A Virtual Globe Using a Discrete Global Grid System to Illustrate the Modifiable Areal Unit Problem

ABSTRACT:In the same way that discrete global grid systems (DGGS) are used to index data on the spherical Earth, they can aggregate point data, with their spherical polygons serving as bins. DGGS are particularly useful at multiple map scales because they are spatially hierarchical and exist on the sphere or ellipsoid, allowing large or small scale binning without projection distortion. We use DGGS in a free and open-source pedagogical tool for teaching students about the modifiable areal unit problem (MAUP). Our software application uses Dutton's quaternary triangular mesh (QTM) to bin global data points geodesically with counts or measures of any theme at multiple levels. Users can interactively select the level to which the data are binned by the QTM, as well as translate the whole tessellation east or west so that points fall into and out of different bins. These two functions illustrate the scaling and zoning aspects of the MAUP with dynamically-drawn choropleths on the surface of a virtual globe that the user can zoom and rotate, allowing visualization at virtually any cartographic scale. Users may also select various quantile classifications to further explore issues in visualizing aggregate data. In addition to presenting this new tool, we highlight the importance, especially at smaller scales, of using geodesic point-in-polygon intersection detection, rather than the projected 2D methods typically used by geographic information systems.RÉSUMÉ:De la même façon qu'ils servent à indexer les données sur le globe terrestre, les systèmes de grilles globales discrètes (DGGS) permettent d'agréger des données ponctuelles, leurs polygones sphériques servant de classes. Les DGGS sont particulièrement utiles lorsque les échelles cartographiques sont multiples, puisqu'ils permettent une analyse spatiale hiérarchique et une projection sphérique ou ellipsoïdale, et ainsi un découpage à grande ou petite échelle sans distorsion de la projection. L'auteur a recours au DGGS dans un outil pédagogique gratuit à code source libre pour entretenir les étudiants du problème de l'unité territoriale modifiable (modifiable areal unit problem—MAUP). L'application logicielle utilisée fait appel au maillage triangulaire quaternaire (quaternary triangular mesh—QTM) de Dutton pour classer les points de données globaux à des fins géodésiques, avec des dénombrements ou mesures multiniveaux de tout type. Les utilisateurs peuvent sélectionner en mode interactif le niveau auquel les données sont classées par le QTM et peuvent également transposer la tessellation complète vers l'est ou l'ouest, de sorte que ces points se situent à l'intérieur ou à l'extérieur de différentes classes. Ces deux fonctions illustrent les aspects de mise à l'échelle et de zonage du MAUP à l'aide du traçage dynamique de choroplèthes sur la surface d'un globe virtuel sur lequel l'utilisateur peut modifier l'échelle de visualisation et qu'il peut faire pivoter, ce qui lui permet d'obtenir pratiquement une représentation à l'échelle cartographique de son choix. L'utilisateur peut également sélectionner diverses classifications par quantile pour explorer de manière plus approfondie les problèmes liés à la visualisation des données agrégées. L'auteur, en plus de présenter ce nouvel outil, souligne l'importance, en particulier dans le cas de plus petites échelles, de l'usage de la détection de l'intersection de points d'inclusion spatiale géodésiques, plutôt que les méthodes de projection bidimensionnelle généralement utilisées par les systèmes d'information géographique.

[1]  G. Yule NOTES ON THE THEORY OF ASSOCIATION OF ATTRIBUTES IN STATISTICS , 1903 .

[2]  C. E. Gehlke,et al.  Certain Effects of Grouping upon the Size of the Correlation Coefficient in Census Tract Material , 1934 .

[3]  E. H. Simpson,et al.  The Interpretation of Interaction in Contingency Tables , 1951 .

[4]  Harry P. Bailey,et al.  Two grid systems that divide the entire surface of the Earth into quadrilaterals of equal area , 1956 .

[5]  E. M. Ballenzweig A practical equal-area grid , 1959 .

[6]  Akio Arakawa,et al.  Integration of the Nondivergent Barotropic Vorticity Equation with AN Icosahedral-Hexagonal Grid for the SPHERE1 , 1968 .

[7]  Eva Elvers,et al.  A System of Domains for Global Sampling Problems , 1974 .

[8]  T. Vincenty DIRECT AND INVERSE SOLUTIONS OF GEODESICS ON THE ELLIPSOID WITH APPLICATION OF NESTED EQUATIONS , 1975 .

