A novel way to present flood hazards using 3D-printing with transparent layers of return period isolines

This paper examines the 3D printed results of a floodplain analysis usually used for hydrological studies to calculate the probabilities in high water stage features. The analysis was performed using probability distributions, including Pearson type III distribution, Log-Pearson type III distribution, Gaussian (normal) distribution, Gumbel distribution, and Log-normal distribution. The maximum theoretical stages of best fitting distribution for different return periods were mapped to the Vardar and Boshava rivers in the Tikvesh Valley. Data to create the model were extracted from digital elevation models of the Vardar river target area. The extracted 3D surface model was covered with a map showing all the flooded areas in the relevant territory for different return periods as transparent layers. The data were converted into a physical model (relief map) using 3D printing methods for visualisation

Developing Versatile Graphic Map Load Metrics

Graphic map load is a property of a map quantifying the amount of map content. It indicates the visual complexity of the map and helps cartographers to adapt maps and other geospatial visualizations to accomplish their purpose. Generally, map design needs to enable the user to quickly, comprehensively, and intuitively obtain the relevant spatial information from a map. Especially, this applies in cases like crisis management, immunology and military. However, there are no widely applicable metrics to assess the complexity of cartographic products. This paper evaluates seven simple metrics for graphic map load calculation based on image analytics using the set of 50 various maps on an easily understandable scale of 0–100%. The metrics are compared to values of user-perceived map load survey joined by 62 respondents. All the suggested metrics are designed for calculation with easy-accessible software and therefore suitable for use in any user environment. Metrics utilizing the principle of edge detection have been found suitable for a diversity of geospatial visualizations providing the best results among other metrics

Automatic Geodata Processing Methods for Real-World City Visualizations in Cities: Skylines

The city-building game Cities: Skylines simulates urban-related processes in a visually appealing 3D environment and thus offers interesting possibilities for visualizations of real-world places. Such visualizations could be used for presentation, participation, or education projects. However, the creation process of the game model from geographical data is inaccurate, complicated, and time consuming, thus preventing the wider use of this game for non-entertainment purposes. This paper presents the automatic methods scripted in the Cities: Skylines application programming interface (API) and bundled into a game modification (commonly referred to as a game mod) named GeoSkylines, to create a geographically accurate visualization of real-world places in Cities: Skylines. Based on various geographical data, the presented methods create road and rail networks, tree coverage, water basins, planning zones, buildings, and services. Using these methods, playable models of the cities of Svit (Slovakia) and Olomouc (Czech Republic) were created in the game. The game mod GeoSkylines also provides methods for exporting game objects such as roads, buildings, and zones into a Geographic Information System (GIS) data format that can be processed further. This feature enables the game Cities: Skylines to be utilized as a data collection tool that could be used in redevelopment design projects

Differences in Thematic Map Reading by Students and Their Geography Teacher

A school world atlas is likely the first systematic cartographic product which students encounter in their lives. However, only a few empirical studies have analysed school atlases in the context of map reading and learning geographical curricula. The present paper describes an eye-tracking study conducted on 30 grammar school students and their geography teacher. The study explored ten tasks using thematic world maps contained in the Czech school world atlas. Three research questions were posed: (i) Are students able to learn using these particular types of maps? (ii) Have the cartographic visualization methods in the school atlas been adequately selected? (iii) Does the teacher read the maps in the same manner as students? The results proved that the students were sufficiently able to learn using thematic maps. The average correctness of their answers exceeded 70%. However, the results highlighted several types of cartographic visualization methods which students found difficult to read. Most of the difficulties arose from map symbols being poorly legible. The most problematic task was estimating the value of the phenomenon from the symbol size legend. Finally, the difference between the students’ and teacher’s manner of reading maps in each task was analysed qualitatively and then quantitatively by applying two different scanpath comparison methods. The study revealed that the geography teacher applied a different method than her students. She avoided looking at the map legend and solved the task using her knowledge

Landslide susceptibility assessment using SVM machine learning algorithm

This paper introduces the current machine learning approach to solving spatial modeling problems in the domain of landslide susceptibility assessment. The latter is introduced as a classification problem, having multiple (geological, morphological, environmental etc.) attributes and one referent landslide inventory map from which to devise the classification rules. Three different machine learning algorithms were compared: Support Vector Machines, Decision Trees and Logistic Regression. A specific area of the Fruska Gora Mountain (Serbia) was selected to perform the entire modeling procedure, from attribute and referent data preparation/processing, through the classifiers' implementation to the evaluation, carried out in terms of the model's performance and agreement with the referent data. The experiments showed that Support Vector Machines outperformed the other proposed methods, and hence this algorithm was selected as the model of choice to be compared with a common knowledge-driven method - the Analytical Hierarchy Process - to create a landslide susceptibility map of the relevant area. The SVM classifier outperformed the AHP approach in all evaluation metrics (kappa index, area under ROC curve and false positive rate in stable ground class)

Application of GIS to Empirical Windthrow Risk Model in Mountain Forested Landscapes

Norway spruce dominates mountain forests in Europe. Natural variations in the mountainous coniferous forests are strongly influenced by all the main components of forest and landscape dynamics: species diversity, the structure of forest stands, nutrient cycling, carbon storage, and other ecosystem services. This paper deals with an empirical windthrow risk model based on the integration of logistic regression into GIS to assess forest vulnerability to wind-disturbance in the mountain spruce forests of Šumava National Park (Czech Republic). It is an area where forest management has been the focus of international discussions by conservationists, forest managers, and stakeholders. The authors developed the empirical windthrow risk model, which involves designing an optimized data structure containing dependent and independent variables entering logistic regression. The results from the model, visualized in the form of map outputs, outline the probability of risk to forest stands from wind in the examined territory of the national park. Such an application of the empirical windthrow risk model could be used as a decision support tool for the mountain spruce forests in a study area. Future development of these models could be useful for other protected European mountain forests dominated by Norway spruce

Joining of the historical research and future prediction as a support tool for the assessment of management strategy for European beech-dominated forests in protected areas

Volume: 22Start Page: 51End Page: 7