World Vision | CSUN Geography and Environmental Studies

The South Pacific island nation of Vanuatu is one of the world’s most vulnerable countries to natural hazards (earthquakes, volcanoes, hurricanes) and part of our research is to provide methods to address the challenges presented by those hazards. World Vision, a humanitarian and development Non-Governmental Organization (NGO), seeks our assistance in learning how Geographic Information Systems (GIS) can help them manage and analyze their data. GIS will be used to analyze coffee farm data to see the benefits and uses of GIS. World Vision Vanuatu is part of the World Vision International partnership – one of the world’s largest non-government humanitarian and development agencies. 

Geographic Information Systems are software which allow for the visualization and spatial analysis of data. These systems allow analysts to create visual representations of their data within the workspace. Once data is visualized, analysts can then perform visual and spatial analysis through tools provided in the software. The beauty of GIS is the connection between data and its visual representation. This connection allows for a range of sophisticated analysis, which may not be done with visual or quantitative data alone.

Having introduced you to what GIS is the video below will inform you on how GIS can help NGOs.

There are two key forms of GIS software that can be used, desktop based and cloud based GIS software. Two desktop softwares we will presenting are ArcMap and QGIS. Both programs have equivalent GIS commuting power, but QGIS is a free application. They have similar tools, and more tools are available online through open source plugins. Cloud-based GIS like ArcGIS Online and Google Earth pro allow for easy and interactive map making. In addition since these maps are created online they are more accessible to the public.

In 2018, World Vision collected data on coffee farms within a study area in Tanna. This data is predominantly in the south west region of Tanna. The survey gathered data of the number of pulpers, seedlings, and farmers within each village. Using GIS we were able to not only spatially display this data but also answer key questions such as the proximity of pulpers to farmers, proximity to savings groups, and geographic responsibility of key farmers and assistant farmers, and which communities do/don't have a Community Disaster and Climate Change Committee (CDCCC). 

The maps and data below display our findings. 

Key farmers and assistant key farmers are essential in helping to establish the coffee farms. The colors represent which farmer is doing the most work in the community, because some key farmers and assistant farmers travel to and work with multiple villages.

Village points were symbolized by key farmers and assistant key farmers. Then we assessed which key farmers/assistant key farmers worked with the most villages in each community. A new attribute field was created in the community layer (polygons). This field contained which key farmer/assistant key farmer worked with the most villages in that community.

CDCCCs are community-based structures mandated by Vanuatu's National Disaster Management Office (NDMO) to facilitate community-based disaster preparedness, response and recovery efforts. CDCCCs can be responsible for training first aid officers, helping local institutions like schools develop risk management plans, and simulating disasters to have response plans in place while focusing on vulnerable groups.

Search by attribute was used to select the villages (point data) that do and do not have CDCCCs. From those selections we created new layers. Once these new layers have been established, we symbolized them by their CDCCC field, making villages which have CDCCCs green and those that do not red. Villages with no data on CDCCCs were made grey.

Once all the layers have been established, a new field in the attribute table of the communities (polygon data) was created about CDCCC. If there was one village in a community that has a CDCCC, YES was added to the CDCCC field. If the community did not have a CDCCC NO was added to the field. If there was no data, NO DATA was added to the field.

Community data was then symbolized, green for having CDCCC, red for no CDCCC, and grey for no data.

Rotating Savings Groups are popular in many parts of the world, and they make up part of the livelihood strategy of coffee farming villages in Tanna Island, Vanuatu. Saving groups are composed of about 15-20 people who all save together. With the money saved, the participants can then take small loans from the savings. Therefore forming a community based banking initiative to save money. The map below demonstrates where these saving groups are located, as well as which villages are far from these saving groups.

The GIS tool Select by Attribute was used to select villages that do and do not have savings groups. From each selection a layer was created. These layers were then symbolized in accordance to if there was or was not a savings group in the village. If there is a savings group the point is green, if not the point is red.

To visualize which villages are far from a savings group, a one-kilometer buffer was ran around the villages to determine which villages do have savings groups. Any villages outside this buffer could be considered far, and those that are within the buffer could be considered close. Different buffers can be created for different distances.

In GIS there are three forms of vector data, these are points, lines, and polygons. Points representing villages can be spatially joined to Community areas. This means that community boundaries are needed. In this case, community boundaries were digitized according to their locations on a hardcopy map. These digitized locations will become the polygon layers representing the community boundaries. Points, line and Polygons, may all be edited using the editor tool in Arcmap. This allows the user to manually adjust any errors that may have occurred while collecting data. 

Data formatting is absolutely critical when doing GIS analysis. Since GIS is database driven, spreadsheets must be formatted in accordance with database rules. This means for column headers it can not start with non alphabetical symbols such as numbers or underscores. In addition, there is a character limit of 10 characters for fields. Therefore it's important to create a general key sheet with all the acronym meanings. 

Geospatial technology can be used in fieldwork to collect accurate latitude and longitude coordinates. The coffee data was collected in two ways, first using a GPS unit and secondly through the usage of local knowledge. GPS units use signals from GPS satellites in order to generate a location. Local knowledge can be used to collect field location points when it is not possible to use GPS units. This can occur when there is a lack of signal in the area, the equipment is simply not working, and when secondary data is not available. In the coffee farm data, there was a lack of signal therefore local experts were able to circle general locations where the villages were located. When using local knowledge it's important to take the necessary steps for quality control and correct any errors that may occur.