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Yongxin Deng, Brendan Wallace, Derek Maassen, and Johnathan Werner

has been reached regarding the mapping methods or parameters (e.g., Brooks et al. 2003 ; Dixon et al. 2011 ; Dixon and Mercer 2012 ; Marsh and Brooks 2012 ). We believe this situation is related to several geographical information system (GIS) complications—present at conceptual as well as methodological levels—that need to be fully recognized and systematically addressed. To say the least, individual tornadoes are discrete real-world events in space–time and are directly observable

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A. Kermanshah, S. Derrible, and M. Berkelhammer

vulnerability of road systems to rainfall-induced flash floods under different future climate change scenarios. Overall, by combining the power of climate models, network science, geographical information systems (GIS), and stochastic modeling, the main goal of this article is to develop and apply a sound method to assess the vulnerability of road systems against climatically driven changes in the character (such as frequency and

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Yao Zhou and Corene J. Matyas

) database ( Knapp et al. 2010 ) and are plotted within a geographic information system (GIS) to identify TCs making landfall over the western Gulf Coast and the Caribbean coast during 1998–2015. A total of 35 TCs are examined, after excluding storms that spend less than 24 h over the ocean, or do not reach tropical storm (TS) intensity during their life cycle. We also exclude one extratropical storm since it experiences different environmental conditions than the tropical storms in this region ( Fig. 1

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M. P. Calef, A. Varvak, A. D. McGuire, F. S. Chapin III, and K. B. Reinhold

Turetsky 2006 ; Miyanishi and Johnson 2001 ; Ward et al. 2001 ). Through advances in fire ecology, the initial U.S. policy of aggressive fire suppression has morphed through time into fire management systems that recognize wildfires as an integral part of ecosystem dynamics ( Todd and Jewkes 2006 ). Additionally, it has never been realistic to suppress all fires in the vast boreal forest of Alaska, which is why the state was classified into four fire management options or fire management zones (FMZs

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Yumei Hu, Esben Almkvist, Torbjörn Gustavsson, and Jörgen Bogren

authority (Lantmäteriet), road vector data, digital elevation model (DEM), land-use data, and a digital surface model (DSM), were all accessed and used to generate the geographical parameters used in this study. Most of the parameters were developed using the geographical information system (GIS) software ArcGIS desktop. The road vector data were divided into 50-m segments, and the thermal-mapping measurements and geographical parameters were spatially related to the segmented scaled road vertices. This

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Michelle E. Saunders, Kevin D. Ash, and Jennifer M. Collins

anthropogenic features ( Diederich et al. 2015 ; Maddox et al. 2002 ). Likewise, access to telecommunication technologies—particularly home and mobile Internet—is generally much more limited in rural than in urban locations ( Cutter et al. 2016 ; Salemink et al. 2017 ). In addition to the geographic limitations of infrastructure, there are individual factors that can limit or deny access to weather information via radar maps. Despite the overall increase in telecommunications technologies over the past 30

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Corene J. Matyas

as the timing of peak areal coverage is dependent upon this threshold ( Muramatsu 1983 ; Lajoie and Butterworth 1984 ). Ground-based radar reflectivity data provide a spatially accurate representation of the areal coverage of TC rain fields over land areas without this limitation. This study employed a geographic information system (GIS) to measure the areal extent of radar reflectivity returns associated with 45 TC landfalls in the United States. The main hypothesis was that the diurnal cycle

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Younghyun Cho and Bernard A. Engel

System Visual Utility Engine (HEC-DSSVue) and Hydrologic Engineering Center’s Grid Utility program (HEC-GridUtil); USACE 2009 , 2011 , 2013 ], but are only applicable in HEC software. On the other hand, Nelson et al. (2003) developed a data browser in a GIS platform instead as a processing tool for radar rainfall data, using a cylindrical equidistant project as a map projection. Similarly, Xie et al. (2005) introduced automated NEXRAD stage III precipitation data processing approaches for GIS

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Walker S. Ashley, Stephen Strader, Troy Rosencrants, and Andrew J. Krmenec

and the coarsest of the three analysis iterations. Population and housing count data at the block level were obtained from the Census Summary File 1 (SF1) archives at NHGIS for each of the three censuses. Boundary and demographic attribute datasets were conflated in a geographic information system (GIS). Table 1. Mean tract (1960–2010) and block (1990–2010) size area (km 2 ) by individual county and all counties (total), as well as the percentage change in population (pop.) and housing units (HU

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Juan Declet-Barreto, Kim Knowlton, G. Darrel Jenerette, and Alexander Buyantuev

bands as follows: where near-infrared (NIR) is TM band 4 (0.76–0.90 μ m), and Red is TM band 3 (0.63–0.69 μ m) of Landsat. The index range is ±1.0, and we recoded negative values to zero (less than one percent for all but one Landsat scene) in order to avoid negative values in regression analyses. The city boundary GIS file ( Cuyahoga County Geographical Information Systems 2016 ) was used to extract pixels within our study area. Because of the large number of pixels in each scene (224 839) and to

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