GIS application for mountain terrains: some considerations and options Errors in GIS applications for mountain environment "Error and uncertainty are common features of cartographic information, so it is hardly surprising that these aspects are also present in digital version of analogue maps" (Openshaw et al. 1991). GIS is a powerful tool in spatial analysis and its power is obvious in that it has the potential to dramatically increase both the magnitude and importance of errors in spatial databases. Burrough (9186) identifies three main groups of factors that govern errors that may be associated with spatial data processing. These are: (a) obvious sources of error (human), (b) errors arising from natural variations or from ordinal measurements, and (c) errors arising through processing. The errors associated with GIS applications specific to mountain regions, our current topic of discussion, are from the second group. No map is entirely error free, but errors due to natural variations in mountainous terrain are significant. Positional error, aerial interpolation error and linear measurement error increases with slope. Error in area calculation Calculation of population density (e.g. bio-density) is exaggerated by underestimation of area for the sloping terrain. The actual surface area on a sloping terrain is greater than the geographical area depicted on the map that represents surface of the earth as a flat surface. The actual area is the product of the geographical area and Cosine of the average slope angle of the place. The smaller the unit size the greater the accuracy (Fig. 1).  Fig. 1: Descrepancy between actual area and geographical area depending on slope of land Error in linear calculation: Similarly the linear distances over sloping land are greater in reality than depicted on the maps. The actual distance in this case can also be calculated by finding the product of the mapped distance and the Cosine of the average slope angle traversed. Thus distances derived from GIS give wrong information, such as shorter road distances and lower drainage density. As a result cost deduced for transmission line or road construction would be an underestimated value, even if the difference is not too large. Positional error: As a result of shorter distance derived from GIS for a mountainous terrain, the placement of points according to linear measurement becomes erroneous when applied on sloping terrain. For example, if sampling points are to be located 1 kilometre from a given point and their positions are worked out with GIS considering a two-dimensional surface, the resultant position would be further away than a kilometre due to slope involved. For that matter how would the measurement of point patterns be carried out using nearest-neighbour analysis or quadrant sampling for mountain areas? Such questions become more relevant as detailed GIS analyses are more frequently being carried out on larger scale maps. Buffer zone error: Since linear distances are underestimated a buffer zone generated by GIS that ignores the land slope would enclose a wider area than actually intended. The proportion of additional distance brought into the buffer zone would be directly proportional to the slope angle of the land. Ineffectiveness of straight-line accessibility derivation: In plain land is derived simply by multiple buffer generation from the road (line) for an area and from a settlement centre (point) for evaluating accessibility of a village to different service centres. This simple method does not hold true for mountain regions, where slope and other physical impediments must be taken into account when working in scales larger than 1:50,000. Some accessibility maps have been made taking the contours into consideration (Bournay & Pradhan, 1994; Trapp, 1995) but existence of un-traversable slopes do not seem to have been considered. Accessibility passages only come below 40o slopes for foot-tracks and within 10o for motorable roads. Therefore the algorithm for accessibility mapping in mountain areas needs to be far more intelligent. Veregin (1995) very well defines the types and sources of error. "Errors result from inadequate data acquisition methods that do not truthfully capture the real world phenomena. This conceptualization has much in common with statistical treatment of error in terms of bias and precision… That is, encoded values represent approximations… Errors can therefore be reduced through the use of more refined data acquisition techniques …and methods of repeated sampling." Apart from this is inherent variability of geographical phenomena. Veregin states that in case thematic attributes, methods for measuring and documenting error can be differentiated in terms of the scale of measurement as approximate, whereas, for interval and ratio data, error can be measured in terms of the mean deviation between actual and observed values at a sample of locations. This provides an error index analogues to the root mean squared error (RMSE) for elevation data. |