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• Urban street networks are poorly served by existing network analysis techniques. • During disasters, street travel rules are significantly different. • A grid-based approach better evaluates a region's traversability during a disaster. A variety of real-world infrastructure systems, including power grids, telecommunications, roads and social networks can be conceptualized and modeled using basic concepts from graph theory and network science. The abstraction of real-world systems into an edge-node topology enables analysts to quantify and characterize a range of network properties, including structure, connectivity, and modularity, as well as the attributes of individual nodes and edges such as degree, criticality, and betweenness. One major problem with the abstraction process associated with traditional network analysis is that edges and nodes are stripped of important proximal, contextual information that is critical for understanding how network elements operate in situ. For example, the geographic context of an elevated causeway is significantly different than a landlocked residential street, yet they are both simply represented as edges in traditional network modeling. The purpose of this paper is to explore the implications associated with network abstractions, including the loss of geographic fidelity, and why it matters for vulnerability analysis and emergency response during extreme events. Specifically, this paper introduces an alternative network conceptualization that preserves important on-network information, but also provides in situ context for local network elements, helping to deepen our understanding of local operating conditions. A case study for hurricane storm surge flooding in Volusia County, Florida is utilized to highlight how the developed approach better captures the realities of how evacuees and emergency response teams can leverage information for both on- and off-network space during a major disaster. [ABSTRACT FROM AUTHOR]

• Demonstrates the use of LBSN to guide managers during natural disasters. • Method for content extraction and sentiment analysis from LBSN data. • Supervised machine learning techniques used to classify tweets. • Tweets joined with ACS 2017 to study the context where tweets emerge. • Longer, less re-tweeted content from less popular tweeters is likely to be useful. Location-based social networks like Twitter can relay not just what is happening but where. To enable efficient resource allocation during natural disasters, managers need to know what citizens are experiencing and where. During Hurricane Irma in 2017 in Florida over six million people were asked to evacuate. The hurricane caused widespread damage. We utilized geospatial and machine learning techniques to categorize geolocated tweets, which were both about Hurricane Irma and located within Florida. We employed sentiment analysis to classify tweets about damage and/or transportation into negative, neutral, or positive groups. We used the American Community Survey (ACS) 2017 geography to provide socio-economic context, and relied on a multinomial logit specification to examine which features of the tweet, tweeter, or location were likely to be associated with negative or positive sentiments. We found that longer tweets were more likely to have useful sentiment-based content. We also discovered that popular tweeters were likely to tweet positive sentiments, and that popular tweets were less likely to have useful information about the disaster. The ACS backdrop suggested that the likelihood of tweets with negative sentiments was higher in census block groups with younger families. The probability of tweets with useful positive or negative sentiments dropped in locations with higher property values or median rents. This paper offers a methodology for extracting information embedded within social media data, and suggests ways to develop a probability-based understanding of needs to guide disaster managers. [ABSTRACT FROM AUTHOR]

• Roadway performance assessment was conducted for Hurricane Irma evacuation in FL. • Evacuation-related performance metrics used for the spatiotemporal traffic analysis. • Major interstates were severely congested while other roads were underutilized. • Roadways typically uncongested experienced severe congestion and bottlenecking. • Lack of relevant evacuation information needs to be addressed for future events. This paper analyzes temporal and spatial traffic patterns and assesses the performance of Florida roadways during the evacuation for Hurricane Irma (2017). Evacuation-specific performance metrics are used to characterize the performance of the roadway systems and granular analyses are conducted using RITIS probe detector data supplemented with hourly continuous counts from Florida Traffic Online. Results from this study show the disparate nature of roadway utilization during mass evacuations – while most interstates remained congested during the major evacuation period, several other highways remained underutilized, potentially due to the lack of information on roadway conditions during evacuation. On the other hand, certain roadways that were typically underutilized suffered from severe congestion and bottlenecking during the evacuation. Detail-oriented studies of this nature could serve as an important baseline to benefit future emergency and disaster planning, and traffic managements during future events of similar nature. [ABSTRACT FROM AUTHOR]

• The spatial and temporal evolution of traffic flow reconstructed for all highways in Florida for hurricane Irma evacuation. • A simplified framework is built to reconstruct evacuation traffic flow based on limited highway camera data. • The data reconstructed could improve survey-based evacuation demand simulators. Hurricane evacuation has long been a difficult problem perplexing local government. Hurricane Irma in 2017 created the most extensive scale of evacuation in Florida's history, involving about 6.5 million people in a mandatory evacuation order and an estimated 4 million evacuation vehicles. Traffic jams emerged in mid-Florida and rapidly spread to involve the entire state. To understand the hurricane evacuation process, the spatial and temporal evolution of the traffic flow is a critical piece of information, but it is usually not fully observed. Based on game theory, this paper employs the available traffic observation of main highways to reconstruct the traffic flow on all highways in Florida during Irma. The reconstructed traffic conditions compare well with those simulated by dynamic models while the reconstruction model is computationally much cheaper to use. Validation with smartphone data further confirms that the reconstruction model captures the traffic conditions for real evacuation processes. The reconstructed data show that the evacuation rates for 5 representative cities -- Key West, Miami, Tampa, Orlando, and Jacksonville-- in Florida were about 90.1%, 38.7%, 52.6%, 22.1%, and 7%, respectively. The peak evacuation traffic flows from Tampa and Miami arrived in the Orlando region at almost the same time, triggering the catastrophic congestion through the entire state. Also, the evacuation for Hurricane Irma was greater than that predicted by an evacuation demand model developed based on previous event and survey data. The detailed evacuation traffic flow reanalysis accomplished in this article lays a foundation for studying evacuation demand as well as developing evacuation management policies. [ABSTRACT FROM AUTHOR]

• GIS-based analysis was conducted to assess the accessibility to mental health services in Florida. • Accessibility of vulnerable population such as seniors was studied. • Counties with the least accessibility are mainly clustered in Northwest Florida. • Counties with a poor access are mostly located in rural areas. The demand for mental health services has been growing stronger over the last couple of decades. This indicates the need to study and assess the access to these mental health services especially with a focus on the vulnerable populations having the greatest need. As such, this paper presents a Geographical Information Systems (GIS)-based analysis in order to study and evaluate the accessibility of mental health facilities using the information on the spatial distributions of population and facilities, and regional traffic characteristics. For this purpose, different age group segments are utilized including the total population as well as those aged between 18 and 21, 22 and 49, 50 and 64, and those aged over 65 and 85. Focusing on the State of Florida, spatially detailed accessibility metrics are calculated with regard to healthcare facilities using travel times between population block groups and these critical mental health facilities. These estimates are used to calculate the weighted county accessibility scores for each county. Findings clearly delineate those counties that lack access to mental facilities, especially those in Northwest Florida, a demographically diverse and substantially rural region. This type of analysis can help planners and policy makers develop better strategies in order to provide adequate mental health care options needed in targeted locations. [ABSTRACT FROM AUTHOR]