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Citizen science initiatives span a wide range of topics, designs, and research needs. Despite this heterogeneity, there are several common barriers to the uptake and sustainability of citizen science projects and the information they generate. One key barrier often cited in the citizen science literature is data quality. Open-source tools for the analysis, visualization, and reporting of citizen science data hold promise for addressing the challenge of data quality, while providing other benefits such as technical capacity-building, increased user engagement, and reinforcing data sovereignty. We developed an operational citizen science tool called the Community Water Data Analysis Tool (CWDAT)—a R/Shiny-based web application designed for community-based water quality monitoring. Surveys and facilitated user-engagement were conducted among stakeholders during the development of CWDAT. Targeted recruitment was used to gather feedback on the initial CWDAT prototype’s interface, features, and potential to support capacity building in the context of community-based water quality monitoring. Fourteen of thirty-two invited individuals (response rate 44%) contributed feedback via a survey or through facilitated interaction with CWDAT, with eight individuals interacting directly with CWDAT. Overall, CWDAT was received favourably. Participants requested updates and modifications such as water quality thresholds and indices that reflected well-known barriers to citizen science initiatives related to data quality assurance and the generation of actionable information. Our findings support calls to engage end-users directly in citizen science tool design and highlight how design can contribute to users’ understanding of data quality. Enhanced citizen participation in water resource stewardship facilitated by tools such as CWDAT may provide greater community engagement and acceptance of water resource management and policy-making.
Annie Gray; Colin Robertson; Rob Feick. CWDAT—An Open-Source Tool for the Visualization and Analysis of Community-Generated Water Quality Data. ISPRS International Journal of Geo-Information 2021, 10, 207 .
AMA StyleAnnie Gray, Colin Robertson, Rob Feick. CWDAT—An Open-Source Tool for the Visualization and Analysis of Community-Generated Water Quality Data. ISPRS International Journal of Geo-Information. 2021; 10 (4):207.
Chicago/Turabian StyleAnnie Gray; Colin Robertson; Rob Feick. 2021. "CWDAT—An Open-Source Tool for the Visualization and Analysis of Community-Generated Water Quality Data." ISPRS International Journal of Geo-Information 10, no. 4: 207.
Droughts on the North American Great Plains once led to elevated levels of out‐migration from rural areas. Large‐scale drought migration has not been observed since the 1950s due to changes in land management and agricultural systems that lessened farm‐level vulnerability to drought. Have droughts had less observable population impacts in subsequent decades? Here, we present findings from an investigation of an unusually severe, localised drought that struck eastern South Dakota in 1976 and caused staggering financial losses to farms. County‐level population and net migration rates show an anomalous increase of migration into drought‐affected counties by male migrants in the age group 30–35 years, likely being return migrants coming to help on the family farm. Newspaper archives and interviews with retired farmers suggest that few people moved away during the 1976 drought; most adapted instead by selling off their livestock herds and taking on greater debt. However, a commonly expressed view is that the drought ‘softened up’ area farmers, increasing their vulnerability to interest rates that quadrupled in the three following years. The early 1980s saw high rates of farm failures, unemployment and population decline in counties that experienced the worst impacts of the 1976 drought, suggesting the drought had a lag effect on population patterns. The findings from this case study are consistent with the ‘lessening hypothesis’ that social and technological innovations reduce economic and population impacts of recurrent climatic risks but elevate vulnerability to less frequent, unusually severe events.
Robert McLeman; Francesca Fontanella; Clara Greig; George Heath; Colin Robertson. Population responses to the 1976 South Dakota drought: Insights for wider drought migration research. Population, Space and Place 2021, e2465 .
AMA StyleRobert McLeman, Francesca Fontanella, Clara Greig, George Heath, Colin Robertson. Population responses to the 1976 South Dakota drought: Insights for wider drought migration research. Population, Space and Place. 2021; ():e2465.
Chicago/Turabian StyleRobert McLeman; Francesca Fontanella; Clara Greig; George Heath; Colin Robertson. 2021. "Population responses to the 1976 South Dakota drought: Insights for wider drought migration research." Population, Space and Place , no. : e2465.
