Exhibiting the Exhibitors: Spatial Visualization for Heterogeneous Cinema Venue Data

Abstract

Cinema data is characteristically complex, heterogeneous and interlinked. Rather than relying on simple information retrieval techniques, researchers are increasingly turning to the creative exploration and reapplication of data in order to more fully explore the meaning of newly available and diverse data sets. In this context, the cinema historian becomes the creator of visual texts which can be assessed for both their interpretive insight and their aesthetic qualities. This paper presents four research projects that use different spatio-temporal visualization techniques to understand the industrial dynamics of post-war film exhibition and distribution in Australia. The research integrates work by a group of inter-disciplinary investigators into the effectiveness of techniques such as dendritic mapping, Circos circular visualizations, animation, cartogram mapping, and multivariate visualization for the study of cinema circuits and operations at a number of scales.     

一种尺度效应指数修正的格网数据示意地图制图算法

示意地图是统计数据可视化的一种新方法,但已有的面域示意地图制图算法不适用于局部数据差别大的应用。针对此问题,结合多尺度格网统计数据的尺度效应,修正了diffusionbasedmethod算法,并将该算法应用于北京市100m格网人口统计数据上,证明了算法的有效性。该算法可应用于格网统计数据示意地图制图。     

Diffusion-based cartogram on spheres

A planar cartogram is a two-dimensional map, on which the area of each closed region is in direct proportion to a chosen extensive property. To date, various algorithms have been proposed to construct planar cartograms. This work extends the two-dimensional, diffusion-based, topologically invariant cartogram algorithm proposed by Gastner and Newman onto spheres. Unlike its planar counterpart, the spherical formulation does not require boundary conditions and is invariant to the rotation of input data on the sphere. An implementation of this spherical cartogram transformation is designed to generate readable topology-preserving cartograms on spheres. Lastly, the method is illustrated with applications to global data such as worldwide human population, gross domestic product (purchasing power parity), carbon dioxide emissions and regional data such as the Electoral College of the United States presidential election of 2016.