Integrated Urban Sensing in the Twenty-First Century

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, highresolution 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 so-called 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 urban areas where the density AQ1

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 2 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.