Which of the following contributes to data quality in GPS data collection?

Using templates or prescribed schemas for feature creation significantly contributes to data quality in GPS data collection by providing a structured approach to organizing and managing spatial data. These templates or schemas set standardized formats and categories, which help to ensure consistency across the data collected. When data collectors use predefined templates, it minimizes the variation in how data is recorded, leading to enhanced interoperability and easier integration with other data sources. Furthermore, this standardization helps in maintaining clarity and precision in the data, making it less prone to errors.

In contrast to this option, other factors such as the age of the GPS technology, geographical diversity of the data points, and the skill level of the data collector can influence data quality but do not directly contribute in the same structured manner. The age of the GPS technology may affect accuracy, but without a consistent methodology like templates, varying results can still occur. Similarly, geographical diversity can provide a broader dataset, but without a structured approach, the quality of the data may not be as reliable. Lastly, while the skill level of the data collector is important for accurate data entry and collection, utilizing a prescribed schema standardizes the process, making it easier for varying skill levels to produce high-quality data.