The Canadian Bikeway Comfort and Safety metrics (Can-BICS): National measures of the bicycling environment for use in research and policy.

Background: The lack of consistent measures of the cycling environment across communities hampers cycling research and policy action. Our goal was to develop the first national dataset in Canada for metrics of the cycling environment at the dissemination area (DA) level - the Canadian Bikeway Comfort and Safety (Can-BICS) metrics. Data and methods: The Can-BICS metrics are area-level metrics based on the quantity of cycling infrastructure within a 1 km buffer of the population-weighted centroid of DAs. The base data are a national cycling network dataset derived from OpenStreetMap (OSM) (extracted January 25, 2022) and classified by high-, medium- and low-comfort facilities. A Can-BICS continuous metric (sum of cycling infrastructure per square kilometre weighted by comfort class) and Can-BICS categorical metric were derived and mapped for all 56,589 DAs in Canada. The Can-BICS metrics were correlated with other national datasets (2016 Canadian Active Living Environments [Can-ALE] and 2016 Census journey-to-work data) to test for associations between Can-BICS and related measures. Additionally, city staff were engaged to provide feedback on metrics during the development phase. Results: One-third (34%) of neighbourhoods in Canada have no cycling infrastructure. According to the categorical measure, 5% of all DAs were assigned as the highest category of Can-BICS (corresponding to 6% of the population) and were nearly all within metro areas. The Can-BICS continuous metric had low correlation with bike-to-work rates (R = 0.29) and was more strongly correlated with sustainable-transportation-to-work rates (R = 0.56) and the Can-ALE metrics (R=0.62). These correlations were variable across cities. Interpretation: The Can-BICS metrics provide national research- and practice-ready measures of cycling infrastructure. The metrics complement existing measures of walking and transit environments (Can-ALE), collectively providing a cohesive set of active living measures. The datasets and code are publicly available, facilitating updates as new infrastructure is built.

INTERACT: A comprehensive approach to assess urban form interventions through natural experiments.

BACKGROUND: Urban form interventions can result in positive and negative impacts on physical activity, social participation, and well-being, and inequities in these outcomes. Natural experiment studies can advance our understanding of causal effects and processes related to urban form interventions. The INTErventions, Research, and Action in Cities Team (INTERACT) is a pan-Canadian collaboration of interdisciplinary scientists, urban planners, and public health decision makers advancing research on the design of healthy and sustainable cities for all. Our objectives are to use natural experiment studies to deliver timely evidence about how urban form interventions influence health, and to develop methods and tools to facilitate such studies going forward. METHODS: INTERACT will evaluate natural experiments in four Canadian cities: the Arbutus Greenway in Vancouver, British Columbia; the All Ages and Abilities Cycling Network in Victoria, BC; a new Bus Rapid Transit system in Saskatoon, Saskatchewan; and components of the Sustainable Development Plan 2016-2020 in Montreal, Quebec, a plan that includes urban form changes initiated by the city and approximately 230 partnering organizations. We will recruit a cohort of between 300 and 3000 adult participants, age 18 or older, in each city and collect data at three time points. Participants will complete health and activity space surveys and provide sensor-based location and physical activity data. We will conduct qualitative interviews with a subsample of participants in each city. Our analysis methods will combine machine learning methods for detecting transportation mode use and physical activity, use temporal Geographic Information Systems to quantify changes to urban intervention exposure, and apply analytic methods for natural experiment studies including interrupted time series analysis. DISCUSSION: INTERACT aims to advance the evidence base on population health intervention research and address challenges related to big data, knowledge mobilization and engagement, ethics, and causality. We will collect ~ 100 TB of sensor data from participants over 5 years. We will address these challenges using interdisciplinary partnerships, training of highly qualified personnel, and modern methodologies for using sensor-based data.

Spatiotemporal patterns of paralytic shellfish toxins and their relationships with environmental variables in British Columbia, Canada from 2002 to 2012.

