DATA STRUCTURES NOTES

This algorithm comes under the greedy method, which means that the objects are chosen to join a growing collection by iteratively picking an object that minimizes some cost function.

Now, taking account of the above mentioned greedy method, the Prim-Jarnik's algorithm is similar to the Dijkstra's shortest-path algorithm to grow the Minimum Spanning Tree (MST) from a single cluster starting with a randomly chosen single root vertex.

The Prim-Jarnik's algorithm can be broken down as follows:
Step 1: Define the starting "cloud" of vertices [C] by selecting some random vertex [v] initially
Step 2: Iteratively choose a min-weight edge [e=(v,u)], thereby connecting a vertex [v] in the cloud [C] to a vertex [u] outside of [C]. After choosing [e] the vertex [u] is brought into the cloud [C] continuously until a spanning tree emerges.


Pseudo-code of the Prim-Jarnik's algorithm
// Prim-Jarnik(G) [Time complexity = O(m * log * n)]
// Input: A weighted connected graph [G] with n number of vertices & m number of edges
// Output: A minimum spanning tree [T] for [G]

// Pick a root vertex [v] of [G]
D[v] <- 0

// All vertices connected to the root vertex, hence it necessary to define the cost function.
// The cost function "D[u] = +infinity" when vertex [u] doesn't connect to vertex [v].
for each vertex u != v do
D[u] <- +infinity

// Initialise the MST
T <- null 

// Initialise a priority queue Q with an entry ((u,null),D[u]) for each vertex [u],
// where (u,null) is the element & D[u] in the key
while Q is not empty do
(u,e) <- Q.removeMin()
Add vertex u and edge e to T

// Perform the relaxation procedure on the edge of the vertex [z] adjacent to [u]
For each vertex z adjacent to u such that z is in Q do
if W((u,z)) <>
D[z] <- W((u,z))
Change to (z,(u,z)) the element of vertex z in Q
Change to D[z] the key of vertex z in Q
return the tree T

Kruskal's algorithm for extracting Minimum Spanning Tree (MST)

This algorithm comes under the greedy method, which means that the objects are chosen to join a growing collection by iteratively picking an object that minimizes some cost function.

Now, taking account of the above mentioned greedy method, the Kruskal's algorithm with a graph considers the edges in order of their weights to grow the Minimum Spanning Tree (MST) in clusters.

The Kruskal's algorithm can be broken down as follows:
Step 1: In a given undirected graph [G] consider each vertex is in its own cluster all by itself i.e. if a [G] has four vertices then you have four cluster initially
Step 2: Look for each edges in turn, ordered by increasing weight i.e. arrange all the edges in ascending order
Step 3: Start with the smallest weighing edge. If this edge connects two different clusters then it is considered as the min-weight "bridge" edge [e] and added to the set of edges of the MST. In this initial stages, due to addition of [e] to the MST (i.e. the growth of the MST), the two clusters then are merged into a single cluster. However, if a given [e] connects to two vertices that are already in the identified cluster then this [e] is discarded.
Step 4: Now iteratively the algorithm adds enough [e] to a set of edges of spanning tree, it terminates and outputs this tree as the MST.


Pseudo-code of the Kruskal's algorithm
// Kruskal(G) [Time complexity = O(m * log * n)]
// Input: A simple connected weight graph [G] with [n] vertices and [m] edges
// Output: A minimum spanning tree [T] for [G]
for each vertex [v] in [G] do
Define an elementary cluster C(v) <- {v}

// Initialise a priority queue Q to contain all edges in [G], using the weights as keys
T <- null 
// The above [T] will ultimately contain the edges of the MST

while [T] has fewer then n-1 edges do
(u,v) <- Q.removeMin()
// Let C(v) be the cluster containing v, and let C(u) be the cluster containing u.
if C(v) != C(u) the
Add edges (v,u) to T
Merge C(v) & C(u)
// The above union of C(v) & C(u) yields a single cluster
return tree TExample below shows the Kruskal's algorithm that extracts the MST from a spanning tree. This spanning tree depicts a subset of Finnish intercity roadways route:

