Geographic Visualization in Archaeology

Archaeologists are often considered frontrunners in employing spatial approaches within the social sciences and humanities, including geospatial technologies such as geographic information systems (GIS) that are now routinely used in archaeology. Since the late 1980s, GIS has mainly been used to support data collection and management as well as spatial analysis and modeling. While fruitful, these efforts have arguably neglected the potential contribution of advanced visualization methods to the generation of broader archaeological knowledge. This paper reviews the use of GIS in archaeology from a geographic visualization (geovisual) perspective and examines how these methods can broaden the scope of archaeological research in an era of more user-friendly cyber-infrastructures. Like most computational databases, GIS do not easily support temporal data. This limitation is particularly problematic in archaeology because processes and events are best understood in space and time. To deal with such shortcomings in existing tools, archaeologists often end up having to reduce the diversity and complexity of archaeological phenomena. Recent developments in geographic visualization begin to address some of these issues, and are pertinent in the globalized world as archaeologists amass vast new bodies of geo-referenced information and work towards integrating them with traditional archaeological data. Greater effort in developing geovisualization and geovisual analytics appropriate for archaeological data can create opportunities to visualize, navigate and assess different sources of information within the larger archaeological community, thus enhancing possibilities for collaborative research and new forms of critical inquiry

Geovisualization

Geovisualization involves the depiction of spatial data in an attempt to facilitate the interpretation of observational and simulated datasets through which Earth's surface and solid Earth processes may be understood. Numerous techniques can be applied to imagery, digital elevation models, and other geographic information system data layers to explore for patterns and depict landscape characteristics. Given the rapid proliferation of remotely sensed data and high-resolution digital elevation models, the focus is on the visualization of satellite imagery and terrain morphology, where manual human interpretation plays a fundamental role in the study of geomorphic processes and the mapping of landforms. A treatment of some techniques is provided that can be used to enhance satellite imagery and the visualization of the topography to improve landform identification as part of geomorphological mapping. Visual interaction with spatial data is an important part of exploring and understanding geomorphological datasets, and a variety of methods exist ranging across simple overlay, panning and zooming, 2.5D, 3D, and temporal analyses. Specific visualization outputs are also covered that focus on static and interactive methods of dissemination. Geomorphological mapping legends and the cartographic principles for map design are discussed, followed by details of dynamic web-based mapping systems that allow for greater immersive use by end users and the effective dissemination of data

Flood hazard hydrology: interdisciplinary geospatial preparedness and policy

Thesis (Ph.D.) University of Alaska Fairbanks, 2017Floods rank as the deadliest and most frequently occurring natural hazard worldwide, and in 2013 floods in the United States ranked second only to wind storms in accounting for loss of life and damage to property. While flood disasters remain difficult to accurately predict, more precise forecasts and better understanding of the frequency, magnitude and timing of floods can help reduce the loss of life and costs associated with the impact of flood events. There is a common perception that 1) local-to-national-level decision makers do not have accurate, reliable and actionable data and knowledge they need in order to make informed flood-related decisions, and 2) because of science--policy disconnects, critical flood and scientific analyses and insights are failing to influence policymakers in national water resource and flood-related decisions that have significant local impact. This dissertation explores these perceived information gaps and disconnects, and seeks to answer the question of whether flood data can be accurately generated, transformed into useful actionable knowledge for local flood event decision makers, and then effectively communicated to influence policy. Utilizing an interdisciplinary mixed-methods research design approach, this thesis develops a methodological framework and interpretative lens for each of three distinct stages of flood-related information interaction: 1) data generation—using machine learning to estimate streamflow flood data for forecasting and response; 2) knowledge development and sharing—creating a geoanalytic visualization decision support system for flood events; and 3) knowledge actualization—using heuristic toolsets for translating scientific knowledge into policy action. Each stage is elaborated on in three distinct research papers, incorporated as chapters in this dissertation, that focus on developing practical data and methodologies that are useful to scientists, local flood event decision makers, and policymakers. Data and analytical results of this research indicate that, if certain conditions are met, it is possible to provide local decision makers and policy makers with the useful actionable knowledge they need to make timely and informed decisions

Extracting patterns from large movement data sets using hybrid spatio-temporal filtering: a case study of geovisual analytics in support of fisheries enforcement activities

The ubiquitous nature of location tracking technologies has resulted in an increase in movement data being collected. These data are used in many contexts, such as understanding animal migration, aiding in fisheries enforcement, or managing fleets of taxicabs. Such large volumes of data call for more efficient data visualization and analysis methods. This research provides a general approach to the analysis of movement data, named Hybrid Spatio-temporal Filtering (HSF), which allows analysts to filter data based on characteristics of movement within a geovisual analytics environment. Filtering signatures are defined by combining movement path complexity (fractal dimension) and velocity, to extract behavioural patterns from data sets. An evaluation within a fisheries enforcement case study (using VMS data), and comparison to other approaches, confirmed the approach is useful, easy to use, and superior to some other approaches. This research demonstrates the value of signature-building filtering approaches for large movement data sets

