Data sources

Data was collected through two primary means. Using open source and freely available data via HDX or performing our own analysis. We discuss all of the different date we gethered below.


We recieve the following data:

  • Census data

  • IOM Data

  • WFP city level costs data

Nightlight analysis

Several studies suggest a strong correlation between nightlight output and GDP, in particular in urban areas for larger areas (e.g. 5x5 kilometers). For this project it is suggested to use nightlight output as proxy indicator for economic recovery.

For the analysis, monthly average radiance composite images from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) were used for both cities, Sana and Aden. The products cover the globe from 75N latitude to 65S in 15 arc-second geographic grids. Nighttime lights global data in GeoTIFF format, were downloaded from the website of Earth Observation Group, NOAA National Centers for Environmental Information (NCEI).

The available, average radiance composite images were filtered by the effects of stray light, lightning, lunar illumination, and cloud-cover, using the VIIRS Cloud Mask product (VCM). The night lights analysis was conducted for three periods. Winter monthly data were selected for the analysis in order to capture the maximum electricity power used during the darkest days of each period.

More info on nightlights

From “When the islamic state comes to town”, Page 52: They are using data from NOAA - how does ours differ?

Note FE = Fixed effect Perhaps we should call brightness VIIRS digital number (which measures nighttime lighting indensity)

We should note that we did not do a processing step remove gas flairs since we were using only the known city boundaries which we believed do not have gas flairs.

“Henderson, Storeygard, and Weil related changes in national nighttime light-ing intensity to changes in national GDP. Using their estimate of the elasticity of nighttime lighting with respect to GDP (0.277) and extrapolating it to cities in Iraq and Syria, an 82-percent reduction in nighttime lighting in the urban core corresponds to a 22.7-percent reduction in the GDP of cities that ISIL controls.”

Satellite damage assessments

The assessment was based on comparative photointerpretation applied to pre-event (2014) and actual (2018), VHR satellite imagery. In some cases, which are addressed in the following, it was not possible to assess a certain damage level to the existing assets:

  • Buildings that were present in the 2014 imagery, while at the 2018 imagery another structure (different shape and/ or material) is identified as in the illustrated example. It is not possible to assess whether this change is due to devastating damages linked with the conflict or not.

  • Buildings, where re-construction works are assessed, although in 2014 these were finalized. The 2018 imagery does not provide damages’ evidence neither for the surrounding buildings nor for the under-reconstruction ones (facades, etc.).

  • In the dense urban fabric areas, it was difficult to confidently identify whether individual buildings were damaged or not as these were obscured by the shadows of attached higher buildings damaged buildings due to intense presence of shadows.

Damage assessments of Aden and Sana’a were conducted by JRC. Damage assessments of Hudaydah, Taizz, Al Hawtah, Sa’ada and Zinjibar were conducted by UNOSAT. The damage categories were harmonized to the following three categories:

  • Moderate damage

  • Severe damage

  • Destroyed

NDVI Analysis

Normalized Difference Vegetation Index is an analysis method to understand the amount of live vegetation in an area. It allows us to understand the effects of the conflict on farming areas. As such, this too will serve as an indicator for economic recovery.

Source :


Ivan where is this data coming from?