An Integrated Workforce Categorization and Geospatial Matching Framework for On-Demand Services

This paper presents an application-oriented architecture for location-aware workforce discovery in an on-demand services environment. The proposed system combines a four-level workforce categorization model with geographic proximity and multi-attribute filtering so that employers can identify workers who are not only suitable by skill but also appropriate in engagement duration, availability, service charge, and practical logistics. The four categories are Quick Workers, Temporary Work, Short-Term Work, and Long-Term Work. Worker positions are represented as GeoJSON Point objects using longitude-latitude coordinates and queried through MongoDB 2dsphere indexing. A nearby-search workflow applies a configurable radius and can be combined with category, specialization, price, food, accommodation, and keyword constraints. A three-tier pincode-resolution strategy is incorporated to provide local lookup, regional approximation, and wider geocoding fallback when direct coordinates are unavailable. Beyond the documented implementation, this paper proposes a relevance-ranking layer, privacy-aware location handling, dynamic availability, and a reproducible evaluation framework as extensions for a stronger production and research system. Existing project observations report sub-15 ms nearby-search responses on dense datasets and an approximately 75% reduction in server API load after 300 ms input debouncing; these values are treated as implementation observations rather than independently reproduced benchmarks. The proposed architecture therefore provides a practical foundation for workforce discovery while identifying measurable directions for scalability, privacy, ranking quality, and real-world validation.