Assessment of a DEM's quality is usually undertaken by deriving a measure of DEM accuracy -how close the DEM's elevation values are to the true elevation. Measures such as Root Mean Squared Error and standard deviation of the error are frequently used. These measures summarise elevation errors in a DEM as a single value. A more detailed description of DEM accuracy would allow better understanding of DEM quality and the consequent uncertainty associated with using DEMs in analytical applications. The research presented addresses the limitations of using a single root mean squared error (RMSE) value to represent the uncertainty associated with a DEM by developing a new technique for creating a spatially distributed model of DEM quality -an accuracy surface. The technique is based on the hypothesis that the distribution and scale of elevation error within a DEM are at least partly related to morphometric characteristics of the terrain. The technique involves generating a set of terrain parameters to characterise terrain morphometry and developing regression models 2 to define the relationship between DEM error and morphometric character. The regression models form the basis for creating standard deviation surfaces to represent DEM accuracy.The hypothesis is shown to be true and reliable accuracy surfaces are successfully created.These accuracy surfaces provide more detailed information about DEM accuracy than a single global estimate of RMSE.Keywords: digital elevation models, quality, error, accuracy, uncertainty 3 IntroductionAssessments of the accuracy of DEMs tend to result in a single measure of how closely the DEM's elevation values represent "reality". Measures such as Root Mean Squared Error and standard deviation of the error are frequently used (Carlisle, 2002;Day and Muller, 1988;Eklundh and Mårtensson, 1995; Fisher, 1998;Kumler, 1994;Li, 1991;Sasowsky, 1992). These measures summarise elevation errors in a DEM as a single value. There is increasing demand for more detailed description of spatial data quality (Canters et al., 2002). A more detailed description of DEM accuracy would allow better understanding of DEM quality and the consequent uncertainty associated with using DEMs in analytical applications.Anecdotal and empirical evidence shows that DEM error is spatially variable, spatially correlated and heteroscedastic, being related to the form of the terrain (Ehlschlaeger and Shortridge, 1997; Fisher, 1998;Hunter and Goodchild, 1997;Kyriakidis et al., 1999;Theobald, 1989;Weibel and Brändli, 1995;Wood, 1994;Zhang and Montgomery, 1994). However, very little research has attempted to model this heteroscedasticity. This paper reports on research to test the hypothesis that DEM error is related to terrain character and then to develop a more detailed description of DEM accuracy by representing the spatial variation in error across a DEM as an accuracy surface that is generated from regression modelling of the relationship between DEM error and terrain characteristics. The resulting spatially varia...

Rattan cane is an important non-timber forest product (NTFP) harvested from Indonesian tropical forests. However, the extraction of NTFPs such as rattan cane may conflict with forest conservation efforts. A better understanding of harvesting practices can help assess the extent of this conflict and guide forest management decisions. This study assesses the accessibility factors that influence rattan cane harvesting levels in Lambusango Forest, Buton Island, Indonesia, and whether the harvesting of rattan cane is affected by the designation of conservation areas. To this end, the analysis adopts participatory mapping, Geographic Information Systems and a questionnaire survey and employs multiple regressions and analysis of covariance. The results show that accessibility, particularly slope and distance, can play a role in the quantity of rattan canes harvested. The presence of conservation forest does not significantly affect rattan cane harvesting levels. This could be due to limited awareness of the harvesters going to the vicinity of the designated conservation areas and mixed sentiments towards conservation efforts due to the long tradition of forest dwelling and harvesting activities. The study concludes that the successful establishment and management of conservation areas require consideration of the specificity of the local context such as the abundance of forest resources, accessibility and historical forest-people interactions, in addition to biological factors.

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This featured graphic is a topological road map of Newcastle upon Tyne, UK, generated using GIS and social network analysis software with Ordnance Survey data. The map formed part of ''en_counter'' (2016), an exhibition of mapping work in Newcastle upon Tyne. The map is derived from Ordnance Survey MasterMap Integrated Transport Network data (Ordnance Survey, 2010), provided in a multi-part fully topologically structured link and node format including information about roads (''RoadLink''), their names (''Road''), and their connections (''RoadNode''). Various data manipulations were made using ArcMap GIS and spreadsheet software to transform the data into the flat file format needed by Gephi social network analysis software. Relevant attributes from the ''RoadNode'' and ''Road'' items were joined to the ''RoadLink'' features. The ''RoadLink'' features were clipped to the boundary of Newcastle Metropolitan District using Ordnance Survey Boundary-Line data (Ordnance Survey, 2015). Only public roads were selected and so excluding alleys and private roads. Some ''RoadLinks,'' mainly segments of roundabouts and slip roads, had no name. A name was added if appropriate, or else the link was deleted. Incidences of the same name being used for different roads were identified and the name was altered to, for example, High Street 1, High Street 2, etc. A series of look-up operations, sorts and filters were used to produce a flat table with each row representing a named road and one of the roads it connects with. The result was data in which connections between roads are maintained, but their location is free to be manipulated. Gephi allows the manipulation and analysis of social network graphs. We used the Force Atlas 2 algorithm (Jacomy et al., 2014) to generate forces of attraction and repulsion within the network, pulling together nodes (in this case roads) which share connections, and pushing apart those which don't. Refinement of labels, lines, and layout was done in Adobe Illustrator. The final map is accurate in the sense it can be used to navigate the city from road to road, but the lack of geospatial references makes it unfamiliar and discombobulating upon viewing. Suburbs and coherent urban areas are grouped together and are located in relation to one another which is logical, but strange at the same time. Topographical scale is lost as map distance is a result of relational connectivity rather than points in physical space. An approach similar to this has been previously theorized (Park and Yilmaz, 2010), albeit without spatializing algorithms in mind. Statistical analysis of the network can be undertaken including measures of centrality to identify significant roads based on the number of connections.

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