Convolutional neural networks (CNNs) are known for their ability to learn shape and texture descriptors useful for object detection, pattern recognition, and classification problems. Deeper layer filters of CNN generally learn global image information vital for whole-scene or object discrimination. In landscape pattern comparison, however, dense localized information encoded in shallow layers can contain discriminative information for characterizing changes across image local regions but are often lost in the deeper and non-spatial fully connected layers. Such localized features hold potential for identifying, as well as characterizing, process–pattern change across space and time. In this paper, we propose a simple yet effective texture-based CNN (Tex-CNN) via a feature concatenation framework which results in capturing and learning texture descriptors. The traditional CNN architecture was adopted as a baseline for assessing the performance of Tex-CNN. We utilized 75% and 25% of the image data for model training and validation, respectively. To test the models’ generalization, we used a separate set of imagery from the Aerial Imagery Dataset (AID) and Sentinel-2 for model development and independent validation. The classical CNN and the Tex-CNN classification accuracies in the AID were 91.67% and 96.33%, respectively. Tex-CNN accuracy was either on par with or outcompeted state-of-the-art methods. Independent validation on Sentinel-2 data had good performance for most scene types but had difficulty discriminating farm scenes, likely due to geometric generalization of discriminative features at the coarser scale. In both datasets, the Tex-CNN outperformed the classical CNN architecture. Using the Tex-CNN, gradient-based spatial attention maps (feature maps) which contain discriminative pattern information are extracted and subsequently employed for mapping landscape similarity. To enhance the discriminative capacity of the feature maps, we further perform spatial filtering, using PCA and select eigen maps with the top eigen values. We show that CNN feature maps provide descriptors capable of characterizing and quantifying landscape (dis)similarity. Using the feature maps histogram of oriented gradient vectors and computing their Earth Movers Distances, our method effectively identified similar landscape types with over 60% of target-reference scene comparisons showing smaller Earth Movers Distance (EMD) (e.g., 0.01), while different landscape types tended to show large EMD (e.g., 0.05) in the benchmark AID. We hope this proposal will inspire further research into the use of CNN layer feature maps in landscape similarity assessment, as well as in change detection.
Karim Malik; Colin Robertson. Landscape Similarity Analysis Using Texture Encoded Deep-Learning Features on Unclassified Remote Sensing Imagery. Remote Sensing 2021, 13, 492 .
AMA StyleKarim Malik, Colin Robertson. Landscape Similarity Analysis Using Texture Encoded Deep-Learning Features on Unclassified Remote Sensing Imagery. Remote Sensing. 2021; 13 (3):492.
Chicago/Turabian StyleKarim Malik; Colin Robertson. 2021. "Landscape Similarity Analysis Using Texture Encoded Deep-Learning Features on Unclassified Remote Sensing Imagery." Remote Sensing 13, no. 3: 492.
Despite numerous studies in statistical downscaling methodologies, there remains a lack of methods that can downscale from precipitation modeled in global climate models to regional level high resolution gridded precipitation. This paper reports a novel downscaling method using a Generative Adversarial Network (GAN), CliGAN, which can downscale large-scale annual maximum precipitation given by simulation of multiple atmosphere-ocean global climate models (AOGCM) from Coupled Model Inter-comparison Project 6 (CMIP6) to regional-level gridded annual maximum precipitation data. This framework utilizes a convolution encoder-dense decoder network to create a generative network and a similar network to create a critic network. The model is trained using an adversarial training approach. The critic uses the Wasserstein distance loss function and the generator is trained using a combination of adversarial loss Wasserstein distance, structural loss with the multi-scale structural similarity index (MSSIM), and content loss with the Nash-Sutcliff Model Efficiency (NS). The MSSIM index allowed us to gain insight into the model’s regional characteristics and shows that relying exclusively on point-based error functions, widely used in statistical downscaling, may not be enough to reliably simulate regional precipitation characteristics. Further use of structural loss functions within CNN-based downscaling methods may lead to higher quality downscaled climate model products.
Chiranjib Chaudhuri; Colin Robertson. CliGAN: A Structurally Sensitive Convolutional Neural Network Model for Statistical Downscaling of Precipitation from Multi-Model Ensembles. Water 2020, 12, 3353 .
AMA StyleChiranjib Chaudhuri, Colin Robertson. CliGAN: A Structurally Sensitive Convolutional Neural Network Model for Statistical Downscaling of Precipitation from Multi-Model Ensembles. Water. 2020; 12 (12):3353.
Chicago/Turabian StyleChiranjib Chaudhuri; Colin Robertson. 2020. "CliGAN: A Structurally Sensitive Convolutional Neural Network Model for Statistical Downscaling of Precipitation from Multi-Model Ensembles." Water 12, no. 12: 3353.
