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11 Nov 2022
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Honiara, Solomon Islands
5 Days
Programme Area
Climate Change, Satellite Imagery and Analysis
Event Focal Point Email
Solomon Islands University (SINU)
MECDM, Government of Solomon Islands
Private – by invitation
Mode of Delivery
United Nations Satellite Centre UNOSAT
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The project “Strengthening Capacities in the Use of Geospatial Information for Improved Resilience in Asia-Pacific and Africa.” (2021-2024) intends to develop sustainable capacities and implement ad-hoc and tailored geospatial solutions. These can help to improve existing policy and decision-making processes to solve priority issues in the fields of Disaster Risk Reduction. This 3-year long project builds on previous experiences and aims to further enhance capacities by leveraging technological advances and innovation and providing integrated geospatial solutions for improved decision making in the fields of Disaster Risk Reduction, Climate Resilience, Environmental Preservation & Food Security in the eight target countries: Solomon Islands, Bangladesh, Bhutan, Fiji, Lao PDR, Nigeria, Uganda, and Vanuatu.

Over the last two decades Geo-spatial Information Technology (GIT) has rapidly developed and is now being also called an “enabling technology” due to the benefit it offers across different application domains. GIT can help us to analyse and to better understand why and where things have happened in the past and it can also show us why and where they might happen in the future allowing us to make informed decision and better use of our resources.

Risk data and information is the foundation of disaster risk management for decision-making and action. Hence, this training is designed to strengthen existing capacities within MECDM, specifically NDMO and the N-DOC sectors, to be able to respond more effectively to the information needs within the different phases of the DRM cycle.

At the end of the course participants should be able to:

  1. Recall the basic concepts and terminologies related to Geospatial Information Systems (GIS);
  2. Recall good practices on field data collection and management to improve data quality;
  3. Create geospatial data using digitization and file-based geospatial database (geopackage) in QGIS;
  4. Use KoboCollect and UN Asign applications for field data collection;
  5. Recall basic methods and functionalities of GIS software (QGIS) and Excel to analyse data;
  6. Apply symbology for proper representation of data and prepare a map layout in QGIS;
  7. Use decision support applications to support decision-making in DRM and Climate Resilience

The course has the main purpose to provide the participants with a comprehensive understanding of the data flow in emergency response and preparedness operations. After a QGIS refresher, module 2 will focus on building capacity to design and improve quantitative surveys and create a questionnaire in a smartphone application. In module 3, participants will learn on how to design and create a file based geospatial database, as well as how to fix errors and topology. Module 4 will show how to analyse existing datasets to provide relevant analysis to assist emergency response operations, preparedness, and awareness raising activities. In module 5, participants will explore how to improve maps, with additional resources, to successfully convey information to decision-makers. In module 6 and 7, participants will be introduced to the INFORM Risk Index and Multi-Criteria Decision Analysis (MCDA), and learn how to use the Decision Support Application tools (DSS). Finally, at the end of the training, UX design session will be held to collect inputs for the design of the decision support system. Benefits and good practices on field data collection, database management, and geospatial analysis will be discussed throughout the training, with the objective of improving data quality in the ministries under the NDM plan arrangements.

This is a full-time, face-to-face course with lectures, GIS lab exercises using GIS datasets and real case scenarios (60% lab exercises, 40% lectures and discussions), and field data collection. This course is divided into 5 modules. Each module is structured into 4 sessions of 1.5 hour each. The average workload is likely to be around 35 hours.  The course is designed in a way to have a balanced approach between theoretical and practical teaching methods consisting in PowerPoint presentations, games, live demos, videos, interactive sessions, and GIS lab exercises. At the end of the course, UNITAR-UNOSAT will set up a community of practice platform to maximize the learning experience of participants and to provide all required technical backstopping and assistance to training participants during and after the training.

The course is designed to accommodate selected participants by NDMO.  Previous GIS experience not required.