ENVS563 – Geographic Data science 563 Computational Essay (60%)

联系我们： 手动添加方式: 微信>添加朋友>企业微信联系人>13262280223 或者 QQ: 1483266981

ENVS563 SUMMER RESIT ASSESSMENT 202526
ENVS563 – Geographic Data science
563 Computational Essay (60%)
Overview
Here’s the premise. You will take the role of a real-world GIS analyst or geographic data scientist tasked to explore datasets on New York City (NYC) and find useful insights for a variety of city decision-makers. It does not matter if you have never been to New York City. 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 New York City, but instead about how much you can show you have learned through analysing data. You will need to 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 if you are doing the assignment in R. A computational essay using Jupyter Notebooks if you are doing the assignment in Python.
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 or Python 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.
ENVS563 SUMMER RESIT ASSESSMENT 202526
Data
The assignment relies on two 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/). You should Download the New York City data. Please Note: that for best results you will need to drop some of the outliers.
Socio-economic variables for New York City. Source: American Community Survey (ACS) 2016–2020, US Census Bureau. Years: 2016–2020.o You have access to ALL variables in the American Community Survey (ACS) 2016–
2020 through the census API — in R via the tidycensus package, or in Python via the census package (or direct API calls with requests/cenpy). o 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.
IMPORTANT – Students of ENVS563 will need to source, at least one additional datasets relating to New York City. Here are some ideas:
https://opendata.cityofnewyork.us/data/ or
https://data.cityofnewyork.us/browse q=trips or
https://earthdata.nasa.gov/earth-observation-data/near-real-time/hazards-and-disasters/air-quality
Part I – Common
This is the one everyone has to do in the same way. Complete the following tasks:
1. Select two variables from the socio-economic dataset.
2. Explore the spatial distribution of the data using choropleths. Comment on the details of your maps and interpret the results.
3. Explore the degree of spatial autocorrelation. Describe the concepts behind your approach and interpret your results.
Part II – Choose your own adventure
For this one, you need to pick one of the following three options. Only one and make the most of it.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. Using the Airbnb 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 airbnbs, which we will call areas of interest (AOI). In completing this, answer the following questions:
What parameters have you used to run DBSCAN Why
What do the clusters help you learn about areas of interest in the city
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.
* If you are working in R, to make sure your html is self-contained, please add the following in the preamble of the qmd file.
—
title: “Assignment Title”author: “Author Name”format:html:self-contained: truetoc: true
—
Submit online via Canvas.
If you experience any difficulty in submitting your assessment, please contact the Student Experience Team (envsci@liverpool.ac.uk) as soon as possible and in advance of the deadline for further advice and support.

ENVS563 – Geographic Data science
563 Computational Essay (60%)最先出现在KJESSAY历史案例。