Manager's Corner The ability to obtain precise information is nothing new. With great patience and skill, mapmakers and land surveyors have long been able to create information with an impressive level of accuracy. However, today the ability to determine and view locations with submeter accuracy is now in the hands of millions of people. Commonly available high-resolution digital terrain and aerial imagery, coupled with GPS-enabled handheld devices, powerful computers, and Web technology, is changing the quality, utility, and expectations of GIS to serve society on a grand scale. This accuracy and precision revolution has raised the bar for GIS quite high. This pervasive capability will be the driver for the next iteration of GIS and the professionals who operate them. When I say there is a "revolution" going on in GIS, I am referring to the change in the fundamental accuracy and precision kernel of commonly used geographic data brought about by new technologies previously mentioned. For many ArcGIS users, this kernel used to be about 10 meters or 40 feet at a scale of 1:24,000. With today's technologies (and those in the future), GIS will be using data with 1-meter and submeter accuracy and precision. There are probably GIS departments—in a large city or metro area—where this standard is already in place. However, this level of detail is far from the case in natural resource management agencies such as Bureau of Land Management (BLM) or the United States Forest Service. But as lidar, GPS, and high-resolution imagery begin to proliferate standard sources for "ground" locations, GIS professionals will begin to feel the consequences in three areas: data quality, analytic methods, and hardware and software. Data Quality As we try to integrate highly resolved data into existing GIS, the errors in legacy data will become more apparent. The expectation is that data is as accurate and precise as possible, so new geometry must be developed either through editing or by capturing new data. We will need to be more careful about documentation and mindful of appropriately mixing data in databases. The four figures accompanying this article illustrate the problems GIS professionals might encounter as they integrate more accurate data into GIS operations. For these illustrations, I used recently acquired lidar elevation data. Figure 1 illustrates a typical base dataset displayed at 1:10,000 scale. Figure 2 This is typical base data displayed at 1:24,000 scale. The hillshade and contours were derived from the National Elevation Dataset, hydrography from the National Hydrography Dataset, and roads from internal files. Red square indicates enlargement area for Figures 3 and 4. Hillshade and contours have been derived from the U.S. Geological Survey National Elevation Dataset. The hydrography came from the U.S. Geological Survey National Hydrography Dataset. Roads were taken from BLM internal files. The standards of accuracy and precision of this data is typical of levels of the data used by natural resource management agencies such as the BLM and Forest Service. Most of the data used in these databases was originally derived from U.S. Geological Survey 1:24,000-scale topographic maps or from existing paper maps of lesser quality. Only in recent years has data been developed using GPS or heads-up digitizing from large-scale imagery or photography. Until recently, I considered the quality of this data pretty good since at commonly used scales ranging from 1:10,000 to 1:100,000, I could not readily detect any flaws. Figure 2 shows hillshade and hydrography displayed at 1:24,000 scale, which is the intended scale of the data. The problem occurs when, because this is the highest resolution in the GIS, this same data is used for scales larger than 1:24,000. Note how hydrography matches the terrain (hillshade) in most areas. Continued on page 36 www.esri.com ArcUser Winter 2010 35