|Exercise 9-1: Creating a Displacement Natural Disaster Resilience Index|
This is the companion website for Exercise 9-1: Creating a Displacement Natural Disaster Resilience Index from the book Geographic Information Systems (GIS) for Disaster Management (second edition). This page contains video walkthroughs of the exercise, datasets associated with the exercise, and other additional materials.
Follow this link to access Exercise 9-1: Creating a Displacement Natural Disaster Resilience Index datasets:
- Download Exercise 9-1: Creating a Displacement Natural Disaster Resilience Index datasets (.zip file, 61.6 mb ) here.
Exercise 9-1: Creating a Displacement Natural Disaster Resilience Index
Skill Level: intermediate to advanced.
Estimated time to complete: 3-4 hours
Additional resources needed: an Internet-connected computer, a zip tool, ArcGIS Pro 2.x (with Spatial Analyst and Advanced licenses) or QGIS 3.x., Exercise 9-1 datasets you can download here.
Purpose: the purpose of this lab will be for you gain experience and knowledge with developing a spatial index using refugee natural disaster resilience in Rwanda as a case study.
ArcGIS Pro and QGIS have similar yet different ways of conducting the tasks presented in this exercise. Wherever possible, equivalent tools in each environment have been used. The overall work flow for this exercise can be summarized as follows:
- Define spatial index variables and their weights as per the discussion made previously in chapter 9.
- Filter datasets to match spatial index variable needs such as removing records not needed in the spatial index.
- Derive distance and/or site parameter datasets from variables using techniques like Euclidean distance, slope calculation, and/or buffering.
- Convert any remaining spatial index variable datasets to raster format.
- Combine spatial index variable datasets in a weighted overlay or equivalent tool and generate scoring outputs.
- Visualize final spatial index results for the final presentation and refinement of model parameters such as changing distance and/or site parameters, and variable weights.
Datasets used in this exercise were compiled from public and open data sources, further demonstrating the value of the ideas of open data for GIS for disaster management first discussed in Chapter 3 and have been used in previous exercises. The following are notes on the datasets used in this exercise:
- Health Sites: From the Global Healthsites Mapping Project
Data source: https://data.humdata.org/dataset/rwanda-healthsites
- Schools: From the National Institute of Statistics of Rwanda (NISR) Geodata Portal
Data source: http://geodata-nisr.opendata.arcgis.com/datasets/1760c0baa0fa4f379e2a5f802e7001fc_0
Data layer renamed to Rwanda_schools
- Settlements: From the VAM Rwanda
Data source: https://data.humdata.org/dataset/rwanda-settlements-0
- Towns: From WFP, VAM Rwanda
Data source: https://data.humdata.org/dataset/rwanda-settlements
Renamed to Rwanda_towns
- Digital Elevation Model (DEM): From the Regional Centre For Mapping Resource For Development (RCMRD)
Data source: http://geoportal.rcmrd.org/layers/servir%3Arwanda_srtm30meters
- Transportation: From OpenStreetMap exports on HDX
Data source: https://data.humdata.org/dataset/hotosm_rwa_roads
Renamed to Rwanda_roads
1. Added a numeric field ‘distance’ to roads for QGIS multi-ring buffering in Task 4, Step 2
- Rivers: From From Intergovernmental Authority on Drought and Development Climate Prediction and Applications Centre (ICPAC)
Data source: http://geoportal.icpac.net/layers/geonode%3Arwa_water_lines_dcw
Renamed to Rwanda_rivers
1. Added a numeric field ‘distance’ to rivers for QGIS multi-ring buffering in Task 4, Step 2
- All datasets were re-projected from their source coordinate system of GCS WGS 1984 (WKID: 4326) to WGS 1984 UTM Zone 36S (WKID: 32736) . UTM Zone 36S covers Rwanda and allows for a projected coordinate system to be used for various spatial analysis tools used in this exercise.
**Note:** QGIS instructions require approximatley 1.9 GB of free disk space with files that are created from the various exercise tasks.
Learning Objectives: after completing the exercise, you will know:
- how to develop a spatial index framework;
- how to convert vector datasets to raster datasets;
- how to use the Euclidean distance (ArcGIS Pro) or proximity analysis (QGIS) tools;
- how to derive slope values from a Digital Elevation model;
- how to reclassify datasets to a common measurement scale;
- how to combine and weight datasets to develop a final spatial index score.
Deliverables: if you are completing this exercise for a class assignment, you can submit a screenshot of your response to the instruction questions as well as answers to the discussion questions.
Displaced populations are at the highest level since WWII. Additionally, impacts of natural disasters continue to escalate worldwide. As you read earlier in this chapter (9) and throughout this book, natural disaster resilience continues to receive attention from interdisciplinary researchers focused on developing new science for characterizing and quantifying natural disaster resilience (National Academy of Sciences 2012). The importance of natural disaster resilience for displaced people has been identified by UNHCR:
A UNHCR survey in 2015 found refugees and internally displaced persons were exposed to 150 disasters in sixteen countries during 2013 and 2014, confirming their vulnerability to disasters associated with natural hazards. Floods, landslides, severe storms, fires and other disasters affected some 380,000. (n.d., 2)
In this exercise, you will create a basic spatial index to quantify the natural disaster resilience of displaced populations using Rwanda as a case study. Rwanda is a unique location in which to spatially index and quantify the natural disaster resilience of displaced populations as per discussions you first learned about in Chapter 1. For example, Rwanda hosts refugee populations from neighboring Democratic Republic of Congo (DRC), Burundi, and ethnic Rwandans who are still returning to Rwanda from after the 1994 Genocide against the Tutsi. Second, Rwandan citizens routinely face internal displacement from natural disasters – most notably floods and landslides – as was discussed in Chapter 1.
When developing a spatial index, it is often useful to first develop a conceptual framework of the spatial index. The conceptual framework for developing a spatial index of the natural disaster resilience of displaced populations used in this exercise is further discussed in the book.
- Page 377: Rivers dataset - a numeric field "distance" was added to rivers (book says it was added to roads)
Exercise 9-1: Creating a Displacement Natural Disaster Resilience Index
ArcGIS Pro Walkthrough (Part 1 of 5)
ArcGIS Pro Walkthrough (Part 2 of 5)
ArcGIS Pro Walkthrough (Part 3 of 5)
ArcGIS Pro Walkthrough (Part 4 of 5)
ArcGIS Pro Walkthrough (Part 5 of 5)
QGIS Pro Walkthrough (Part 1 of 8)
QGIS Pro Walkthrough (Part 2 of 8)
QGIS Pro Walkthrough (Part 3 of 8)
QGIS Pro Walkthrough (Part 4 of 8)
QGIS Pro Walkthrough (Part 5 of 8)
QGIS Pro Walkthrough (Part 6 of 8)
QGIS Pro Walkthrough (Part 7 of 8)
QGIS Pro Walkthrough (Part 8 of 8)