# Syrian Refugee Settlement Clinic Locations

[This article was first published on

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

**More or Less Numbers**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Previously I posted about the location of refugee settlements and how that had grown in density over time as well as in numbers. As many NGOs and non-profits work in the area, they are providing much needed assistance to the people living around the Zahle area. I wanted to look at the area again because of the breath of the crisis with Syria and the potential long-term locating of Syrians in Lebanon. Services such as clinics have been established in these camps, which may or may not have taken into account the ability to service refugees (such planning considerations may not be possible in these circumstances) at optimal locations. For long-term planning these are more important considerations by whomever the governing body for these settlements becomes.

Below is a map of settlement locations in the Zahle district provided by the UN Syria Data Portal. Each point represents multiple tents in the settlement.

The overall consideration for clinic location will be on the basis for the level of service per person. Based on a general criteria of having 1 clinic per 15,000-20,000 people, we can allocate about 4 clinics to the area. The method(s) to determine these locations utilized both kmeans method of determining mean point in a cluster and a location analysis algorithm that considers the weights of points for determining a location (special thanks to the author(s) of orloca, kmeans, and the always helpful ggplot2 packages in R).

For these purposes latitude and longitude of tent settlement locations are the most helpful. Here the settlements or points are colored according to the population of that settlement.

The clinics are located most closely to those settlements with the highest number of people. In the central Zahle area, these locations are about in the middle from a Latitude standpoint. Other locations are perhaps less intuitive if the population of settlements were not considered. Obviously with more clinics these points would change, but this is considered a minimum service level.

Using only this method to determine the location of a clinic would be problematic from the standpoint of what is actually on the ground with reference to street access or other local contingencies. Planning for medical facilities is more of an exercise for long-term planning considerations than emergency or relief medicine which may have more short-term goals such as providing care at all. Starting with taking into account the number of people being serviced and their location are important considerations as these camps become potentially longer-term obligations.

Those interested in the R code can find it here.

Below is a map of settlement locations in the Zahle district provided by the UN Syria Data Portal. Each point represents multiple tents in the settlement.

The overall consideration for clinic location will be on the basis for the level of service per person. Based on a general criteria of having 1 clinic per 15,000-20,000 people, we can allocate about 4 clinics to the area. The method(s) to determine these locations utilized both kmeans method of determining mean point in a cluster and a location analysis algorithm that considers the weights of points for determining a location (special thanks to the author(s) of orloca, kmeans, and the always helpful ggplot2 packages in R).

For these purposes latitude and longitude of tent settlement locations are the most helpful. Here the settlements or points are colored according to the population of that settlement.

As you can see, some settlements hold many more people than others and the average settlement is about 187 people (again we’re talking many tents per settlement). Since the distribution of people in settlements is not equal we consider the “weight” (settlement population) for each point when determining a clinic location.

The clinics are located most closely to those settlements with the highest number of people. In the central Zahle area, these locations are about in the middle from a Latitude standpoint. Other locations are perhaps less intuitive if the population of settlements were not considered. Obviously with more clinics these points would change, but this is considered a minimum service level.

Using only this method to determine the location of a clinic would be problematic from the standpoint of what is actually on the ground with reference to street access or other local contingencies. Planning for medical facilities is more of an exercise for long-term planning considerations than emergency or relief medicine which may have more short-term goals such as providing care at all. Starting with taking into account the number of people being serviced and their location are important considerations as these camps become potentially longer-term obligations.

Those interested in the R code can find it here.

To

**leave a comment**for the author, please follow the link and comment on their blog:**More or Less Numbers**.R-bloggers.com offers

**daily e-mail updates**about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.