## Recently Published

##### Topic Modeling

This is example code for topic modeling from https://ladal.edu.au/topicmodels.html#Topic_distributions

##### Natural Language Processing

This is example code from Simon’s AI workshop which explores NLP and its reseaerch uses.

##### Partial Dependence Plot DALEX Workflow

This document provides an example workflow for producing PDPs and grouping them by a categorical variable. This uses the Ames Housing dataset along with Pakistan Conflict data.

##### Introduction to Tidyverse

This document is to practice using the tidyverse package using the code in the Guide to R book (https://bookdown.org/yih_huynh/Guide-to-R-Book/ifelse.html).

##### HMA Conflict - Data Cleaning

This is a consolidated document containing the data cleaning steps for HMA conflict research.

##### Intro the Interpretable Machine Learning (iml) package

This code below works through the iml package. The code and examples are from cran’s website. I added additional code related to partial dependency plots and visualizing surrogate model trees.

##### HMA Conflict Modeling - Data Cleaning and Exploration

This code contains the initial data cleaning steps and model runs for an analysis of conflict in High Mountain Asia.

##### Interactive Israel - Hamas Conflict Map

This is an interactive map where users can examine 5,669 observed conflict events in Israeli and Palestinian territories.

##### Pakistan Conflict Modeling

This document uses a self organizing map and random forest to examine conflict in Pakistan.

##### Pakistan Dataframe Build

This document outlines the procedures for building a district/year long data frame for Pakistan. It includes the following features/variables: Seasonal temperature (t2m) and precipitation (tp), SPEI, deprivation index, built, total conflict, and conflict by event type. This document also contains useful scripts for NetCDF processing, raster processing, zonal statistics, point data, and GIS functions.

##### Intro to Self Organizing Maps

This document introduces self-organized maps - an unsupervised machine learning method. This method is demonstrated with data from the GapMinder project as well as a California housing dataset. GEOG6160 - Lab 10.

##### ML Pipelines

This document demonstrates the use of pipelines in the mlr3 package using credit and sonar datasets. GEOG6160 - Lab 9

##### Intro to Neural Networks

This document introduces regression and classification neural networks using the mlr3 package in R. Examples of both approaches are presented using cereal and credit score data. GEOG6160-Lab 8.

##### Ensemble Tree ML Methods

This lab explores ensemble tree machine learning methods using Pinyon Pine tree and sonar datasets. This lab examines CART, Random Forests, and Boosted Regression Trees. GEOG6160 - Lab 7.

##### Introduction to Machine Learning (mlr3 package)

This document introduces the mlr3 package and its use in machine learning. This uses a California housing data set as an example. GEOG6160 - Lab 6

##### Initial Dissertation Research

This project is an exploration of climate change and conflict in Pakistan. This project uses the following datasets:
Pakistan Conflict Data - Acquired from the Armed Conflict Location and Event Data Project (ACLED) on 21SEP2022. See https://acleddata.com/terms-of-use/
Regional Climate Data - ERA5 Dataset with monthly averaged temperature and precipitation data for January and July beginning in 2010 and ending in 2022. The spatial resolution is approximately 9 kilometers. Temperature data is calculated at 2m above the land surface. Precipitation data is total precipitation between forecast steps (monthly). Reanalysis model. See https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land-monthly-means?tab=overview
Log Gross National Income Per Capital Variable - From: https://globaldatalab.org/shdi/metadata/lgnic/
Population Data acquired on 07NOV2022 from: https://datacommons.org/place/country/PAK?utm_medium=explore&mprop=count&popt=Person&hl=en
Pakistan Gross Domestic Product acquired on 07NOV2022 from: https://data.worldbank.org/indicator/NY.GDP.MKTP.CD?end=2021&locations=PK&start=2010

##### Point Pattern Processes

This document provides example code for examining point patterns and processes using quadrant counts, kernel density, distance functions, marked point processes, and point process models. GEOG6000 lab 14.

##### Geostatistics II (Kriging Methods II)

This document outlines kriging with external drift, regression kriging, indicator kriging, and geostatistical simulations. These methods are explored using a Swiss precipitation and midwestern US ozone data set. GEOG6000 - Lab 13

##### Introduction to Spatial Interpolation

This document outlines variograms and the basics of spatial interpolation using ordinary kriging. GEOG6000 - Lab 12

##### Spatial Regression II

This document walks through the steps for testing for spatial autocorrelation and building spatial models. The initial portion walks through spatial filtering, geographically weighted regression, and spatial hierarchical models. GEOG6000 - Lab 10

##### Spatial Regression Models I

This document is a basic introduction to spatial regression models. Statistical tests include the Moran's I and Getis Ord. Models include spatial lag, spatial error, and spatial durbin. GEOG-6000 lab 9.

##### Spatial Data Visualization - tmap and mapview

This document is a basic guide for using tmap and mapview. It also highlights different code to adjust the elements and legend of the map. Please see the links at the beginning of the page for additional resources. GEOG 6000 - Lab 8b

##### Introduction to Spatial Data and Simple Features

This document is a basic walkthrough of using spatial data using simple features (sf) and raster packages in R. There are some example visualizations and basic statistical analysis using spatial data as well. GEOG 6000 - Lab 8

##### Clustering and Principle Component Analysis

This document outlines the procedures to perform hierarchical clustering, k-means clustering, and principle component analysis. This code uses climate and housing data. Additionally, this document explores some basic mapping features for spatial data. GEOG6000 - Lab 7

##### Visualization Practice - ggplot and plotly

This document contains examples of several visualization techniques using ggplot and plotly.

##### Generalized Linear Models - Binomial and Poisson

This document introduces generalized linear models using both binomial and poisson families of data. This document also includes an example of combining boxplots into one figure for better visualization.
GEOG6000 - Lab 5

##### Basic Linear Modeling

This page uses basic linear modeling techniques to explore basic datasets. There are also methods to compare between models as well.
GEOG6000 - Lab 4

##### Basic Plotting

This document explores basic plotting techniques using base R. There are some basic ggplot examples in this document as well.
GEOG6000 - Lab 3

##### Mixed Effects Model Demo

This document explores hierarchical linear models, generalized hierarchical models, and generalized additive models. This is a great basic overview of using different mixed effects model approaches. Additionally, this uses ggplot and ggplotly to produce interactive figures.
GEOG6000 - Lab 6