Volunteered geographic information (VGI) refers to geo-referenced data created by citizen volunteers. VGI has proliferated in recent years due to the advancement of technologies that enable the public to contribute geographic data. VGI is not only an innovative mechanism for geographic data production and sharing, but also may greatly influence GIScience and geography and its relationship to society. Despite the advantages of VGI, VGI data quality is under constant scrutiny as quality assessment is the basis for users to evaluate its fitness for using it in applications. Several general approaches have been proposed to assure VGI data quality but only a few methods have been developed to tackle VGI biases. Analytical methods that can accommodate the imperfect representativeness and biases in VGI are much needed for inferential use where the underlying phenomena of interest are inferred from a sample of VGI observations. VGI use for inference and modeling adds much value to VGI. Therefore, addressing the issue of representativeness and VGI biases is important to fulfill VGI’s potential. Privacy and security are also important issues. Although VGI has been used in many domains, more research is desirable to address the fundamental intellectual and scholarly needs that persist in the field.
Organizations with a responsibility for maintaining large-scale, multi-user spatial databases often turn to server-based relational database management systems to achieve their goals. The administration of such databases has many dimensions. Industry standards in the areas of data storage and services should be researched and applied to ensure a sound, comprehensive database design as well as to promote interoperability with external entities. Data validation tools should be implemented to improve the accuracy and efficiency of data maintenance activities. Metadata should be maintained according to industry standards to protect the organization’s investment in data and to increase the likelihood of the data being located by clearinghouse and portal search tools. Database security strategies can prevent unauthorized access to data and lessen the chances of data loss due to accidental data corruption. Database performance should be monitored and strategies implemented to ensure that data can be retrieved from the system with acceptable response times. Finally, trends in the field such as the increasing need to manage large volumes of data call for spatial database managers to be knowledgeable of non-relational data models as well, such as NoSQL data models.
Balancing data access, security, and privacy is a significant challenge in the digital environment. This entry examines the conflicts among these elements, which enable innovation but create vulnerabilities. Open data access fuels advancements in tools like navigation apps and AI systems but risks breaches, as seen with ransomware targeting cloud systems. Data privacy laws aim to protect autonomy but can hinder progress, while emerging cyberthreats, such as quantum computers cracking encryption, erode public trust. Technological solutions offer potential solutions: homomorphic encryption enables secure data computations, federated learning preserves privacy in AI training, and differential privacy shields individual identities in analytics. Decentralized systems empower users, and AI-driven security detects threats effectively, though scalability and ethical concerns persist. Real-world applications, from healthcare collaborations to smart city planning, demonstrate how these technologies align these elements, fostering innovation without compromising safety. Emerging trends, such as secure multi-party computation and personal data vaults, offer improved control, but ethical issues, including surveillance risks, require careful consideration. This enrty identifies strategies to balance these elements, supporting innovation while protecting trust, autonomy, and ethical responsibility.