Despite numerous studies in statistical downscaling methodology, there remains a lack of methods that can downscale from precipitation modeled in global climate models to regional level high resolution gridded precipitation. This paper reports a novel downscaling method using a Generative Adversarial Network (GAN), CliGAN, which can downscale large-scale annual maximum precipitation given by simulation of multiple atmosphere-ocean global climate models (AOGCM) from Coupled Model Inter-comparison Project 6 (CMIP6) to regional-level gridded annual maximum precipitation data. This framework utilizes a convolution encoder-dense decoder network to create a generative network and a similar network to create a critic network. The model is trained using an adversarial training approach. The critic uses the Wasserstein distance loss function and the generator is trained using a combination of adversarial loss Wasserstein distance, structural loss with the multi-scale structural similarity index (MSSIM), and content loss with the Nash-Sutcliff Model Efficiency (NS). The MSSIM index allowed us to gain insight into the model’s regional characteristics and shows that relying exclusively on point-based error functions, widely used in statistical downscaling, may not be enough to reliably simulate regional precipitation characteristics. Further use of structural loss functions within CNN-based downscaling methods may lead to higher quality downscaled climate model products.
Chiranjib Chaudhuri; Colin Robertson. CliGAN: A Structurally Sensitive Convolutional Neural Network Model for Statistical Downscaling of Precipitation from Multi-Model Ensembles. 2020, 1 .
AMA StyleChiranjib Chaudhuri, Colin Robertson. CliGAN: A Structurally Sensitive Convolutional Neural Network Model for Statistical Downscaling of Precipitation from Multi-Model Ensembles. . 2020; ():1.
Chicago/Turabian StyleChiranjib Chaudhuri; Colin Robertson. 2020. "CliGAN: A Structurally Sensitive Convolutional Neural Network Model for Statistical Downscaling of Precipitation from Multi-Model Ensembles." , no. : 1.
Social media and other forms of volunteered geographic information (VGI) are used frequently as a source of fine-grained big data for research. While employing geographically referenced social media data for a wide array of purposes has become commonplace, the relevant scales over which these data apply to is typically unknown. For researchers to use VGI appropriately (e.g., aggregated to areal units (e.g., neighbourhoods) to elicit key trend or demographic information), general methods for assessing the quality are required, particularly, the explicit linkage of data quality and relevant spatial scales, as there are no accepted standards or sampling controls. We present a data quality metric, the Spatial-comprehensiveness Index (S-COM), which can delineate feasible study areas or spatial extents based on the quality of uneven and dynamic geographically referenced VGI. This scale-sensitive approach to analyzing VGI is demonstrated over different grains with data from two citizen science initiatives. The S-COM index can be used both to assess feasible study extents based on coverage, user-heterogeneity, and density and to find feasible sub-study areas from a larger, indefinite area. The results identified sub-study areas of VGI for focused analysis, allowing for a larger adoption of a similar methodology in multi-scale analyses of VGI.
Haydn Lawrence; Colin Robertson; Rob Feick; Trisalyn Nelson. The Spatial-Comprehensiveness (S-COM) Index: Identifying Optimal Spatial Extents in Volunteered Geographic Information Point Datasets. ISPRS International Journal of Geo-Information 2020, 9, 497 .
AMA StyleHaydn Lawrence, Colin Robertson, Rob Feick, Trisalyn Nelson. The Spatial-Comprehensiveness (S-COM) Index: Identifying Optimal Spatial Extents in Volunteered Geographic Information Point Datasets. ISPRS International Journal of Geo-Information. 2020; 9 (9):497.
Chicago/Turabian StyleHaydn Lawrence; Colin Robertson; Rob Feick; Trisalyn Nelson. 2020. "The Spatial-Comprehensiveness (S-COM) Index: Identifying Optimal Spatial Extents in Volunteered Geographic Information Point Datasets." ISPRS International Journal of Geo-Information 9, no. 9: 497.
This chapter reviews Collins and Evans’ “Periodic Table of Expertises” and applies its expanded model of expertise to geographical expertise—the area of expertise related to places. Taking the position that expertise can be acquired through both experience and formal training, this chapter explores how thinking about geographic knowledge and expertise has evolved to encompass both place-based and process-based dimensions. Specifically, it demonstrates how recent developments in mobile phone and geolocation technologies change the dimensional reconstruction of particular forms of geographic expertise.
Colin Robertson; Rob Feick. Geographical Expertise: From Places to Processes and Back Again. The Third Wave in Science and Technology Studies 2019, 87 -104.
AMA StyleColin Robertson, Rob Feick. Geographical Expertise: From Places to Processes and Back Again. The Third Wave in Science and Technology Studies. 2019; ():87-104.
