Where to put bike counters? Stratifying bicycling patterns in the city using crowdsourced data

This work was supported by a grant from the Public Health Agency of Canada to BikeMaps.org.When designing bicycle count programs, it can be difficult to know where to locate counters to generate a representative sample of bicycling ridership. Crowdsourced data on ridership has been shown to represent patterns of temporal ridership in dense urban areas. Here we use crowdsourced data and machine learning to categorize street segments into classes of temporal patterns of ridership. We used continuous signal processing to group 3,880 street segments in Ottawa, Ontario into six classes of temporal ridership that varied based on overall volume and daily patterns (commute vs non-commute). Transportation practitioners can use this data to strategically place counters across these strata to efficiently capture bicycling ridership counts that better represent the entire city.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

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

Multi-source data fusion of optical satellite imagery to characterize habitat selection from wildlife tracking data

This work was supported by CAPES (Coordination for the Improvement of Higher Education Personnel) [BEX-13438-13-1].Wildlife tracking data allow monitoring of how organisms respond to spatio-temporal changes in resource availability. Remote sensing data can be used to quantify and qualify these variations to understand how movement is related to these changes. The use of remote sensing data with concurrent high levels of spatial and temporal detail may hold potential to improve our understanding of habitat selection. However, no current orbital sensor produces data with simultaneous high temporal and high spatial resolution, therefore alternative methods are required to generate remote sensing data that matches the high spatial-temporal resolution of modern wildlife tracking data. We present an analytical framework, not yet used in movement ecology, for data fusion of optical remote sensing data from multiple satellites and wildlife tracking data to study the impact of seasonal vegetation patterns on the movement of maned wolves (Chrysocyon brachyurus). We use multi-source data fusion to combine MODIS data with higher spatial resolution data (ASTER, Landsat 4-5-7-8, CBERS 2-2B) and create a synthetic NDVI product with a 15 m spatial detail and daily temporal resolution. We also use the higher spatial resolution data to create a multi-source NDVI product with same level of spatial detail but coarser temporal resolution and data from MODIS to create a single-source NDVI product with high temporal resolution but coarse spatial resolution. We combine the three different spatial-temporal resolution NDVI products with GPS tracking data of maned wolves to create step-selection functions (SSF), which are models used in ecology to investigate and predict habitat selection by animals. The SSF model based on multi-source NDVI had the best performance predicting the probability of use of visited locations given its NDVI value. The SSF based on the raw MODIS NDVI product, one which is commonly employed by ecologists, had the poorest performance for our study species. These findings indicate that, in contrast with current practice in movement ecology, a detailed spatial resolution of contextual environmental variable may be more important than a detailed temporal resolution, when investigating wildlife habitat selection regarding vegetation, although this result will be highly dependent on species. The choice of data set should therefore take into account not only the scale of movement but also the spatial and temporal scales at which dynamic environmental variables are changing.PostprintPostprintPeer reviewe

Intraurban Analysis of Surface Urban Heat Island From Disagregated Thermal Radiance Images

