Visualization of Large-Scale Narrative Data Describing Human Error

2017 ◽  
Vol 59 (4) ◽  
pp. 520-534 ◽  
Author(s):  
William J. Irwin ◽  
Saul D. Robinson ◽  
Stephen M. Belt
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
Latent Semantic Analysis ◽  
Human Error ◽  
Large Scale ◽  
Semantic Analysis ◽  
Difficult Problem ◽  
Geographic Information ◽  
Data Sets ◽  
Systems Software

Objective Introduced is a visual data exploration technique for compiling, reducing, organizing, visually rendering, and filtering text-based narratives for detailed analysis. Background The analysis of data sets provides an increasingly difficult problem. The method of visual representation is considered an effective tool in many applications. The focus of this study was to determine if a latent semantic analysis–based projection of narrative data into a geographic information systems software program provided a useful tool for reducing and organizing large sums of narrative data for analysis. Method This approach utilizes latent semantic analysis to reduce narratives to a high-dimensional vector, truncates the vector to a two-dimensional projection through application of isometric mapping, and then visually renders the result with geographic information systems software. This method is demonstrated on aviation self-reported safety narratives sourced from the Aviation Safety Reporting System. Results Thematic regions from the corpus are illustrated along with the first five topics identified. Conclusion Shown is the ability to assimilate a large number of narratives, identify contextual themes, recognize common events and outliers, and organize resultant topics. Application Large narrative-based data sets present in aviation and other domains may be visualized to facilitate efficient analysis, enhance comprehension, and improve safety.

scholarly journals Fully Automated Pose Estimation of Historical Images in the Context of 4D Geographic Information Systems Utilizing Machine Learning Methods

2021 ◽  
Vol 10 (11) ◽  
pp. 748
Author(s):  
Ferdinand Maiwald ◽  
Christoph Lehmann ◽  
Taras Lazariv
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
Image Retrieval ◽  
Pose Estimation ◽  
Large Scale ◽  
Feature Matching ◽  
Geographic Information ◽  
Content Based Image Retrieval ◽  
Matching Methods ◽  
Historical Images

The idea of virtual time machines in digital environments like hand-held virtual reality or four-dimensional (4D) geographic information systems requires an accurate positioning and orientation of urban historical images. The browsing of large repositories to retrieve historical images and their subsequent precise pose estimation is still a manual and time-consuming process in the field of Cultural Heritage. This contribution presents an end-to-end pipeline from finding relevant images with utilization of content-based image retrieval to photogrammetric pose estimation of large historical terrestrial image datasets. Image retrieval as well as pose estimation are challenging tasks and are subjects of current research. Thereby, research has a strong focus on contemporary images but the methods are not considered for a use on historical image material. The first part of the pipeline comprises the precise selection of many relevant historical images based on a few example images (so called query images) by using content-based image retrieval. Therefore, two different retrieval approaches based on convolutional neural networks (CNN) are tested, evaluated, and compared with conventional metadata search in repositories. Results show that image retrieval approaches outperform the metadata search and are a valuable strategy for finding images of interest. The second part of the pipeline uses techniques of photogrammetry to derive the camera position and orientation of the historical images identified by the image retrieval. Multiple feature matching methods are used on four different datasets, the scene is reconstructed in the Structure-from-Motion software COLMAP, and all experiments are evaluated on a newly generated historical benchmark dataset. A large number of oriented images, as well as low error measures for most of the datasets, show that the workflow can be successfully applied. Finally, the combination of a CNN-based image retrieval and the feature matching methods SuperGlue and DISK show very promising results to realize a fully automated workflow. Such an automated workflow of selection and pose estimation of historical terrestrial images enables the creation of large-scale 4D models.

Untangling origin-destination flows in geographic information systems

2017 ◽  
Vol 18 (1) ◽  
pp. 153-172 ◽  
Author(s):  
Anita Graser ◽  
Johanna Schmidt ◽  
Florian Roth ◽  
Norbert Brändle
Keyword(s):  
Information System ◽  
Information Systems ◽  
Geographic Information System ◽  
Geographic Information Systems ◽  
Spatial Data ◽  
Geographic Information ◽  
Data Sets ◽  
Flow Maps ◽  
Local Strength ◽  
Edge Bundling

Origin–destination flow maps are a popular option to visualize connections between different spatial locations, where specific routes between the origin and destination are unknown or irrelevant. Visualizing origin–destination flows is challenging mainly due to visual clutter which appears quickly as data sets grow. Clutter reduction techniques are intensively explored in the information visualization and cartography domains. However, current automatic techniques for origin–destination flow visualization, such as edge bundling, are not available in geographic information systems which are widely used to visualize spatial data, such as origin–destination flows. In this article, we explore the applicability of edge bundling to spatial data sets and necessary adaptations under the constraints inherent to platform-independent geographic information system scripting environments. We propose (1) a new clustering technique for origin–destination flows that provides within-cluster consistency to speed up computations, (2) an edge bundling approach based on force-directed edge bundling employing matrix computations, (3) a new technique to determine the local strength of a bundle leveraging spatial indexes, and (4) a geographic information system–based technique to spatially offset bundles describing different flow directions. Finally, we evaluate our method by applying it to origin–destination flow data sets with a wide variety of different data characteristics.

scholarly journals A GIS-Based Analysis for Mapping the Distribution of Seaweed Cultivation Area in East Lombok Southern Coastal Waters

