Contents lists available at ScienceDirect

Journal of Structural Geology

journal homepage: www.elsevier.com/locate/jsg

Modern approaches to field data collection and mapping: Digital methods,crowdsourcing, and the future of statistical analyses

S.J. Whitmeyera,∗, E.J. Pylea, T.L. Pavlisb, W. Swangera, L. Robertsa

a James Madison University, Harrisonburg, VA, 22807, USAbUniversity of Texas at El Paso, El Paso, TX, 79902, USA

A R T I C L E I N F O

Keywords:SmartphonesDigital compassesCrowdsourcingGeological mappingStatistics

A B S T R A C T

Modern use of mobile devices for field geology has facilitated new approaches to, and methodologies for, fielddata collection. Here, we highlight current, state-of-the-art methods, including digital-compass measurementsand field data collection with mobile devices, which facilitate crowdsourcing by novice geologists. Crowd-sourced collection of field data is advocated as a means of assembling big datasets for the construction ofdetailed geologic maps. However, expert control of field data is necessary to address inconsistencies in crowd-sourced novice datasets. Digital compasses on mobile devices can facilitate collection of field data by less-experienced geologists. However, concerns exist regarding instrument-related data quality. We incorporatediscussions of statistical methods that are relevant to evaluating the precision and accuracy of digital compassesas compared with analogue compasses. All compass platforms tested (Brunton Pocket Transits, iPhones, iPads,and Android-based phones) exhibited inconstancies in precision. However, the least reliable were Android-baseddevices. We argue that redundancy in measurements, coupled with assessing instrument drift through time, isnecessary for all types of compasses. Statistical evaluation of compass measurements and other field data isarguably an important component of future mapping and data collection methods, as we adapt to the oppor-tunities and challenges of assembling massive field datasets.

1. Introduction

Mobile technologies have become ubiquitous in today's society,with significant impacts on modern communications. The effective andefficient use of mobile devices in the field has likewise facilitated therevolution in digital approaches to field data collection that was high-lighted almost a decade ago (De Paor and Whitmeyer, 2009; Pavliset al., 2010; Whitmeyer et al., 2010). In many ways, technology hasnow caught up with the early visions of mobile, digital platforms formapping geology in the field.

As a result of modern mobile technologies, geoscience professionalsmap and collect data in the field using approaches that our predecessorslikely never envisioned. Gone are the tried and true methods of map-ping geology with an analogue compass, hardback field book, papertopographic map, and mylar overlay. Then again, we don't use planetables and alidades or travel to field sites on horseback anymore either.Today's data collection tools include apps on smartphones and mobiletablets, UAVs (unmanned aerial vehicles/drones), and high-resolutionimagery and digital elevation models (DEMs) assembled with LiDARand/or photogrammetry (e.g. Bemis et al., 2014; Cawood et al., 2017;

Pavlis and Mason, 2017).It should come as no surprise that these new tools have precipitated

a change in our methods of field data collection and mapping. A legacyof publications have already documented the advantages of digitalmethods for geologic mapping (e.g. Schetselaar, 1995; McCaffrey et al.,2005; Knoop and van der Pluijm, 2006; De Paor and Whitmeyer, 2009;Pavlis et al., 2010). As we do not need to duplicate these efforts, thispaper will focus on new approaches and methodologies for field datacollection that are enabled by modern mobile technologies. These newapproaches, such as using digital compasses to measure geologic fea-tures, crowd-sourcing data collection, and statistical analyses of out-crop data, differ significantly from the nineteenth century stereotype ofan isolated, bearded geologist tromping through a remote field areawith a Brunton compass, field book, and map board.

Our main tenet is that modern mobile equipment enables the rapidcollection of field data by less-experienced, or novice, geologists, andthus facilitates the collection of significantly larger field datasets thanwere previously possible. However, several caveats should be con-sidered with respect to data collected with mobile devices and by re-latively inexperienced persons. In the sections that follow, we start by

https://doi.org/10.1016/j.jsg.2018.06.023Received 18 December 2017; Received in revised form 25 June 2018; Accepted 25 June 2018

∗ Corresponding author.E-mail address: whitmesj@jmu.edu (S.J. Whitmeyer).

Journal of Structural Geology xxx (xxxx) xxx–xxx

0191-8141/ © 2018 Elsevier Ltd. All rights reserved.

Please cite this article as: Whitmeyer, S.J., Journal of Structural Geology (2018), https://doi.org/10.1016/j.jsg.2018.06.023

examining the use of mobile devices for fieldwork, and follow withtechniques for using mobile technologies to support crowd-sourcedfield data collection. We also briefly speculate on possible future ap-plications of new technologies. We fully understanding that some of ourspeculations likely will prove to be somewhat incorrect, but we trustthat they at least will be provocative.

2. Orientation measurements and field data collection withmobile devices

The advent of mobile devices, such as smartphones with iOS andAndroid operating systems, has put digital collection of field datawithin the capabilities of almost everyone, whether geology expert orrelative novice. The incorporation of GPS (global positioning system)chips in many smartphones and tablets meant that these devices had thepotential to be useful tools for fast and efficient geologic mapping anddata collection in the field. The internal sensors within these devices,such as accelerometers, gyroscopes, and magnetometers, led tech-savvygeoscientists to realize that smartphones could be used as digitalcompasses (McCarthy et al., 2009; Weng et al., 2012; Lee et al., 2013).However, it was unclear whether digital compass apps could produceorientation measurements accurate enough for professional geologicfieldwork. The variability in reported accuracy of digital compasses onassorted hardware platforms (Vanderlip and Mitchell, 2016;Allmendinger et al., 2017; Novakova and Pavlis, 2017) prompted us tostatistically analyze and compare planar orientation data from iPhones,iPads, and Android-based devices using the FieldMove Clino app(Midland Valley, 2017) with more traditional analogue measurementsfrom Brunton Pocket Transits.

2.1. Methods

2.1.1. Data collectionUndergraduate geology students at James Madison University

measured limestone bedding planes on ten outcrops that collectivelyrepresent limbs of a sequence of folds. In a second exercise, studentsmeasured plywood planes in an indoor laboratory that were arranged tomodel an anticlinal fold. Students measured each planar feature with aBrunton Pocket Transit compass and with the digital compass in theFieldMove Clino app on their personal smartphone (iPhones andAndroid-based Samsung and Motorola smartphones; Fig. 1a). A secondgroup of students collected a similar dataset on limestone outcropsusing both Brunton compasses and the FieldMove Clino app on iPadPros (Fig. 1b).