BACKGROUND: Harmful algal blooms produce paralytic shellfish toxins that accumulate in the tissues of filter feeding shellfish. Ingestion of these toxic shellfish can cause a serious and potentially fatal condition known as paralytic shellfish poisoning (PSP). The coast of British Columbia is routinely monitored for shellfish toxicity, and this study uses data from the monitoring program to identify spatiotemporal patterns in shellfish toxicity events and their relationships with environmental variables. METHODS: The dinoflagellate genus Alexandrium produces the most potent paralytic shellfish toxin, saxitoxin (STX). Data on all STX measurements were obtained from 49 different shellfish monitoring sites along the coast of British Columbia for 2002-2012, and monthly toxicity events were identified. We performed hierarchical cluster analysis to group sites that had events in similar areas with similar timing. Machine learning techniques were used to model the complex relationships between toxicity events and environmental variables in each group. RESULTS: The Strait of Georgia and the west coast of Vancouver Island had unique toxicity regimes. Out of the seven environmental variables used, toxicity in each cluster could be described by multivariable models including monthly sea surface temperature, air temperature, sea surface salinity, freshwater discharge, upwelling, and photosynthetically active radiation. The sea surface salinity and freshwater discharge variables produced the strongest univariate models for both geographic areas. CONCLUSIONS: Applying these methods in coastal regions could allow for the prediction of shellfish toxicity events by environmental conditions. This has the potential to optimize biotoxin monitoring, improve public health surveillance, and engage the shellfish industry in helping to reduce the risk of PSP.

Evaluation of different radon guideline values based on characterization of ecological risk and visualization of lung cancer mortality trends in British Columbia, Canada.

BACKGROUND: There is no safe concentration of radon gas, but guideline values provide threshold concentrations that are used to map areas at higher risk. These values vary between different regions, countries, and organizations, which can lead to differential classification of risk. For example the World Health Organization suggests a 100 Bq m(-3)value, while Health Canada recommends 200 Bq m(-3). Our objective was to describe how different thresholds characterized ecological radon risk and their visual association with lung cancer mortality trends in British Columbia, Canada. METHODS: Eight threshold values between 50 and 600 Bq m(-3) were identified, and classes of radon vulnerability were defined based on whether the observed 95(th) percentile radon concentration was above or below each value. A balanced random forest algorithm was used to model vulnerability, and the results were mapped. We compared high vulnerability areas, their estimated populations, and differences in lung cancer mortality trends stratified by smoking prevalence and sex. RESULTS: Classification accuracy improved as the threshold concentrations decreased and the area classified as high vulnerability increased. Majority of the population lived within areas of lower vulnerability regardless of the threshold value. Thresholds as low as 50 Bq m(-3) were associated with higher lung cancer mortality, even in areas with low smoking prevalence. Temporal trends in lung cancer mortality were increasing for women, while decreasing for men. CONCLUSIONS: Radon contributes to lung cancer in British Columbia. The results of the study contribute evidence supporting the use of a reference level lower than the current guideline of 200 Bq m(-3) for the province.

A critical examination of indices of dynamic interaction for wildlife telemetry studies.

Wildlife scientists continue to be interested in studying ways to quantify how the movements of animals are interdependent - dynamic interaction. While a number of applied studies of dynamic interaction exist, little is known about the comparative effectiveness and applicability of available methods used for quantifying interactions between animals. We highlight the formulation, implementation and interpretation of a suite of eight currently available indices of dynamic interaction. Point- and path-based approaches are contrasted to demonstrate differences between methods and underlying assumptions on telemetry data. Correlated and biased correlated random walks were simulated at a range of sampling resolutions to generate scenarios with dynamic interaction present and absent. We evaluate the effectiveness of each index at identifying different types of interactive behaviour at each sampling resolution. Each index is then applied to an empirical telemetry data set of three white-tailed deer (Odocoileus virginianus) dyads. Results from the simulated data show that three indices of dynamic interaction reliant on statistical testing procedures are susceptible to Type I error, which increases at fine sampling resolutions. In the white-tailed deer examples, a recently developed index for quantifying local-level cohesive movement behaviour (the di index) provides revealing information on the presence of infrequent and varying interactions in space and time. Point-based approaches implemented with finely sampled telemetry data overestimate the presence of interactions (Type I errors). Indices producing only a single global statistic (7 of the 8 indices) are unable to quantify infrequent and varying interactions through time. The quantification of infrequent and variable interactive behaviour has important implications for the spread of disease and the prevalence of social behaviour in wildlife. Guidelines are presented to inform researchers wishing to study dynamic interaction patterns in their own telemetry data sets. Finally, we make our code openly available, in the statistical software R, for computing each index of dynamic interaction presented herein.

Human disturbance in riparian areas disrupts predator-prey interactions between grizzly bears and salmon.

