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Über dieses Buch

This book constitutes the refereed proceedings of the International Conference on Geographical Information Systems Theory, Applications and Management, held in Barcelona, Spain, in April 2015.

The 10 revised full papers presented were carefully reviewed and selected from 45 submissions. The papers address new challenges in geo-spatial data sensing, observation, representation, processing, visualization, sharing and managing. They concern information and communications technology (ICT) as well as management of information and knowledge-based systems.

Inhaltsverzeichnis

Frontmatter

Reasoning Geo-Spatial Neutral Similarity from Seismic Data Using Mixture and State Clustering Models

Abstract
Conventionally, earthquake events are recognized by guided and well established geographical region confines. However, explicit regional schemes are prone to overlook patterns manifested by cross-boundary seismic relations that are regarded vital to seismological research. Rather, we investigate a statistically motivated system that clusters earthquake impacted places by similarity in seismic feature space, and is hence impartial to geo-spatial proximity constraints. To facilitate our study, we have acquired hundreds of thousands recordings of earthquake episodes that traverse an extended time period of forty years. Episodes are split into groups singled out by their affiliated geographical place, and from each, we have extracted objective seismic features expressed in both a compact term-frequency of scales format, and as a discrete signal representation that captures magnitude samples spaced in regular time intervals. Attribute vectors of the distributional and temporal domains are further applied towards our mixture model and Markov chain frameworks, respectively, to conduct clustering of presumed unlabeled, shake affected locations. We performed comprehensive cluster analysis and classification experiments, and report robust results that support the intuition of geo-spatial neutral similarity.
Avi Bleiweiss

Web-Based Geoinformation System for Exploring Geomagnetic Field, Its Variations and Anomalies

Abstract
In the modern World, specialists in many scientific and applied spheres consider parameters of geomagnetic field, its variations and anomalies as one of the key factors, which can influence on systems and objects of various origins. The estimation of the influence requires an effective approach to analyze the principles of distribution of geomagnetic field parameters on the Earth’s surface, its subsoil and in circumterrestrial space. The approach causes a complicated problem to be solved, which is concerned with modeling and visualization of parameters of geomagnetic field, its variations and anomalies. The most effective and obvious solution to this problem is supposed to be a geoinformation system, because of the geodata-centric character of the problem itself. In this paper the authors suggest the solution, which is based on modern geoinformation and web technologies and provides the mechanisms to calculate, analyze and visualize parameters of geomagnetic field and its variations.
Andrei V. Vorobev, Gulnara R. Shakirova

Identifying Local Deforestation Patterns Using Geographically Weighted Regression Models

Abstract
This study aimed at identifying drivers and patterns of deforestation in Mexico by applying Geographically Weighted Regression (GWR) models to cartographic and statistical data. We constucted a nation-wide multidate GIS database incorporating digital data about deforestation from the Global Forest Change database (2000–2013); along with ancillary data (topography, road network, settlements and population disribution, socio-economical indices and government policies). We computed the rate of deforestation during the period 2008–2011 at the municipal level. Local linear models were fitted using the rate of deforestation as dependent variable. In comparison with the global model, the use of GWR increased the goodness-of-fit (adjusted R2) from 0.20 (global model) to 0.63. The mapping of GWR models’ parameters and its significance, anables us to highlight the spatial variation of the relationship between the rate of deforestation and its drivers. Factors identified as having a major impact on deforestation were related to topography, accessibility, cattle ranching and marginalization. Results indicate that the effect of these drivers varies over space, and that the same driver can even exhibit opposite effects depending on the region.
Jean-François Mas, Gabriela Cuevas

XQuery-Based Query Processing in Open Street Map

Abstract
Volunteered geographic information (VGI) makes available a very large resource of geographic data. The exploitation of data coming from such resources requires an additional effort in the form of tools and effective processing techniques. One of the most established VGI is Open Street Map (OSM) offering data of urban and rural maps from the earth. In this paper we present a library for querying OSM with XQuery. This library is based on the well-known spatial operators defined by Clementini and Egenhofer, providing a repertoire of XQuery functions which encapsulate the search on the XML document representing a layer of OSM, and make the definition of queries on top of OSM layers easy. In essence, the library is equipped with a set of OSM Operators for OSM elements which, in combination with Higher Order facilities of XQuery, facilitates the composition of queries and the definition of keyword based search geo-localized queries. OSM data are indexed by an R-tree structure, in which OSM elements are enclosed by Minimum Bounding Rectangles (MBRs), in order to get shorter answer time.
Jesús M. Almendros-Jiménez, Antonio Becerra-Terón

The K Group Nearest-Neighbor Query on Non-indexed RAM-Resident Data

Abstract
Data sets that are used for answering a single query only once (or just a few times) before they are replaced by new data sets appear frequently in practical applications. The cost of buiding indexes to accelerate query processing would not be repaid for such data sets. We consider an extension of the popular (K) Nearest-Neighbor Query, called the (K) Group Nearest Neighbor Query (GNNQ). This query discovers the (K) nearest neighbor(s) to a group of query points (considering the sum of distances to all the members of the query group) and has been studied during recent years, considering data sets indexed by efficient spatial data structures. We study (K) GNNQs, considering non-indexed RAM-resident data sets and present an existing algorithm adapted to such data sets and two Plane-Sweep algorithms, that apply optimizations emerging from the geometric properties of the problem. By extensive experimentation, using real and synthetic data sets, we highlight the most efficient algorithm.
George Roumelis, Michael Vassilakopoulos, Antonio Corral, Yannis Manolopoulos

