ENVS363代做、R设计编程代写

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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.
ENVS363-563 Computational Essay
• 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/hazardsand-disasters/air-quality
Part 1 – Common
1.1 Collecting and importing the data
1.1.1 Import and explore
1.2 Preparing the data
1.2.1 What CRS are you going to use? Justify your answer.
1.3 Discussion of the data
• Present and describe the data sets used for this project.
1.4 Mapping and Data visualisation
1.4.1 Airbnb in the BAY AREA at Neighbourhood Level
• Summarise the data. Using Bay Area zipcodes/ ZCTAs obtained from Berkeley Library.
This is slightly different from the Airbnb neighbourhood file. Obtain a count of listings by
neighbourhood.
ENVS363-563 Computational Essay
• Map 1.1: Number of listings per zipcode. Explore the spatial distribution of the data using
choropleths. Style the layers using a colour ramp.
• Map 1.2: Average price per zipcode. Explore the spatial distribution of the data using
choropleths. Style the layers using a colour ramp.
Justify your data classification methods and visualization choices. You should include these maps
in your assessment submission. The maps should be well-presented and include a short
description.
Questions to answer within your analysis: How does the Inside Airbnb data compare to other ‘new’
forms of spatial data? Discuss the potential insights and biases, as well as opportunities and
limitations of the Airbnb data.
1.4.2. Socio-economic variables from the ACS data
Select two variables from American Community Survey data. These could be but are not limited
to population density, median income, median age, unemployed, percentage of black population,
percentage of Hispanic population or education level. See the Appendix in this document for help.
If you chose to calculate population percentages, make sure you standardise the table by the
population size of each tract.
• Map2: Explore the spatial distribution of your chosen variables using choropleths. Style the
variables using a colour ramp. Justify your data classification methods and visualization
choices. You should include these maps in your assessment submission. The maps should
be well-presented and include a short description.
Questions to answer within your analysis. Comment on the details of your map and analyse the
results. What are the main types of neighbourhoods you identify? Which characteristics help you
delineate this typology? What can you say about the spatial distribution of your socio-economic
variable of interest? If you had to use this classification to evaluate where Airbnbs would cluster,
what would your hypothesis be? Why?
For some stylised (not necessarily accurate) facts about the Bay Area see here.
1.4.3. Combining Data sets
• Map 3: Plot the natural logarithm of price (ln of price) of Airbnbs in the San Francisco Bay
Area together (point plot) with one of your chosen socio-economic variables of interest
at zipcode level using ggplot or tmap or mapsf (polygon plot). There are various ways of
doing this. The maps should be well-presented.
Questions to answer within your analysis. Comment on the details of your map and analyse the
results. Does this map tell you more about the relationship between Airbnb location/price and
your socio-economic variable of choice? Explain your answer.
1.4.4. Autocorrelation
• Map 4: Explore the degree of spatial autocorrelation. Describe the concepts behind your
approach and interpret your results.
ENVS363-563 Computational Essay
Part 2 – Chose your own analysis
For this one, you need to pick one of the following three options. Only one, and make the most
of it.
Please Note: This part of the assignment can be done on the Bay Area as a whole or you can
zoom in on one of the counties. For example, you could just focus on San Francisco.
1. Create a geodemographic classification and interpret the results. In the process, answer
the following questions:
• What are the main types of neighbourhoods you identify?
• Which characteristics help you delineate this typology?
• If you had to use this classification to target areas in most need, how would you use it?
why?
2. Create a regionalisation and interpret the results. In the process, answer at least the
following questions:
• How is the city partitioned by your data?
• What do you learn about the geography of the city from the regionalisation?
• What would one useful application of this regionalisation in the context of urban policy?
3. Use the OpenStreetMap package to osmdata download Point of Interest (POIs) Data for
the Bay Area or San Francisco. Using this this data, complete the following tasks:
• Visualise the dataset appropriately and discuss why you have taken your specific
approach
• Use DBSCAN to identify areas of the city with high density of POIs, which we will call
areas of interest (AOI). In completing this, answer the following questions:
o What parameters have you used to run DBSCAN? Why?
o What do the clusters help you learn about areas of interest in the city?
o Name one example of how these AOIs can be of use for the city. You can take
the perspective of an urban planner, a policy maker, an operational
practitioner (e.g. police, trash collection), an urban entrepreneur, or any
other role you envision.
Resources to help you. See also suggested bibliography in slides throughout the course.
• https://www.r-bloggers.com/2017/11/programming-meh-lets-teach-how-to-writecomputational-essays-instead/
• https://rmarkdown.rstudio.com/
• https://www.rstudio.com/wp-content/uploads/2015/02/rmarkdown-cheatsheet.pdf
• https://vizual-statistix.tumblr.com/post/114850050736/i-find-the-spread-of-airbnb-to-beas-fascinating
• https://carto.com/blog/airbnb-impact/
• https://cran.r-project.org/web/packages/biscale/vignettes/biscale.html
Appendix
American Community Survey (ACS) 2016-2020, US Census Bureau. Observations: 1039; Variables:
472; Years: 2016-2020
ENVS363-563 Computational Essay
Variable Description
B19013_001E Median household income in the past 12 months (in 2020 inflation-adjusted
dollars). Coded as hh_income
B02001 (list of vars) Population by race
See https://api.census.gov/data/2020/acs/acs5/variables.html
I have already recoded black (n of black people) and all_ppl_race (total
population by census tract)
B23006 (list of vars) Population by education
See https://api.census.gov/data/2020/acs/acs5/variables.html
C15002A (list of vars) Population by Sex by Education
See https://api.census.gov/data/2020/acs/acs5/variables.html
C27012 (list of vars) Population by Health insurance
See https://api.census.gov/data/2020/acs/acs5/variables.html
B08006 (list of vars) Commuting variable
See https://api.census.gov/data/2020/acs/acs5/variables.html
B09010 (list of vars) Supplementary income variables
See https://api.census.gov/data/2020/acs/acs5/variables.html
B09019 (list of vars) Household type counts
See https://api.census.gov/data/2020/acs/acs5/variables.html
B17001 (list of vars) Poverty Status
See https://api.census.gov/data/2020/acs/acs5/variables.html
B28011 (list of vars) Internet Access
See https://api.census.gov/data/2020/acs/acs5/variables.html
B99084 (list of vars) Work From Home
See https://api.census.gov/data/2020/acs/acs5/variables.html