We also performed a precision test on 12 new Brunton PocketTransits, and 12 iPad Pros running the FieldMove Clino app. EachBrunton compass was taken straight out of the box from the factory, andused to measure strike and dip orientations on shallowly-, moderately-,and steeply-dipping plywood surfaces (Fig. 1c). iPad Pros measureddifferent, but similarly-oriented surfaces with shallow (∼15° dip),moderate (∼40° dip), and steep (∼60° dip) plywood surfaces. Theplywood models were positioned in random orientations on wooden orplastic horizontal surfaces to minimize magnetic or electrical inter-ference. To control for variables outside of the instruments, authorWhitmeyer took all of the measurements, using each Brunton compassor iPad Pro in turn, during a single session. Each device measured eachsurface twice, producing a total of 24 measurements per station for bothBrunton compasses and iPads (see Appendix A). Data were plotted aspoles to planes on a stereonet, where three distinct clusters of points,representing shallow, moderate, and steep dips, are apparent for eachdevice (Fig. 1d; Brunton poles in black, iPad poles in red).

Data from all exercises were disaggregated by measurement plat-form (Brunton compass, iPhone, or Android-based phone) for eachstation. Because of potentially small sample sizes, specific digital devicemodels within the collective categories of iPhones and Android phoneswere noted (see Appendix B), but not separated out in the analyses.

Data were displayed with Stereonet (Allmendinger et al., 2013) andOrient (Vollmer, 2018) software by plotting poles to bedding planes.Fisher distribution statistics were calculated for each device category(iPhones, Android-based phones, Brunton compasses), providing a re-sultant mean vector (R-bar) direction and length for data collected ateach measurement station. This analysis also provided dispersion va-lues (kappa) and 95% confidence cones. To facilitate the initial analysesand identify suspected systematic errors, data were further dis-aggregated by dip angle, assigning dip angles of 0–29° as “shallow,”30–59° as “moderate,” and 60–90° as “steep.”

In addition, data were used to represent either suspected (in thefield) or constructed (in the laboratory) fold patterns by fitting cy-lindrical best-fit great circles to poles to planes. Resultant fold axes(poles to best-fit great circles) were calculated in Stereonet for each ofthe three platforms: Brunton compasses, iPhones, Android-basedphones (Fig. 1e). Fold axes from field and laboratory datasets usingBrunton compasses and iPad Pros were similarly calculated and com-pared (Fig. 1f).

2.1.2. – statistical methodsA central research question in the analyses was to determine if any

statistically significant differences existed between platforms, de-termined by both the dispersion of data and the length and orientationof the mean vector for each platform. An ancillary question was toidentify any systematic patterns of error in measurement by platform,based on shallow, moderate, or steep dip angles. Analyzing sphericaldata is complex, but has analogues in parametric and non-parametricstatistics. Since we had three sets of essentially parametric data ofpresumably similar variance (i.e. dispersion), a multi-sample Analysisof Variance (ANOVA) test was applied using the method of Mardia(1972) with ∝=0.05 for a Fisher distribution. As with multisampleANOVA tests, the F-ratio is calculated as the variance between data sets,compared to the total variance with the system. Where the R-bar valueis greater than 0.747 and k is high, Mardia and Jupp (2000) recommendthat the F ratio can be approximated by:

∑ − − −

− ∑ − − −∼=

=

− − − −

R R q pn R R n q p

F( )/( 1)( 1)

( )/( )( 1)˙̇i

qi

iq

iq p n q p

1

1( 1)( 1),( )( 1)

In such cases where the F-ratio was less than a critical F value for theappropriate degrees of freedom, the null hypothesis Ho, that the plat-forms' mean vectors are the same, could not be rejected. In our analyses,no distinction could be made between platforms (iPhone, Android-based phone, Brunton compass). Where the F-ratio was greater than thecritical F value, a set of Watson-Williams tests (Watson and Williams,1956) were conducted to determine which platform was distinct fromthe others, using the approximation:

+ − −

− − − −∼ − − −

R R R pn R R n p

F( )/( 1)

( )/( 2)( 1)˙̇ p p n

1 2

1 21,( 1)( 2)

F-values greater than the critical value indicated a difference be-tween platforms. This method is analogous to pair-wise post-hoc tests inlinear ANOVA tests. Plots of the Fisher analyses can be found inFig. 2a–f.

Using a Fisher Distribution has limitations, as it requires that thedistribution of the poles be symmetrical about the mean vector.Preliminary analysis suggested that there might be conditions whereasymmetrical distribution of poles about the mean direction would beobserved. Therefore, data were also compared using a BinghamDistribution, which is more appropriate for asymmetrical data (Tauxe,2010). The calculation of eigenvalues associated with this distributionis challenging, and confidence cones are less meaningful where datasets are n < 25 (Joshua Davis, personal communication). As the datasets arranged by platform in this study frequently had fewer than 25samples, we elected to extend the analysis by using the bootstrappingfeature of Orient 3.7.1 (Vollmer, 2015, 2018), which generates a

S.J. Whitmeyer et al. Journal of Structural Geology xxx (xxxx) xxx–xxx

2

maximum eigenvalue for each distribution and a 95% confidence conebased on 10,000 resamples of the data for each platform. The a95confidence ellipse that results has major and minor axes that are pro-portional to the minimum and intermediate eigenvalues, with the lar-gest eigenvalue representing the principal direction of the distribution.Although there is an ANOVA procedure for bootstrap data sets(Figueiredo, 2017), the calculations are complex and will be the subjectof continuing research. In our bootstrapping analyses, we comparedmaximum eigenvalues and confidence cones for each platform, ex-amining them for mutual overlap (Joshua Davis, personal commu-nication; Fig. 3a–f). While this procedure lacks a firm theoreticalfooting at present, it is sufficient to identify those conditions underwhich bootstrapping analyses might be necessary. Tables 1 and 2 showthe Fisher and bootstrap calculations (respectively) for the three sta-tions in each setting that represent shallow, moderate, and steep dipangles.

2.2. Results

2.2.1. – Fisher statistics analysesFor the field data, analyses of the shallowly-dipping (Station H8)

and steeply-dipping (Station H7) limestone outcrops returned sig-nificant values for F (ANOVA Significance; Table 1). Where F(4,72) = 3.174, p < 0.05, the Ho is rejected, with a large effect size of0.1499 indicated. Subsequent post-hoc analysis showed a significantdifference between the iPhone and Android-based phone platforms (F(1,20) = 5.0368, p < 0.05). R-bar values exceed the 0.747 threshold, andkappa values are much greater than 1 and of the same order of mag-nitude. In this analysis, caution is urged, as the data set overall is rathersmall, and the Android data set particularly small.

