The aim of the research was to develop conceptual model for determination of functional areas with abstract borders within indoor space and to illustrate how determined functional areas can be applied to facilitate wayfinding process in indoor environments. The objectives of the research were to define criteria for determination of functional areas of objects by adopting principles of human behaviour and human perception of the environment, to develop rules how to subdivide indoor space into navigable and non navigable areas and to incorporate functional areas in a navigation model. Based on literature review conceptual model for functional area determination was developed. The designed model suggests that functional areas of indoor objects depend on their characteristics such as attractiveness, necessity, limited capacity, closeness to central areas, possession of transition area. Additionally, the developed model introduces private space concept to delineate functional areas taking into account human behavioural rules. Mathematical expressions and rules to deal with special cases in order to delineate functional areas were established. The proposed model was implemented for two case studies: Rotterdam Central Station (for peak and off-peak hours) and Faculty of Architecture and the Built Environment, TU Delft. The partition of indoor environment for wayfinding purposes was performed in two steps. Firstly, semantic indoor space partition was performed: separate functional areas were determined and the indoor space was partitioned into navigable and non-navigable areas using GIS tools (buffer, union, difference, aggregation). Secondly, geometric space subdivision was executed where navigable area was subdivided applying constrained Delaunay triangulation to generate navigation network in which functional areas were represented as dead-end nodes. The results derived applying the proposed model were verified using photographs and video records. Functional areas of objects were determined by measuring areas occupied by people around specific objects and distances that people keep between each other and objects. Measurements obtained from photographs were compared with the results obtained using the proposed model. Video records were inspected to examine people movement trajectories. The results of the image and video analysis indicate that the research successfully integrated findings of previous studies and new concepts introduced in this research in order to provide more realistic abstraction of the indoor environment and more accurate navigation path. Eight cases of determined functional areas were examined and the outcome of the validation showed that only one case was not supported. On the basis of the results of the validation, it was concluded that the selected criteria are appropriate measures to determine separate functional areas within indoor space and provide reliable results. In this research the determined functional areas were represented as dead-end nodes in the generated navigation networks. The results of the performed path computation tests showed that indication of these functional areas in computations of navigation paths allows generation of more realistic routes that adopt principles of human natural movement and avoidance of areas that are usually occupied. However, the findings of research showed that additional attention has to be paid while determining ranges of the criteria, analysis of object’s closeness to surrounding location has to be improved. Furthermore, the findings revealed that time impact on properties of objects has to be carefully evaluated taking into consideration different factors such as occurring events in the environment, total number of people within the environment, habits of people and other. Moreover, the research suggests that in the future studies different navigation models could be built such as navigation network based on a visibility graph or grid based navigation model, in order to investigate how functional areas can be indicated in these models and analyse which approach provides better path calculation results.