Edited by Stan Geertman and John Stillwell
Chapter 3: Hard and soft data integration in geocomputation: mixed methods for data collection and processing in urban planning
As human society evolves, it’s spatial and aspatial imprint becomes more complex owing to increasingly sophisticated interactions. The twenty-first century presents a set of opportunities and challenges that result from a key development: the use of digital means. This chapter reviews key cutting-edge approaches that are informing this new digital world. A mixed-methods approach can capture both hard-physical (quantitative) and soft-aspatial (qualitative) analysis as a hybrid approach depending on the task at hand. Using case studies from the UK, China, Germany and South Korea, this chapter first introduces current data availability, its opportunities and challenges, and the required need to integrate with more classical methods and existent data sets. The chapter discusses crowdsourcing, one of the fast-growing data collection methods that bridges the quantitative/hard and qualitative/soft data analysis; uses these methods and sentiment analysis for public policy analysis; presents an application of hard data collection associated with local/remote sensing linked with soft data collection of field surveys; pinpoints the key role of a new generation of learning algorithms towards data harvesting, mining and calibration; and explores the role of behavioural theories in support of these new learning algorithms.
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