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Dr. Trisalyn NelsonProfessor, UVic Geography

Lansdowne Research Chair in Spatial SciencesDirector of GeomaticsDirector of SPAR Lab

From Bears to Bikes: Transdisciplinary Spatial

Research

100 Islands

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Area

Spec

ies

Marine Subsidies?

Island biogeography

Indoor Radon Exposure Vulnerability PM2.5 – Air Quality

Grizzly Bears

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Grizzly Bears Grizzly Bears

Data collected 2001 – present230 individual bears; >630,000 telemetry locations

Grizzly Bears

Hair cortisol concentrations (HCC) as a non-invasive indicator of stress

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): e83768.

Habitat variables• Topography, vegetation productivity, crown closure, percent

conifer

Anthropogenic variables• Roads, oil & gas well-sites, harvests, parks & protected areas

Grizzly Bears

Grizzly Bears

MALE FEMALE

Grizzly Bears

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Grizzly Bears

Long, Nelson et al. 2014. A critical examination of indices of dynamic interaction for wildlife telemetry studies. .Journal of Animal Ecology.

Long & Nelson. 2013. A review of quantitative methods for movement data. International Journal of Geographical Information Science.

Long & Nelson. 2013. Measuring dynamic interaction in movement data. Transactions in GIS.

Long & Nelson. 2012. Time geography and wildlife home range delineation. The Journal of Wildlife Management.

Movement (i.e., Dynamic Interaction)

Grizzly Bears

Step length

• Fast ~ travelling

• Slow ~ utilizing resources

How does movement near roads impact survival and mortality?

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, 24-32.

Grizzly Bears

No, I’m not being tracked by scientists,

just by my wife…

Grizzly Bears

Data drive & pattern based approach

Grizzly Bears

Autocorrelation in movement as a function of distance from road

Grizzly Bears

Distance between telemetry observation to road (m)Co

-eff

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of

Var

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on

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ove

men

t (s

tep

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Response scale

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Grizzly Bears

BreedingNon-

breeding

Adult Female 90m 35m

Sub-adult female

80m 25m

Male 55m 55m

Sub-adult Male

75m 70m

Scales of Response to Roads

Grizzly Bears

Higher mortality when bears move fast near roads

Grizzly Bears

Lower mortality when near roads at night

Grizzly Bears

• 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

Grizzly Bears: Future Direction Movement & Spatial Ecology @ ASU

• Movement • Geospatial Analysis

• Pattern, Space and time, “Big” spatial data sets

• Remote Sensing• Lidar, Landsat archive

• Spatial Ecology • Environment & sustainability; human-

environment management

Within ASU: Biological Sciences & Conservation and Ecology; School of Life Sciences – Animal Behavior program

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•Healthy

• Sustainable

• Economic

•Happiness

Cycling

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): 451-475.

Annual federal funding for cycling and walking, 1988–2009

4% of Tempe Residents Bike to Work

Safety

• Only ~30% of bike incident data captured

• No centralized or near miss reporting

Ridership

• Counters – limited in space or time

Lack of Data for Planning

Technology

Nelson et al. (2015) BikeMaps. org: a global tool for collision and near miss mapping. Frontiers in public health 3.

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Technology

• Ethics requires data be anonymous

Technology

Technology Technology

Technology ~2500 Downloads Technology

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Outreach

Photo Credit: Corey Burger

Data

~2150 locations in 35 Countries: Canada, US, Australia, UK, Germany, Costa Rica, Belgium, France, Denmark, New Zeland…

Victoria

Victoria769 reports

Year Insurance Reports

2009 112

2010 119

2011 119

2012 125

2013 134

•Crashes: 19%

•Near-Miss: 47%

•Hazards: 27%

• Thefts: 7%

Data: e.g., Victoria Cycling safety hot spotsData

Insurance 09-13

Data –Daily Trends

Re

lati

ve P

rop

ort

ion

(%

)

Data – Weekday by Hour

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Near miss / no injury Doctor required Hospital required

Re

lati

ve P

rop

ort

ion

(%

)

Data – Weekday by Hour Weather

Web Scraped (Dark Sky Forecast API):

Near Miss 74% 51%

Collision 26% 49%

Weather Need for Exposure Data

Ridership

Jestico, Nelson, & Winters. (2015) Mapping Ridership with Fitness App Data. Journal of Transport Geography (Revised and Resubmitted).

Victoria – Manual Counts

Ridership

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Ridership Description Relevance

Slope (%) Cyclists deterred by hills and large

slope (Broach et al. 2012)

Population density

(population per km2)

Denser population have more cyclists

(Winters et al. 2010)

Pavement widths (m) Wide roads deter cyclists (Allen-

Munley & Daniel 2006)

On-street parking Parked vehicles on roadways deter

cyclists (Stinson & Bhat 2003)

Posted traffic speed

limit

Cyclists prefer low speed (Hood et al.

2011; Landis et al. 1997)

Bike Facilities (painted

lanes & multi-use trails)

Cyclists prefer bike facilities,

especially off-street pathway (Stinson

& Bhat 2003; Winters et al. 2013)

Variable Category Cycling volume change per

1 unit increase

Strava cyclist volume Continuous + 51

Segment Slope (%) Continuous - 72

Posted Speed Limit

(reference 20km/h)

50km/h -740

40km/h -834

30km/h +291

On Street Parking

(reference none)

Yes -237

Ridership Ridership

Jestico, B., Nelson, T.A., and Winters, M. (2015). Mapping Ridership with Fitness App Data. Journal of Transport Geography (Revised and Resubmitted).

Higher Ridership Lower Ridership

Summary

• 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

Future work

• Weather

• Lighting

• Mapping Ridership

• Surveillance methods

• Map of cost of cycling injury Vs. infrastructure

• Long term – safe routing

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@ ASU – Smart Cities!

• 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

HUGE THANKS TO….

Sponsors

@BikeMapsTeam