From the Average Nearest Neighbor test, there

is statistically significant clustering in the

drumlins (Graph 1).

There was a strong linear correlation found

between the area of a unit in the bedrock

geology and the number of drumlins it

contained. The Student’s T-Test resulted in a

14% likelihood that the density of drumlins was

due to random chance. Since it was larger than

5%, it is not statistically significant-i.e.,

drumlins are evenly distributed over each

type of bedrock.

There was a less strong linear correlation found between the area of a unit in the surficial

geology and the number of drumlins it contained. Furthermore, for the surficial geology, the

Students T test gave a 2.3% likelihood that the density of drumlins was the result of random

chance, and since it is less than 5%, it is statistically significant. i.e., drumlins are not

evenly distributed over each type of surficial geology. Indeed, a disproportionately large

number of drumlins are found above till. This is unsurprising since drumlins are made of till.

A disproportionately small number of drumlins are found over drift poor sediment.

The underlying process for the formation of drumlins is still unknown. Various

explanations have been suggested as to how drumlins begin to form, including catastrophic

flooding of highly pressurized glacial water [1], or small changes in the overlying glacier

creating distinct points of nucleation [2]. However, the available studies did not investigate

how the underlying bedrock affects drumlin formation. By using statistical methods, it may

be possible to determine whether there is structure within the spatial distribution of

drumlins or correlation with the underlying bedrock which would better allow the process

of nucleation to be investigated.

Drumlins in Newfoundland and Labrador were selected as the study dataset due to the

large number of drumlins in the area and quality of data. Drumlins were modeled as point

features, and statistical tests were performed to observe (1) whether the distribution of

drumlins was random, and (2) if the density of drumlins over any given rock type was

statistically different from the mean density.

The drumlin data was extracted from the Geological Survey of Canada glacial data by

performing a SQL query. After inspecting the data, the area of study (required for the density

tests) was chosen as the main landmass from the same dataset.

The first statistical test was Average Nearest Neighbor, which tests how the points are

distributed by taking the average distance of the nearest neighbor from every point.

Comparing this against knowns distribution can help determine if the points are randomly

distributed or have structure associated with them, such as clustering or dispersion. The

“Multi Distance Spatial Analysis (Ripley’s K Function)” was also used to test clustering or

dispersion at a variety of scales, but due to software issues the tool did not yield useable

results.

Next, it was investigated whether the drumlins had higher densities over any geologic units.

For both surficial geology and bedrock geology, the number of drumlins in any given rock

unit was found, and compared against the area of the rock unit in the area of study. A density

of drumlins over each rock unit was found. The effectiveness of the linear correlation was

found by using Students T- Test [3] to compare the average density of drumlins over each

rock type to the average density of drumlins over the full area of study.

The distribution of drumlins in Newfoundland and Labrador are clustered, with no relation

to the underlying bedrock. There is a relation to the underlying surficial geology, in that

drumlins are more likely to occur above till. The analysis of the distribution of drumlins

over the underlying rock could be improved if more distinctive criteria were selected, i.e. by

choosing more specific categories of underlying rock.

Additional data linking drumlins with the hardness of the rocks they are on top of could be

used to produce a more subtle analysis. Furthermore, a similar type of analysis could be

performed on a larger area with more accuracy, and could also be performed on any

presumed random point feature, for instance on the distribution of glacial erratics.

Introduction

Data Sources:

Klassen, R.A., Paradis, S., Bolduc, A.M., and Thomas, R.D.

1992: Glacial landforms and deposits, Labrador, Newfoundland and

eastern Québec; Geological Survey of Canada, Map 1814A,

scale 1:1 000 000.

Wheeler, J.O., Hoffman, P.F., Card, K.D., Davidson, A., Sanford,

B.V., Okulitch, A.V., and Roest, W.R. (comp.)

1997: Geological Map of Canada, Geological Survey of Canada,

Map D1860A.

Publications:

[1]Shaw, John, and Robert Gilbert. "Evidence for large-scale

subglacial meltwater flood events in southern Ontario and

northern New York State." Geology 18.12 (1990): 1169. Web.

[2] John Menzies, Dale P. Hess, Jessey M. Rice, Kaleb G. Wagner,

Edouard Ravier, A case study in the New York Drumlin

Field, an investigation using microsedimentology, resulting

in the refinement of a theory of drumlin formation,

Sedimentary Geology, Volume 338, 1 June 2016, Pages 84-

96, ISSN 0037-0738, https://doi.org/10.1016/

j.sedgeo.2016.01.017.

[3]Wong, David S., and Jay Lee. Statistical Analysis of Geographic

Information. Hoboken: John Wiley & Sons, 2005. Print.

Drum’lin up some Statistics: Geostatistics of Drumlins

in Newfoundland and Labrador.

By Zachary Kaplan

Sources

Methods

Results

Map 1: Map of the area of study and all of the drumlins within that area.

Graph 1: The result of the Average Nearest Neighbor test

Map 2: Map of the drumlins overlaid above the bedrock

units

Graph 2: plot of number of drumlins in a given bedrock

unit vs. its area

Graph 3: plot of the number of drumlins in a given

surficial geology unit vs. its area

Map 3: Map of the drumlins overlaid above the surficial

geology units

Conclusions