Geological surface reconstruction from 3D point clouds

The numerical simulation of phenomena such as subsurface fluid flow or rock deformations are based on geological models, where volumes are typically defined through stratigraphic surfaces and faults, which constitute the geometric constraints, and then discretized into blocks to which relevant petrophysical or stress-strain properties are assigned. This paper illustrates the process by which it is possible to reconstruct the triangulation of 3D geological surfaces assigned as point clouds. These geological surfaces can then be used in codes dedicated to volume discretization to generate models of underground rocks. The method comprises the following: - Characterization of the best fitting plane and identification of the concave hull of the point cloud which is projected on it - Triangulation of the point cloud on the plane, constrained to the Planar Straight Line Graph constituted by the concave hull The algorithm, implemented in C ++ , depends exclusively on two parameters (nDig, maxCut) which allow one to easily evaluate the optimal refinement level of the hull on a case by case basis

rangemap: An R Package to Explore Species' Geographic Ranges

Data exploration is a critical step in understanding patterns and biases in information about species’ geographic distributions. We present rangemap, an R package that implements tools to explore species’ ranges based on simple analyses and visualizations. The rangemap package uses species occurrence coordinates, spatial polygons, and raster layers as input data. Its analysis tools help to generate simple spatial polygons summarizing ranges based on distinct approaches, including spatial buffers, convex and concave (alpha) hulls, trend-surface analysis, and raster reclassification. Visualization tools included in the package help to produce simple, high-quality representations of occurrence data and figures summarizing resulting ranges in geographic and environmental spaces. Functions that create ranges also allow generating extents of occurrence (using convex hulls) and areas of occupancy according to IUCN criteria. A broad community of researchers and students could find in rangemap an interesting means by which to explore species’ geographic distributions

rangemap: An R Package to Explore Species' Geographic Ranges

Data exploration is a critical step in understanding patterns and biases in information about species’ geographic distributions. We present rangemap, an R package that implements tools to explore species’ ranges based on simple analyses and visualizations. The rangemap package uses species occurrence coordinates, spatial polygons, and raster layers as input data. Its analysis tools help to generate simple spatial polygons summarizing ranges based on distinct approaches, including spatial buffers, convex and concave (alpha) hulls, trend-surface analysis, and raster reclassification. Visualization tools included in the package help to produce simple, high-quality representations of occurrence data and figures summarizing resulting ranges in geographic and environmental spaces. Functions that create ranges also allow generating extents of occurrence (using convex hulls) and areas of occupancy according to IUCN criteria. A broad community of researchers and students could find in rangemap an interesting means by which to explore species’ geographic distributions

Multi-objective optimisation of a commercial vehicle complex network

In this project we build on research that has been done by Joubert and Axhausen (2013), who built a commercial vehicle complex network for Gauteng. Two shortcomings are identi ed in the approach they followed. The rst shortcoming is the approximations used to determine whether an activity formed part of a cluster. These approximations resulted in some activities to be assigned to the wrong clusters, and other activities to not be assigned to any cluster. The second shortcoming is that the completeness of the complex network was never explicitly considered when they evaluated the di erent combinations of input clustering parameters. We address the rst shortcoming by generating a concave hull for each cluster. The concave hull envelopes all points in the cluster, and one can accurately determine whether an activity forms part of a cluster. To generate the concave hulls, we integrate the Duckham Algorithm with the existing clustering algorithm used by Joubert and Axhausen (2013). The rst step of the Duckham Algorithm is to generate the Delaunay triangulation of the cluster. For some combinations of input clustering parameters, more than 2% of the clusters were degenerate. A degenerate Delaunay triangulation occurs when three or more points in a cluster are colinear (lie on a straight line), or when four points in a cluster are cocircular (lie on the circumference of a circle). No valid Delaunay triangulations can be generated for these clusters. We suggest to deal with these degeneracies by using the weighted average of the points as a reference to the cluster, instead of simply ignoring it. We consider the completeness of the complex network as part of a multi-objective problem: we cannot maximise completeness without making a trade-o with computational complexity. We address this multi-objective problem by conducting a multiple response surface experiment and performing multi-objective evaluation by constructing two e cient frontiers. From the multiple response surface experiment, we found that the input clustering parameters ( , pmin) that optimises the completeness of the complex network, while minimising the computational complexity, is (1, 2). From the multi-objective evaluation, we determined that in general, using = 1 will result in an e cient point. To conclude, we use input clustering parameters (1, 2) to build a commercial vehicle complex network in the Nelson Mandela Bay Municipality, and perform various network analyses on this network.Mini-Dissertation (BEng) -- University of Pretoria, 2014.Industrial and Systems EngineeringUnrestricte

Visualization of Uncertain Boundaries of Undersea Features

There have been several studies that detect, measure, analyze, and visualize the undersea features by using technologies in multiple disciplines including geography and oceanography. However, definitions of the undersea features often vary among the existing leading literature. Due to this reason the geographical boundary for a certain undersea feature is sometimes not identical among the definitions. In this study, we explore semantic uncertainty in the definitions of some undersea features and apply approaches from fuzzy-set theory and geographic information science on empirical bathymetric data to visualize the uncertain boundaries of the undersea features. Results from this study demonstrate that the representation based on the fuzzy-set approach can be useful for dealing with the semantic uncertainty of the undersea features

