in_G364_Lab4_Ozone.Rmd

---
title: "Lab 4: Voronoi Polygons & Ozone pollution"
subtitle: <h4 style="font-style:normal">One week lab!</h4>
output:
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---

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\

```{r, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message = FALSE)
```

# 1. Overview

**Welcome to Lab 4.** The objective of this lab is to introduce you to
the concept of Voronoi polygons and demonstrate their practical
application in designing an air quality monitoring network for
California. You will learn how to create Voronoi polygons from ozone
measurement data, identify suitable monitoring station locations, and
explore the significance of such a network in air quality management.
You will also compare this with spatial demographic data from the
CDC/ATSDR Social Vulnerability Index. <br><br>

See the rubric on canvas (this is up!) for how it will be graded and the
checklist at the end for what you need for 100%. <br><br>

See The Canvas page here, or go to canvas for assignment guidelines. You
can use either the website base RStudio/Posit Cloud OR R-Desktop for
this lab.<br><br>

**This is a ONE WEEK LAB.**

<br><br>


<br>

```{r,include=FALSE,echo=FALSE}
# invisible data read
library(tidyverse)
library(sp)
library(sf)
library(readxl)
library(skimr)
library(tmap)
library(viridis)
library(kableExtra)
library(spatstat)
library(spatstat.data)
library(car)

data(longleaf)
market    <- read_csv("./Data/Farmers_Markets_NYShort.csv")
market.sp <- st_as_sf(market,coords=c("Longitude","Latitude"),crs=4326)

```

# 2. Background (please read!)

<br>

## A. Ozone pollution

<br> 


![*Ozone in LA.Figure from
<https://www.rifs-potsdam.de/en/news/new-database-global-surface-ozone-levels-shows-regions-most-affected-air-pollution>*](Figures/Lab4Fig1%20Smog%20Daniel%20Stein.png){width="800"}

<br>

![*Figure from Oregon Dept
Ecology*[^1]](Figures/Lab4Fig1%20Ozone.jpeg){width="500"}

[^1]: <https://ecology.wa.gov/Air-Climate/Air-quality/Air-quality-targets/Air-quality-standards/Ozone-pollution>

<br>

As the global population and city sizes increase, it is increasingly
important to understand atmospheric air pollution. Two major pollutants
include Ozone (O3) and particulates (PM 2.5). In this lab, we will map
them and explore their impact on populations in California. <br>

Ozone is a naturally occurring molecule made up of three Oxygen atoms.
90% of the plant's ozone exists in the "ozone layer", which exists
approximately 20-25km above sea level, in the stratosphere.
Stratospheric ozone is crucial for human life, as it absorbs damaging
solar ultra-violet radiation (see the "ozone hole" for the importance of
natural ozone). <br>

10% of the planet's ozone is found in the lowest levels of the
atmosphere, in the air which we breathe. It is created when pollutants
such as NOx and VOCs (emitted by cars and industry) react with sunlight.
Strong evidence exists that the risk of premature death increases with
higher levels of ozone. High levels of ozone can cause immediate health
problems such as asthma. Over the longer term, ozone exposure can
exacerbate pre-existing lung and heart health problems. Read more here:
<https://aircentraltexas.org/en/regional-air-quality/who-is-at-risk>
<br>

It is important to map the spread of ozone to support public health
policy. We have access to a point dataset of ozone data, showing the
location of the monitoring network. We also have access to demographic
data from the US census and their Social Vulnerability Index. We will
use Voronoi polygons to assess the intersection between these and the
impact on environmental justice.

<br><br>

## B. Voronoi Polygons

<br>

#### What are they?

![*These are voronoi polygons.*](Figures/Lab4Fig2%20voronoi.png)

<br>

Read these two articles on voronoi polygons to understand more:

-   <https://fbellelli.com/posts/2021-07-08-the-fascinating-world-of-voronoi-diagrams/>
-   <https://gisgeography.com/voronoi-diagram-thiessen-polygons/>

<br>

Here is how they might be useful in our case:

#### Air Quality Monitoring Network Design:

When setting up or optimizing an air quality monitoring network, Voronoi
polygons can help determine the optimal locations for monitoring
stations. Each monitoring station can be located at the centroid of its
corresponding Voronoi polygon, ensuring that the network covers the
entire area efficiently.

#### Environmental Justice Studies:

Voronoi polygons can be utilized in environmental justice studies to
assess whether certain communities, particularly those with vulnerable
populations, are disproportionately affected by high ozone levels. AND
if those communities have a dense enough monitoring network.
Understanding ozone concentration patterns can contribute to health risk
assessments. By overlaying Voronoi polygons with demographic data, you
can assess the exposure of different populations to high ozone levels,
potentially leading to health warnings or policy recommendations.