Wildlife must increasingly balance trade-offs between the need to access important foods and the mortality risks associated with human-dominated landscapes. Human disturbance can profoundly influence wildlife behavior, but managers know little about the relationship between disturbance-behavior dynamics and associated consequences for foraging. We address this gap by empirically investigating the consequences of human activity on a keystone predator-prey interaction in a region with limited but varied industrial disturbance. Using stable isotope data from 226 hair samples of grizzly bears (Ursus arctos horribilis) collected from 1995 to 2014 across 22 salmon-bearing watersheds (88,000 km2) in British Columbia, Canada, we examined how human activity influenced their consumption of spawning salmon (Oncorhynchus spp.), a fitness-related food. Accounting for the abundance of salmon and other foods, salmon consumption strongly decreased (up to 59% for females) with increasing human disturbance (as measured by the human footprint index) in riparian zones of salmon-bearing rivers. Declines in salmon consumption occurred with disturbance even in watersheds with low footprints. In a region currently among the least influenced by industrial activity, intensification of disturbance in river valleys is predicted to increasingly decouple bears from salmon, possibly driving associated reductions in population productivity and provisioning of salmon nutrients to terrestrial ecosystems. Accordingly, we draw on our results to make landscape-scale and access-related management recommendations beyond current streamside protection buffers. This work illustrates the interaction between habitat modification and food security for wildlife, highlighting the potential for unacknowledged interactions and cumulative effects in increasingly modified landscapes.

Erratum to "Branching out: Feasibility of examining the effects of greenspace on mental health after traumatic brain injury" [Dialogues in Health, Volume 2, 2023, 100129].

[This corrects the article DOI: 10.1016/j.dialog.2023.100129.].

Exploring the ecological processes driving geographical patterns of breeding bird richness in British Columbia, Canada.

British Columbia (BC), Canada, has a diverse landscape that provides breeding habitat for > 300 avian species, and the recent development of the BC Breeding Bird Atlas data set presents key information for exploring the landscape conditions which lead to biological richness. We used the volunteer-collected raw breeding bird evidence data set to analyze the effects of sampling biases on spatial distribution of observed breeding bird species and implemented regression tree analysis (Random Forests) to examine the influence of productivity, ambient energy, and habitat heterogeneity on independently measured breeding bird richness. Results indicated that total breeding species richness is correlated with total survey effort (alpha < 0.001). By stratifying species richness by survey effort, we observed that ambient energy is the top-ranked environmental predictor of breeding bird richness across BC, which, when used in combination with a number of other environmental variables, explains -40% of the variation in richness. Using our modeled relationships, we predicted breeding bird species richness in the areas of BC not presently surveyed between three and six hours. The majority of the productive Boreal Plains, the southern portion of the Taiga Plains region, the lowlands of the Southern and Central Interior, along the Rocky Mountain Trench, and the coastal areas of the Georgia Depression are predicted to have the highest categories of breeding richness (35-57 unique species). Our results support ongoing species diversity gradient research, which identifies ambient energy as an important factor influencing species diversity distributions in the Northern Hemisphere. By linking breeding bird richness to environmental data derived from remotely sensed data and systematically collected climate data, we demonstrate the potential to monitor shifts in ambient energy as a surrogate for vertebrate habitat condition affecting population levels. By analyzing the influence of survey effort on species richness metrics, we also highlight the need to consider adding attributes to the raw breeding bird data set to describe observer experience, such as hours or seasons spent surveying, and provide survey dates to allow greater flexibility for removing survey bias. These additions can increase the utility of atlas data for species richness studies useful for conservation planning.

Ecosystem classifications based on summer and winter conditions.