From the dataset of plywood models in the laboratory, only theanalysis of the station with a moderate dip (Station M1) returned asignificant value for F (ANOVA Significance; Table 1). Where F(4,152) = 3.180, p < 0.05, the Ho is rejected, with a moderate effect sizeof 0.0777 indicated. Subsequent post-hoc analysis showed a significantdifference between the Brunton and iPhone platforms (F(1, 65)= 4.391,p < 0.05) for Station M1. It is worth noting that R-bar values exceedthe 0.747 threshold of Mardia (1972) for this type of analysis, and thatthe kappa values are much greater than 1 and of the same order ofmagnitude. When examining the distribution patterns of poles, thereappears to be a systematic dispersion in distribution patterns foriPhones, which tend to follow a small circle (Fig. 2e), indicating po-tential precision errors with device azimuth measurements.

2.2.2. – Bootstrap analysesAs suspected, several distributions displayed azimuthal variation

along a small circle by device, as are shown by the highly eccentricellipses (Fig. 3). As a result, the fit of a symmetrical confidence cone(Fisher distribution; Fig. 2) to the plots is not necessarily representative.Therefore, plots of the data were generated using Orient, plotting abootstrapped a95 confidence ellipse (10,000 resamples) around themaximum eigenvalue representing the principal direction for each de-vice. The results of the bootstrapped analyses are reported Table 2 anddescribed in more detail in Appendix C.

At moderate dip angles (stations H1, M1), all three platforms arerelatively consistent with each other, but at steep angles (stations H7,M4), the azimuth performance of the digital devices varies considerablyalong a small circle. At shallow dip angles (stations H8, M2), eachplatform shows a tight distribution, but variation exists between plat-forms that suggests more detailed analyses are needed. Of particular

Fig. 1. Digital compass in FieldMove Clino on an (a.) iPhone, and (b) iPad, measuring the orientation of a limestone bedding plane; c. a Brunton Pocket Transitanalogue compass measuring an inclined plywood model; d. Stereonet plot incorporating poles to bedding planes for limestone outcrops that show a folded surface -Android data in red, iPhone data in blue, Brunton data in green; e. Stereonet plot incorporating poles to bedding planes for limestone outcrops that show a foldedsurface - iPad data in blue, Brunton data in green; f. Stereonet plot showing poles to shallowly-, moderately-, and steeply-dipping planar plywood surfaces asmeasured with iPads (blue) and Brunton Pocket Transits (green).

S.J. Whitmeyer et al. Journal of Structural Geology xxx (xxxx) xxx–xxx

3

note, the field measurements for shallow depth (station H8), whileshowing relatively tight distributions within each platform, show nooverlap of the a95 confidence ellipses, suggesting that a more detailedanalysis is warranted to determine if there is a true difference betweenplatforms. In most instances, the Android platform shows the greatestazimuthal variation. Detailed descriptions of each plot are found inAppendix C.

2.2.3. - Precision test comparisonsOrientation measurements using new Brunton compasses and iPad

Pros also demonstrate considerable variability in precision. The newBrunton compasses yielded three relatively-tightly clustered sets ofpoints (Fig. 1f). However, these apparently tight clusters still representspreads of 4˚-5° on strike measurements, and spreads of 6˚-7° for dipmeasurements. The iPad measurements were less tightly clustered, withspreads of 6–26° for strike measurements and spreads of 3° for dipmeasurements. These data suggest that Brunton compasses are moreprecise when measuring strike, while iPads are more precise whenmeasuring dip. In addition, iPad measurements of strike are apparentlymore precise on steeper planes. The measurement spreads would likelybe greater on natural surfaces that have a greater degree of undulation,as well as in situations where more than one individual is taking themeasurements. Nevertheless, these results highlight the need for carefulcalibration and redundancy of measurements, regardless of device type.

2.3. Interpretations

In comparing the results of the analyses, several general inter-pretations can be made. First, Brunton compass and iPhone

measurements are similar, except at relatively steep dip angles. For thefield data, the Fisher distribution differences were seen in the moderatedip set, while the laboratory data showed the greatest differences at lowdips. Under these circumstances, a strong indicator of consistency inmeasurement (reliability or precision) can be seen in the kappa valuescalculated for each data set, where large values indicate a tight dis-tribution and small dispersion. The bootstrap-based analyses showedmore consistency in determining conditions of greatest variation, suchthat steep dip angles displayed the greatest azimuthal variation.

In this examination, a pattern of variance is observed in the digitalplatforms, such that dip values are fairly consistent, but variation isobserved along a small circle of less-well constrained strike measure-ments. Key observations from these analyses include:

1. Given the considerable difference in sample size, where more datawas collected for the Brunton compasses than the digital devices,performance assertions made with respect to the Android-basedphones should be treated as tentative.

2. The least levels of consistency in the Fisher analyses, with thegreatest dispersion and smallest k values, are observed for strikemeasurements taken at relatively shallow dip angles, with thegreatest consistency observed at steep dip angles. This issue is wellknown for analogue devices like the Brunton Pocket Transit wheremeasuring strike is difficult for shallow dips. However, the origin ofthis issue in digital devices is less apparent.

3. The bootstrap-based analyses identified a more consistent pattern ofvariation between platforms, such that the relative significance ofthe azimuthal variation of the digital platforms at steep dip angles ismore apparent in the confidence ellipses that were calculated. This

Fig. 2. Stereonet plots of poles to planes for Brunton compass measurements (green), iPhone measurements (blue), and Android-based phone measurements (red), onlimestone bedding planes (a, b, c.) and plywood models (d, e, f.) Plots represent planes with shallow dips (a, d.), moderate dips (b, e.), and steeper dips (c, f.); ellipsesrepresent Fisher distribution-based cones of confidence (a95) for each dataset.

S.J. Whitmeyer et al. Journal of Structural Geology xxx (xxxx) xxx–xxx

4

Fig. 3. Stereonet plots of poles to planes for Brunton compass measurements (green), iPhone measurements (blue), and Android phone measurements (red), onlimestone bedding planes (a, b, c.) and plywood models (d, e, f.) Plots represent planes with shallow dips (a, d.), moderate dips (b, e.), and steeper dips (c, f.); ellipsesrepresent bootstrap-based cones of confidence (a95) for each dataset.

