What type of data is collected from a large group of people?

Crowdsourced data refers to information that is gathered from a large number of individuals, often through collaborative efforts or contributions from the public. This type of data collection leverages the collective knowledge and skills of the crowd to generate, share, and analyze information, which can be particularly valuable in various fields, including geography and social sciences.

Crowdsourcing can take many forms, such as surveys, mobile applications that allow users to report data about their environment, or community mapping projects where people contribute geographic information based on their knowledge or experiences. The key characteristic of crowdsourced data is that it is produced by non-experts, and it often reflects real-time updates and perspectives from a diverse population, making it rich in local insights.

In contrast, open-source data is not specifically tied to a large group's input but rather refers to any data made available for public use, often including datasets that are freely available for use, modification, and distribution. Remotely sensed data is collected via satellite or aerial platforms and does not rely on input from individuals. Spatial data is a broader term that encompasses any data with geographic attributes but does not imply a collective or participatory method of data collection. Thus, crowdsourced data is specifically distinguished by its reliance on contributions from a widespread