## 3 Current Developments
### Data Quality Analysis
#### 3.1.3 Data Trust and Vandalism  
Similarly to other open source related projects, Linus' Law [106] can have a major impact on the success of VGI. The assumption behind Linus' Law is that with the number of contributors, the quality of the product increases, an assumption which has been proven for Wikipedia, where the quality of an article increases with the number of contributors who work on it [107, 108, 109]. While [PERSON] _et al_. [110] found that the law generally applies to OSM positional accuracy, [PERSON] and [PERSON] [111, 112] could not identify a similar pattern when analyzing heavily edited objects in OSM. Nearly all presented studies discussed thus far showed indications of similar data completeness or improved data quality for densely populated areas in OSM in comparison to proprietary and governmental datasets [19, 21, 113]. [PERSON] _et al_. [3] summarize that the OSM project proves to be heterogeneous with an urban bias and chances are that: \"When one moves away from large urban centers the major issue for quality becomes one of coverage--in many rural areas there is little or no OSM coverage at all\" [111]. While most conducted analyses focused on different areas in Europe and the U.S., [PERSON] _et al_. [114] investigated the aforementioned general pattern of OSM data on a larger scale. The study revealed that when comparing selected world regions, the data quality and contributor activity does not necessarily always show the same pattern in all urban areas, particularly outside of Europe. In prior research, it had already been shown that the number of contributors can strongly influence the geometric data quality and spatial concentration of OSM data in different areas [19, 68]; additionally, it was also determined that the temporal dataset quality is highly affected by the same criteria [114]. It has been criticized that most prior studies about OSM with the objective of a data quality analysis only consider certain object types (e.g., roads) for descriptive measurements [90]. However, other studies also highlighted the lack of attribute information, such as turn restrictions, speed limits or street names [19, 21, 83], the lack of a well-defined data standard [12, 68, 115] or some formal quality control process [99] in OSM and VGI data in general. All of these factors lead to the questionable statement by [PERSON] and [PERSON] [89] that \"it is probably better to have no mapping at all, rather than some inaccurate, possibly incomplete, user generated content\". The best approach to answer whether OSM or any other VGI source should be utilized or not is to assess the OSM dataset quality for the selected area of interest and its particular role or purpose in the project [60, 116, 117]. Therefore, it is important not to look only at the completeness of the map data, but also to review the collected information in more detail, especially in areas where data imports or automated scripts took place and no active contributor community is available. Furthermore, it needs to be noted that the availability of aerial imagery in OSM editors introduces \"armchair-mapping\" patterns, in which case, the contributors of the project only trace objects from the satellite images and no local knowledge is needed [114]. In most cases, areas with lower OSM community member numbers tend to have higher contributions based on armchair mapping, which stands in contrast to the \"local knowledge\" most people identify with when they refer to VGI [1, 118]. To simplify the evaluation of the OSM dataset for the users' areas of interest, many free and online quality assessment and assurance tools are available to get detailed quality information. Interested users are also able to report errors in the map by using OSM Notes or OpenStreetBugs. Other tools, such as Keep Right, Osmose or OSM Inspector, can be used to visualize detected errors in the map data. However, establishing some sort of trust in the collected VGI dataset is a really important factor.