Five classes of geospatial data and the barriers to using them

Reynard, Darcy

doi:10.1111/gec3.12364

Cited by 7 publications

(12 citation statements)

References 51 publications

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“…Big data can be defined in a variety of ways depending on the disciplines and subjects being studied. However, there are three components that can be considered the fundamentals of big data termed the "three Vs": (1) volume-the quantity of data that are collected, stored, and processed; (2) velocity-how fast the data are collected and processed; and (3) variety-the types/sources of data [38][39][40]. This study aimed to highlight the usefulness of the longest track records of the Atlantic hurricane public database in tandem with multiple geospatial data and hurricane modeling techniques in order to identify the most vulnerable areas to hurricanes in a spatially explicit manner.…”

Section: Modeling Large-scale and Long-term Historical Hurricanesmentioning

confidence: 99%

A Comprehensive Analysis of Hurricane Damage across the U.S. Gulf and Atlantic Coasts Using Geospatial Big Data

Park

2021

IJGI

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(1) Background: Hurricane events are expected to increase as a consequence of climate change, increasing their intensity and severity. Destructive hurricane activities pose the greatest threat to coastal communities along the U.S. Gulf of Mexico and Atlantic Coasts in the conterminous United States. This study investigated the historical extent of hurricane-related damage, identifying the most at-risk areas of hurricanes using geospatial big data. As a supplement to analysis, this study further examined the overall population trend within the hurricane at-risk zones. (2) Methods: The Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model and the HURRECON model were used to estimate the geographical extent of the storm surge inundation and wind damage of historical hurricanes from 1950 to 2018. The modeled results from every hurricane were then aggregated to a single unified spatial surface to examine the generalized hurricane patterns across the affected coastal counties. Based on this singular spatial boundary coupled with demographic datasets, zonal analysis was applied to explore the historical population at risk. (3) Results: A total of 777 counties were found to comprise the “hurricane-prone coastal counties” that have experienced at least one instance of hurricane damage over the study period. The overall demographic trends within the hurricane-prone coastal counties revealed that the coastal populations are growing at a faster pace than the national average, and this growth puts more people at greater risk of hurricane hazards. (4) Conclusions: This study is the first comprehensive investigation of hurricane vulnerability encompassing the Atlantic and Gulf Coasts stretching from Texas to Maine over a long span of time. The findings from this study can serve as a basis for understanding the exposure of at-risk populations to hurricane-related damage within the coastal counties at a national scale.

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Section: Modeling Large-scale and Long-term Historical Hurricanesmentioning

confidence: 99%

A Comprehensive Analysis of Hurricane Damage across the U.S. Gulf and Atlantic Coasts Using Geospatial Big Data

Park

2021

IJGI

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“…Previous academics have investigated various aspects that have influenced the use of geospatial technology. Reynard [9] has reviewed past studies on geospatial technologies using the World Wide Web (Geo Web). The results have identified two barrier factors: financial barriers and technological barriers.…”

Section: Literature Review On Factors Influencing Usage Of Geospatial...mentioning

confidence: 99%

“…Among the examples of geospatial technology are Global Positioning System (GPS), Geographic Information Systems (GIS), spectral imagery, Light Detention and Ranging (LiDAR), and aerial photography are examples of geospatial technology [2][3][4][5][6][7]. Although geospatial technology is getting more stable, some obstacles are found to hinder its development; including financial [8,9], lack of knowledge [4], lack of experience [10], lack of training [9], data sharing data is not manageable [11], lack of support from institutional [12], and poor communication among organizations [8].…”

