Understanding Map Interpretation and Shadow Projection on a Plane

In summary: Therefore, the map is not injective and not surjective, but it has a fixpoint, meaning that it maps some vectors to themselves. In summary, the map $\psi:\mathbb{R}^3\rightarrow \mathbb{R}^3$, $v\mapsto \frac{1}{3}\begin{pmatrix}2 & - 1& - 1\\ - 1& 2 & -1\\ - 1&-1 & 2\end{pmatrix}\cdot v$ describes the shadow of the sun on a plane. The direction of the sun can be found by taking a vector in the direction of the sun, which will have a shadow of
  • #1
mathmari
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Hey! :eek:

Let $\phi_1:\mathbb{R}^3\rightarrow \mathbb{R}^3$, $v\mapsto \begin{pmatrix}0 & -1 & 0 \\ 1 & 0 & 0 \\ 0 & 0 & 1\end{pmatrix}\cdot v$, $\phi_2:\mathbb{R}^3\rightarrow \mathbb{R}^3$, $v\mapsto \frac{1}{3}\begin{pmatrix}2 & - 1& - 1\\ - 1& 2 & -1\\ - 1&-1 & 2\end{pmatrix}\cdot v$ which describes the shadow of the sun on a plane.
Give the direction of the sun by giving a vector. How do we find this vector? (Wondering)

To give the equation of the plane do we have to find the image of the map? (Wondering)

How can we explain why at a projection $\psi$ it holds that $\psi\circ\psi=\psi$ and $\text{im} \psi=\text{fix} \psi$? (Wondering)
 
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  • #2
mathmari said:
Hey! :eek:

Let $\phi_1:\mathbb{R}^3\rightarrow \mathbb{R}^3$, $v\mapsto \begin{pmatrix}0 & -1 & 0 \\ 1 & 0 & 0 \\ 0 & 0 & 1\end{pmatrix}\cdot v$, $\phi_2:\mathbb{R}^3\rightarrow \mathbb{R}^3$, $v\mapsto \frac{1}{3}\begin{pmatrix}2 & - 1& - 1\\ - 1& 2 & -1\\ - 1&-1 & 2\end{pmatrix}\cdot v$ which describes the shadow of the sun on a plane.
Give the direction of the sun by giving a vector. How do we find this vector?

Hey mathmari!

Suppose we have a vector in the direction of the sun.
Then its shadow will have length 0, won't it? (Wondering)
Put otherwise, its image must be the zero vector.

mathmari said:
To give the equation of the plane do we have to find the image of the map?

Suppose we have a vector in the plane.
Then its shadow will coincide with this vector, won't it? (Wondering)
Put otherwise, its image must be the same as the vector.
It also means that the vector is a fixpoint of the transformation.

mathmari said:
How can we explain why at a projection $\psi$ it holds that $\psi\circ\psi=\psi$ and $\text{im} \psi=\text{fix} \psi$?

What is $\psi$?
Did you mean $\phi_2$? (Wondering)
 
  • #3
Klaas van Aarsen said:
Suppose we have a vector in the direction of the sun.
Then its shadow will have length 0, won't it? (Wondering)
Put otherwise, its image must be the zero vector.

Suppose we have a vector in the plane.
Then its shadow will coincide with this vector, won't it? (Wondering)
Put otherwise, its image must be the same as the vector.
It also means that the vector is a fixpoint of the transformation.

Could you explain to me these two statements? I haven't really understood why these hold. (Wondering)

Klaas van Aarsen said:
What is $\psi$?
Did you mean $\phi_2$? (Wondering)

Oh yes, you are right
 
  • #4
mathmari said:
Could you explain to me these two statements? I haven't really understood why these hold.

Consider a sundial.
One that has a long thin rod that casts a shadow on the plane of the sundial.
View attachment 9553

Now suppose we change the direction of that rod, so that it points directly towards the sun.
Which shadow will it cast? (Wondering)

Alternatively, suppose we lower the rod, so that it comes closer and closer to the plane of the sundial, until it finally reaches the plane of the sundial.
What happens to its shadow? (Wondering)
 

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  • #5
Klaas van Aarsen said:
Now suppose we change the direction of that rod, so that it points directly towards the sun.
Which shadow will it cast? (Wondering)

If that rod has the direction to the sun, then there is no shadow. Therefore, the vector has to in the kernel of the map, right? (Wondering)
Klaas van Aarsen said:
Alternatively, suppose we lower the rod, so that it comes closer and closer to the plane of the sundial, until it finally reaches the plane of the sundial.
What happens to its shadow? (Wondering)

Then if we turn in that direction so that the shadow gets maximum, then the shadow is equal to the length of rod. That means thst this vector has to be in the fix set of the map, right? (Wondering)
 
  • #6
mathmari said:
If that rod has the direction to the sun, then there is no shadow. Therefore, the vector has to in the kernel of the map, right?

