Data gap analysis

Goal: Make an overview of where you have data and where there are gaps. The data gap analysis will help you determine where you (1) need to do additional calculations OR (2) find additional data to close these gaps and then downscale if needed.

The task that accompanies this module is for you to conduct the data gap analysis of your city and sector. This task can be split up into the below steps, as was described in the video:


The data gap analysis is best carried out when first data has already been processed and the available data and its quality become evident.

  1. Locate your data gap matrix tab: Actually, you can work in your worksheet right away, see SCA Construction Worksheet and SCA Biomass Worksheet.
  2. Fill in what you have: If you have so far filled in your data sources here, great, you can move to the next step. If you have not used this template so far, then now is the time to fill it in, following the path of the decision tree diagram.
  3. Check if economic activity is taking place: Start with answering if the economic activity takes place in the city or not.
    • In general, it is very likely that you will not have any extraction of metals and fossil fuel materials (MF 2 and MF 4), but this is something that you can easily verify on google maps.
    • If you still don’t know, you can check the EW-MFA dataset of your country and see whether there are values for the materials you are interested in, e.g. you check whether extraction activities happen in your country.
      • To do so, click on the + sign next to Materials, check them all and save.
      • You can then select the Domestic Extraction indicator which will give you the results for extraction. If the value for the extraction of one material is 0 for your country (for a number of years), you can safely assume that there is no extraction in your city. In the case the value is different from 0 you should find a way to downscale it.
  4. Find more data for where there are gaps: After filling in the table, you will realise where you have gaps and therefore need to collect and upload additional data. Following the decision tree, try to find more data, giving preference to the lowest spatial scale possible, starting from low and going to high (NUTS3, NUTS2, country). You can use the data from the Eurostat library as a good starting point for NUTS data.
  5. Process the additional data: Once you have found the additional data, you can already process it too, using the respective reference space. For example, if the data is on Andalucía (NUTS2), then put Andalucía into the reference space column. You do not have to make any calculations to adjust the data for your city. This will be done in M4: Downscaling.

Outline of the video

  • Now that you have been processing data and needed to actually work with the data, you are very familiar with what you have and might also have realised what you don’t have. This is where this week’s module comes in, the data gap analysis.
  • Goal: Make an overview of where you have data and where there are gaps. The data gap analysis will help you determine where you (1) need to do additional calculations OR (2) find additional data to close these gaps and then downscale if needed.
  • The actual downscaling and calculations will take place in the next module. So make sure to focus on the analysis of the gaps and the collecting and processing of additional data where needed.
  • M2:48, What are gaps?
    • Stating that you don’t have economic activity is NOT a gap. Stating that the activity doesn’t exist in the city is a confirmation for someone else that there is no gap here, but an informed assessment of the local situation.
    • Data on flows and/or stock is missing although the economic activity takes place in your city
    • Data that exists only for one year that doesn’t match the reference year;
  • M6:06, How do you go about determining if there is a data gap?
  • go through the decision tree diagram
  • M8:00, We will rely on the template that you have worked with so far and extend it by a number of columns.
  • Don’t worry, the order of the first columns haven’t changed. If you have used it so far, you can simply copy paste your data here and continue.
  • There are 4 cases of possible situations that we can go through.
  • If you do have data on a higher spatial scale, then you also need to add the proxy data.
  • M11:58, recap: Once you’ve assessed where you have gaps, you need to look to higher spatial scales to fill them. Ideally you start with low scales, as explained in the decision making tree. If you don’t know where to find additional data, be sure to remember the Eurostat data.
  • The Eurostat grid, shows you which data there is on which scale.
  • In the economy-wide Material Flow Accounts dataset, the data can also be found. There you can browse by different materials, as well as different lifecycle stages (extraction, consumption) on the national level.
  • Finally, we strongly recommend that you fill in this template. It is not just an exercise to make an overview and keep track of the status of your data collection. This data gap analysis matrix will also become part of your SCA report. It will help to share with others where gaps indeed exist and where data on higher scales were found and used or where simply there was no data at all.