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With the advent of new technologies and broader participation in geospatial data production, new challenges emerge for spatial data sharing. Spatial data sharing practices are increasingly transacted through and, to varying degrees, controlled by a handful of privately controlled corporate services. Data production has evolved from being largely centralized, expert-oriented, and authoritative in nature to now also include hybrid data collection processes involving distributed assemblages of individuals who share and co-produce spatial data while interacting through centralized architectures and control regimes. These changes have resulted mainly from technological and social changes linked to the emergence of Web 2.0 and widely available Internet participation tools. Concerns about how spatial data access and sharing are controlled, particularly for sensitive or personally-identifying data, have increased interest in distributed file technologies that allow users to share resources independently of centralized platforms. This paper examines how spatial data sharing practices may move towards a more decentralized sharing ecosystem as technologies for a further distributed web mature. We identify this transition as increasingly hybridized forms of data ownership and access control concerns are coupled with new distributed systems (e.g., Web 3.0). We also discuss opportunities and barriers to distributed spatial data sharing, including possible benefits for big geographic data management and the need for protocols to share, integrate, and process spatial data shared on distributed networks.

The widespread availability of tools to collect and share spatial data enables us to produce a large amount of geographic information on a daily basis. This enormous production of spatial data requires scalable data management systems. Geospatial architectures have changed from clusters to cloud architectures and more parallel and distributed processing platforms to be able to tackle these challenges. Peer-to-peer (P2P) systems as a backbone of distributed systems have been established in several application areas such as web3, blockchains, and crypto-currencies. Unlike centralized systems, data storage in P2P networks is distributed across network nodes, providing scalability and no single point of failure. However, managing and processing queries on these networks has always been challenging. In this work, we propose a spatio-temporal indexing data structure, DSTree. DSTree does not require additional Distributed Hash Trees (DHTs) to perform multi-dimensional range queries. Inserting a piece of new geographic information updates only a portion of the tree structure and does not impact the entire graph of the data. For example, for time-series data, such as storing sensor data, the DSTree performs around 40% faster in spatio-temporal queries for small and medium datasets. Despite the advantages of our proposed framework, challenges such as 20% slower insertion speed or semantic query capabilities remain. We conclude that more significant research effort from GIScience and related fields in developing decentralized applications is needed. The need for the standardization of different geographic information when sharing data on the IPFS network is one of the requirements.

Increasing data resources are available for documenting and detecting changes in environmental, ecological, and socioeconomic processes. Currently, data are distributed across a wide variety of sources (e.g. data silos) and published in a variety of formats, scales, and semantic representations. A key issue, therefore, in building systems that can realize a vision of earth system monitoring remains data integration. Discrete global grid systems (DGGSs) have emerged as a key technology that can provide a common multi-resolution spatial fabric in support of Digital Earth monitoring. However, DGGSs remain in their infancy with many technical, conceptual, and operational challenges. With renewed interest in DGGS brought on by a recently proposed standard, the demands of big data, and growing needs for monitoring environmental changes across a variety of scales, we seek to highlight current challenges that we see as central to moving the field(s) and technologies of DGGS forward. For each of the identified challenges, we illustrate the issue and provide a potential solution using a reference DGGS implementation. Through articulation of these challenges, we hope to identify a clear research agenda, expand the DGGS research footprint, and provide some ideas for moving forward towards a scaleable Digital Earth vision. Addressing such challenges helps the GIScience research community to achieve the real benefits of DGGS and provides DGGS an opportunity to play a role in the next generation of GIS.

The Rocky Mountain ridged mussel (Gonidea angulata) is a bivalve species whose Canadian range is limited to the Okanagan Valley, British Columbia. In 2019, conflicts between habitat protection for the mussel and potential habitat alteration to control the invasive Eurasian watermilfoil (Myriophyllum spicatum) (milfoil), led to a decision to maintain the status of the mussels as Special Concern under Canada’s Species at Risk Act (SARA) rather than classify it as Endangered. Milfoil control can cause direct mortality and/or burial of the mussels, but there had been no systematic study of the impacts of milfoil control on mussel beds. The purpose of this study was to address knowledge gaps by delineating known mussel beds and potential overlap with milfoil to provide information for management decisions that balance the needs of native species protection and invasive species control. Rocky Mountain ridged mussels in three reference locations were enumerated using snorkel surveys. The presence and distribution of milfoil was documented in relation to five sites within these three locations. Milfoil was encroaching on one site, causing some changes to the substrate. At other sites, the differences in the depth and distribution of the mussel and the milfoil could allow milfoil control without damaging the mussel beds. It is recommended that, before milfoil removal near known mussel beds be undertaken, a detailed site evaluation be conducted to determine potential impacts. This study suggests presumed impediments to co-managing the mussels and controlling an invasive species should not preclude classifying the mussels as Endangered and affording protections under SARA.