Surface Urban Heat Islands (SUHI) are areas with higher surface temperatures than their surroundings. Several studies have used thermal images from satellites to research the influence of urbanization on surface temperature patterns, however the low spatial resolution of thermal sensors limits the analysis of LST intraurban variations. Attempting to overcome this limitation, we used the Enhanced Physical Model (EPM) for disaggregation of land surface temperature (DLST) to generate fine scale LST for Sao Paulo city in Brazil. This method uses a linear regression and Planck’s law to combine NDVI, NDWI and UI to estimate LST at finer spatial detail. First, we calibrate the method by upscaling an ASTER thermal band to 1000 m and using EPM to estimate the original 100 m thermal band. The original and estimated ASTER thermal bands achieved and R² of 0.66. Following, we apply the EPM model to estimate the LST at 15 m and compare it with data from meteorological stations. The 15 m LST image facilitated the identification of potential SUHIs. The EPM model provides an enhanced product with higher level of spatial detail, which allows researchers to identify changes of surface temperature that would not be evident from an ASTER LST (90 m spatial resolution) product. In summary, the model allowed us to quantify and map the influence of different urbanization patterns on the LST distribution.Ilhas de calor de superfície (ICS)são áreas com temperature de superfície maior do que as áreas ao redor. Vários estudos tem usado imagens termais de satélite para investigar a influência da urbanização nos padrões de temperatura de superfície; entretanto a baixa resolução espacial dos atuais sensores termais limita a análise dos padrões de variação intraurbana de temperatura de superfície. Com o objetivo de surpassar essa limitação, nós utilizamos o the Enhanced Physical Model (EPM) para gerar dados de temperatura de superfície com maior nível de detalhamento para a cidade de São Paulo- Brasil. Esse método utiliza um modelo de regressão linear e a lei de Planck para combinar NDVI, NDWI e UI para estimar a temperatura de superfície com maior nível de detalhes espaciais.  Primeiro, para calibrar o modelo, nós reamostramos uma banda termal ASTER para 1000 m e utilizamos o método EPM para estimar a banda original de 100 m. A banda termal estimatada de 100 m atingiu um R2= 0.66 em relação a banda termal original. A seguir,  nós aplicamos o método EPM para estimar a temperatura de superfície à 15 m. A imagem de temperatura de superfície de 15 m facilitou a identificação de potenciais ilhas de calor de superfície. O modelo EPM fornece um produto com alto grau de detalhamento espacial, o que permite que pesquisadores identifiquem as mudanças de temperatura de superfície que não seriam evidentes na imagem  termal ASTER original (90 m de resolução espacial). Em suma, o modelo nos permitiu quantificar e mapear a influência de diferentes padrões de urbanização na distribuição dos padrões de temperatura de superfície

Access to human-mobility data is essential for building a sustainable future

Funding: This article is a contribution of the COVID-19 Bio-Logging Initiative, which is funded in part by the Gordon and Betty Moore Foundation (GBMF9881) and the National Geographic Society (NGS-82315R-20) (both grants to C.R.) and endorsed by the United Nations Decade of Ocean Science for Sustainable Development. The authors also gratefully acknowledge support from the Kuni Endowed Junior Faculty Fellowship at the Bren School of Environmental Science & Management (to R.Y.O.); NASA FINESST (80NSSC22K1535) and the Yale Institute for Biospheric Studies (to D.E.S.); the National Biodiversity Future Center via the PNRR funds (Mission 4, Component 2, Investment 1.4) of the Italian Ministry of University and Resarch, Project CN00000033 (to F.C.); and the Natural Sciences and Engineering Research Council of Canada (to J.L.).Mobile devices, and other tracking technologies, generate detailed data on the movements and behavior of billions of people worldwide. At present, these data are predominantly used to pursue corporate interests. We argue that improving access to human-mobility data is essential for addressing urgent conservation and sustainability goals. Close collaboration between industry and the research community has the potential to generate substantive environmental and societal benefits.Peer reviewe

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

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

New methods and applications for context aware movement analysis (CAMA)

Recent years have seen a rapid growth in movement research owing to new technologies contributing to the miniaturization and reduced costs of tracking devices. Similar trends have occurred in how environmental data are being collected (e.g., through satellites, unmanned aerial vehicles, and sensor networks). However, the development of analytical techniques for movement research has failed to keep pace with the data collection advances. There is a need for new methods capable of integrating increasingly detailed movement data with a myriad of contextual data - termed context aware movement analysis (CAMA). CAMA investigates more than movement geometry, by including biological and environmental conditions that may influence movement. However, there is a shortage of methods relating movement patterns to contextual factors, which is still limiting our ability to extract meaningful information from movement data. This thesis contributes to this methodological research gap by assessing the state-of-the art for CAMA within movement ecology and human mobility research, developing innovative methods to consider the spatio-temporal differences between movement data and contextual data and exploring computational methods that allow identification of patterns in contextualized movement data. We developed new methods and demonstrated how they facilitated and improved the integration between high frequency tracking data and temporally dynamic environmental variables. One of the methods, multi-channel sequence analysis, is then used to discover varying human behaviour relative to weather conditions in a large human GPS tracking dataset from Scotland. The second method is developed for combing multi-sensor satellite imagery (i.e., image fusion) of differing spatial and temporal resolutions. This method is applied to a GPS tracking data on maned wolves in Brazil to understand fine-scale movement behaviours related to vegetation changes across seasons. In summary, this thesis provides a significant development in terms of new ideas and techniques for performing CAMA for human and wildlife movement studies."The research leading to these results has received funding from the Science Without Borders Programme (CAPES BEX 3438/13 - 1) in the form of the author's PhD scholarship." - p. v