--------------------------------------------
Stage 1.
--------------------------------------------
Main cluster (C) contains the following vertices: null
Elementary Cluster (C1) contains the following vertices: null

Priority Queue (Q)
------------------
----
110
----
137
---
141
---
164
---
177
---
294
---
381
---
409
---
534
---
549
---
------------------

Set of min-weight "bridge" edges e = {null}
Set of discarded edges d = {null}

--------------------------------------------
Stage 2.
--------------------------------------------
Main cluster (C) contains the following vertices:null
Elementary Cluster (C1) contains the following vertices: Pori+Tampere

Priority Queue (Q)
------------------
----
137
---
141
---
164
---
177
---
294
---
381
---
409
---
534
---
549
---
------------------

Set of min-weight "bridge" edges e = {110}
Set of discarded edges d = {null}

--------------------------------------------
Stage 3.
--------------------------------------------
Main cluster (C) contains the following vertices:null
Elementary Cluster (C1) contains the following vertices: Pori+Tampere
Elementary Cluster (C2) contains the following vertices: Helsinki+Kouvola

Priority Queue (Q)
------------------
---
141
---
164
---
177
---
294
---
381
---
409
---
534
---
549
---
------------------

Set of min-weight "bridge" edges e = {110,137,}
Set of discarded edges d = {null}

--------------------------------------------
Stage 4.
--------------------------------------------
Main cluster (C) contains the following vertices:null
Elementary Cluster (C1) contains the following vertices: Pori+Tampere+Turku
Elementary Cluster (C2) contains the following vertices: Helsinki+Kouvola

Priority Queue (Q)
------------------
---
164
---
177
---
294
---
381
---
409
---
534
---
549
---
------------------

Set of min-weight "bridge" edges e = {110,137,141}
Set of discarded edges d = {null}

--------------------------------------------
Stage 5.
--------------------------------------------
Main cluster (C) contains the following vertices:null
Elementary Cluster (C1) contains the following vertices: Pori+Tampere+Turku+Helsinki+Kouvola

Priority Queue (Q)
------------------
---
177
---
294
---
381
---
409
---
534
---
549
---
------------------

Set of min-weight "bridge" edges e = {110,137,141,164,}
Set of discarded edges d = {null}

--------------------------------------------
Stage 6.
--------------------------------------------
Main cluster (C) contains the following vertices:null
Elementary Cluster (C1) contains the following vertices: Pori+Tampere+Turku+Helsinki+Kouvola

Priority Queue (Q)
------------------
---
294
---
381
---
409
---
534
---
549
---
------------------

Set of min-weight "bridge" edges e = {110,137,141,164,}
Set of discarded edges d = {177,}

--------------------------------------------
Stage 7.
--------------------------------------------
Main cluster (C) contains the following vertices:null
Elementary Cluster (C1) contains the following vertices: Pori+Tampere+Turku+Helsinki+Kouvola+Kuopio

Priority Queue (Q)
------------------
---
381
---
409
---
534
---
549
---
------------------

Set of min-weight "bridge" edges e = {110,137,141,164,294}
Set of discarded edges d = {177,}

--------------------------------------------
Stage 8.
--------------------------------------------
Main cluster (C) contains the following vertices:null
Elementary Cluster (C1) contains the following vertices: Pori+Tampere+Turku+Helsinki+Kouvola+Kuopio

Priority Queue (Q)
------------------
---
409
---
534
---
549
---
------------------

Set of min-weight "bridge" edges e = {110,137,141,164,294,}
Set of discarded edges d = {177,381}

--------------------------------------------
Stage 9.
--------------------------------------------
Main cluster (C) contains the following vertices:null
Elementary Cluster (C1) contains the following vertices: Pori+Tampere+Turku+Helsinki+Kouvola+Kuopio

Priority Queue (Q)
------------------
---
534
---
549
---
------------------

Set of min-weight "bridge" edges e = {110,137,141,164,294}
Set of discarded edges d = {177,381,409}

--------------------------------------------
Stage 10.
--------------------------------------------
Main cluster (C) contains the following vertices:null
Elementary Cluster (C1) contains the following vertices: Pori+Tampere+Turku+Helsinki+Kouvola+Kuopio+Oulu

