A Combined Data Mining and Decision Support Approach to Educational Planning

2003 ◽  
pp. 203-212 ◽  
Author(s):  
Silvana Gasar ◽  
Marko Bohanec ◽  
Vladislav Rajkovič
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Educational Planning ◽  
Combined Data

Lessons Learned From Data Mining, Decision Support and Collaboration

2003 ◽  
pp. 237-246
Author(s):  
Dunja Mladenić ◽  
Nada Lavrač ◽  
Marko Bohanec
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Lessons Learned

scholarly journals Dynamic Clinical Data Mining: Search Engine-Based Decision Support

2014 ◽  
Vol 2 (1) ◽  
pp. e13 ◽  
Author(s):  
Leo Anthony Celi ◽  
Andrew J Zimolzak ◽  
David J Stone
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Search Engine ◽  
Clinical Data ◽  
Clinical Data Mining

scholarly journals Review of current approaches to spatially explicit urban vulnerability assessments: hazard complexity, data sources, and cartographic representations

GeoScape ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 47-61
Author(s):  
Pavel Raška ◽  
Martin Dolejš ◽  
Jan Pacina ◽  
Jan Popelka ◽  
Jan Píša ◽  
...  
Keyword(s):  
Climate Change ◽  
Decision Support ◽  
Data Sources ◽  
Small Scale ◽  
Spatially Explicit ◽  
Decision Support Tools ◽  
Multiple Hazards ◽  
Vulnerability Assessments ◽  
Support Tools ◽  
Combined Data

AbstractSocio-ecological hazards are processes that − depending on the vulnerability of societal systems − may have profound adverse impacts. For this reason, the current discourse in disaster risk reduction (DRR) has been experiencing a shift toward a vulnerability-led paradigm, raising new questions about how to address (i) the complexity of vulnerabilities to multiple hazards, (ii) their cultural, dynamic, and subjective character, and (iii) the effectiveness and legitimacy of vulnerability assessments as decision-support tools. In this paper, we present a review of 707 vulnerability studies (derived from the Clarivate WoS database; 1988−2018) with a particular focus on urban settings and spatially explicit assessments in order to evaluate current efforts to meet the aforementioned issues. The reviewed studies assessed vulnerabilities to 35 hazard types that were predominantly (n=603, 85%) analysed as single hazards (mostly seismic, flood, and groundwater contamination hazards, as well as climate change), whereas only 15% (n=104) of studies focused on multiple hazards (mostly atmospheric hazards). Within the spatially explicit vulnerability studies, almost 60% used data collected by the study itself (mostly seismic hazards), while statistical and combined data were both employed in 20% of cases (mostly floods, climate change, and social and political hazards). Statistical data were found to have only limited transferability, often being generalised to be applicable in small-scale studies, while reducing the role of cultural and contextual factors. Field research data provided high-resolution information, but their acquisition is time-consuming, and therefore fixed at a local scale and single temporal stage. Underlying hazard types and suitable data sources resulting in other differences found a preference towards the specific coverage and resolution of vulnerability maps that appeared in 44% of all reviewed studies. Altogether, the differences we found indicated a division of spatially explicit vulnerability research in two major directions: (i) geological and geomorphological studies focusing on physical vulnerability, using their own data surveys at a detailed scale and lacking links to other hazards, and (ii) other studies (mostly atmospheric hazards and socialpolitical hazards) focusing on social or combined vulnerabilities, using primarily statistical or combined data at a municipal, regional, and country scale with occasional efforts to integrate multiple hazards. Finally, although cartographic representations have become a frequent component of vulnerability studies, our review found only vague rationalisations for the presentation of maps, and a lack of guidelines for the interpretation of uncertainties and the use of maps as decision-support tools.

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