This chapter provides basic concepts on quality, definitions, sources of the problem with quality, and the distinction between “internal quality” and “external quality”.
The question about crowdsourced geospatial data quality is the first one to come up. When it comes to crowdsourced geographic data, it is very common to hear suggestions that the data is not good enough and that contributors cannot collect data at a good quality, because unlike trained researchers, they don’t have enough experience and expertise of geospatial data. Therefore, we should pay particular attention to the issues stemming from the quality of crowdsourced geospatial data. Also, note that any crowdsourced data is biased in on or more ways. Contributors can have different aspects and levels of quality of judgment and decision making. Their decisions and preferences could significantly influence their data. I am curious about how to identify and estimate biases in crowdsourced data?
Furthermore, the authors mention that users can evaluate external quality based on internal quality. However, nowadays, geographical resources (both data and applications) are mostly accessible via web services. Data producers do not always provide internal quality of data. In this situation, how users evaluate the external quality of resources? Last, while the internal and external quality measures are applied to measure the quality of data which is factual in nature, how to assess the quality of information aiming at opinions or vague concepts? (QZ)