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With the development of Information and Communications Technology (ICT), new forms of geospatial data are emerging in recent years. Among the new and emerging forms of geospatial data, volunteered geographic information (or crowdsourced geographic information) and geosocial networking data are gaining much interest [1–4]. Sources of volunteered geographic information (VGI) include collaborative mapping projects (e.g., OpenStreetMap and WikiMapia) and social media (e.g., Flickr, Foursquare, Twitter, Facebook). As the term ‘VGI’ has been widely accepted and used, VGI refers to geographic information generated voluntarily. Collaborative mapping projects like OpenStreetMap (OSM) is intentionally contributed by people as data creators are always aware that they are creating geographic information; whereas geographic information, such as geotagged posts, images and videos, publicly shared in social media is likely to be unintentionally contributed by people as most of the data creators (social media users) might not be aware or never care that they are creating geographic information. As the most up-to-date open access mapping platform in the world, OSM is playing an increasingly important role in both research and commercial applications. More and more researchers use OSM as a base map in their studies, and develop thematic maps (transport maps, disaster maps, etc.) or specific services (route planning). To offer users a complete and up-to-date understanding of local environment, a number of popular location-based services switch to OSM from commercial counterparts like Google Map, Yahoo Map and Bing Map. OSM is a crowdsourced map, which means the features and attributes are likely to be voted by the crowd. This enables OSM to maintain an acceptable level of data quality [5]. Unlike OSM data is intentionally contributed and voted by the crowed, social media data is likely to be unintentionally contributed and lack of votes from the crowed. As a result, it is hard to implement quality assurance of social media data, which leads to a high risk of bias. Apart from popular social media, other online social networks for certain communities (tourists, cyclists, hikers, researchers, etc.) and other forms of social networks (e.g., mobile phone networks) offer new forms of geospatial data. Those new forms of geospatial data all belong to geosocial networking data. Geosocial networking data tend to be ‘big data’ due to its large volume, unstructured forms and risk of bias. In spite of risk of bias, geosocial networking data gain an increasing attention because it has a high spatial accuracy and tracks human activities at the individual level. Therefore, VGI data and geosocial networking data will pave the way for a better understanding of some key issues or new issues in urban studies and environmental sciences. Compared to traditional geospatial data, geospatial big data require different data processing and analysis approaches.

Big data and sustainable cities: applications of new and emerging forms of geospatial data in urban studies