Chicago/Turabian StyleColin Robertson; Rob Feick. 2019. "Geographical Expertise: From Places to Processes and Back Again." The Third Wave in Science and Technology Studies , no. : 87-104.
Social media has greatly expanded opportunities to study place and well-being through the availability of human expressions tagged with physical location. Such research often uses social media content to study how specific places in the offline world influence well-being without acknowledging that digital platforms (e.g., Twitter, Facebook, Youtube, Yelp) are designed in unique ways that structure certain types of interactions in online and offline worlds, which can influence place-making and well-being. To expand our understanding of the mechanisms that influence social media expressions about well-being, we describe an ecological framework of person-place interactions that asks, “at what broad levels of interaction with digital platforms and physical environments do effects on well-being manifest?” The person is at the centre of the ecological framework to recognize how people define and organize both digital and physical communities and interactions. The relevance of interactions in physical environments depends on the built and natural characteristics encountered across modes of activity (e.g., domestic, work, study). Here, social interactions are stratified into the meso-social (e.g., local social norms) and micro-social (e.g., personal conversations) levels. The relevance of interactions in digital platforms is contingent on specific hardware and software elements. Social interactions at the meso-social level include platform norms and passive use of social media, such as observing the expressions of others, whereas interactions at the micro-level include more active uses, like direct messaging. Digital platforms are accessed in a physical location, and physical locations are partly experienced through online interactions; therefore, interactions between these environments are also acknowledged. We conclude by discussing the strengths and limitations of applying the framework to studies of place and well-being.
Ketan Shankardass; Colin Robertson; Krystelle Shaughnessy; Martin Sykora; Rob Feick. A unified ecological framework for studying effects of digital places on well-being. Social Science & Medicine 2018, 227, 119 -127.
AMA StyleKetan Shankardass, Colin Robertson, Krystelle Shaughnessy, Martin Sykora, Rob Feick. A unified ecological framework for studying effects of digital places on well-being. Social Science & Medicine. 2018; 227 ():119-127.
Chicago/Turabian StyleKetan Shankardass; Colin Robertson; Krystelle Shaughnessy; Martin Sykora; Rob Feick. 2018. "A unified ecological framework for studying effects of digital places on well-being." Social Science & Medicine 227, no. : 119-127.
With access to collections of continuous satellite imagery over a 40-year period, spectral-temporal patterns extracted from multi-temporal imagery offer a potential new tool to model mechanisms of forest succession and monitor changes in forested landscapes. Specifically, spectral-temporal trajectories associated with successional forest change occurring over prolonged periods of time may enhance periodic ‘snapshot’ monitoring methods, especially for species that exhibit complex and non-linear dynamics. In this paper, Landsat time-series are used to examine the spectral-temporal signatures of bamboo-dominated forest succession occurring within the critically threatened Araucaria Forest, a pine-dominated subtype of the Atlantic Forest in southern Brazil. Alteration of canopy structure through ongoing anthropogenic disturbance has increased understorey light climate and given opportunity for native invasive bamboos to flourish, resulting in drastic reduction of tree regeneration and loss of biodiversity. We aimed to evaluate how spectral-temporal signatures could be used to (1) characterize stages of bamboo-dominated forest succession, (2) identify synchrony of bamboo lifecycle dynamics and (3) classify regions of bamboo-dominated forest. Changepoint analysis was performed using an extracted sample spectral-temporal signature and trajectories were fit to the resulting segments using linear regression. Based on slope values of the fitted segments, a novel description incorporating temporal information of bamboo-dominated forest succession was developed which identified four broad phases: pioneer predominance, mature bamboo, dieback and pioneer regeneration. To determine the spatial and temporal synchrony of bamboo-dominated forest succession, a hybrid model was developed by combining the modelled segments and compared to a 32-year Landsat time-series of vegetation indices by calculating root-mean square error between each pixel in the study area. The hybrid model proficiently classified regions of bamboo-dominance, achieving between 77% and 90% accuracy, which also indicated lifecycle synchrony of bamboo populations within the study area. To further assess the performance of the hybrid model, a time-weighted dynamic time warping model approach was used to determine synchrony and classify regions of bamboo. The time-weighted dynamic time warping classifier had lower overall accuracy (68%–82%), but is still considered a useful tool for automated classification purposes that take advantage of multi-temporal imagery. To compare classification performance between ‘snapshot’ and multi-temporal imagery classifiers, a maximum-likelihood classification was performed, which attained lower overall accuracies than the hybrid model (75%–84%). Overall, the use of spectral-temporal signatures offers a novel and effective approach to both describing and modelling bamboo-dominated forest succession (and forest successional processes more generally)...