Table 1Data from Fisher statistics analyses of shallowly-, moderately-, and steeply-dipping limestone outcrops (H8, H1, H7 respectively) and shallowly-, moderately-, andsteeply-dipping plywood models (M2, M1, M4 respectively) for Brunton Pocket Transits, iPhones, and Android-based phones. N=number of measurements, R-Bar= resultant mean vector (R/n), Kappa= dispersion values (higher= tighter distribution), Trend & Plunge= average orientations of poles to bedding planes,a95= 95% confidence cones, ANOVA Significance= indicates where F-values are greater than critical values, representing a difference between platforms.

Station/Platform N R-Bar Kappa Trend Plunge a95 ANOVA Significance?

Station H1 - moderate 42 0.9648 27.7 319.7 51.6 4.3 noBrunton compass 19 0.9956 215.8 320.0 48.6 2.3 –iPhone 17 0.9534 20.2 316.9 53.2 8.1 –Android phone 6 0.9131 8.0 327.4 57.0 22.8 –

Station H7 - steep 40 0.9264 13.2 154.2 23.2 6.5 yesBrunton compass 18 0.9895 90.2 152.4 19.1 3.7 vs. iPhoneiPhone 16 0.9104 10.5 155.2 23.6 12.0 vs. AndroidAndroid phone 6 0.9368 13.2 157.9 25.6 19.2 vs. iPhone

Station H8 - shallow 39 0.9909 106.9 321.6 70.9 2.8 yesBrunton compass 17 0.9915 110.5 324.4 71.2 4.3 –iPhone 16 0.9940 155.5 322.4 72.9 3.8 vs. AndroidAndroid phone 6 0.9898 68.4 313.8 64.8 7.4 vs. iPhone

Station M1 - moderate 79 0.9804 50.5 298.0 36.0 2.3 yesBrunton compass 40 0.9878 79.8 300.1 36.0 2.5 vs. iPhoneiPhone 27 0.9736 36.5 293.0 35.3 4.7 –Android phone 12 0.9810 44.2 302.6 34.2 6.3 –

Station M2 - shallow 79 0.9955 217.5 269.8 73.2 1.1 noBrunton compass 40 0.9993 1419.6 267.6 72.3 0.6 –iPhone 27 0.9894 91.3 273.9 74.8 2.9 –Android phone 12 0.9982 500.1 269.2 72.4 1.9 –

Station M4 - Steep 79 0.9597 24.5 311.2 28.9 3.3 noBrunton compass 40 0.9881 81.9 314.1 27.8 2.5 –iPhone 27 0.9183 11.8 306.8 31.9 8.5 –Android phone 12 0.9707 28.6 310.5 25.8 7.9 –

S.J. Whitmeyer et al. Journal of Structural Geology xxx (xxxx) xxx–xxx

5

approach compensates for small sample sizes, making comparisonsbetween platforms more meaningful. Significant future work will befocused on refining a multi-sample analysis approach using boot-strapped data.

4. Each instrument is sensitive to calibration issues, regardless ofwhether it is digital or analogue, as was demonstrated for both iPadPros and brand-new Brunton Pocket Transits that both showed no-ticeable dispersion.

5. One cannot assume a priori that digital compasses on smartphonesare as accurate or consistent in measurement as Brunton compassmeasurements. Android-based phone measurements are generallysuspect, while iPhone strike measurements show dispersion at steepdip angles.

6. ANOVA analysis of spherical data is an accessible technique, andshould be considered when evaluating digital and analogue plat-forms.

3. Crowd-sourced field data collection

The almost universal availability of mobile devices, such as smart-phones and mobile tablets that incorporate chips and sensors that fa-cilitate rapid and relatively accurate collection of field data (see above),has enabled a new “crowdsourcing” approach to collecting big datasetsof publicly-accessible information. The term “crowdsourcing” has beendefined as “a type of participative online activity in which an in-dividual, an institution, a nonprofit organization, or company proposesto a group of individuals of varying knowledge, heterogeneity, andnumber, via a flexible open call, the voluntary undertaking of a task”(Estellés-Arolas and González-Ladrón-de-Guevara, 2012). For scientificdata collection, crowdsourcing is a component of “citizen science”,where “members of the public participate in the scientific process inways that may include identifying research questions, making newdiscoveries, collecting and analyzing data, interpreting results, devel-oping technologies and applications, or problem solving” (White HouseOffice of Science and Technology Policy, 2015). As can be deducedfrom these statements, a key component of crowdsourcing for scientific

data collection is the use of a group of relative novices to collect datathat was traditionally the purview of scientific professionals. By enga-ging a larger pool of less-experienced, but enthusiastic, citizen scientistsin data collection activities, large datasets can be compiled, albeit witha greater potential for inaccuracies.

About a dozen years ago we recognized the potential for crowd-sourcing field data collection for geologic maps through engaging un-dergraduate geology students as a “crowd” of novice mappers(Johnston et al., 2005). Of course, undergraduate geology students arenot complete novices with regards to collecting geologic data in thefield. However, they are a “crowd” of relatively inexperienced personswith respect to producing professional geologic maps and interpreta-tions.

As a component of our efforts to modernize geoscience field edu-cation (e.g. Whitmeyer and Mogk, 2009), we incorporated a digitalmapping exercise within a capstone field course for James MadisonUniversity (De Paor and Whitmeyer, 2009). During this exercise, wecollected students' orientation measurements and lithologic data into alarge dataset that expanded each year as we incrementally progressedacross a several kilometer-square field area (Whitmeyer and De Paor,2014). Several years of data collection produced a dense dataset ofoutcrop bedding orientation measurements (color-coded by lithology,Fig. 4b). The density of this crowd-sourced student dataset is im-pressive, especially as compared with data collected by a single pro-fessional field geologist during the same period of time (Fig. 4a).However, close (qualitative) examination of the crowd-sourced datasetreveals several areas with conflicting lithologic information (i.e. sym-bols of more than one color in a small area, as highlighted by whiteboxes in Figs. 4b and 6b). This effect prompts the question: How can wehighlight and potentially resolve the inconsistencies in data on thecrowd-sourced map?

