Come learn about some of the most widely adopted machine learning methods used for clustering of spatial data. This forum will illustrate how the algorithms work, how to interpret the results, and how and when to apply them.  We’ll go beyond the basics of several key spatial data science techniques, including density-based clustering and multivariate…

A spatial perspective isn’t (just) about making compelling visualizations, but also investigating how complex human-environment interactions impact the theory, design, methods and infrastructure of research. Detangling how place impacts, interacts with and/or drives factors of health outcomes for different people and neighborhoods is essential to reducing health disparities. In this forum talk, Dr. Kolak highlights…

Biomass feedstock utilization has the potential to reduce dependency on fossil fuels and improve energy security while decarbonizing our energy needs. Energy crops, short rotation woody crops, municipal solid waste, and agricultural and forest residues are considered promising sources of renewable energy. Computer modeling has proven to be a key tool towards minimizing logistics cost.…

As data, including geospatial data, are increasingly mobilized in the service of governments and corporations, their unequal conditions of production, their asymmetrical methods of application, and their unequal effects on both individuals and groups have become increasingly difficult for data scientists--and others who rely on data in their work--to ignore. But it is precisely this…

From deforestation to urbanization, the human footprint on the land surface is ever expanding, converting natural land covers into land uses or intensifying current land uses. Land use change results in loss of biodiversity, increased greenhouse gas emissions, and alteration of hydrological systems, among other impacts. Rates of land use change can be quantified using…

Is nature predictable? If so, how can we better manage and conserve ecosystems? Iterative ecological forecasting is an emerging interdisciplinary research area that aims to improve researchers' ability to predict ecological processes on near-term timescales that can be validated and updated. In this forum, Dietze will discuss the challenges and opportunities in iterative ecological forecasting,…

Seasonal (0-3 months) climate forecasts and near-term decadal (10-30 years) climate change projections provide valuable information for improving seasonal and long-term planning of water and energy management. Given that the climatic attributes over these time periods are typically expressed as probabilistic information, we propose an adaptive water and energy management framework that uses probabilistic inflow…