamics over the evolution of a disaster from satellite images, and such information is very important in disaster mitigation and reduction (Liu et al., 2015; Ghaffarian et al., 2018).

Location-aware big data, such as smartphone call records, signalling data, and social media posts, have been widely used to infer real-time human activities (Yi et al., 2019; Wang et al., 2020, 2019; Yue et al., 2017), estimate disaster-induced losses (Kryvasheyeu et al., 2016; Liu et al., 2019b), monitor resettlement and restoration (Martín et al., 2020a, b; Wang and Taylor, 2018; Yabe et al., 2020), and study disaster-related resilience (Hong et al., 2021; Huang and Ling, 2018; Kasmalkar et al., 2020; Zou et al., 2018). Urban residents adjust their activities when their living environments are socially and physically impacted by an adverse event, and such adjustment can be inferred from location-aware big data (Qian et al., 2021b). In other words, the changes in human activities extracted from location-aware big data could be used to study the resilience capacity of an urban system in response to an adverse event. For example, Hong et al. (2021) quantified changes in mobility behaviour before, during, and after the Hurricane Harvey using smartphone geolocation data, and they analysed the spatial variable of community resilience capacity, which was defined as the function of the magnitude of impact and time to recovery.

Human activities may also change in response to mild yet frequent adverse natural events, such as urban rainstorms. Unlike in the case of hurricanes, dwellers are usually not mobilized by relevant authorities to prepare for and resettle after such events. Instead, nearly 90 % of flood-related tweets in a city are released during heavy rains (Wang et al., 2020). Consequently, human activities mainly show how an urban system responds to but not how it prepares for and recovers from such adverse natural events (Qian et al., 2022; Zhang et al., 2022). As a result, urban resilience to mild and frequent adverse events refers to the ability of an urban system to respond to adverse events. Furthermore, urban resilience could significantly differ from that of destructive disasters and may show significant spatiotemporal variations due to the areal difference in local natural settings, socioeconomic status, and infrastructure completeness (Adger et al., 2005; Guan and Chen, 2014; Östh et al., 2015; Zou et al., 2019, 2018). Study of such regional inequality is therefore of great value to disaster relief and mitigation.

However, previous resilience metrics, which have mainly focused on unique disaster events, were not suitable for making assessments at a large scale. Two resilience metrics were introduced into this study from other fields. Sensitivity is a widely used tool for understanding resilience in different regions in many other weather events, such as heat waves and air pollution (Hong et al., 2021; Wang et al., 2021). For example, Zheng et al. (2019) defined the links between a city-level happiness index calculated from social media data and a daily local air quality metric as the perception sensitivity and explored its spatial variation. However, response sensitivity has not yet been studied for rainstorm events through analysing the relation between a city-scale human activity response metric and a rainstorm event index. Another index, the rainfall threshold, is commonly used to study rainfall events that have resulted in landslides (Marra et al., 2016; Naidu et al., 2018). In this study, the rainfall threshold, which is defined as the minimum rainfall index that corresponds to a significant urban human activity response anomaly, is introduced into the study of urban resilience. These two metrics can effectively depict urban resilience at different levels of focus.

In this study, we propose a method for measuring and evaluating how urban systems respond to heavy rains as reflected in location-aware big data. We extracted human activities from the Tencent location request (TLR) data in 346 cities across mainland China from May to August 2017 and used two indicators – rainfall threshold and response sensitivity – to quantify the urban resilience across our study area. We found significant regional inequality of urban resilience in mainland China, and the inequality could be explained by variations in the regional natural, socioeconomic, and infrastructure variables. The findings from this study provide a new perspective and method to quantify urban resilience to frequent yet not so destructive adverse events across a large geographic scale. Practically, our findings suggest an urgent need to revise current unified rainstorm warning standards to better serve residents.

