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Author  |
Christopher W. Zobel; Milad Baghersad; Yang Zhang |
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Title |
Calling 311: evaluating the performance of municipal services after disasters |
Type |
Conference Article |
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Year |
2017 |
Publication |
Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management |
Abbreviated Journal |
Iscram 2017 |
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Volume |
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Issue |
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Pages |
164-172 |
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Keywords |
Resilience; Municipal Departments; 311 Service Center; Disaster; Critical Infrastructure |
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Abstract |
As part of a movement towards enabling smart cities, a growing number of urban areas in the USA, such as New York City, Boston, and Houston, have established 311 call centers to receive service requests from their citizens through a variety of platforms. In this paper, for the first time, we propose to leverage the large amount of data provided by these non-emergency service centers to help characterize their operational performance in the context of a natural disaster event. We subsequently develop a metric based on the number of open service requests, which can serve as the basis for comparing the relative performance of different departments across different disasters and in different geographic locations within a given urban area. We then test the applicability and usefulness of the approach using service request data collected from New York City's 311 service center. |
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Address |
Virginia Tech |
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Corporate Author |
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Thesis |
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Publisher |
Iscram |
Place of Publication |
Albi, France |
Editor |
Tina Comes, F.B., Chihab Hanachi, Matthieu Lauras, Aurélie Montarnal, eds |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
2411-3387 |
ISBN |
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Medium |
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Track |
Analytical Modeling and Simulation |
Expedition |
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Conference |
14th International Conference on Information Systems for Crisis Response AndManagement |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
2008 |
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Author |
Derya Ipek Eroglu; Duygu Pamukcu; Laura Szczyrba; Yang Zhang |
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Title |
Analyzing and Contextualizing Social Vulnerability to Natural Disasters in Puerto Rico |
Type |
Conference Article |
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Year |
2020 |
Publication |
ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2020 |
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Volume |
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Issue |
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Pages |
389-395 |
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Keywords |
Data Analytics, Hurricane María, Principal Component Analysis, Social Vulnerability Index. |
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Abstract |
As the third hurricane the U.S. experienced in 2017, Hurricane María generated impacts that resulted in both short term and long term suffering in Puerto Rico. In this study, we aim to quantify the vulnerability of Puerto Ricans by taking region and society specific characteristics of the island into account. To do this, we follow Cutter et al.'s social vulnerability calculation, which is an inductive approach that aims to represent a society based on its characteristics. We adapted the Social Vulnerability Index (SoVI) for Puerto Rico by using data obtained from the U.S. Census Bureau. We analyzed the newly calculated SoVI for Puerto Rico and compared it with the existing deductive approach developed by the Center for Disease Control (CDC). Our findings show that the new index is able to capture some characteristics that the existing vulnerability index is unable to do. |
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Address |
Virginia Tech; Virginia Tech; Virginia Tech; Virginia Tech |
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Corporate Author |
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Thesis |
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Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Amanda Hughes; Fiona McNeill; Christopher W. Zobel |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
978-1-949373-27-37 |
ISBN |
2411-3423 |
Medium |
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Track |
Data and resilience: opportunities and challenges |
Expedition |
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Conference |
17th International Conference on Information Systems for Crisis Response and Management |
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Notes |
deryaipek@vt.edu |
Approved |
no |
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Call Number |
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Serial |
2238 |
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Author |
Laura Szczyrba; Yang Zhang; Duygu Pamukcu; Derya Ipek Eroglu |
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Title |
A Machine Learning Method to Quantify the Role of Vulnerability in Hurricane Damage |
Type |
Conference Article |
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Year |
2020 |
Publication |
ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2020 |
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Volume |
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Issue |
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Pages |
179-187 |
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Keywords |
Vulnerability, Impact, Damage, Machine Learning, Hurricane María. |
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Abstract |
Accurate pre-disaster damage predictions and post-disaster damage assessments are challenging because of the complicated interrelationships between multiple damage drivers, including various natural hazards, as well as antecedent infrastructure quality and demographic characteristics. Ensemble decision trees, a family of machine learning algorithms, are well suited to quantify the role of social vulnerability in disaster impacts because they provide interpretable measures of variable importance for predictions. Our research explores the utility of an ensemble decision tree algorithm, Random Forest Regression, for quantifying the role of vulnerability with a case study of Hurricane Mar\'ia. The contributing predictive power of eight drivers of structural damage was calculated as the decrease in model mean squared error. A measure of social vulnerability was found to be the model's leading predictor of damage patterns. An additional algorithm, other methods of quantifying variable importance, and future work are discussed. |
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Address |
Virginia Tech; Virginia Tech; Virginia Tech; Virginia Tech |
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Corporate Author |
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Thesis |
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Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Amanda Hughes; Fiona McNeill; Christopher W. Zobel |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
978-1-949373-27-17 |
ISBN |
2411-3403 |
Medium |
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Track |
Analytical Modeling and Simulation |
Expedition |
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Conference |
17th International Conference on Information Systems for Crisis Response and Management |
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Notes |
lszczyrba@vt.edu |
Approved |
no |
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Call Number |
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Serial |
2218 |
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Author |
Yang Zhang; William Drake; Yuhong Li; Christopher Zobel; Margaret Cowell |
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Title |
Fostering Community Resilience through Adaptive Learning in a Social Media Age: Municipal Twitter Use in New Jersey following Hurricane Sandy |
Type |
Conference Article |
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Year |
2015 |
Publication |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2015 |
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Issue |
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Pages |
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Keywords |
Adaptive learning; disaster resilience; Hurricane Sandy; social media; Twitter |
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Abstract |
Adaptive learning capacity is a critical component of community resilience that describes the ability of a community to effectively gauge its vulnerability to the external environment and to make appropriate changes to its coping strategies. Traditionally, the relationship between government and community learning was framed within a deterministic paradigm. Learning outcomes were understood to result from the activities of central actors (i.e., government) and flow passively into the community. The emergence of social media is fundamentally changing the ways organizations and individuals collect and share information. Despite its growing acceptance, it remains to be determined how this shift in communication will ultimately affect community adaptive learning, and therefore, community resilience. This paper presents the initial results of a mixed-methods research effort that examined the use of Twitter in local municipalities from Monmouth County, NJ after Hurricane Sandy. Using a conceptual model of organizational learning, we examine the learning outcomes following the Hurricane Sandy experience. |
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Corporate Author |
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Publisher |
University of Agder (UiA) |
Place of Publication |
Kristiansand, Norway |
Editor |
L. Palen; M. Buscher; T. Comes; A. Hughes |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
2411-3387 |
ISBN |
9788271177881 |
Medium |
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Track |
Social Media Studies |
Expedition |
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Conference |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
yes |
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Call Number |
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Serial |
1236 |
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