Sunday, October 26, 2014

Syrian Refugee Informal Settlement Animation

This is an animation of the growth in Syrian refugee informal camp settlements in Lebanon over the past several years.  You can see from the .gif that the growth was most dramatic in terms of the number of settlements in the Summer of 2013.  The border has recently been closed and the number of additional settlements you can tell has dramatically decreased.   



This was inspired by a much cooler animation by Nathan Yau of Walmart/Sams club store locations over time.  But this was my best attempt using what I knew in R, code here.  Special thanks to Kay Cichini for his helpful examples/tips and Yihui Xie for creating the awesome 'animation' package.

Tuesday, September 30, 2014

Syrian Refugee Settlement Clinic Locations

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.


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.

Thursday, July 10, 2014

Syrian Refugee Density in Lebanon

I've done a few posts on Syria and have used data provided by the UNHCR for different analysis or visualization.  There are several links on their Syrian refugee data portal that communicate the breadth of this crisis numerically and visually.

One such link had the locations of settlements in Lebanon and the number of people in each settlement.  This information is undoubtedly helpful for coordinating the location of services within camps and in general tracking how they grow.  I was interested in seeing the growth of these camps, where and during what time periods the most growth is seen.

Below is a map of the country (or most of it) showing where all the tents are located that have been documented by the UNHCR.  Overlaid is a density plot communicating the concentration of structures (tents) with the number of people housed per structure.


The concentration of settlements has clearly been just outside of a town called Zahle.  If we look more closely at Zahle we can more clearly see the number of people per tent in this settlement.  On average across Lebanon there are over 6 people per tent based on this UNHCR dataset, some as high as 12.



If looked at year by year, we can see how during different years this area was settled more heavily.  2013 was a year where a significant amount of tents were constructed or setup.  


No doubt this data is being used to coordinate the location of different public facilities such as clinics, etc.  Data such as this provided by UNHCR serves burgeoning communities with much needed information in how to setup a town or "plan" for how this settlement could be organized or mitigated differently.  The code for this data and graphs, or at least most of it is available on my Github account.