Mapping water stations to determine current access and how to create equity.
This study was conducted as a final project in 2019 in the Yale undergraduate course Geographic Information Systems using ArcGIS.
Because much of the data regarding camps is not publicly available, this project has relied on maps created by the UNHCR in pdf format. The rest of the data, which was used to georeferenced the camp and add context was acquired through the United States Geological Survey (elevation data), the Humanitarian Data Exchange (Iraqi districts), and was available in the files associated to this course (countries).
This project explores the current state of water access within the camp and proposes new locations based on the outcomes. My goal is to evaluate which regions of the camp are in highest need of new water sources based on distance, and calculate which water sources are experiencing the greatest demand. Using this information and the parameters established by the UNHCR, I will propose new sites for water sources, and examine their potential impacts.
Settled in 2012, the Domiz refugee camp was originally designed to accommodate 10,000 Syrian refugee families. Today, conservative figures estimate 32,600 people in the camp, while other estimates can go as high as 65,000 people.
Though the UNHCR guarantees decent living conditions within the camp, these have never been met. According to UNHCR guidelines, each person requires 20 L of water per day, and camps should provide one tap stand per 80 people, at a maximum distance of 200 m away. In 2015, the year in which my data was current, Domiz had 12 sources of water, including boreholes and tanks. While my research has not shown that there is a shortage of water, this may be because many people rely on water trucks to drive around and fill their water containers. While this may sound convenient, wait times can be unpredictable, and this leaves residents powerless if something were to prevent the trucks from making their rounds. Additionally, this is inefficient for the camp, as allowing refugees to collect their own water would allow more personnel to be available to help in other camp tasks.
This is a simple camp density map. To input population density data into my map, I used the ‘Region Group’ tool in order to create individual entries for each block. Then, I added a new field in the attribute table of the layer, and input the approximate densities as documented by the UNHCR.
This map is a reference tool; it calculates the cost for someone travel through a specific path, with high friction corresponding to traveling between shelters and low friction being walking on designated paths. To do this, first I used the ‘Reclassify’ tool to create a new raster set for the friction data. I classified roads as having a friction of ‘1,’ gave undeveloped land areas a friction of ‘3,’ and classified developed areas as having a friction of ‘10.’ I used the ‘Reclassify tool again to create a raster set for the water sources, setting all areas that were not water sources as ‘NoData.’ I did not make distinctions between tanks and boreholes for this step.
This map portrays which areas have the greatest cost to accessing water. I used the ‘Cost Distance’ tool, inputting the friction layer and the water sources layer as a target, to determine which camp areas had the greatest distance to water. Because I wanted to avoid the possibility of considering putting a water source in an uninhabited region that happens to be far from sources, I used the ‘Extract Mask’ tool to only focus on the costs for areas in which there are people living. To create the mask layer, I again used the ‘Reclassify’ tool and set all areas with a population density of 0 to ‘NoData.’
This map illustrates the demand at each source of water, assuming that everyone is minimizing their cost in fetching water. To make it, I used the layer with only the water sources, and used the ‘Region Group’ tool to make each one distinct. I used this layer with the ‘Cost Allocation’ tool, which gave me the zones I was looking for.
This map demonstrates population served by each water demand zone and shows where the greatest need for a new water source is. Using the water demand zones, I used the ‘Zonal Statistic’ tool in order to calculate the average density per water demand zone. To create a layer with the area of each zone, I made a new model with the water demand zone layers and used the ‘Raster to Polygon’ tool to create a layer that I could use the ‘Add Geometry Attributes’ tool on. For this, I selected for the file to calculate the Geodesic Area of each feature and made sure that the units were in hectares. I then converted this file back into raster. Finally, I used the 'Raster Calculator' to multiply the areas by the population density.
This map combines populations that rely on a water source with their cost to the water source. This depicts the areas where people face the greatest challenges to accessing water. To create this map, I combined the cost distance and demand population layers through ‘Raster Calculator.’ Specifically, I chose to multiply the cost distance with the population, as this reduced the range of my numbers and resulted in low numbers (0-100).
This map depicts two new proposed water sources along with the previous water sources.
Here, the present and proposed water sources are overlaid on a map depicting water need.