Room: Track 1 - Talks
Friday, 20:45 UTC
Duration: 20 minutes (plus Q&A)
Over the past few years, the world has been experiencing the impacts of climate change with an increase in extreme weather events. Coastal communities are among the most vulnerable and face a range of unique flooding hazards including storm surge, wave impact, and erosion causing damage to homes, businesses, and infrastructure. Timely emergency response relies on high quality spatial datasets to support 911 calls, disaster planning, and response & recovery efforts. It is imperative that road network and water feature (ocean and inland) quality be as accurate as possible when used to support emergency operations. Our team has selected a few coastal cities to run a subset of relevant map error checks to identify the location and density of key errors that would impede response activities. With these data our team uses the Map Quality Measurement process to generate a heat map and narrow down the most problematic areas for communities to focus and improve data.
Map Quality Measurement (MQM) is an analysis and visualization tool revealing the distribution of errors within a given geography. MQM works by running a series of checks, referred to as Atlas-checks, that identify geometric, topologic, and attribution errors. Atlas-check outputs show the density of data errors, the types of errors they are, and assign priority to critical fixes. The checks are written to review core map features such as roadways, buildings, waterways, coastlines, and their relationships with one another.