# An Inspection of IFC Models from Practice
## 3 The Importance of IFC Models Quality for IFC Data Downstream Automation Use Cases
[42] describe it together with some of the usual errors of IFC models). These are necessary to perform a correct assessment of the energy requirements according to the occupancy of the building (which has to be stored as well). As a counterpart to this, climate parameters and information related to the planned geographic location of the building are useful (i.e., maximum and minimum temperatures in summer and winter, wind speed, humidity, etc.). The accuracy required for retrieving such information is not a centimeter-precision position of the building (as given by an accurate georeferencing), but mostly the identification of the city where it will be built, together with the orientation of the building is sufficient. However, a building's context and its surrounding buildings could affect its energy use indirectly (e.g., by reducing a building's exposure to wind or blocking sunlight). Urban planning and building permit issuing is a further relevant use case, which is likely to become more and more automated in the future [43]. A number of different analyses are required to check a design against relevant regulations (e.g., building codes and city ordinances), and the BIM provided as a set of IFC files is likely to be processed by different tools to check them. For this reason, a comprehensive good quality of the models is essential [30; 44; 45; 46; 47]. Entities have to be correctly identified by IFC classes; grouped consistently as _IfcSite_, _IfcBuilding_, _IfcBuildingStoreys_; their grouping aspartments or similar building units would also be appropriate; spaces should be modelled correctly, possibly with the association of functions or foreseen use, since many rules are based on such information, which is also hard to infer in an automated process. The geometry should also follow known criteria and avoid irregularities, such as intersections, redundancies, gaps, unforeseen kinds of storage (as boundary representation, solids, etc.) and so on. Any error can prevent the success of automatic processing, such as: the measurements of rooms, doors and other elements to check accessibility and minimum dimensions requirements; the identification and calculation of paths and routes necessary for emergency evacuations or other uses; the extraction of the building envelope to define external dimensions, as useful for regulations checking facades and other outer elements; volumes and surfaces measurements as input to further checks (for example, the parking places to be provided, the taxes and fees to be paid for the constructions, amount of green areas to be designed, in some cases). The attributes necessary in this case can vary and an exhaustive list could be provided case by case, as a consequence of the applied regulations and used tools. Since the integration with geoinformation represents a strong opportunity and a necessity for checking many regulations, an accurate georeferencing is also essential. Finally, the conversion use case, also as a basis for integration with further data sets from other fields, including geoinformation, gains from (and is enabled by) a good quality of the IFC models [10]. However, specific relevance has to be awarded to the representation and storage of geometries. Geometric clashes between elements will significantly impede fully automated conversions, since it is not possible to know which element is actually present at a given location. Gaps are also a problem, since elements need to fully enclose spaces, such as rooms, in order for their correct semantics to be automatically derived from the model. Grouping can also be useful to isolate the elements to be converted to autonomous objects. The correct assignments of semantic classes is also necessary to support the automated mapping, as well as attributes, for any kind of translation of the present information. An accurate georeferencing information is again the premise for a correct conversion to and integration with georeferenced data.