Journal of Structural xxx (xxxx) xxx–xxx

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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, USA b University of Texas at El Paso, El Paso, TX, 79902, USA

ARTICLE INFO ABSTRACT

Keywords: Modern use of mobile devices for field geology has facilitated new approaches to, and methodologies for, field Smartphones data collection. Here, we highlight current, state-of-the-art methods, including digital- measurements Digital and field data collection with mobile devices, which facilitate crowdsourcing by novice . Crowd- Crowdsourcing sourced collection of field data is advocated as a means of assembling big datasets for the construction of Geological mapping detailed geologic maps. However, expert control of field data is necessary to address inconsistencies in crowd- Statistics 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 incorporate discussions of statistical methods that are relevant to evaluating the precision and accuracy of digital compasses as 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-based devices. We argue that redundancy in measurements, coupled with assessing instrument drift through time, is necessary for all types of compasses. Statistical evaluation of compass measurements and other field data is arguably 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 Pavlis and Mason, 2017). It should come as no surprise that these new tools have precipitated Mobile technologies have become ubiquitous in today's society, a change in our methods of field data collection and mapping. A legacy with significant impacts on modern communications. The effective and of publications have already documented the advantages of digital efficient use of mobile devices in the field has likewise facilitated the methods for geologic mapping (e.g. Schetselaar, 1995; McCaffrey et al., revolution in digital approaches to field data collection that was high- 2005; Knoop and van der Pluijm, 2006; De Paor and Whitmeyer, 2009; lighted almost a decade ago (De Paor and Whitmeyer, 2009; Pavlis Pavlis et al., 2010). As we do not need to duplicate these efforts, this et al., 2010; Whitmeyer et al., 2010). In many ways, technology has paper will focus on new approaches and methodologies for field data now caught up with the early visions of mobile, digital platforms for collection that are enabled by modern mobile technologies. These new mapping geology in the field. approaches, such as using digital compasses to measure geologic fea- As a result of modern mobile technologies, geoscience professionals tures, crowd-sourcing data collection, and statistical analyses of out- map and collect data in the field using approaches that our predecessors crop data, differ significantly from the nineteenth century stereotype of likely never envisioned. Gone are the tried and true methods of map- an isolated, bearded tromping through a remote field area ping geology with an analogue compass, hardback field book, paper with a Brunton compass, field book, and map board. topographic map, and mylar overlay. Then again, we don't use plane Our main tenet is that modern mobile equipment enables the rapid tables and alidades or travel to field sites on horseback anymore either. collection of field data by less-experienced, or novice, geologists, and Today's data collection tools include apps on smartphones and mobile thus facilitates the collection of significantly larger field datasets than tablets, UAVs (unmanned aerial vehicles/drones), and high-resolution were previously possible. However, several caveats should be con- imagery and digital elevation models (DEMs) assembled with LiDAR sidered with respect to data collected with mobile devices and by re- and/or photogrammetry (e.g. Bemis et al., 2014; Cawood et al., 2017; latively inexperienced persons. In the sections that follow, we start by