Residents in different cities may adjust and change their activities in different ways in response to rainfall. Such activity changes and adjustment, also known as urban resilience, could be characterized and studied from location-aware big data. In this study, we used Tencent aggregated location request data to examine the changes in collective human activities in major cities in China in response to rainfall over summer 2017. Our results show that the rainfall time, peak intensity, and duration are the three most important indicators that determine whether a rain event would trigger human activity anomalies or not. We also proposed two indices, the rainfall threshold and response sensitivity, to describe urban resilience; they show significant spatial variations across our study area. Furthermore, the unequal urban resilience could be explained by a series of explanatory variables.

We believe this paper provides a new perspective for studying urban resilience and that the results bridge the knowledge gap between heavy rains, collective human activities, and urban resilience. Such knowledge is of great significance to urban planning, traffic management, and emergency response.

We also believe this study has three other contributions regarding urban resilience research. First, this study expanded the research framework of urban resilience in response to high-frequency yet mild adverse events, and such a framework could be used to study the variations in urban resilience across a large area. Previous studies have mainly focused on urban resilience to a specific adverse event such as a typhoon or a hurricane. For example, Zou et al. (2018) used a normalized ratio index to assess the regional variations in urban resilience to Hurricane Sandy. In this study, we examined urban resilience to rains over a relatively long period and across a large area. Such a study can better show how residents in different regions change their activities in different ways in response to rains with different durations, peak intensities, and accumulative rainfall levels.

Secondly, our research analysed the impacts of different features of the rainfall events on human activities. Previous studies have often simply characterized a disaster using its threat levels. For example, Zou et al. (2018) used the average hurricane track kernel density and its wind speed to define the threat levels. In this study, instead, we extracted five major elements of a rainfall event and employed the random forest model to study the impacts of different elements on collective human activities.

Thirdly, we used the rainfall threshold to quantify urban resilience, and the threshold is valuable for authorities to revise heavy-rain alerts. Conventionally, heavy-rain alerts are usually based on rainfall intensity and precipitation only and seldom consider the areal difference in infrastructures. According to current Chinese standards, Chinese authorities issue a blue, yellow, and orange alert when precipitation is or will be over 50 mm in 12, 6, and 3 h and the rain might not stop (Mendiondo, 2005). A red alert is issued when precipitation is or will be over 100 mm in 3 h and the rain might not stop. Results from this study show that it is not appropriate to apply such a unified alert standard to different groups of cities across China. In the less resilient, fragile areas (Fig. 7), for instance, a rainfall event with 3.25 mm of precipitation per hour (i.e. 19.5 mm in 6 h) already triggers significant human activity anomalies. As a result, the national heavy-rain-alert standard significantly underestimates the impacts of rainfall on the residents in northwestern and central China.

Table 1 shows the precipitation thresholds of different city groups that trigger human activity anomalies and the 6 h accumulative precipitation based on which a heavy-rain warning should be issued. For example, for the LH cities, a rainfall intensity of 1.8 mm and 6 h accumulative precipitation of 10.8 mm should trigger a yellow heavy-rain warning. Such a value of the 6 h accumulative precipitation is much lower than the current yellow Chinese heavy-rain-warning standard (50 mm in 6 h). By contrast, for the HH cities, the rainfall threshold and 6 h accumulative precipitation that trigger human activity anomalies are 22.34 and 134.05 mm, respectively. The accumulative precipitation is much higher than the yellow heavy-rain-warning standard. In other words, it is amateurish to issue a heavy-rain warning when the 6 h accumulative precipitation is 50 mm for the HH cities. Results from this study therefore are of great value for the authorities who revise heavy-rain alerts across China to help local residents be better prepared for such adverse events.

The study could be expanded. Rather than representing all the residents of a city, the Tencent location request dataset is generated by over 1 billion monthly active users. The Tencent dataset's aggregate geotagged human activities may underestimate the effects of rainstorms on infrequent users, particularly the elderly and children. To address this limitation and further investigate human responses to various weather events, our future studies will aim to integrate multisource geospatial datasets. Furthermore, identifying disaster types such as rainstorm, waterlogging, and flood from social media data and then analysing the regional response variation in large-scale human activity in different disasters can improve deep understanding of urban resilience.