ENVS363 A Computational Essay

ENVS363/563.3 - A Computational Essay 2023/24

Overview and Instructions

Due Date: 8th January 2024

50% of the final mark

Overview

Here’s the premise. You will take the role of a real-world GIS analyst or spatial data scientist tasked to explore datasets on the San Francisco Bay Area (often just called the Bay Area) and find useful insights for a variety of city decision-makers. It does not matter if you have never been to the Bay

Area. In fact, this will help you focus on what you can learn about the city through the data, without the influence of prior knowledge. Furthermore, the assessment will not be marked based on how much you know about the San Francisco Bay Area but instead about how much you can show you

have learned through analysing data. You will need contextualise your project by highlighting the opportunities and limitations of ‘old’ and ‘new’ forms of spatial data and reference relevant literature.

Format

A computational essay using Quarto. The assignment should be carried out fully in Quarto.

What is a Computational Essay?

A computational essay is an essay whose narrative is supported by code and computational results

that are included in the essay itself. This piece of assessment is equivalent to 4,000 words.

However, this is the overall weight. Since you will need to create not only narrative but also code and figures, here are the requirements:

• Maximum of 1,000 words (ordinary text) (references do not contribute to the word count). You should answer the specified questions within the narrative. The questions should be included within a wider analysis.

• Up to five maps or figures (a figure may include more than one map and will only count as one but needs to be integrated in the same overall output)

• Up to one table

There are three kinds of elements in a computational essay.

1. Ordinary text (in English)

2. Computer input (R code)

3. Computer output

These three elements all work together to express what’s being communicated.

Submission

You must submit 1 electronic copy of your assessment via Canvas by the published

deadline . The format of the file must be an html document. Please do not include your name anywhere in the documents.

• Please refer to the ENVS363/563 Assessment criteria. This document includes the parts you should include in your Computational Essay.

Data

The assignment relies on datasets and has two parts. Each dataset is explained with more detail below.

Data made available on Murray Cox’s website as part of his “Inside Airbnb” project which you can download ( http://insideairbnb.com/ ). The website periodically publishes snapshots of Airbnb listings around the world. You should Download the San Francisco data , the San Mateo data and the Oakland data . These are all part of the Bay Area.

Please Note: that for best results you will need to drop some of the outliers.

• Socio-economic variables for the Bay Area. Source: American Community Survey (ACS)

2016-2020, US Census Bureau. Observations: 1039; Variables: 472; Years: 2016-2020 . o A subset of variables from the latest ACS has already been retrieved for you in ACS_2016_2020_vars.csv. However, you have access to ALL variables in the American Community Survey (ACS) 2016-2020 through the R package Tidycensus .

o You are strongly recommended to use the census API in the R package

Tidycensus to extract your variables of interest instead of the csv. For more

information about the ACS (2016-2020) you can have a look at:

https://www.census.gov/data/developers/data-sets/acs-5year.html and

https://api.census.gov/data/2020/acs/acs5/variables.html .

If you want to visualise some aspects at different Subnational Administrative boundaries, you can

download USA boundaries from GADM. You can also find other geodata for the Bay Area in the

Berkeley Library .

IMPORTANT - Students of ENVS563 will need to source, at least, two additional datasets relating to San Francisco or the Bay Area. You can use any dataset that will help you complete the tasks below but, if you need some inspiration, have a look at the following:

• Geodata for the Bay Area in the Berkeley Library .

• San Francisco Open Data Portal: https://datasf.org/opendata/

• Data World: https://data.world/datasets/san-francisco

• NASA Data: https://earthdata.nasa.gov/earth-observation-data/near-real-time/hazards

and-disasters/air-quality