∗ Corresponding author. E-mail address: [email protected] (S.J. Whitmeyer). https://doi.org/10.1016/j.jsg.2018.06.023 Received 18 December 2017; Received in revised form 25 June 2018; Accepted 25 June 2018 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 S.J. Whitmeyer et al. Journal of Structural Geology xxx (xxxx) xxx–xxx examining the use of mobile devices for fieldwork, and follow with Data were displayed with Stereonet (Allmendinger et al., 2013) and techniques for using mobile technologies to support crowd-sourced Orient (Vollmer, 2018) software by plotting poles to bedding planes. field data collection. We also briefly speculate on possible future ap- Fisher distribution statistics were calculated for each device category plications of new technologies. We fully understanding that some of our (iPhones, Android-based phones, Brunton compasses), providing a re- speculations likely will prove to be somewhat incorrect, but we trust sultant mean vector (R-bar) direction and length for data collected at that they at least will be provocative. each measurement station. This analysis also provided dispersion va- lues (kappa) and 95% confidence cones. To facilitate the initial analyses 2. Orientation measurements and field data collection with and identify suspected systematic errors, data were further dis- mobile devices aggregated by dip angle, assigning dip angles of 0–29° as “shallow,” 30–59° as “moderate,” and 60–90° as “steep.” The advent of mobile devices, such as smartphones with iOS and In addition, data were used to represent either suspected (in the Android operating systems, has put digital collection of field data field) or constructed (in the laboratory) patterns by fitting cy- within the capabilities of almost everyone, whether geology expert or lindrical best-fit great circles to poles to planes. Resultant fold axes relative novice. The incorporation of GPS (global positioning system) (poles to best-fit great circles) were calculated in Stereonet for each of chips in many smartphones and tablets meant that these devices had the the three platforms: Brunton compasses, iPhones, Android-based potential to be useful tools for fast and efficient geologic mapping and phones (Fig. 1e). Fold axes from field and laboratory datasets using data collection in the field. The internal sensors within these devices, Brunton compasses and iPad Pros were similarly calculated and com- such as accelerometers, gyroscopes, and , led tech-savvy pared (Fig. 1f). geoscientists to realize that smartphones could be used as digital compasses (McCarthy et al., 2009; Weng et al., 2012; Lee et al., 2013). 2.1.2. – statistical methods However, it was unclear whether digital compass apps could produce A central research question in the analyses was to determine if any orientation measurements accurate enough for professional geologic statistically significant differences existed between platforms, de- fi eldwork. The variability in reported accuracy of digital compasses on termined by both the dispersion of data and the length and orientation assorted hardware platforms (Vanderlip and Mitchell, 2016; of the mean vector for each platform. An ancillary question was to Allmendinger et al., 2017; Novakova and Pavlis, 2017) prompted us to identify any systematic patterns of error in measurement by platform, statistically analyze and compare planar orientation data from iPhones, based on shallow, moderate, or steep dip angles. Analyzing spherical iPads, and Android-based devices using the FieldMove Clino app data is complex, but has analogues in parametric and non-parametric (Midland Valley, 2017) with more traditional analogue measurements statistics. Since we had three sets of essentially parametric data of from Brunton Pocket Transits. presumably similar variance (i.e. dispersion), a multi-sample Analysis of Variance (ANOVA) test was applied using the method of Mardia 2.1. Methods (1972) with ∝ = 0.05 for a Fisher distribution. As with multisample ANOVA tests, the F-ratio is calculated as the variance between data sets, 2.1.1. Data collection compared to the total variance with the system. Where the R-bar value Undergraduate geology students at James Madison University is greater than 0.747 and k is high, Mardia and Jupp (2000) recommend measured limestone bedding planes on ten outcrops that collectively that the F ratio can be approximated by: represent limbs of a sequence of folds. In a second exercise, students q measured plywood planes in an indoor laboratory that were arranged to ()∑i=1 RRqi −−−/(1)(1 p ) ∼˙ F(1)(1),()(1qp−− nqp −−) model an anticlinal fold. Students measured each planar feature with a q ˙ ()nRRnqp− ∑i=1 i −−−/()(1) Brunton Pocket Transit compass and with the digital compass in the FieldMove Clino app on their personal smartphone (iPhones and In such cases where the F-ratio was less than a critical F value for the Android-based Samsung and Motorola smartphones; Fig. 1a). A second appropriate degrees of freedom, the null hypothesis Ho, that the plat- group of students collected a similar dataset on limestone outcrops forms' mean vectors are the same, could not be rejected. In our analyses, using both Brunton compasses and the FieldMove Clino app on iPad no distinction could be made between platforms (iPhone, Android- Pros (Fig. 1b). based phone, Brunton compass). Where the F-ratio was greater than the We also performed a precision test on 12 new Brunton Pocket critical F value, a set of Watson-Williams tests (Watson and Williams, Transits, and 12 iPad Pros running the FieldMove Clino app. Each 1956) were conducted to determine which platform was distinct from Brunton compass was taken straight out of the box from the factory, and the others, using the approximation: used to measure orientations on shallowly-, moderately-, ()/(1)RRRp12+− − and steeply-dipping plywood surfaces (Fig. 1c). iPad Pros measured ∼˙ Fppn−−1,( 1)( − 2) ()nR−−12 Rn/(2 −)(1 p −)˙ different, but similarly-oriented surfaces with shallow (∼15° dip), moderate (∼40° dip), and steep (∼60° dip) plywood surfaces. The F-values greater than the critical value indicated a difference be- plywood models were positioned in random orientations on wooden or tween platforms. This method is analogous to pair-wise post-hoc tests in plastic horizontal surfaces to minimize magnetic or electrical inter- linear ANOVA tests. Plots of the Fisher analyses can be found in ference. To control for variables outside of the instruments, author Fig. 2a–f. Whitmeyer took all of the measurements, using each Brunton compass Using a Fisher Distribution has limitations, as it requires that the or iPad Pro in turn, during a single session. Each device measured each distribution of the poles be symmetrical about the mean vector. surface twice, producing a total of 24 measurements per station for both Preliminary analysis suggested that there might be conditions where Brunton compasses and iPads (see Appendix A). Data were plotted as asymmetrical distribution of poles about the mean direction would be poles to planes on a stereonet, where three distinct clusters of points, observed. Therefore, data were also compared using a Bingham representing shallow, moderate, and steep dips, are apparent for each Distribution, which is more appropriate for asymmetrical data (Tauxe, device (Fig. 1d; Brunton poles in black, iPad poles in red). 2010). The calculation of eigenvalues associated with this distribution Data from all exercises were disaggregated by measurement plat- is challenging, and confidence cones are less meaningful where data form (Brunton compass, iPhone, or Android-based phone) for each sets are n < 25 (Joshua Davis, personal communication). As the data station. Because of potentially small sample sizes, specifi c digital device sets arranged by platform in this study frequently had fewer than 25 models within the collective categories of iPhones and Android phones samples, we elected to extend the analysis by using the bootstrapping were noted (see Appendix B), but not separated out in the analyses. feature of Orient 3.7.1 (Vollmer, 2015, 2018), which generates a

