Automatic data classification based on the triangular graph for thematic maps

A triangular point graph helps in the process of data classification for a thematic map. A triangular graph can be used for a situation that is described by three variables. The total sum of variables is 100%. The proportion of three variables is plotted in an equilateral triangular graph where each side represents a coordinate for one variable. A triangular graph displays the proportions of the three variables. The position of the point indicates the type (class) of the situation in the triangular graph. The typology of the situation can be subsequently expressed in the map. We have created a “Triangular Graph” program which represents a helpful automatic tool for ArcGIS software. This new program classifies input data based on a triangular graph. It is realized by two python scripts located in a custom toolbox as two programs. The first program calculates X and Y coordinates in an equilateral triangular graph. The second program compares plotted points and suggested zones of a division produced by the first program. Finally, a new attribute is added to the source data. The user can create a new thematic map, based on that attribute in order to express the typology of the given situation. The programming language Python and essential module ArcPy have been used for solving these tasks. To test the created programs several maps were made, based on the classification often used in demography. For example, the new program helped to create a sample map of age categories in districts of the Czech Republic. The program is available to download from the Esri web pages and web pages of the Department of Geoinformatics, Palacký University Olomouc

Program extension for diagram maps

Absolute statistical data are very often expressed by a diagram in thematic maps. The ArcGIS 9 software from ESRI is commonly used for the production of cartographic output, but there are only a few possibilities for how to express data by diagram maps. A program extension called “Diagram map creator” was developed at the Department of Geoinformatics, Palacký University in 2010. This extension serves as a supplement for the automatic generation of various diagram maps. The program code, user interface and the possibilities of and the use of the “Diagram map creator” extension are presented in this article. Some examples of thematic maps are also shown. Article in English. Programų priedai diagramų žemėlapiams kurti  Santrauka Dažnai statistiniai duomenys teminiuose žemėlapiuose pristatomi diagramomis. ESRI kompanijos kompiuterinė programa ArcGIS 9 yra populiari kartografinei produkcijai kurti, tačiau joje numatyta tik kelios duomenų išraiškos diagramų žemėlapiuose galimybės. 2010 m. Palacký universito Geoinformatikos katedroje sukurtas programos priedas „Diagramų žemėlapių kūrimo priemonė” (Diagram map creator). Tai priedėlis įvairiems diagramų žemėlapiams kurti automatizuotai. Analizuojama programos pradinis tekstas, vartotojo aplinka ir taikymo galimybės. Pateikta keletas teminių žemėlapių pavyzdžių. Приложение к программам для создания диаграммных карт Резюме Зачастую статистические данные на тематических картах представляются в виде диаграмм. Для создания картографической продукции популярна компьютерная программа ArcGIS 9, созданная компанией ESRI, однако в ней предусмотрено лишь несколько возможностей для представления данных на диаграммных картах. На кафедре геоинформатики университета в городе Оломоуце (Palacky University, Чехия) в 2010 г. было создано приложение к программе „Средство для создания диаграммных карт” (Diagram map creator). Это приложение для автоматизированного создания разных диаграммных карт. В статье проанализирован начальный текст программы, среда пользователя и возможности ее применения. Представлено несколько примеров тематических карт. Reikšminiai žodžiai: ArcGIS , teminė kartografija, programa, taškų diagrama, linijų diagrama, plotų diagrama, UDK 528.948 Ключевые слова: тематическая картография, программа, диаграмма точек, диаграмма линий, диаграмма площадей, УДК 528.94

Database modelling in Cartography for the “Atlas of Election”

Today maps are prepared in Geographic Information Systems (GIS software) and based on data stored in a database. In the stage of the conceptual database design, the graphic editor of a database model is recommended. The structure of data is often under the influence of the cartographic requests. For example, new data may be added only for visualization purposes. All database structures for a base data and a cartographical data can be defined in a conceptual database model before creation of a physical database model. Database modelling is demonstrated in ArcGIS Diagrammer software in this article. Design of a cartographic database model for the book “Atlas of Election to the Olomouc Region Council” is used as an example. Moreover, steps of a model creation, detail structure and relationships in the model are also mentioned. The cartographical database model of the “Atlas of Election” illustrates cartographical influence to the database structure

