Using Building Permits to Monitor Disaster Recovery: A Spatio-Temporal Case Study of Coastal Mississippi Following Hurricane Katrina

Stevenson, Joanne; Emrich, Christopher T.; Mitchell, Jerry T.; Cutter, Susan L.

doi:10.1559/152304010790588052

Cited by 59 publications

(32 citation statements)

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“…Therefore, in order to avoid these constraints, more well-documented events have to be collected. Moreover, such restrictions may be a major concern with respect to scale since local assessments of natural hazard vulnerability are hardly comparable with studies undertaken on a regional scale (Cutter and Finch 2008;Stevenson et al 2010). Additionally, a further analysis of data, such as according to the type and year of construction, would enrich our understanding beyond space; such information would be of particular interest with respect to the overall discussion on multi-temporal and spatial assessment of risk Keiler et al 2005Keiler et al , 2006aZischg et al 2005a, b), and with respect to advances in multi-temporal vulnerability assessments Papathoma-Köhle et al 2011) and multi-hazard vulnerability studies .…”

Section: Discussionmentioning

confidence: 99%

Spatial scan statistics in vulnerability assessment: an application to mountain hazards

2012

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In the European Alps, the concept of risk has increasingly been applied in order to reduce the susceptibility of society to mountain hazards. Risk is defined as a function of the magnitude and frequency of a hazard process times consequences; the latter being quantified by the value of elements at risk exposed and their vulnerability. Vulnerability is defined by the degree of loss to a given element at risk resulting from the impact of a natural hazard. Recent empirical studies suggested a dependency of the degree of loss on the hazard impact, and respective vulnerability (or damage-loss) functions were developed. However, until now, only little information is available on the spatial characteristics of vulnerability on a local scale; considerable ranges in the loss ratio for medium process intensities only provide a hint that there might be mutual reasons for lower or higher loss rates. In this paper, we therefore focus on the spatial dimension of vulnerability by searching for spatial clusters in the damage ratio of elements at risk exposed. By using the software SaTScan, we applied an ordinal data model and a normal data model in order to detect spatial distribution patterns of five individual torrent events in Austria. For both models, we detected some significant clusters of high damage ratios, and consequently high vulnerability. Moreover, secondary clusters of high and low values were found. Based on our results, the assumption that lower process intensities result in lower damage ratios, and therefore in lower vulnerability, and vice versa, has to be partly rejected. The spatial distribution of vulnerability is not only dependent on the process intensities but also on the overall land use pattern and the individual constructive characteristics of the buildings exposed. Generally, we suggest the use of a normal data model for test sites exceeding a minimum of 30 elements at risk exposed. As such, the study enhanced our understanding of spatial vulnerability patterns on a local scale.

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Section: Discussionmentioning

confidence: 99%

Spatial scan statistics in vulnerability assessment: an application to mountain hazards

2012

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“…Maximum damage indicators for F0/F1-F3 ratings are noted by missing roofs and, in some cases, removal of walls, whereas well-constructed houses are completely leveled in F4 zones with even more catastrophic damage in F5 regions [31]. As a result, recovery in lesser damaged areas (F0/F1-F3) would typically require less financial resources to draw upon and time to complete the repairs, thereby explaining similar and faster recovery rates [19], [46]. Unlike lesser damaged areas, recovery in the most severely damaged areas (F4 and F5) would require more financial resources, decision-making policies regarding construction, and, consequently, more time to rebuild with the possibility of relocating to another site [19], [26], [46].…”

Section: Discussionmentioning

confidence: 99%

Geospatial Assessment of Recovery Rates Following a Tornado Disaster

Wagner

Myint

Cerveny

2012

IEEE Trans. Geosci. Remote Sensing

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Remote sensing has proven to be instrumental in monitoring land alterations from natural disasters and anthropogenic processes. Additionally, geospatial analyses of tornado disasters have provided damage assessments, whereas hazard research has limited recovery evaluation to economic and migration perspectives. This study examines recovery from the 1999 Moore, OK, tornado disaster across three consecutive years, utilizing medium-resolution imagery and a series of image-processing algorithms. Spectral enhancements including normalized difference vegetation index, soil-adjusted vegetation index, urban index (UI), and two new indices, i.e., ShortWave Radiation Index (SWIRI) and Coupled Vegetative Urban Index (CVUI), were utilized in conjunction with a recovery index and statistical thresholds to assess recovery. Classification accuracy assessments prove that geospatial techniques and medium-resolution imagery can capture the rate of recovery with the most effective results noted with SWIRI using the 1.5 standard deviation threshold. Computed annual and Fujita Scale recovery rates indicate that 1) the most severely damaged areas associated with an F5 rating were the slowest to recover whereas the lesser damaged areas (F1-F3) were the quickest to rebuild and 2) complete recovery was never attained, even three years after the event, regardless of the F-scale damage zones. Recovery appears to be a significant and direct function of the level of damage sustained. With these results, decision makers and other policyholders could implement more resilient approaches in reconstructing the more severely damaged areas.

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“…Therefore, Twitter is becoming a key source of open and free geospatial data generated by citizens [12]. For example, geo-tagged tweets have been primarily utilized in disaster management [8,13,14]. With accurate location information, tweets are proven to be highly spatiotemporally reliable and useful in such applications [7].…”

Section: Introductionmentioning

confidence: 99%

Discover Patterns and Mobility of Twitter Users—A Study of Four US College Cities

Shan

2017

IJGI

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Geo-tagged tweets provide useful implications for studies in human geography, urban science, location-based services, targeted advertising, and social network. This research aims to discover the patterns and mobility of Twitter users by analyzing the spatial and temporal dynamics in their tweets. Geo-tagged tweets are collected over a period of six months for four US Midwestern college cities: (1) West Lafayette, IN; (2) Bloomington, IN; (3) Ann Arbor, MI; (4) Columbus, OH. Various analytical and statistical methods are used to reveal the spatial and temporal patterns of tweets, and the tweeting behaviors of Twitter users. It is discovered that Twitter users are most active between 9:00 p.m. and 11:00 .pm. In smaller cities, tweets aggregate at campuses and apartment complexes, while tweets in residential areas of bigger cities make up the majority of tweets. We also found that most Twitter users have two to four places of frequent visits. The mean mobility range of frequent Twitter users is linearly correlated to the size of the city, specifically, about 40% of the city radius. The research therefore confirms the feasibility and promising future for using geo-tagged microblogging services such as Twitter to understand human behavior patterns and carry out other geo-social related studies.

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Using Building Permits to Monitor Disaster Recovery: A Spatio-Temporal Case Study of Coastal Mississippi Following Hurricane Katrina

Cited by 59 publications

References 0 publications

Spatial scan statistics in vulnerability assessment: an application to mountain hazards

Spatial scan statistics in vulnerability assessment: an application to mountain hazards

Geospatial Assessment of Recovery Rates Following a Tornado Disaster

Discover Patterns and Mobility of Twitter Users—A Study of Four US College Cities

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