18-Activity-Point-Pattern-Analysis-V.Rmd

# Activity 8: Point Pattern Analysis V

*NOTE*: The source files for this book are available with companion package [{isdas}](https://paezha.github.io/isdas/). The source files are in Rmarkdown format and packed as templates. These files allow you execute code within the notebook, so that you can work interactively with the notes. 

## Practice questions

Answer the following questions:

1. Describe the process to use simulation for hypothesis testing
2. Why is the selection of an appropriate region critical for the analysis of point patterns?
3. Discuss the issues associated with the edges of a region.
4. What is a sampled point pattern?

## Learning objectives

In this activity, you will:

1. Explore a dataset using single scale distance-based techniques.
2. Explore the characteristics of a point pattern at multiple scales.
3. Discuss ways to evaluate how confident you are that a pattern is random.

## Suggested reading

O'Sullivan D and Unwin D (2010) Geographic Information Analysis, 2nd Edition, Chapter 5. John Wiley & Sons: New Jersey.

## Preliminaries

It is good practice to begin with a clean session to make sure that you do not have extraneous items there when you begin your work. The best practice is to restart the `R` session, which can be accomplished for example with `command/ctrl + shift + F10`. An alternative to _only_ purge user-created objects from memory is to use the `R` command `rm` (for "remove"), followed by a list of items to be removed. To clear the workspace from _all_ objects, do the following:
```{r}
rm(list = ls())
```

Note that `ls()` lists all objects currently on the workspace.

Load the libraries you will use in this activity. In addition to `tidyverse`, you will need `spatstat`, a package designed for the analysis of point patterns (you can learn about `spatstat` [here](https://cran.r-project.org/web/packages/spatstat/vignettes/getstart.pdf) and [here](http://spatstat.org/resources/spatstatJSSpaper.pdf)):
```{r message=FALSE}
library(isdas) # Companion Package for Book An Introduction to Spatial Data Analysis and Statistics
library(spatstat) # Spatial Point Pattern Analysis, Model-Fitting, Simulation, Tests
library(tidyverse) # Easily Install and Load the 'Tidyverse'
```

Load a dataset of your choice. It could be one of the datasets that we have used before (Toronto Business Points, Bear GPS Locations), or one of the datasets included with the package `spatstat`. To see what datasets are available through the package, do the following:
```{r}
vcdExtra::datasets("spatstat.data")
```

Load a dataset of your choice.

You can do this by using the `load()` function if the dataset is in your drive (e.g., the GPS coordinates of the bear).

On the other hand, if the dataset is included with the `spatstat` package you can do the following, for example to load the `gorillas` dataset:
```{r}
gorillas.ppp <- gorillas
```

As usual, you can check the object by means of the `summary` function:
```{r}
summary(gorillas.ppp)
```

## Activity

::: {.infobox .caution data-latex="{caution}"}
**Capstone Activity**

This is a capstone activity where you can work free-style
on a data set of your choice, and put in practice what you
have learned with respect to the analysis of point patterns.
:::

1. Partner with a fellow student to analyze the chosen dataset.

2. Discuss whether the pattern is random, and how confident you are in your decision.

3. The analysis of the pattern is meant to provide insights about the underlying process. Create a hypothesis using the data generated and can you answer that hypothesis using the plots generated?

4. Discuss the limitations of the analysis, for instance, choice of modeling parameters (size of region, kernel bandwidths, edge effects, etc.)