As the need for more broad-scale solutions to environmental problems is increasingly recognized, traditional hierarchical, government-led models of coordination are being supplemented by or transformed into more collaborative inter-organizational networks (i.e., collaboratives, coalitions, partnerships). As diffuse networks, such regional environmental planning and design (REPD) efforts often face challenges in sharing and using spatial and other types of information. Recent advances in semantic knowledge management technologies, such as knowledge graphs, have the potential to address these challenges. In this paper, we first describe the information needs of three multi-stakeholder REPD initiatives in the western USA using a list of 80 need-to-know questions and concerns. The top needs expressed were for help in tracking the participants, institutions, and information products relevant to the REDP’s focus. To address these needs, we developed a prototype knowledge graph based on RDF and GeoSPARQL standards. This semantic approach provided a more flexible data structure than traditional relational databases and also functionality to query information across different providers; however, the lack of semantic data expertise, the complexity of existing software solutions, and limited online hosting options are significant barriers to adoption. These same barriers are more acute for geospatial data, which also faces the added challenge of maintaining and synchronizing both semantic and traditional geospatial datastores.

Sustainability describes a broad set of themes centered on current human uses of the planet’s resources. The multiple uses and users of the term have led to a proliferation of salient issues and associated indicators. We present a new method to systematically link these issues and indicators under two conceptual frameworks of sustainability in order to enable quantitative analyses. We demonstrate this approach with a specific use case focused on the global sourcing of agricultural products. We use the optimization software Marxan in a novel way to develop minimum sets of indicators that provide maximum coverage of sustainability issues. Minimum covering sets were identified and accumulation curves were developed to measure the contribution of each indicator in each set to overall issues coverage. While greater detail in the assessment of each indicator would likely provide more effective sets of indicators, those that were generated provide optimism that this approach can bring better focus to sustainability assessments.

A foodshed is a concept analogous to a watershed, describing the catchment of the sources of food for a region. As such, it portrays linkages ranging from local communities out to the global food system. Inefficiencies exist at all stages of the food supply chain, resulting in the challenges of inequitable access to healthy and safe food. Many of these inefficiencies are informational; for instance, food being wasted that could be donated to food banks were there communication of the need. These informational inefficiencies can be ameliorated by a stronger semantic characterization of the links between actors and resources in the food system, allowing for the development of smarter software technologies to facilitate interconnections. We discuss an iterative process to improve informatics frameworks for the foodshed by engaging with regional stakeholders to identify important issues and information needs. Key Words: food systems, ontologies, semantic web, smart foodsheds, stakeholder engagement.

The Internet of Food (IoF) is an emerging field in smart foodsheds, involving the creation of a knowledge graph (KG) about the environment, agriculture, food, diet, and health. However, the heterogeneity and size of the KG present challenges for downstream tasks, such as information retrieval and interactive exploration. To address those challenges, we propose an interactive knowledge and learning environment (IKLE) that integrates three programming and modeling languages to support multiple downstream tasks in the analysis pipeline. To make IKLE easier to use, we have developed algorithms to automate the generation of each language. In addition, we collaborated with domain experts to design and develop a dataflow visualization system, which embeds the automatic language generations into components and allows users to build their analysis pipeline by dragging and connecting components of interest. We have demonstrated the effectiveness of IKLE through three real-world case studies in smart foodsheds.