Validation and Integration of Wheat Seed Emergence Prediction Model with GIS and Numerical Weather Prediction Models

Abstract
The main factors affecting wheat emergence are climatic condition, soil properties and planting depth. Time and percentage of the wheat emergence depend on the interaction between above factors which can be predicted by using wheat simulation model (WSM). WSM is based on three main factors which are soil water potential, soil temperature and planting depth. The general objective of this study was to delineate the best location for wheat production in arid regions such as Oman through linking Wheat Simulation Model (WSM) with Numeric Weather Prediction Model (NWPM) in the platform of the Geographical Information Systems (GIS). Soil temperature and water potential raster layers which were obtained from NWPM were analyzed using spatial analysis tools in ESRI ArcGIS software. Four field trials, over two seasons, have validated positively the linkage of the developed WSM with GIS. The developed model can be promoted as a tool for decision makers to delineate the best location for wheat production in arid regions.
R. Al-Habsi, Y. A. Al-Mulla, Y. Charabi, H. Al-Busaidi, M. Al-Belushi

Towards Geospatial Tangible User Interfaces: An Observational User Study Exploring Geospatial Interactions of the Novice

Abstract
Tangible user interfaces (TUI) such as tangible tabletops have potential as novel and innovative learning environments for mapping applications across a wide range of geospatial learning activities. This is because they offer a more natural and intuitive class of interface to users and they are fun to use. For realising their potential as a new type of geo-technology, they must be easy and straightforward to learn and remember how to use. Furthermore, the different types of tangible object interactions should align to the mental models and cultural perceptions of different types of users. This paper reports on the results of an initial observational of a small set of novice users. Users were recorded completing six tasks whilst thinking aloud. The resulting analysis revealed how easy it was for the novice to discover the different types of geospatial tangible interactions (e.g., zoom, pan, adding layers, working with layers). Formed around the categories of (1) everyday cartographic elements and their everyday metaphors, (2) object manipulations, and (3) offline interactions we propose a set usability guidelines for geospatial tangible tables. The aim is to provide an evidence base on which to improve future iterations of the improving their usability, usefulness and increasing their potential as a learning interface.
Catherine Emma Jones, Valérie Maquil

Integration of a Real-Time Stochastic Routing Optimization Software with an Enterprise Resource Planner

Abstract
In order to manage their activities in a centralized manner, an Enterprise Resource Planning (ERP) software is a fundamental tool to many companies. As a generic software, many times it’s necessary to add new functionalities to the ERP in order to improve and to adapt/suite it to the companies’ processes. The Intelligent Fresh Food Fleet Router (i3FR) project aims to meet the needs expressed by several companies, namely the usefulness of a tool that makes “intelligent” management of the food distribution logistics. This “intelligence” presupposes interconnection capacity of various platforms (e.g., fleet management, GPS, and logistics), and active communication between them in order to optimize and enable integrated decisions.
This paper presents a multi-layered architecture to integrate existing ERPs with a route optimization and a temperature data acquisition module. The optimization module is prepared to deal with dynamic scenarios, as new demands may appear during the optimization process and the routes will admit several states (e.g., open, locked and closed), according with the ERP manager instructions. The data aquisition module implements the retrieve of some vehicles parameters (e.g., chambers’ temperatures and vehicle’s global positioning system data), used to validate the routes and provide information to the company’s manager.
A distribution company was selected as case-study, having up to 5000 daily deliveries and a fleet of 120 vehicles. The integration of the developed modules with the company’s ERP allowed the maintainance of most of the existing procedures, avoiding routines disruption.
Pedro J. S. Cardoso, Gabriela Schütz, Jorge Semião, Jânio Monteiro, João Rodrigues, Andriy Mazayev, Emanuel Ey, Micael Viegas

Using Conditional Probability and a Nonlinear Kriging Technique to Predict Potato Early Die Caused by Verticllium Dahliae

Abstract
Verticillium dahliae is a plant pathogenic fungus that can be devastating to commercial potato production. Potato growers in the state of Michigan have experienced yield declines and decreased marketability as a direct result of the persistence of V. dahliae in soil. A team of researchers at Michigan State University conducted a soil evaluation using geostatistics and geographic information systems (GIS). The use of a nonlinear Kriging method allowed the team to predict where infection may occur. Nonlinear Kriging is a useful tool for creating conditional probability maps based on a threshold, which can be built into the equation. Verticillium dahliae has an inoculum threshold needed to cause infection in a potato plant. Using this threshold, maps can be created based on a probability of any point in space being greater than the threshold. The methods used in this paper show how geostatistics can be a valuable tool for commercial growers.
Luke Steere, Noah Rosenzweig, William Kirk

Using Linked Open Data in Geographical Information Systems

Abstract
Linked Open Data is becoming increasingly important for Geographical Information Systems because most of the sources available on the Web are free of charge. In this work, we present an approach for integrating heterogeneous data located in various public organizations. We address the concepts and technologies which allow for visualizing flood information available from linked open data sources using geographical information systems. The proposed approach adds to the decision-making process, specially in the context of minimizing damage caused by floods. This work also contributes to reducing costs to obtain information beyond organization boundaries by using Semantic Web technologies.
Patricia Carolina Neves Azevedo, Vitor Afonso Pinto, Guilherme Sousa Bastos, Fernando Silva Parreiras

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