“Unrivaled tolerance to a vast range of environmental extremes, the adaptive sensory processing capabilities of the Mesa 2 Rugged Tablet extend from its auxiliary Geode companion to orbital satellites in the furthest reaches of Earth’s biosphere.”
Earlier this year, I resumed my duties as a groundskeeper around the office of a small ecological consulting company in Pennsylvania. Having received my Bachelor of Arts in geography from West Chester University of Pennsylvania, a few decimal degrees of GIS still lingered in my head, so when I was called into the office mid-January to help operate a Mesa 2 Rugged Tablet demo model, I was happy to comply. It seemed like a curious portable device that could definitely take a punch and continue to run without skipping a beat, and I was discovering new and exciting features on a day-to-day basis. Using the property grounds and the surrounding forest, which I had become familiar with in the previous years of work I had done outside, I began to establish a series of waypoint markers to mimic the Flagging Tape used in actual field work data analysis; I then tested different methods of data collection and cross-referencing in order to establish the most efficient and practical means to carry out potential field work applications in the future. I was intrigued by the node-locked copy of Global Mapper™ v18.0, which my boss had purchased from Blue Marble Geographics – Global Mapper was a GIS software company staple, but completely foreign to me (I had settled into Esri’s ArcGIS™ software during my college years). With my experience in GIS and the novelty of a portable touchscreen device capable of running Global Mapper on Windows 10 OS, the Mesa 2 tablet became the perfect catalyst to boost my familiarity with the software. My comfort in performing GIS analysis through the Juniper Systems interface grew exponentially compared to that of my experiences using ArcMap on a desktop computer, and it wasn’t long before I was certain that the Mesa 2 would become an exceptional company asset. Specifically, it seemed that it would be invaluable in determining the relative accuracy of spatial datasets provided by external sources, in real-time, to account for the margin of error and establish new parameters that would better represent our firsthand data observations.