Architecture of an Information System for Geospatial Data Integration in Circular Organic Waste Management

Introduction. The development of a circular economy requires advanced information systems for monitoring, analyzing, and predicting environmental risks associated with organic waste management. The integration of heterogeneous technological, environmental, and geospatial data is essential for ensuring reliable information processing and supporting environmental decision-making. However, differences in data structures, incomplete facility attributes, and duplicate records complicate the development of integrated information systems. This study addresses these challenges by developing a modular architecture for integrating heterogeneous data and establishing a consistent information basis for subsequent environmental risk prediction. Materials and Methods. The study utilized open datasets from the U.S. Environmental Protection Agency (EPA), including the Excess Food Opportunities Map (EFOM) and AgSTAR Livestock Anaerobic Digester Database. A modular information system architecture was developed to support data acquisition, schema harmonization, entity resolution, geospatial processing, and visualization. Facility records were matched using name similarity, administrative location, and geographical coordinates. The system was implemented in Python using Pandas, NumPy, and RapidFuzz. Data completeness, matching outcomes, and computational performance were evaluated experimentally. Results. The proposed architecture and data integration algorithm were implemented and tested using 2,323 initial facility records, including 1,850 EFOM and 473 AgSTAR records. The matching algorithm identified 312 strict correspondences, representing 65.96% of AgSTAR records, and produced an integrated dataset of 2,011 facility records. Geographical coordinates were available for 91.99% of the integrated records. The median processing time of the evaluated information-processing pipeline was 35.31 ms. Experimental results demonstrated the functionality of data harmonization, facility matching, geospatial integration, and analytical processing components. Conclusions. The developed information system architecture enables the integration and consistent processing of heterogeneous datasets related to circular organic waste management. Experimental evaluation confirmed the functionality of the implemented components and their ability to establish a geospatially referenced information basis. Further research will focus on independently validating facility correspondences, integrating measured environmental indicators, and implementing machine learning models for environmental risk prediction.