An Innovative System to Understand the Development of Epidemics Using GIS Spatial Analysis and Based on AI and Big Data

2021 ◽  
pp. 229-261
Author(s):  
Giovanni Rinaldi ◽  
Fabio Capello
GI_Forum ◽  
2015 ◽  
Vol 1 ◽  
pp. 475-483
Author(s):  
Bakhtiar Feizizadeh ◽  
Samereh Pourmoradian ◽  
Samira Pourmoradian

2020 ◽  
Vol 1 ◽  
pp. 1-23
Author(s):  
Majid Hojati ◽  
Colin Robertson

Abstract. With new forms of digital spatial data driving new applications for monitoring and understanding environmental change, there are growing demands on traditional GIS tools for spatial data storage, management and processing. Discrete Global Grid System (DGGS) are methods to tessellate globe into multiresolution grids, which represent a global spatial fabric capable of storing heterogeneous spatial data, and improved performance in data access, retrieval, and analysis. While DGGS-based GIS may hold potential for next-generation big data GIS platforms, few of studies have tried to implement them as a framework for operational spatial analysis. Cellular Automata (CA) is a classic dynamic modeling framework which has been used with traditional raster data model for various environmental modeling such as wildfire modeling, urban expansion modeling and so on. The main objectives of this paper are to (i) investigate the possibility of using DGGS for running dynamic spatial analysis, (ii) evaluate CA as a generic data model for dynamic phenomena modeling within a DGGS data model and (iii) evaluate an in-database approach for CA modelling. To do so, a case study into wildfire spread modelling is developed. Results demonstrate that using a DGGS data model not only provides the ability to integrate different data sources, but also provides a framework to do spatial analysis without using geometry-based analysis. This results in a simplified architecture and common spatial fabric to support development of a wide array of spatial algorithms. While considerable work remains to be done, CA modelling within a DGGS-based GIS is a robust and flexible modelling framework for big-data GIS analysis in an environmental monitoring context.


Geosciences ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 421 ◽  
Author(s):  
Elzbieta Bielecka

The paper aimed to express the cognitive and intellectual structure of research executed in the field of GIS-based land use change modeling. An exploration of the Web of Science database showed that research in GIS spatial analysis modeling for land use change began in the early 1990s and has continued since then, with a substantial growth in the 21st century. By science mapping methods, particularly co-coupling, co-citation, and citation, as well as bibliometric measures, like impact indices, this study distinguishes the most eminent authors, institutions, countries, and journals in GIS-based land use change modeling. The results showed that GIS-based analysis of land use change modeling is a multi- and interdisciplinary research topic, as reflected in the diversity of WoS research categories, the most productive journals, and the topics analyzed. The highest impact on the world sciences in the field have can be attributed to European Universities, particularly from The Netherlands, Belgium, Switzerland, and Great Britain. However, China and the United States published the highest number of research papers.


Marine Policy ◽  
2020 ◽  
Vol 113 ◽  
pp. 103803 ◽  
Author(s):  
Laura Castro-Santos ◽  
María Isabel Lamas-Galdo ◽  
Almudena Filgueira-Vizoso

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