Then if we turn in that direction so that the shadow gets maximum, then the shadow is equal to the length of rod. That means that this vector has to be in the fix set of the map, right?

Yep. (Nod)
 
  • #7
Klaas van Aarsen said:
Yep. (Nod)

The echelon form of the matrix of the map is \begin{align*}\frac{1}{3}\begin{pmatrix}2 & - 1& - 1\\ - 1& 2 & -1\\ - 1&-1 & 2\end{pmatrix}\ \underset{R_3:R_3+\frac{1}{2}\cdot R_1}{\overset{R_2:R_2+\frac{1}{2}\cdot R_1}{\longrightarrow}} \ \frac{1}{3}\begin{pmatrix}2 & - 1& - 1\\ 0 & \frac{3}{2} & -\frac{3}{2}\\ 0&-\frac{3}{2} & \frac{3}{2}\end{pmatrix} \ \overset{R_3:R_3+R_2}{\longrightarrow} \ \frac{1}{3}\begin{pmatrix}2 & - 1& - 1\\ 0 & \frac{3}{2} & -\frac{3}{2}\\ 0&0 & 0\end{pmatrix}\ \underset{R_2:2\cdot R_2}{\overset{R_1:\left (\frac{3}{2}\right )\cdot R_1}{\longrightarrow}} \ \begin{pmatrix}1 & - \frac{1}{2}& - \frac{1}{2}\\ 0 & 1 & -1\\ 0&0 & 0\end{pmatrix}\end{align*}

The kernel of $\psi$ is $\text{ker}(\psi)=\left \{v\in \mathbb{R}^3\mid \psi (v)=0\in \mathbb{R}^3\right \} $.

We have that \begin{equation*}\psi (v)=0\Rightarrow \frac{1}{3}\begin{pmatrix}2 & - 1& - 1\\ - 1& 2 & -1\\ - 1&-1 & 2\end{pmatrix}\begin{pmatrix}v_1 \\ v_2 \\ v_3\end{pmatrix}=\begin{pmatrix}0 \\ 0 \\\ 0\end{pmatrix}\end{equation*}

Using the echelon form we get the equations:
\begin{align*}v_1-\frac{1}{2}v_2-\frac{1}{2}v_3=&0 \\ v_2-v_3=&0 \end{align*}

The solution vector is therefore $\begin{pmatrix}v_1 \\ v_2 \\ v_3\end{pmatrix}=\begin{pmatrix}v_3 \\ v_3 \\ v_3\end{pmatrix}$

So the kernel of the map is $\text{Kern}(\psi)=\left \{\begin{pmatrix}v_3 \\ v_3 \\ v_3\end{pmatrix}\mid v_3\in \mathbb{R}\right \}=\left \{v_3\begin{pmatrix}1 \\ 1 \\ 1\end{pmatrix}\mid v_3\in \mathbb{R}\right \}$.
A basis of the kernel is $\left \{\begin{pmatrix}1 \\ 1\\ 1\end{pmatrix}\right \}$.

Since the kernel doesn't contain only the zero vector, the map $\psi$ is not injective. The image of $\psi$ is $\text{Im}(\psi)=\left \{w\in \mathbb{R}^3\mid w=\psi (v) \ \text{ für ein } v\in \mathbb{R}^3\right \}$.

A basis of the image contains the linear independent columns of the matrix of the map, so $\left \{\begin{pmatrix}2 \\ -1\\ -1\end{pmatrix}, \ \begin{pmatrix}-1 \\ 2\\ -1\end{pmatrix}\right \}$.

Since the basis of the image contains only two elements and a basis of $\mathbb{R}^3$ three, the map is not surjective. The set of fixed points of $\psi$ is $\text{Fix}(\psi)=\left \{v\in \mathbb{R}^3\mid \psi (v)=v \right \}$.