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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 Transit analogue 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 folded surface - iPad data in blue, Brunton data in green; f. Stereonet plot showing poles to shallowly-, moderately-, and steeply-dipping planar plywood surfaces as measured with iPads (blue) and Brunton Pocket Transits (green). maximum eigenvalue for each distribution and a 95% confidence cone From the dataset of plywood models in the laboratory, only the based on 10,000 resamples of the data for each platform. The a95 analysis of the station with a moderate dip (Station M1) returned a confidence ellipse that results has major and minor axes that are pro- significant value for F (ANOVA Significance; Table 1). Where F(4, portional to the minimum and intermediate eigenvalues, with the lar- 152) = 3.180, p < 0.05, the Ho is rejected, with a moderate effect size gest eigenvalue representing the principal direction of the distribution. of 0.0777 indicated. Subsequent post-hoc analysis showed a significant

Although there is an ANOVA procedure for bootstrap data sets difference between the Brunton and iPhone platforms (F(1, 65) = 4.391, (Figueiredo, 2017), the calculations are complex and will be the subject p < 0.05) for Station M1. It is worth noting that R-bar values exceed of continuing research. In our bootstrapping analyses, we compared the 0.747 threshold of Mardia (1972) for this type of analysis, and that maximum eigenvalues and confidence cones for each platform, ex- the kappa values are much greater than 1 and of the same order of amining them for mutual overlap (Joshua Davis, personal commu- magnitude. When examining the distribution patterns of poles, there nication; Fig. 3a–f). While this procedure lacks a firm theoretical appears to be a systematic dispersion in distribution patterns for footing at present, it is sufficient to identify those conditions under iPhones, which tend to follow a small circle (Fig. 2e), indicating po- which bootstrapping analyses might be necessary. Tables 1 and 2 show tential precision errors with device azimuth measurements. the Fisher and bootstrap calculations (respectively) for the three sta- tions in each setting that represent shallow, moderate, and steep dip 2.2.2. – Bootstrap analyses angles. As suspected, several distributions displayed azimuthal variation along a small circle by device, as are shown by the highly eccentric 2.2. Results ellipses (Fig. 3). As a result, the fit of a symmetrical confidence cone (Fisher distribution; Fig. 2) to the plots is not necessarily representative. 2.2.1. – Fisher statistics analyses Therefore, plots of the data were generated using Orient, plotting a For the field data, analyses of the shallowly-dipping (Station H8) bootstrapped a95 confidence ellipse (10,000 resamples) around the and steeply-dipping (Station H7) limestone outcrops returned sig- maximum eigenvalue representing the principal direction for each de- nificant values for F (ANOVA Significance; Table 1). Where F(4, vice. The results of the bootstrapped analyses are reported Table 2 and 72) = 3.174, p < 0.05, the Ho is rejected, with a large effect size of described in more detail in Appendix C. 0.1499 indicated. Subsequent post-hoc analysis showed a significant At moderate dip angles (stations H1, M1), all three platforms are difference between the iPhone and Android-based phone platforms (F(1, relatively consistent with each other, but at steep angles (stations H7, 20) = 5.0368, p < 0.05). R-bar values exceed the 0.747 threshold, and M4), the azimuth performance of the digital devices varies considerably kappa values are much greater than 1 and of the same order of mag- along a small circle. At shallow dip angles (stations H8, M2), each nitude. In this analysis, caution is urged, as the data set overall is rather platform shows a tight distribution, but variation exists between plat- small, and the Android data set particularly small. forms that suggests more detailed analyses are needed. Of particular

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Fig. 2. Stereonet plots of poles to planes for Brunton compass measurements (green), iPhone measurements (blue), and Android-based phone measurements (red), on limestone 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.); ellipses represent Fisher distribution-based cones of confidence (a95) for each dataset. note, the field measurements for shallow depth (station H8), while measurements are similar, except at relatively steep dip angles. For the showing relatively tight distributions within each platform, show no field data, the Fisher distribution differences were seen in the moderate overlap of the a95 confidence ellipses, suggesting that a more detailed dip set, while the laboratory data showed the greatest differences at low analysis is warranted to determine if there is a true difference between dips. Under these circumstances, a strong indicator of consistency in platforms. In most instances, the Android platform shows the greatest measurement (reliability or precision) can be seen in the kappa values azimuthal variation. Detailed descriptions of each plot are found in calculated for each data set, where large values indicate a tight dis- Appendix C. tribution and small dispersion. The bootstrap-based analyses showed more consistency in determining conditions of greatest variation, such 2.2.3. - Precision test comparisons that steep dip angles displayed the greatest azimuthal variation. Orientation measurements using new Brunton compasses and iPad In this examination, a pattern of variance is observed in the digital Pros also demonstrate considerable variability in precision. The new platforms, such that dip values are fairly consistent, but variation is Brunton compasses yielded three relatively-tightly clustered sets of observed along a small circle of less-well constrained strike measure- points (Fig. 1f). However, these apparently tight clusters still represent ments. Key observations from these analyses include: spreads of 4˚-5° on strike measurements, and spreads of 6˚-7° for dip ff measurements. The iPad measurements were less tightly clustered, with 1. Given the considerable di erence in sample size, where more data spreads of 6–26° for strike measurements and spreads of 3° for dip was collected for the Brunton compasses than the digital devices, measurements. These data suggest that Brunton compasses are more performance assertions made with respect to the Android-based precise when measuring strike, while iPads are more precise when phones should be treated as tentative. measuring dip. In addition, iPad measurements of strike are apparently 2. The least levels of consistency in the Fisher analyses, with the more precise on steeper planes. The measurement spreads would likely greatest dispersion and smallest k values, are observed for strike be greater on natural surfaces that have a greater degree of undulation, measurements taken at relatively shallow dip angles, with the as well as in situations where more than one individual is taking the greatest consistency observed at steep dip angles. This issue is well measurements. Nevertheless, these results highlight the need for careful known for analogue devices like the Brunton Pocket Transit where ffi calibration and redundancy of measurements, regardless of device type. measuring strike is di cult for shallow dips. However, the origin of this issue in digital devices is less apparent. 3. The bootstrap-based analyses identified a more consistent pattern of 2.3. Interpretations variation between platforms, such that the relative significance of the azimuthal variation of the digital platforms at steep dip angles is In comparing the results of the analyses, several general inter- more apparent in the confidence ellipses that were calculated. This pretations can be made. First, Brunton compass and iPhone