Ecosystem classifications map an area into relatively homogenous units for environmental research, monitoring, and management. However, their effectiveness is rarely tested. Here, three classifications are (1) defined and characterized for Canada along summertime productivity (moderate-resolution imaging spectrometer fraction of absorbed photosynthetically active radiation) and wintertime snow conditions (special sensor microwave/imager snow water equivalent), independently and in combination, and (2) comparatively evaluated to determine the ability of each classification to represent the spatial and environmental patterns of alternative schemes, including the Canadian ecozone framework. All classifications depicted similar patterns across Canada, but detailed class distributions differed. Class spatial characteristics varied with environmental conditions within classifications, but were comparable between classifications. There was moderate correspondence between classifications. The strongest association was between productivity classes and ecozones. The classification along both productivity and snow balanced these two sets of variables, yielding intermediate levels of association in all pairwise comparisons. Despite relatively low spatial agreement between classifications, they successfully captured patterns of the environmental conditions underlying alternate schemes (e.g., snow classes explained variation in productivity and vice versa). The performance of ecosystem classifications and the relevance of their input variables depend on the environmental patterns and processes used for applications and evaluation. Productivity or snow regimes, as constructed here, may be desirable when summarizing patterns controlled by summer- or wintertime conditions, respectively, or of climate change responses. General purpose ecosystem classifications should include both sets of drivers. Classifications should be carefully, quantitatively, and comparatively evaluated relative to a particular application prior to their implementation as monitoring and assessment frameworks.

A framework for modern time geography: emphasizing diverse constraints on accessibility.

Time geography is widely used by geographers as a model for understanding accessibility. Recent changes in how access is created, an increasing awareness of the need to better understand individual variability in access, and growing availability of detailed spatial and mobility data have created an opportunity to build more flexible time geography models. Our goal is to outline a research agenda for a modern time geography that allows new modes of access and a variety of data to flexibly represent the complexity of the relationship between time and access. A modern time geography is more able to nuance individual experience and creates a pathway for monitoring progress toward inclusion. We lean on the original work by Hägerstrand and the field of movement GIScience to develop both a framework and research roadmap that, if addressed, can enhance the flexibility of time geography to help ensure time geography will continue as a cornerstone of accessibility research. The proposed framework emphasizes the individual and differentiates access based on how individuals experience internal, external, and structural factors. To enhance nuanced representation of inclusion and exclusion, we propose research needs, focusing efforts on implementing flexible space-time constraints, inclusion of definitive variables, addressing mechanisms for representing and including relative variables, and addressing the need to link between individual and population scales of analysis. The accelerated digitalization of society, including availability of new forms of digital spatial data, combined with a focus on understanding how access varies across race, income, sexual identity, and physical limitations requires new consideration for how we include constraints in our studies of access. It is an exciting era for time geography and there are massive opportunities for all geographers to consider how to incorporate new realities and research priorities into time geography models, which have had a long tradition of supporting theory and implementation of accessibility research.

WalkRollMap.org: Crowdsourcing barriers to mobility.

Walking is a simple way to improve health through physical activity. Yet many people experience barriers to walking from a variety of physical, social, and psychological factors that impact their mobility. A challenge for managing and studying pedestrian environments is that barriers often occur at local scales (e.g., sidewalk features), yet such fine scale data on pedestrian facilities and experiences are often lacking or out of date. In response, our team developed WalkRollMap.org an online mapping tool that empowers communities by providing them with tools for crowdsourcing their own open data source. In this manuscript we highlight key functions of the tool, discuss initial approaches to community outreach, and share trends in reporting from the first nine months of operation. As of July 27, 2022, there have been 897 reports, of which 53% served to identify hazards, 34% missing amenities, and 14% incidents. The most frequently reported issues were related to sidewalks (15%), driver behavior (19%), and marked crosswalks (7%). The most common suggested amenities were sidewalks, marked crosswalks, connections (i.e., pathways between streets), and curb cuts. The most common types of incidents all included conflicts with vehicles. Data compiled through WalkRollMap.org offer unique potential for local and timely information on microscale barriers to mobility and are available for use by anyone as data are open and downloadable.

Branching out: Feasibility of examining the effects of greenspace on mental health after traumatic brain injury.

Aim: This pilot study's aim was to determine the feasibility of examining the effects of an environmental variable (i.e., tree canopy coverage) on mental health after sustaining a brain injury. Methods: A secondary data analysis was conducted leveraging existing information on mental health after moderate to severe traumatic brain injury (TBI) from the TBI Model System. Mental health was measured using PHQ-9 (depression) and GAD-7 (anxiety) scores. The data were compared with data on tree canopy coverage in the state of Texas that was obtained from the Multi-Resolution Land Characteristics (MRLC) Consortium using GIS analysis. Tree canopy coverage as an indicator of neighborhood socioeconomic status was also examined using the Neighborhood SES Index. Results: Tree canopy coverage had weak and non-significant correlations with anxiety and depression scores, as well as neighborhood socioeconomic status. Data analysis was limited by small sample size. However, there is a higher percentage (18.8%) of participants who reported moderate to severe depression symptoms in areas with less than 30% tree canopy coverage, compared with 6.6% of participants who endorsed moderate to severe depression symptoms and live in areas with more than 30% tree canopy coverage (there was no difference in anxiety scores). Conclusion: Our work confirms the feasibility of measuring the effects of tree canopy coverage on mental health after brain injury and warrants further investigation into examining tree canopy coverage and depression after TBI. Future work will include nationwide analyses to potentially detect significant relationships, as well as examine differences in geographic location.