We have the following:
\begin{align*}\frac{1}{3}\begin{pmatrix}2 & - 1& - 1\\ - 1& 2 & -1\\ - 1&-1 & 2\end{pmatrix}\begin{pmatrix}v_1 \\ v_2\\ v_3\end{pmatrix}=\begin{pmatrix}v_1 \\ v_2\\ v_3\end{pmatrix} &\Rightarrow \left (\frac{1}{3}\begin{pmatrix}2 & - 1& - 1\\ - 1& 2 & -1\\ - 1&-1 & 2\end{pmatrix}-I_3\right )\begin{pmatrix}v_1 \\ v_2\\ v_3\end{pmatrix}=\begin{pmatrix}0 \\ 0\\ 0\end{pmatrix} \\ & \Rightarrow \left (\begin{pmatrix}\frac{2}{3} & - \frac{1}{3}& - \frac{1}{3}\\ - \frac{1}{3}& \frac{2}{3} & -\frac{1}{3}\\ - \frac{1}{3}&-\frac{1}{3} & \frac{2}{3}\end{pmatrix}-\begin{pmatrix}1 & 0 & 0\\ 0 & 1 & 0 \\ 0 & 1 & 0\end{pmatrix}\right )\begin{pmatrix}v_1 \\ v_2\\ v_3\end{pmatrix}=\begin{pmatrix}0 \\ 0\\ 0\end{pmatrix} \\ & \Rightarrow \begin{pmatrix}- \frac{1}{3} & - \frac{1}{3}& - \frac{1}{3}\\ - \frac{1}{3}& - \frac{1}{3} & -\frac{1}{3}\\ - \frac{1}{3}&-\frac{1}{3} & - \frac{1}{3}\end{pmatrix}\begin{pmatrix}v_1 \\ v_2\\ v_3\end{pmatrix}=\begin{pmatrix}0 \\ 0\\ 0\end{pmatrix}\end{align*}

We calculate the echelon form of the matrix:
\begin{equation*}\begin{pmatrix}- \frac{1}{3} & - \frac{1}{3}& - \frac{1}{3}\\ - \frac{1}{3}& - \frac{1}{3} & -\frac{1}{3}\\ - \frac{1}{3}&-\frac{1}{3} & - \frac{1}{3}\end{pmatrix} \longrightarrow \begin{pmatrix}- \frac{1}{3} & - \frac{1}{3}& - \frac{1}{3}\\ 0 & 0 & 0 \\ 0 & 0 & 0\end{pmatrix} \longrightarrow \begin{pmatrix} 1 & 1 & 1\\ 0 & 0 & 0 \\ 0 & 0 & 0\end{pmatrix}\end{equation*}
And so we get the equation $v_1+v_2+v_3=0$ and so $v_1=-v_2-v_3$.

The solution vector is $\begin{pmatrix}v_1 \\ v_2 \\ v_3\end{pmatrix}=\begin{pmatrix}-v_2-v_3 \\ v_2 \\ v_3\end{pmatrix}$.

Therefore the set of fixed points is:
\begin{align*}\text{Fix}(\psi)&=\left \{\begin{pmatrix}-v_2-v_3 \\ v_2 \\ v_3\end{pmatrix}\mid v_2, v_3\in \mathbb{R} \right \}=\left \{\begin{pmatrix}-v_2 \\ v_2 \\ 0\end{pmatrix}+\begin{pmatrix}-v_3 \\ 0 \\ v_3\end{pmatrix}\mid v_2, v_3\in \mathbb{R} \right \} \\ &=\left \{v_2\begin{pmatrix}-1 \\ 1 \\ 0\end{pmatrix}+v_3\begin{pmatrix}-1 \\ 0 \\ 1\end{pmatrix}\mid v_2, v_3\in \mathbb{R} \right \}\end{align*}
A basis of the set of fixed points is $\left \{\begin{pmatrix}-1 \\ 1 \\ 0\end{pmatrix},\ \begin{pmatrix}-1 \\ 0 \\ 1\end{pmatrix}\right \}$. Is so far everything correct? (Wondering) From the above we get that the direction of the sun is a multiple of $\begin{pmatrix}1 \\ 1\\ 1\end{pmatrix}$, or not? (Wondering)

The equation of the plane is a linear combination of the basis elements of the set of fixed points, i.e. $a\begin{pmatrix}-1 \\ 1 \\ 0\end{pmatrix}+b\begin{pmatrix}-1 \\ 0 \\ 1\end{pmatrix}$, or not? (Wondering)
 
  • #8
Yep. (Nod)

Did you notice that the basis of the image spans the same plane as the basis of the fixed points? (Wondering)
 
  • #9
Klaas van Aarsen said:
Did you notice that the basis of the image spans the same plane as the basis of the fixed points? (Wondering)

Ahh yes! Does this mean that all the vectors of the image are fixed points? (Wondering)
 
  • #10
mathmari said:
Ahh yes! Does this mean that all the vectors of the image are fixed points?

Yep.
It also means that they are eigenvectors that correspond to eigenvalue 1.
And the vector to the sun is an eigenvector that corresponds to eigenvalue 0. (Nerd)
 
  • #11
Klaas van Aarsen said:
Yep.
It also means that they are eigenvectors that correspond to eigenvalue 1.
And the vector to the sun is an eigenvector that corresponds to eigenvalue 0. (Nerd)

Ah ok! Do we need these two statements? Are these equivalent with what we said before? I got stuck right now. (Wondering)
 
  • #12
mathmari said:
Ah ok! Do we need these two statements? Are these equivalent with what we said before? I got stuck right now.

