Using treemaps for variable selection in spatio-temporal visualisation

We demonstrate and reflect upon the use of enhanced treemaps that incorporate spatial and temporal ordering for exploring a large multivariate spatio-temporal data set. The resulting data-dense views summarise and simultaneously present hundreds of space-, time-, and variable-constrained subsets of a large multivariate data set in a structure that facilitates their meaningful comparison and supports visual analysis. Interactive techniques allow localised patterns to be explored and subsets of interest selected and compared with the spatial aggregate. Spatial variation is considered through interactive raster maps and high-resolution local road maps. The techniques are developed in the context of 42.2 million records of vehicular activity in a 98 km(2) area of central London and informally evaluated through a design used in the exploratory visualisation of this data set. The main advantages of our technique are the means to simultaneously display hundreds of summaries of the data and to interactively browse hundreds of variable combinations with ordering and symbolism that are consistent and appropriate for space- and time- based variables. These capabilities are difficult to achieve in the case of spatio-temporal data with categorical attributes using existing geovisualisation methods. We acknowledge limitations in the treemap representation but enhance the cognitive plausibility of this popular layout through our two-dimensional ordering algorithm and interactions. Patterns that are expected (e.g. more traffic in central London), interesting (e.g. the spatial and temporal distribution of particular vehicle types) and anomalous (e.g. low speeds on particular road sections) are detected at various scales and locations using the approach. In many cases, anomalies identify biases that may have implications for future use of the data set for analyses and applications. Ordered treemaps appear to have potential as interactive interfaces for variable selection in spatio-temporal visualisation. Information Visualization (2008) 7, 210-224. doi: 10.1057/palgrave.ivs.950018

Luotettavuuden indikaattorien valintaprosessin määrittäminen: Case Helsingin seudun liikenne

It is important to have information on the day-to-day operational level events and planning objectives in public transport. Performance indicators are needed to inform related decisions in these domains. As one aspect of public transport level of service, problems with reliability appear to passengers as delays in different parts of the journey, such as increasing overall travel times or adding extra waiting time at transfer points. Factors affecting reliability can be classified in many different ways, but the factors mainly consist of decisions and choices at the planning and operational level, as well as environmental variables. This work considers that reliability consists of four components: punctuality, regularity, waiting time, and cancellation. As Helsinki Region Transport (HSL) is under-going a continuous effort of developing performance indicators, the aim of this thesis was to develop a process for defining and selecting reliability performance indicators for HSL case. Development in this thesis. starts with defining alternative performance indicators for punctuality, regularity and waiting time. The selection of a single indicator to describe the reliability was performed using the analytical hierarchy process (AHP). The AHP method was used to create an assessment framework consisting of a subjective and an objective part. The purpose of the evaluation framework was to help assess the superiority and suit-ability of the different indicator alternatives. The objective part evaluated the technical and computational details of the indicators, while the subjective part evaluated the indicators from the perspective of the user’s needs. Based on the evaluation, the selected indicators were subjected to spatio-temporal analysis to find the most effective aggregates. The analysis also outlined possible graphical visualizations for the indicators. In addition, a workshop was organized for users to define the criteria for subjective evaluation and to evaluate the indicator options. The workshop was evaluated as positive for its involvement and information sharing, as well as development wishes for the way the assessment was collected and for exploring the alternatives in advance. In general, the AHP method and assessment framework were found to work well as part of the selection process. Performed spatio-temporal analysis yielded the desired result, but the analysis was also found to need improvement in certain points. For example, the partial combination of analysis with a workshop was found to be a necessary addition. Overall, the indicator selection process outlined in this work can be considered successful, as details of the process are almost at the level where they can be implemented in the ongoing HSL processes.On tärkeää saada tietoa joukkoliikenteen päivittäisistä operatiivisen tason tapahtumista ja suunnittelutavoitteista. Suorituskykyindikaattoreita tarvitaan avuksi näihin asioihin liittyvissä kysymyksissä. Yhtenä julkisen liikenteen palvelutason näkökulmana luotettavuusongelmat näyttäytyvät matkustajille viivästyksinä matkan eri osissa, kuten kasvavina kokonaismatka-aikoina tai ylimääräisenä odotusaikana vaihdoissa. Luotettavuuteen vaikuttavat tekijät voidaan luokitella monin eri tavoin, mutta tekijät koostuvat pääasiassa suunnittelu- ja toimintatason päätöksistä ja valinnoista sekä ympäristömuuttujista. Työssä katsottiin, että luotettavuus koostuu neljästä osasta: täsmällisyydestä, säännöllisyydestä, odotusajasta ja ajamattomuudesta. Koska Helsingin seudun liikenne (HSL) pyrkii jatkuvasti kehittämään suoritusindikaattoreita, tämän työn tavoitteena oli kehittää prosessi luotettavuuden suorituskykyindikaattorien määrittelemiseksi ja valitsemiseksi. Tämän työ alkaa vaihtoehtoisten suorituskykyindikaattorien määrittämisellä täsmällisyydelle, säännöllisyydelle ja odotusajalle. Yksittäisen indikaattorin valinta luotettavuuden kuvaamiseksi suoritettiin käyttämällä analyyttistä hierarkia prosessia (AHP). AHP-menetelmää käytettiin luomaan arviointijärjestelmä, joka koostui subjektiivisesta ja objektiivisesta osasta. Arvioinnin tarkoituksena oli auttaa arvioimaan eri indikaattorivaihtoehtojen paremmuutta ja soveltuvuutta. Objektiivisessa osassa arvioitiin indikaattorien teknisiä ja laskennallisia yksityiskohtia, kun taas subjektiivisessa osassa arvioitiin indikaattoreita käyttäjän tarpeiden näkökulmasta. Arvioinnin perusteella valituille indikaattoreille tehtiin spatio-temporaalinen analyysi tehokkaimpien koosteiden löytämiseksi. Analyysi hahmotteli myös indikaattorien mahdollisia graafisia visualisointeja. Lisäksi tuleville käyttäjille järjestettiin työpaja subjektiivisen arvioinnin perusteiden määrittelemiseksi ja indikaattorivaihtoehtojen arvioimiseksi. Työpajaa arvioitiin positiivisesti sen osallistamisesta ja tiedon jakamisesta. Kehitystarpeita nähtiin arvioinnin keräystavassa ja tunnistettiin tarve saada tutkia vaihtoehtoja etukäteen. Yleisesti AHP-menetelmän ja arviointikehyksen todettiin toimivan hyvin osana valintaprosessia. Suoritettu spatio-temporaalinen analyysi tuotti halutun tuloksen, mutta analyysin todettiin myös tarvitsevan parannusta tietyissä kohdissa. Esimerkiksi analyysin osittainen yhdistäminen työpajaan todettiin olevan välttämätön lisä. Kaiken kaikkiaan tässä työssä hahmotettua indikaattorivalintaprosessia voidaan pitää onnistuneena, koska prosessin yksityiskohdat ovat melkein sillä tasolla, missä ne voidaan toteuttaa osana meneillään olevassa HSL:n suorituskykyindikaattorien kehittämisprosessia

