ABSTRACT
Today, vast volumes of highly diverse sensor data are generated, and this amount is growing exponentially. As highlighted in several chapters of this book (e.g., Chapters 2, 5, 8, or 10), high-resolution remotely sensed data serve day-to-day applications. Virtual Globes such as Google Earth have brought such images to everybody’s fingertips. Lesser known to the wider public are two other fields of data generation: real-time in situ sensing of environmental parameters and sensing of human behavior in space and time. Environmental data are mainly sensor-generated. Examples include weather stations or intelligent mobile sensor pods. We call these “machine-generated” data. On the contrary, direct measurements of humans in space and time are predominantly restricted for privacy reasons. Information about persons or groups and their behavior in space and time is either derived from socalled volunteered geographic information (VGI) or it may be derived from proxy data, for example, from mobile communication networks or social media. In this chapter, we argue that multiple coordinated views of spatiotemporal data provide unprecedented opportunities for geographic analysis in times of “big data.” Together, these different types of data generation enable an integrated sensing. We focus on
14.1 Introduction .................................................................................................. 270 14.2 Triple D: Dimensions, Domains, and Data of Integrated Urban Sensing .... 272
14.2.1 Characterizing Domains of Urban Sensing ...................................... 273 14.2.2 Why Are Remote Sensing Data Left Out Here? ............................... 275 14.2.3 Environmental Sensing: “Machine-Generated” Data ...................... 276 14.2.4 Human Sensing: “User-Generated” Data ......................................... 277 14.2.5 Combining Environmental with Social Sensing .............................. 277
14.3 Case Studies .................................................................................................. 278 14.3.1 Collective Urban Dynamics .............................................................. 279 14.3.2 Context-Aware Urban Spaces ...........................................................280 14.3.3 Integrated Sensing for a More Holistic Geo-Process Understanding ... 281
14.4 Discussion and Conclusion ........................................................................... 282 References ..............................................................................................................284
