Why focus on this particular typhoon? Well, this category 5 tropical cyclone was responsible for destroying more than 400 thousand homes and taking over 6 thousand lives, making it the deadliest typhoon in Philippine history.
Based on what we found, 1.9 billion dollars were promised but only 650 million dollars were delivered. This looks bad but it is not telling you the whole story. A serious limitation that we face when studying aid is that we can only examine what is reported and made publicly available. All the aid that goes unreported or is kept private is not reflected in these findings.
Fortunately for us, there are several open data initiatives working to make international aid as transparent as possible. We obtained the aid data for this study from two open aid databases: (1) the Government of the Philippines’ Foreign Aid Transparency Hub (FAiTH), and (2) the International Aid Transparency Initiative (IATI).
Aside from learning how much aid was delivered, we were also interested in learning where in the Philippines the aid funds went: what region, province, city, barangay, and so on. This proved difficult because out of the 483 aid projects that we found, only 79 contained information at the subnational level. This accounts for only 14% of the total funds disbursed.
The fellows and I geocoded those 79 projects and made several maps. The first map shows the number of aid project locations by province and the second map is looking at the total funds disbursed by province.
After looking at these maps, you might be alarmed to see so many provinces in white. According to the data that we found, only 12 out of 82 provinces received humanitarian assistance in the aftermath of Typhoon Haiyan.
This could be a consequence of the lack of data with subnational information. As I mentioned before, only 79 out of the 483 projects that we found contained sufficient information to be assigned a precision code below 6 or 8 - that is, the project descriptions included location information more specific than “the Philippines”. Maybe if development actors included more details about their projects when reporting their aid, these maps would look a lot more colorful.
However, if we isolate the top ten flood risk and storm surge provinces in the Philippines, we end up with a pretty similar map. This offers an alternative explanation for why so much of the country is white. Rather than being neglected by the humanitarian response, it is possible that the provinces receiving zero funds were simply not among the most affected by Typhoon Haiyan.
Now let’s look at the state of disaster infrastructure in these ten high risk provinces. There are three types of infrastructure that are critical in a disaster situation: health facilities, evacuation centers, and roads. Our fellow summer fellow, Prabesh Basnet, did an in-depth analysis of the road infrastructure in the Philippines in the context of disaster resilience, leaving me to focus on health facilities and evacuation centers.
From this side-by-side comparison of evacuation infrastructure and aid disbursements in the top ten flood risk and storm surge provinces, there doesn’t seem to be a correlation between the amount of funding received and the amount of evacuation centers in each province. However, we could take this analysis a step further if we had a time frame for when these evacuation centers where built. With that additional information, we might be able to determine whether the funding received in response to Typhoon Haiyan had any impact on the evacuation infrastructure in these provinces.
We observe a similar lack of correlation between the amount of funding received and the amount of health facilities in each province. We could also further this analysis if we had information about when these health facilities were built.
When it comes to health infrastructure, however, we face an additional obstacle: the health infrastructure map only captures those health facilities that are mapped on OpenStreetMap (OSM).
Why is this a problem? We ran a small experiment comparing how many health facilities exist versus how many are mapped on OSM and Google. Our findings did not fare well for OSM.
Of all the hospitals that exist in the top ten flood risk and storm surge provinces, 42.67% are mapped on OSM, as opposed to 89.22% on Google Maps. That means that 57.33% of the hospitals that exist in these provinces are not included in the health infrastructure map above.
We ran the same experiment for evacuation centers in these provinces and found that only 8.68% of all evacuation centers in these provinces are mapped on OSM, as opposed to 84.5% on Google Maps.
Why did we use OSM if Google Maps does a better job of mapping disaster infrastructure? The key advantage of OSM is that it is free and open source, whereas Google Maps is not. This means that anyone with a computer and access to the internet can contribute and download data from OSM.
That is precisely why the Map the Philippines initiative strongly advocates the use of OSM. It is no coincidence that most of our trainings this summer focused on teaching OSM to members of NGOs, local government, universities, and civil society organizations. We even took a trip to Leyte, one of the provinces most affected by Typhoon Haiyan, and held an OSM workshop there. Our hope is to foster disaster resilience in the Philippines by building a large community of OSM mappers. Ideally these mappers will fill in the gaps by mapping more roads, evacuation centers, health facilities, and disaster aid.
In order to get a more comprehensive picture of the humanitarian response to Typhoon Haiyan, it is imperative that all donors report their contributions to open data platforms such as FAiTH or IATI. It is also important that donors include more detailed project descriptions and disclose the amounts of not only the funds committed but also the funds disbursed.
To determine whether the funding received in response to Typhoon Haiyan had any impact on the quality and quantity of disaster infrastructure in the top ten flood risk and storm surge provinces, we need more detailed information about said infrastructure. For example, it would be useful to know when the health facilities and evacuation centers were built.
Lastly, we recommend holding more OSM, GIS, and geocoding workshops in the Philippines. Doing so will build capacity among NGOs, universities, government agencies, and civil society organizations to use geospatial tools, foster the growth of the local OSM community, and make disaster resilience in the Philippines a more realistic and sustainable goal.