How to Call Machine Learning Algorithms on R for Spatial Analysis.

Author(s): Stephen Chege-Tierra Insights

Originally published on Towards AI.

R has become ideal for GIS, especially for GIS machine learning as it has topnotch libraries that can perform geospatial computation. R has simplified the most complex task of geospatial machine learning and data science.

As GIS is slowly embracing data science, mastery of programming is very necessary regardless of your perception of programming. Hopefully, this article will serve as a roadmap for leveraging the power of R, a versatile programming language, for spatial analysis, data science and visualization within GIS contexts.

We shall look at various machine learning algorithms such as decision trees, random forest, K nearest neighbor, and naïve Bayes and how you can install and call their libraries in R studios, including executing the code.

R, GIS and Machine learning

I have written about the amazing wonders of R for GIS in my previous articles, but I will sum it up.

When it comes to the geospatial industry, R seems to be punching above its weight with numerous benefits, such as its easy integration with popular GIS applications like ArcGIS, QGIS, Google Earth engine, and GRASS GIS, which lets users combine the analytical power of R with the geospatial features of these applications. As a result of this integration, geospatial specialists can more effectively analyze and interpret spatial data by taking advantage of the benefits of both R and GIS.

RStudio is a multifunctional open-source IDE (integrated development environment) that is extensively used as a graphical front-end to work with R of version 3.0.1 or higher. In addition, it’s also adapted to many other programming languages, such as Python or SQL.

What makes it ideal for GIS?

1. Importing and exporting GIS data — importing and exporting data from various sources and formats is a key task. You can get assistance with this work using several R packages, including sf, raster, sp, and rgdal. Numerous spatial data formats, including shapefiles, GeoJSON, GeoTIFF, and NetCDF, can be read and written by these programs.

2. Data Visualization — R is primarily used by GIS professionals for statistical analysis and data plotting by utilizing packages such as ggplot2. Numerous mapping and data visualization programs, such as ggplot2 and map, are available. For visualizations, these tools are quite simple if you are already familiar with them.

3. Data Support — R supports both spatiotemporal arrays, data cubes, and programs such as tidy-census, which allow one to collect data from the census bureau. R provides a tool to assist with these kinds of simple tasks for anyone dealing with government data and seeking crucial data for analysis.

4. Robust online community — R has built a devoted online community that assists you with tutorials, documentation, code and articles if you are just starting. Such online platforms including Udemy, Edx, and Stackoverflow are filled with a vast amount of details about R studios to utilize.

Lets go!

1. Install Packages

install.packages(c("sp", "sf", "raster", "caret", "randomForest", "e1071", "xgboost"))

2. Load Packages

library(sp)
library(sf)
library(raster)
library(caret)
library(randomForest)
library(e1071)
library(xgboost)

3. Radom Forest

install.packages("randomForest")
library(randomForest)

4. Support Vector Machine (SVM)

# Install and load necessary packages
install.packages("e1071")
library(e1071)

# Train the SVM model
model_svm <- svm(target_variable ~ ., data = trainData)

5. Gradient Boosting Machine (GBM)

# Install and load necessary packages
install.packages("gbm")
library(gbm)

6. K-means

# Install and load necessary packages (k-means is part of the base R distribution, so no need to install extra packages)
# Load the necessary package
library(stats)

# Specify the number of clusters
k <- 3

# Train the K-Means model
model_kmeans <- kmeans(trainData[, -which(names(trainData) == "target_variable")], centers = k)

1. R offers clear and illustrative code– R is easier to work with than Python, for instance, if you are just starting on a machine learning project and need to describe the work you perform. This is because R offers the appropriate statistical way to work with data in fewer lines of code.

2. The R language is ideal for visualizing data- The finest prototype for working with machine learning models is provided by the R language, you can make beautiful and interactive machine learning-generated chats using R studios.

3. Robust Library –The greatest tools and library packages for working on machine learning projects are available in the R language. These packages allow developers to design the best possible pre-, model, and post-models for machine learning projects. R is the language of choice for machine learning applications because its packages are more sophisticated and comprehensive than those of Python.

4. Integration with GIS and Spatial Analysis– R offers smooth integration with spatial data using packages like SF, raster, and spatial for machine learning applications linked to GIS. R is a great option for geographic data science applications because of these packages, which let users process, analyze, and visualize spatial data in addition to performing machine learning tasks.

5. In-depth Documentation- R facilitates repeatability by analyzing data using a script-based methodology. Transparency and cooperation are promoted in machine learning projects by the ease with which users may share code, document their analyses, and repeat results when developing code in R scripts.

6. Community Support- R is known to have a dedicated following that provides support on Github, stackoverflow and other software collaboration platforms in case you need support or to cross-reference your work.

Conclusion

R machine learning with GIS authorizes users to excerpt valuable intuitions from spatial data, make informed decisions, and address complex spatial challenges across domains such as environmental science, urban planning, agriculture, and public health.

I highly recommend you explore R if you have not already, the features are mind-blowing, especially if you are into data and geospatial analytics. The maps you can create with a few lines of code are astonishing, plus you can make them interactive with features like R shiny.

I wrote about Python ML here.

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Published via Towards AI