This document discusses applying decision tree algorithms in R to classify landslide occurrence predictors from GIS data. It introduces decision trees and different algorithms like C4.5, ID3, and CART. It also covers topics like data preparation, model training and testing, and interpreting results. The goal is to predict landslide probability based on variables like elevation, slope, and NDVI using decision trees.

1. Decision tree in GIS using R environment
Omar F. Althuwaynee, Ph.D.

2. Evaluate and compare the results of applying different
decision trees algorithms, to classify and understand
landslide occurrence predictors distributions, using
GIS and R environment.
Course objectives
Omar F. Althuwaynee, PhD in Geomatics engineering

3. You have to go through the following videos on my
channel, regarding data preparation in ArcGIS:
1. How to process Logistic regression in GIS: Prepare binary training data by
ArcGIS ?
2. How to easily produce testing binary data set for prediction mapping
validation?
Course preparations
Omar F. Althuwaynee, PhD in Geomatics engineering

4. 1. Create dichotomous (0,1) training and testing data.
2. Effectively set your project environment , and install
packages related to current application.
3. Read spatial data in R environment .
5. Run Statistical analysis, using various decision trees
algorithms.
6. Run statistical tests and produce decision tree.
End of this course, you will be able to
Omar F. Althuwaynee, PhD in Geomatics engineering

5. Data mining approaches, based on the successive
division of the problem into several sub-problems with
a smaller number of dimensions, until a solution for
each of the simpler problems can be found.
Omar F. Althuwaynee, PhD in Geomatics
engineering
Decision Trees philosophy

6. 1. Mostly use supervised learning methods.
2. Predictive , high accuracy, stability .
3. Mapping non-linear relationships.
4. Used for classification or regression solving methods.
5. Easy to understand: no analytical or statistical background
needed (intuitive graphical representation)
6. Useful in data exploration: (finding significant variables and
its relations).
7. Less data cleaning required: (fairly not influenced by outliers
and missing values).
8. Data type is not a constraint: (handling both numerical and
categorical variables).
Omar F. Althuwaynee, PhD in Geomatics
engineering
Why to use Tree based learning algorithms?

7. 1. Categorical Variable Decision Tree: (categorical target variable
Example:- Target variable, Student will play cricket or not” i.e. YES or NO.
Natural hazards susceptibility, Yes=1, No=0.
2. Continuous Variable Decision Tree: (continuous target variable).
Example: Target variable, continuous Students age classification <=10 &
Age>20,. Earthquake intensity <=x1 & >x2
Omar F. Althuwaynee, PhD in Geomatics
engineering
Types of decision tree

8. Omar F. Althuwaynee, PhD in Geomatics engineering
Regression trees Classification trees
Dependent variable is continuous Dependent variable is categorical
Value of terminal nodes is the mean
response of observation. (Make its
prediction with mean value).
Value of terminal node is the mode
response of observations . (make its
prediction with mode value.
Similarity
Divide the predictors (independent variables) into distinct and non-overlapping
boxes.
Splits the predictor space down into two new branches down the tree (looks for
best variable available), and looks about only the current split, and not about
future splits
Splitting process is continued until a user defined stopping criteria is reached.
But, the fully grown tree is likely to over fit data, leading to poor accuracy on
unseen data. Therefore, we need to do ‘Pruning’.
Regression vs. Classification

9. The algorithm stops when any one of the conditions is
true:
• All the samples belong to the same class.
• There are no remaining attributes on which the
samples may be further partitioned
• There are no samples for the branch test attribute
Omar F. Althuwaynee, PhD in Geomatics
engineering
Stopping Criteria

10. Reference:
https://www.analyticsvidhya.com/blog/2016/04/complete-tutorial-tree-based-modeling-scratch-in-python/
Omar F. Althuwaynee, PhD in Geomatics
engineering
To remove anomalies in the training data due to noise or outliers, and
tackle overfitting.
• The pruned trees are smaller and less complex.( look at a few
steps ahead and make a choice).
• Pre-pruning and Post-pruning (removes a sub-tree from a fully
grown tree).
Pruning