Clara Greig; Colin Robertson; André E.B. Lacerda. Spectral-temporal modelling of bamboo-dominated forest succession in the Atlantic Forest of Southern Brazil. Ecological Modelling 2018, 384, 316 -332.
AMA StyleClara Greig, Colin Robertson, André E.B. Lacerda. Spectral-temporal modelling of bamboo-dominated forest succession in the Atlantic Forest of Southern Brazil. Ecological Modelling. 2018; 384 ():316-332.
Chicago/Turabian StyleClara Greig; Colin Robertson; André E.B. Lacerda. 2018. "Spectral-temporal modelling of bamboo-dominated forest succession in the Atlantic Forest of Southern Brazil." Ecological Modelling 384, no. : 316-332.
The ways in which geographic information are produced have expanded rapidly over recent decades. These advances have provided new opportunities for geographical information science and spatial analysis—allowing the tools and theories to be expanded to new domain areas and providing the impetus for theory and methodological development. In this light, old problems of inference and analysis are rediscovered and need to be reinterpreted, and new ones are made apparent. This article describes a new typology of geographical analysis problems that relates to uncertainties in the relationship between individual‐level data, represented as point features, and the geographic context(s) that they are associated with. We describe how uncertainty in context linkage (uncertain geographic context problem) is also related to, but distinct from, uncertainty in point‐event locations (uncertain point observation problem) and how these issues can impact spatial analysis. A case study analysis of a geosocial dataset demonstrates how alternative conclusions can result from failure to account for these sources of uncertainty. Sources of point observation uncertainties common in many forms of user‐generated and big spatial data are outlined and methods for dealing with them are reviewed and discussed.
Colin Robertson; Rob Feick. Inference and analysis across spatial supports in the big data era: Uncertain point observations and geographic contexts. Transactions in GIS 2018, 22, 455 -476.
AMA StyleColin Robertson, Rob Feick. Inference and analysis across spatial supports in the big data era: Uncertain point observations and geographic contexts. Transactions in GIS. 2018; 22 (2):455-476.
Chicago/Turabian StyleColin Robertson; Rob Feick. 2018. "Inference and analysis across spatial supports in the big data era: Uncertain point observations and geographic contexts." Transactions in GIS 22, no. 2: 455-476.
Jed Long; Colin Robertson; Trisalyn Nelson. stampr: Spatial-Temporal Analysis of Moving Polygons in R. Journal of Statistical Software 2018, 84, 1 -19.
AMA StyleJed Long, Colin Robertson, Trisalyn Nelson. stampr: Spatial-Temporal Analysis of Moving Polygons in R. Journal of Statistical Software. 2018; 84 (1):1-19.
Chicago/Turabian StyleJed Long; Colin Robertson; Trisalyn Nelson. 2018. "stampr: Spatial-Temporal Analysis of Moving Polygons in R." Journal of Statistical Software 84, no. 1: 1-19.
The comparison of spatial patterns is a fundamental task in geography and quantitative spatial modelling. With the growth of data being collected with a geospatial element, we are witnessing an increased interest in analyses requiring spatial pattern comparisons (e.g., model assessment and change analysis). In this paper, we review quantitative techniques for comparing spatial patterns, examining key methodological approaches developed both within and beyond the field of geography. We highlight the key challenges using examples from widely known datasets from the spatial analysis literature. Through these examples, we identify a problematic dichotomy between spatial pattern and process—a widespread issue in the age of big geospatial data. Further, we identify the role of complex topology, the interdependence of spatial configuration and composition, and spatial scale as key (research) challenges. Several areas ripe for geographic research are discussed to establish a consolidated research agenda for spatial pattern comparison grounded in quantitative geography. Hierarchical scaling and the modifiable areal unit problem are highlighted as ideas which can be exploited to identify pattern similarities across spatial and temporal scales. Increased use of “time-aware” comparisons of spatial processes are suggested, which properly account for spatial evolution and pattern formation. Simulation-based inference is identified as particularly promising for integrating spatial pattern comparison into existing modelling frameworks. To date, the literature on spatial pattern comparison has been fragmented, and we hope this work will provide a basis for others to build on in future studies.
Jed Long; Colin Robertson. Comparing spatial patterns. Geography Compass 2017, 12, e12356 .
AMA StyleJed Long, Colin Robertson. Comparing spatial patterns. Geography Compass. 2017; 12 (2):e12356.
