General algorithm of assigning raster features to vector maps at any resolution or scale

TL;DR

This paper introduces a versatile algorithm for assigning raster data features, like pollution levels, to vector map components across various scales, enhancing multi-source geographic data fusion.

Contribution

The proposed method offers a generalized, scale-independent approach for fusing raster and vector data, demonstrated on global pollution datasets for urban analysis.

Findings

Successfully assigned pollution data to roads in 1692 cities worldwide.

Provides a scalable, resolution-agnostic algorithm for data fusion.

Enables more accurate and flexible geographic analyses.

Abstract

The fusion of multi-source data is essential for a comprehensive analysis of geographic applications. Due to distinct data structures, the fusion process tends to encounter technical difficulties in terms of preservation of the intactness of each source data. Furthermore, a lack of generalized methods is a problem when the method is expected to be applicable in multiple resolutions, sizes, or scales of raster and vector data, to what is being processed. In this study, we propose a general algorithm of assigning features from raster data (concentrations of air pollutants) to vector components (roads represented by edges) in city maps through the iterative construction of virtual layers to expand geolocation from a city centre to boundaries in a 2D projected map. The construction follows the rule of perfect squares with a slight difference depending on the oddness or evenness of the ratio of city size to raster resolution. We demonstrate the algorithm by applying it to assign accurate PM$_{2.5}$ and NO$_{2}$ concentrations to roads in 1692 cities globally for a potential graph-based pollution analysis. This method could pave the way for agile studies on urgent climate issues by providing a generic and efficient method to accurately fuse multiple datasets of varying scales and compositions.