Space matters : geographic variability of electoral turnout determinants in the 2012 London mayoral election

Electoral participation is an important measure of the health of a liberal democracy. The determinants of voter turnout have been examined across a range of elections, but geographical approaches are relatively rare and are mostly performed at large scale aggregations and for national elections. This paper addresses this gap by exploring geographic variability in relationships between the turnout at a local election and socio-demographic variables at a detailed spatial level. Specifically, we focus on the London mayoral election, an important element of the 21st century local government reform in Britain, which, until now, has seldom been analysed from a geographical perspective. By linking the turnout from the 2012 mayoral election to socio-demographic data from the 2011 Census and doing this at the level of London’s 625 wards, for the first time a more detailed picture of the spatially uneven nature of turnout is evidenced than in previous studies which have focused on larger aggregations, typically constituencies. Analysis is approached through spatial analysis using geographically weighted regression (GWR), which enables the investigation of local variations in voting patterns. The results demonstrate that electoral processes do vary over geographic space and that some of the variables that are traditionally assumed to affect the turnout in a specific way, do not do so uniformly over space or even change the direction to the opposite of the traditionally assumed affect in certain locations. Our findings present a starting point for a more detailed investigation as to why this heterogeneity exists and which social processes it relates to.PostprintPeer reviewe

Crowdsourcing indicators for cultural ecosystem services : a geographically weighted approach for mountain landscapes

This study was partially supported by the OpenNESS project funded from the European Union's Seventh Programme for research; technological development and demonstration under grant agreement n° 308428.Integrating cultural dimensions into the ecosystem service framework is essential for appraising non-material benefits stemming from different human-environment interactions. This study investigates how the actual provision of cultural services is distributed across the landscape according to spatially varying relationships. The final aim was to analyse how landscape settings are associated to people’s preferences and perceptions related to cultural ecosystem services in mountain landscapes. We demonstrated a spatially explicit method based on geo-tagged images from popular social media to assess revealed preferences. A spatially weighted regression showed that specific variables correspond to prominent drivers of cultural ecosystem services at the local scale. The results of this explanatory approach can be used to integrate the cultural service dimension into land planning by taking into account specific benefiting areas and by setting priorities on the ecosystems and landscape characteristics which affect the service supply. We finally concluded that the use of crowdsourced data allows identifying spatial patterns of cultural ecosystem service preferences and their association with landscape settings.PostprintPeer reviewe

Activity triangles : a new approach to measure activity spaces

Funding: This work was supported by the EU FP7 Marie Curie ITN GEOCROWD grant (FP7- PEOPLE-2010-ITN-264994) and the ESRC Grant (grant number ES/L011921/1).There is an on-going challenge to describe, analyse and visualise the actual and potential extent of human spatial behaviour. The concept of an activity space has been used to examine how people interact with their environment and how the actual or potential spatial extent of individual spatial behaviour can be defined. In this paper we introduce a new method for measuring activity spaces. We first focus on the definitions and the applications of activity space measures, identifying their respective limitations. We then present our new method, which is based on the theoretical concept of significant locations, that is, places where people spent most of their time. We identify locations of significant places from GPS trajectories and define the activity space of an individual as a set of the first three significant places forming a so-called "activity triangle”. Our new method links the distances travelled for different activities to whether or not people group their activities, which is not possible using existing methods of measuring activity spaces. We test our method on data from a GPS-based travel survey across three towns is Scotland and look at the variations in size and shape of the designed activity triangle among people of different age and gender. We also compare our activity triangle with five other activity spaces and conclude by providing possible routes for improvement of activity space measures when using real human movement data (GPS data).Publisher PDFPeer reviewe

Revisiting the past : replicating fifty-year-old flow analysis using contemporary taxi flow data