Stacking-based visualization of trajectory attribute data

Visualizing trajectory attribute data is challenging because it involves showing the trajectories in their spatio-temporal context as well as the attribute values associated with the individual points of trajectories. Previous work on trajectory visualization addresses selected aspects of this problem, but not all of them. We present a novel approach to visualizing trajectory attribute data. Our solution covers space, time, and attribute values. Based on an analysis of relevant visualization tasks, we designed the visualization solution around the principle of stacking trajectory bands. The core of our approach is a hybrid 2D/3D display. A 2D map serves as a reference for the spatial context, and the trajectories are visualized as stacked 3D trajectory bands along which attribute values are encoded by color. Time is integrated through appropriate ordering of bands and through a dynamic query mechanism that feeds temporally aggregated information to a circular time display. An additional 2D time graph shows temporal information in full detail by stacking 2D trajectory bands. Our solution is equipped with analytical and interactive mechanisms for selecting and ordering of trajectories, and adjusting the color mapping, as well as coordinated highlighting and dedicated 3D navigation. We demonstrate the usefulness of our novel visualization by three examples related to radiation surveillance, traffic analysis, and maritime navigation. User feedback obtained in a small experiment indicates that our hybrid 2D/3D solution can be operated quite well

Easier surveillance of climate-related health vulnerabilities through a Web-based spatial OLAP application

