Geospatial big data and cartography : research challenges and opportunities for making maps that matter

Geospatial big data present a new set of challenges and opportunities for cartographic researchers in technical, methodological, and artistic realms. New computational and technical paradigms for cartography are accompanying the rise of geospatial big data. Additionally, the art and science of cartography needs to focus its contemporary efforts on work that connects to outside disciplines and is grounded in problems that are important to humankind and its sustainability. Following the development of position papers and a collaborative workshop to craft consensus around key topics, this article presents a new cartographic research agenda focused on making maps that matter using geospatial big data. This agenda provides both long-term challenges that require significant attention as well as short-term opportunities that we believe could be addressed in more concentrated studies.PostprintPeer reviewe

A Multicriteria Model for Estimating <i>Coffea arabica</i> L. Productive Potential Based on the Observation of Landscape Elements

Understanding a crop’s productive potential is crucial for optimizing resource use in agriculture, encouraging sustainable practices, and effectively planning planting and preservation efforts. Achieving precise and tailored management strategies is equally important. However, this task is particularly challenging in coffee cultivation due to the absence of accurate productivity maps for this crop. In this article, we created a multicriteria model to estimate the productive potential of coffee trees based on the observation of landscape elements that determine environmental fragility (EF). The model input parameters were slope and terrain shape data, slope flow power, and orbital image data (Landsat 8), allowing us to calculate the NDVI vegetation index. We applied the model developed to coffee trees planted in Bambuí, Minas Gerais, Brazil. We used seven plots to which we had access to yield data in a recent historical series. We compared the productivity levels predicted by the EF model and the historical productivity data of the coffee areas for the years 2016, 2018, and 2020. The model showed a high correlation between the calculated potential and the annual productivity. We noticed a strong correlation (R2) in the regression analyses conducted between the predicted productive potential and the actual productivity in 2018 and 2020 (0.91 and 0.93, respectively), although the correlation was somewhat weaker in 2016 (0.85). We conclude that our model could satisfactorily estimate the yearly production potential under a zero-harvest system in the study area