Section: Introductionmentioning

confidence: 99%

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Factors Influencing the Use of Geospatial Technology with LiDAR for Road Design: Case of Malaysia

et al. 2022

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This study seeks a better understanding of the acceptance of geospatial technology with Light Detention and Ranging (LIDAR) in road design in a developing country, Malaysia. Existing surveying measurement methods to provide quick, accurate, and reliable information are unsuccessful in producing an expected result, especially in large areas. In addition, topographic data cannot be observed well with the conventional total station method in areas under thick canopies, which is challenging to identify road areas at risk to the environment, such as slope failure. Geospatial surveying technology by LiDAR helps in measuring fields over a wide area and provides a broader spatial extent. At the same time, the laser capability of airborne LiDAR, which penetrates the canopy, helps give accurate readings on the terrain. However, the use of LiDAR geospatial technology for use in road design is still insufficient to date. Thus, this study is developed to identify the factors that influence the use of LiDAR in road design among engineers. Factors identified are barriers, motivation, and strategy. Barrier factors consist of lack of knowledge, risk, cost, and human aspects that slow down the development of LiDAR use. On the other hand, motivational factors consist of encouraging engineers to obtain knowledge about LiDAR and to use it more widely. Meanwhile, a strategy factor form increases LiDAR measurement methods through activities or work procedures. The finding shows that barriers and strategy factors are the significant factors that affect the acceptance of LiDAR among engineers. However, motivational factors have no significant effect to engineers in accepting the use of LiDAR. The advantages of this study and its limitations are also discussed. Finally, this study also provides compilation of few suggestions pertaining this topic to improve future research.

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“…Despite the fact that geographic data attracts growing interest from data users, and also the scholars reviewed show that it is valuable to users and therefore should be more open, data authorities nevertheless continue to ignore the potential of geographic initiatives. In a recent study, Reynard (2018) revealed that despite the relevant and significant contributions of a Public Participatory Geographic Information System (PPGIS) developed to work in mobility issues throughout Edmonton (Canada), the city hall ignored its results and took a top-down approach without considering the data generated by citizens and the empirical results that showed where the city infrastructure could be improved.…”

Section: Problemmentioning

confidence: 99%

A user-centric framework to improve the reusability of open geodata in cities

Páez¹

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There are many people who contributed in certain ways to achieve the results presented in this research. First, to all, I would like to thank my main supervisor Joaquin Huerta, to the guide and all support along this research also to introducing me to the European geospatial community and its challenges, projects, communities and overall to help me to start an academic carrier in the European research community. Special thanks to Michael Gould who always provided me with new insights and different ways to tackle different open data issues. Special thanks to professor Christian Kray for his support, new ideas, comments and always helping to get better results in my publications and the GeoC outcomes. I am very grateful to Sergi Trilles for his support in all my research, and pushing me to move forward and reach always better results. Special thanks to professor Auriol Degbelo of the Institute of geoinformatics of the University of Münster for his patience, guidance, support and especially to helping me to understand how the scientific contributions should be and the right method to follow. Special thanks also to my co-supervisor Roberto Henriques for helping me in my external semester in Lisbon finding the way to reach the initial publication presented in this research. I am very grateful to professor Alexis Comber from the school of the geography of the University of Leeds for the guide in my second publication presented in this research. Besides my advisors, I would like to thank the two international experts Dr. Rolf de By and Dr. Çetin Cömert for their comments, suggestions, and questions that make of this thesis a better contribution. To my colleagues and friends in the GeoTec research group and the GeoC project, their interesting comments, and help with the activities that this research carried out. I want to express my acknowledgement to the four data authorities who allow me to perform the participatory workshops and always be interested in the topic presented in this investigation. To Pamela Mayorga in IDECA-Bogotá, Julio Muñoz in IDECS

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Five classes of geospatial data and the barriers to using them

Cited by 7 publications

References 51 publications

A Comprehensive Analysis of Hurricane Damage across the U.S. Gulf and Atlantic Coasts Using Geospatial Big Data

A Comprehensive Analysis of Hurricane Damage across the U.S. Gulf and Atlantic Coasts Using Geospatial Big Data

Factors Influencing the Use of Geospatial Technology with LiDAR for Road Design: Case of Malaysia

A user-centric framework to improve the reusability of open geodata in cities

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