Integrating Social Innovation and Geoinformatics for Community-Based Disaster Risk Reduction: A Sandbox Approach in Mueang Tak District, Thailand

Abstract

Background and Objectives: Natural disasters, particularly landslides, floods, and droughts, pose increasing threats to the socio-economic stability and environmental security of communities in Mueang Tak District, Tak Province, Thailand. Conventional top-down disaster management strategies often suffer from a critical data-action gap in which advanced spatial technologies and risk information are not effectively translated into locally actionable practices. To address this challenge, this study aimed to integrate social innovation with geoinformatics technology to strengthen community-based disaster risk reduction. The specific objectives were to analyze natural disaster risk areas using Geographic Information Systems (GIS) and Remote Sensing (RS), investigate community-based social innovation needs, and develop and evaluate “Sandbox Innovation Areas” for landslide, flood, and drought management.

Methodology: The study employed an integrated research design structured into four phases: spatial risk assessment, community survey, participatory sandbox development, and evaluation. Spatial data were processed using Google Earth Engine (GEE) and GIS to identify priority risk areas. Social data were collected from 462 residents using stratified multistage cluster random sampling across landslide-prone, flood-prone, and drought-prone areas to assess existing practices and innovation needs. Spatial and social findings were integrated to guide the selection of three pilot sandbox areas and the design of capacity-building activities for Community Innovators. The sandbox process included participatory GIS, field validation, co-creation of community risk maps, and evaluation of perceived knowledge gain and stakeholder opinions.

Results: The spatial analysis identified critical risk areas, revealing that 40.13% of Mae Tho Subdistrict was classified as high to very high landslide risk, 16.98% of Wang Hin Subdistrict faced high to very high flood risk, and 22.05% of Pa Mamuang Subdistrict was exposed to high to very high agricultural drought risk. The social survey indicated strong demand for practical and participatory innovations, particularly nature-based solutions, village-based response teams, and community drills. The sandbox intervention developed 32 Community Innovators who applied GPS and geospatial tools to validate risk locations. Post-training self-assessment indicated a high level of perceived knowledge gain (x̄ = 3.86), while stakeholder opinions toward the sandbox model were also rated at a high level (x̄ = 4.16), reflecting favorable perceptions of the approach.

Discussions: The findings show that the sandbox mechanism can help bridge the gap between academic spatial technology and local disaster-management practice. By integrating scientific risk information with community knowledge and participatory learning, the approach enabled local actors to verify risk conditions, interpret spatial information, and co-create context-specific responses. This process supported a “Hybrid Knowledge Model” in which geoinformatics complements rather than replaces local wisdom, thereby strengthening participation, ownership, and local decision-making.

Conclusions: Integrating social innovation with geoinformatics can strengthen community preparedness and support locally responsive disaster risk reduction. Sustainable implementation requires local administrative organizations to invest in community-level spatial information, participatory risk-management processes, and continued development of community innovators. The sandbox approach provides a practical mechanism for translating risk evidence into local action, while future applications in other disaster-prone areas are recommended to assess its transferability and scalability.