Maps of fishing locations are important in assessing fishery exposure to management alternatives and facilitates stakeholder outreach (e.g. the New England Fishery Management Council’s Omnibus Habitat Amendment 2, http://www.nefmc.org/library/omnibus-habitat-amendment-2 and the Mid-Atlantic Fishery Management Council’s Amendment 16 to the Atlantic Mackerel, Squid, and Butterfish Fishery Management Plan, https://www.greateratlantic.fisheries.noaa.gov/regs/2016/September/16msbamend16ea.pdf). Fishing location data is also a primary input into behavioral location choice models. Issues of fishing location accuracy and precision thus affect both a manager’s ability to govern effectively and a researcher’s ability to model welfare changes. Using data from the limited access scallop fishery in the Northeastern US, this study compares revenue maps created by different approaches for the fishing years 2000-2015. Besides the commonly used aggregation approach of logbook data into statistical areas and ten-minutes-square grids, two probability models will be employed. One of the probability models is based on the work of DePiper (2014) and employs statistical representations of logbook point data, while the second approach follows the approach of Münch/DePiper/Demarest (2017) and incorporates a kernel smoother on Vessel Monitoring System track data. This works aims to highlight the differences in the spatial distribution of revenue between the method applied and to discuss the drawbacks of simply aggregating logbook data into standardized grids, which serves as the most common approach to its spatial representation. This research indicates that statistical models can substantially improve the ability to define fishing locations when compared to traditional point aggregation methods.