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13
Pedro Cabral
2020 Brazilian Wild and Criminal Fires: Analysis and Visualization
Pedro Cabral, Researcher at the Federal Institute of Ceará
Posted
4 months ago
2615 Views
|
7 Replies
|
21 Total Likes
Follow this post
|
UPDATED:
Added the data from September ─ 2020.
2020 Brazilian Wild Fires and Criminal Fires: Analysis and Visualization on the NASA MODIS Data.
Pedro Gomes Cabral — Intern Consultant at
Wolfram Research, Inc.
—
pcabral@wolfram.com
ABSTRACT:
In this article, inspired by
Arnoud Buzing's "Mapping Wildfires"
and
Mads Bahrami's "US Fire Map"
, I perform some data parsing, analysis and visualization on the data from the “
INPE's Fire Database
” that uses the
NASA MODIS
(Moderate Resolution Imaging Spectroradiometer) data to search for wildfires and automatically extract the geographical position, administrative division and the radioactive fire power. Through some parsing functions, the raw CSV dataset can be transformed into a more computable dataset with entities and quantities.
Introduction
O
u
t
[
]
=
Photography of “Illegal loggers, 2020” and “Apui fire, 2020” taken by
Bruno Kelly
.
The year of 2020 has been a cruel year, with both wildfires and criminal fires to all the
six biomes of Brazil
, the
Atlantic Forest
,
Amazon
,
Pampa
,
Pantanal
,
Caatinga
, and
Cerrado
. A couple of news articles claim devastating effects in the 2020 wildfires and criminal fires through all biomes, some include: “
On 14 days of this month, the Amazon already has more wildfires than in all September of last year.
”, “
Burning Amazon 20: Burns consume trees and animals in southern Amazonas
”, and “
Wildfires leave deep marks in the Amazon
”.
In this article, I’ll be analysing the complete
NASA MODIS Land
data from
January 1st
to
August 31th
of the year 2020, performing statistical analysis, and geographical visualization of the data.
Data Parsing
I’ve wrote one helper function that automatically parses the
INPE’s Fire Database
datasets into a more computable form of dataset, with entities and with the Wolfram Language knowledge.
Set the current directory to the notebook directory.
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This notebook already contains this raw piece of data, so there is no need for importing or downloading.
Iconized March 2020 dataset.
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Definition of the helper function
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=
For parsing the data, just call
ParseINPEDataset
with a proper
INPE’s Fire Database
CSV dataset.
Parse the first 10 elements of the unprocessed fire dataset with the
ParseINPEDataset
function.
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Downloading the Datasets
The dataset can be downloaded directly from the Wolfram Cloud. The file consists of a
Wolfram Binary Dump
, it’s a space and memory efficient way of holding computable data without any effort, the only downside is that you cannot read it as a raw file. This file will be
monthly updated
until the end of 2020 by me.
Define
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For importing, you can use
CloudImport[]
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Import[]
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Retrieve the
FireData
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BrazilianFireData
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Dataset Information
The dataset is a list of associations, containing
8 rules
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8 rules
for each list. Here is a description of all columns.
Generate a dataset called
ColumnsDescription
with the description of all columns.
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Get five random samples of the
BrazilianFireData
dataset and display as a formatted dataset.
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O
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t
[
]
=
Dataset Segmentation
For the easiness of manipulation of the dataset, it’s possible to segment the data by month and date.
Segment the
BrazilianFireData
dataset into lists by month.
B
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]
&
]
;
I
n
[
]
:
=
Segment the
BrazilianFireData
dataset into lists by day.
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I
n
[
]
:
=
Statistical Analysis and Visualization
In this section, many statistical functions and different plotting methods will be applied on the dataset. For the easiness of visualization and interaction of this notebook,
every interactive plot is saved
, so it’s not necessary to run them again.
Let’s visualize the trend of the amount of fires per day, and the mean fire radiative power per day.
Plot a list line graphic of the amount of fires reported per day.
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[
]
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t
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=
Plot a list line graphic of the mean fire radiative power per day.
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P
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〛
]
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[
]
:
=
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t
[
]
=
Plot a histogram of the amount of days without rain.
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〛
]
,
5
,
,
{
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1
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h
[
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]
}
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d
T
r
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e
I
n
[
]
:
=
O
u
t
[
]
=
Plot a bar chart of the fires and their biomes.
L
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t
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m
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a
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e
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〛
]
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,
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]
}
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d
T
r
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e
I
n
[
]
:
=
O
u
t
[
]
=
Data Querying and Comparison
Some interesting questions can be computationally extracted from the datasets.
Q:
What was the most affected biome by the fires?
Dataset of the count for every biome from the reverse sorted dataset.
D
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t
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t
[
C
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[
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〛
]
/
/
R
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]
I
n
[
]
:
=
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