<p>Abstract</p> <p>Background</p> <p>Climate change has a significant impact on population health. Population vulnerabilities depend on several determinants of different types, including biological, psychological, environmental, social and economic ones. Surveillance of climate-related health vulnerabilities must take into account these different factors, their interdependence, as well as their inherent spatial and temporal aspects on several scales, for informed analyses. Currently used technology includes commercial off-the-shelf Geographic Information Systems (GIS) and Database Management Systems with spatial extensions. It has been widely recognized that such OLTP (On-Line Transaction Processing) systems were not designed to support complex, multi-temporal and multi-scale analysis as required above. On-Line Analytical Processing (OLAP) is central to the field known as BI (Business Intelligence), a key field for such decision-support systems. In the last few years, we have seen a few projects that combine OLAP and GIS to improve spatio-temporal analysis and geographic knowledge discovery. This has given rise to SOLAP (Spatial OLAP) and a new research area. This paper presents how SOLAP and climate-related health vulnerability data were investigated and combined to facilitate surveillance.</p> <p>Results</p> <p>Based on recent spatial decision-support technologies, this paper presents a spatio-temporal web-based application that goes beyond GIS applications with regard to speed, ease of use, and interactive analysis capabilities. It supports the multi-scale exploration and analysis of integrated socio-economic, health and environmental geospatial data over several periods. This project was meant to validate the potential of recent technologies to contribute to a better understanding of the interactions between public health and climate change, and to facilitate future decision-making by public health agencies and municipalities in Canada and elsewhere. The project also aimed at integrating an initial collection of geo-referenced multi-scale indicators that were identified by Canadian specialists and end-users as relevant for the surveillance of the public health impacts of climate change. This system was developed in a multidisciplinary context involving researchers, policy makers and practitioners, using BI and web-mapping concepts (more particularly SOLAP technologies), while exploring new solutions for frequent automatic updating of data and for providing contextual warnings for users (to minimize the risk of data misinterpretation). According to the project participants, the final system succeeds in facilitating surveillance activities in a way not achievable with today's GIS. Regarding the experiments on frequent automatic updating and contextual user warnings, the results obtained indicate that these are meaningful and achievable goals but they still require research and development for their successful implementation in the context of surveillance and multiple organizations.</p> <p>Conclusion</p> <p>Surveillance of climate-related health vulnerabilities may be more efficiently supported using a combination of BI and GIS concepts, and more specifically, SOLAP technologies (in that it facilitates and accelerates multi-scale spatial and temporal analysis to a point where a user can maintain an uninterrupted train of thought by focussing on "what" she/he wants (not on "how" to get it) and always obtain instant answers, including to the most complex queries that take minutes or hours with OLTP systems (e.g., aggregated, temporal, comparative)). The developed system respects Newell's cognitive band of 10 seconds when performing knowledge discovery (exploring data, looking for hypotheses, validating models). The developed system provides new operators for easily and rapidly exploring multidimensional data at different levels of granularity, for different regions and epochs, and for visualizing the results in synchronized maps, tables and charts. It is naturally adapted to deal with multiscale indicators such as those used in the surveillance community, as confirmed by this project's end-users.</p

Enhancing Exploratory Analysis across Multiple Levels of Detail of Spatiotemporal Events

Crimes, forest fires, accidents, infectious diseases, human interactions with mobile devices (e.g., tweets) are being logged as spatiotemporal events. For each event, its spatial location, time and related attributes are known with high levels of detail (LoDs). The LoD of analysis plays a crucial role in the user’s perception of phenomena. From one LoD to another, some patterns can be easily perceived or different patterns may be detected, thus requiring modeling phenomena at different LoDs as there is no exclusive LoD to study them. Granular computing emerged as a paradigm of knowledge representation and processing, where granules are basic ingredients of information. These can be arranged in a hierarchical alike structure, allowing the same phenomenon to be perceived at different LoDs. This PhD Thesis introduces a formal Theory of Granularities (ToG) in order to have granules defined over any domain and reason over them. This approach is more general than the related literature because these appear as particular cases of the proposed ToG. Based on this theory we propose a granular computing approach to model spatiotemporal phenomena at multiple LoDs, and called it a granularities-based model. This approach stands out from the related literature because it models a phenomenon through statements rather than just using granules to model abstract real-world entities. Furthermore, it formalizes the concept of LoD and follows an automated approach to generalize a phenomenon from one LoD to a coarser one. Present-day practices work on a single LoD driven by the users despite the fact that the identification of the suitable LoDs is a key issue for them. This PhD Thesis presents a framework for SUmmarizIng spatioTemporal Events (SUITE) across multiple LoDs. The SUITE framework makes no assumptions about the phenomenon and the analytical task. A Visual Analytics approach implementing the SUITE framework is presented, which allow users to inspect a phenomenon across multiple LoDs, simultaneously, thus helping to understand in what LoDs the phenomenon perception is different or in what LoDs patterns emerge