Clustering uncertain data using voronoi diagrams and R-tree index

We study the problem of clustering uncertain objects whose locations are described by probability density functions (pdfs). We show that the UK-means algorithm, which generalizes the k-means algorithm to handle uncertain objects, is very inefficient. The inefficiency comes from the fact that UK-means computes expected distances (EDs) between objects and cluster representatives. For arbitrary pdfs, expected distances are computed by numerical integrations, which are costly operations. We propose pruning techniques that are based on Voronoi diagrams to reduce the number of expected distance calculations. These techniques are analytically proven to be more effective than the basic bounding-box-based technique previously known in the literature. We then introduce an R-tree index to organize the uncertain objects so as to reduce pruning overheads. We conduct experiments to evaluate the effectiveness of our novel techniques. We show that our techniques are additive and, when used in combination, significantly outperform previously known methods. © 2006 IEEE.published_or_final_versio

Improving the Quality of Citizen Contributed Geodata through Their Historical Contributions:The Case of the Road Network in OpenStreetMap

OpenStreetMap (OSM) has proven to serve as a promising free global encyclopedia of maps with an increasing popularity across different user communities and research bodies. One of the unique characteristics of OSM has been the availability of the full history of users’ contributions, which can leverage our quality control mechanisms through exploiting the history of contributions. Since this aspect of contributions (i.e., historical contributions) has been neglected in the literature, this study aims at presenting a novel approach for improving the positional accuracy and completeness of the OSM road network. To do so, we present a five-stage approach based on a Voronoi diagram that leads to improving the positional accuracy and completeness of the OSM road network. In the first stage, the OSM data history file is retrieved and in the second stage, the corresponding data elements for each object in the historical versions are identified. In the third stage, data cleaning on the historical datasets is carried out in order to identify outliers and remove them accordingly. In the fourth stage, through applying the Voronoi diagram method, one representative version for each set of historical versions is extracted. In the final stage, through examining the spatial relations for each object in the history file, the topology of the target object is enhanced. As per validation, a comparison between the latest version of the OSM data and the result of our approach against a reference dataset is carried out. Given a case study in Tehran, our findings reveal that the completeness and positional precision of OSM features can be improved up to 14%. Our conclusions draw attention to the exploitation of the historical archive of the contributions in OSM as an intrinsic quality indicator

Modeling Of Socio-economic Factors And Adverse Events In An Active War Theater By Using A Cellular Automata Simulation Approach

Department of Defense (DoD) implemented Human Social Cultural and Behavior (HSCB) program to meet the need to develop capability to understand, predict and shape human behavior among different cultures by developing a knowledge base, building models, and creating training capacity. This capability will allow decision makers to subordinate kinetic operations and promote non-kinetic operations to govern economic programs better in order to initiate efforts and development to address the grievances among the displeased by adverse events. These non-kinetic operations include rebuilding indigenous institutions’ bottom-up economic activity and constructing necessary infrastructure since the success in non-kinetic operations depends on understanding and using social and cultural landscape. This study aims to support decision makers by building a computational model to understand economic factors and their effect on adverse events. In this dissertation, the analysis demonstrates that the use of cellular automata has several significant contributions to support decision makers allocating development funds to stabilize regions with higher adverse event risks, and to better understand the complex socio-economic interactions with adverse events. Thus, this analysis was performed on a set of spatial data representing factors from social and economic data. In studying behavior using cellular automata, cells in the same neighborhood synchronously interact with each other to determine their next states, and small changes in iteration may yield to complex formations of adverse event risk after several iterations of time. The modeling methodology of cellular automata for social and economic analysis in this research was designed in two major implementation levels as follows: macro and micro-level. In the macro-level, the modeling framework integrates iv population, social, and economic sub-systems. The macro-level allows the model to use regionalized representations, while the micro-level analyses help to understand why the events have occurred. Macro-level subsystems support cellular automata rules to generate accurate predictions. Prediction capability of cellular automata is used to model the micro-level interactions between individual actors, which are represented by adverse events. The results of this dissertation demonstrate that cellular automata model is capable of evaluating socio-economic influences that result in changes in adverse events and identify location, time and impact of these events. Secondly, this research indicates that the socioeconomic influences have different levels of impact on adverse events, defined by the number of people killed, wounded or hijacked. Thirdly, this research shows that the socio-economic, influences and adverse events that occurred in a given district have impacts on adverse events that occur in neighboring districts. The cellular automata modeling approach can be used to enhance the capability to understand and use human, social and behavioral factors by generating what-if scenarios to determine the impact of different infrastructure development projects to predict adverse events. Lastly, adverse events that could occur in upcoming years can be predicted to allow decision makers to deter these events or plan accordingly if these events do occur

Does Time Smoothen Space? Implications for Space-Time Representation

The continuous nature of space and time is a fundamental tenet of many scientific endeavors. That digital representation imposes granularity is well recognized, but whether it is possible to address space completely remains unanswered. This paper argues Hales' proof of Kepler's conjecture on the packing of hard spheres suggests the answer to be "no", providing examples of why this matters in GIS generally and considering implications for spatio-temporal GIS in particular. It seeks to resolve the dichotomy between continuous and granular space by showing how a continuous space may be emergent over a random graph. However, the projection of this latent space into 3D/4D imposes granularity. Perhaps surprisingly, representing space and time as locally conjugate may be key to addressing a "smooth" spatial continuum. This insight leads to the suggestion of Face Centered Cubic Packing as a space-time topology but also raises further questions for spatio-temporal representation

Computers from plants we never made. Speculations

We discuss possible designs and prototypes of computing systems that could be based on morphological development of roots, interaction of roots, and analog electrical computation with plants, and plant-derived electronic components. In morphological plant processors data are represented by initial configuration of roots and configurations of sources of attractants and repellents; results of computation are represented by topology of the roots' network. Computation is implemented by the roots following gradients of attractants and repellents, as well as interacting with each other. Problems solvable by plant roots, in principle, include shortest-path, minimum spanning tree, Voronoi diagram, $\alpha$-shapes, convex subdivision of concave polygons. Electrical properties of plants can be modified by loading the plants with functional nanoparticles or coating parts of plants of conductive polymers. Thus, we are in position to make living variable resistors, capacitors, operational amplifiers, multipliers, potentiometers and fixed-function generators. The electrically modified plants can implement summation, integration with respect to time, inversion, multiplication, exponentiation, logarithm, division. Mathematical and engineering problems to be solved can be represented in plant root networks of resistive or reaction elements. Developments in plant-based computing architectures will trigger emergence of a unique community of biologists, electronic engineering and computer scientists working together to produce living electronic devices which future green computers will be made of.Comment: The chapter will be published in "Inspired by Nature. Computing inspired by physics, chemistry and biology. Essays presented to Julian Miller on the occasion of his 60th birthday", Editors: Susan Stepney and Andrew Adamatzky (Springer, 2017