Quantifying gaze and mouse interactions on spatial visual interfaces with a new movement analytics methodology

This research was supported by the Royal Society International Exchange Programme (grant no. IE120643).Eye movements provide insights into what people pay attention to, and therefore are commonly included in a variety of human-computer interaction studies. Eye movement recording devices (eye trackers) produce gaze trajectories, that is, sequences of gaze location on the screen. Despite recent technological developments that enabled more affordable hardware, gaze data are still costly and time consuming to collect, therefore some propose using mouse movements instead. These are easy to collect automatically and on a large scale. If and how these two movement types are linked, however, is less clear and highly debated. We address this problem in two ways. First, we introduce a new movement analytics methodology to quantify the level of dynamic interaction between the gaze and the mouse pointer on the screen. Our method uses volumetric representation of movement, the space-time densities, which allows us to calculate interaction levels between two physically different types of movement. We describe the method and compare the results with existing dynamic interaction methods from movement ecology. The sensitivity to method parameters is evaluated on simulated trajectories where we can control interaction levels. Second, we perform an experiment with eye and mouse tracking to generate real data with real levels of interaction, to apply and test our new methodology on a real case. Further, as our experiment tasks mimics route-tracing when using a map, it is more than a data collection exercise and it simultaneously allows us to investigate the actual connection between the eye and the mouse. We find that there seem to be natural coupling when eyes are not under conscious control, but that this coupling breaks down when instructed to move them intentionally. Based on these observations, we tentatively suggest that for natural tracing tasks, mouse tracking could potentially provide similar information as eye-tracking and therefore be used as a proxy for attention. However, more research is needed to confirm this.Publisher PDFPeer reviewe

A spatially aware method for mapping movement-based and place-based regions from spatial flow networks

This work was supported by the Economic and Social Research Council and The Scottish Graduate School of Social Science.Community detection (CD) is a frequent method for analysing flow networks in geography. It allows us to partition the network into a set of densely interconnected regions, called communities. We introduce a new technique for including geographical weighting into existing methods for detecting spatially coherent communities. We take a link-based CD algorithm and adjust it to incorporate geographical weighting. We call this approach geographically weighted community detection (GWCD). Our method is demonstrated on two case studies of commonly encountered flow networks: commuter flows and taxi pick-up/drop-off flows. Further, we test different measures of distance for geographic weighting and compare our results with the unmodified CD algorithm. Our results show that GWCD can capture the geographical nature of flow regions, generating spatially smaller and more compact areas than if geography is omitted and that it can be used to distinguish between different types of movement-type communities.Publisher PDFPeer reviewe

Combining Geographically Weighted Regression and Geovisual Analytics to investigate temporal variations in house price determinants across London in the period 1980-1998

Hedonic price modelling attempts to uncover information on the determinants of prices - in this case the prices are those of houses in the Greater London area for the period between 1980 and 1998. The determinants of house prices can include house attributes (such as size, type of building, age, etc.), neighbourhood attributes (such as proportion of unemployed people in the neighbourhood or local tax rates) and geographic attributes (such as distance from the city centre or proximity to various amenities) (Orford 1999). Almost all applications of hedonic price models applied to housing are in the form of multiple linear regression models where price is regressed on various attributes. The parameter estimates from the calibration of this type of regression model are assumed to yield information on the relative importance of various attributes in influencing price. One major problem with this approach is that it assumes that the determinants of prices are the same in all parts of the study area. This seems particularly illogical in this type of application where there could easily be local variations in preferences and also in supply and demand relationships. Hence, it seems reasonable to calibrate local hedonic price models rather than global ones – that is, to calibrate a model form which is flexible enough to allow the determinants of house prices to vary spatially. Geographically Weighted Regression (GWR) (Fotheringham et al. 2002) is a statistical technique that allows local calibrations and which yields local estimates of the determinants of house prices. GWR was recently used to investigate spatial variations in house price determinants across London separately for each of the years between 1980 and 1998 (Crespo et al. 2007). The result of the GWR analysis is a set of continuous localised parameter estimate surfaces which describe the geography of the parameter space. These surfaces are typically visualised with a set of univariate choropleth maps for each surface which are used to examine the plausibility of the stationarity assumption of the traditional regression and different possible causes of non-stationarity for each separate parameter (Fotheringham and al. 2002). The downside of these separate univariate visualisations is that multivariate spatial and non-spatial relationships and patterns in the parameter space can not be seen. In an attempt to counter this inadequacy, in a previous study we suggested to treat the result space of one single GWR analysis as a multivariate dataset and visually explore it (Demšar et al. 2007). The goal was to identify spatial and multivariate patterns that the separate univariate mapping could not recognise. In this paper we extend this approach with the temporal dimension: we use Geovisual Analytical exploration to investigate the spatio-temporal dynamics in a time series of GWR hedonic price models. The idea is to merge the time series of GWR result spaces (one space per year) into one single highly-dimensional spatio-temporal dataset, which we then visually explore in an attempt to uncover information about the temporal and spatio-temporal behaviour of parameter estimates of GWR and consequently of underlying geographical processes

