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Abstract Aggregated community-scale data could be harnessed to provide insights into the disparate impacts of managed power outages, burst pipes, and food inaccessibility during extreme weather events. During the winter storm that brought historically low temperatures, snow, and ice to the entire state of Texas in February 2021, Texas power-generating plant operators resorted to rolling blackouts to prevent collapse of the power grid when power demand overwhelmed supply. To reveal the disparate impact of managed power outages on vulnerable subpopulations in Harris County, Texas, which encompasses the city of Houston, we collected and analyzed community-scale big data using statistical and trend classification analyses. The results highlight the spatial and temporal patterns of impacts on vulnerable subpopulations in Harris County. The findings show a significant disparity in the extent and duration of power outages experienced by low-income and minority groups, suggesting the existence of inequality in the management and implementation of the power outage. Also, the extent of burst pipes and disrupted food access, as a proxy for storm impact, were more severe for low-income and minority groups. Insights provided by the results could form a basis from which infrastructure operators might enhance social equality during managed service disruptions in such events. The results and findings demonstrate the value of community-scale big data sources for rapid impact assessment in the aftermath of extreme weather events. 

The emergence of mobile platforms equipped with Global Positioning System technology enables real-time data collection affording opportunities for mining data applicable to rapid flood inundation assessment. The collected data can be employed to complement existing methods for rapid flood inundation assessment, such as remote sensing, to enhance situational awareness. In particular, telemetry-based digital trace data related to human activity have intrinsic advantages to be used for inundation assessment. In this study, we investigate the use of Mapbox telemetry data, which provides human activity indices with high spatial and temporal resolutions, for application in rapid flood inundation assessment. Using data from Hurricane Harvey in 2017 in Harris County, Texas, we (1) study anomalous fluctuations in human activities and analyze the differences in activity level between inundated and non-inundated areas and (2) investigate changes in the concentration of human activity, to explore the disruption of human activity as an indicator of flood inundation. Results show that both analyses can provide valuable rapid insights regarding flood inundation status. Anomalous activities can be significantly higher/lower in flooded areas compared with non-flooded areas. Also, the concentration of human activity during the flood propagation period across affected watersheds can be observed. This study contributes to the state of knowledge in smart flood resilience by investigating the application of ubiquitous telemetry-based digital trace data to enhance rapid flood inundation assessment. Accordingly, the use of such digital trace data could provide emergency managers and public officials with valuable insights to inform impact evaluation and response actions. 

The spread of pandemics such as COVID-19 is strongly linked to human activities. The objective of this article is to specify and examine early indicators of disease spread risk in cities during the initial stages of outbreak based on patterns of human activities obtained from digital trace data. In this study, the Venables distance ( D v ) and the activity density ( D a ) are used to quantify and evaluate human activities for 193 United States counties, whose cumulative number of confirmed cases was greater than 100 as of March 31, 2020. Venables distance provides a measure of the agglomeration of the level of human activities based on the average distance of human activities across a city or a county (less distance could lead to a greater contact risk). Activity density provides a measure of level of overall activity level in a county or a city (more activity could lead to a greater risk). Accordingly, Pearson correlation analysis is used to examine the relationship between the two human activity indicators and the basic reproduction number in the following weeks. The results show statistically significant correlations between the indicators of human activities and the basic reproduction number in all counties, as well as a significant leader-follower relationship (time lag) between them. The results also show one to two weeks’ lag between the change in activity indicators and the decrease in the basic reproduction number. This result implies that the human activity indicators provide effective early indicators for the spread risk of the pandemic during the early stages of the outbreak. Hence, the results could be used by the authorities to proactively assess the risk of disease spread by monitoring the daily Venables distance and activity density in a proactive manner.