In this study, the authors created an overview of the usage of heat maps as a GIS visualization method. In the first part of the paper, a significant number of studies was evaluated, and the technique was thoroughly described to set up a base level for further research. At this moment, the most used input data for heat maps are point data. While these data fit the method very well, also studies based on line and polygon data were found. The second part of the paper is devoted to an exploratory study on traffic accident data of the Olomouc city, Czech Republic. Even spatial distribution of the dataset by geographical information system makes it the perfect example of heat map usage. These data were visualized in multiple ways changing color range, kernel size, radius, and transparency. Two groups of users were created in order to evaluate these heat maps. One group was consisting of those educated or working in cartography. The second one was consisting of the general public. Created heat maps were shown to these volunteers and their task was to decide their preferred solution. Most of the users chose bright colors with a negative feeling, such as red, for traffic accident visualization. The best settings for transparency was identified to be around 50%. The final questions were about map readability based on radius. This setting is tied to map scale but follows a common trend throughout the research. The results of this work are a general set of recommendations and specific evaluation of the exploratory study regarding traffic accidents spatial data. The general recommendations include basic principles of the method, implementation by GIS, suitable data and correct usage of heat maps. The evaluation is answering specific questions regarding heat map settings, style and presentation in the specific case.

We are now generating exponentially more data from more sources than a few years ago. Big data, an already familiar term, has been generally defined as a massive volume of structured, semi-structured, and/or unstructured data, which may not be effectively managed and processed using traditional databases and software techniques. It could be problematic to visualize easily and quickly a large amount of data via an Internet platform. From this perspective, the main aim of the paper is to test point data visualization possibilities of selected JavaScript Mapping Libraries to measure their performance and ability to cope with a big amount of data. Nine datasets containing 10,000 to 3,000,000 points were generated from the Nature Conservation Database. Five libraries for marker clustering and two libraries for heatmap visualization were analyzed. Loading time and the ability to visualize large data sets were compared for each dataset and each library. The best-evaluated library was a Mapbox GL JS (Graphics Library JavaScript) with the highest overall performance. Some of the tested libraries were not able to handle the desired amount of data. In general, an amount of less than 100,000 points was indicated as the threshold for implementation without a noticeable slowdown in performance. Their usage can be a limiting factor for point data visualization in such a dynamic environment as we live nowadays.

There has been an enormous technological boom that impacted all areas of geoscience in the past few decades. Part of the change was also the process of democratization of cartography as well as geographic information systems (GIS), together with new approaches that have emerged, bringing social dimension into cartography and GIS. These new approaches were variously labelled as critical cartography, collaborative mapping, digital citizenship, Bottom-up GIS and Participatory GIS. The paper describes the role of collaborative mapping and digital participation in the process of community building and community assets mapping. Secondly, we will use the examples of Kenya and Peru to support our findings of community development. Thirdly, we will discuss a possible further development within the use of OpenStreetMap (OSM) for remote communities. The analysis compares approaches and experiences in different countries on different continents.

Recent developments in web map applications have widely affected how background maps are rendered. Raster tiles are currently considered as a regular solution, while the use of vector tiles is becoming more widespread. This article describes an experiment to test both raster and vector tile methods. The concept behind raster tiles is based on pre-generating an original dataset including a customized symbology and style. All tiles are generated according to a standardized scheme. This method has a few disadvantages: if any change in the dataset is required, the entire tile-generating process must be redone. Vector tiles manipulate vector objects. Only vector geometry is stored on the server, while symbology, rendering, and defining zoom levels run on the client-side. This method simplifies changing symbology or topology. Based on eight pilot studies, performance testing on loading time, data size, and the number of requests were performed. The observed results provide a comprehensive comparison according to specific interactions. More data, but only one or two tiles, were downloaded for vector tiles in zoom and move interactions, while 40 tiles were downloaded for raster tiles for the same interactions. Generally, the WebP format downloaded about three times fewer data than Portable Network Graphics (PNG).

No abstract

ABSTRACT:See-Think-Do is a framework originally used as an approach focused on a service and product marketing on the Internet. Customers can be classified into three groups according to their involvement from potential users to real customers. The article presents an idea of public involvement in community mapping in three levels: "See"-almost any user; "Think"-potential contributors; and "Do"-interested users. The case study implements the See-Think-Do framework as an awareness-based approach used for The Crisis Map of the Czech Republic. It is an Ushahidi-based crowdsourcing platform for sharing spatial and multimedia information during crisis situations, e.g. disaster floods in 2013. While the current crisis projects use public mapping just at the onset of the disaster, according to See-Think-Do any user can be considered as a potential contributor even during the dormant period. The focus is put on the "See" and "Think" groups of contributors, which are currently ignored. The objective of this paper is to summarize approaches (social networks, mass-media, emailing, gamification, …) and tools (GIT/GIS, ICT, multimedia) for increasing the awareness about the project within the resting phase. That recruits a higher number of both active and passive users during the disaster. It allows the training in ICT, cartographical, spatial and GIS skills in a nonstressful way and the targeting on specific operators. Volunteers from the "Think" group may be used for data processing or rectification, GIS professionals from the "Do" group for data verification. The results refer that contributors with already established skills and required literacy (interface, data uploading) provide data faster and more accurate, the usability of the project increases based on users' comments.

ABSTRACT:See-Think-Do is a framework originally used as an approach focused on a service and product marketing on the Internet. Customers can be classified into three groups according to their involvement from potential users to real customers. The article presents an idea of public involvement in community mapping in three levels: "See"-almost any user; "Think"-potential contributors; and "Do"-interested users. The case study implements the See-Think-Do framework as an awareness-based approach used for The Crisis Map of the Czech Republic. It is an Ushahidi-based crowdsourcing platform for sharing spatial and multimedia information during crisis situations, e.g. disaster floods in 2013. While the current crisis projects use public mapping just at the onset of the disaster, according to See-Think-Do any user can be considered as a potential contributor even during the dormant period. The focus is put on the "See" and "Think" groups of contributors, which are currently ignored. The objective of this paper is to summarize approaches (social networks, mass-media, emailing, gamification, …) and tools (GIT/GIS, ICT, multimedia) for increasing the awareness about the project within the resting phase. That recruits a higher number of both active and passive users during the disaster. It allows the training in ICT, cartographical, spatial and GIS skills in a nonstressful way and the targeting on specific operators. Volunteers from the "Think" group may be used for data processing or rectification, GIS professionals from the "Do" group for data verification. The results refer that contributors with already established skills and required literacy (interface, data uploading) provide data faster and more accurate, the usability of the project increases based on users' comments.

The article describes the process of aggregation of media-based data about the coronavirus pandemic in the Olomouc region, the Czech Republic. Originally non-spatially located news from different sources and various platforms (government, social media, news portals) were automatically aggregated into a centralized database. The application “COVID-map” is an interactive web map solution which visualizes records from the database in a spatial way. The COVID-map has been developed within the Ad hoc online hackathon as an academic project at the Department of Geoinformatics, Palacký University Olomouc, Czech Republic. Alongside spatially localized data, the map application collects statistical data from official sources e.g., from the governmental crisis management office. The impact of the application was immediate. Within a few days after the launch, tens of thousands users per day visited the COVID-map. It has been published by regional and national media. The COVID-map solution could be considered as a suitable implementation of the correctly used cartographical method for the example of the coronavirus pandemic.