From Bears to Bikes: Transdisciplinary Spatial Research

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1 Species From Bears to Bikes: Transdisciplinary Spatial Research Dr. Trisalyn Nelson Professor, UVic Geography Lansdowne Research Chair in Spatial Sciences Director of Geomatics Director of SPAR Lab 100 Islands Indoor Radon Exposure Vulnerability PM 2.5 Air Quality Island biogeography Marine Subsidies? Area 1

2 Data collected 2001 present 230 individual bears; >630,000 telemetry locations Hair cortisol concentrations (HCC) as a noninvasive indicator of stress Habitat variables Topography, vegetation productivity, crown closure, percent conifer Anthropogenic variables Roads, oil & gas well-sites, harvests, parks & protected areas Source: Boonstra 2013 Bourbonnais, Nelson et al. (2013). Spatial analysis of factors influencing long-term stress in the grizzly bear (Ursus arctos) population of Alberta, Canada. PloS one 8.12 (2013): e MALE FEMALE 2

3 Co-efficient of Variation in Movement (step length) Movement (i.e., Dynamic Interaction) Long, Nelson et al A critical examination of indices of dynamic interaction for wildlife telemetry studies..journal of Animal Ecology. How does movement near roads impact survival and mortality? Step length Fast ~ travelling Slow ~ utilizing resources Long & Nelson A review of quantitative methods for movement data. International Journal of Geographical Information Science. Long & Nelson Measuring dynamic interaction in movement data. Transactions in GIS. Long & Nelson Time geography and wildlife home range delineation. The Journal of Wildlife Management. Kite, Nelson et al. (2016). A movement-driven approach to quantifying grizzly bear (Ursus arctos) near-road movement patterns in west-central Alberta. Biological Conservation, 195, Data drive & pattern based approach No, I m not being tracked by scientists, just by my wife Autocorrelation in movement as a function of distance from road Response scale Distance between telemetry observation to road (m) 3

4 Scales of Response to Roads Breeding Nonbreeding Adult Female 90m 35m Sub-adult female 80m 25m Higher mortality when bears move fast near roads Male 55m 55m Sub-adult Male 75m 70m Lower mortality when near roads at night Implement/ support management strategies Any telemetry data Spatial pattern analysis Other features (traffic) R code More landscape data & more movement data = new methods for integration : Future Direction Movement & Spatial ASU Movement Geospatial Analysis Pattern, Space and time, Big spatial data sets Remote Sensing Lidar, Landsat archive Spatial Ecology Environment & sustainability; humanenvironment management Within ASU: Biological Sciences & Conservation and Ecology; School of Life Sciences Animal Behavior program 4

5 Cycling Healthy Sustainable Economic Happiness Annual federal funding for cycling and walking, % of Tempe Residents Bike to Work Lack of Data for Planning Safety Only ~30% of bike incident data captured No centralized or near miss reporting Counters limited in space or time Pucher, John, Ralph Buehler, and Mark Seinen. "Bicycling renaissance in North America? An update and re-appraisal of cycling trends and policies."transportation research part A: policy and practice 45.6 (2011): Technology Nelson et al. (2015) BikeMaps. org: a global tool for collision and near miss mapping. Frontiers in public health 3. 5

6 Technology Technology Ethics requires data be anonymous Technology Technology Technology ~2500 Downloads Technology 6

7 Relative Proportion (%) Outreach Data ~2150 locations in 35 Countries: Canada, US, Australia, UK, Germany, Costa Rica, Belgium, France, Denmark, New Zeland Victoria Photo Credit: Corey Burger Data: e.g., Victoria Data Cycling safety hot spots Crashes: 19% Near-Miss: 47% Hazards: 27% Thefts: 7% Victoria 769 reports Year Insurance Reports Data Daily Trends Data Weekday by Hour Insurance

8 Relative Proportion (%) Data Weekday by Hour Weather Web Scraped (Dark Sky Forecast API): Near Miss 74% 51% Collision 26% 49% Near miss / no injury Doctor required Hospital required Weather Need for Exposure Data Victoria Manual Counts Jestico, Nelson, & Winters. (2015) Mapping with Fitness App Data. Journal of Transport Geography (Revised and Resubmitted). 8

9 Description Relevance Slope (%) Cyclists deterred by hills and large slope (Broach et al. 2012) Population density (population per km 2 ) Pavement widths (m) On-street parking Posted traffic speed limit Bike Facilities (painted lanes & multi-use trails) Denser population have more cyclists (Winters et al. 2010) Wide roads deter cyclists (Allen- Munley & Daniel 2006) Parked vehicles on roadways deter cyclists (Stinson & Bhat 2003) Cyclists prefer low speed (Hood et al. 2011; Landis et al. 1997) Cyclists prefer bike facilities, especially off-street pathway (Stinson & Bhat 2003; Winters et al. 2013) Variable Category Cycling volume change per 1 unit increase Higher Lower Strava cyclist volume Continuous + 51 Segment Slope (%) Continuous - 72 Posted Speed Limit (reference 20km/h) On Street Parking (reference none) 50km/h km/h km/h +291 Yes -237 Jestico, B., Nelson, T.A., and Winters, M. (2015). Mapping with Fitness App Data. Journal of Transport Geography (Revised and Resubmitted). Summary Future work Smarter cities = better data Crowdsourcing and VGI (direct and indirect) = novel spatial data for planning smarter cities Conflate with traditional data Appropriate statistical modeling Demonstration of careful and effective use Weather Lighting Mapping Surveillance methods Map of cost of cycling injury Vs. infrastructure Long term safe routing 9

10 @ ASU Smart Cities! HUGE THANKS TO. Technology and information to improve city function SGSUP ideal transdisciplinary skills (Links to GeoDesign?) GIScience Urban & transportation planning Climate, heat, land use, and air quality ASU School of Sustainability; LTER; Center of Urban Innovation; Civil, Environmental and Sustainable Engineering 10