Wave 1 results of the INTerventions, Research, and Action in Cities Team (INTERACT) cohort study: Examining spatio-temporal measures for urban environments and health.

Built environment interventions have the potential to improve population health and reduce health inequities. The objective of this paper is to present the first wave of the INTErventions, Research, and Action in Cities Team (INTERACT) cohort studies in Victoria, Vancouver, Saskatoon, and Montreal, Canada. We examine how our cohorts compared to Canadian census data and present summary data for our outcomes of interest (physical activity, well-being, and social connectedness). We also compare location data and activity spaces from survey data, research-grade GPS and accelerometer devices, and a smartphone app, and compile measures of proximity to select built environment interventions.

Making Space in Geographical Analysis.

In this commentary we reflect on the potential and power of geographical analysis, as a set of methods, theoretical approaches, and perspectives, to increase our understanding of how space and place matter for all. We emphasize key aspects of the field, including accessibility, urban change, and spatial interaction and behavior, providing a high-level research agenda that indicates a variety of gaps and routes for future research that will not only lead to more equitable and aware solutions to local and global challenges, but also innovative and novel research methods, concepts, and data. We close with a set of representation and inclusion challenges to our discipline, researchers, and publication outlets.

Riding through the pandemic: Using Strava data to monitor the impacts of COVID-19 on spatial patterns of bicycling.

COVID-19 prompted a bike boom and cities around the world responded to increased demand for space to ride with street reallocations. Evaluating these interventions has been limited by a lack of spatially-temporally continuous ridership data. Our paper aims to address this gap using crowdsourced data on bicycle ridership. We evaluate changes in spatial patterns of bicycling during the first wave of the COVID-19 pandemic (Apr - Oct 2020) in Vancouver, Canada using Strava data and a local indicator of spatial autocorrelation. We map statistically significant change in ridership and reference clusters of change to a high-resolution base map. Amongst streets where bicycling increased, we measured the proportion of increase occurring on pre-existing bicycle facilities or street reallocations compared to streets without. In all our analyses, we evaluate patterns across subsets of Strava data representing recreation, commuting, and ridership generated by women and older adults (55 + ). We found consistent and unique patterns by trip purpose and demographics: samples generated by women and older adults showed increases near green and blue spaces and on street reallocations that increased access to parks, and these patterns were also mirrored in the recreation sample. Commute ridership highlighted distinct patterns of increase around the hospital district. Across all subsets most increases occurred on bicycle facilities (pre-existing or provisional), with a strong preference for high-comfort facilities. We demonstrate that changes in spatial patterns of bicycle ridership can be monitored using Strava data, and that nuanced patterns can be identified using trip and demographic labels in the data.

Sentinel surveillance of SARS-CoV-2 rates and equity impacts using labor and delivery patients in Phoenix, Arizona.

Proactive management of SARS-CoV-2 requires timely and complete population data to track the evolution of the virus and identify at risk populations. However, many cases are asymptomatic and are not easily discovered through traditional testing efforts. Sentinel surveillance can be used to estimate the prevalence of infections for geographical areas but requires identification of sentinels who are representative of the larger population. Our goal is to evaluate applicability of a population of labor and delivery patients for sentinel surveillance system for monitoring the prevalence of SARS-CoV-2 infection. We tested 5307 labor and delivery patients from two hospitals in Phoenix, Arizona, finding 195 SARS-CoV-2 positive. Most positive cases were associated with people who were asymptomatic (79.44%), similar to statewide rates. Our results add to the growing body of evidence that SARS-CoV-2 disproportionately impacts people of color, with Black people having the highest positive rates (5.92%). People with private medical insurance had the lowest positive rates (2.53%), while Medicaid patients had a positive rate of 5.54% and people without insurance had the highest positive rates (6.12%). With diverse people reporting for care and being tested regardless of symptoms, labor and delivery patients may serve as ideal sentinels for asymptomatic detection of SARS-CoV-2 and monitoring impacts across a wide range of social and economic classes. A more robust system for infectious disease management requires the expanded participation of additional hospitals so that the sentinels are more representative of the population at large, reflecting geographic and neighborhood level patterns of infection and risk.

