Tablet-assisted collection and mapping of human disturbance activities across complex social-ecological landscapes

Background/Question/Methods

Modeling the bidirectional relationship between human behavior and ecosystems often requires precise spatial data of human disturbance, such as harvesting and land management activities. In the past, these data have been collected using rapid rural appraisal or participatory mapping techniques in which respondents annotate paper maps. However, these existing methods do not allow a high level of resolution, they require additional labor and introduce error during digitization, and symbolic representations on traditional maps (e.g., topographic lines or political boundaries) are not intuitive to many people. To directly capture a large number of participants' recalled resource collection behaviors, we created a hand-held, tablet-based field application with a people-focused, intuitive, zoomable user interface that presents the respondent's environment using high-resolution satellite imagery. The touch-based interface records point, line, or polygon data to digitally capture features; data are easily exported from the tablet for use with GIS software. The application uses cached imagery so that it is fully functional in the field without cellular or internet connection. We tested this application in a rural, subtropical region of Nepal to record features of the built environment and people's natural resource collection behaviors in community forests.

Results/Conclusions

In a field test, 26 out of 28 respondents identified and recorded point features of their built environment near them (e.g., schools, bus stops, markets, banks). In a separate test, 30 of 30 respondents recorded polygons in their local community forest to indicate areas where they harvested fodder for livestock, fuel wood, and medicinal plants, as well as engaged in activities such as animal grazing and picnic/recreation. Our results show the feasibility of using tablet technology to collect highly detailed, spatially explicit data on human disturbance, even among people with low familiarity of computer technology. The cost-effective technology presents the opportunity to develop large-N household or individual resource collection GIS datasets, which will improve our understanding of social-ecological systems. This application contributes to the emerging literature on mobile GIS and participatory mapping for social-ecological systems and presents a promising technique for future social-ecological studies.