JR wishes to acknowledge the support of the EPSRC (Grant # EP/I018433/1). JR, UD and MB were supported by the European Commission grant COSMIC: Complexity in Spatial Dynamics (Complexity-NET/FP6 ERANET) during the early stages of this work. MB further wishes to thank the European Research Council for support under 249393-ERC-2009-AdG.Over sixty years ago, geography began its so-called quantitative revolution, where for the first time statistical methods were used to explain the spatial nature of geographic phenomena. Computers made some of this possible, but their limited power did not allow for more than relatively small analytic explorations and consequently many of these earlier ideas are now buried in the mists of time. Here we attempt to replicate one of these early analyses using taxi flow data collected in 1962 and originally used by Goddard (1970 Goddard, J. B. 1970. Functional regions within the city centre: A study by factor analysis of taxi flows in Central London. Transactions of the Institute of British Geographers 49:161–82; then at the London School of Economics) to extract functional regions within London's city center. Our experiment attempts to replicate Goddard's methodology on a modern taxi flow data set, acquired through Global Positioning System tracking. We initially expected that our analysis would be directly comparable with Goddard's, potentially providing insights into temporal change in the spatial structure of the city core. Attempts at replicating the original analysis have proved enormously difficult, however, for several reasons, including the many subjective choices made by the researcher in articulating and using the original method and the specific characteristics of contemporary taxi flow data. We therefore opt to replicate Goddard's approach as closely and as logically as possible and to fill in gaps based on statistically informed choices. We have also run the analysis on two spatial scales—Central London and a wider area—to explore how scales of analyses that were beyond the capacities of Goddard's early computations also help to shape our understanding of the results he obtained.Publisher PDFPeer reviewe

Long-term exposure to air pollution and mortality in Scotland : a register-based individual-level longitudinal study

Funding: This study is funded by the St Leonard’s interdisciplinary PhD scholarship, School of Geography and Sustainable Development, and School of Medicine, University of St Andrews, Scotland, UK.Background Air pollution is associated with several adverse health outcomes. However, heterogeneity in the size of effect estimates between cohort studies for long-term exposures exist and pollutants like SO2 and mental/behavioural health outcomes are little studied. This study examines the association between long-term exposure to multiple ambient air pollutants and all-cause and cause-specific mortality from both physical and mental illnesses. Methods We used individual-level administrative data from the Scottish-Longitudinal-Study (SLS) on 202,237 individuals aged 17 and older, followed between 2002 and 2017. The SLS dataset was linked to annual concentrations of NO2, SO2, and particulate-matter (PM10, PM2.5) pollution at 1 km2 spatial resolution using the individuals’ residential postcode. We applied survival analysis to assess the association between air pollution and all-cause, cardiovascular, respiratory, cancer, mental/behavioural disorders/suicides, and other-causes mortality. Results Higher all-cause mortality was associated with increasing concentrations of PM2.5, PM10, NO2, and SO2 pollutants. NO2, PM10, and PM2.5 were also associated with cardiovascular, respiratory, cancer and other-causes mortality. For example, the mortality hazard from respiratory diseases was 1.062 (95%CI = 1.028–1.096), 1.025 (95%CI = 1.005–1.045), and 1.013 (95%CI = 1.007–1.020) per 1 μg/m3 increase in PM2.5, PM10 and NO2 pollutants, respectively. In contrast, mortality from mental and behavioural disorders was associated with 1 μg/m3 higher exposure to SO2 pollutant (HR = 1.042; 95%CI = 1.015–1.069). Conclusion This study revealed an association between long-term (16-years) exposure to ambient air pollution and all-cause and cause-specific mortality. The results suggest that policies and interventions to enhance air quality would reduce the mortality hazard from cardio-respiratory, cancer, and mental/behavioural disorders in the long-term.Publisher PDFPeer reviewe

