How well do we really know the world? Uncertainty in GIScience

There are many reasons why geospatial data are not geography, but merely representations of it. Thus geospatial data will always leave their user uncertain about the true nature of the world. Over the past three decades uncertainty has become the focus of significant research in GIScience. This paper reviews the reasons for uncertainty, its various dimensions from measurement to modeling, visualization, and propagation. The later sections of the paper explore the implications of current trends, specifically data science, new data sources, and replicability, and the new questions these are posing for GIScience research in the coming years

Twenty years of progress: GIScience in 2010

It is 20 years since the term “geographic information science” was suggested to encompass the set of fundamental research issues that surround GIS. Two decades of GIScience have produced a range of accomplishments, in an expanding literature of research results as well as in the infrastructure of research. Several themes are suggested for future research, based both on gaps in what has been accomplished thus far, and on technology trends that will themselves raise research questions

Citizens as Voluntary Sensors: Spatial Data Infrastructure in the World of Web 2.0

Much progress has been made in the past two decades, and increasingly since the popularizing of the Internet and the advent of the Web, in exploiting new technologies in support of the dissemination of geographic information. Data warehouses, spatial data libraries, and geoportals have proliferated, and today’s users of geographic information have a wealth of potential sources that can be searched for suitable data sets. Standards have been established, issues of syntactic interoperability have been largely addressed, and rich descriptions are available in metadata to allow the suitability of a given data set to be assessed. Table digitizers used to be an essential asset for any spatial data center in the days when most sources of geographic information were in the form of paper maps, and skill in digitizing was a major part of any introduction to geographic information systems (GIS). Today, however, users rely heavily on digital sources, and virtually all digitizing is heads-up on-screen

Generating Geospatial Footprints For Geoparsed Text From Crowdsourced Platial Data

The research paper reports on the generation of geospatial footprints from geoparsed text associated with geocrowdsourced platial data collected and stored in the George Mason University Geocrowdsourcing Testbed (GMU-GcT). The GMU-GcT facilitates study of social dynamics, quality assessment, data contribution patterns, and position validation for geocrowdsourced geo data, with a primary purpose of mapping transient obstacles and navigation hazards in a dynamic urban environment. This paper reports on the automated generation of spatial footprints using open-source software, and discusses the role of automated spatial footprints in quality assessment for automated position validation. A detailed, local gazetteer is used to store placenames and placename variants including abbreviated, slang, former, and jargon-based instances. Obstacle reports containing location descriptions are geoparsed and processed with the help of the GMU-GcT gazetteer to generate geospatial footprints, which are used in a quality assessment process to validate the position of obstacle reports. Continuing research with the GMU-GcT has produced fifteen characteristic footprints types, which are generated and grouped into simple, complex, and ambiguous categories. The opensource tools used for generating these footprints are MapBox, MapBox.js, TURF.js, jQuery, and Bootstrap

SIG y análisis espacial de datos: perspectivas convergentes

En este artículo se identifican algunos de los desarrollos más importantes experimentados por los SIG y el análisis espacial de datos desde los inicios de los 50. Aunque tanto los SIG como el análisis espacial de datos comenzaron como dos áreas de investigación y aplicación más o menos separadas, han crecido unidos estrechamente a lo largo del tiempo. En el trabajo se mantiene que estas dos disciplinas se unen en el terreno de la Ciencia de la Información Geográfica, proporcionando cada una de ellas apoyo o añadiendo valor a la otra. El artículo comienza proporcionando una visión crítica retrospectiva de los desarrollos que han tenido lugar en los últimos cincuenta años. A continuación, se reflexiona acerca de los desafíos actuales y se especula sobre el futuro. Por último se comenta el potencial de convergencia del desarrollo de los SIG y del análisis espacial de datos bajo la rubrica de la Ciencia de la Información Geográfica (o SIGciencia)

Combining Spatial-Temporal and Phylogenetic Analysis Approaches for Improved Understanding on Global H5N1 Transmission

Background Since late 2003, the highly pathogenic influenza A H5N1 had initiated several outbreak waves that swept across the Eurasia and Africa continents. Getting prepared for reassortment or mutation of H5N1 viruses has become a global priority. Although the spreading mechanism of H5N1 has been studied from different perspectives, its main transmission agents and spread route problems remain unsolved. Methodology/Principal Findings Based on a compilation of the time and location of global H5N1 outbreaks from November 2003 to December 2006, we report an interdisciplinary effort that combines the geospatial informatics approach with a bioinformatics approach to form an improved understanding on the transmission mechanisms of H5N1 virus. Through a spherical coordinate based analysis, which is not conventionally done in geographical analyses, we reveal obvious spatial and temporal clusters of global H5N1 cases on different scales, which we consider to be associated with two different transmission modes of H5N1 viruses. Then through an interdisciplinary study of both geographic and phylogenetic analysis, we obtain a H5N1 spreading route map. Our results provide insight on competing hypotheses as to which avian hosts are responsible for the spread of H5N1. Conclusions/Significance We found that although South China and Southeast Asia may be the virus pool of avian flu, East Siberia may be the source of the H5N1 epidemic. The concentration of migratory birds from different places increases the possibility of gene mutation. Special attention should be paid to East Siberia, Middle Siberia and South China for improved surveillance of H5N1 viruses and monitoring of migratory birds

Next-Generation Digital Earth: A position paper from the Vespucci Initiative for the Advancement of Geographic Information Science

This position paper is the outcome of a joint reflection by a group of international geographic and environmental scientists from government, industry, and academia brought together by the Vespucci Initiative for the Advancement of Geographic Information Science, and the Joint Research Centre of the European Commission. It argues that the vision of Digital Earth put forward by Vice-President Al Gore 10 years ago needs to be re-evaluated in the light of the many developments in the fields of information technology, data infrastructures, and earth observation that have taken place since. It focuses the vision on the next-generation Digital Earth and identifies priority research areas to support this vision. The paper is offered as input for discussion among different stakeholder communities with the aim to shape research and policy over the next 5-10 years

Mining Big Data for Tourist Hot Spots: Geographical Patterns of Online Footprints

Understanding the complex, and often unequal, spatiality of tourist demand in urban contexts requires other methodologies, among which the information base available online and in social networks has gained prominence. Innovation supported by Information and Communication Technologies in terms of data access and data exchange has emerged as a complementary supporting tool for the more traditional data collection techniques currently in use, particularly, in urban destinations where there is the need to more (near)real-time monitoring. The capacity to collect and analise massive amounts of data on individual and group behaviour is leading to new data-rich research approaches. This chapter addresses the potential for discovering geographical insights regarding tourists’ spatial patterns within a destination, based on the analysis of geotagged data available from two social networks. ·info:eu-repo/semantics/publishedVersio