A GIS-enabled workflow for georeferenced veterinary KAP surveys: a proof-of-concept study in Kazakhstan

One Health surveillance at the animal–human–environment interface requires practical tools that integrate epidemiological, behavioral, and geographic information to support evidence-based decision-making. This study presents a GIS-enabled workflow for integrating georeferenced Knowledge, Attitude, and Practice (KAP) surveys into veterinary surveillance in Kazakhstan. Using ArcGIS Survey123, disease-specific questionnaire modules were developed for anthrax, blackleg, and mastitis and implemented as a proof-of-concept field application. The framework integrates mobile data collection, automated geolocation, standardized KAP indicators, quality-control procedures, livestock-density context, and GIS-enabled spatial visualization within a unified digital workflow. Rather than providing a nationally representative assessment of livestock owners’ knowledge and practices, the study demonstrates the feasibility of combining georeferenced behavioral data with disease-specific veterinary surveillance layers to support spatially informed interpretation and decision-making. The resulting GIS framework illustrates how KAP information can be linked with epidemiological and geographic datasets to support targeted risk communication, farmer education, vaccination planning, and veterinary outreach. As a proof-of-concept, the proposed workflow provides a practical methodological foundation for veterinary surveillance and has potential for future One Health applications through integration with human-health, environmental, and climatic data.