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This paper presents data on Prolonged Unplanned School Closures (PUSCs) caused by hurricanes and affecting school districts along the East and Gulf Coasts of the United States between the 2011/12 and 2018/19 academic years. PUSCs are school closures lasting at least seven days that were not part of the school calendar at the start of the academic year. The dataset additionally includes counterfactual observations, meaning information pertaining to school districts affected by hurricanes, but that either did not close, or that did not experience a prolonged closure. We additionally incorporate school-district level data on socioeconomic characteristics, geography, school district capacity, and hazard characteristics. These data are used in the paper titled “Learning after the storm: Characterizing and Understanding Prolonged Unplanned School Closures After Hurricane". This dataset can be leveraged to uncover patterns of PUSCs, evaluate the impacts of various factors on school closure duration, and identify appropriate policies and strategies to enhance community resilience by minimizing the potential and the impacts of school closures. Looking ahead, the expected change of hurricane frequency and intensity under climate change makes such systematic data compilation an especially critical resource for both public and academic use. 

Dataset and code for the following paper: Abbasi, D., Safari, S., Nateghi, R., & Reilly, A. C. (2025). Learning after the Storm: Characterizing and Understanding Prolonged Unplanned School Closures after Hurricanes. International Journal of Disaster Risk Reduction, 105611. Schools are vital for providing both education and social services, but when closures extend for long periods due to disasters, they can disrupt student learning and cause widespread negative consequences for families and the broader community. This dataset compiles information on Prolonged Unplanned School Closures (PUSCs)—those lasting seven or more unexpected days—linked to hurricanes from the 2011/12 to 2018/19 school years across East and Gulf Coast school districts. It includes data on districts that closed, as well as counterfactuals where closures were avoided or brief. Supplementary district-level data cover socioeconomic factors, geography, school district capacity, and hazard characteristics, along with code used for analysis. This dataset (CSV and XLSX) and code underpin the study “Learning after the Storm: Characterizing and Understanding Prolonged Unplanned School Closures After Hurricanes.” 

Abstract Cooling energy demand is sensitive to urban form and socioeconomic characteristics of cities. Climate change will impact how these characteristics influence cooling demand. We use random forest machine learning methods to analyze the sensitivity of cooling demand in Chicago, IL, to weather, vegetation, building type, socioeconomic, and control variables by dividing census tracts of the city into four groups: below-Q1 income–hot days; above-Q1 income–hot days; below-Q1 income–regular days; and above-Q1 income–regular days. Below-Q1 census tracts experienced an increase in cooling demand on hot days while above-Q1 census tracts did not see an increase in demand. Weather (i.e. heat index and wind speed) and control variables (i.e. month of year, holidays and weekends) unsurprisingly had the most influence on cooling demand. Among the variables of interest, vegetation was associated with reduced cooling demand for below-Q1 income on hot days and increased cooling demand for below-Q1 income on regular days. In above-Q1 income census tracts building type was the most closely associated non-weather or control variable with cooling demand. The sensitivity of cooling demand for below-Q1 income census tracts to vegetation on hot days suggests vegetation could become more important for keeping cities cool for low-income populations as global temperatures increase. This result further highlights the importance of considering environmental justice in urban design. 

Abstract Building community resilience in the face of climate disasters is critical to achieving a sustainable future. Operational approaches to resilience favor systems’ agile return to the status quo following a disruption. Here, we show that an overemphasis on recovery without accounting for transformation entrenches ‘resilience traps’–risk factors within a community that are predictive of recovery, but inhibit transformation. By quantifying resilience including both recovery and transformation, we identify risk factors which catalyze or inhibit transformation in a case study of community resilience in Florida during Hurricane Michael in 2018. We find that risk factors such as housing tenure, income inequality, and internet access have the capability to trigger transformation. Additionally, we find that 55% of key predictors of recovery are potential resilience traps, including factors related to poverty, ethnicity and mobility. Finally, we discuss maladaptation which could occur as a result of disaster policies which emphasize resilience traps. 

Accurate forecasting of electricity demand is vital to the resilient management of energy systems. Recent efforts in harnessing smart-meter data to improve forecasting accuracy have primarily centered around cluster-based approaches (CBAs), where smart-meter data are grouped into a small number of clusters and separate prediction models are developed for each cluster. The cluster-based predictions are then aggregated to compute the total demand. CBAs have provided promising results compared to conventional approaches that are generally not conducive to integrating smart-meter data. However, CBAs are computationally costly and suffer from the curse of dimensionality, especially under scenarios involving smart-meter data from millions of customers. In this work, we propose an efficient reduced model approach (RMA) that leverages a novel hierarchical dimension reduction algorithm to enable the integration of fine-resolution high-dimensional smart-meter data for millions of customers in load prediction. We demonstrate the applicability of our proposed approach by using data from a utility company, based in Illinois, United States, with more than 3.7 million customers and present model performance in-terms of forecast accuracy. The proposed hierarchical dimension reduction approach enables utilizing the high-resolution data from smart- meters in a scalable manner that is not exploitable otherwise. The results shows significant improvements in forecast accuracy compared to the available approaches that either do not harness fine-resolution data or are not scalable to large-scale smart-meter big data.