The article is devoted to the problem of evaluating the detailing of spatial data. In geoinformatics, spatial data detailing determines how detailed a particular object is representeda map image, and the detail score allows you to analyze the permissible accuracy of spatial objects for a specific user task. An approach to the definition of detailing concept is proposed. The evaluation of the object’s detail depends on its characteristics: geometric, semantic, and topological. A study is being conducted to select the geometric characteristics of the object that reflect its detail. For linear objects, in addition to the characteristics of the line as a whole (length, number of points, sinuosity, average rotation angle), it is suggested to consider its smaller details, such as bends and triplets. A bend is a section of a line where the angle of rotation retains its sign. A triplet is a combination of three consecutive points. Based on the results of the study, the geometric characteristics that change in the trend depending on the scale were selected. The paper presents the developed software for assessing map detail—the MapAnalyser toolbar for the QGIS geoinformation system. The functional capabilities of the developed software are described. The toolbar allows you to get the geometric, semantic, and topological characteristics of a layer or set of layers, as well as to evaluate the graphical complexity of a map image based on RlE encoding. The program code is written in the PyQGIS language. The software has passed state registration and is hosted on the github server. With its help, new results were obtained on the evaluation of spatial data granularity. New software, embedded in QgIS, to assess the detail of the map and spatial data, based on taking into account geometric and symbolic (used in the display) parameters. The software allows to calculate the metrics of spatial data detail, as well as to assess the complexity of the cartographic image. It’s can be used in the integration of data obtained from different sources, assess the compliance of data detail and the map scale, to assess the complexity of the map for different purposes and scales.

Digital topographic maps are created in a series of scales from large to small, and the underlying spatial data is commonly organized as a multiscale database consisting of several levels of detail (LoDs). Spatial density of features (or spatial objects) in such database varies both between LoDs (coarser levels are less densely populated with features) and within each LoD (feature density changes over the area). While the former type of density variation is caused by generalization, the latter one is mainly conditioned by geographic location and its properties, such as landscape complexity or fraction of urban areas. Since topographic database LoDs are derived using different data sources and generalization techniques, there is a need for a method that can help with automated evaluation of resulting feature density in terms of its appropriateness for the specified location and level of detail. This paper provides such method by uncovering dependencies between the location properties and the density of spatial data in multiscale topographic database. Changes in feature density are modeled as a function of spatial (landscape complexity and terrain ruggedness) and non-spatial (land cover types ratio) measures estimated via independent data sources. Resulting model predicts how much higher or lower is the expected spatial density of features over the area in comparison to the average density for the LoD. This information can be used further to assess the fitness of the data to the desired level of detail of the topographic map.