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

Nan Xu, Mark Stevenson, Kerry A. Nice, Sachith Seneviratne

TL;DR

The paper tackles the problem of fusing raster and vector geospatial data across arbitrary resolutions and city sizes, specifically mapping raster pollutant concentrations to road networks. It introduces a general algorithm that rasterises city maps using a perfect-square tiling and constructs virtual layers to expand from city centers to boundaries, using quantities such as $s = floor(A/r)$ and $G = s^2$ to define layer structure. Raster values $D_r$ (e.g., PM2.5, NO2) are sampled at generated grid centers and assigned to vector roads via bounding boxes, updating road attributes $D_v^{(1)} = D_v^{(0)} \cup P$, with generalization to any raster resolution $r$, city size $A$, and number of cities. The method is demonstrated on 1692 global cities, preserving graph topology while enabling graph-based pollution analyses, and is positioned as a scalable, low-cost approach for multi-source data fusion in climate studies.

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.

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

TL;DR

The paper tackles the problem of fusing raster and vector geospatial data across arbitrary resolutions and city sizes, specifically mapping raster pollutant concentrations to road networks. It introduces a general algorithm that rasterises city maps using a perfect-square tiling and constructs virtual layers to expand from city centers to boundaries, using quantities such as and to define layer structure. Raster values (e.g., PM2.5, NO2) are sampled at generated grid centers and assigned to vector roads via bounding boxes, updating road attributes , with generalization to any raster resolution , city size , and number of cities. The method is demonstrated on 1692 global cities, preserving graph topology while enabling graph-based pollution analyses, and is positioned as a scalable, low-cost approach for multi-source data fusion in climate studies.

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 and NO 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.
Paper Structure (6 sections, 3 theorems, 15 equations, 3 figures, 3 algorithms)

This paper contains 6 sections, 3 theorems, 15 equations, 3 figures, 3 algorithms.

Table of Contents

  1. Introduction
  2. Main results
  3. Algorithm
  4. Experimental results
  5. Discussion
  6. Conclusions

Key Result

Theorem 2.1

\newlabelthm:rast0 If $I_{totale(o)} > 1$, the number of distinct vertices $V_{vire(o)}$ of constructed virtual grids with the areas of $B_{vire(o)}^2$ expands through $I_{vire(o)}$ iterations as perfect squares started from 4. The number of distinct vertices $V_{reale(o)}$ of real grids with the for s is even: for s is odd: $V_{reale(o)}$ should be equal to the number of target grids $G$,

Figures (3)

  • Figure 1: Map Rasterisation using Perfect Squares: step is even(left) or odd(right) number
  • Figure 1: $PM_{2.5}$ added into $OSM$
  • Figure 2: Sample images of $PM_{2.5}$ and $NO_{2}$ shown on each road in 1692 cities

Theorems & Definitions (3)

  • Theorem 2.1: Map Rasterisation using Perfect Squares
  • Theorem 2.2: Assign Raster Features to Vector Maps
  • Theorem 2.3: General Algorithm at Any Resolution or Scale