1. Mapping diversity in citizen science participation in the northeastern US
Erika Barthelmess1 and Jacob Malcomb2
Biology Department and Nature Up North
St. Lawrence University
Canton, NY 13617 1
@porcupinedoc
@natureupnorth 2
Department of Environmental Sciences
University of Virginia

2. Background Boston NYC Albany http://natureupnorth.org

3. Citizen Science and inclusion
We stand for equity as a society
CS has the potential to:
Engage communities who have traditionally remained uninvolved in science (Newman et al. 2012)
Make environmental knowledge more robust and democratic (Ottinger 2010)
Connect people to science (Pandya 2012).
Participants in CS are not representative
Highly educated, more affluent participants (Evans et al )

4. Are we meeting this goal?
Premise
“…the environmental imperative is to get people of all ethnic and economic backgrounds on board with what we need to do to save what we can of the natural world; and the scientific imperative is to figure out what it is that can be done.” -- Dickinson and Bonney 2012
Are we meeting this goal?

5. Question
Can we use data on access to education, income, and race to predict where citizen science happens?
Approach
Combine US Census data with citizen science data from three large projects.

9. USA NPN Data

10. Budburst Data

11. iNaturalist Data

12. Process PREDICTOR VARIBLES: Hectares
Number of ISE centers (Informal Sci Ed)
Number of Colleges & Universities
Number of Educational Institutions (# Schools + # C&U)
Number of Educational Institutions per/100 people
Number of Schools (K-12)
Population Size
Per Capita Income
Region (Urbanized, Not Urbanized)
Percent of Population White (vs. other races)
Population Density (people per 100 ha)
Bachellors’ Degrees per Capita
% with Income to Poverty ratio 50% or lower
% with Income to Poverty 99% or lower (At_Below)
% with Income to Poverty > 100% (Above)
RESPONSE VARIABLE:
Are there Cit Sci data in the Block Group? (Yes/No)

13. Results 62,500 possible models Best model:
CS ~ 1 + Region + Hectares + NumSch + SumISE + PopSize + PerCapIncome + SumCU + PerWhite + Pop100HA + BAperCap + NumED + Edper100 + Below
10 models with AICc < 2.0
Used model averaging to look at them together.

14. Results 16 Estimate Std. Error Adjusted SE z value Pr(>|z|)
(Intercept)
-1.90E+00
1.03E-01
18.364
<<<0.001
***
RegionUrban
-1.04E+00
4.23E-02
24.583
Hectares
9.44E-05
6.80E-06
13.879
NumSch
8.26E-02
6.56E-02
1.259
NumISE
1.33E+00
1.17E-01
11.307
PopSize
3.14E-04
2.63E-05
2.64E-05
11.922
PerCapIncome
1.55E-06
1.14E-06
1.362
0.1733
NumCU
4.33E-01
8.02E-02
5.399
<< 0.001
PerWhite
3.49E-03
9.27E-04
3.767
Pop100HA
-3.53E-05
2.52E-06
13.996
BAperCap
4.86E+00
2.50E-01
19.468
NumED
4.93E-02
3.14E-02
1.57
Edper100
1.46E-01
1.18E-01
1.233
Below
6.69E-03
2.86E-03
2.343 *
AboveP
-9.25E-04
2.55E-03
0.364
AtBelow
1.90E-05
6.88E-04
0.028

15. Results

16. Results

17. Results

18. Concluding thoughts How can we improve access for all?
Where there are more people (absolute) and the area is larger, there are more citizen science data (not a surprise).
Where people have more education and greater access to education, including to informal science education centers, there are more citizen science data.
Race and poverty status also appear to have a role
Need further exploration.
We can use Cit Sci data to assess ourselves on access.
How can we improve access for all?