  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.
