Methodology
The main criteria for selecting relevant data layers and sources were accuracy, relevance, and spatial granularity to measure current street environmental conditions, avoid measurement duplication, and ensure appropriateness for aggregation. After a review of 29 potentially relevant data sets and data layers, 15 were selected. The Green Infrastructure Focus (GIF) project, which is aimed at identifying where green infrastructure improvements and investments might be best targeted, is the source of the majority of data layers.
The compound index integrates ten datasets, each weighted according to its impact on identifying and prioritizing neighbourhoods with the greatest need and opportunity to repurpose kerbside space currently allocated to on-street parking for new street trees and green spaces. The datasets and their weightings are as follows:
- High level (13% each): Urban Heat Island, NO2, and PM2.5.
- Medium level (9% each): Tree Canopy Cover, Pavement Width, and Parking.
- Lower level (4.5% each): Pedestrian Activity, Access to Open Green Space, IMD Score, and IMD Health.
The selected geographical unit for reporting results is the Lower Super Output Area (LSOA). When necessary, these results are aggregated by Borough boundaries to address London-level analysis. The initial step in the data analysis involved consolidating all data layers at the LSOA level. Various analytical methods were applied depending on the geometry types and attributes of the original data layers.
Each LSOA was then ranked. A rank of 1 assigned to the area with highest priority according to performance. For example, the area with highest urban heat island is ranked 1, and the area with lowest tree canopy is also ranked 1. This ranking is based on two key factors: the estimated impact of the indicators on the street-level environmental conditions and the suitability for planting interventions. For example, areas ranked highest for noise are prioritized because trees help mitigate noise, while areas ranked highest for pavement width are prioritized due to the assumption that more space is available for planting without obstructing pedestrian space. The decision to use a ranking system is motivated by the need to establish clear priorities and facilitate the communication of results, particularly when the units of measurement for the indicators are complex or not easily interpretable.
As with any data-led prioritisation analysis, the reliability of the results is inherently tied to the quality of the underlying data. In this case, the majority of the data used in the prioritisation matrix originates from the Green Infrastructure Focus (GIF) project. While it was possible to sense-check general patterns within these data layers, a full assessment of their accuracy and precision was not feasible. As a result, the limitations of the GIF dataset should be considered as also applying to this project. Furthermore, the process of aggregating data to the LSOA level introduces additional uncertainties, due to spatial interpolation, clipping, and other methodological decisions. Despite these limitations, the resulting outputs provide a valuable strategic lens for urban greening.