Geo-visual Analytics of Canada-U.S. Transborder Traffic Data

This research aims to investigate new geo-visual analytics methods and techniques for visually analyzing the large amount of historical and near real time geospatial and temporal traffic data at the border crossings between Canada and the U.S. Historical traffic-related time-series data are available from different agencies in both countries for at least the last four decades for different modes of transportation and different purposes. Supplementary historical and near real-time data about delays, weather conditions, and different types of alerts and conditions at the ports of entry can be used to analyze the decision processes behind changes in traffic patterns. The data are gathered, processed, and linked to a web-based Geographic Information System (GIS) that can be accessed by authorized users over the Internet using an intuitive graphical user interface (GUI) to support different types of queries. The resulting database and information system can be beneficial for understanding the impact of the different factors affecting delays at the ports of entry and the impacts of these delays on the decision-making of travelers, planners, and supply chain operators

Subsurface Characterization by Means of Geovisual Analytics

This Thesis is concerned with one of the major problems in subsurface characterizations emerging from ever-increasing loads of data in the last decades: What kind of technologies suit well for extracting novel, valid and useful knowledge from persistent data repositories for the characterization of subsurface regions and how can such technologies be implemented in an integrated, community-open software platform? In order to address those questions, an interactive, open-source software platform for geoscientific knowledge discovery has been developed, which enables domain experts to generate, optimize and validate prognostic models of the subsurface domain. Such a free tool has been missing in the geoscientific community so far. The extensible software platform GeoReVi (Geological Reservoir Virtualization) implements selected aspects of geovisual analytics with special attention being paid to an implementation of the knowledge discovery in databases process. With GeoReVi the human expert can model and visualize static and dynamic systems in the subsurface in a feedback cycle. The created models can be analyzed and parameterized by means of modern approaches from geostatistics and data mining. Hence, knowledge that is useful to both the assessment of subsurface potentials and to support decision-making during the utilization process of the subsurface regions can be extracted and exchanged in a formalized manner. The modular software application is composed of both integrated and centralized databases, a graphical user interface and a business logic. In order to fulfill the needs of low computing time in accordance with high computational complexity of spatial problems, the software system makes intense use of parallelism and asynchronous programming. The competitiveness of industry branches, which are aimed at utilizing the subsurface in unknown regions, such as the geothermal energy production or carbon capture and storage, are especially dependent on the quality of spatial forecasts for relevant rock and fluid properties. Thus, the focus of this work has been laid upon the implementation of algorithms, which enhance the predictability of properties in space under consideration of uncertainty. The software system was therefore evaluated in ample real-world scenarios by solving problems from scientific, educational and industrial projects. The implemented software system shows an excellent suitability to generically address spatial problems such as interpolation or stochastic simulation under consideration of numerical uncertainty. In this context, GeoReVi served as a tool for discovering new knowledge with special regard to investigating the heterogeneity of rock media on multiple scales of investigation. Among others, it could be demonstrated that the three-dimensional scalar fields of different petrophysical and geochemical properties in sandstone media may diverge significantly at small-scales. In fact, if the small-scale variability is not considered in field-scale projects, in which the sampling density is usually low, statistical correlations and thus empirical relationships might be feigned. Furthermore, it could be demonstrated that the simple kriging variance, which is used to simulate the natural variability in sequential simulations, systematically underestimates the intrinsic variability of the investigated sandstone media. If the small-scale variability can be determined by high-resolution sampling, it can be used to enhance conditional simulations at the scale of depositional environments

Gendered Activities and Vegetation Change: A Study of Vegetation of Bunkpurugu-Yunyoo District

This paper looks at the change in the vegetation cover in Bunkpurugu-Yunyoo District in the Northern Region of Ghana as contributed to by gendered domestic economic activities. The study became necessary due to the increasing dependence on the biodiversity for fuel wood (mainly), timber and livestock grazing, amidst persistent bush burning in the district. The study adopted Remote Sensing and GIS technologies for the determination of the changes in vegetation. It considered 2000 and 2015 as base and current years respectively for the determination of the change. Specifically, it used ArcGIS, ENVI and NDVI software to process the LandSat 7 images it acquired from the study area. The study sought to determine the nature of vegetation of the area in each of the years, determine the extent of change and explain the possible drivers to the change. The results showed that vegetation decreased inversely with the increases in settlement, bare land, and burnt land sizes. The main drivers of the change were identified as human related – gendered roles of women, urbanization and drive for development. It concluded that the vegetation of Bunkpurugu-Yunyoo District detected at 25% is rather a drastic change, given the short time period of six years. Keywords: Change detection, Pixel, Bunkpurugu-Yunyoo, Gendered roles