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Fig. 3. Stereonet plots of poles to planes for Brunton compass measurements (green), iPhone measurements (blue), and Android phone measurements (red), on limestone 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.); ellipses represent bootstrap-based cones of confidence (a95) for each dataset.

Table 1 Data from Fisher statistics 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, 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 no Brunton 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 yes Brunton compass 18 0.9895 90.2 152.4 19.1 3.7 vs. iPhone iPhone 16 0.9104 10.5 155.2 23.6 12.0 vs. Android Android 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 yes Brunton 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. Android Android 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 yes Brunton compass 40 0.9878 79.8 300.1 36.0 2.5 vs. iPhone iPhone 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 no Brunton 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 no Brunton 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 –

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Table 2 Data 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 and Zemrock (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 −13.090 Brunton compass 19 0.9913 319.97 48.601 −25.550 −25.550 yes iPhone 17 0.9335 314.794 51.269 −25.570 −10.996 yes Android 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 −9.043 Brunton 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, broad Android 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 −25.55 Brunton compass 17 0.9832 324.42 71.23 −25.55 −25.55 w/iPhone iPhone 16 0.988 322.52 72.9 −25.55 −25.55 w/Brunton Android 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 −25.55 Brunton compass 40 0.9771 300.24 35.744 −25.55 −25.55 tight iPhone 27 0.9559 294.08 35.018 −25.56 −13.09 overlap, great circle variation Android 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 −25.55 Brunton compass 40 0.9986 267.563 72.311 −25.55 −25.55 tight iPhone 27 0.9798 273.389 74.624 −25.55 −25.55 tight Android 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 −13.09 Brunton compass 40 0.98 314.256 27.327 −25.55 −25.55 tight iPhone 27 0.904 309.539 28.883 −25.6 −6.977 overlap, great circle variation Android phone 12 0.9444 310.277 25.728 −25.57 −10.996 overlap, great circle variation

approach compensates for small sample sizes, making comparisons data collection is the use of a group of relative novices to collect data between platforms more meaningful. Significant future work will be that was traditionally the purview of scientific professionals. By enga- focused on refining a multi-sample analysis approach using boot- ging a larger pool of less-experienced, but enthusiastic, citizen scientists strapped data. in data collection activities, large datasets can be compiled, albeit with 4. Each instrument is sensitive to calibration issues, regardless of a greater potential for inaccuracies. whether it is digital or analogue, as was demonstrated for both iPad About a dozen years ago we recognized the potential for crowd- Pros and brand-new Brunton Pocket Transits that both showed no- sourcing field data collection for geologic maps through engaging un- ticeable dispersion. dergraduate geology students as a “crowd” of novice mappers 5. One cannot assume a priori that digital compasses on smartphones (Johnston et al., 2005). Of course, undergraduate geology students are are as accurate or consistent in measurement as Brunton compass not complete novices with regards to collecting geologic data in the measurements. Android-based phone measurements are generally field. However, they are a “crowd” of relatively inexperienced persons suspect, while iPhone strike measurements show dispersion at steep with respect to producing professional geologic maps and interpreta- dip angles. tions. 6. ANOVA analysis of spherical data is an accessible technique, and As a component of our efforts to modernize geoscience field edu- should be considered when evaluating digital and analogue plat- cation (e.g. Whitmeyer and Mogk, 2009), we incorporated a digital forms. mapping exercise within a capstone field course for James Madison University (De Paor and Whitmeyer, 2009). During this exercise, we collected students' orientation measurements and lithologic data into a 3. Crowd-sourced field data collection large dataset that expanded each year as we incrementally progressed across a several kilometer-square field area (Whitmeyer and De Paor, The almost universal availability of mobile devices, such as smart- 2014). Several years of data collection produced a dense dataset of phones and mobile tablets that incorporate chips and sensors that fa- outcrop bedding orientation measurements (color-coded by lithology, cilitate rapid and relatively accurate collection of field data (see above), Fig. 4b). The density of this crowd-sourced student dataset is im- has enabled a new “crowdsourcing” approach to collecting big datasets pressive, especially as compared with data collected by a single pro- of publicly-accessible information. The term “crowdsourcing” has been fessional field geologist during the same period of time (Fig. 4a). defined as “a type of participative online activity in which an in- However, close (qualitative) examination of the crowd-sourced dataset dividual, an institution, a nonprofit organization, or company proposes reveals several areas with conflicting lithologic information (i.e. sym- to a group of individuals of varying knowledge, heterogeneity, and bols of more than one color in a small area, as highlighted by white number, via a flexible open call, the voluntary undertaking of a task” boxes in Figs. 4b and 6b). This effect prompts the question: How can we (Estellés-Arolas and González-Ladrón-de-Guevara, 2012). For scientific highlight and potentially resolve the inconsistencies in data on the data collection, crowdsourcing is a component of “citizen science”, crowd-sourced map? where “members of the public participate in the scientific process in Our current approach to this challenge is semi-qualitative: we use a ways that may include identifying research questions, making new less-dense, but more accurate, “expert” dataset to constrain the map discoveries, collecting and analyzing data, interpreting results, devel- interpretations of the larger and denser “novice” dataset. The expert oping technologies and applications, or problem solving” (White House dataset represents the mapping efforts of one author (Whitmeyer), Office of Science and Technology Policy, 2015). As can be deduced which we assume is an accurate representation of the sampled geology from these statements, a key component of crowdsourcing for scientific