[9]  H. Kenner Geodesic Math and How to Use It , 1976 .

[10]  Lloyd A. Treinish,et al.  Sphere quadtrees: a new data structure to support the visualization of spherically distributed data , 1990, Other Conferences.

[11]  Denis White,et al.  Cartographic and Geometric Components of a Global Sampling Design for Environmental Monitoring , 1992 .

[12]  Michael F. Goodchild,et al.  A hierarchical spatial data structure for global geographic information systems , 1992, CVGIP Graph. Model. Image Process..

[13]  R. Heikes,et al.  Numerical Integration of the Shallow-Water Equations on a Twisted Icosahedral Grid , 1995 .

[14]  Max Tegmark,et al.  An Icosahedron-based Method for Pixelizing the Celestial Sphere , 1996, The Astrophysical Journal.

[15]  Geoffrey H. Dutton A Hierarchical Coordinate System for Geoprocessing and Cartography , 1998 .

[16]  K. Sahr,et al.  Comparing Geometrical Properties of Global Grids , 1999 .

[17]  Todd D. Ringler,et al.  Modeling the Atmospheric General Circulation Using a Spherical Geodesic Grid: A New Class of Dynamical Cores , 2000 .

[18]  Hanan Samet,et al.  Navigating through triangle meshes implemented as linear quadtrees , 2000, TOGS.

[19]  DNA lattices: A method for molecular-scale patterning and computation , 2002, Comput. Sci. Eng..

[20]  Todd D. Ringler,et al.  Climate modeling with spherical geodesic grids , 2002, Comput. Sci. Eng..

[21]  Tony Rees,et al.  "C-Squares", a New Spatial Indexing System and its Applicability to the Description of Oceanographic Datasets , 2003 .

[22]  K. Sahr,et al.  Geodesic Discrete Global Grid Systems , 2003 .

[23]  Benjamin Watson,et al.  StorySpace: technology supporting reflection, expression, and discourse in classroom narrative , 2004, IEEE Computer Graphics and Applications.

[24]  Mark Gahegan,et al.  Geovisualization for knowledge construction and decision support , 2004, IEEE Computer Graphics and Applications.

[25]  Jeong Chang Seong,et al.  Implementation of an Equal-area Gridding Method for Global-scale Image Archiving , 2005 .

[26]  K. Gorski,et al.  HEALPix: A Framework for High-Resolution Discretization and Fast Analysis of Data Distributed on the Sphere , 2004, astro-ph/0409513.

[27]  Kristin A. Cook,et al.  Illuminating the Path: The Research and Development Agenda for Visual Analytics , 2005 .

[28]  T. Ringler,et al.  Analysis of Discrete Shallow-Water Models on Geodesic Delaunay Grids with C-Type Staggering , 2005 .

[29]  Jianguo Wu,et al.  The modifiable areal unit problem and implications for landscape ecology , 1996, Landscape Ecology.

[30]  Declan Butler,et al.  Virtual globes: The web-wide world , 2006, Nature.

[31]  Daniel A. Keim,et al.  Geovisual analytics for spatial decision support: Setting the research agenda , 2007, Int. J. Geogr. Inf. Sci..

[32]  S. Dark,et al.  The modifiable areal unit problem (MAUP) in physical geography , 2007 .

[33]  Thomas C. Hales,et al.  Jordan ’ s Proof of the Jordan Curve Theorem , 2007 .

[34]  P. Combes,et al.  Dots to Boxes: Do the Size and Shape of Spatial Units Jeopardize Economic Geography Estimations? , 2008 .

[35]  Alan M MacEachren,et al.  International Journal of Health Geographics Geovisual Analytics to Enhance Spatial Scan Statistic Interpretation: an Analysis of U.s. Cervical Cancer Mortality , 2022 .

[36]  Geoffrey Dutton Planetary Modelling via Hierarchical Tessellation , 2008 .

[37]  Stan Openshaw,et al.  Modifiable Areal Unit Problem , 2008, Encyclopedia of GIS.

[38]  William Ribarsky,et al.  Alleviating the Modifiable Areal Unit Problem within Probe‐Based Geospatial Analyses , 2010, Comput. Graph. Forum.

[39]  K. Gade A Non-singular Horizontal Position Representation , 2010 .