Potential path volume (PPV) : a geometric estimator for space use in 3D

Urška Demšar is supported by a Leverhulme Trust Research Project Grant (RPG-2018-258).Background: Many animals move in three dimensions and many animal tracking studies collect the data on their movement in three physical dimensions. However, there is a lack of approaches that consider the vertical dimension when estimating animal space use, which is problematic, as this can lead to mistakes in quantification of spatial differentiation, level of interaction between individuals or species, and the use of resources at different vertical levels. Methods: This paper introduces a new geometric estimator for space use in 3D, the Potential Path Volume (PPV). The concept is based on time geography and generalises the accessibility measure, the Potential Path Area (PPA) into three dimensions. We derive the PPV mathematically and present an algorithm for their calculation. Results: We demonstrate the use of the PPV in a case study using an open data set of 3D bird tracking data. We also calculate the size of the PPV to see how this corresponds to trip type (specifically, we calculate PPV sizes for departure/return foraging trips from/to a colony) and evaluate the effect of the temporal sampling on the PPV size. PPV sizes increase with the increased temporal resolution, but we do not see the expected pattern than return PPV should be smaller than departure PPV. We further discuss the problem of different speeds in vertical and horizontal directions that are typical for animal movement and to address this rescale the PPV with the ratio of the two speeds. Conclusions: The PPV method represents a new tool for space use analysis in movement ecology where object movement occurs in three dimensions, and one which can be extended to numerous different application areas.Publisher PDFPeer reviewe

Does long-term air pollution exposure affect self-reported health and limiting long term illness disproportionately for ethnic minorities in the UK? A census-based individual level analysis

This study is part of a PhD project that was supported by the St Leonard’s interdisciplinary PhD scholarship, University of St Andrews, Scotland, UK.Previous studies have investigated the impact of air pollution on health and mortality. However, there is little research on how this impact varies by individuals’ ethnicity. Using a sample of more than 2.5-million individuals aged 16 and older from the 2011 UK census linked to 10-years air pollution data, this article investigates the effect of air pollution on self-reported general health and limiting long-term illness (LLTI) in five main ethnic groups and by country of birth in UK. The association of air pollution with self-reported health and LLTI by individual’s ethnicity was examined using two levels mixed-effects generalised-linear models. Pakistani/Bangladeshi, Indian, Black/African/Caribbean, and other ethnic minorities and people born outside UK/Ireland were more likely to report poorer health and the presence of LLTI than White-group and UK/Ireland born individuals. Higher concentrations of NO2, SO2 and CO pollutants were associated with poorer self-reported health and the presence of LLTI in the UK population. Analysis by ethnicity showed a more pronounced effect of NO2, PM10, PM2.5, and CO air pollution on poor self-reported health and the presence of LLTI among ethnic minorities, mostly for people from Black/African/Caribbean origin compared to White people, and among non-UK/Ireland born individuals compared to natives. Using a large-scale individual-level census data linked to air pollution spatial data, our study supports the long-term deteriorating effect of air pollution on self-reported health and LLTI, which is more pronounced for ethnic minorities and non-natives.Publisher PDFPeer reviewe

