scholarly journals Analysing malaria incidence at the small area level for developing a spatial decision support system: A case study in Kalaburagi, Karnataka, India

2017 ◽  
Vol 20 ◽  
pp. 9-25 ◽  
Author(s):  
S. Shekhar ◽  
E-H. Yoo ◽  
S.A. Ahmed ◽  
R. Haining ◽  
S. Kadannolly
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Small Area ◽  
Malaria Incidence ◽  
Spatial Decision Support System ◽  
Spatial Decision Support ◽  
System A ◽  
Small Area Level

scholarly journals Web-Based Spatial Decision Support System andWatershed Management with a Case Study

2011 ◽  
Vol 02 (03) ◽  
pp. 195-203 ◽  
Author(s):  
Yanli Zhang ◽  
Ramanathan Sugumaran ◽  
Matthew McBroom ◽  
John DeGroote ◽  
Rebecca L Kauten ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Spatial Decision Support System ◽  
Spatial Decision Support ◽  
Web Based

Towards Spatial Decision Support System for Animals Traceability

2011 ◽  
pp. 389-411
Author(s):  
Marcos Visoli ◽  
Sandro Bimonte ◽  
Sônia Ternes ◽  
François Pinet ◽  
Jean-Pierre Chanet
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Decision Makers ◽  
Spatial Decision Support System ◽  
Private Institutions ◽  
Accurate Data ◽  
Spatial Decision Support ◽  
New Generation

Animal traceability is a very important question for several government and private institutions from many points of view: economical, sanitary, etc. Traditional systems are able to memorize the main bovine movements, or to capture the geolocation of an animal using RFID. Now it should be possible to envisage a new generation of traceability systems in which the different locations are automatically recorded several times per day for each animal. These systems should also be coupled with analysis techniques to help decision-makers to take decisions, validate and/or reformulate their hypothesis. In this chapter the authors present a spatial decision support system dedicated to the animal geolocation acquisitions and analysis of possible sanitary problems. Indeed, in case of sanitary alerts, the system is able to determine the animals which have been in contact with a diseased animal exploiting historical trajectories of animals. It is applied to traceability of beef cattle using the Brazilian production system as a case study. OTAG focuses on improving methods and geotechnologies for recording reliable and accurate data on beef production.

scholarly journals Planning and Managing the Integrated Water System: A Spatial Decision Support System to Analyze the Infrastructure Performances

2020 ◽  
Vol 12 (16) ◽  
pp. 6432
Author(s):  
Michele Grimaldi ◽  
Monica Sebillo ◽  
Giuliana Vitiello ◽  
Vincenzo Pellecchia
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Water System ◽  
Decision Makers ◽  
Spatial Decision Support System ◽  
Human Beings ◽  
Spatial Decision Support ◽  
System A ◽  
The Individual

The demand for water is constantly increasing, while there are factors related to climate change and pollution that make it less and less available. Addressing this problem means being able to face it with a global approach, which takes into account that human beings need water to survive, as well as all the systems on which they rely, namely sanitation, health, education, business, and industry. While human behavior is influenced by the growing awareness on this topic promoted by organizations specifically targeting this mission, the need to protect water resources in operational terms has led mainly to the need for smart urban infrastructure planning, consistent with the objective of promoting sustainable development. To this aim, the authorities in charge of monitoring the implementation of the investment plans by operators need to perform accurate evaluations of the technical quality of the services provided. The present paper introduces a framework to design a Multi-criteria Spatial Decision Support System, conceived to help decision-makers define and analyze the investment priorities of the individual service operators. By building a knowledge model of the network under investigation, decision-makers are aware of physical components of the whole system and are provided with an intervention priority index related to the network objects that could be affected by the planning action to be implemented.

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