Schematic map of Uttarakhand

This was a part of a 5 day workshop on geoMapping with Arun Ganesh(@planemad). It started with Introduction to maps and how do you go about making a map. I will talk about this in another post, combining my learning’s from the workshop and other resources that I have been looking at. This is all about “How do you make a schematic map”, the process and my experience with this exercise.

1. Pick a location

Decide a location for which you want to make a map about. It could be a country,state,city or any locality. I chose Uttarakhand, A state in north India because I spent maximum time of my childhood in this beautiful state. You could chose any place but it helps if you have a little knowledge about the place.

2.Select a theme

In this step we decide what do you want to show on the map, i.e what is the map about? I chose to show tourist places of Uttarakhand as it has a lot of hill stations and other tourist spots. While doing this you also might want to ask yourself these questions Who wants your map?, Where will it be seen? and What is its purpose? 

Simple map

Simple map

3.Collect Data

In this step one collects all possible data about the location that you chose, which is relevant to your theme. Again, it depends what you want to show through the map so answering previously mentioned questions helps.  I collected maps about the tourist places, railmap and roadmap in Uttarakhand.

uttaranchalrails     uttaranchalroads

uttranchal-tourist-map

4.Classify/group features and trace

Next step is to take these maps into Illustrator or any tool you prefer, overlay these maps on top of each other like layers. It looks pretty messed up but here is the place the classification and grouping of features helps. Trace the map features according to the classifications made by you. I started with tracing of state and district boundaries. Try to make it as simple as possible.

Layered maps

Layered maps

Sstate and District boundaries

State and District boundaries

5. Pick a Grid

Here we pick a grid on which we follow as a language in the entire process. It makes things look less cluttered and neat. Some of us were using 45 & 90 degree grid, to simplify the shapes.  It took me time to understand that schematic map is not the exact or accurate representation of maps, hence you can see the messed up geometry in the start. This is what it was looking like at this point.

Main highways

Main highways and roads

6. Simplify the geometry

The next few steps you just want to spend in simplifying the geometry more and more. Once you add more data layers it will become more complex and so it needs to be simplifies as much as possible. The next few steps show how I did that. And, I got the geometry simplified too! 🙂 Add icons, typography and the legend.

map31 map3

 

7.Upload it somewhere to share

We all, as a part of this workshop uploaded our maps to wikipedia. Link your map to an article on wikipedia and upload it there. I uploaded my map on the article about Uttarakhand in the Tourism section and Tourist places in Uttarakhand.

Final - Tourist  map of Uttarakhand

Final – Tourist map of Uttarakhand

This is what it finally looks like. Feel free to share.

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List of Work

 
GeoMapping Workshop

Indian rail map
Untitled

Schematic Map – Tourist map of Uttarakhand
map6

Other experiments with maps
pop_den1

UFO Sightings Visualization
Untitled

Short papers

S anand Workshop
chernoff1
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Ufo Sightings

Data Modelling

This exercise was taken by Sridhar Pabbisetty (VF) , where we decided on what main project are we going to work on for an interactive visualization. Each one in class acquired their data from some resources and tried to understand and build associations, dependencies between them. We tried to organize them in tables so data is logically stored and efficiently fetched through appropriate queries.

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This exercise helped us in taking some major decisions about our visualization. Also, it gave us a brief idea about how will things be carried on in later steps.

This is the basic list of deliverable that we prepared by the end of this exercise.

  • Theme for visualization
  • How data will be stored
  • Rough sketch/outline of visualization
  • Interaction
  • Query/Form variables
  • Choice of platform


Cleaning of data

I spent most of the initial time in cleaning of data. The data set had 60,000+ rows and was quite messed up. Values were not put into correct categories throughout the file.

Someone didn’t think that putting data  into correct fields was important. :p

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How should it look?

During this phase I was thinking about how the visualization should look and what all things i really want to focus on. Decisions taken at this state were mostly based on what i really wanted to show using this data.

The whole quest started with looking up for all possible links online about Ufo Sightings and trying to relate the data to it. I also saw a few visualizations related to the topic which will help me clear out my thoughts more.

This is the initial sketch of what i had in mind.

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Prototype

While the thinking and exploring was going on about the visualization i was also trying to learn Tilemill to make my visualization interactive. This is one of the initial experiments with it using 5000 rows of data.

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Problems realized

After having a look at just 5000 rows of sightings it was looking like a cluster of almost nothing but markers. One could hardly differentiate between two markers because of the closeness in the geotags.

After this, decision made was to create a density map for UFO Sightings considering in mind that ” Where Should you go if you want to see a UFO ?”.

Each cluster would be suggestive of what areas should you be visiting. So the final outcome focuses on this aspect.

Final Outcome

This is what it looks like finally.Online link to this is here.

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Feedback

Since this topic/theme was not that serious like other things that my friends were working on, I was suggested to make some sort of art installation/visualization to it which could be fun and interesting than trying to link or make sense out of this data.

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