Humans as Sensors: Citizen Science Data to Assess Climate Change
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1 Humans as Sensors: Citizen Science Data to Assess Climate Change Prof. People, Space and Place Research Cluster School of Environmental Sciences Roxby Building The University of Liverpool Liverpool L69 7ZT August 2012
2
3 Overview Three Ideas About Data Open Data Volunteered Geographical Information Citizen Science Methodological Issues Implications for Data Analysis Illustration by Example Monitoring Climate Change via Citizen Science
4 Volunteered Geographic Information See for example Goodchild in GeoJournal (2007) DOI: /s y... the widespread engagement of large numbers of private citizens, often with little in the way of formal qualifications, in the creation of geographic information, a function that for centuries has been reserved to official agencies.
5 Example:Open Streetmap
6 Is VGI a new outlet for Citizen Science?
7 Issues analysing Open Data Quite a lot of this data is geotagged and time-stamped in some way But there are special special issues Experimental design there often is none! Metadata Not always formal...
8 Climate Change: First Bloom of North American Lilac Monitoring first bloom dates of certain plants Warmer climate earlier dates? Here we consider the lilac in North America
9 Open data source (VGI): Source created by Mark Schwartz
10 Details Data consists of: First bloom dates for years at 1126 observation locations across USA (with a handful of observations just outside) A total of observations Latitude and longitude of each observation location is also recorded
11 Results d ij = S + Ry ij + ɛ ij : Estimate Std. Error t value Pr(> t ) (Intercept) year Counts First Bloom Day: B i Year: Y i
12 What!?! A rather surprising result Implies first bloom date getting later by about 1 day every 5 years opposite of expected effect!
13 Consider things further: How evenly balanced are the observations? What geographical effects might influence first bloom dates? Elevation Onset of spring Average annual temperature Soil type
14 Even space/time balance? Year Longitude
15 In tabular form Eastern Western
16 Split the Data into Two Estimate Std. Error t value Pr(> t ) a (Eastern) a (Western) b Y (Eastern) b Y (Western) Table: Regression analysis for models fitted separately to each network
17 An Explanation and A New Approach The green wave Spring comes sooner in some parts of the US In the regression model, allow intercept to change between locations This allows for the different baseline problem. The random coefficient model: Intercept in regression is a random variable at the locational level
18 Estimates of Random Coefficients Random Coefficient Model (MLM) d ij = s i + Ry ij + ɛ ij
19 Results d ij = s i + Ry ij + ɛ ij : Value Std.Error DF t-value p-value (Intercept) year Note the change in sign of the year coefficient Implies first bloom date getting earlier by about 1 day every 6 years This estimate allows for imbalance in observation locations over time
20 A Non-Linear Time Effect d ij = A + τ i + υ j + ɛ ij τ i Time effect υ j Observation station effect Thus, variation over time is modelled as an individual random variable each year... in the same way as geographical effect ɛ ij is a Gaussian error term
21 Results τ j Year (j)
22 Estimates of Random Coefficients Random Coefficient Model (MLM) - Varying Slope PS. Why not GWR? d ij = s i + R i y ij + ɛ ij
23 Lessons Learned The key thing here is working without experimental design. The lack of design can lead to misleading outcomes Unless you use an appropriate technique Learning from data is good You need to learn about the data as well. Statistical analysis is an aid to intelligent thought, not a substitute for it! Need to think about space and geography... Thank You
24 References Schwartz, M.D. and Reiter, B.E. Changes in North American Spring International journal of Climatology,2000,v20,pp Laird, N.M. and Ware, J.H. Random-Effects Models for Longitudinal Data Biometrics,1982, v38, pp Brunsdon, C. and Comber, A. Assessing the changing flowering date of the common lilac in North America: a random coefficient model approach GeoInformatica: DOI: /s Multilevel Models in R Random Effects in R
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