The main contribution of this dissertation is the prototypic implementation of a visualization and analysis tool for mobile urban microclimate data sets. Therefore, three chapters of this dissertation (Chapter 2 through Chapter 4) are dedicated to this topic. Chapter 2 provides an overview of the research background, data set characteristics, the framework components, and its use case for the detailed analysis of one single mobile measurement run. In Chap- ter 3, an improved method to correct air temperature measurements for sensor lags is portrayed, before Chapter 4 explains, how the visual delineation of cli- matic microenvironments can be facilitated based on the spatial aggregation of a set of mobile transect measurements.
A smaller project within this thesis focuses on the analysis of thermal in- frared images for urban microclimate research. In this context, Chapter 5 describes a prototypic tool that assists the analysis of a set of time-varying thermographs.
Chapter 6 focuses on the visualization of relationships between architectural design and microclimate. In particular, the state of the art of the visualiza- tion in current building performance simulation tools is described based on a literature review and feedback from an architect.
TraVis – A Protoypic Software
for the Visualization and
Analysis of Mobile Urban
Microclimate Measurements
This chapter introduces the main project of this dissertation: The implementa- tion of TraVis, a prototypic visualization tool for the analysis of mobile transect measurements in an urban microclimate context. TraVis is designed to support the workflow of analyzing mobile measurements by providing functionalities for data preprocessing, data representation, and data analysis. The framework complements domain-specific state-of-the-art visualization techniques, which mainly use standard mapping and timeline plots, by incorporating spatial context and multivariate relationships into the (visual) analysis. I developed TraVis in close collaboration with two urban climatologists (Ariane Middel and Benjamin L. Ruddell), who use mobile transect measurements to observe the impact of the build environment on microclimate in a desert city. They continuously evaluated the design decisions, and gave important advice about domain-specific analysis techniques.
While this chapter provides an overview of the background of the described research, the sample data set with which it was developed, and the basic sys- tem components of the tool, the following chapters describe work that extend the core visualization and analysis functionalities. In this context, Chapter 3
introduces an improved method for sensor lag correction, which is an important preprocessing step for mobile measurements conducted with slow sensors. Fi- nally, Chapter 4 describes a purely data-driven approach for the identification of climatic microenvironments, which can handle diverse mobile measurement routes and spatial data-sparsety.
To a large part, this chapter is based on a peer-reviewed full-paper, which was published in the proceedings of PacificVis 2015 [60]. The description of the data set is taken from a paper that I recently submitted to Urban Climate [63].
2.1
Introduction
Mobile transect measurements are an important tool for urban climatology. A sensor platform is mounted to a vehicle, which is then moved along a predeter- mined, potentially interesting route in order to investigate the spatial variation of observed parameters. In contrast to stationary measurements, which are collected only pointwise in space, mobile measurements deliver high-resolution spatial data along a line. This is useful to examine the extend and properties of contiguous areas of similar climate, as well as the transition between them. Relating these patterns to the surrounding urban form can inform the design of sustainable and comfortable urban neighborhoods [108].
Data sets from mobile measurements are multivariate and (often-times) time-varying trajectory data. A trajectory is defined as a time-ordered se- quence of spatial locations visited by an entity [7, 8, 154, 124]. Mobile measure- ments build on these two elementary components, although sensors mounted on the moving platform add additional environmental attributes, such as air temperature, surface temperature, relative humidity, short- and longwave ra- diation or air quality data, varying dynamically along the transect route. The time-varying component of the data set is introduced by repeating the tran- sect measurements periodically. Furthermore, each sensor and therefore each trajectory attribute has a unique and dynamically varying spatial context and representative source area corresponding to the observation.
In this Chapter, I introduce a framework for the visualization and analysis of these complex movement data sets. It is structured as follows: The domain- specific background of this work is described in Section 2.2, leading to the goals
and contribution to the urban climate as well as the visualization community in Section 2.3. Related work, as seen from a visualization point-of-view, is por- trayed in Section 2.4. Section 2.5 provides details on the data set, which was used for implementation and testing of the prototype. The main components of the framework are shown in Section 2.6, while Section 2.7 demonstrates, how they can be used to analyze a single mobile measurement run in detail. Finally, a conclusion is provided in Section 2.8.