Data Mining Practical Machine Learning Tools and Techniques

In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

Social Network Analysis

An Introduction to MultiAgent Systems.

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Navigation services communicate optimal routes to users by providing sequences of instructions for these routes. Each single instruction guides the wayfinder from one decision point to the next. The instructions are based on geometric data from the street network, which is typically the only dataset available. This paper addresses the question of enriching such wayfinding instructions with local landmarks. We propose measures to formally specify the landmark saliency of a feature. Values for these measures are subject to hypothesis tests in order to define and extract landmarks from datasets. The extracted landmarks are then integrated in the wayfinding instructions. A concrete example from the city of Vienna demonstrates the applicability and usefulness of the method.

Enriching Wayfinding Instructions with Local Landmarks

Cultural Studies.- Progress on Yindjibarndi Ethnophysiography.- Study of Cultural Impacts on Location Judgments in Eastern China.- Cross-Cultural Similarities in Topological Reasoning.- Thalassographein: Representing Maritime Spaces in Ancient Greece.- Semantics.- From Top-Level to Domain Ontologies: Ecosystem Classifications as a Case Study.- Semantic Categories Underlying the Meaning of 'Place'.- Spatial Semantics in Difference Spaces.- Similarity.- Evaluation of a Semantic Similarity Measure for Natural Language Spatial Relations.- Affordance-Based Similarity Measurement for Entity Types.- An Image-Schematic Account of Spatial Categories.- Mapping and Representation.- Specifying Essential Features of Street Networks.- Spatial Information Extraction for Cognitive Mapping with a Mobile Robot.- Spatial Mapping and Map Exploitation: A Bio-inspired Engineering Perspective.- Scale-Dependent Simplification of 3D Building Models Based on Cell Decomposition and Primitive Instancing.- Perception and Cognition.- Degradation in Spatial Knowledge Acquisition When Using Automatic Navigation Systems.- Stories as Route Descriptions.- Three Sampling Methods for Visibility Measures of Landscape Perception.- Reasoning and Algorithms.- Reasoning on Spatial Semantic Integrity Constraints.- Spatial Reasoning with a Hole.- Geospatial Cluster Tessellation Through the Complete Order-k Voronoi Diagrams.- Drawing a Figure in a Two-Dimensional Plane for a Qualitative Representation.- Navigation and Landmarks.- Linguistic and Nonlinguistic Turn Direction Concepts.- A Uniform Handling of Different Landmark Types in Route Directions.- Effects of Geometry, Landmarks and Orientation Strategies in the 'Drop-Off' Orientation Task.- Uncertainty and Imperfection.- Data Quality Ontology: An Ontology for Imperfect Knowledge.- Triangulation of Gradient Polygons: A Spatial Data Model for Categorical Fields.- Relations in Mathematical Morphology with Applications to Graphs and Rough Sets.

Spatial Information Theory

Big data has now become a strong focus of global interest that is increasingly attracting the attention of academia, industry, government and other organizations. Big data can be situated in the disciplinary area of traditional geospatial data handling theory and methods. The increasing volume and varying format of collected geospatial big data presents challenges in storing, managing, processing, analyzing, visualizing and verifying the quality of data. This has implications for the quality of decisions made with big data. Consequently, this position paper of the International Society for Photogrammetry and Remote Sensing (ISPRS) Technical Commission II (TC II) revisits the existing geospatial data handling methods and theories to determine if they are still capable of handling emerging geospatial big data. Further, the paper synthesises problems, major issues and challenges with current developments as well as recommending what needs to be developed further in the near future.

https://orbit.dtu.dk/files/117885360/PHOTO_D_15_00278R1B.pdf

Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges

This paper complements landmark research with an approach to formalize the structural salience of objects along routes. The aim is to automatically integrate salient objects—landmarks—into route directions. To this end, two directions of research are combined: the formalization of salience of objects and the conceptualization of wayfinding actions. We approach structural salience with some taxonomic considerations of point-like objects with respect to their positions along a route and detail the effects of different positions on the conceptualization process. The results are used to extend a formal language of route knowledge, the wayfinding choreme theory. This research contributes to a cognitive foundation for next generation navigation support and to the aim of formalizing geosemantics.

/pdf/structural-salience-of-landmarks-for-route-directions-5d82cngnbf.pdf

Structural salience of landmarks for route directions

Big data has now become a strong focus of global interest that is increasingly attracting the attention of academia, industry, government and other organizations. Big data can be situated in the disciplinary area of traditional geospatial data handling theory and methods. The increasing volume and varying format of collected geospatial big data presents challenges in storing, managing, processing, analyzing, visualizing and verifying the quality of data. This has implications for the quality of decisions made with big data. Consequently, this position paper of the International Society for Photogrammetry and Remote Sensing (ISPRS) Technical Commission II (TC II) revisits the existing geospatial data handling methods and theories to determine if they are still capable of handling emerging geospatial big data. Further, the paper synthesises problems, major issues and challenges with current developments as well as recommending what needs to be developed further in the near future. 
Keywords: Big data, Geospatial, Data handling, Analytics, Spatial Modeling, Review

Stephan Winter

Papers

Enriching Wayfinding Instructions with Local Landmarks

Spatial Information Theory

Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges

Structural salience of landmarks for route directions

Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges