# Visualising the Relevance of Climate Change for Spatial Planning by the Example of Serbia
## 2 Data and Methods
### Data Analysis and Visualisation Methods  
Content analysis is a tool for determining the presence of certain words, themes, or concepts within qualitative data (i.e., spatial-planning documents) [47; 48]; Content analysis enables researchers to analyse and quantify the presence and frequency of selected terms, thus interpreting their relevance. It is commonly used in social sciences and the context of climate change e.g., [49; 50]., although not in the field of spatial planning, which is the intention of this paper. We used content analysis with the aim of developing a visual method to present it. Therefore, we used information visualisation methods in order to communicate in a graphical, interactive, and understandable way, since 90% of the information transmitted to the brain is visual [51; 52]. Until recently, research in social sciences posed fundamental challenges to the application of visualisation techniques [53]. However, in the field of spatial planning, it has been acknowledged since the very beginning that visual communication, featuring significantly more graphic constituents than text, has an indispensable role in providing the necessary information. A review analysis by [PERSON] et al. [54] showed that most common visualisation methods are based on lines, plots, maps, parallel coordinates, and radial coordinates. Based on a comparative assessment of some of the most popular visualisation methods (presented in Table 1), we further elaborated our content analysis findings through a _colour-graded classification_ and the _spider_ method as the simplest, most readable, affordable, and fastest to produce. Charts (line charts, bar charts, pie charts, and histograms) are one of the most common visualisation methods that are simple and fast to produce and which do not require basic software and skills, but are rather easy to read and understand. Diverse types of charts correspond to different needs. A line chart displays data points connected by straight lines, illustrating trends and variations over a continuous range. It is effective for showing changes in data over time or across categories. A bar chart represents data using rectangular bars of varying lengths that correspond to the value it represents, making it easy to compare values among different categories. A histogram represents the distribution of a dataset. It consists of bars where the width represents intervals and the height represents the frequency or count of data points within each interval. It is commonly used in statistical analysis to show the frequency distribution of continuous data. Finally, a pie chart represents data in a circular graph, divided into slices to illustrate the proportion of each category relative to the whole. It is effective for displaying the composition of a whole and showing the relationship of parts to the whole. Maps as cartographic representations are inextricably linked with spatial planning. They are graphical representations of geographic information, providing a visual depiction of spatial relationships and features on the Earth's surface. They use symbols, colours, and scale to convey information about locations, landscapes, and various spatial attributes. However, their production requires time, costly software and experts with specific skills that require time to develop. They are easy to read, but cannot be applied in the representation of all aspects (measures) of climate change since they do not all have spatial connotations nor are they convenient to present on a map. A word cloud is mainly used for text summarisation and the analysis of qualitative data (in business and academia); it is simple to produce but requires software and has limited clarity/readability. A word cloud is a visual representation of text data where words are arranged in a graphical manner, and the size of each word is determined by its frequency or importance in the given text. Commonly used words appear larger and more prominent.