Context-aware movement analysis in ecology : a systematic review

This work was supported by the Coordination for the Improvement of Higher Education Personnel (BEX:13438/13-1), the Leverhulme Trust Research Project Grant (RPG-2018-258); the Discovery grant from the Natural Sciences and Engineering Research Council of Canada the Polish National Science Centre (UMO-2019/35/O/ST6/04127).Research on movement has increased over the past two decades, particularly in movement ecology, which studies animal movement. Taking context into consideration when analysing movement can contribute towards the understanding and prediction of behaviour. The only way for studying animal movement decision-making and their responses to environmental conditions is through analysis of ancillary data that represent conditions where the animal moves. In GIScience this is called Context-Aware Movement Analysis (CAMA). As ecology becomes more data-oriented, we believe that there is a need to both review what CAMA means for ecology in methodological terms and to provide reliable definitions that will bridge the divide between the content-centric and data-centric analytical frameworks. We reviewed the literature and proposed a definition for context, develop a taxonomy for contextual variables in movement ecology and discuss research gaps and open challenges in the science of movement more broadly. We found that the main research for CAMA in the coming years should focus on: 1) integration of contextual data and movement data in space and time, 2) tools that account for the temporal dynamics of contextual data, 3) ways to represent contextualized movement data, and 4) approaches to extract meaningful information from contextualized data.Publisher PDFPeer reviewe

Red deer exhibit spatial and temporal responses to hiking activity

Funding: This project is funded through a joint James Hutton Institute & University of St Andrews collaborative PhD Studentship, the Carnegie Trust and the British Deer Society.Outdoor recreation has the potential to impact the spatial and temporal distribution of animals. We explore interactions between red deer Cervus elaphus and hikers along a popular hiking path in the Scottish Highlands. We placed camera traps in transects at different distances (25, 75 and 150 m) from the path to study whether distance from hiker activity influences the number of deer detected. We compared this with the detection of red deer in an additional, spatially isolated area (one km away from any other transects and the hiking path). We collected count data on hikers at the start of the path and explored hourly (red deer detection during daytime), daily, diurnal (day versus night) and monthly spatial distributions of red deer. Using generalized linear mixed models with forward model selection, we found that the distribution of deer changed with the hiking activity. We found that fewer red deer were detected during busy hourly hiking periods. We found that during daytime, more red deer were detected at 150 m than at 25 m. Moreover, during the day, red deer were detected at a greater rate in the isolated area than around the transects close to the path and more likely to be found close to the path at night. This suggests that avoidance of hikers by red deer, in this study area, takes place over distances greater than 75 m and that red deer are displaced into less disturbed areas when the hiking path is busy. Our results suggest that the impact of hikers is short-term, as deer return to the disturbed areas during the night.Publisher PDFPeer reviewe

Fusion of wildlife tracking and satellite geomagnetic data for the study of animal migration

This work was supported by the Leverhulme Trust [Research Project Grant RPG-2018-258].Background: Migratory animals use information from the Earth’s magnetic field on their journeys. Geomagnetic navigation has been observed across many taxa, but how animals use geomagnetic information to find their way is still relatively unknown. Most migration studies use a static representation of geomagnetic field and do not consider its temporal variation. However, short-term temporal perturbations may affect how animals respond - to understand this phenomenon, we need to obtain fine resolution accurate geomagnetic measurements at the location and time of the animal. Satellite geomagnetic measurements provide a potential to create such accurate measurements, yet have not been used yet for exploration of animal migration. Methods: We develop a new tool for data fusion of satellite geomagnetic data (from the European Space Agency’s Swarm constellation) with animal tracking data using a spatio-temporal interpolation approach. We assess accuracy of the fusion through a comparison with calibrated terrestrial measurements from the International Real-time Magnetic Observatory Network (INTERMAGNET). We fit a generalized linear model (GLM) to assess how the absolute error of annotated geomagnetic intensity varies with interpolation parameters and with the local geomagnetic disturbance. Results: We find that the average absolute error of intensity is − 21.6 nT (95% CI [− 22.26555, − 20.96664]), which is at the lower range of the intensity that animals can sense. The main predictor of error is the level of geomagnetic disturbance, given by the Kp index (indicating the presence of a geomagnetic storm). Since storm level disturbances are rare, this means that our tool is suitable for studies of animal geomagnetic navigation. Caution should be taken with data obtained during geomagnetically disturbed days due to rapid and localised changes of the field which may not be adequately captured. Conclusions: By using our new tool, ecologists will be able to, for the first time, access accurate real-time satellite geomagnetic data at the location and time of each tracked animal, without having to start new tracking studies with specialised magnetic sensors. This opens a new and exciting possibility for large multi-species studies that will search for general migratory responses to geomagnetic cues. The tool therefore has a potential to uncover new knowledge about geomagnetic navigation and help resolve long-standing debates.Publisher PDFPeer reviewe