Chicago/Turabian StyleJed Long; Colin Robertson. 2017. "Comparing spatial patterns." Geography Compass 12, no. 2: e12356.
The Internet is increasingly a source of data for geographic information systems, as more data becomes linked, available through application programing interfaces (APIs), and more tools become available for handling unstructured web data. While many web data extraction and structuring methods exist, there are few examples of comprehensive data processing and analysis systems that link together these tools for geographic analyses. This paper develops a general approach to the development of spatial information context from unstructured and informal web data sources through the joint analysis of the data’s thematic, spatial, and temporal properties. We explore the utility of this derived contextual information through a case study into maritime surveillance. Extraction and processing techniques such as toponym extraction, disambiguation, and temporal information extraction methods are used to construct a semi-structured maritime context database supporting global scale analysis. Geographic, temporal, and thematic content were analyzed, extracted and processed from a list of information sources. A geoweb interface is developed to allow user visualization of extracted information, as well as to support space-time database queries. Joint keyword clustering and spatial clustering methods are used to demonstrate extraction of documents that relate to real world events in official vessel information data. The quality of contextual geospatial information sources is evaluated in reference to known maritime anomalies obtained from authoritative sources. The feasibility of automated context extraction using the proposed framework and linkage to external data using standard clustering tools is demonstrated.
Colin Robertson; Kevin Horrocks. Spatial Context from Open and Online Processing (SCOOP): Geographic, Temporal, and Thematic Analysis of Online Information Sources. ISPRS International Journal of Geo-Information 2017, 6, 193 .
AMA StyleColin Robertson, Kevin Horrocks. Spatial Context from Open and Online Processing (SCOOP): Geographic, Temporal, and Thematic Analysis of Online Information Sources. ISPRS International Journal of Geo-Information. 2017; 6 (7):193.
Chicago/Turabian StyleColin Robertson; Kevin Horrocks. 2017. "Spatial Context from Open and Online Processing (SCOOP): Geographic, Temporal, and Thematic Analysis of Online Information Sources." ISPRS International Journal of Geo-Information 6, no. 7: 193.
Understanding how people move and interact within urban settings has been greatly facilitated by the expansion of personal computing and mobile studies. Geosocial data derived from social media applications have the potential to both document how large segments of urban populations move about and use space, as well as how they interact with their environments. In this paper we examine spatial and temporal clustering of individuals’ geosocial messages as a way to derive personal activity centres for a subset of Twitter users in the City of Toronto. We compare the two types of clustering, and for a subset of users, compare to actual self-reported activity centres. Our analysis reveals that home locations were detected within 500 m for up to 53% of users using simple spatial clustering methods based on a sample of 16 users. Work locations were detected within 500 m for 33% of users. Additionally, we find that the broader pattern of geosocial footprints indicated that 35% of users have only one activity centre, 30% have two activity centres, and 14% have three activity centres. Tweets about environment were more likely sent from locations other than work and home, and when not directed to another user. These findings indicate activity centres defined from Twitter do relate to general spatial activities, but the limited degree of spatial variability on an individual level limits the applications of geosocial footprints for more detailed analyses of movement patterns in the city.
Colin Robertson; Rob Feick; Martin Sykora; Ketan Shankardass; Krystelle Shaughnessy. Personal Activity Centres and Geosocial Data Analysis: Combining Big Data with Small Data. Lecture Notes in Geoinformation and Cartography 2017, 145 -161.
AMA StyleColin Robertson, Rob Feick, Martin Sykora, Ketan Shankardass, Krystelle Shaughnessy. Personal Activity Centres and Geosocial Data Analysis: Combining Big Data with Small Data. Lecture Notes in Geoinformation and Cartography. 2017; ():145-161.
Chicago/Turabian StyleColin Robertson; Rob Feick; Martin Sykora; Ketan Shankardass; Krystelle Shaughnessy. 2017. "Personal Activity Centres and Geosocial Data Analysis: Combining Big Data with Small Data." Lecture Notes in Geoinformation and Cartography , no. : 145-161.
