## 1 Introduction  
The period of the past twenty years can be characterized by a shift from conventional agriculture through precision agriculture [1] towards the latest innovations in the form of data-driven agriculture [2]. A crucial part of precision agriculture is estimating crop yield. Information on yield is important for two main reasons: it can be used to maximise economic profitability and to reduce the environmental burden caused by agricultural activities [3, 4]. Both objectives need as much precise information on yield as possible. It has been defined, for example, by [PERSON] [5], that a yield in a plot is not homogeneous owing to variable soil conditions, weather, climate, and so on. Consequently, all plots have their strong and weak zones in terms of crop yield. Data from field harvesters represent the most detailed, as well as the most credible, source of yield information. On the other hand, field harvesters provide measurements with a bias. Conventional approaches of reducing these biases through filtering the data were described, for example, in [PERSON] [6], [PERSON] and [PERSON] [7], [PERSON] et al. [8], [PERSON] et al. [9], [PERSON] et al. [10], and [PERSON] et al. [11]. A different approach relies on so-called Big Data, which is more than a term for vast volumes of data, equivalent to Terabytes of memory and larger in the context of geoinformatics. Big Data approaches also process all the available data, while conventional approaches usually deal with only a subset of data (e.g., a representative and random sample; a subset without erroneous data) [12] (pp. 19-25). Processing of all available data is related to'veracity', which is considered \"the fourth V (volume--velocity--variety--veracity)\" and seems to be the most relevant in the agricultural domain [13]. Processing and visualization of all the available data from farm machinery forms the basic premise of the presented paper, with its main objective being an innovative approach to dealing with visualization of uncertainties of point Big Data. In this context, the term \"point Big Data\" is defined as all the available farm machinery measurements of yield and uncertainty thereof in the Rostenice pilot farm in the Czech Republic (see Section 2.1 for details). Point data were selected for the purpose of this paper as they are the original source of uncertainty, in contrast to more common visualizations of interpolated and/or density surfaces as described by [PERSON] et al. [14], [PERSON] et al. [15], [PERSON] et al. [16], [PERSON] et al. [17], and [PERSON] et al. [18]. Interpolated/density surfaces add an additional level of uncertainty during their creation. Data quality and uncertainty are crucial issues in geographical information science (GIS), because uncertainties occur in the real world and at each stage of knowledge extraction from spatial data, as well as from Big Data in general [19]. The quality definition started with the positional errors and topology at the early stage of GIS development, and both concepts developed with an increasing interest in the use of spatial data in decision-making processes [20; 21]. There exist several definitions of both concepts, although international standards have only evolved with respect to the quality. In the following text, we use the definition by [PERSON] et al. [22]: \"uncertainty is the difference between a real geographic phenomenon and the user's understanding of the geographic phenomenon\". The quality of data can be generally defined as \"the degree to which a set of inherent characteristics fulfils requirements\" [23]. This definition was further developed and consistently used in ISO (International Organization for Standardization) standards 19101 [24], 19157 [25] and 19158 [26] for geographical information. The history of uncertainty visualization is connected with the National Center for Geographic Information and Analysis (NCGIA) [27; 28].