Priority Queue (Q)
------------------
---
549
---
------------------

Set of min-weight "bridge" edges e = {110,137,141,164,294,534,}
Set of discarded edges d = {177,381,409}

--------------------------------------------
Stage 11.
--------------------------------------------
Main cluster (C) contains the following vertices = C1
Elementary Cluster (C1) contains the following vertices: Pori+Tampere+Turku+Helsinki+Kouvola+Kuopio+Oulu+Ivalo

Priority Queue (Q)
------------------
null
------------------

Set of min-weight "bridge" edges e = {110,137,141,164,294,534,549}
Set of discarded edges d = {177,381,409}

Spanning Tree & Minimum Spanning Tree (MST)

Spanning Tree - A tree that contains every vertex of a connected graph G is referred to as a spanning tree. The below figure depicts the Finnish intercity roadways route as a sapnning tree.



Minimum Spanning Tree (MST) - the problem of computing a spanning tree with the smallest total weight is known as the Minimum Spanning Tree problem. The below figure depicts the MST that houses a collection of smallest weighted routes between the Finnish cities. Routes not taken into the MST are: 177,381,409.

Friday, March 27, 2009

Application of a Binary Tree

The binary tree are applicable in the below mentioned fields:
- Used in the compilers of high-level programming languages for intermediate representation
- Used as a searching technique
- Used in databases for storing data
- To solve arithmetic expressions

Exercise-Binary Tree Construction

Given data items: 17, 6, 3, 9 ,12, 15, 1, 18, 22
The resulting binary tree is:

What is a Binary Tree?

A binary tree embodies a finite set of data items that is either empty or partitioned into three disjoint subsets. The first part contains a single data item referred to as the root of the binary tree, other two data items are left and right subtrees. These data items is referred to as nodes of the binary tree.

From this figure we can portray the following:
A - Root node of the binary tree
A - Parent node of the nodes B & C
B & C - Children nodes of A and these nodes are in a level one more than the root node A
C - Right child node of A
B - Left child node of A
D & E - These node are without a child, so they are referred to as leafs or external nodes
B & C - These nodes have a child each, so they are referred to as internal nodes

Depth of this binary tree - The longest path from the root node A to any leaf node, for example D node. Therefore, the depth of this binary tree is two.
Indegree of a node - The number of edges merging into a node. For example, indegree of the node B is one i.e. one edge merges.
Outdegree of a node - The number of edges coming out from a node. For example, outdegree of the node A is two i.e. two edges comes out of this root node.

Concerning the binary trees, do refer to the following blog entries:
- Exercise-Binary Tree Construction
- Application of a Binary Tree

What is a Complete Binary Tree?

A strictly binary tree of depth 'd' in which all the leaves are at level 'd' i.e. there is non empty left or right subtrees and leaves are populated at the same level.
In a complete binary tree all the internal nodes have equal degree, which means that one node at the root level, two nodes at level 2, four nodes at level 3, eight nodes at level 4, etc, which the below figure represents:

Kruskal's algorithm for extracting Minimum Spanning Tree (MST)

This algorithm comes under the greedy method, which means that the objects are chosen to join a growing collection by iteratively picking an object that minimizes some cost function.

Now, taking account of the above mentioned greedy method, the Prim-Jarnik's algorithm is similar to the Dijkstra's shortest-path algorithm to grow the Minimum Spanning Tree (MST) from a single cluster starting with a randomly chosen single root vertex.

The Prim-Jarnik's algorithm can be broken down as follows:
Step 1: Define the starting "cloud" of vertices [C] by selecting some random vertex [v] initially
Step 2: Iteratively choose a min-weight edge [e=(v,u)], thereby connecting a vertex [v] in the cloud [C] to a vertex [u] outside of [C]. After choosing [e] the vertex [u] is brought into the cloud [C] continuously until a spanning tree emerges.


