Geographically Referenced Data for Social Science

An estimated 80% of all information has a spatial reference. Information about households as well as environmental data can be linked to precise locations in the real world. This offers benefits for combining different datasets via the spatial location and, furthermore, spatial indicators such as distance and accessibility can be included in analyses and models. HSpatial patterns of real-world social phenomena can be identified and described and possible interrelationships between datasets can be studied. Michael F. GOODCHILD, a Professor of Geography at the University of California, Santa Barbara and principal investigator at the Center for Spatially Integrated Social Science (CSISS), summarizes the growing significance of space, spatiality, location, and place in social science research as follows: "(...) for many social scientists, location is just another attribute in a table and not a very important one at that. After all, the processes that lead to social deprivation, crime, or family dysfunction are more or less the same everywhere, and, in the minds of social scientists, many other variables, such as education, unemployment, or age, are far more interesting as explanatory factors of social phenomena than geographic location. Geographers have been almost alone among social scientists in their concern for space; to economists, sociologists, political scientists, demographers, and anthropologists, space has been a minor issue and one that these disciplines have often been happy to leave to geographers. But that situation is changing, and many social scientists have begun to talk about a "spatial turn," a new interest in location, and a new "spatial social science" that crosses the traditional boundaries between disciplines. Interest is rising in GIS (Geographic Information Systems) and in what GIS makes possible: mapping, spatial analysis, and spatial modelling. At the same time, new tools are becoming available that give GIS users access to some of the big ideas of social science."

Geographically referenced data for social science

"Die Autoren beschreiben in ihrem Beitrag den Mehrwert der Nutzung von Geodaten in den Sozialwissenschaften. Sie liefern vor allem eine Reihe praktischer Hinweise auf die Bezugsquellen von Geodaten. Beispielhaft legen sie dar, wie sich Geodaten mit Daten des Sozio-oekonomischen Panels (SOEP) kombinieren lassen. Der erste Teil ihres Beitrags enthält eine nähere Beschreibung von Geodaten und des Geographischen Informationssystems (GIS). Das zweite Kapitel beschäftigt sich mit den Zielgruppen und Nutzern, es erläutert die der Dokumentation zugrunde liegende Definition der Geodaten und zeigt die Vorzüge und Herausforderungen beim Einsatz von Geodaten in der sozialwissenschaftlichen Forschung auf. Das dritte Kapitel verdeutlicht anhand zahlreicher Beispiele das Potenzial von Geodaten als eine zusätzliche Informationsquelle für die empirische Forschung. Abschließend wird ein Überblick über verfügbare Geodaten in Deutschland und die am meisten nachgefragten Daten in den Sozialwissenschaften gegeben. Der Anhang des Beitrags enthält Internet-Links für Datenquellen, Hinweise zur verfügbaren GIS-Software und weitere Informationen." (ICI

Geoscience after IT: Part J. Human requirements that shape the evolving geoscience information system

The geoscience record is constrained by the limitations of human thought and of the technology for handling information. IT can lead us away from the tyranny of older technology, but to find the right path, we need to understand our own limitations. Language, images, data and mathematical models, are tools for expressing and recording our ideas. Backed by intuition, they enable us to think in various modes, to build knowledge from information and create models as artificial views of a real world. Markup languages may accommodate more flexible and better connected records, and the object-oriented approach may help to match IT more closely to our thought processes

Usos actuales de los SIG para la ingeniería civil en Colombia

This document presents and reviews the GIS (Geographic Information System) in Colombia and its possible current uses in civil engineering. Since a model with georeferenced graphic elements (maps) with additional information associated with a database is a very complete design tool, it facilitates engineering analysis to carry out effective construction projects.Este documento presenta y reseña los SIG (Sistema de Información Geográfica) en Colombia y sus posibles usos actuales en la ingeniería civil. Ya que un modelo con elementos gráficos geo referenciados (mapas) que cuenta con información adicional asociada a una base de datos conforma una herramienta de diseño muy completa, la misma facilita a la ingeniería la realización de análisis con el fin de realizar proyectos constructivos efectivos

The State of the Art in Cartograms

Cartograms combine statistical and geographical information in thematic maps, where areas of geographical regions (e.g., countries, states) are scaled in proportion to some statistic (e.g., population, income). Cartograms make it possible to gain insight into patterns and trends in the world around us and have been very popular visualizations for geo-referenced data for over a century. This work surveys cartogram research in visualization, cartography and geometry, covering a broad spectrum of different cartogram types: from the traditional rectangular and table cartograms, to Dorling and diffusion cartograms. A particular focus is the study of the major cartogram dimensions: statistical accuracy, geographical accuracy, and topological accuracy. We review the history of cartograms, describe the algorithms for generating them, and consider task taxonomies. We also review quantitative and qualitative evaluations, and we use these to arrive at design guidelines and research challenges

Geospatial Data Management Research: Progress and Future Directions

Without geospatial data management, today´s challenges in big data applications such as earth observation, geographic information system/building information modeling (GIS/BIM) integration, and 3D/4D city planning cannot be solved. Furthermore, geospatial data management plays a connecting role between data acquisition, data modelling, data visualization, and data analysis. It enables the continuous availability of geospatial data and the replicability of geospatial data analysis. In the first part of this article, five milestones of geospatial data management research are presented that were achieved during the last decade. The first one reflects advancements in BIM/GIS integration at data, process, and application levels. The second milestone presents theoretical progress by introducing topology as a key concept of geospatial data management. In the third milestone, 3D/4D geospatial data management is described as a key concept for city modelling, including subsurface models. Progress in modelling and visualization of massive geospatial features on web platforms is the fourth milestone which includes discrete global grid systems as an alternative geospatial reference framework. The intensive use of geosensor data sources is the fifth milestone which opens the way to parallel data storage platforms supporting data analysis on geosensors. In the second part of this article, five future directions of geospatial data management research are presented that have the potential to become key research fields of geospatial data management in the next decade. Geo-data science will have the task to extract knowledge from unstructured and structured geospatial data and to bridge the gap between modern information technology concepts and the geo-related sciences. Topology is presented as a powerful and general concept to analyze GIS and BIM data structures and spatial relations that will be of great importance in emerging applications such as smart cities and digital twins. Data-streaming libraries and “in-situ” geo-computing on objects executed directly on the sensors will revolutionize geo-information science and bridge geo-computing with geospatial data management. Advanced geospatial data visualization on web platforms will enable the representation of dynamically changing geospatial features or moving objects’ trajectories. Finally, geospatial data management will support big geospatial data analysis, and graph databases are expected to experience a revival on top of parallel and distributed data stores supporting big geospatial data analysis