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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 the mountain 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 conflicting lithologic information (i.e. symbols of more than one color in a small area). Background orthoimagery from the Ordinance Survey of Ireland.

(Fig. 4a). As such, it functions as a control group of accurate data to consists of Ordovician conglomeratic sandstones and basalts un- help identify inconsistencies in the novice dataset and better constrain conformably overlain by a Silurian sequence of terrestrial red beds, the construction of a for the field area. near shore sandstones and siltstones, and shelf/slope turbidites (Fig. 5; Graham et al., 1989; Chew et al., 2007; Whitmeyer et al., 2010). The area is broadly folded and cut by Late Devonian (Mohr, 2003; Johnson 3.1. Field data collection et al., 2011) steeply-dipping normal and transverse faults. Students collected field data on iPads using apps such as iGIS, and During 2009 to 2015, students collected orientation (strike and dip more recently, FieldMove. All student data from the seven-year period of bedding) and lithologic data from outcrops along a section of the were collected into an ArcGIS database, which covers most of the mountain Bencorragh in the lakes region of western Ireland (De Paor mountain (Fig. 4b). Students mapped at a scale of about 1:5000, and fi and Whitmeyer, 2009; Whitmeyer and De Paor, 2014). The eld area is thus their data collection is significantly more dense and detailed than in the southern section of the South Mayo Trough, just north of the is evident in previously published geologic maps of this area (e.g. the ffi boundary with the Connemara of Dalradian a nity (Williams 1:63,360 map of Graham et al., 1989; and the 1:100,000 map of Morris and Rice, 1989; Dewey and Ryan, 2016). Geology of the field area

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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). et al., 1995). The expert dataset was collected with the same equip- mapping experience can lead to differing interpretations. ment, during the same time periods, and covered the same field area at Comparing Fig. 7a to the expert interpretation (Fig. 7b), even the same scale. The significant reduction in data density on the expert though the dataset is sparser, the cleaner contacts can be used to con- map is due to the efforts of a single mapper, as opposed to the efforts of strain the areas of conflicting unit characterization (blue-green zone at 14–16 student teams each year. the right side of Fig. 7a). This approach assumes that the expert char- acterizations of the lithologic units are correct. However, there are 3.2. Creating a geologic map: comparison of novice and expert datasets areas where the lack of data in the expert dataset yielded poor con- straints on faults and unit contacts. For example, several polygons are We built a geologic map from the expert dataset, drawing contact shown as extensive, continuous regions on the expert map (e.g. the lines, faults, and fold axes as constrained by orientation and lithological right side of Fig. 7d). In contrast, the increased density of data within data (Fig. 6a). Contacts and faults were rarely evident in the field (c.f. the same region on the novice map enabled better characterization of ff the occasional white orientation symbols that represent evidence), fault o sets and orientation (Fig. 7c). In this area, the expert dataset ff and thus unit boundaries were best constrained by high densities of was too sparse to illustrate the o set contacts that are clear in the no- data. Semi-transparent colored polygons represent the geologic forma- vice dataset. Consequently, the optimum strategy is to use the large tions present in the mapping area (see Formation Names inset box, novice dataset for the main geologic map interpretation, and then use fi Figs. 4 and 6). the expert dataset to ne-tune areas of inconstancy on the novice map. The second step was to create a geologic map of the same area using Additional constraints on digital datasets and geologic maps can be the significantly larger student-sourced (novice) dataset (Fig. 6b). This provided by high-resolution terrain models (DEMs) and imagery. For fi high-density map exhibits some locations with inconsistent data col- this eld area, Google Earth imagery proved helpful for resolving the fi lected by students (e.g. boxed areas in Fig. 6b). We have previously orientation and extent of some of the more signi cant faults that postulated that a benefit of crowdsourcing data is that the volume of transect the region. For example, the placement of a northwest-striking ff fi accurate data outweighs the incorrect data (Whitmeyer and De Paor, fault with tens of meters of o set was ne-tuned with the assistance of 2014). When datasets are large, spurious geologic interpretations can overhead/oblique views of 3D terrain and imagery (see the black fault “ ” more easily be seen as outlying data (e.g. Fig. 6b – the white box labeled with an A in Fig. 8). The same imagery assisted in the pla- highlighting brown and blue dots and symbols in the yellow field). cement of an eastward-plunging synclinal axis near the southern margin fi Throughout much of the field area, stratigraphic contacts can be fairly of the eld area, as the exposed rock in the Google Earth imagery well constrained using the crowd-sourced data (Fig. 7a left side). showed the location and orientation of a nose of the (see the “ ” However, in some areas, precise contacts are difficult to produce due to red fold axis labeled with a B in Fig. 8). the variability in lithologic characterizations of different mappers (e.g. Fig. 7a - green symbols along the right margin). These areas likely show gradational stratigraphic contacts, where students' lack of field

