Step 2: Gather building typologies

Goal: Define building typologies and assign them to the buildings that you have collected in Step 1.

Situation: Building typologies, also known as archetypes, represent different types of construction technologies or methods and are classified by buildings’ land use, height and year of construction. These typologies need to be found or defined, because each has its own material composition. For example, a single family house from the 1930s is very distinct from a high-rise apartment building of the 1960s, or a new low-rise office building (also in terms of material composition (t/m2)).

Approach

There are two parts to Step 2:

  1. Identify or define building typologies
  2. Assign the building typologies to the buildings
(1) Identify or define building typologies

Here are some options for you to gather the building typologies, listed in the order of how you should try to find them. Therefore, if the first option does not work, try the next one.

  1. Identify local building typologies: Try to identify building typologies that exist for and within the city. It is unlikely that a city has its own list of typologies, but it is worth exploring that first with the respective department (e.g. urban planning).
  2. Identify national building typologies in TABULA or EPISCOPE: If a city does not have its own building typologies, you can consult its national residential building typologies from the TABULA and EPISCOPE projects WebTool: https://webtool.building-typology.eu/#bm. (For example, the webtool does have data for Denmark, the Netherlands, Norway and Spain, but not for Finland nor Portugal, the corresponding countries of the CityLoops cities.) When using the typologies of these two projects, they need to be matched with those of the cadastre’s land use classification, for the correct calculation in MSA method step 4.
  3. Identify national building typologies through web search: For cities that do not have the building typologies for their country in the TABULA WebTool, the next step of the search can be in looking for scientific articles or other publications on building typologies, for the respective city’s country or with large cities of that country in the publication’s title.
  4. Create own building typologies: A more complex option remains, which is the creation of own typologies for the UCA. Using the information from the city’s land use and building footprint database (from step 1) building typologies can be put together. These should have land use, height, and year of construction as their distinguishing parameters. Below, two examples are given of how this can be done.

Example 1:

Stephan & Athanassiadis (2017) took advantage of the “Census of Land Use and Employment (CLUE) database” of Melbourne, Australia to create 47 archetypes (see right column in first image), using “the floor area by land-use, the age and the number of stories for each of the 14,385 buildings within the city council” (Stephan and Athanassiadis 2017, 13). This many archetypes might not necessarily be needed for a smaller or younger city, but the applied approach for establishing them remains the same. (See more in their article [”Quantifying and mapping embodied environmental requirements of urban building stocks”] (https://doi.org/10.1016/j.buildenv.2016.11.043))

Example 2:

In the case of Apeldoorn, we have chosen to create typologies (Residential and Utility) and sub-typologies (Commercial, Offices, Other, Row, Single Apartment, High Rise) because we had material intensities for them. Our typologies do not coincide with those of the cadastre. Therefore, we have calculated them by means of spatial methods. This means, we made use of the height (transformed into the number of floors) and we distinguished if these buildings were physically touching or not. For an example, you can see the definition and transformation of the typologies and those of the sub-typologies.

Next, we needed to transform the data into age cohorts. In order to move from construction years to age cohorts we had to pre-process the data. Although there are many ways to do it (with a CASE THEN in QGIS) one way to do it in google sheets is with the following formula =IFS(E2<1945, "<1945", AND(E2>=1945, E2<=1970), "1945-1970", AND(E2>1970, E2<=2000), "1971-2000", E2>2000, ">2000"), where:

  • the IFS function checks multiple conditions and returns a corresponding value if a condition is met.
  • the first condition checks if the year of construction (in cell E2) is less than 1945. If it is, the formula returns "<1945".
  • the second condition checks if the year of construction is between 1945 and 1970 (inclusive). We use the AND function to check if the year is greater than or equal to 1945 and less than or equal to 1970. If this condition is met, the formula returns "1945-1970".
  • the third condition checks if the year of construction is between 1971 and 2000 (inclusive). Again, we use the AND function to check if the year is greater than 1970 and less than or equal to 2000. If this condition is met, the formula returns "1971-2000".
  • the fourth condition checks if the year of construction is greater than 2000. If it is, the formula returns ">2000".

By using IFS in this way, we can categorise buildings into different age cohorts based on their year of construction. Using AND within the IFS formula allows us to check if a value is within a specific range.

(2) Assign the building typologies to the buildings

Once you have identified or defined the building typologies, you then need to assign them to the buildings:

  1. Open the database with all buildings.
  2. Ensure that all unique building entries have the distinguishing parameters of land use, height, and year of construction.
  3. Assign the building typologies to each of the buildings by writing them down in an extra column. For example, all buildings that are a low-rise apartment that were built between 1980 and 2000, could be marked with “Apartment_low[1980-2000]” in an extra spreadsheet column and those that are high-rise retail buildings from 2010 until now could be assigned the “Retail_high[2010;now[“ building typology. Fill this in for every building row.
  4. Check that each single building has one corresponding building typology.