Pseudo-code of the Prim-Jarnik's algorithm
// Prim-Jarnik(G) [Time complexity = O(m * log * n)]
// Input: A weighted connected graph [G] with n number of vertices & m number of edges
// Output: A minimum spanning tree [T] for [G]

// Pick a root vertex [v] of [G]
D[v] <- 0

// All vertices connected to the root vertex, hence it necessary to define the cost function.
// The cost function "D[u] = +infinity" when vertex [u] doesn't connect to vertex [v].
for each vertex u != v do
D[u] <- +infinity

// Initialise the MST
T <- null 

// Initialise a priority queue Q with an entry ((u,null),D[u]) for each vertex [u],
// where (u,null) is the element & D[u] in the key
while Q is not empty do
(u,e) <- Q.removeMin()
Add vertex u and edge e to T

// Perform the relaxation procedure on the edge of the vertex [z] adjacent to [u]
For each vertex z adjacent to u such that z is in Q do
if W((u,z)) <>
D[z] <- W((u,z))
Change to (z,(u,z)) the element of vertex z in Q
Change to D[z] the key of vertex z in Q
return the tree T

Kruskal's algorithm for extracting Minimum Spanning Tree (MST)

This algorithm comes under the greedy method, which means that the objects are chosen to join a growing collection by iteratively picking an object that minimizes some cost function.

Now, taking account of the above mentioned greedy method, the Kruskal's algorithm with a graph considers the edges in order of their weights to grow the Minimum Spanning Tree (MST) in clusters.

The Kruskal's algorithm can be broken down as follows:
Step 1: In a given undirected graph [G] consider each vertex is in its own cluster all by itself i.e. if a [G] has four vertices then you have four cluster initially
Step 2: Look for each edges in turn, ordered by increasing weight i.e. arrange all the edges in ascending order
Step 3: Start with the smallest weighing edge. If this edge connects two different clusters then it is considered as the min-weight "bridge" edge [e] and added to the set of edges of the MST. In this initial stages, due to addition of [e] to the MST (i.e. the growth of the MST), the two clusters then are merged into a single cluster. However, if a given [e] connects to two vertices that are already in the identified cluster then this [e] is discarded.
Step 4: Now iteratively the algorithm adds enough [e] to a set of edges of spanning tree, it terminates and outputs this tree as the MST.


Pseudo-code of the Kruskal's algorithm
// Kruskal(G) [Time complexity = O(m * log * n)]
// Input: A simple connected weight graph [G] with [n] vertices and [m] edges
// Output: A minimum spanning tree [T] for [G]
for each vertex [v] in [G] do
Define an elementary cluster C(v) <- {v}

// Initialise a priority queue Q to contain all edges in [G], using the weights as keys
T <- null 
// The above [T] will ultimately contain the edges of the MST

while [T] has fewer then n-1 edges do
(u,v) <- Q.removeMin()
// Let C(v) be the cluster containing v, and let C(u) be the cluster containing u.
if C(v) != C(u) the
Add edges (v,u) to T
Merge C(v) & C(u)
// The above union of C(v) & C(u) yields a single cluster
return tree T
Example below shows the Kruskal's algorithm that extracts the MST from a spanning tree. This spanning tree depicts a subset of Finnish intercity roadways route:

--------------------------------------------
Stage 1.
--------------------------------------------
Main cluster (C) contains the following vertices: null
Elementary Cluster (C1) contains the following vertices: null

Priority Queue (Q)
------------------
----
110
----
137
---
141
---
164
---
177
---
294
---
381
---
409
---
534
---
549
---
------------------

Set of min-weight "bridge" edges e = {null}
Set of discarded edges d = {null}

--------------------------------------------
Stage 2.
--------------------------------------------
Main cluster (C) contains the following vertices:null
Elementary Cluster (C1) contains the following vertices: Pori+Tampere

Priority Queue (Q)
------------------
----
137
---
141
---
164
---
177
---
294
---
381
---
409
---
534
---
549
---
------------------

Set of min-weight "bridge" edges e = {110}
Set of discarded edges d = {null}

--------------------------------------------
Stage 3.
--------------------------------------------
Main cluster (C) contains the following vertices:null
Elementary Cluster (C1) contains the following vertices: Pori+Tampere
Elementary Cluster (C2) contains the following vertices: Helsinki+Kouvola

Priority Queue (Q)
------------------
---
141
---
164
---
177
---
294
---
381
---
409
---
534
---
549
---
------------------

