(2019). GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond. International Journal of Geographical Information Science. Ahead of Print.

In this commentary, we describe the current state of the art of points of interest (POIs) as digital, spatial datasets, both in terms of their quality and affordings, and how they are used across research domains. We argue that good spatial coverage and high-quality POI features — especially POI category and temporality information — are key for creating reliable data. We list challenges in POI geolocation and spatial representation, data fidelity, and POI attributes, and address how these challenges may affect the results of geospatial analyses of the built environment for applications in public health, urban planning, sustainable development, mobility, community studies, and sociology. This commentary is intended to shed more light on the importance of POIs both as standalone spatial datasets and as input to geospatial analyses.

The volume, velocity, and variety of data that are now becoming available allow us to study urban environments based on human behaviour with a spatial, temporal, and thematic granularity that was not achievable until now. Such data-driven approaches open up additional, complementary perspectives on how urban systems function, especially if they are based on user-generated content (UGC). While the data sources, such as social media, introduce specific biases, they also open up new possibilities for scientists and the broader public. For instance, they provide answers to questions that previously could only be addressed by complex simulations or extensive human-participant surveys. Unfortunately, many of the required data sets are locked in data silos that are accessible only via restricted APIs. Even if these data could be fully accessed, their naïve processing and visualization would surpass the abilities of modern computer architectures. Finally, the established place schemata used to study urban spaces differ substantially from UGC-based point-of-interest (POI) schemata. In this work, we present a multi-granular, data-driven, and theory-informed approach that addresses the key issues outlined above by introducing a theoretical and technical framework to interactively explore the pulse of a city based on social media.