Mobile phone-based infectious disease surveillance system, Sri Lanka.

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.

Review of software for space-time disease surveillance.

Disease surveillance makes use of information technology at almost every stage of the process, from data collection and collation, through to analysis and dissemination. Automated data collection systems enable near-real time analysis of incoming data. This context places a heavy burden on software used for space-time surveillance. In this paper, we review software programs capable of space-time disease surveillance analysis, and outline some of their salient features, shortcomings, and usability. Programs with space-time methods were selected for inclusion, limiting our review to ClusterSeer, SaTScan, GeoSurveillance and the Surveillance package for R. We structure the review around stages of analysis: preprocessing, analysis, technical issues, and output. Simulated data were used to review each of the software packages. SaTScan was found to be the best equipped package for use in an automated surveillance system. ClusterSeer is more suited to data exploration, and learning about the different methods of statistical surveillance.

Regionalization of landscape pattern indices using multivariate cluster analysis.

Regionalization, or the grouping of objects in space, is a useful tool for organizing, visualizing, and synthesizing the information contained in multivariate spatial data. Landscape pattern indices can be used to quantify the spatial pattern (composition and configuration) of land cover features. Observable patterns can be linked to underlying processes affecting the generation of landscape patterns (e.g., forest harvesting). The objective of this research is to develop an approach for investigating the spatial distribution of forest pattern across a study area where forest harvesting, other anthropogenic activities, and topography, are all influencing forest pattern. We generate spatial pattern regions (SPR) that describe forest pattern with a regionalization approach. Analysis is performed using a 2006 land cover dataset covering the Prince George and Quesnel Forest Districts, 5.5 million ha of primarily forested land base situated within the interior plateau of British Columbia, Canada. Multivariate cluster analysis (with the CLARA algorithm) is used to group landscape objects containing forest pattern information into SPR. Of the six generated SPR, the second cluster (SPR2) is the most prevalent covering 22% of the study area. On average, landscapes in SPR2 are comprised of 55.5% forest cover, and contain the highest number of patches, and forest/non-forest joins, indicating highly fragmented landscapes. Regionalization of landscape pattern metrics provides a useful approach for examining the spatial distribution of forest pattern. Where forest patterns are associated with positive or negative environmental conditions, SPR can be used to identify similar regions for conservation or management activities.

What does crowdsourced data tell us about bicycling injury? A case study in a mid-sized Canadian city.

With only ∼20 % of bicycling crashes captured in official databases, studies on bicycling safety can be limited. New datasets on bicycling incidents are available via crowdsourcing applications, with opportunity for analyses that characterize reporting patterns. Our goal was to characterize patterns of injury in crowdsourced bicycle incident reports from BikeMaps.org. We extracted 281 incidents reported on the BikeMaps.org global mapping platform and analyzed 21 explanatory variables representing personal, trip, route, and crash characteristics. We used a balanced random forest classifier to classify three outcomes: (i) collisions resulting in injury requiring medical treatment; (ii) collisions resulting in injury but the bicyclist did not seek medical treatment; and (iii) collisions that did not result in injury. Results indicate the ranked importance and direction of relationship for explanatory variables. By knowing conditions that are most associated with injury we can target interventions to reduce future risk. The most important reporting pattern overall was the type of object the bicyclist collided with. Increased probability of injury requiring medical treatment was associated with collisions with animals, train tracks, transient hazards, and left-turning motor vehicles. Falls, right hooks, and doorings were associated with incidents where the bicyclist was injured but did not seek medical treatment, and conflicts with pedestrians and passing motor vehicles were associated with minor collisions with no injuries. In Victoria, British Columbia, Canada, bicycling safety would be improved by additional infrastructure to support safe left turns and around train tracks. Our findings support previous research using hospital admissions data that demonstrate how non-motor vehicle crashes can lead to bicyclist injury and that route characteristics and conditions are factors in bicycling collisions. Crowdsourced data have potential to fill gaps in official data such as insurance, police, and hospital reports.