Placer mining is a mineral extraction method in floodplains that involves the removal of earth material to access mineral-laden sediments, a process that can have significant and long-term impacts on aquatic ecosystems. Given the widespread nature of mining, new tools are required to monitor the potential watershed-scale ecological impacts of placer mining. This study adapted and evaluated a deep learning model – a U-Net convolution neural network, and compared it to a traditional image classification method – random forests (RF) – to detect and quantify the area of post-placer mining disturbance at the watershed scale. Overall, both random forest and U-Net models performed well at classifying digitized image samples where placer disturbances were mapped. Sensitivity in placer classification was high, with both modelling frameworks achieving at least 75% accuracy in the classification of digitized placer samples in 7 out of 12 modelling scenarios. Misclassification of non-placer pixels as placer was highly variable among different models, data configurations, study sites, and time periods. Commission errors (i.e., incorrectly classifying a non-placer pixel as placer) were typically the result of models labelling water areas or forest areas as placer – errors which may have only marginal practical significance. In general, U-Net models performed better in terms of minimizing misclassification errors, whereas RF models performed slightly better in classifying known placer pixels. We conclude with discussions on the advantages of deploying U-Net and RF models for placer detection, challenges that may be encountered in operational systems that employ the models, and identifying outstanding issues which need to be addressed in future placer modelling studies.

Brain pericytes reside on the abluminal surface of capillaries, and their processes cover ~90% of the length of the capillary bed. These cells were first described almost 150 years ago (Eberth, 1871; Rouget, 1873) and have been the subject of intense experimental scrutiny in recent years, but their physiological roles remain uncertain and little is known of the complement of signaling elements that they employ to carry out their functions. In this review, we synthesize functional data with single-cell RNAseq screens to explore the ion channel and G protein-coupled receptor (GPCR) toolkit of mesh and thin-strand pericytes of the brain, with the aim of providing a framework for deeper explorations of the molecular mechanisms that govern pericyte physiology. We argue that their complement of channels and receptors ideally positions capillary pericytes to play a central role in adapting blood flow to meet the challenge of satisfying neuronal energy requirements from deep within the capillary bed, by enabling dynamic regulation of their membrane potential to influence the electrical output of the cell. In particular, we outline how genetic and functional evidence suggest an important role for Gs-coupled GPCRs and ATP-sensitive potassium (KATP) channels in this context. We put forth a predictive model for long-range hyperpolarizing electrical signaling from pericytes to upstream arterioles, and detail the TRP and Ca2+ channels and Gq, Gi/o, and G12/13 signaling processes that counterbalance this. We underscore critical questions that need to be addressed to further advance our understanding of the signaling topology of capillary pericytes, and how this contributes to their physiological roles and their dysfunction in disease.

The R package stampr implements functions for analyzing movement in mapped polygon data. Methods described in this paper include deriving change events based on spatial relationships, plotting change events, summarizing measures of distance and direction of movement, characterizing changes in polygon shape changes, and characterizing sequences of polygons over time using graphs. Two examples are used to demonstrate the core functionality available in the stampr package.

For Donald Trump ‘America First’ means ‘America First.’ Canada and likeminded nations will have to get used to it. Canada will have to actively engage with Congress, the states and the private and public interests that drive the American agenda. We will also have to put more effort and contribute more to the rules-based order of which we have been a beneficiary. Traditional statecraft is based on predictability and stability, both hallmarks of U.S. post-war foreign policy practised by both Democrats and Republicans. Predictable, Mr. Trump is not. The deliberation and careful planning that characterized the Obama administration have been replaced by Mr. Trump’s reliance on gut and instinct. Such unpredictability will continue to create heartburn inside foreign chancelleries, whether friend or foe. Where once the USA was prepared to cover the spread on trade and security, under Donald Trump there will be more take than give. Now, Canada and the allies will have to make their own investments in hard power to preserve collective security. But less dependence and reliance on US leadership and more collective responsibility would be a good thing. Middle powers, like Canada, will have to step up their diplomacy, both collective and individual. Focusing on their own niche capacities they will have to shore up the space left by Trump Administration decisions on climate, migration and at the international institutions that sustain the rules-based order. Ironically, one effect of the Trump presidency may be to make the western alliance stronger.

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.

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.

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.

Limited surveillance programs and lack of diagnostic laboratory testing capacity in many low and middle income Asian countries have made it difficult to validate epidemiological patterns and anticipate future changes in disease risk. In this study, we consider the case of Japanese Encephalitis in Asia and examine how populations of human hosts and animal reservoirs are expected to change over the next three decades. Growth was modelled at the sub-national level for rural and urban areas to estimate where high-density, susceptible populations will potentially overlap with populations of the virus' amplifying host. High-risk areas based on these projections were compared to the current distribution of Japanese Encephalitis, and known immunization activities in order to identify areas of highest priority for concern. Results indicated that mapping JE risk factors at the sub-national level is an effective way to contextualize and supplement JE surveillance data. New patterns of risk factor change occurring in Southeast Asia were identified, including around major urban areas experiencing both urbanization and growth in pig populations. A hotspot analysis of pig-to-population ratio found a significant spatial cluster extending northward through Southeast Asia and interior China. Mapping forecasted changes in risk factors for JE highlights regions vulnerable to emerging zoonoses and may be an important tool for developing effecting transnational health policies.

