Co-Visualization of Air Temperature and Urban Data for Visual Exploration
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Co-Visualization of Air Temperature and Urban Data for Visual Exploration Jacques Gautier* Mathieu Bredif´ † Sidonie Christophe‡ LASTIG, Univ Gustave Eiffel, ENSG, IGN, F-94160 Saint-Mande, France ABSTRACT or separately horizontal and vertical spatial distributions of air tem- Urban climate data remain complex to analyze regarding their spatial perature data: horizontally, at the building block level, which is at distribution. The co-visualization of simulated air temperature into the time the finest possible spatial resolution for atmospheric simu- urban models could help experts to analyze horizontal and verti- lation models (air temperature data is now typically simulated with cal spatial distributions. We design a co-visualization framework 500m - 2km resolutions); vertically to observe vertical profiles into enabling simulated air temperature data exploration, based on the and above the city. Co-visualizing climate and urban morphological graphic representation of three types of geometric proxies, and their data, at the building block level, is less explored, because it requires co-visualization with a 3D urban model with various possible ren- precise urban representation data and models. dering styles. Through this framework, we aim at allowing meteoro- We present an integrated framework for co-visualizing urban sim- logical researchers to visually analyze and interpret the relationships ulated air temperature and urban morphology, at a building block between simulated air temperature data and urban morphology. level, which remains complex because of the following data charac- teristics and their possible visual integration into a 3D scene: Index Terms: Human-centered computing—Visualization— Geographic visualization;——Visualization design and evaluation • 3D Urban model. A topographic model of metric resolution methods; Visualization techniques— enables the visualization of buildings, blocks, shapes, heights, structures, empty area, urban canyon depth and direction. 1 INTRODUCTION • Temperature data, simulated by atmospheric models, is sam- Understanding urban climate phenomena is one of the main issues pled on a 3D grid (Fig.1.left) which is : regarding global warming. Amongst others, the urban heat island – Horizontally: a regular square grid. Horizontal resolu- effect (UHI; [18]) causes higher temperatures in cities than in rural ar- tion being larger than the resolution of the urban model, eas, accentuated during heat waves episodes and characterized by the this scale difference may cause over-interpretation. lack of night-time cooling. UHIs are largely addressed [2, 7, 19, 22], regarding diverse causes such as ground artificialization in cities, – Vertically: grid sample altitudes start at the ground level, lower albedo causing more solar energy absorption, building den- following the topographic elevation, with a spacing that sity and higher urban surface roughness causing reduced air ve- gradually goes from metric at the ground level to much locity [3, 5, 15, 17, 21, 24]. A better understanding of the urban sparser in the levels above the urban canopy. heat islands needs then more accurate knowledge of urban morphol- ogy [21,27]. Climate models simulate temperature, wind or humid- • Co-visualization of simulated air temperature and urban ity distributions and values, based on multiple indicators, including morphology implies to visually integrate, into a 3D visualiza- vegetation ratio and mean albedo of buildings surface, such as the tion environment, heterogeneous and multi-dimensional data, Local Climate Zone (LCZ) classification characterizing regions by with various spatial resolutions, geometries and data structures. surface cover, structure, material and human activity [21, 23, 28]. These characteristics of the data, and so the characteristics of their Co-visualizing simulated climate data with morphological urban co-visualization, require to work on the data possible graphic repre- data would allow supporting the validation of climate simulation sentation and rendering styles, in order to ease their visual integration models, by comparing simulation results with morphological indica- and representation (lowering visual heterogeneity, avoiding visual tors used as simulation input data, such as a suitable visual analysis clutter), and on the interaction capacities which would support the of relationships between UHIs and the urban environment. Regard- navigation into the 3D scene to explore the data and the visual analy- ing specifically temperature data, existing visualizations work as sis of their spatial relationships. In order to fit these expectations, we final media, to present global trends, breaks and changes, based on design and develop a co-visualization framework based on the long-time series of temperature values as impactful stripes [8] or representation of air temperature data as geometric proxies, in- as spatialized data [12, 25], or to inform and explain the tempera- tegrating and transforming the initial irregular 3D grid into hor- ture profiles as vertical and horizontal cross-sections at city scale. izontal planes, 3D point clouds, or road-based vertical planes, The visualization of plane representations of air temperature into and their related style capacities and interaction modes. We aim the city have been proposed, projecting temperature into 3D urban at providing tools, to help the users to navigate into those graphic space [20], or representing local effects of urban structures by repre- representations of air temperature data, and to validate and/or to senting temperature on static/animated vertical cross-sections [10]. open questions about UHI explanations and interpretations. Meteorology researchers claim for more local and finer visual exploration of their simulated data integrated into real urban data, to 2 RELATED WORK ease the interpretation of relationships between urban heat islands and urban morphology. They particularly expect to analyze together Visualization aims at supporting visual perception and interpretation of data, enhanced by data interaction to go to visual analytics. In the *e-mail: [email protected] scope of geovisualization, we handle issues regarding geospatial or †e-mail:[email protected] spatialized data visualization. Especially we target spatio-temporal ‡e-mail:[email protected] phenomena on Earth, that could be represented by raw, predicted, simulated, or learned data coming from various acquisition sensors and input models, and their related imprecision, to be (visually) propagated. In particular, climate data visualization attempts mostly to verify input/output data and models, and demonstrate global and Figure 1: From the 3D grid of temperature data (left) to geometric proxies (middle), stylized and co-visualized with urban data (right). local patterns, whereas urban climate data visualization focuses more respectively the spatial distribution of specific temperature interval, on spatialization of climate data, and visual exploration of spatial or the value of temperature data at a specific location. These tools relationships between climate specificities and urban morphology. are efficient to represent the physics of a phenomenon, but avoid to go deeper in its spatial analysis. Color palettes for geophysics 2.1 Climate data Visualization phenomena are still in question to improve scientific analysis and Climate data visualization mostly exists to validate and communi- sharing [6,26], but also remain questionable. Those considerations cate about simulation results, and especially about air temperature reach to graphic semiology issues to help visual perception and profiles, dynamic wind representation [13] or possible air corri- spatial cognition, based on suitable and efficient visualizations. dors [4], with the expectations to validate input/output data and Due to both climate and urban data dimensions, we propose models characteristics. Many representation types are used, most to design and implement the representation of air temperature as of all as vertical and horizontal cross-sections, long time series spa- particular 3D data: enabling graphic representation and interaction tialized data [12] or global trends into long time series [8]. Sets of would provide a co-visualization framework to represent and explore multi-dimensional spatial 2D views from very small scales (at Euro- temperature data in their urban spatial context at finer spatial scale. pean level for instance), to wider scales (at city scales for instance) are very often proposed to visualize the spatial distribution of air tem- 3 3D URBAN TEMPERATURE EXPLORATION perature coming from simulation models integrating morphological The air temperature data co-visualized in this work was simulated by indicators, in addition to visualizations of their temporal profile dis- the two following atmospheric models: Meso-NH model (Mesoscale tributions through graphs and histograms [12,25]. Spatio-temporal Non-Hydrostatic atmospheric model), simulating climate data for animations are very useful to improve the global perception of a the atmosphere until altitudes of 15000 meters [11]; TEB model spatio-temporal phenomenon and its general patterns, but it remains (Town Energy Balance), extending the Meso-NH simulations in the difficult to detect and analyze changes, at any scales. In addition, the urban environment, below the building roofs [14]. Simulated air beautiful and so efficient graphic representation of warming stripes temperature is represented by a 3D grid (Fig.1.left). Each value