The use of Internet-based sources of information for health surveillance applications has increased in recent years, as a greater share of social and media activity happens through online channels. The potential surveillance value in online sources of information about emergent health events include early warning, situational awareness, risk perception and evaluation of health messaging among others. The challenge in harnessing these sources of data is the vast number of potential sources to monitor and developing the tools to translate dynamic unstructured content into actionable information. In this paper we investigated the use of one social media outlet, Twitter, for surveillance of avian influenza risk in North America. We collected AI-related messages over a five-month period and compared these to official surveillance records of AI outbreaks. A fully automated data extraction and analysis pipeline was developed to acquire, structure, and analyze social media messages in an online context. Two methods of outbreak detection; a static threshold and a cumulative-sum dynamic threshold; based on a time series model of normal activity were evaluated for their ability to discern important time periods of AI-related messaging and media activity. Our findings show that peaks in activity were related to real-world events, with outbreaks in Nigeria, France and the USA receiving the most attention while those in China were less evident in the social media data. Topic models found themes related to specific AI events for the dynamic threshold method, while many for the static method were ambiguous. Further analyses of these data might focus on quantifying the bias in coverage and relation between outbreak characteristics and detectability in social media data. Finally, while the analyses here focused on broad themes and trends, there is likely additional value in developing methods for identifying low-frequency messages, operationalizing this methodology into a comprehensive system for visualizing patterns extracted from the Internet, and integrating these data with other sources of information such as wildlife, environment, and agricultural data.
Colin Robertson; Lauren Yee. Avian Influenza Risk Surveillance in North America with Online Media. PLOS ONE 2016, 11, e0165688 .
AMA StyleColin Robertson, Lauren Yee. Avian Influenza Risk Surveillance in North America with Online Media. PLOS ONE. 2016; 11 (11):e0165688.
Chicago/Turabian StyleColin Robertson; Lauren Yee. 2016. "Avian Influenza Risk Surveillance in North America with Online Media." PLOS ONE 11, no. 11: e0165688.
Linkages between human, environmental, and animal health have been an increasingly important topic of geographical research in recent years. As more data become available for explicitly representing the geographies of these systems, and how they interact, geographers are playing an important role in shaping this research area. Whereas previously these linkages have been known, but rarely quantified, geographical data are now enabling surveillance of environmental changes, animal populations, and human populations in order to realize fine-grained estimates of disease risk. In this paper, we consider the role of spatial data in this new research area, and characterize challenges of integrating and analyzing data across these domains. We explore these issues through three case studies into emerging zoonoses; avian influenza, Japanese encephalitis, and syndromic animal health surveillance. Issues of scale, availability and access, and linkage uncertainties are found to be key data issues. We anticipate these issues will be important research challenges for geographers working on zoonoses, and as part of multidisciplinary research teams. Finally, we suggest that geographers working in this area adopt the concept of vulnerability surveillance to address these issues and refocus research on vulnerable populations, interfaces, and areas. Ces dernières années, les liens entre les êtres humains, l'environnement et la santé animale ont constitué un sujet d'étude d'importance croissante en géographie. À mesure que de nouvelles données sont disponibles pour représenter de manière précise les contours géographiques de ces systèmes et définir leurs modes d'interaction, les géographes contribuent à étendre ce domaine de recherche. Alors que l'existence de ces liens était déjà connue sans qu'on puisse établir clairement leur valeur quantitative, les données géographiques fournissent l'assise qui permet d'effectuer un suivi des changements environnementaux, des populations animales et des populations humaines afin de brosser un tableau détaillé du risque de maladie. Cet article s'intéresse au rôle des données spatiales dans ce domaine de recherche émergent et aux défis propres à l'intégration et à l'analyse des données dans ces champs. Nous étudions ces enjeux à partir de trois études de cas portant sur des zoonoses récentes : la grippe aviaire, l'encéphalite japonaise et la surveillance syndromique de la santé animale. Il en ressort que les principaux enjeux relatifs aux données concernent l'échelle, la disponibilité et l'accès, et les incertitudes qui planent sur les liens. Nous prévoyons que ces enjeux poseront un défi majeur pour la recherche menée par les géographes dont les travaux portent sur les zoonoses et qui font partie d'équipes multidisciplinaires. Pour terminer, il est proposé aux géographes œuvrant dans ce domaine d'utiliser le concept de suivi de la vulnérabilité afin d'aborder les enjeux et de recentrer les travaux de recherche sur les populations vulnérables, les interfaces et les milieux.
Colin Robertson; Lauren Yee; Julia Metelka; Craig Stephen. Spatial data issues in geographical zoonoses research. The Canadian Geographer/Le Géographe canadien 2016, 60, 300 -319.
AMA StyleColin Robertson, Lauren Yee, Julia Metelka, Craig Stephen. Spatial data issues in geographical zoonoses research. The Canadian Geographer/Le Géographe canadien. 2016; 60 (3):300-319.
Chicago/Turabian StyleColin Robertson; Lauren Yee; Julia Metelka; Craig Stephen. 2016. "Spatial data issues in geographical zoonoses research." The Canadian Geographer/Le Géographe canadien 60, no. 3: 300-319.
