The State of the Art in Cartograms

Cartograms combine statistical and geographical information in thematic maps, where areas of geographical regions (e.g., countries, states) are scaled in proportion to some statistic (e.g., population, income). Cartograms make it possible to gain insight into patterns and trends in the world around us and have been very popular visualizations for geo-referenced data for over a century. This work surveys cartogram research in visualization, cartography and geometry, covering a broad spectrum of different cartogram types: from the traditional rectangular and table cartograms, to Dorling and diffusion cartograms. A particular focus is the study of the major cartogram dimensions: statistical accuracy, geographical accuracy, and topological accuracy. We review the history of cartograms, describe the algorithms for generating them, and consider task taxonomies. We also review quantitative and qualitative evaluations, and we use these to arrive at design guidelines and research challenges

Exploring Uncertainty in Geodemographics with Interactive Graphics

Geodemographic classifiers characterise populations by categorising geographical areas according to the demographic and lifestyle characteristics of those who live within them. The dimension-reducing quality of such classifiers provides a simple and effective means of characterising population through a manageable set of categories, but inevitably hides heterogeneity, which varies within and between the demographic categories and geographical areas, sometimes systematically. This may have implications for their use, which is widespread in government and commerce for planning, marketing and related activities. We use novel interactive graphics to delve into OAC – a free and open geodemographic classifier that classifies the UK population in over 200,000 small geographical areas into 7 super-groups, 21 groups and 52 sub-groups. Our graphics provide access to the original 41 demographic variables used in the classification and the uncertainty associated with the classification of each geographical area on-demand. It also supports comparison geographically and by category. This serves the dual purpose of helping understand the classifier itself leading to its more informed use and providing a more comprehensive view of population in a comprehensible manner. We assess the impact of these interactive graphics on experienced OAC users who explored the details of the classification, its uncertainty and the nature of between – and within – class variation and then reflect on their experiences. Visualization of the complexities and subtleties of the classification proved to be a thought-provoking exercise both confirming and challenging users’ understanding of population, the OAC classifier and the way it is used in their organisations. Users identified three contexts for which the techniques were deemed useful in the context of local government, confirming the validity of the proposed methods

Kahden muuttujan sävyjen sekoitus – väriasteikoiden suunnittelu kahden muuttujan koropleettikartoille räätälöidyllä työkalulla