11. What are the various decision tree algorithms and how
do they differ from each other
Common algorithms: Like; C4.5, ID3, CART, CHAID, Random
forest
1. Classification and regression
2. Numeric (continuous) or categorical targets and factors data.
3. Using tree pruning or not.
4. Amount of memory usage
5. Amount of information and outcomes provided
6. Statistical background
7. Stand alone or ensemble learning based classifiers
Omar F. Althuwaynee, PhD in Geomatics
engineering

12. To predict whether a landslide will happen in a certain
areas (yes/ no).
• Slope angle is a significant variable but we don’t have enough
details about all the related conditions of previous events.
• Now, as we know there are additional important variables,
then we can build a decision tree to predict landslide (or any
target) based on:
Elevation, Aspect, soil type, vegetation density and various other
variables.
Omar F. Althuwaynee, PhD in Geomatics
engineering
Case study

13. Slope
Angle
Slope
Angle
Landslides
Elevation
NDVI
Soil type Aspect
Yes=20
No=50
Yes=80
No= 50
Slope ≤5° Slope >5°
NDVI=0.5
S.Type=Silt Aspect=NE Elev.≥300m
Yes=60
No= 10
Yes=20
No= 40
Yes=20
No= 10
Yes=40
No= 0
Yes=100
No=100
Typical Decision Tree

14. • To predict the probability, whether a landslide will
occur in a particular places, or not.
Data:
• Dependent factor Landslide training (75 observations) and
testing (25 observations) data locations.
• Independent factors (Elevation, slope, NDVI, Curvature).
Note:
• Analysis will depend only on the number of the observations,
more training observations will increase the model efficiency.
Current Application
Omar F. Althuwaynee, PhD in Geomatics engineering

15. 1. Prepare GIS data
2. Resample to similar extent and resolution.
3. Data quality
4. Convert into statistical data format, like, .txt, .csv!!
5. Check the data in R environment, like, summary, str, head,
plot.
Omar F. Althuwaynee, PhD in Geomatics
engineering
Data input
Testing_points Elevation Curvature Slope NDVI
1 275 -0.0625 13.55703 0.516273
0 363 0.0625 16.73342 0.469728
0 267 0.1875 13.2819 0.435414
0 92 0.125 10.01578 0.396327
… …. ….. …. ….

16. 8399= respondents to the survey, 57% = best customers(1) - 43%= other (0).
Left side (Total life time)
• Females ( F) are more likely to be best customers than males (M).
• 1st row: difference between males and females is statistically significant (59 -54)%= 5% females are
more likely to be a best customer .
• Is 5% is significant from a business point of view or not? (ask a business analyst)
Right side (Net sales)
• This suggests that Net sales is a stronger or more relevant predictor of MEN customer status than
Total lifetime visits( used to analyse Females)
To conclude: female behaviour and male behaviour have different nuances.
Further illustration using Categorical Target (1–0)

17. 1. Download the current course data
2. Open R studio, and connect to internet
3. And, Let us begin!
Happy learning..!
Omar F. Althuwaynee, PhD in Geomatics
engineering

18. • https://www.tutorialspoint.com/data_mining/dm_dti.html
• http://scikit-learn.org/stable/modules/tree.html
• https://goo.Gl/uk6i3x
• https://www.analyticsvidhya.com/blog/2016/04/complete-tutorial-tree-based-modeling-
scratch-in-python/
• https://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Classification/Decision_Trees
• https://cran.r-project.org/web/views/MachineLearning.html
• http://machinelearningmastery.com/non-linear-classification-in-r-with-decision-trees/
• http://dni-institute.in/blogs/random-forest-using-r-step-by-step-tutorial/
References
Omar F. Althuwaynee, PhD in Geomatics engineering