Our current approach to this challenge is semi-qualitative: we use aless-dense, but more accurate, “expert” dataset to constrain the mapinterpretations of the larger and denser “novice” dataset. The expertdataset represents the mapping efforts of one author (Whitmeyer),which we assume is an accurate representation of the sampled geology

Table 2Data from bootstrap analyses of shallowly-, moderately-, and steeply-dipping limestone outcrops (H8, H1, H7 respectively) and shallowly-, moderately-, and steeply-dipping plywood models (M2, M1, M4 respectively) for Brunton Pocket Transits, iPhones, and Android-based phones. N=number of measurements, Trend &Plunge= average orientations of poles to bedding planes, Max Eigenvalue= rough equivalent of R-bar value (resultant vector length) in a Fisher distribution, k(2) &k(1)=major and minor ellipse axes (respectively) that are proportional to the minimum and intermediate eigenvalues. k(1) and k(2) values derived from Mardia andZemrock (1977).

Station/Platform N Max Eigenvalue Trend Plunge k(2) k(1) Ellipse Overlap?

Station H1 - moderate 42 0.9452 318.29 50.54 −25.560 −13.090Brunton compass 19 0.9913 319.97 48.601 −25.550 −25.550 yesiPhone 17 0.9335 314.794 51.269 −25.570 −10.996 yesAndroid phone 6 0.8498 324.061 55.391 −25.640 −4.295 yes, broad

Station H7 - steep 40 0.8903 153.28 20.74 −9.043 −9.043Brunton compass 18 0.9797 152.5 19.01 −25.55 −25.55 yes

iPhone 16 0.7951 152.34 22.5 −7.042 −5.798 yes, broadAndroid phone 6 0.888 158.09 22.45 −25.62 −5.79 yes, narrow

Station H8 - shallow 39 0.982 329.61 63.98 −25.55 −25.55Brunton compass 17 0.9832 324.42 71.23 −25.55 −25.55 w/iPhoneiPhone 16 0.988 322.52 72.9 −25.55 −25.55 w/BruntonAndroid phone 6 0.9799 313.79 62.77 −25.55 −25.55 none

Station M1 - moderate 79 0.9657 298.569 35.304 −25.55 −25.55Brunton compass 40 0.9771 300.24 35.744 −25.55 −25.55 tightiPhone 27 0.9559 294.08 35.018 −25.56 −13.09 overlap, great circle variationAndroid phone 12 0.9638 302.682 34.255 −25.56 −13.09 overlap, great circle variation

Station M2 - shallow 79 0.9913 269.599 73.112 −25.55 −25.55Brunton compass 40 0.9986 267.563 72.311 −25.55 −25.55 tightiPhone 27 0.9798 273.389 74.624 −25.55 −25.55 tightAndroid phone 12 0.9963 269.224 72.399 −25.55 −25.55 tight

Station M4 - Steep 79 0.9472 312.188 27.569 −25.56 −13.09Brunton compass 40 0.98 314.256 27.327 −25.55 −25.55 tightiPhone 27 0.904 309.539 28.883 −25.6 −6.977 overlap, great circle variationAndroid phone 12 0.9444 310.277 25.728 −25.57 −10.996 overlap, great circle variation

S.J. Whitmeyer et al. Journal of Structural Geology xxx (xxxx) xxx–xxx

6

(Fig. 4a). As such, it functions as a control group of accurate data tohelp identify inconsistencies in the novice dataset and better constrainthe construction of a geologic map for the field area.

3.1. Field data collection

During 2009 to 2015, students collected orientation (strike and dipof bedding) and lithologic data from outcrops along a section of themountain Bencorragh in the lakes region of western Ireland (De Paorand Whitmeyer, 2009; Whitmeyer and De Paor, 2014). The field area isin the southern section of the South Mayo Trough, just north of theboundary with the Connemara terrane of Dalradian affinity (Williamsand Rice, 1989; Dewey and Ryan, 2016). Geology of the field area

consists of Ordovician conglomeratic sandstones and basalts un-conformably overlain by a Silurian sequence of terrestrial red beds,near shore sandstones and siltstones, and shelf/slope turbidites (Fig. 5;Graham et al., 1989; Chew et al., 2007; Whitmeyer et al., 2010). Thearea is broadly folded and cut by Late Devonian (Mohr, 2003; Johnsonet al., 2011) steeply-dipping normal and transverse faults.

Students collected field data on iPads using apps such as iGIS, andmore recently, FieldMove. All student data from the seven-year periodwere collected into an ArcGIS database, which covers most of themountain (Fig. 4b). Students mapped at a scale of about 1:5000, andthus their data collection is significantly more dense and detailed thanis evident in previously published geologic maps of this area (e.g. the1:63,360 map of Graham et al., 1989; and the 1:100,000 map of Morris

Fig. 4. Outcrop-based orientation data (strike and dip of bedding) collected by (a.) a single professional geologist, and (b.) student digital mapping projects, on themountain of Bencorragh, County Galway, western Ireland. Data collected during the years 2009–2015. Strike and dip symbols and dots (locations without mea-surable planar surfaces) are color coded by lithology (see Formation Names in upper left corners of maps). White boxes in Fig. 4b highlight examples of conflictinglithologic information (i.e. symbols of more than one color in a small area). Background orthoimagery from the Ordinance Survey of Ireland.

S.J. Whitmeyer et al. Journal of Structural Geology xxx (xxxx) xxx–xxx

7

et al., 1995). The expert dataset was collected with the same equip-ment, during the same time periods, and covered the same field area atthe same scale. The significant reduction in data density on the expertmap is due to the efforts of a single mapper, as opposed to the efforts of14–16 student teams each year.

3.2. Creating a geologic map: comparison of novice and expert datasets

We built a geologic map from the expert dataset, drawing contactlines, faults, and fold axes as constrained by orientation and lithologicaldata (Fig. 6a). Contacts and faults were rarely evident in the field (c.f.the occasional white orientation symbols that represent fault evidence),and thus unit boundaries were best constrained by high densities ofdata. Semi-transparent colored polygons represent the geologic forma-tions present in the mapping area (see Formation Names inset box,Figs. 4 and 6).

