Data-analytiikka pelisuunnittelun työkaluna

Tiivistelmä. Tämän tutkielman lähtökohtana on ollut tutkielman tekijän intohimo pelialaa kohtaan ja kiinnostus data-analytiikasta. Data-analytiikka on ollut liikealalle tarpeellista, koska se auttaa ymmärtämään asiakasryhmiä ja asiakkaiden tarpeita. Pelialalla data-analytiikkaa voidaan käyttää samaan tapaan ja myös parantamaan pelin suunnittelua pelinkehitys prosessin aikana. Tutkien kirjallisuutta pyrin vastaamaan kysymykseen mitä hyötyä data-analytiikasta on videopelien suunnittelussa. Useat tutkimukset ja artikkelit ovat osoittaneet, kuinka datan kerääminen ja sen analysointi auttavat pelin kehitystä ja ratkaisee ongelmia, joita pelin suunnittelijat eivät voisi huomata tai edes keksiä, mistä lähteä niitä ratkaisemaan. Data-analytiikan aloittamista pelin suunnittelussa voi estää se, että analytiikka prosessin implementaatio on kallista ja vaikeaa. Näistä järjestelmistä on kuitenkin olemassa ilmaisia versioita ja internetissä on ohjeita, jotka voivat auttaa jopa noviisia luomaan analytiikka järjestelmän heidän peliinsä ja analysoimaan pelidataa. Kirjallisuuden avulla data-analytiikka on todettu olevan tärkeä työkalu pelien monetisaation seuraamisessa, mutta sen hyötyjä pelin suunnittelussa tulisi tutkia mielestäni vielä enemmän. Koska data-analytiikka pelialalla on uutta, toiveeni on että tämä tutkielma tulee motivoimaan pelin suunnittelijoita käyttämään data-analytiikkaa heidän peleissä. Tällä tavalla, videopelit voivat kehittyä eteenpäin suuntaan, joka auttaa pelaajia nauttimaan niistä entistä enemmän

Map-based interaction with trajectory data

Tese de doutoramento, Informática (Engenharia Informática), Universidade de Lisboa, Faculdade de Ciências, 2017With the increasing popularity of location based services and mobile tracking technologies, the collection of large amounts of spatio-temporal data became an increasingly common, easier, and more reliable task. In turn, this has emphasized the possibility of analysing georeferenced information, particularly associated with human trajectory data, to identify and understand movement patterns and activities, ultimately, supporting decision making in various contexts. In order to properly analyse and understand the spatio-temporal and the thematic properties associated with these data, adequate visualization techniques are needed. Due to the spatial properties of trajectories, map-based techniques, such as 2D static maps or 3D space-time cubes (STCs) are considered as essential tools for their visualization. However, despite the increasing number of visualization systems, the study regarding their usability, alongside the role of the human user, sometimes with a limited background in data visualization and analysis, are often neglected. In addition to the somewhat disperse, and sometimes even contradictory, results in the literature, these factors, ultimately, emphasize the lack of knowledge to support the choice of particular visualizations, and their design, in different types of tasks. This dissertation addresses these issues through three main sets of contributions, focusing on inexperienced users, in terms of data visualization and analysis: i) the characterization of the dis/advantages of existing map-based techniques (2D static maps and STCs), depending on the types of visual analysis tasks and the focus of the analysis; ii) the improvement of existing visualization techniques, either through the inclusion of additional spatial cues within the STC, or combining both types of techniques in various ways; and iii) the identification of design guidelines for trajectory data visualization, describing various considerations/criteria for the selection of different map-based visualization techniques and their possible interactive features

Visualising player data for video game designers

The collection and analysis of videogame players' actions in the game world, known as game telemetry, is a common technique for understanding the behaviour of players. This process, known as Game Analytics, often uses data visualisation to allow designers to manually analyse features of the data. Heatmap visualisation, a grid-based visualisation showing how often an event occurs across the game world (e.g. �ring of weapons), is used widely in the games industry for visualising aggregated data, but has limitations when used to classify player behaviour at an individual or group level. Existing works using clustering to identify player behaviour yield results that must be interpreted by an expert, a problem acknowledged by existing research. Motivated by these limitations, this work presents the novel application of dendrogram visualisation as a means to interpret large datasets of heatmaps, through the use of hierarchical clustering, to aid designers in exploring and analysing player behaviour. This allows an intuitive and well- understood visualisation technique (heatmaps) to be used for cluster analysis, presenting intelligible results to a game designer, in a format they are familiar with. To evaluate dendrograms as a design tool, a system was designed and implemented to visualise player data, using heatmaps, with hierarchical clustering being performed on these heatmaps, the results displayed as a dendrogram. A feasibility study was con- ducted with a set of game designers, to understand the opportunities and limitations of dendrograms as a game analytics tool. The results a�rmed the utility of heatmaps for visualising aggregate data, but visual complexity increases in large quantities. Den- drograms were found to be initially di�cult to read, but showed promise for analysing large sets of data and guiding the designer to interesting areas of the data, provided they could \drill down" into the base data (heatmaps). In light of these �ndings, a us- ability study was designed and conducted with a set of 40 game development students, where they were presented with realistic game design scenarios, and asked to �nd an- swers to analytics questions using heatmaps and dendrograms. The results showed that whilst dendrograms were initially di�cult to understand, they were used to successfully explore and understand cluster relationships, with participants providing the correct answers grounded in the data. Furthermore participants reiterated the need to explore the base data (heatmaps) to understand the cluster relationships of the dendrogram. This work concludes that dendrograms represent a viable and useful tool for identifying interesting behaviour patterns within a heatmap dataset. Whilst some familiarity is required with the tool, it is possible to use dendrograms to explore behaviour clusters within a large dataset, and this work presents a solution to the limitations of analysing player behaviour through the use of heatmaps in large datasets. This work highlights a number of avenues for future work, such as deploying and studying dendrograms in a game production setting, or evaluating the dendrogram visualisation in di�erent game genres