Volume 15, Issue 1

Social media platforms are increasingly being used by public agencies and emergency managers to communicate with the public in times of crisis. However, while the adoption of these technologies has been well documented at the federal level, little is known empirically about the extent to which social media are being utilized for emergency management communications by local agencies, and less still is known about how rates of adoption vary based on the organizational and demographic characteristics of local municipalities. This exploratory study provides an empirical analysis of social media use by local municipalities in the State of Florida and examines the organizational and demographic factors related to social media adoption for emergency and crisis communications. The study suggests that social media are still underutilized by local agencies in many regards. Findings also indicate that larger municipalities which serve younger, more highly educated populations are more likely to adopt social media for crisis communications, while agencies representing traditionally under-served populations are less likely to utilize social media for these same purposes. The implications of these findings are discussed, particularly the role of higher education in preparing public administrators and emergency managers for careers in a networked society.

Illegal fishing activities in the Gulf of Mexico pose a threat to the US national security, as well as damage to the economy. The US Coast Guard (USCG) estimates over 1100 incursions by Mexican fisherman into US regulated waters in the Gulf of Mexico annually. Fishermen enter the water borders to catch red snapper, which is one of the Gulf of Mexico’s signature and most valuable fish. There are a number of academic contributions which have sought to improve the understanding of the problem of illegal fishing, and to try to generate better solutions. In this study, we investigate the relationship between illegal fishing activities and environmental factors with one-year of historical sight, weather, and moon phase data. Descriptive analysis provides some interesting insights such as sight patterns depending on wave height, moon phase, and hours of a day. Also, we develop logistic regression models that shows wave height is negatively correlated with sight occurrences for all sight types. In addition, we oversample the data and develop two pre diction models using logistic regression and k-nearest neighbor algorithm and compare prediction accuracies. The results show that k-nearest neighbor algorithm performs better in most of the cases.

Electric distribution utilities have an obligation to inform the public and government regulators about when they expect to complete service restoration after a major storm. In this study, we explore methods for calculating the estimated time of restoration (ETR) from weather impacts, defined as the time it will take for 99.5% of customers to be restored. Actual data from Storm Irene (2011), the October Nor’easter (2011) and Hurricane Sandy (2012) within the Eversource Energy-Connecticut service territory were used to calibrate and test the methods; data used included predicted outages, the peak number of customers affected, a ratio of how many outages a restoration crew can repair per day, and the count of crews working per day. Data known before a storm strikes (such as predicted outages and available crews) can be used to calculate ETR and support pre-storm allocation of crews and resources, while data available immediately after the storm passes (such as customers affected) can be used as motivation for securing or releasing crews to complete the restoration in a timely manner. Used together, the methods presented in this paper will help utilities provide a reasonable, data-driven ETR without relying solely on qualitative past experiences or instinct.