Practical Value of User‐Centred Spatial Statistics for Responsive Urban Planning

This chapter addresses spatial statistics via an alternative perspective, focusing on evidence‐based people‐spatial relationships and related measures, quantifications and qualifications, and by this, it provides rather specific spatial information and spatial statistics about urban environments. It is based on time quality assessment (TQA), a time‐people‐place‐oriented approach for the analysis and simulation of the quality of living environments, backgrounded with the method of behaviour mapping. It shows that the quality of the time spent on a certain activity in a certain place indicates the quality of the living environment. It also shows that the quality of the time spent depends on what a person can afford, and it provides an evaluation of the quality of living environments with a measure of good/bad time. The practical value is in the provision of empirical knowledge to support planning guidance based on user‐centred small‐scale spatial statistics, which is able to inform top‐down and bottom‐up decision‐making processes for people‐friendly living environments

Concurrent software architectures for exploratory data analysis

Decades ago, increased volume of data made manual analysis obsolete and prompted the use of computational tools with interactive user interfaces and rich palette of data visualizations. Yet their classic, desktop-based architectures can no longer cope with the ever-growing size and complexity of data. Next-generation systems for explorative data analysis will be developed on client–server architectures, which already run concurrent software for data analytics but are not tailored to for an engaged, interactive analysis of data and models. In explorative data analysis, the key is the responsiveness of the system and prompt construction of interactive visualizations that can guide the users to uncover interesting data patterns. In this study, we review the current software architectures for distributed data analysis and propose a list of features to be included in the next generation frameworks for exploratory data analysis. The new generation of tools for explorative data analysis will need to address integrated data storage and processing, fast prototyping of data analysis pipelines supported by machine-proposed analysis workflows, preemptive analysis of data, interactivity, and user interfaces for intelligent data visualizations. The systems will rely on a mixture of concurrent software architectures to meet the challenge of seamless integration of explorative data interfaces at client site with management of concurrent data mining procedures on the servers