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

4. Discussion crowdsourcing approach to mapping geology in the field. This approach is a significant change from traditional methods of mapping geology in Perhaps the most important new capability that mobile technologies the field, and comes with a new set of caveats and challenges. They bring to the collection of geologic data in the field is the ability to include concerns about the accuracy of digital compass measurements, quickly and easily amass large, dense datasets. The automation of si- concerns about novices to collecting reliable data, and challenges re- multaneously recording location, orientation of a planar or linear fea- lated to evaluating large and dense field datasets. Nonetheless, these ture, and the type of data recorded, vastly accelerates collection of field new methods have the potential to transform the way field-oriented data. Using a modern mobile device (smartphone, tablet) a field geol- structural and mapping studies are conducted. ogist can easily capture an order of magnitude more orientation data in Our experiments with mobile mapping equipment highlight situa- a day, with every measurement precisely located. More importantly, tions where digital compass measurements may not be as precise as smartphones and tablets with mapping apps that incorporate digital field geologists require. Novakova and Pavlis (2017) observed that compasses facilitate field data collection though an intuitive interface some devices suffer from imprecise estimates of azimuth whereas in- that can be used by anyone with even a rudimentary knowledge of clination measurements tend to be more precise. This behavior is par- geology. Thus, modern digital technologies have enabled a ticularly apparent in our Android-based phone datasets (see Appendix

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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 the right 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 the novice 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 different from the geologic map interpretation of the same area using the sparser expert dataset (d.).

B), where the phones were from more than one manufacturer (Sam- same surface, then plot the data on a stereonet. If the errors are random sung, Motorola) and a variety of models. Our experiments with iPhones between dip estimate and azimuth measurement, the data cluster will suggest that not all of these devices are as precise as those used in be approximately circular and Fisher statistics should provide a clear studies by Allmendinger et al. (2017). However, our iPhone datasets assessment of the accuracy of the measurement group relative to the also reflect a variety of models (see Appendix B). Collectively, our ob- analogue compass. In our experience, a device with a poor magnetic servations together with these previous studies, as well as anecdotal sensor appears immediately in a test of this sort by simple inspection of observations, indicate that each device almost certainly has its own the stereonet; i.e. a scattering of the azimuth measurements produces a unique signature in terms of precision and accuracy. Indeed, in line small circle grouping of the measurements (e.g. Fig. 2e and f). If a with a question raised by Allmendinger et al. (2017), the data presented device fails this initial test, it is likely not a usable device, although here indicate that this issue even extends to the analogue devices occasionally it can be corrected with a recalibration. Alternatively, a (Brunton compasses) that geologists have been using for over a century. bootstrap analysis of several data points can quickly determine the Thus, all observations to date support a statement by Midland Valley in constraints of any small circle variation, so that the user can then reference to use of FieldMove Clino, that users should know the lim- evaluate whether the dispersion is within acceptable limits. itations of their device before trusting the data obtained with it Resolution of issues with instrument drift through time or abrupt (Midland Valley, 2017). changes in readings, like those observed by Novakova and Pavlis These mobile device limitations likely derive, in part, from the (2017), is a more challenging problem. One simple solution is to quality of the sensors embedded in each device. Specifically, the mag- carefully monitor azimuth measurements with hourly comparisons of netic sensor used in mobile devices is typically a single chip or chip set azimuth measurements to a conventional compass. A more thorough that contains a three-component . If that basic device is comparison might require periodic precision tests during the day, imprecise, or behaves erratically, it will lead to major azimuthal errors, which could be done through a simple mental comparison of repeated like those seen in some of our experiments. Because these chips are azimuth measurements to evaluate the data scatter. In either case, some inexpensive, mass-produced items, they likely vary in quality both field time will be required, but until a device's reliability is thoroughly among devices of a given model and between different devices and known, these steps are essential to insure accuracy and reliability of manufacturers. This lack of consistent quality is probably the cause of digital field measurements. A clear take-home message is to take the the variability among individual devices that we observed. Thus, the time to know your device and its variability. best solution seems to be that each device needs to be individually The use of mobile devices for orientation measurements and field tested for its feasibility and reliability as a field tool. mapping is an important component to the crowd-sourcing method of To evaluate the reliability of an individual mobile device, we re- field data collection that we outline. Even given the caveats about the commend a simple precision test prior to extensive field use, similar to precision of mobile device measurements, relative novices are more what is shown for Brunton compasses and iPad Pros in Fig. 1d. That is, likely to produce robust field data with a digital compass than from use a uniformly planar surface (e.g. a plywood sheet, oriented carefully inexperienced use of an analogue Brunton compass. Our collective ex- with an analogue compass) and perform repeated measurements on the perience is that it takes several months of consistent practice before

