# Spatial Crowdsourcing Task Allocation Scheme for Massive Data with Spatial Heterogeneity

arXiv: https://arxiv.org/abs/2310.12433 · HTML: https://mlsanity.com/paper/2310.12433/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2023.

## Built on by (most cited first, 1 of 1)

- [Coordination Matters: Evaluation of Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2605.06557/) (2026)

## Abstract (arXiv)

Spatial crowdsourcing (SC) engages large worker pools for location-based tasks, attracting growing research interest. However, prior SC task allocation approaches exhibit limitations in computational efficiency, balanced matching, and participation incentives. To address these challenges, we propose a graph-based allocation framework optimized for massive heterogeneous spatial data. The framework first clusters similar tasks and workers separately to reduce allocation scale. Next, it constructs novel non-crossing graph structures to model balanced adjacencies between unevenly distributed tasks and workers. Based on the graphs, a bidirectional worker-task matching scheme is designed to produce allocations optimized for mutual interests. Extensive experiments on real-world datasets analyze the performance under various parameter settings.