We don't need these two statements.
They are merely equivalent with what we said before. (Nerd)
 
  • #13
Klaas van Aarsen said:
We don't need these two statements.
They are merely equivalent with what we said before. (Nerd)

Ok!

That the vectors of the image are fixed points is just in this case or does this hold every time if we consider a projection? (Wondering)
 
  • #14
mathmari said:
Ok!

That the vectors of the image are fixed points is just in this case or does this hold every time if we consider a projection?

It holds every time if we consider a projection.
It is diiferent for a rotation. (Thinking)
 
  • #15
Klaas van Aarsen said:
It holds every time if we consider a projection.

Does this hold because the projection doesn't change the length of the vector, it just "brings" it to an other position? (Wondering)
 
  • #16
mathmari said:
Does this hold because the projection doesn't change the length of the vector, it just "brings" it to an other position? (Wondering)

A projection does change the length of a vector in general.
The result of a projection is a vector in the plane of projection, which is generally shorter than the original vector.
However, the result of the projection of a vector that is already in the plane, is the same vector. (Thinking)

Did you perhaps mean a rotation? (Wondering)
 
  • #17
Klaas van Aarsen said:
A projection does change the length of a vector in general.
The result of a projection is a vector in the plane of projection, which is generally shorter than the original vector.
However, the result of the projection of a vector that is already in the plane, is the same vector. (Thinking)

Did you perhaps mean a rotation? (Wondering)

No, I mean projection.. I just want to understand why the image equal the set of fixed points and why it holds that $\phi_2\circ\phi_2=\phi_2$, but I got stuck. (Wondering)
 
  • #18
mathmari said:
No, I mean projection.. I just want to understand why the image equal the set of fixed points and why it holds that $\phi_2\circ\phi_2=\phi_2$, but I got stuck.

It seems there is some confusion what exactly a projection is.
What do you think it is? (Wondering)

For reference, wiki gives as definition:
In an abstract setting we can generally say that a projection is a mapping of a set (or of a mathematical structure) which is idempotent, which means that a projection is equal to its composition with itself.
It says in words that $\phi_2$ is called a projection if $\phi_2\circ\phi_2=\phi_2$. (Thinking)
 
  • #19
Klaas van Aarsen said:
For reference, wiki gives as definition:
In an abstract setting we can generally say that a projection is a mapping of a set (or of a mathematical structure) which is idempotent, which means that a projection is equal to its composition with itself.
It says in words that $\phi_2$ is called a projection if $\phi_2\circ\phi_2=\phi_2$. (Thinking)

I am trying to understand this because I am confused now. Can we see that graphically? (Wondering)
 
  • #20
mathmari said:
I am trying to understand this because I am confused now. Can we see that graphically? (Wondering)

How about this picture.
View attachment 9556
It shows an orthogonal projection in 2D onto a line.
(edit: just noticed the 3rd axis, making it 3D. Well forget about that axis for now, and consider it 2D. (Blush))
The vector with the person on top is 'dropped' onto the line.
As you can see, the resulting projection vector is shorter.
If we project the result again it doesn"t change.
The image of all possible vectors form the line.
And all vectors on the line are fixpoints. That is, repeated application yields the same vector. Put otherwise, the transformation is 'idempotent'. (Thinking)
 

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FAQ: Understanding Map Interpretation and Shadow Projection on a Plane

What is map interpretation?

Map interpretation is the process of analyzing and understanding the information presented on a map. This includes understanding symbols, colors, and other elements to determine the features and characteristics of a particular area.

How do you interpret shadows on a map?

Shadows on a map are created by the projection of light onto a flat surface. They can be interpreted by understanding the direction of the light source and how it affects the features on the map. For example, shadows can indicate the elevation of certain features, such as mountains or valleys.

What is shadow projection on a plane?

Shadow projection on a plane is the process of creating a two-dimensional representation of a three-dimensional object by projecting its shadow onto a flat surface. This technique is commonly used in cartography to create maps.

How does understanding map interpretation and shadow projection benefit us?

Understanding map interpretation and shadow projection allows us to accurately read and interpret maps, which are essential tools for navigation, planning, and decision making. It also helps us to better understand the physical features and characteristics of a particular area.

What are some common challenges in map interpretation and shadow projection?

Some common challenges in map interpretation and shadow projection include accounting for distortions caused by the projection method used, understanding the scale of the map, and interpreting symbols and colors correctly. It also requires knowledge and understanding of geography, cartography, and mathematics.

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