Python代写 | DATA2001/DATA2901 Practical Assignment: Bushfire Risk Analysis


DATA2001/DATA2901 Practical Assignment: Bushfire Risk Analysis

Data Set Description and Preparation
Your task in this assignment is to calculate a bushfire risk score with regard to bushfire protection
for different neighbourhoods in Sydney. The neighbourhood ’score’ is expressed as a measure
of several factors which we assume to affect the risk of bush fires within an area — vegetation,
population density, number of dwellings etc.
In order to calculate this score, you will need to integrate different data sources. As a starting
point, we provide you with some census-based datasets which give you input on at least three
factors: population density, dwelling and business locations. We also provide some spatial data
with the vegetation and risk categories provided by the NSW Rural Fire Service. We leave it up-
to you to integrate further data and to refine the suggested risk score. Some ideas would be the
availability of specific emergency services, or the prevalence of waterways etc.
Based on your computed risk scores, perform then a correlation analysis against the ABS pro-
vided median income and median rent costs of each neighbourhood.
Your submission should consist of your Jupyter notebook that you used for integrating the data
sets and for performing and visualising your analysis.
Milestone 1: Load and integrate the provided datasets into the university provided PostgreSQL
database by the tutorials in Week 11.
Provided datasets: We provide in Canvas several CSV files with Statistical Area 2 (SA2) data
from the Australian Bureau of Statistics (ABS), as well as some bush fire prone land vegetation
spatial data from the NSW Rural Fire Service (keep checking Canvas/Ed for any later additions or
updates at
StatisticalAreas.csv: area id, area name, parent area id
Neighbourhoods.csv: area id, area name, land area, population, dwellings, businesses, median income, av
BusinessStats.csv: area id, number of businesses, accommodation and food, retail trade, agriculture for
RFSNSW BFPL.shp: gid, category, shape leng, shape area, geom

Task 1: Data Integration and Database Generation
Build a database using PostgreSQL that integrates data from the following sources:
1. Sydney neighbourhood dataset (based on provided CSV files with SA2-data from ABS).
2. Spatial data in the SA2 ESRI Shape data file from the ABS at
3. Census data for the given neighbourhoods including population count, dwelling and busi-
nesses counts.
4. Bush Fire Prone Land in NSW; Originally from the Rural Fire service, but modified for this
task – you will need to do some transforming of this data
5. You are encouraged to extend and refine both scoring function and source data. For
full points when integrating at least one additional data set.
Milestone 1: Load and integrate the provided datasets into PostgreSQL by the tutorials in Week 11.
Task 2: Fire Risk Analysis
1. Compute the fire risk score for all given neighbourhoods according to the following formula
and definitions (adjust as needed if you integrated any additional datasets):
re risk = S(z(population density)+z(dwelling & business density)+z(bfpl density) z(assistive service density)
With S being the logistic function (sigmoid function), and z the z-score (”standard score”) of a
measure – the number of standard deviations from the mean (assuming a normal distribution):
z(measure; x) = x avgmeasure
Measure Definition Risk Data Source
population density population divided by neighbourhood’s land area + Neighbourhoods.csv
dwelling density number of dwellings divided by neighbourhood land area + Neighbourhoods.csv
business density number of businesses divided by neighbourhood land area + BusinessStats.csv
bfpl density area and category of BFPL divided by neighbourhood land area + RFSNSW BFPL.shp
assistive service density number of assistive services divided by neighbourhood land area – BusinessStats.csv
2. Store the computed measures and scores of each neighbourhood in your database. Create
at least one index which is helpful for data integration or the fire risk score computation.
3. Determine whether there is a correlation between your fire risk score and the median income
and rent of a neighbourhood.



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