Analysis of human mobility patterns from GPS trajectories and contextual information

This work was supported by the EU FP7 Marie Curie ITN GEOCROWD grant (FP7- PEOPLE-2010-ITN-264994).Human mobility is important for understanding the evolution of size and structure of urban areas, the spatial distribution of facilities, and the provision of transportation services. Until recently, exploring human mobility in detail was challenging because data collection methods consisted of cumbersome manual travel surveys, space-time diaries or interviews. The development of location-aware sensors has significantly altered the possibilities for acquiring detailed data on human movements. While this has spurred many methodological developments in identifying human movement patterns, many of these methods operate solely from the analytical perspective and ignore the environmental context within which the movement takes place. In this paper we attempt to widen this view and present an integrated approach to the analysis of human mobility using a combination of volunteered GPS trajectories and contextual spatial information. We propose a new framework for the identification of dynamic (travel modes) and static (significant places) behaviour using trajectory segmentation, data mining and spatio-temporal analysis. We are interested in examining if and how travel modes depend on the residential location, age or gender of the tracked individuals. Further, we explore theorised “third places”, which are spaces beyond main locations (home/work) where individuals spend time to socialise. Can these places be identified from GPS traces? We evaluate our framework using a collection of trajectories from 205 volunteers linked to contextual spatial information on the types of places visited and the transport routes they use. The result of this study is a contextually enriched data set that supports new possibilities for modelling human movement behaviour.PostprintPeer reviewe

Simulation experiment to test strategies of geomagnetic navigation during long-distance bird migration

The project was funded by the Leverhulme Trust (Research Project Grant RPG-2018-258).Background Different theories suggest birds may use compass or map navigational systems associated with Earth’s magnetic intensity or inclination, especially during migratory flights. These theories have only been tested by considering properties of the Earth’s magnetic field at coarse temporal scales, typically ignoring the temporal dynamics of geomagnetic values that may affect migratory navigational capacity. Methods We designed a simulation experiment to study if and how birds use the geomagnetic field during migration by using both high resolution GPS tracking data and geomagnetic data at relatively fine spatial and temporal resolutions in comparison to previous studies. Our simulations use correlated random walks (CRW) and correlated random bridge (CRB) models to model different navigational strategies based on underlying dynamic geomagnetic data. We translated navigational strategies associated with geomagnetic cues into probability surfaces that are included in the random walk models. Simulated trajectories from these models were compared to the actual GPS trajectories of migratory birds using 3 different similarity measurements to evaluate which of the strategies was most likely to have occurred. Results and conclusion We designed a simulation experiment which can be applied to different wildlife species under varying conditions worldwide. In the case of our example species, we found that a compass-type strategy based on taxis, defined as movement towards an extreme value, produced the closest and most similar trajectories when compared to original GPS tracking data in CRW models. Our results indicate less evidence for map navigation (constant heading and bi-gradient taxis navigation). Additionally, our results indicate a multifactorial navigational mechanism necessitating more than one cue for successful navigation to the target. This is apparent from our simulations because the modelled endpoints of the trajectories of the CRW models do not reach close proximity to the target location of the GPS trajectory when simulated with geomagnetic navigational strategies alone. Additionally, the magnitude of the effect of the geomagnetic cues during navigation in our models was low in our CRB models. More research on the scale effects of the geomagnetic field on navigation, along with temporally varying geomagnetic data could be useful for further improving future models.Publisher PDFPeer reviewe