Set of min-weight "bridge" edges e = {110,137,}
Set of discarded edges d = {null}

--------------------------------------------
Stage 4.
--------------------------------------------
Main cluster (C) contains the following vertices:null
Elementary Cluster (C1) contains the following vertices: Pori+Tampere+Turku
Elementary Cluster (C2) contains the following vertices: Helsinki+Kouvola

Priority Queue (Q)
------------------
---
164
---
177
---
294
---
381
---
409
---
534
---
549
---
------------------

Set of min-weight "bridge" edges e = {110,137,141}
Set of discarded edges d = {null}

--------------------------------------------
Stage 5.
--------------------------------------------
Main cluster (C) contains the following vertices:null
Elementary Cluster (C1) contains the following vertices: Pori+Tampere+Turku+Helsinki+Kouvola

Priority Queue (Q)
------------------
---
177
---
294
---
381
---
409
---
534
---
549
---
------------------

Set of min-weight "bridge" edges e = {110,137,141,164,}
Set of discarded edges d = {null}

--------------------------------------------
Stage 6.
--------------------------------------------
Main cluster (C) contains the following vertices:null
Elementary Cluster (C1) contains the following vertices: Pori+Tampere+Turku+Helsinki+Kouvola

Priority Queue (Q)
------------------
---
294
---
381
---
409
---
534
---
549
---
------------------

Set of min-weight "bridge" edges e = {110,137,141,164,}
Set of discarded edges d = {177,}

--------------------------------------------
Stage 7.
--------------------------------------------
Main cluster (C) contains the following vertices:null
Elementary Cluster (C1) contains the following vertices: Pori+Tampere+Turku+Helsinki+Kouvola+Kuopio

Priority Queue (Q)
------------------
---
381
---
409
---
534
---
549
---
------------------

Set of min-weight "bridge" edges e = {110,137,141,164,294}
Set of discarded edges d = {177,}

--------------------------------------------
Stage 8.
--------------------------------------------
Main cluster (C) contains the following vertices:null
Elementary Cluster (C1) contains the following vertices: Pori+Tampere+Turku+Helsinki+Kouvola+Kuopio

Priority Queue (Q)
------------------
---
409
---
534
---
549
---
------------------

Set of min-weight "bridge" edges e = {110,137,141,164,294,}
Set of discarded edges d = {177,381}

--------------------------------------------
Stage 9.
--------------------------------------------
Main cluster (C) contains the following vertices:null
Elementary Cluster (C1) contains the following vertices: Pori+Tampere+Turku+Helsinki+Kouvola+Kuopio

Priority Queue (Q)
------------------
---
534
---
549
---
------------------

Set of min-weight "bridge" edges e = {110,137,141,164,294}
Set of discarded edges d = {177,381,409}

--------------------------------------------
Stage 10.
--------------------------------------------
Main cluster (C) contains the following vertices:null
Elementary Cluster (C1) contains the following vertices: Pori+Tampere+Turku+Helsinki+Kouvola+Kuopio+Oulu

Priority Queue (Q)
------------------
---
549
---
------------------

Set of min-weight "bridge" edges e = {110,137,141,164,294,534,}
Set of discarded edges d = {177,381,409}

--------------------------------------------
Stage 11.
--------------------------------------------
Main cluster (C) contains the following vertices = C1
Elementary Cluster (C1) contains the following vertices: Pori+Tampere+Turku+Helsinki+Kouvola+Kuopio+Oulu+Ivalo

Priority Queue (Q)
------------------
null
------------------

Set of min-weight "bridge" edges e = {110,137,141,164,294,534,549}
Set of discarded edges d = {177,381,409}

Spanning Tree & Minimum Spanning Tree (MST)

Spanning Tree - A tree that contains every vertex of a connected graph G is referred to as a spanning tree. The below figure depicts the Finnish intercity roadways route as a sapnning tree.



Minimum Spanning Tree (MST) - the problem of computing a spanning tree with the smallest total weight is known as the Minimum Spanning Tree problem. The below figure depicts the MST that houses a collection of smallest weighted routes between the Finnish cities. Routes not taken into the MST are: 177,381,409.