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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. In several 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 Earth terrain and can be visually correlated to geologic interpretations. undergraduate students produce reliable orientation measurements interpretations. Less apparent are the optimal strategies for evaluating with a Brunton compass. In contrast, relative novices can easily and and constraining large field datasets that incorporate areas with con- quickly produce a robust dataset of field measurements with digital flicting data. The availability of expert field geologists can help resolve compasses, where the accurate data visibly overwhelms the poor data. these inconsistencies, but new statistical and computational meth- However, strategies are needed to highlight and resolve mapped areas odologies ultimately may be the most efficient solution. that show inconsistencies in data. Our approach to using large crowd-sourced datasets for building better geologic maps uses smaller expert (control) datasets to constrain Acknowledgements areas of conflicting field data. This method requires that experts are available to map the same field area along with the novices (students). The authors thank students at JMU and UTEP for testing geologic Alternatively, experts could field check a draft crowd-sourced map and compasses in the field. Lauren Roberts and Catherine Whitmeyer helped then use the comparative techniques outlined above to constrain the with the precision tests. The authors are grateful to Bill Dunne, Randy final map interpretations. Ultimately, the main benefit of crowd-sour- Williams, and a second reviewer for their reviews, which have im- cing field data collection is in assembling much larger field datasets, proved this manuscript. The authors also extend special thanks to Josh which in turn, yield better, more constrained, geologic map inter- Davis for his constructive, insightful, and above all tolerant advice and pretations. input into the statistical analyses in this manuscript. Richard Additional approaches to refining crowd-sourced map interpreta- Allmendinger's Stereonet software and Frederick W. Vollmer's Orient tions will likely derive from increased use of remote UAVs to gather software were used to prepare some figures. This work was supported high-resolution, 3-D terrain datasets and detailed outcrop models. by National Science Foundation award #1714587 to Whitmeyer & Pyle. However, we envision that large crowd-sourced datasets will also benefit from statistical analyses. We suggest that the next fundamental development in field data collection for geologic maps will include the Appendices development of statistical algorithms for highlighting areas of sig- nificant dispersion within large field datasets, to identify spurious and/ Appendix A or conflicting data. Semi-automated dispersion analyses would not be able to resolve all areas of conflicting data, but these types of analyses Tabulated data for precision tests of 12 new Brunton Pocket Transits should be able to highlight regions of conflict with more precision than and 12 iPad Pros using the FieldMove Clino app. Each compass or iPad the comparative visual strategies that we outline in this manuscript. took two strike and dip measurements of shallowly-, moderately- and One thing is clear: We now have the ability to assemble big field steeply-dipping plywood models. All measurements were taken by datasets, which should enable the creation of better geologic map Whitmeyer.

