GeoLinter: A Linting Framework for Choropleth Maps

Visualization linting is a proven effective tool in assisting users to follow established visualization guidelines. Despite its success, visualization linting for choropleth maps, one of the most popular visualizations on the internet, has yet to be investigated. In this paper, we present GeoLinter, a linting framework for choropleth maps that assists in creating accurate and robust maps. Based on a set of design guidelines and metrics drawing upon a collection of best practices from the cartographic literature, GeoLinter detects potentially suboptimal design decisions and provides further recommendations on design improvement with explanations at each step of the design process. We perform a validation study to evaluate the proposed framework's functionality with respect to identifying and fixing errors and apply its results to improve the robustness of GeoLinter. Finally, we demonstrate the effectiveness of the GeoLinter - validated through empirical studies - by applying it to a series of case studies using real-world datasets.Comment: to appear in IEEE Transactions on Visualization and Computer Graphic

Mapping the COVID-19 pandemic - The influence of map design choices by media outlets on people’s perception of the state of the pandemic

Following the outbreak of the COVID-19 pandemic in 2019, numerous online newspapers took it upon themselves to inform the public on a daily basis on the spread of the virus. Given their visually appealing, informative, and comprehensive nature, maps were often used for this purpose. The Swiss media have also resorted to this form of data journalism. Numerous studies in recent years have demonstrated that maps can affect people’s perceptions and emotions. Particularly the colours used in the maps can contribute to these effects. In this thesis, it was therefore of interest to find out what emotional, perceptual, and behavioural impact these many COVID-19 maps had on readers. The study shows that especially the COVID-19 topic had significant influence on people's emotions and that warm colour scales evoked more concern and led to more cautious behaviour than cold ones. Maps are therefore powerful tools, which is why it is important to make thoughtful decisions when designing them. This is especially important when maps reach a wide public and provide information about important events. This study therefore seeks to raise awareness and, in the best case scenario, contribute to a more considered and improved map design. This thesis is divided into two parts. Firstly, it examines what the COVID-19 case rate maps published by the Swiss media looked like and how they were created. Secondly, it is being analysed which emotions, perceptions, and behaviour they generated

Assessing visual variables of cartographic text design

This dissertation presents a series of usability studies, which examines the usability of the application of visual variables on cartographic text. Labels’ size, shape, orientation, texture, and colour were tested. The study also examines different lettering systems and their impact on cartographic text design. The obtained users’ preference, time measurement, questionnaires and eye tracking data were analysed both qualitatively and quantitatively. Insights are acquired to improve the quality of map through Improving cartographic text design

Using Geovisual Analytics to investigate the performance of Geographically Weighted Discriminant Analysis

Geographically Weighted Discriminant Analysis (GWDA) is a method for prediction and analysis of categorical spatial data. It is an extension of Linear Discriminant Analysis (LDA) that allows the relationship between the predictor variables and the categories to vary spatially. This is also referred to spatial non-stationarity. If spatial non-stationarity exists, GWDA should model the relationship between the categories and predictor variables more accurately, thus resulting in a lower classification uncertainty and ultimately a higher classification accuracy. The GWDA output also requires interpretation to understand which variables are important in driving the classification in different geographical regions. This research uses interactive visualisations from the field of geovisual analytics to investigate the performance of GWDA in terms of classification accuracy, classification uncertainty and spatial non-stationarity. The methodology is demonstrated in a case study that uses GWDA to examine the relationship between county level voting patterns in the 2004 US presidential election and five socio-economic indicators. This research builds on existing techniques to interpret the GWDA output and provides additional insight into the processes driving the classification. It also demonstrates a practical application of geovisual analytic tools

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

Spationomy

This open access book is based on "Spationomy – Spatial Exploration of Economic Data", an interdisciplinary and international project in the frame of ERASMUS+ funded by the European Union. The project aims to exchange interdisciplinary knowledge in the fields of economics and geomatics. For the newly introduced courses, interdisciplinary learning materials have been developed by a team of lecturers from four different universities in three countries. In a first study block, students were taught methods from the two main research fields. Afterwards, the knowledge gained had to be applied in a project. For this international project, teams were formed, consisting of one student from each university participating in the project. The achieved results were presented in a summer school a few months later. At this event, more methodological knowledge was imparted to prepare students for a final simulation game about spatial and economic decision making. In a broader sense, the chapters will present the methodological background of the project, give case studies and show how visualisation and the simulation game works