A time geographic approach to delineating areas of sustained wildlife use

Geographic information systems (GIS) are widely used for mapping wildlife movement patterns, and observed wildlife locations are surrogates for inferring on wildlife movement and habitat selection. We present a new approach to mapping areas where wildlife exhibit sustained use, which we term slow movement areas (SMAs). Nested within the habitat selection concepts of home range and core areas, SMAs are an additional approach to identifying areas important for wildlife. Our method for delineating SMAs is demonstrated on a grizzly bear (Ursus arctos) case study examining road density. Our results showed that subadult females had significantly higher road densities within SMAs than in their potential path area home ranges. The lowest road density was found in the SMAs of adult male grizzly bears. Given increased mortality risks associated with roads, female encampment near roads may have negative conservation implications. The methods presented in this manuscript compliment recent developments to identify movement suspension and intensively exploited areas defined from wildlife telemetry data. SMA delineation is sensitive to missing data and best applied to telemetry data collected with a consistent resolution.PostprintPeer reviewe

Promoting Crowdsourcing for Urban Research: Cycling Safety Citizen Science in Four Cities

People generate massive volumes of data on the Internet about cities. Researchers may engage these crowds to fill data gaps and better understand and inform planning decisions. Crowdsourced tools for data collection must be supported by outreach; however, researchers typically have limited experience with marketing and promotion. Our goal is to provide guidance on effective promotion strategies. We evaluated promotion efforts for BikeMaps.org, a crowdsourced tool for cycling collisions, near misses, hazards, and thefts. We analyzed website use (sessions) and incidents reported, and how they related to promotion medium (social, traditional news, or in-person), intended audience (cyclists or general), and community context (cycling mode share, cycling facilities, and a survey in the broader community). We compared four Canadian cities, three with active promotion, and one without, over eight months. High-use events were identified in time periods with above average web sessions. We found that promotion was essential for use of the project. Targeting cycling specific audiences resulted in more data submitted, while targeting general audiences resulted in greater age and gender diversity. We encourage researchers to use tools to monitor and adapt to promotion medium, audience, and community context. Strategic promotion may help achieve more diverse representation in crowdsourced data

BikeMaps.org: A Global Tool for Collision and Near Miss Mapping

There are many public health benefits to cycling, such as chronic disease reduction and improved air quality. Real and perceived concerns about safety are primary barriers to new ridership. Due to limited forums for official reporting of cycling incidents, lack of comprehensive data is limiting our ability to study cycling safety and conduct surveillance. Our goal is to introduce BikeMaps.org, a new website developed by the authors for crowd-source mapping of cycling collisions and near misses. BikeMaps.org is a global mapping system that allows citizens to map locations of cycling incidents and report on the nature of the event. Attributes collected are designed for spatial modelling research on predictors of safety and risk, and to aid surveillance and planning. Released in October 2014, within two months the website had more than 14,000 visitors and mapping in 14 countries. Collisions represent 38% of reports (134/356) and near misses 62% (222/356). In our pilot city, Victoria, Canada, citizens mapped data equivalent to about one year of official cycling collision reports within two months via BikeMaps.org. Using report completeness as an indicator, early reports indicate that data are of high quality with 50% being fully attributed and another 10% having only one missing attribute. We are advancing this technology, with the development of a mobile App, improved data visualization, real-time altering of hazard reports, and automated open-source tools for data sharing. Researchers and citizens interested in utilizing the BikeMaps.org technology can get involved by encouraging citizen mapping in their region

‘All Ages and Abilities’: exploring the language of municipal cycling policies

ABSTRACTAs cities work to support greater uptake and equity in cycling, the terminology ‘All Ages and Abilities’ (or AAA) is increasingly common in cycling research and practice vernacular. However, it is unclear the values that underlie this. We undertook a policy scan of Canadian municipal and regional policy documents to understand: the language used to describe ‘All Ages and Abilities’; the infrastructure specified; how municipalities and regions define a cycling network; and how equity and priority populations are incorporated into these plans. Of 35 plans, 25 mentioned ‘All Ages and Abilities’. Fourteen mentioned specific ‘All Ages and Abilities’ infrastructure, with cycle tracks, local street bikeways, and multi-use paths most frequent. Reference to the idea of a network was common (32 plans), with some defining this as a minimum grid. Within plans that used ‘All Ages and Abilities’ language, children and older adults were the most common populations mentioned (e.g. ‘Ages’), but there was more ambiguity around who was being referred to with ‘Abilities’. As use of this terminology continues, clarity is needed on the meaning and values that underpin it. A lack of specificity in design standards and whom this infrastructure serves is a barrier to concrete, consistent implementation

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

Generalized model for mapping bicycle ridership with crowdsourced data

Fitness apps, such as Strava, are a growing source of data for mapping bicyclingridership, due to large samples and high resolution. To overcome bias introduced by data generated from only fitness app users, researchers build statistical models that predict total bicycling by integrating Strava data with official counts and geographic data. However, studies conducted on single cities provide limited insight on best practices for modeling bicycling with Strava as generalizability is difficult to assess. Our goal is to develop a generalized approach to modeling bicycling ridership using Strava data which enables detailed mapping that is more inclusive of all bicyclists and will support better and more equitable decision-making across cities. We used Strava data, official counts, and geographic data to model Average Annual Daily Bicycling (AADB) in five cities: Boulder, Ottawa, Phoenix, San Francisco, and Victoria. Using a machine learning approach, LASSO, we identify variables important for predicting ridership in all cities, and independently in each city. Using the LASSO-selected variables as predictors in Poisson regression, we built generalized and city-specific models andcompared accuracy. Our results indicate generalized prediction of bicycling ridership on a road segment in concert with Strava data should include the following variables: number of Strava riders, percentage of Strava trips categorized as commuting, bicycling safety, and income. Inclusion of city-specific variables increased model performance, as the R2 for generalized and city-specific models ranged from 0.08–0.80 and 0.68–0.92, respectively. However, model accuracy was influenced most by the official count data used for model training. For best results, official count data should capture diverse street conditions, including low ridership areas. Counts collected continuously over a long time period, rather than at peak periods, may also improve modeling. Modeling bicycling from Strava and geographic data enablesmapping of bicycling ridership that is more inclusive of all bicyclists and better able tosupport decision-making