#### Identifying Ozone Hotspots:

Voronoi polygons can help identify areas with high ozone concentrations,
known as "ozone hotspots." By visualizing the Voronoi polygons, you can
quickly pinpoint regions where ozone levels are consistently elevated.
This information is crucial for understanding air quality and targeting
pollution control efforts. Not the best way but fast

#### Pollution Source Identification:

Voronoi diagrams can assist in identifying potential sources of ozone
pollution. High ozone concentrations in a specific Voronoi polygon could
indicate nearby pollution sources that need further investigation. This
can be valuable for regulatory agencies and environmental scientists.
Not the best way but fast

#### Data Validation and Quality Control:

Voronoi diagrams can be used to validate and control the quality of
ozone measurement data. Outliers or inconsistencies in data within a
Voronoi polygon may indicate measurement errors or the need for further
investigation.

<br><br>

# 3. Data (please read!)

<br>

## A. Ozone Data

Ozone measurements were taken across the State of California. Population
density has also been recorded at each site using this source:
<https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density-rev11>.

-   The units of the ozone measurements are in "1000 parts per billion".
-   The units of population density are people per square km.

<br>

## B. SOVI Social Vulnerability Index

![*The components of SOVI*](Figures/Lab4Fig3%20SOVI.png){width="500"}

<br>

SOVI, or the Social Vulnerability Index, is a quantitative measure used
to assess the resilience and susceptibility of communities to various
hazards and disasters, such as natural disasters or public health
crises. It considers factors like socioeconomic status, access to
resources, and community infrastructure to determine the level of
vulnerability within a given population. It is based on US-Census data,
so we have variables as vector POLYGONS (either census tracts or
counties). There are many places to access and download the data.

-   Read more here:
    <https://sc.edu/study/colleges_schools/artsandsciences/centers_and_institutes/hvri/data_and_resources/sovi/index.php>

-   <br> Here's the SOVI "recipe"
    <https://sc.edu/study/colleges_schools/artsandsciences/centers_and_institutes/hvri/documents/sovi/sovi_recipe_2016.pdf>

-   <br> You can see what individual column names mean here. \
    I suggest using Edit/Find or Ctrl-F to quickly search for what you
    need
    <https://www.atsdr.cdc.gov/placeandhealth/svi/documentation/pdf/SVI2020Documentation_08.05.22.pdf>

<br><br>

# 4. Getting started.

<br>

To make it easier for a one week lab, I have provided much of the code
and data. FIRST, GO BACK AND READ THE BACKGROUND ABOVE.

<br><br>

## A. Create your project

In Desktop R, select your 364 directory, create a new project, then name
the project Lab 3. If you are stuck on this process, see these tutorials

         
 - For R-Desktop, see [Tutorial 3B](https://psu-spatial.github.io/Geog364-2023/in_Tutorial03LabSetUp.html#Projects_using_Desktop)

 - For Posit Cloud, see [Tutorial 3C](https://psu-spatial.github.io/Geog364-2023/in_Tutorial03LabSetUp.html#Projects_using_Rstudioposit_Cloud)
    <br><br>


You can check this has worked by looking on the file explorer on your
computer, then selecting your GEOG364 folder and checking that a Lab 4
folder has appeared with your `.Proj` file inside it. This is what you
double click to access this lab in R.

<br><br>

## B. Get the lab script and data

Go to [CANVAS LAB
4](https://psu.instructure.com/courses/2260204/assignments/15455245),
download the lab report template and the ozone dataset. Rename the lab
report to include your email ID and place in your Lab 4 folder.

<br><br>

# 5. Your analysis

Open your lab script and follow the instructions. Most of the code
chunks will run automatically. Some, I might ask you to edit.

<br><br>

# 6. Submitting your work

Remember to save your work throughout and to spell check! (next to the
save button). Now, press the knit button one final time.

On R-Desktop

-   If you have not made any mistakes in the code then R should create a
    html file in your lab 4 folder which includes your answers.

-   If you look at your lab 4 folder on your computer, you should see
    the html there - complete with a very recent time-stamp. Double
    click on the html file. This will open it in your web-browser.\
    CHECK THAT THIS IS WHAT YOU WANT TO SUBMIT.<br>

On Rstudio cloud,

-   See [TUTORIAL 3 Cloud - LINK
    FIXED](https://psu-spatial.github.io/Geog364-2023/in_Tutorial03LabSetUp.html#Projects_using_Rstudioposit_Cloud)
    for how to download your files <br>

Finally, go to Canvas and submit BOTH your html and your .Rmd file in
Lab 3.

```{r, echo=FALSE}
knitr::include_graphics("./Figures/LabFig_WhatToSubmit.png")
```

<br><br>

## How am I graded?

Overall, here is what your lab should correspond to:

```{r, echo=FALSE}
rubric <- readxl::read_excel("in_G364_Lab0Rubric.xlsx")
knitr::kable(rubric) %>%   
  kable_classic_2() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "responsive"))


```