Spatial cluster analysis is a uniquely interdisciplinary endeavour, and so it is important to communicate and disseminate ideas, innovations, best practices and challenges across practitioners, applied epidemiology researchers and spatial statisticians. In this research we conducted a scoping review to systematically search peer-reviewed journal databases for research that has employed spatial cluster analysis methods on individual-level, address location, or x and y coordinate derived data. To illustrate the thematic issues raised by our results, methods were tested using a dataset where known clusters existed. Point pattern methods, spatial clustering and cluster detection tests, and a locally weighted spatial regression model were most commonly used for individual-level, address location data (n = 29). The spatial scan statistic was the most popular method for address location data (n = 19). Six themes were identified relating to the application of spatial cluster analysis methods and subsequent analyses, which we recommend researchers to consider; exploratory analysis, visualization, spatial resolution, aetiology, scale and spatial weights. It is our intention that researchers seeking direction for using spatial cluster analysis methods, consider the caveats and strengths of each approach, but also explore the numerous other methods available for this type of analysis. Applied spatial epidemiology researchers and practitioners should give special consideration to applying multiple tests to a dataset. Future research should focus on developing frameworks for selecting appropriate methods and the corresponding spatial weighting schemes.

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.

Because many infectious diseases are emerging in animals in low-income and middle-income countries, surveillance of animal health in these areas may be needed for forecasting disease risks to humans. We present an overview of a mobile phone–based frontline surveillance system developed and implemented in Sri Lanka. Field veterinarians reported animal health information by using mobile phones. Submissions increased steadily over 9 months, with ≈4,000 interactions between field veterinarians and reports on the animal population received by the system. Development of human resources and increased communication between local stakeholders (groups and persons whose actions are affected by emerging infectious diseases and animal health) were instrumental for successful implementation. The primary lesson learned was that mobile phone–based surveillance of animal populations is acceptable and feasible in lower-resource settings. However, any system implementation plan must consider the time needed to garner support for novel surveillance methods among users and stakeholders.

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.

Japanese Encephalitis (JE) is a vector-borne disease of major importance in Asia. Recent increases in cases have spawned the development of more stringent JE surveillance. Due to the difficulty of making a clinical diagnosis, increased tracking of common symptoms associated with JE-generally classified as the umbrella term, acute encephalitis syndrome (AES) has been developed in many countries. In Nepal, there is some debate as to what AES cases are, and how JE risk factors relate to AES risk. Three parts of this analysis included investigating the temporal pattern of cases, examining the age and vaccination status patterns among AES surveillance data, and then focusing on spatial patterns of risk factors. AES and JE cases from 2007-2011 reported at a district level (n = 75) were examined in relation to landscape risk factors. Landscape pattern indices were used to quantify landscape patterns associated with JE risk. The relative spatial distribution of landscape risk factors were compared using geographically weighted regression. Pattern indices describing the amount of irrigated land edge density and the degree of landscape mixing for irrigated areas were positively associated with JE and AES, while fragmented forest measured by the number of forest patches were negatively associated with AES and JE. For both JE and AES, the local GWR models outperformed global models, indicating spatial heterogeneity in risks. Temporally, the patterns of JE and AES risk were almost identical; suggesting the relative higher caseload of AES compared to JE could provide a valuable early-warning signal for JE surveillance and reduce diagnostic testing costs. Overall, the landscape variables associated with a high degree of landscape mixing and small scale irrigated agriculture were positively linked to JE and AES risk, highlighting the importance of integrating land management policies, disease prevention strategies and promoting healthy sustainable livelihoods in both rural and urban-fringe developing areas.

Surveillance systems tracking health patterns in animals have potential for early warning of infectious disease in humans, yet there are many challenges that remain before this can be realized. Specifically, there remains the challenge of detecting early warning signals for diseases that are not known or are not part of routine surveillance for named diseases. This paper reports on the development of a hidden Markov model for analysis of frontline veterinary sentinel surveillance data from Sri Lanka. Field veterinarians collected data on syndromes and diagnoses using mobile phones. A model for submission patterns accounts for both sentinel-related and disease-related variability. Models for commonly reported cattle diagnoses were estimated separately. Region-specific weekly average prevalence was estimated for each diagnoses and partitioned into normal and abnormal periods. Visualization of state probabilities was used to indicate areas and times of unusual disease prevalence. The analysis suggests that hidden Markov modelling is a useful approach for surveillance datasets from novel populations and/or having little historical baselines.