The second step was to create a geologic map of the same area usingthe significantly larger student-sourced (novice) dataset (Fig. 6b). Thishigh-density map exhibits some locations with inconsistent data col-lected by students (e.g. boxed areas in Fig. 6b). We have previouslypostulated that a benefit of crowdsourcing data is that the volume ofaccurate data outweighs the incorrect data (Whitmeyer and De Paor,2014). When datasets are large, spurious geologic interpretations canmore easily be seen as outlying data (e.g. Fig. 6b – the white boxhighlighting brown and blue dots and symbols in the yellow field).Throughout much of the field area, stratigraphic contacts can be fairlywell constrained using the crowd-sourced data (Fig. 7a left side).However, in some areas, precise contacts are difficult to produce due tothe variability in lithologic characterizations of different mappers (e.g.Fig. 7a - green symbols along the right margin). These areas likely showgradational stratigraphic contacts, where students' lack of field

mapping experience can lead to differing interpretations.Comparing Fig. 7a to the expert interpretation (Fig. 7b), even

though the dataset is sparser, the cleaner contacts can be used to con-strain the areas of conflicting unit characterization (blue-green zone atthe right side of Fig. 7a). This approach assumes that the expert char-acterizations of the lithologic units are correct. However, there areareas where the lack of data in the expert dataset yielded poor con-straints on faults and unit contacts. For example, several polygons areshown as extensive, continuous regions on the expert map (e.g. theright side of Fig. 7d). In contrast, the increased density of data withinthe same region on the novice map enabled better characterization offault offsets and orientation (Fig. 7c). In this area, the expert datasetwas too sparse to illustrate the offset contacts that are clear in the no-vice dataset. Consequently, the optimum strategy is to use the largenovice dataset for the main geologic map interpretation, and then usethe expert dataset to fine-tune areas of inconstancy on the novice map.

Additional constraints on digital datasets and geologic maps can beprovided by high-resolution terrain models (DEMs) and imagery. Forthis field area, Google Earth imagery proved helpful for resolving theorientation and extent of some of the more significant faults thattransect the region. For example, the placement of a northwest-strikingfault with tens of meters of offset was fine-tuned with the assistance ofoverhead/oblique views of 3D terrain and imagery (see the black faultlabeled with an “A” in Fig. 8). The same imagery assisted in the pla-cement of an eastward-plunging synclinal axis near the southern marginof the field area, as the exposed rock in the Google Earth imageryshowed the location and orientation of a nose of the syncline (see thered fold axis labeled with a “B” in Fig. 8).

Fig. 5. Simplified geologic map of the lakes region of western Ireland showing the field area (the mountain of Bencorragh, indicated by red arrow) of Ordovician-Silurian volcanic and sedimentary rocks along the northern boundary of the Connemara terrane. Map modified from Chew et al. (2007).

S.J. Whitmeyer et al. Journal of Structural Geology xxx (xxxx) xxx–xxx

8

4. Discussion

Perhaps the most important new capability that mobile technologiesbring to the collection of geologic data in the field is the ability toquickly and easily amass large, dense datasets. The automation of si-multaneously recording location, orientation of a planar or linear fea-ture, and the type of data recorded, vastly accelerates collection of fielddata. Using a modern mobile device (smartphone, tablet) a field geol-ogist can easily capture an order of magnitude more orientation data ina day, with every measurement precisely located. More importantly,smartphones and tablets with mapping apps that incorporate digitalcompasses facilitate field data collection though an intuitive interfacethat can be used by anyone with even a rudimentary knowledge ofgeology. Thus, modern digital technologies have enabled a

crowdsourcing approach to mapping geology in the field. This approachis a significant change from traditional methods of mapping geology inthe field, and comes with a new set of caveats and challenges. Theyinclude concerns about the accuracy of digital compass measurements,concerns about novices to collecting reliable data, and challenges re-lated to evaluating large and dense field datasets. Nonetheless, thesenew methods have the potential to transform the way field-orientedstructural and mapping studies are conducted.

Our experiments with mobile mapping equipment highlight situa-tions where digital compass measurements may not be as precise asfield geologists require. Novakova and Pavlis (2017) observed thatsome devices suffer from imprecise estimates of azimuth whereas in-clination measurements tend to be more precise. This behavior is par-ticularly apparent in our Android-based phone datasets (see Appendix

Fig. 6. Geological map interpretations of data collected by (a.) a single professional geologist, and (b.) student (novice) digital mapping projects, on the mountain ofBencorragh, County Galway, western Ireland. White box highlights examples of spurious data (brown and blue spots in a yellow field). Yellow boxes indicatelocations of detail maps in Fig. 7a–d. Data collected during the years 2009–2015. Background orthoimagery from the Ordinance Survey of Ireland.

S.J. Whitmeyer et al. Journal of Structural Geology xxx (xxxx) xxx–xxx

9

B), where the phones were from more than one manufacturer (Sam-sung, Motorola) and a variety of models. Our experiments with iPhonessuggest that not all of these devices are as precise as those used instudies by Allmendinger et al. (2017). However, our iPhone datasetsalso reflect a variety of models (see Appendix B). Collectively, our ob-servations together with these previous studies, as well as anecdotalobservations, indicate that each device almost certainly has its ownunique signature in terms of precision and accuracy. Indeed, in linewith a question raised by Allmendinger et al. (2017), the data presentedhere indicate that this issue even extends to the analogue devices(Brunton compasses) that geologists have been using for over a century.Thus, all observations to date support a statement by Midland Valley inreference to use of FieldMove Clino, that users should know the lim-itations of their device before trusting the data obtained with it(Midland Valley, 2017).

These mobile device limitations likely derive, in part, from thequality of the sensors embedded in each device. Specifically, the mag-netic sensor used in mobile devices is typically a single chip or chip setthat contains a three-component magnetometer. If that basic device isimprecise, or behaves erratically, it will lead to major azimuthal errors,like those seen in some of our experiments. Because these chips areinexpensive, mass-produced items, they likely vary in quality bothamong devices of a given model and between different devices andmanufacturers. This lack of consistent quality is probably the cause ofthe variability among individual devices that we observed. Thus, thebest solution seems to be that each device needs to be individuallytested for its feasibility and reliability as a field tool.

To evaluate the reliability of an individual mobile device, we re-commend a simple precision test prior to extensive field use, similar towhat is shown for Brunton compasses and iPad Pros in Fig. 1d. That is,use a uniformly planar surface (e.g. a plywood sheet, oriented carefullywith an analogue compass) and perform repeated measurements on the

same surface, then plot the data on a stereonet. If the errors are randombetween dip estimate and azimuth measurement, the data cluster willbe approximately circular and Fisher statistics should provide a clearassessment of the accuracy of the measurement group relative to theanalogue compass. In our experience, a device with a poor magneticsensor appears immediately in a test of this sort by simple inspection ofthe stereonet; i.e. a scattering of the azimuth measurements produces asmall circle grouping of the measurements (e.g. Fig. 2e and f). If adevice fails this initial test, it is likely not a usable device, althoughoccasionally it can be corrected with a recalibration. Alternatively, abootstrap analysis of several data points can quickly determine theconstraints of any small circle variation, so that the user can thenevaluate whether the dispersion is within acceptable limits.