Bivariate maps are a type of map visualization where two related data series are displayed at once for each data point. They can answer questions of how two variables interrelate in a geographical context using several kinds of encodings — visual variables — such as shape or color. The most common types are choropleth maps that use color hue and lightness to encode data and symbol-based maps that use shape size for both data series. Bivariate maps have seen a minor surge in popularity with new software tools but remain an understudied visualization type with a lack of clear usage recommendations. The thesis consists of a theoretical and a practical part. The purpose was to collate existing recommendations about the design of bivariate maps and determine whether they are considered a useful type of visualization. The theoretical part was a literature survey of relevant visualization and cartography literature, including empirical studies. I also sought to see whether bivariate choropleths are considered more effective than other types. The practical part was building a web tool prototype for bivariate color scale creation limited to choropleth maps, the Bivariate hue blender. The tool uses the Hue-Chroma-Lightness (HCL) color space for scheme design. By rotating the hue angle of an input color by a user-defined amount, a new color can be created. Intermediate colors are generated by blending these two with each other and a light secondary input color. The primary purpose of the tool was to improve color scheme creation and the building process used the framework of research-based design. It involved building the tool, using it to evaluate seven existing palettes, and creating three new palettes. These were applied to four different bivariate maps using statistical data from Finland in two different geographical divisions. Test data was selected using contingency table visualizations to ensure that all classes contain values. In addition to the color scales, a bivariate ordinal texture design was created. Bivariate maps were found to be grouped in categories using the concept of integral and separable dimensions. Bivariate choropleth maps were found to be a relevant visualization type, provided that the data is suitable, and that the number of classes is no larger than 9. An issue pertaining to color contrast was identified — accessibility guidelines stipulate a lightness difference between adjacent hues that require the use of strokes in most choropleth maps. Questions concerning effectiveness of other types, how bivariate symbols interact and how viewers can use bivariate maps for analytical tasks remain unresolved. The tool was subjectively found to enable better control over bivariate color scale creation than other similar software. The evaluated bivariate palettes had issues in lightness uniformity and separation of colors, which could be resolved in the three new palettes. These were found to be at least as practical as the seven initial palettes. This work has concluded that bivariate maps can be considered useful in special cases with the right data, which should encourage visualization designers to employ them. It has contributed a prototype tool that aids the creation of new perceptually uniform color scales for bivariate choropleth maps. Three new colorblind-safe 3×3 palettes are an addition to the limited set of schemes in active use. The method of selecting data using contingency tables can aid in creating bivariate maps.Kahden muuttujan tietokartat ovat visualisointityyppi, jossa kaksi toisiinsa liittyvää tietosarjaa näytetään kunkin datapisteen kohdalla. Niillä voidaan tutkia kuinka kaksi muuttujaa ovat yhteydessä toisiinsa maantieteellisessä kontekstissa, käyttämällä useita erilaisia visuaalisia muuttujia – kuten muotoa tai väriä. Yleisimpiä tyyppejä ovat koropleettikartat, joissa käytetään värin sävyä ja vaaleutta tietojen esittämiseen, sekä symbolikartat, joissa käytetään muodon kokoa molemmille datasarjoille. Kahden muuttujan karttojen suosio on kasvanut uusien ohjelmistotyökalujen myötä, mutta ne ovat edelleen vähän tutkittu visualisointityyppi, josta puuttuvat selkeät käyttösuositukset. Opinnäytetyöni koostuu teoreettisesta ja käytännön osasta. Tarkoituksena on ollut koota olemassa olevia suosituksia kahden muuttujan kartoista ja selvittää, pidetäänkö niitä hyödyllisenä visualisointityyppinä. Teoriaosuus on kirjallisuuskatsaus visualisointi- ja kartografiakirjallisuuteen, mukaan luettuna myös empiiriset tutkimukset. Pyrin myös selvittämään, pidetäänkö kahden muuttujan koropleettikarttoja tehokkaampina kuin muita kahden muuttujan karttatyyppejä. Käytännön osuus on verkkotyökalun prototyyppi, Bivariate hue blender, joka on tehty kahden muuttujan väriasteikkojen luomista varten. Työkalu käyttää Hue-Chroma-Lightness (HCL; sävy, kromaattisuus, vaaleus) -väriavaruutta. Kun syötetyn värin sävykulmaa kääntää, syntyy uusi väri. Alkuperäisestä ja uudesta väristä luodaan kaksi erillistä väriasteikkoa vaaleasta aloitussävystä ja näitä yhdistämällä muodostetaan asteikon välivärit. Työkalun ensisijaisena tarkoituksena on ollut helpottaa väriasteikkojen luomista. Sen kehittämisessä on sovellettu tutkimukseen perustuvaa suunnittelua. Työkalun avulla on arvioitu seitsemän palettia ja luotu kolme uutta. Näitä on sovellettu neljään erilaiseen kahden muuttujan karttaan, joissa on käytetty tilastotietoja Suomesta kahden eri maantieteellisen jaon mukaan. Väriasteikkojen lisäksi on luotu kuviotekstuuri. Tutkimuksessa todetaan, että kahden muuttujan kartat voidaan jakaa luokkiin käyttäen kokonaisten ja eroteltavien ulottuvuuksien käsitettä. Koropleettikarttojen todetaan olevan toimiva laji, kunhan aineisto on sopiva ja luokkia enintään yhdeksän. Työssä tunnistettiin värikontrastiin liittyvä ongelma – esteettömyysohjeissa määrätyt vierekkäisten sävyjen vaaleuserot edellyttävät ääriviivojen käyttöä useimmissa kartoissa. Tutkimuksessa auki jäävät kysymykset koskevat muiden tyyppien tehokkuutta, kaksimuuttujaisten symbolien vuorovaikutusta ja sitä, kuinka katsoja lukee ja käyttää näitä karttoja. Työkalun voidaan todeta subjektiivisesti mahdollistavan paremman hallinnan kaksimuuttujaväriasteikkojen luomisessa vastaaviin ohjelmiin verrattuna. Arvioiduissa paleteissa oli ongelmia vaaleuden tasaisuudessa ja värien erottelussa, jotka nyt voitiin ratkaista kolmessa uudessa paletissa. Näiden todetaan olevan ainakin yhtä käytännöllisiä kuin seitsemän alkuperäistä palettia. Työn loppupäätelmä on, että kaksimuuttujaisia karttoja voidaan pitää hyödyllisinä tietyissä tapauksissa ja niihin soveltuvalla datalla, mikä voi kannustaa visualisointisuunnittelijoita käyttämään niitä. Työn tuloksena on prototyyppityökalu, joka auttaa luomaan uusia tasajakoisia väriskaaloja kahden muuttujan koropleettikarttoja varten. Kolme uutta palettia on lisäys aktiivisessa käytössä olevien kaksimuuttujaisten palettien rajalliseen joukkoon. Kontingenssitaulukoihin perustuva aineiston valintamenetelmä voi auttaa suunnittelijoita kahden muuttujan karttojen luomisessa