Colin Robertson. Space-Time Topological Graphs. International Conference on GIScience Short Paper Proceedings 2016, 1, 1 .
AMA StyleColin Robertson. Space-Time Topological Graphs. International Conference on GIScience Short Paper Proceedings. 2016; 1 (1):1.
Chicago/Turabian StyleColin Robertson. 2016. "Space-Time Topological Graphs." International Conference on GIScience Short Paper Proceedings 1, no. 1: 1.
The ability to explicitly represent infectious disease distributions and their risk factors over massive geographical and temporal scales has transformed how we investigate how environment impacts health. While landscape epidemiology studies have shed light on many aspects of disease distribution and risk differentials across geographies, new computational methods combined with new data sources such as citizen sensors, global spatial datasets, sensor networks, and growing availability and variety of satellite imagery offer opportunities for a more integrated approach to understanding these relationships. Additionally, a large number of new modelling and mapping methods have been developed in recent years to support the adoption of these new tools. The complexity of this research context results in study-dependent solutions and prevents landscape approaches from deeper integration into operational models and tools. In this paper we consider three common research contexts for spatial epidemiology; surveillance, modelling to estimate a spatial risk distribution and the need for intervention, and evaluating interventions and improving healthcare. A framework is proposed and a categorization of existing methods is presented. A case study into leptospirosis in Sri Lanka provides a working example of how the different phases of the framework relate to real research problems. The new framework for geocomputational landscape epidemiology encompasses four key phases: characterizing assemblages, characterizing functions, mapping interdependencies, and examining outcomes. Results from Sri Lanka provide evidence that the framework provides a useful way to structure and interpret analyses. The framework reported here is a new way to structure existing methods and tools of geocomputation that are increasingly relevant to researchers working on spatially explicit disease-landscape studies.
Colin Robertson. Towards a geocomputational landscape epidemiology: surveillance, modelling, and interventions. GeoJournal 2015, 82, 397 -414.
AMA StyleColin Robertson. Towards a geocomputational landscape epidemiology: surveillance, modelling, and interventions. GeoJournal. 2015; 82 (2):397-414.
Chicago/Turabian StyleColin Robertson. 2015. "Towards a geocomputational landscape epidemiology: surveillance, modelling, and interventions." GeoJournal 82, no. 2: 397-414.
As momentum and interest build to leverage new forms of user-generated content that contains geographical information, classical issues of data quality remain significant research challenges. In this article we explore issues of representativeness for one form of user-generated content, geotagged photographs in US urban centers. Generalized linear models were developed to associate photograph distribution with underlying socioeconomic descriptors at the city-scale, and examine intra-city variation in relation to income inequality. We conclude our analyses with a detailed examination of Dallas, Seattle, and New Orleans. Our findings add to the growing volume of evidence outlining uneven representativeness in user-generated data, and our approach contributes to the stock of methods available to investigate geographic variations in representativeness. In addition to city-scale variables relating to distribution of user-generated content, variability remains at localized scales that demand an individual and contextual understanding of their form and nature. The findings demonstrate that careful analysis of representativeness at both macro and micro scales can simultaneously provide important insights into the processes giving rise to user-generated data sets and potentially shed light on their embedded biases and suitability as inputs to analysis.
Colin Robertson; Robert Feick. Bumps and bruises in the digital skins of cities: unevenly distributed user-generated content across US urban areas. Cartography and Geographic Information Science 2015, 43, 283 -300.
AMA StyleColin Robertson, Robert Feick. Bumps and bruises in the digital skins of cities: unevenly distributed user-generated content across US urban areas. Cartography and Geographic Information Science. 2015; 43 (4):283-300.
Chicago/Turabian StyleColin Robertson; Robert Feick. 2015. "Bumps and bruises in the digital skins of cities: unevenly distributed user-generated content across US urban areas." Cartography and Geographic Information Science 43, no. 4: 283-300.
Robert Feick; Colin Robertson. A multi-scale approach to exploring urban places in geotagged photographs. Computers, Environment and Urban Systems 2015, 53, 96 -109.
AMA StyleRobert Feick, Colin Robertson. A multi-scale approach to exploring urban places in geotagged photographs. Computers, Environment and Urban Systems. 2015; 53 ():96-109.
Chicago/Turabian StyleRobert Feick; Colin Robertson. 2015. "A multi-scale approach to exploring urban places in geotagged photographs." Computers, Environment and Urban Systems 53, no. : 96-109.