This paper presents a systematic literature review that reflects the current state of research in the field of algorithms and models for map generalization, the existing solutions for automatic (tactile) map generation, as well as good practices for designing spatial databases for the purposes of automatic map development. A total number of over 500 primary studies were screened in order to identify the most relevant research on automatic (tactile) map generation from the last decade. The reviewed papers revealed many existing solutions in the field of automatic map production, as well as algorithms (e.g., Douglas–Peucker, Visvalingam–Whyatt) and models (e.g., GAEL, CartACom) for data generalization that might be used to transform traditional spatial data into the haptic form, suitable for blind and visually impaired people. However, it turns out that a comprehensive solution for automatic tactile map generation does not exist.
In this study, we detected which means of transportation is beneficial from a travel time perspective in specific districts of Warsaw, Poland. To achieve this goal, we proposed a framework to perform a spatial analysis to describe the as-is situation in the city (the state that the situation is in at the present time). The framework contains the following elements: attractiveness analysis, travel time and speed analysis, and potential accessibility analysis. The relationship between the averaged nominal travel speed and the number of residents was also investigated. We used data from a journey planner, as well as land use and population statistics, and employed descriptive analytics. The results are presented as maps of travel times, travel speed, and potential accessibility, as well as scatter plots of dependencies between travel speed and number of residents. Unfortunately, public transportation ranks behind car and bike transport in terms of travel time, speed, and potential accessibility. The largest positive influence on effectiveness of traveling by public transportation is the metro and railway system; also, bikes can perfectly complement the public transportation system. The obtained results can be used to indicate directions of changes in the transportation system of Warsaw.
The research presented in this paper proposes a method for the development of photorealistic, physical terrain models using full-color 3D printing, along with an efficiency assessment (i.e., cartographic correctness). The rapid development of 3D printing technology in recent years has caused a growth in the number of solutions allowing the automatic or semi-automatic generation of digital terrain representations that can be then 3D printed. Unfortunately, most of these solutions do not consider cartographic principles and their products cannot be referred to as 3D maps. The methodology proposed in this paper takes into account issues related to proper cartographic design, so that highly reliable models can be created. The main challenges identified during the research include choosing the optimal pixel size for Digital Elevation Model (DEM) generation, which is used for landform representation, and integrating raster and vector data. Printing accuracy assessment is of high importance and, thus, a method for its implementation has been proposed. In order to verify the usability of the proposed methodology, the natural heritage site of the Valley of Five Polish Ponds, in the Tatra Mountains, Poland, has been chosen as the case study area. The results suggest that the proposed methodology can be successfully used for the preparation of highly accurate 3D maps that can be used for natural heritage documentation, promotion and visualization, as well as for the purposes of spatial planning and education.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.