OpenStreetMap – building data completeness visualization in terms of “Fitness for purpose”

The purpose of this article was to provide the user with information about the number of buildings in the analyzed OpenStreetMap (OSM) dataset in the form of data completeness indicators, namely the standard OSM building areal completeness index (C Index), the numerical completeness index (COUNT Index) and OSM building location accuracy index (TP Index). The official Polish vector database BDOT10k (Database of Topographic Objects) was designated as the reference dataset. Analyses were carried out for Piaseczno County in Poland, differentiated by land cover structure and urbanization level. The results were presented in the form of a bivariate choropleth map with an individually selected class interval suitable for the statistical distribution of the analyzed data. The results confirm that the completeness of OSM buildings close to 100% was obtained mainly in built-up areas. Areas with a commission of OSM buildings were distinguished in terms of area and number of buildings. Lower values of completeness rates were observed in less urbanized areas. The developed methodology for assessing the quality of OSM building data and visualizing the quality results to assist the user in selecting a dataset is universal and can be applied to any OSM polygon features, as well as for peer review of other spatial datasets of comparable thematic scope and detail

Design and Interpretability of Contour Lines for Visualizing Multivariate Data

Multivariate geospatial data are commonly visualized using contour plots, where the plots for various attributes are often examined side by side, or using color blending. As the number of attributes grows, however, these approaches become less efficient. This limitation motivated the use of glyphs, where different attributes are mapped to different pre-attentive features of the glyphs. Since both contour plot overlays and glyphs clutter the underlying map, in this paper we examine whether contour lines, which are already present in map space, can be leveraged to visualize multivariate geospatial data. We present five different designs for stylizing contour lines, and investigate their interpretability using three crowdsourced studies. We evaluated the designs through a set of common geospatial data analysis tasks on a four-dimensional dataset. Our first two studies examined how the contour line width and the number of contour intervals affect interpretability, using synthetic datasets where we controlled the underlying data distribution. Study 1 revealed that the increase of width improves the task performance in most of the designs, specially in completion time, except some scenarios where reducing width does not affect performance where the visibility of the background is critical. In Study 2, we found out that fewer contour intervals lead to less visual clutter, hence improved performance. We then compared the designs in a third study that used both synthetic and real-life meteorological data. The study revealed that the results found using synthetic data were generalizable to the real-life data, as hypothesized. Moreover, we formulated a design recommendation table tuned to give users task- and category-specific design suggestions under various environment constraints. At last, we discuss the comparison between the lab and online versions of study 1 with respect to display size (lab study was done on big screen and vice versa). Our studies show the effectiveness of stylizing contour lines to represent multivariate data, reveal trade-offs among design parameters, and provide designers with important insights into the factors that influence multivariate interpretability. We also show some real-life scenarios where our visualization approach may improve decision making

Artificial Intelligence in geospatial analysis: applications of self-organizing maps in the context of geographic information science.