Resolution of issues with instrument drift through time or abruptchanges in readings, like those observed by Novakova and Pavlis(2017), is a more challenging problem. One simple solution is tocarefully monitor azimuth measurements with hourly comparisons ofazimuth measurements to a conventional compass. A more thoroughcomparison might require periodic precision tests during the day,which could be done through a simple mental comparison of repeatedazimuth measurements to evaluate the data scatter. In either case, somefield time will be required, but until a device's reliability is thoroughlyknown, these steps are essential to insure accuracy and reliability ofdigital field measurements. A clear take-home message is to take thetime to know your device and its variability.

The use of mobile devices for orientation measurements and fieldmapping is an important component to the crowd-sourcing method offield data collection that we outline. Even given the caveats about theprecision of mobile device measurements, relative novices are morelikely to produce robust field data with a digital compass than frominexperienced use of an analogue Brunton compass. Our collective ex-perience is that it takes several months of consistent practice before

Fig. 7. a. Detail from the novice geologic map (Fig. 6b) showing well-constrained lithologic contacts on the left side and poorly-constrained lithologic contacts on theright that likely derive from gradational contacts between units, b. Detail from the expert geologic map with sparser data (Fig. 6a) that can be compared with thenovice map (6b.) to constrain contacts, c. Detail from the novice geologic map (Fig. 6b) showing denser data that yielded a map interpretation that is quite differentfrom the geologic map interpretation of the same area using the sparser expert dataset (d.).

S.J. Whitmeyer et al. Journal of Structural Geology xxx (xxxx) xxx–xxx

10

undergraduate students produce reliable orientation measurementswith a Brunton compass. In contrast, relative novices can easily andquickly produce a robust dataset of field measurements with digitalcompasses, where the accurate data visibly overwhelms the poor data.However, strategies are needed to highlight and resolve mapped areasthat show inconsistencies in data.

Our approach to using large crowd-sourced datasets for buildingbetter geologic maps uses smaller expert (control) datasets to constrainareas of conflicting field data. This method requires that experts areavailable to map the same field area along with the novices (students).Alternatively, experts could field check a draft crowd-sourced map andthen use the comparative techniques outlined above to constrain thefinal map interpretations. Ultimately, the main benefit of crowd-sour-cing field data collection is in assembling much larger field datasets,which in turn, yield better, more constrained, geologic map inter-pretations.

Additional approaches to refining crowd-sourced map interpreta-tions will likely derive from increased use of remote UAVs to gatherhigh-resolution, 3-D terrain datasets and detailed outcrop models.However, we envision that large crowd-sourced datasets will alsobenefit from statistical analyses. We suggest that the next fundamentaldevelopment in field data collection for geologic maps will include thedevelopment of statistical algorithms for highlighting areas of sig-nificant dispersion within large field datasets, to identify spurious and/or conflicting data. Semi-automated dispersion analyses would not beable to resolve all areas of conflicting data, but these types of analysesshould be able to highlight regions of conflict with more precision thanthe comparative visual strategies that we outline in this manuscript.

One thing is clear: We now have the ability to assemble big fielddatasets, which should enable the creation of better geologic map

interpretations. Less apparent are the optimal strategies for evaluatingand constraining large field datasets that incorporate areas with con-flicting data. The availability of expert field geologists can help resolvethese inconsistencies, but new statistical and computational meth-odologies ultimately may be the most efficient solution.

Acknowledgements

The authors thank students at JMU and UTEP for testing geologiccompasses in the field. Lauren Roberts and Catherine Whitmeyer helpedwith the precision tests. The authors are grateful to Bill Dunne, RandyWilliams, and a second reviewer for their reviews, which have im-proved this manuscript. The authors also extend special thanks to JoshDavis for his constructive, insightful, and above all tolerant advice andinput into the statistical analyses in this manuscript. RichardAllmendinger's Stereonet software and Frederick W. Vollmer's Orientsoftware were used to prepare some figures. This work was supportedby National Science Foundation award #1714587 to Whitmeyer & Pyle.

Appendices

Appendix A

Tabulated data for precision tests of 12 new Brunton Pocket Transitsand 12 iPad Pros using the FieldMove Clino app. Each compass or iPadtook two strike and dip measurements of shallowly-, moderately- andsteeply-dipping plywood models. All measurements were taken byWhitmeyer.

Fig. 8. Screen capture of an oblique aerial view from Google Earth with field data and geological interpretations overlain onto the Google Earth terrain model. Inseveral places, meso-scale faults (the fault with the greatest offset is labeled with an “A”) and a synclinal fold (labeled with a “B”) are apparent on the Google Earthterrain and can be visually correlated to geologic interpretations.

S.J. Whitmeyer et al. Journal of Structural Geology xxx (xxxx) xxx–xxx

11

Appendix B

All strike and dip data for all platforms collected for this study.Group 1 data are measurements of limestone bedding planes usingBrunton Pocket Transits, iPhones, and Android-based phones; collectedin Fall (2016). Group 2 data are measurements of plywood surfacesusing Brunton Pocket Transits, iPhones, and Android-based phones;collected in Spring (2017). Group 3 data are measurements of limestonebedding planes using Brunton Pocket Transits and iPad Pros.

Appendix C

Detailed interpretation for each bootstrap analysis of the six stationshighlighted in Fig. 3 and Table 2.

References

Allmendinger, R.W., Cardozo, N.C., Fisher, D., 2013. Structural Geology Algorithms:Vectors & Tensors. Cambridge University Press, Cambridge.

Allmendinger, R.W., Siron, C.R., Scott, C.P., 2017. Structural data collection with mobiledevices: accuracy, redundancy, and best practices. J. Struct. Geol. 102, 98–112.

Bemis, S.P., Micklethwaite, S., Turner, D., James, M.R., Akciz, S., Thiele, S.T., Bangash,H.A., 2014. Ground-based and UAV-based photogrammetry: a multi-scale, high-re-solution mapping tool for structural geology and paleoseismology. J. Struct. Geol. 69,163–178.

Cawood, A.J., Bond, C.E., Howell, J.A., Butler, R.W.H., Totake, Y., 2017. LiDAR, UAV orcompass-clinometer? Accuracy, coverage and the effects on structural models. J.Struct. Geol. 98, 67–82.

Chew, D.M., Graham, J.R., Whithouse, M.J., 2007. U-Pb zircon geochronology of plagi-ogranites from the Lough Nafooey (= Midland Valley) arc in western Ireland: con-straints on the onset of the Grampian orogeny. J. Geol. Soc. 164, 747–750.