GeoEco ◽  
2018 ◽  
Vol 4 (2) ◽  
pp. 171
Author(s):  
Mohammad Faisal ◽  
Sukuryadi Sukuryadi ◽  
Fatma Wulandari
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
Geographic Information ◽  
Problem Solution ◽  
Seaweed Cultivation ◽  
The People ◽  
Systems Software ◽  
Disk Basic ◽  
Observation Method ◽  
Suitable Area

The analysis employed in geographic information systems is an analysis using GIS (Geographic Information Systems) software as in land mapping or the like. GIS-based analysis can provide information that the potential distribution of seaweed cultivation area owned by the local territory is assuredly propitious; and for that reason, it needs to be maintained wisely for the people welfare. The objectives of this study are to find out the distribution of the potential seaweed cultivation area and to obtain the database on the suitability of seaweed cultivation area in the south waters of East Lombok. This research employs survey approach since it utilizes the existing data for gaining the problem solution rather than hypothesis testing. The instruments deployed in the process of the research are some equipment such as ships, GPS, current kites, measuring signals, thermometer, geological compasses, stopwatch, secchi disk, basic diving equipment, salinometer, and a GIS-based analysis software. The data of the research are carefully collected through observation method, documentation, and experiments. The result of the study shows that the total area of 606.936 ha are classified into suitable category (S2) 47.27%, not suitable area (N) 46.37% and highly suitable area (S1) 6.36%.

Geographic Information Systems for Tracking Bridge Component Inspection and Repairs

1997 ◽  
Vol 1575 (1) ◽  
pp. 60-65
Author(s):  
Raymond D. Thierrin
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
Computer Aided Design ◽  
Large Scale ◽  
Geographic Information ◽  
Digital Information ◽  
Bridge Rehabilitation ◽  
Digital Format ◽  
Management Procedures ◽  
Available Technology

Bridge component inspection and repair information has been traditionally collected on paper forms by field personnel and stored in project files. Because of the industrywide use of computer-aided design and drafting technology in bridge rehabilitation design, digital information for bridge components is often available as a by-product of the design process. In addition, projects are becoming more sophisticated and, as a result, the construction field office is becoming more automated. It is now possible to automate field data collection and management procedures so that information can be captured in a digital format in the field and used throughout the construction documentation process. The available technology includes pen-based computers, pen-enabled database software, and digital color cameras, all of which can be integrated into systems that are easily used by field inspection personnel. By using databases and geographic information systems, inspectors and engineers can readily review component information and track the progress of repairs for large-scale rehabilitation projects.

Developing geographic information systems platforms for multijurisdictional transportation analyses: framework and techniques for spatial data sharing

2003 ◽  
Vol 30 (5) ◽  
pp. 807-818 ◽  
Author(s):  
Kai Han ◽  
Scott Minty ◽  
Alan Clayton
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
Data Sharing ◽  
Spatial Data ◽  
Rural Areas ◽  
Geographic Information ◽  
Data Sets ◽  
Data Interoperability ◽  
Urban And Rural Areas ◽  
Integration Data

Geographic information systems (GISs) have been presented as a powerful analysing tool for civil engineers to help their decision-making processes. Building GIS platforms for transportation analysis involving multiple jurisdictions has been challenging, however, because of the complexity and difficulty associated with conducting data sharing and ensuring spatial data interoperability among GISs for transportation (GIS-T) data sets. In the context of western Canadian urban and rural areas, this paper investigates the issues related to GIS-T data sharing, establishes a conceptual framework, develops techniques supporting the framework by solving recurring data-sharing problems, and constructs a number of GIS-T platforms facilitating comprehensive multijurisdictional transportation analyses. In addition, based on the knowledge gained through solving real-world problems, the authors propose an open GIS-T platform consisting of a series of customized base maps, each being tailored to suit the needs of individual application and, as a whole, linked together by interoperability to better support transportation applications.Key words: transportation engineering analysis, GIS, GIS-T, spatial data, interoperability, integration, data sharing.

Geographic Information Systems (GIS) in International Relations

2016 ◽  
Vol 70 (4) ◽  
pp. 845-869 ◽  
Author(s):  
Jordan Branch
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
International Relations ◽  
Selection Bias ◽  
Geographic Information ◽  
Data Sets ◽  
Gis Data ◽  
Technical Details ◽  
Default Data ◽  
Do So

AbstractGeographic Information Systems (GIS) are being applied with increasing frequency, and with increasing sophistication, in international relations and in political science more generally. Their benefits have been impressive: analyses that simply would not have been possible without GIS are now being completed, and the spatial component of international politics—long considered central but rarely incorporated analytically—has been given new emphasis. However, new methods face new challenges, and to apply GIS successfully, two specific issues need to be addressed: measurement validity and selection bias. Both relate to the challenge of conceptualizing nonspatial phenomena with the spatial tools of GIS. Significant measurement error can occur when the concepts that are coded as spatial variables are not, in fact, validly measured by the default data structure of GIS, and selection bias can arise when GIS systematically excludes certain types of units. Because these potential problems are hidden by the technical details of the method, GIS data sets and analyses can sometimes appear to overcome these challenges when, in fact, they fail to do so. Once these issues come to light, however, potential solutions become apparent—including some in existing applications in international relations and in other fields.

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