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Appendix B 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 on All strike and dip data for all platforms collected for this study. Education: Vignettes, Evaluations, and Future Directions. Purdue University Press, Group 1 data are measurements of limestone bedding planes using West Lafayette, Indiana, pp. 103–113. Brunton Pocket Transits, iPhones, and Android-based phones; collected Lee, S., Suh, J., Park, H-d, 2013. Smart Compass-Clinometer: a smartphone applications for easy and rapid geological site investigation. Comput. Geosci. 61, 32–42. in Fall (2016). Group 2 data are measurements of plywood surfaces Mardia, K.V., 1972. Statistics of Directional Data. Academic Press, London. using Brunton Pocket Transits, iPhones, and Android-based phones; Mardia, K.V., Jupp, P.E., 2000. Directional Statistics. Wiley, New York. collected in Spring (2017). Group 3 data are measurements of limestone Mardia, K.V., Zemrock, P.J., 1977. Table of maximum likelihood estimates for the – bedding planes using Brunton Pocket Transits and iPad Pros. Bingham distribution. J. Stat. Comput. Simul. 6, 29 34. McCa ffrey, 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 technologies Appendix C and 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 General Detailed interpretation for each bootstrap analysis of the six stations Assembly. Vienna, Austria, . http://meetingorganizer.copernicus.org/EGU2009/ highlighted in Fig. 3 and Table 2. 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. References Earth Sci. 21, 71–104. Morris, J.H., Long, C.B., McConnell, B., Archer, J.B., 1995. Geology of Connemara. Allmendinger, R.W., Cardozo, N.C., Fisher, D., 2013. Structural Geology Algorithms: Geological Survey of Ireland, Dublin. Vectors & Tensors. Cambridge University Press, Cambridge. Novakova, L., Pavlis, T.L., 2017. Assessment of the precision of smart phones and tablets – Allmendinger, R.W., Siron, C.R., Scott, C.P., 2017. Structural data collection with mobile for measurement of planar orientations: a case study. J. Struct. Geol. 97, 93 103. devices: accuracy, redundancy, and best practices. J. Struct. Geol. 102, 98–112. Pavlis, T.L., Mason, K.A., 2017. The new world of 3D geologic mapping. GSA Today 27, – Bemis, S.P., Micklethwaite, S., Turner, D., James, M.R., Akciz, S., Thiele, S.T., Bangash, 4 10. http://dx.doi.org/10.1130/GSATG313A.1. ‐ H.A., 2014. Ground-based and UAV-based photogrammetry: a multi-scale, high-re- Pavlis, T.L., Langford, R., Hurtado, J., Serpa, L., 2010. Computer based data acquisition fi solution mapping tool for structural geology and paleoseismology. J. Struct. Geol. 69, and visualization systems in eld geology: results from 12 years of experimentation – 163–178. and future potential. Geosphere 6, 275 294. fi Cawood, A.J., Bond, C.E., Howell, J.A., Butler, R.W.H., Totake, Y., 2017. LiDAR, UAV or Schetselaar, E., 1995. Computerized eld-data capture and GIS analysis for generation of – compass-clinometer? Accuracy, coverage and the effects on structural models. J. cross sections in 3-D perspective views. Comput. Geosci. 21, 687 701. Struct. Geol. 98, 67–82. Tauxe, L., 2010. Essentials of Paleomagnetism. University of California Press. … Chew, D.M., Graham, J.R., Whithouse, M.J., 2007. U-Pb zircon geochronology of plagi- Vanderlip, C., Mitchell, J., 2016. There's an app for that Testing geologic smartphone ogranites from the Lough Nafooey (= Midland Valley) arc in western Ireland: con- apps against the Brunton Pocket Transit. GSA Abstr. Progr 48. http://dx.doi.org/10. straints on the onset of the Grampian . J. Geol. Soc. 164, 747–750. 1130/abs/2016AM-283596. De Paor, D.G., Whitmeyer, S.J., 2009. Innovations and redundancies in geoscience field Vollmer, F.W., 2015. Orient 3: a new integrated software program for orientation data courses: past experiences and proposals for the future. In: Whitmeyer, S.J., Mogk, D., analysis, kinematic analysis, spherical projections, and Schmidt plots. Geolog. Soc. Pyle, E.J. (Eds.), Field Geology Education: Historical Perspectives and Modern Am. Abstr. Progr. 47 (7), 49. Approaches, pp. 45–56. http://dx.doi.org/10.1130/2009.2461(05). GSA Special Vollmer, F.W., 2018. Orient: Spherical Projection and Orientation Data Analysis Software. Paper 461. www.frederickvollmer.com. fi Dewey, J.F., Ryan, P.D., 2016. Connemara: its position and role in the grampian orogeny. Watson, G.S., Williams, E., 1956. On the construction of signi cance tests on the circle – Can. J. Earth Sci. 53, 1246–1257. http://dx.doi.org/10.1139/cjes-2015-0125. and the sphere. Biometrila 43, 344 352. Estellés-Arolas, E., González-Ladrón-de-Guevara, F., 2012. Towards an integrated Weng, Y., Sun, F., Grigsby, J.D., 2012. GeoTools: an Android phone application in – crowdsourcing definition. J. Inf. Sci. 38, 189–200. http://dx.doi.org/10.1177/ geology. Comput. Geosci. 44, 24 30. ffi 0165551512437638. White House O ce of Science and Technology Policy, 2015. Fact Sheet: Empowering Figueiredo, A., 2017. Bootstrap and permutation tests in ANOVA for directional data. Students and Others through Citizen Science and Crowdsourcing. Washington. fi Comput. Stat. 32, 1213–1240. http://dx.doi.org/10.1007/s00180-017-0739-x. Whitmeyer, S.J., Mogk, D.W., 2009. Geoscience eld education: a recent resurgence. Eos – Graham, J.R., Leake, B.E., Ryan, P.D., 1989. The Geology of South Mayo, Western Ireland. 90, 385 386. http://dx.doi.org/10.1029/2009EO430001. Scottish Academic Press, Edinburgh. Whitmeyer, S.J., De Paor, D.G., 2014. Crowdsourcing digital maps using citizen geolo- – Johnson, E.A., Sutherland, S., Logan, M.A.V., Samson, S.D., Feely, M., 2011. gists. Eos 95, 397 399. http://dx.doi.org/10.1002/2014EO440001. Emplacement conditions of a porphyritic felsite dyke and timing of motion along the Whitmeyer, S.J., Nicoletti, J., De Paor, D.G., 2010. The digital revolution in geologic – Coolin fault at Ben Levy, Co. Galway. Ir. J. Earth Sci. 29, 1–13. http://dx.doi.org/10. mapping. GSA Today 20, 4 10. http://dx.doi.org/10.1130/GSATG70A.1. 3318/IJES.2011.29.1. Williams, D.M., Rice, A.H.N., 1989. Low-angle extensional faulting and the emplacement – Johnston, S., Whitmeyer, S.J., De Paor, D., 2005. New developments in digital mapping of the Connemara Dalradian, Ireland. 8, 417 428. http://dx.doi.org/10. and visualization as part of a capstone field geology course. Geol. Soc. Am. Abst. 1029/TC008i002p00417.

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