De Paor, D.G., Whitmeyer, S.J., 2009. Innovations and redundancies in geoscience fieldcourses: past experiences and proposals for the future. In: Whitmeyer, S.J., Mogk, D.,Pyle, E.J. (Eds.), Field Geology Education: Historical Perspectives and ModernApproaches, pp. 45–56. http://dx.doi.org/10.1130/2009.2461(05). GSA SpecialPaper 461.

Dewey, J.F., Ryan, P.D., 2016. Connemara: its position and role in the grampian orogeny.Can. J. Earth Sci. 53, 1246–1257. http://dx.doi.org/10.1139/cjes-2015-0125.

Estellés-Arolas, E., González-Ladrón-de-Guevara, F., 2012. Towards an integratedcrowdsourcing definition. J. Inf. Sci. 38, 189–200. http://dx.doi.org/10.1177/0165551512437638.

Figueiredo, A., 2017. Bootstrap and permutation tests in ANOVA for directional data.Comput. Stat. 32, 1213–1240. http://dx.doi.org/10.1007/s00180-017-0739-x.

Graham, J.R., Leake, B.E., Ryan, P.D., 1989. The Geology of South Mayo, Western Ireland.Scottish Academic Press, Edinburgh.

Johnson, E.A., Sutherland, S., Logan, M.A.V., Samson, S.D., Feely, M., 2011.Emplacement conditions of a porphyritic felsite dyke and timing of motion along theCoolin fault at Ben Levy, Co. Galway. Ir. J. Earth Sci. 29, 1–13. http://dx.doi.org/10.3318/IJES.2011.29.1.

Johnston, S., Whitmeyer, S.J., De Paor, D., 2005. New developments in digital mappingand visualization as part of a capstone field geology course. Geol. Soc. Am. Abst.

Prog. 37 (7), 145.Knoop, P.A., van der Pluijm, B., 2006. GeoPad: Tablet PC-enabled field science education.

In: Berque, D., Prey, J., Reed, R. (Eds.), The Impact of Pen-Based Technology onEducation: Vignettes, Evaluations, and Future Directions. Purdue University Press,West Lafayette, Indiana, pp. 103–113.

Lee, S., Suh, J., Park, H-d, 2013. Smart Compass-Clinometer: a smartphone applicationsfor easy and rapid geological site investigation. Comput. Geosci. 61, 32–42.

Mardia, K.V., 1972. Statistics of Directional Data. Academic Press, London.Mardia, K.V., Jupp, P.E., 2000. Directional Statistics. Wiley, New York.Mardia, K.V., Zemrock, P.J., 1977. Table of maximum likelihood estimates for the

Bingham distribution. J. Stat. Comput. Simul. 6, 29–34.McCaffrey, K.J.W., Jones, R.R., Holdsworth, R.E., Wilson, R.W., Clegg, P., Imber, J.,

Holliman, N., Trinks, I., 2005. Unlocking the spatial dimension—Digital technologiesand the future of geoscience fieldwork. J. Geol. Soc. 162, 927–938.

McCarthy, A., Cosgrave, R., Meere, P., 2009. Use of the iPhone as a geological field tool:practical benefits and technical limitations. In: Proceedings of the EGU GeneralAssembly. Vienna, Austria, . http://meetingorganizer.copernicus.org/EGU2009/EGU2009-11727.pdf.

Midland Valley, 2017. Fieldmove Clino: Android Users Guide. Midland Valley, Glasgow.Mohr, P., 2003. Late magmatism of the Galway Granite batholith: I. Dacite dykes. Ir. J.

Earth Sci. 21, 71–104.Morris, J.H., Long, C.B., McConnell, B., Archer, J.B., 1995. Geology of Connemara.

Geological Survey of Ireland, Dublin.Novakova, L., Pavlis, T.L., 2017. Assessment of the precision of smart phones and tablets

for measurement of planar orientations: a case study. J. Struct. Geol. 97, 93–103.Pavlis, T.L., Mason, K.A., 2017. The new world of 3D geologic mapping. GSA Today 27,

4–10. http://dx.doi.org/10.1130/GSATG313A.1.Pavlis, T.L., Langford, R., Hurtado, J., Serpa, L., 2010. Computer‐based data acquisition

and visualization systems in field geology: results from 12 years of experimentationand future potential. Geosphere 6, 275–294.

Schetselaar, E., 1995. Computerized field-data capture and GIS analysis for generation ofcross sections in 3-D perspective views. Comput. Geosci. 21, 687–701.

Tauxe, L., 2010. Essentials of Paleomagnetism. University of California Press.Vanderlip, C., Mitchell, J., 2016. There's an app for that… Testing geologic smartphone

apps against the Brunton Pocket Transit. GSA Abstr. Progr 48. http://dx.doi.org/10.1130/abs/2016AM-283596.

Vollmer, F.W., 2015. Orient 3: a new integrated software program for orientation dataanalysis, kinematic analysis, spherical projections, and Schmidt plots. Geolog. Soc.Am. Abstr. Progr. 47 (7), 49.

Vollmer, F.W., 2018. Orient: Spherical Projection and Orientation Data Analysis Software.www.frederickvollmer.com.

Watson, G.S., Williams, E., 1956. On the construction of significance tests on the circleand the sphere. Biometrila 43, 344–352.

Weng, Y., Sun, F., Grigsby, J.D., 2012. GeoTools: an Android phone application ingeology. Comput. Geosci. 44, 24–30.

White House Office of Science and Technology Policy, 2015. Fact Sheet: EmpoweringStudents and Others through Citizen Science and Crowdsourcing. Washington.

Whitmeyer, S.J., Mogk, D.W., 2009. Geoscience field education: a recent resurgence. Eos90, 385–386. http://dx.doi.org/10.1029/2009EO430001.

Whitmeyer, S.J., De Paor, D.G., 2014. Crowdsourcing digital maps using citizen geolo-gists. Eos 95, 397–399. http://dx.doi.org/10.1002/2014EO440001.

Whitmeyer, S.J., Nicoletti, J., De Paor, D.G., 2010. The digital revolution in geologicmapping. GSA Today 20, 4–10. http://dx.doi.org/10.1130/GSATG70A.1.

Williams, D.M., Rice, A.H.N., 1989. Low-angle extensional faulting and the emplacementof the Connemara Dalradian, Ireland. Tectonics 8, 417–428. http://dx.doi.org/10.1029/TC008i002p00417.

S.J. Whitmeyer et al. Journal of Structural Geology xxx (xxxx) xxx–xxx

12