CartoEvaluation method for assessment of GIS software

The article offers proposals for a new approach to evaluating GIS programs in cartographic functions. The newly proposed CartoEvaluation method is a comprehensive guide in monitoring all cartographic features and in subsequent selection of the program. The CartoEvaluation method is based on Gold-Question-Metric method. CartoEvaluation method was applied for assessment of some desktop GIS software. The result of assessment brings the rank of software. Method can help a user in the choice of GIS program when a user requires a higher cartographic functionality

Maps of native ranges of tropical and subtropical plants created by GIS

This article presents various methods employed for construction of maps of native ranges of plants using geographical information system (GIS). The maps were originally created for a set of publications on important tropical and subtropical plants species kept in the collections of Flora Olomouc Exhibition Grounds, JSC., Czech Republic. Two different approaches were applied using outlined and chorochromatic methods. The former was used for construction of maps depicting approximate ranges, i.e., ranges which cannot be constructed exactly due to the objective lack of biogeographical data (e.g. early domesticated crops, which no longer occur in the wild). The latter approach was used for construction of maps showing known ranges, i.e., ranges that can be constructed more or less exactly because there is no considerable lack of biogeographical data. The maps of known ranges could be further divided according to the total area of the depicted range, its shape or location. The paper also presents plans of the greenhouses and the exhibition complex at Flora Olomouc Exhibition Grounds, using a different type of thematic maps useful for large-scale mapping of living collections

Evaluation of Effective Cognition for the QGIS Processing Modeler

This article presents an evaluation of the QGIS Processing Modeler from the point of view of effective cognition. The QGIS Processing Modeler uses visual programming language for workflow design. The functionalities of the visual component and the visual vocabulary (set of symbols and line connectors) are both important. The form of symbols affects how workflow diagrams may be understood. The article discusses the results of assessing the Processing Modeler’s visual vocabulary in QGIS according to the Physics of Notations theory. The article evaluates visual vocabularies from the older QGIS 2.x and newer 3.x versions. The paper identifies serious design flaws in the Processing Modeler. Applying the Physics of Notations theory resulted in certain practical recommendations, such as changing the fill colour of symbols, increasing the size and variety of inner icons, removing functional icons, and using a straight connector line instead of a curved line. Another recommendation was to provide a supplemental preview window for the entire model in order to improve user navigation in huge models. Objective eye-tracking measurements validated some results of the evaluation using the Physics of Notations. The respondents read workflows to solve different tasks and their gazes were tracked. Evaluation of the eye-tracking metrics revealed the respondents’ reading patterns of the diagram. Evaluation using both Physics of Notation theory and eye-tracking measurements inspired recommendations for improving visual notation. A set of recommendations for users is also given, which can be applied easily in practice using a contemporary visual notation

Experiment in Finding Look-Alike European Cities Using Urban Atlas Data

The integration of geography and machine learning can produce novel approaches in addressing a variety of problems occurring in natural and human environments. This article presents an experiment that identifies cities that are similar according to their land use data. The article presents interesting preliminary experiments with screenshots of maps from the Czech map portal. After successfully working with the map samples, the study focuses on identifying cities with similar land use structures. The Copernicus European Urban Atlas 2012 was used as a source dataset (data valid years 2015–2018). The Urban Atlas freely offers land use datasets of nearly 800 functional urban areas in Europe. To search for similar cities, a set of maps detailing land use in European cities was prepared in ArcGIS. A vector of image descriptors for each map was subsequently produced using a pre-trained neural network, known as Painters, in Orange software. As a typical data mining task, the nearest neighbor function analyzes these descriptors according to land use patterns to find look-alike cities. Example city pairs based on land use are also presented in this article. The research question is whether the existing pre-trained neural network outside cartography is applicable for categorization of some thematic maps with data mining tasks such as clustering, similarity, and finding the nearest neighbor. The article’s contribution is a presentation of one possible method to find cities similar to each other according to their land use patterns, structures, and shapes. Some of the findings were surprising, and without machine learning, could not have been evident through human visual investigation alone