Intervento del rappresentante degli studenti, dott.ssa Gisella De Rosa

Research presented in this paper was funded by a Strategic Research Cluster grant [07/SRC/I1168] by the Science Foundation Ireland under the National Development Plan. Special Issue: Web and wireless GISThe quantity and quality of spatial data are increasing rapidly. This is particularly evident in the case of movement data. Devices capable of accurately recording the position of moving entities have become ubiquitous and created an abundance of movement data. Valuable knowledge concerning processes occurring in the physical world can be extracted from these large movement data sets. Geovisual analytics offers powerful techniques to achieve this. This article describes a new geovisual analytics tool specifically designed for movement data. The tool features the classic space-time cube augmented with a novel clustering approach to identify common behaviour. These techniques were used to analyse pedestrian movement in a city environment which revealed the effectiveness of the tool for identifying spatiotemporal patterns.PostprintPeer reviewe

Interpreting Pedestrian Behaviour by Visualising and Clustering Movement Data

Recent technological advances have increased the quantity of movement data being recorded. While valuable knowledge can be gained by analysing such data, its sheer volume creates challenges. Geovisual analytics, which helps the human cognition process by using tools to reason about data, offers powerful techniques to resolve these challenges. This paper introduces such a geovisual analytics environment for exploring movement trajectories, which provides visualisation interfaces, based on the classic space-time cube. Additionally, a new approach, using the mathematical description of motion within a space-time cube, is used to determine the similarity of trajectories and forms the basis for clustering them. These techniques were used to analyse pedestrian movement. The results reveal interesting and useful spatiotemporal patterns and clusters of pedestrians exhibiting similar behaviour

Air pollution and individuals’ mental well-being in the adult population in United Kingdom : a spatial-temporal longitudinal study and the moderating effect of ethnicity

This paper is part of a PhD project that is funded by the St Leonard’s PhD scholarship, University of St Andrews, Scotland, United Kingdom.Background Recent studies suggest an association between ambient air pollution and mental well-being, though evidence is mostly fragmented and inconclusive. Research also suffers from methodological limitations related to study design and moderating effect of key demographics (e.g., ethnicity). This study examines the effect of air pollution on reported mental well-being in United Kingdom (UK) using spatial-temporal (between-within) longitudinal design and assesses the moderating effect of ethnicity. Methods Data for 60,146 adult individuals (age:16+) with 349,748 repeated responses across 10-data collection waves (2009–2019) from “Understanding-Society: The-UK-Household-Longitudinal-Study” were linked to annual concentrations of NO2, SO2, PM10, and PM2.5 pollutants using the individuals’ place of residence, given at the local-authority and at the finer Lower-Super-Output-Areas (LSOAs) levels; allowing for analysis at two geographical scales across time. The association between air pollution and mental well-being (assessed through general-health-questionnaire-GHQ12) and its modification by ethnicity and being non-UK born was assessed using multilevel mixed-effect logit models. Results Higher odds of poor mental well-being was observed with every 10μg/m3 increase in NO2, SO2, PM10 and PM2.5 pollutants at both LSOAs and local-authority levels. Decomposing air pollution into spatial-temporal (between-within) effects showed significant between, but not within effects; thus, residing in more polluted local-authorities/LSOAs have higher impact on poor mental well-being than the air pollution variation across time within each geographical area. Analysis by ethnicity revealed higher odds of poor mental well-being with increasing concentrations of SO2, PM10, and PM2.5 only for Pakistani/Bangladeshi, other-ethnicities and non-UK born individuals compared to British-white and natives, but not for other ethnic groups. Conclusion Using longitudinal individual-level and contextual-linked data, this study highlights the negative effect of air pollution on individuals’ mental well-being. Environmental policies to reduce air pollution emissions can eventually improve the mental well-being of people in UK. However, there is inconclusive evidence on the moderating effect of ethnicity.Publisher PDFPeer reviewe

Designing geovisual analytics environments and displays with humans in mind

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