Friday, March 27, 2009

Application of a Binary Tree

The binary tree are applicable in the below mentioned fields:
- Used in the compilers of high-level programming languages for intermediate representation
- Used as a searching technique
- Used in databases for storing data
- To solve arithmetic expressions

Exercise-Binary Tree Construction

Given data items: 17, 6, 3, 9 ,12, 15, 1, 18, 22
The resulting binary tree is:

What is a Binary Tree?

A binary tree embodies a finite set of data items that is either empty or partitioned into three disjoint subsets. The first part contains a single data item referred to as the root of the binary tree, other two data items are left and right subtrees. These data items is referred to as nodes of the binary tree.

From this figure we can portray the following:
A - Root node of the binary tree
A - Parent node of the nodes B & C
B & C - Children nodes of A and these nodes are in a level one more than the root node A
C - Right child node of A
B - Left child node of A
D & E - These node are without a child, so they are referred to as leafs or external nodes
B & C - These nodes have a child each, so they are referred to as internal nodes

Depth of this binary tree - The longest path from the root node A to any leaf node, for example D node. Therefore, the depth of this binary tree is two.
Indegree of a node - The number of edges merging into a node. For example, indegree of the node B is one i.e. one edge merges.
Outdegree of a node - The number of edges coming out from a node. For example, outdegree of the node A is two i.e. two edges comes out of this root node.

Concerning the binary trees, do refer to the following blog entries:
- Exercise-Binary Tree Construction
- Application of a Binary Tree

What is a Complete Binary Tree?

A strictly binary tree of depth 'd' in which all the leaves are at level 'd' i.e. there is non empty left or right subtrees and leaves are populated at the same level.
In a complete binary tree all the internal nodes have equal degree, which means that one node at the root level, two nodes at level 2, four nodes at level 3, eight nodes at level 4, etc, which the below figure represents:

Create a magazine article spread with InDesign

Adobe’s In Design may not be as widely used as Illustrator or Photoshop, but it is no less full of powerful tools for getting your design job done. It’s a publisher’s best friend – handy for print and layout design of all sorts.
For this beginner’s tutorial, we’re going to look at how to create a magazine in InDesign. I used an article published on 99designs as the source material to create the first spread of an article: Massive impact design: the world’s subway maps. Follow this step-by-step guide and try it out for yourself!

1. Create a new document

Create a new document by going to File > New > Document (Ctrl/Cmd + N), and change the following settings:

• Number of pages: 3
  • Make sure “Facing Pages” is clicked so that you will see a spread and not just single pages.
• Start page #: 2
• Page size: Letter
• Margins: Leave at 3
• Bleed: 1p0,
• Slug is optional, but it should be included if you want to leave notes in the file but not printed on the document itself.

2. Set up a grid layout

To create guides start by Layout > Create Guides…

Then set a grid by creating guides:

• Row number: 40
• Gutter: 0
• Columns number: 4
• Gutter: 1p0
• Fit guide to: margins
• Click preview to view the guides as you create them

And voila! Note that these settings are my own particular preference. When you’re creating your own document, it’s possible you’ll be provided with a grid that’s already been created for the publication. If not, you get to create one yourself!
Experiment with different combinations of rows, columns, and gutters to see what works best for you. And when you’re done, your document is ready for design.

3. Working with images

InDesign’s treatment of images is a bit different from Adobe’s other programs. While it can be difficult to adjust to at first, the program gives the designer a powerful set of tools that make it easy and flexible to work with type and imagery together in a layout.
Here are the basics:

To import an image select File > Place… (Ctrl/Cmd D)

In InDesign, the image and frame for the image can be edited separately. This gives you tons of flexibility when it comes to cropping images to fit your layout in the exact way you want to.
Start by setting the frame for where you want your image on the page. When you import an image, you’ll see a light blue line surrounding the picture. That’s the frame. You can shift the size of this frame while maintaining the original image’s aspect ratio by dragging the frame while holding down the shift button. Otherwise, you can drag the frame to fit the exact space you would like, and it will not distort the appearance original image.
Pro tip: Make sure that you include the bleed when sizing your frames, to make sure that your images will reach the edge of the page after the paper is cut in the printing process.