Finding the polygon hull of a network without conditions on the starting vertex

Many real‐life problems arising within the fields of wireless communication, image processing, combinatorial optimization, etc, can be modeled by means of Euclidean graphs. In the case of wireless sensor networks, the overall topology of the graph is not known because sensor nodes are often randomly deployed. One of the significant problems in this field is the search for boundary nodes. This problem is important in cases such as the surveillance of an area of interest, image contour reconstruction, graph matching problems, routing or clustering data, etc. In the literature, many algorithms are proposed to solve this problem, a recent one of which is the least polar‐angle connected node (LPCN) algorithm and its distributed version D‐LPCN, which are both based on the concept of a polar angle visit. An inconvenience of these algorithms is the determination of the starting vertex. In effect, the point with the minimum x ‐coordinate is a possible starting point, but it has to be known at the beginning, which considerably increases the algorithms' complexity. In this article, we propose a new method called RRLPCN (reset and restart with least polar‐angle connected node), which is based on the LPCN algorithm to find the boundary vertices of a Euclidean graph. The main idea is to start the LPCN algorithm from an arbitrary vertex, and whenever it finds a vertex with an x ‐coordinate smaller than that of the starting one, LPCN is reset and restarted from this new vertex. The algorithm stops as soon as it visits the same edge for the second time in the same direction. In addition to finding the boundary vertices, RRLPCN also finds the vertex with minimum x ‐coordinate, which is the last starting point of our algorithm. The distributed version of the proposed algorithm, called D‐RRLPCN, is then applied to boundary node detection in the wireless sensor network. It has been implemented using real sensor nodes (Arduino/XBee and TelosB). The simulation results have shown our algorithm to be very effective in comparison to other algorithms

Visual analytics of geo-related multidimensional data

In recent years, both the volume and the availability of urban data related to various social issues, such as real estate, crime and population are rapidly increasing. Analysing such urban data can help the government make evidence-based decisions leading to better-informed policies; the citizens can also benefit in many scenarios such as home-seeking. However, the analytic design process can be challenging since (i) the urban data often has multiple attributes (e.g., the distance to supermarket, the distance to work, schools zone in real estate data) that are highly related to geography; and (ii) users might have various analysis/exploration tasks that are hard to define (e.g., different home-buyers might have requirements for housing properties and many of them might not know what they want before they understand the local real estate market). In this thesis, we use visual analytics techniques to study such geo-related multidimensional urban data and answer the following research questions. In the first research question, we propose a visual analytics framework/system for geo-related multidimensional data. Since visual analytics and visualization designs are highly domain-specific, we use the real estate domain as an example to study the problem. Specifically, we first propose a problem abstraction to satisfy the requirements from users (e.g., home buyers, investors). Second, we collect, integrate and clean the last ten year's real estate sold records in Australia as well as their location-related education, facility and transportation profiles, to generate a real multi-dimensional data repository. Third, we propose an interactive visual analytic procedure to help less informed users gradually learn about the local real estate market, upon which users exploit this learned knowledge to specify their personalized requirements in property seeking. Fourth, we propose a series of designs to visualize properties/suburbs in different dimensions and different granularity. Finally, we implement a system prototype for public access (http://115.146.89.158), and present case studies based on real-world datasets and real scenario to demonstrate the usefulness and effectiveness of our system. Our second research question extends the first one and studies the scalability problem to support cluster-based visualization for large-scale geo-related multidimensional data. Particularly, we first propose a design space for cluster-based geographic visualization. To calculate the geographic boundary of each cluster, we propose a concave hull algorithm which can avoid complex shapes, large empty area inside the boundary and overlaps among different clusters. Supported by the concave hull algorithm, we design a cluster-based data structure named ConcaveCubes to efficiently support interactive response to users' visual exploration on large-scale geo-related multidimensional data. Finally, we build a demo system (http://115.146.89.158/ConcaveCubes) to demonstrate the cluster-based geographic visualization, and present extensive experiments using real-world datasets and compare ConcaveCubes with state-of-the-art cube-based structures to verify the efficiency and effectiveness of ConcaveCubes. The last research question studies the problem related to visual analytics of urban areas of interest (AOIs), where we visualize geographic points that satisfy the user query as a limited number of regions (AOIs) instead of a large number of individual points (POIs). After proposing a design space for AOI visualization, we design a parameter-free footprint method named AOI-shapes to effectively capture the region of an AOI based on POIs that satisfy the user query and those that do not. We also propose two incremental methods which generate the AOI-shapes by reusing previous calculations as per users' update of their AOI query. Finally, we implement an online demo (http://www.aoishapes.com) and conduct extensive experiments to demonstrate the efficiency and effectiveness of the proposed AOI-shapes