Computers & Geosciences, Volume 42, May 2012, Pages 64-70

Igor Rychkov, James Brasington, and Damià Vericat

“Highlights

• We present a software toolkit for processing terrestrial point clouds.
• The toolkit can be applied to TLS surveys of gravel river beds.
• Improved DEM differencing is one outcome.
• Estimating surface roughness and grain size distribution is possible now with point-based, statistical metrics.
• Other applications and extensions are enabled by the library being freely available and open source.
  Keywords

“Processing of high-resolution terrestrial laser scanning (TLS) point clouds presents methodological and computational challenges before a geomorphological analysis can be carried out. We present a software library that effectively deals with billions of points and implements a simple methodology to study the surface profile and roughness. Adequate performance and scalability were achieved through the use of 64-bit memory mapped files, regular 2D grid sorting, and parallel processing. The plethora of the spatial scales found in a TLS dataset were grouped into the “ground” model at the grid scale and per cell, sub-grid surface roughness. We used centroid-thinning to build a piecewise linear ground model, and studied “detrended” standard deviation of relative elevations as a measure of surface roughness. Two applications to the point clouds from gravel river bed surveys are described. Linking empirically the standard deviation to the grain size allowed us to retrieve morphological and sedimentological models of channel topology evolution and movement of the gravel with richer quantitative results and deeper insights than the previous survey techniques.”

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GIScience 2012, Columbus, Ohio, 18-21 September 2012

Jared Aldstadt, Michael Widener, and Neal Crago

“In the age of “Big Data” GIScientists are faced with the challenging task of processing and interpreting increasingly large spatial datasets. To fully exploit these large and high-resolution data, researchers must adapt existing algorithms and utilize new computational technologies, such as high performance computers (HPCs) (Armstrong 2000). This research concentrates on improving the performance of local irregular cluster detection algorithms, focusing on one flexible, but computationally expensive, algorithm: AMOEBA (Aldstadt and Getis 2006). The ability to locally detect clusters within a geographic region is a powerful tool that can be applied in many settings. For example, in a health context, it is important to explicitly locate the clustering of disease incidents so an appropriate medical intervention can occur. AMOEBA is one of a growing number of cluster detection techniques that do not restrict the search to regular geometric shapes (Duczmal and Assuncao 2004, Tango and Takahashi 2005, Assuncao et al. 2006, Yiannakoulias et al. 2007). These methods have the potential to more accurately delineate clusters, thereby improving the value of clustering as an exploratory data analysis technique.

“While the ability to detect irregularly shaped clusters is useful, the underlying process of AMOEBA involves organically growing the cluster from every spatial data point, known as seed locations, in a predefined study area. This research builds on previous work (Duque et al. 2011a, Widener et al. 2012) by developing a heuristic and decomposition strategies for parallel computing platforms to improve the runtime of the algorithm. The heuristic is designed to intelligently sample seed locations in sub-regions of a large dataset in order to eliminate redundant cluster discovery. Current AMOEBA algorithms spend valuable computation time rediscovering the same irregular cluster, as all seed locations apart of a particular cluster will grow approximately or exactly the same hotspot (or coldspot). With the heuristic reducing the number of computations necessary in some sub-regions but not in others, new decomposition strategies are necessary to equitably distribute the computational load of growing clusters from those seed locations that are tested. In addition to providing researchers with a faster tool for detecting irregularly shaped clusters in large or detailed datasets, this research also provides general insights into how efficient local spatial cluster detection can be achieved on parallel computing platforms.”

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GIScience 2012, Columbus, Ohio, 18-21 September 2012

Thomas Blaschke and Clemens Eisank

“GIScientists find themselves sometimes in a somewhat defending role when to position Geographic Information Science. Kemp et al. (2012) state that researchers in this field often find it difficult to argue in established disciplines like Geography, Statistics, or Computer Science. Kemp et al. diagnose reasons for this to include problems of a narrow focus on indices like Thomson-Reuters’ for use in assessment metrics, the relative importance of conferences versus journals, or different criteria used in geography and computer science (as well as other fields, such as statistics or economics), or the highly variable meaning of “strong impact factors” across fields, and so on (Kemp et al. 2012: 268).

“Reitsma (2012) argued that GIScience may be considered as a science if using similar criteria as Stamos (2007), namely simplicity, predictive accuracy, coherence with known facts and testability and pleads not for just a yes or no classification, but for some kind of degree classification. What distinguishes GIScience from man other sciences is the fact that GIScientists study the representation of the world and not the world in concrete. For this knowledge and principles from other disciplines are needed and used (Reitsma 2012:9). Following this line of arguments one may reason that GIScience exists in symbiosis with other disciplines and can hardly exist without them. This could be one reason why it is still in the process of self-justification. Nevertheless, in this short article, we will avoid to discuss whether or not GIScience is a science or a discipline and if it is scientific at all. Rather, we try to analyse in an unbiased and neutral way how well GIScience is reflected in the literate. We are well aware of the limitations of this approach. We of course reduce a positive hit as a “citations” if a search term is found in the title or keywords of a particular database. This will produce quite a high number of errors of omission and – less often – errors of commission: we – as many other studies which analyse publications and the number of their citations in other publications – hypothesize that an entry is a confirmation of its contents.”

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GIScience 2012, Columbus, Ohio, 18-21 September 2012

M.W. Horner and J. A. Downs

“Mobile object analysis continues to be well-studied in GIScience (Hornsby and Egenhofer 2002; Laube et al. 2005; Neutens et al. 2011). Time geography remains the key theoretical framework for understanding mobile objects’ movement possibilities (Miller 2005). Within time geography, recent efforts have sought to enhance its ‘probabilistic’ potential through exploring questions of data uncertainty, spatial representation, and limitations of classical approaches (Kuijpers et al. 2010; Neutens et al. 2011; Winter and Lin 2011). Along these lines, Downs (2010) fused time geography and kernel density estimation in developing timegeographic density estimation (TGDE), which may be used to estimate mobile objects’ probable locations in continuous space, given a time budget between control points (Downs 2010). Downs and Horner (2012) extend TGDE to discrete network space, demonstrating its application with GPS-based vehicle tracking data (Downs and Horner 2012) and using it in searches for travellers’ destinations missing in travel surveys (Horner et al. 2012).

Intensity Values for Traveller 1 (eq. 1).

“The present paper explores a new direction for TGDE, namely the creation of a densitybased accessibility measure for mobile objects. Related to time geography, accessibility measures have also garnered widespread attention in the literature (Kwan 1998; Miller 1999; O’Sullivan et al. 2000; Yu and Shaw 2008; Delafontaine et al. 2012). Our new metrics gauge how accessible a moving object is to particular opportunities of interest, given the constraints inherent to its movement plan. Thus, we are able not only visualize where the object most likely could have been (Downs and Horner 2012), but we also capture the configuration and magnitude of activities relative to its travel path from both a visual and analytic perspective.”

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