First public release of the KnowWhereGraph Project

KnowWhereGraph is an open, cross-domain knowledge graph and associated toolset that rapidly raises the situational awareness of data scientists and decision makers by providing detailed area briefings for any place on Earth.

May 6, 2022
KnowWhereGraph webpage screenshot

KnowWhereGraph: A Cross-Domain Geo-Knowledge Graph and Service Stack

We announce Vienna, the first public release of the KnowWhereGraph Project (KWG; KWG is an open, cross-domain knowledge graph and associated toolset that rapidly raises the situational awareness of data scientists and decision makers by providing detailed area briefings for any place on Earth within seconds to answer questions such as:

  •     "What is here?"
  •     "What happened here before?"
  •     "Who knows more?"
  •     "How does it compare to other regions or previous events?"

KWG features services for representing, exploring, visualizing, and analyzing data at the interface between humans and their environment that are open, cross-domain, deeply integrated, and densely connected. At the project's core is the KnowWhere Graph, a geo-knowledge graph that is based on existing standards like RDF, OWL, SHACL, SSN/SOSA, OWL-Time, and GeoSPARQL, and also incorporates custom ontologies. KnowWhereGraph uses the S2 hierarchical discrete global grid (DGG) for spatial representations and inference (

In a nutshell, KWG is a gazetteer of gazetteers. It contains more than ten different types of region identifiers (e.g., ZIP codes, FIPS codes, administrative boundaries, climate zones, named places) and links those to over 20 data layers about transportation, health, climate, disasters, soil and agriculture, and so on. Because many phenomena, such as storms, floods, or earthquakes, do not respect human-made boundaries, users can represent any region of their interest with a collection of S2 cells (currently at level 13). While KWG aims to serve global data, most of the current layers are US-centric. Please see for an overview table. KWG also links out to other graphs.

Its current size exceeds 12.5 billion triples (graph statements) with industry and non-governmental organization pilots in disaster relief, agricultural land use, applications in credit and risk evaluations, and food-related supply chains. KWG data include observations of past and present natural hazards (e.g., hurricanes, wildfires, landslides, smoke plumes) and spatial characteristics related to climate (e.g., temperature, precipitation, air quality), soil properties, crop/land-cover types, demographics (e.g., health data about obesity and diabetes), experts (and their expertise), and transportation infrastructure, among many others. Essentially, we are centrally incurring the high cost of data integration to substantially reduce the time spent on data wrangling for all end users.

Altogether, KWG and its supporting toolset enable queries such: "For a given earthquake simulation, show me the population at risk based on some demographic criteria, nearby major transportation/evacuation infrastructure, highlight soils at risk of liquefaction, and propose experts familiar with the region, e.g., in terms of local health matters."

The graph can be queried directly using the SPARQL querying interface at, or browsed with the Knowledge Explorer at The Knowledge Explorer helps users focus their data search by providing a set of filters corresponding to data characteristics, and hyperlinked results allow the user to navigate and de-reference the graph. Together, these tools help reveal the graph's content and structure.

We are leveraging the graph with additional tools being developed for specific contexts. The open-source Geo-Enrichment Toolbox plugins hosted on both ArcGIS and QuantumGIS software enable Geographic Information Systems (GIS) users to easily incorporate a growing variety of natural hazards, climate, and socioeconomic data from KWG into their own spatial analysis. To assist humanitarian response efforts following a natural disaster, the GeoGraphVis tool provides situational awareness, visualizing, for example, physical properties of a hurricane in relation to health characteristics of populations in its path.

In combination with the Knowledge Explorer, the Expert Similarity Search helps responders to identify people with expertise relevant to the disaster situation quickly.

The Crop Impact Assessment tool enhances strategic planning during disasters by providing online analysis, forecasting, and alerts to ensure key stakeholders throughout the supply chain are ready with backup strategies to keep products moving. It also allows farmers and growers to identify mitigation strategies and build resilience in the face of such events.

The National Science Foundation funds KWG as part of its Convergence Accelerator program's Open Knowledge Network (OKN) initiative. The team includes members from academia (University of California, Santa Barbara; Kansas State University; Michigan State University; Arizona State University; University of Southern California), the nonprofit sector (Direct Relief); industry (Esri; Oliver Wyman; Hydronos Labs), and the US federal government (US Geological Survey; US Department of Agriculture).

At this early stage in the public release of KWG, we hope to collect constructive feedback and anticipate downtimes due to substantial query load. Vienna is a rolling release, so we will fix issues (and add new one ;-)) as we receive feedback. Developing and deploying a 12B triples-large graph by integrating 30 complex data layers is one thing; keeping such a graph stable and running is a very different story. We would love to hear and learn from you about your experience in scaling and sustaining such graphs and about your ideas for new data and denser links among datasets. KWG and OKN are community efforts; please get involved. After browsing the graph and trying the tools, please consider answering a few questions in the survey at Thanks for your time and interest.

Finally, this release is dedicated to our team member E. Lynn Usery (USGS), who passed away in March 2022, and our advisory board member Peter A. Fox (RPI), who passed away in March 2021. Both were very prominent, long-term supporters of the geo-semantics, spatial data science, and knowledge graph communities and inspired all of us. They left a deep mark on the broader community. Their contributions and kindness shall not be forgotten.