Once you’ve set the frame for your image, which I did by including the entire left page, you can adjust the size of your image to fill the frame. Access this by double clicking on your image, and a brown outline will appear, displaying the actual image’s edges.
The toolbar at the top of the image will display several ways to adjust the size of the image based on the frame:

1. Fill frame proportionally: Maintains the image’s proportions, but makes sure the entire frame is filled.
2. Fit content proportionally: Maintains image’s proportions while fitting the entire image in the frame, so the frame may not be completely filled.
3. Fit Content to frame: Skews the proportions of the image to make it fit into the frame.
4. Fit Frame to content: Skews the proportions of the frame to make it fit the image.
5. Center content: Centers the content within the frame.
6. This group aligns the image to the left, right, center, top, and bottom.

My preference is to manually adjust the size of the image to the frame to get it the exact way that I want it.

4. Working with title text

When your image is in place, it’s time to work with text.

Select the Type tool and drag a text box across the blank page.

Place the text box in the center of the page, so there will be white space above it but also room for the body text underneath. When you’re moving your images, violet colored guide lines will appear to indicate the center of the page, both horizontally and vertically.
For the font, I selected Helvetica Neue (to play off of the use of Helvetica in the map), size 100 for the word “Massive” and 56 for “Impact Design.” I then added the subtitle, in the same font for simplicity’s sake, in an even smaller size — 30, and all lower case letters to maintain a hierarchy with the title. Make sure you keep all text on the inside of the safety margins for the edges of the page (indicated in purple), to make sure the text won’t be cut off in the printing process.

5. Working with body text

In the next step, we pair the sans serifed bold title with a serifed font for the text. I selected Adobe Garamond Pro in size 12.

To the to right of the toolbar there is an “A” for character formatting and “P” for paragraph formatting. Click P to get the tools for paragraph formatting, then look half way in the middle of the toolbar for a button with three columns. This allows you to create automated columns in your text box. I chose two columns, and left the margins at 1p0. Then, you can use the Selection tool to adjust the box size for columns and fit your text appropriately.

6. Adding Color

Finally, let’s add a little bit of color to this page.

Use the Eyedropper tool in the table on the left to select color for the title, this color is R: 252 G:201 B:58

Next, use the rectangle tool that you’ll find in the toolbar, to create a horizontal green box around the subtitle — mirroring the effect of the subway lines. I made my line fit the grid — two rows wide.
In addition, at this final stage, I’ll tweak the spacing between the three elements by sight, using the grid to create balance between white space, text, and color.

6. Adding page numbers

There are ways to automate page numbers in InDesign, but for the use of this basic layout we’re just going to do it ourselves, by creating a text box with the name of the magazine and the page number. We skipped this on the left page of the spread, as it’s a completely image-based page and we don’t want to disrupt the image.

7. Exporting your final design

And we’re done! To preview what your work looks like without the guidelines and make sure it’s all on the up-and-up, head to the bottom left tool bar:

There are five different viewing options.

• Normal: This is what the document looks like with all of the rulers, guides, and other marks you need to create your design.
• Preview: Preview removes these marks, giving you a clean view.
• Bleed: Just like Preview, but includes the bleeds
• Slug: Same as Bleed, but includes the Slug as well
• Presentation: Presents your preview against a black background

Here’s the final image when exported:

The format you need to use to export your design will depend on the project, but you’ll probably need in .INDD or .PDF document. Here’s the dialog for exporting as a PDF:

To get here, hit File > Export (Ctrl/Cmd + E). In exporting a design, I keep it pretty simple, just making sure that I’m exporting the image as a high quality PDF and making sure that it’s exported as a spread and not just a page. do this by making sure the “Spreads” buttong under the heading “Pages” is clicked. You can also determine whether or not you are exporting the Marks and Bleeds, by heading to the “Marks and Bleeds” section on the side of the menu, and selecting which you want included or excluded.
And there you have it, a beginner’s guide to a selection of the most useful tools and how to create a magazine in InDesign. There’s a ton more that you can do with this program, so get experimenting!