Trouble in Paradise: Natural Disaster Preparation in Jamaica: Looking at Flooding, Landslides and Human Vulnerability

The above poster was made by Mwahaki King for the GIS at Tufts University Poster Exhibition 2016 (GIS@Tufts) and has also been published on the GIS@Tufts website here: 
https://sites.tufts.edu/gis/files/2016/01/King_Mwahaki_MCM1009_2016.pdf

The purpose of the project was to ascertain high risk areas and thus areas prioritisation in Jamaica during natural disasters; specifically landslides, flooding and hurricanes. High risk areas were determined primarily by weak housing infrastructure, access to hospitals and environmental factors such as the slope of the terrain and proximity to rivers.

The maps were made by Mwahaki King using ArcGIS software and the poster itself was produced using Microsoft Publisher.

Data Sources:

GDAM, DIVA-GIS, CARISKA

• GDAM: http://www.gadm.org/
• DIVA-GIS http://www.diva-gis.org/gdata
• CARISKA (N.B. This website is originally in French) http://ckan.c-read.net/fr/organization/cariska

The accompanying paper can be found below, which further outlines Jamaica's environmental background, conclusions, recommendations and limitations of the project.

China Wind Farm Suitability Analysis

The above maps were made by Mwahaki King in May 2016, using raster data in ArcGIS. Mwahaki King performed by an unweighted and a weighted suitability analysis to determine optimal locations for a wind farm facility in China, north of Beijing. In both suitability analyses the suitability of a wind farm facility was dependent on the following criteria:

1. The site needed to be close to high wind areas
2. It must be close to existing power lines
3. It must be far from the The Great Wall of China so as not to disrupt tourism
4. It must be on fairly level land i.e. a low slope is preferable

In the unweighted suitability analysis, these criteria were assessed on a system of 1 - 5, with the lowest score and thus worst location having a score of 4 and the best location having a score of 20.

In the weighted analysis, these criteria were assessed by importance. Distance to high wind areas was deemed to be the most important and weighted at 50%. Second, was distance to power lines, weighted at 25%. Third, was distance from The Great Wall weighted at 12.5%. The final factor was slope, also weighted at 12.5%. This produced a grid of 1 - 5. One being the least ideal location for a wind farm facility and five being the best.

Data Sources:
ESRI
Gfk (Tufts University GIS has a license for this data and these maps were made as a part of Tufts University GIS coursework)
National Renewable Energy Lab

Households with a Computer, South Africa

The above map was created by Mwahaki King using ArcGIS software on February 24, 2016 to demonstrate the percentage of households in South Africa with a computer in the home, by municipality. The most recent data available to the cartographer at the time of the map's creation was from the year 2001.

Data Sources:
Statistics South Africa
ESRI
US National Park Service

High Income Households, South Africa

The above map was created by Mwahaki King using ArcGIS software on February 24, 2016 to demonstrate the percentage of high income households in South Africa, by municipality. The most recent geographic and financial data available to the cartographer at the time of the map's creation was from the year 2001.

Data Sources:
Statistics South Africa
ESRI
US National Park Service

Haiti Livelihood Zones

The above map was created by Mwahaki King on February 17, 2016, using ArcGis software and the most recent data at the time from FEWS NET Data Center regarding livelihood zones in Haiti. Livelihood zones are specific geographic regions of a country in which people share the same environmental conditions, similar income and access to food and marketplaces. Given these components of livelihood zones, they are an important tool non-profits and aid agencies when analysing food security in developing countries and areas to prioritise.

Data Sources:

FEWSNet

ESRI
GIST
USAid