Identifying Hard to Heat Homes
Exploring how the attributes of the National Geographic Database could be used to help identify homes which are harder to heat.
Heating homes, particularly in the current economic climate, is not an easy task for every household and the term 'fuel poverty' is now a term we sadly hear on a regular basis. Various scientific studies have revealed that cold, damp homes can have adverse effects on the health of its residents, particularly for those who are already classed vulnerable and/or suffer from conditions such as asthma, arthritis or circulatory problems. So how can we start to identify which households are at risk of having cold, damp homes?
The physical characteristics of our homes, may have an impact on how easy it is to heat. This case study explores how the latest enhancements (March 2024) to the OS National Geographic Database (NGD) Building Feature data, could be used to help identify areas where homes might be harder to heat and how this data could be augmented with other data sources to build a picture of which households may be at risk.
Physical Building Characteristics
The physical characteristics of buildings will impact how easy it is to heat. The NGD Building feature type has rich attribution about these physical characteristics, including Period of Construction, Construction Material and Building Connectivity. Although there are many other factors which contribute to the ability to heat a property, for this example, we are just focusing on the three attributes listed above based on the following rationale:
- Connectivity - Properties which share walls with adjacent properties will retain heat better than those which are standalone (detached).
- Period of Construction (building age) - Older properties are less likely to have good insulation, cavity walls or double-glazed windows.
- Construction Material - Properties built of a more insulating material will likely have slower heat transfer.
Data Analysis - Creating an Index
In order to identify 'hard to heat homes', a risk index can be created. This can be achieved by giving each residential building a score based on each physical attribute: Building Connectivity, Period of Construction and Construction Material. For this example, the scores have been allocated on a very simple basis with a score of '1' being allocated to those buildings which are considered to have an attribute which could make them harder to heat. Once each residential building has been given a score for each attribute, these can be combined using the OSID of each building to give an overall index score from 0 (easier to heat) to 3 (harder to heat).
Our simple index system is shown in the table below, however, it is important to note that this simple system has been created to showcase a methodology and that, in reality, a more complex index system would ideally be generated. For example, other datasets which include a Unique Property Reference Number (UPRN) , such as Energy Performance Certificates (EPC), could be added into the index. By linking the EPC to the OS NGD Building data via the UPRN, a more detailed picture of the energy efficiently of a residential property would be given.
It is also important to recognise that the physical characteristics of a building are not the only factors which influence how easy a home is to heat or indeed the ability of a household to heat a home. This index could therefore be augmented with other data which may have an impact on the ability of a household to heat a home such as Index of Multiple Deprivation statistics related to income deprivation, council tax bands and other population characteristics.
Visualising the Data
Data visualisation is often key to helping us answer questions related to location, such as: 'Where are the hard to heat homes?'. They allow us to identify spatial patterns and trends that are not visible in graphs and tables and can help us to make better and more informed decisions.
Read more about using maps to inform decision making here .
By visualising the OS NGD Building data with the added hard to heat home index scores in a GIS or using other geospatial data visualisation methods (e.g. python or R), spatial patterns and trends can start to be identified.
Simple Visualisation
The image on the right just shows the data in its raw form, showing the building footprints styled simply by their index score. Red properties received a score of 3 (harder to heat) whilst the green buildings received a score of 0 (easier to heat). The harder to heat homes, as you can see, are older, standalone buildings built of less insulating materials such as timber or wood. In contrast, the easier to heat homes are newer, built of more insulating materials and are connected to another property.
Viewing the data at the individual building level however doesn't really help identify patterns the wider spatial scale at which decisions might be made or intervention planned. When viewed at the city level however, patterns and clustering of homes which may be harder to heat start to appear.
The map on the right clearly shows spatial clustering of harder and easier to heat homes across the city of Southampton. However, this method of visualisation is not quantifiable, meaning that you can't easily count the number of properties in a given area or compare areas easily.
Thematic Mapping
In order to quantify our data and allow us to easily compare index scores between different geographical areas of the city, it is necessary to quantify our data against an area or boundary. This could be a District or Ward boundary or a statistical census boundary such the Office for National Statistics (ONS) Output Area (OA) or Lower Super Output Area (LSOA).
One major benefit of aggregating data to a statistical census boundary level is that it allows for the addition of other datasets to the analysis such as population data or Indices of Multiple Deprivation (IMD). These additional datasets may help to build a more comprehensive picture as to where properties may be at risk of being cold and damp.
The map on the right is a choropleth map showing the average index score for each ONS Output Area in Southampton. A choropleth map is a form of statistical map which uses different colours, shades or patterns to represent the magnitude of a given variable within a geographical region.
This map allows us to easily identify areas of the city which have a higher average score than others, meaning they have a greater proportion of harder to heat homes. For example, Output Area E0086691 in the east of the city has a mean score of 1.87 with 87% of homes receiving a score of 2 or 3. In contrast, Output Area E0086768 to the North West, around Lordswood received a very low mean score of 0.05 with no homes being allocated a score of 2 or 3.
The areas with higher mean scores (in reds and oranges on the map) may therefore warrant further investigation to better understand the population and health characteristics of the areas and assess whether household may be at risk of having a cold and damp home.
Further Analysis
The analysis could be taken further by supplementing it with additional datasets such as the Department for Levelling Up, Housing and Communities Indices of Multiple Deprivation (IMD) 2019 data which provides details on income and employment deprivation for each LSOA. An IMD Decile of 1 represents the most deprived 10% of LSOAs nationally whilst a IMD Decile of 10 represents the least deprived 10% of LSOAs nationally. This dataset, for example, could be used to help understand where areas of hard to heat homes coincide with more deprived areas where residents may be less likely to have the finances available to keep their homes warm and free from damp.
Let's look again at the area to the east of Southampton around Thornhill which we examined above (Output Area: E0086691). This area was highlighted as an area with a high proportion of harder to heat homes. This area falls within a LSOA with a IMD index score of 4, meaning it is in the lower 40% of deprived households nationally. It also borders an area with an IMD index score of 1. Therefore this area, and areas like this (such as Redbridge in the far west) may warrant further exploration and analysis around the factors that influence the ability of a household to heat a home.
(c) Crown copyright and database rights. Ordnance Survey 2023. Indices of Multiple Deprivation 2019 - Ministry of Housing, Communities & Local Government
As noted above, other health and income related datasets could also be explored and added to the analysis to help identify those at risk of having a cold, damp home. In addition, other attributes of the OS NGD Building features dataset such as the presence of self-contained basement flats could also be explored when trying to identify properties which may be hard to heat or prone to damp.
Conclusion
In summary, we have explored how the attribution of the NGD Building features could be used to help understand which properties may be at risk of being hard to heat based on their physical properties. By creating a simple index for each building and visualising the result, it gives a clear picture of the spatial pattern of where these properties might be. When augmented with other data, such as that relating to population and income, a more detailed view of at risk areas emerges and could form a starting point for understanding where people are more likely to be put a risk of a cold, damp home.
Further Resources