Recruiting Participants for Population Health Intervention Research: Effectiveness and Costs of Recruitment Methods for a Cohort Study

BackgroundPublic health research studies often rely on population-based participation and draw on various recruitment methods to establish samples. Increasingly, researchers are turning to web-based recruitment tools. However, few studies detail traditional and web-based recruitment efforts in terms of costs and potential biases. ObjectiveThis study aims to report on and evaluate the cost-effectiveness, time effectiveness, and sociodemographic representation of diverse recruitment methods used to enroll participants in 3 cities of the Interventions, Research, and Action in Cities Team (INTERACT) study, a cohort study conducted in Canadian cities. MethodsOver 2017 and 2018 in Vancouver, Saskatoon, and Montreal, the INTERACT study used the following recruitment methods: mailed letters, social media (including sponsored Facebook advertisements), news media, partner communications, snowball recruitment, in-person recruitment, and posters. Participation in the study involved answering web-based questionnaires (at minimum), activating a smartphone app to share sensor data, and wearing a device for mobility and physical activity monitoring. We describe sociodemographic characteristics by the recruitment method and analyze performance indicators, including cost, completion rate, and time effectiveness. Effectiveness included calculating cost per completer (ie, a participant who completed at least one questionnaire), the completion rate of a health questionnaire, and the delay between completion of eligibility and health questionnaires. Cost included producing materials (ie, printing costs), transmitting recruitment messages (ie, mailing list rental, postage, and sponsored Facebook posts charges), and staff time. In Montreal, the largest INTERACT sample, we modeled the number of daily recruits through generalized linear models accounting for the distributed lagged effects of recruitment campaigns. ResultsOverall, 1791 participants were recruited from 3 cities and completed at least one questionnaire: 318 in Vancouver, 315 in Saskatoon, and 1158 in Montreal. In all cities, most participants chose to participate fully (questionnaires, apps, and devices). The costs associated with a completed participant varied across recruitment methods and by city. Facebook advertisements generated the most recruits (n=687), at a cost of CAD $15.04 (US$11.57; including staff time) per completer. Mailed letters were the costliest, at CAD $108.30 (US$83.3) per completer but served to reach older participants. All methods resulted in a gender imbalance, with women participating more, specifically with social media. Partner newsletters resulted in the participation of younger adults and were cost-efficient (CAD $5.16 [US$3.97] per completer). A generalized linear model for daily Montreal recruitment identified 2-day lag effects on most recruitment methods, except for the snowball campaign (4 days), letters (15 days), and reminder cards (5 days). ConclusionsThis study presents comprehensive data on the costs, effectiveness, and bias of population recruitment in a cohort study in 3 Canadian cities. More comprehensive documentation and reporting of recruitment efforts across studies are needed to improve our capacity to conduct inclusive intervention research

Generalized model for mapping bicycle ridership with crowdsourced data

This work was supported by a grant (#1516-HQ-000064) from the Public Health Agency of Canada. MW is supported by a Scholar Award from the Michael Smith Foundation for Health Research.Fitness apps, such as Strava, are a growing source of data for mapping bicycling ridership, due to large samples and high resolution. To overcome bias introduced by data generated from only fitness app users, researchers build statistical models that predict total bicycling by integrating Strava data with official counts and geographic data. However, studies conducted on single cities provide limited insight on best practices for modeling bicycling with Strava as generalizability is difficult to assess. Our goal is to develop a generalized approach to modeling bicycling ridership using Strava data which enables detailed mapping that is more inclusive of all bicyclists and will support better and more equitable decision-making across cities. We used Strava data, official counts, and geographic data to model Average Annual Daily Bicycling (AADB) in five cities: Boulder, Ottawa, Phoenix, San Francisco, and Victoria. Using a machine learning approach, LASSO, we identify variables important for predicting ridership in all cities, and independently in each city. Using the LASSO-selected variables as predictors in Poisson regression, we built generalized and city-specific models and compared accuracy. Our results indicate generalized prediction of bicycling ridership on a road segment in concert with Strava data should include the following variables: number of Strava riders, percentage of Strava trips categorized as commuting, bicycling safety, and income. Inclusion of city-specific variables increased model performance, as the R2 for generalized and city-specific models ranged from 0.08–0.80 and 0.68–0.92, respectively. However, model accuracy was influenced most by the official count data used for model training. For best results, official count data should capture diverse street conditions, including low ridership areas. Counts collected continuously over a long time period, rather than at peak periods, may also improve modeling. Modeling bicycling from Strava and geographic data enablesmapping of bicycling ridership that is more inclusive of all bicyclists and better able to support decision-making.Publisher PDFPeer reviewe

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.Other UBCNon UBCReviewedFacult