[40]  K. Morita,et al.  Building Construction , 2010 .

[41]  Brian M. Tomaszewski Situation awareness and virtual globes: Applications for disaster management , 2011, Comput. Geosci..

[42]  Peng Guang-xiong,et al.  The Study on Error Analysis of Discretization Area in Discrete Global Grid System , 2011 .

[43]  Robin Flowerdew,et al.  How serious is the Modifiable Areal Unit Problem for analysis of English census data? , 2011, Population trends.

[44]  Le Yu,et al.  Google Earth as a virtual globe tool for Earth science applications at the global scale: progress and perspectives , 2012 .

[45]  Huadong Guo,et al.  Next-generation Digital Earth , 2012, Proceedings of the National Academy of Sciences.

[46]  Charles F. F. Karney Algorithms for geodesics , 2011, Journal of Geodesy.

[47]  Raúl Sierra-Alcocer,et al.  Exploratory analysis of the interrelations between co-located boolean spatial features using network graphs , 2012, Int. J. Geogr. Inf. Sci..

[48]  Huadong Guo,et al.  Digital Earth 2020: towards the vision for the next decade , 2012, Int. J. Digit. Earth.

[49]  Marcos De Oliveira,et al.  Applying Geovisual Analytics to Volunteered Crime Data , 2013 .

[50]  Philip W. Jones,et al.  A multi-resolution approach to global ocean modeling , 2013 .

[51]  Ken Sexton,et al.  Modifiable Areal Unit Problem (MAUP) , 2008 .

[52]  Calvin Klatt,et al.  Geodesy ∗ , 2014, Encyclopedia of Remote Sensing.

[53]  Mohamed Abdel-Aty,et al.  Sensitivity analysis in the context of regional safety modeling: identifying and assessing the modifiable areal unit problem. , 2014, Accident; analysis and prevention.

[54]  Michał Bernard Pietrzak REDEFINING THE MODIFIABLE AREAL UNIT PROBLEM WITHIN SPATIAL ECONOMETRICS, THE CASE OF THE SCALE PROBLEM , 2014 .

[55]  Douglas Houston,et al.  Implications of the modifiable areal unit problem for assessing built environment correlates of moderate and vigorous physical activity , 2014 .

[56]  Ali Mahdavi-Amiri,et al.  Categorization and Conversions for Indexing Methods of Discrete Global Grid Systems , 2015, ISPRS Int. J. Geo Inf..

[57]  Jianya Gong,et al.  A virtual globe-based vector data model: quaternary quadrangle vector tile model , 2016, Int. J. Digit. Earth.

[58]  Christian Heipke,et al.  Information from imagery: ISPRS scientific vision and research agenda , 2016 .

[59]  J. Ben,et al.  Radix Representation of Triangular Discrete Grid System , 2016 .

[60]  Faramarz F. Samavati,et al.  The OGC® Discrete Global Grid System core standard: A framework for rapid geospatial integration , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[61]  Christopher Gold Spatial Context: An Introduction to Fundamental Computer Algorithms for Spatial Analysis , 2016 .

[62]  G. Percivall,et al.  ADVANCEMENTS IN OPEN GEOSPATIAL STANDARDS FOR PHOTOGRAMMETRY AND REMOTE SENSING FROM OGC , 2016 .

[63]  Manne Gerell Smallest is Better? The Spatial Distribution of Arson and the Modifiable Areal Unit Problem , 2017 .

[64]  F. Roy The Weather Book: A Manual of Practical Meteorology , 2017 .

[65]  Min Sun,et al.  Spatial aggregation as a means to improve attribute reliability , 2017, Comput. Environ. Urban Syst..

[66]  Mark Pesce,et al.  The Web-Wide World , 2017, WWW.

[67]  Cynthia A. Brewer,et al.  Evaluating data stability in aggregation structures across spatial scales: revisiting the modifiable areal unit problem , 2017 .

[68]  Pontus Hennerdal,et al.  A Multiscalar Approach for Identifying Clusters and Segregation Patterns That Avoids the Modifiable Areal Unit Problem , 2017 .

[69]  Paulo Raposo Scale and Generalization , 2017 .

[70]  Sarah E. Battersby,et al.  Shapes on a plane: evaluating the impact of projection distortion on spatial binning , 2017 .