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsThe size and dimensionality of available geospatial repositories increases every day, placing additional pressure on existing analysis tools, as they are expected to extract more knowledge from these databases. Most of these tools were created in a data poor environment and thus rarely address concerns of efficiency, dimensionality and automatic exploration. In addition, traditional statistical techniques present several assumptions that are not realistic in the geospatial data domain. An example of this is the statistical independence between observations required by most classical statistics methods, which conflicts with the well-known spatial dependence that exists in geospatial data. Artificial intelligence and data mining methods constitute an alternative to explore and extract knowledge from geospatial data, which is less assumption dependent. In this thesis, we study the possible adaptation of existing general-purpose data mining tools to geospatial data analysis. The characteristics of geospatial datasets seems to be similar in many ways with other aspatial datasets for which several data mining tools have been used with success in the detection of patterns and relations. It seems, however that GIS-minded analysis and objectives require more than the results provided by these general tools and adaptations to meet the geographical information scientist‟s requirements are needed. Thus, we propose several geospatial applications based on a well-known data mining method, the self-organizing map (SOM), and analyse the adaptations required in each application to fulfil those objectives and needs. Three main fields of GIScience are covered in this thesis: cartographic representation; spatial clustering and knowledge discovery; and location optimization.(...

Choriented Maps: Visualizing SDG Data on Mobile Devices

Choropleth maps and graduated symbol maps are often used to visualize quantitative geographic data. However, as the number of classes grows, distinguishing between adjacent classes increasingly becomes challenging. To mitigate this issue, this work introduces two new visualization types: choriented maps (maps that use colour and orientation as variables to encode geographic information) and choriented mobile (an optimization of choriented maps for mobile devices). The maps were evaluated in a graphical perception study featuring the comparison of SDG (Sustainable Development Goal) data for several European countries. Choriented maps and choriented mobile visualizations resulted in comparable, sometimes better effectiveness and confidence scores than choropleth and graduated symbol maps. Choriented maps and choriented mobile visualizations also performed well regarding efficiency overall and performed worse only than graduated symbol maps. These results suggest that the use of colour and orientation as visual variables in combination can improve the selectivity of map symbols and user performance during the exploration of geographic data in some scenarios.Comment: Accepted for publication in the Cartographic Journa

Measuring Segregation Patterns and Change: a Co-Location Quotient Approach

There are many segregation measures introduced and utilized in geographic research up to this date. Because residential segregation can be defined in more than one way the measure’s formulation is dependent on the particular definition the researcher is trying to reflect. Another distinctive feature of the quantitative exploration of segregation is the role of geographic scale. In contrast, global indices focus on overall level of spatial separation of population in the urban area while local indices assume that the index magnitude varies from place to place across the city. The main purpose of this study is to introduce a new measure of segregation that focuses on the lack of interactions of the population groups and to explore its properties. The proposed measure is a modified co-location quotient (CLQ) that was originally applied to point data as a measure of spatial association between two categorical variables. The first part of this dissertation introduces two versions of modified CLQ that are applicable to categories of areally aggregated population. One is the global measure that captures the overall exposure of one population group given the presence of another group. The local version of the measure describes levels of exposure for every single spatial unit. Both, global and local quotients have two basic specifications – two-group CLQ and same-group CLQ. Each variant of the measure allows the option to include the neighborhood size in computation, which theoretically defines the space within which people have the possibility for interaction. The use of CLQ in the proposed mathematical configuration expands the discussion of dimensions of segregation by suggesting the connection between different dimensions that are covered by co-location measure. Using publicly available data from U.S. Census Bureau on racial composition of population CLQs were computed for thirty urban areas, where twenty nine are metro areas and one is Washington D.C. The basic units of analysis are census tracts and block groups that contain aggregated population counts. Three decennial releases are used: 1990, 2000 and 2010. The results suggest an overall, but uneven, increase in the exposure of white people in given urban areas. Patterns of concentration for white people remained stable over the time span. But the concentration of black people shows a substantial decrease indicating an increasing exposure of blacks in the global sense. Conversely, same-group CLQs for whites and for blacks indicate unequal experiences for these two population groups in America. Additionally, various visualization techniques related to co-location measure were explored. The pointillist approach, suggested in this study, is found to be particularly effective technique for displaying CLQ results compared to widely utilized choropleth mapping