I presented this deck for Paper: Map based interaction in Mobile Phones, published at India HCI and IDID 2010 conference. This research was done during my Master's of Interaction Design 2006-08 at IDC, IIT Boambay.

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Map-based interaction for inputting
in Mobile phones
Atish Patel I Project II I Interaction Design I M. Des. 2006-08 I IDC, IIT Bombay

2. atishpatel06@gmail.com
1,112m
people
population of india

3. atishpatel06@gmail.com
1,112m
people
population of india
468m
illiterate

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1,112m
people
population of india
303m
semi-illiterate
468m
illiterate

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1,112m
people
population of india
303m
semi-illiterate
76% +
below primary
education 468m
illiterate

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471m
mobile phone

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471m
mobile phone
60m
internet

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18m
rail travel

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18m
rail travel
0.22m
e/i - ticket

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increase net ticketing

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provide usable
mobile-ticketing

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focus on semi-literates
and illiterates

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problems ?

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problems ?
Inputting Source & Destination Station
why?

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problems ?
Text input

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problems ?
Text input
1. ~8000 station in IR, 3467 + for booking
2. ~76% of population semi-literate + Illiterate
3. People not mobile savvy, no SMS, no Contact Book
4. ‘BAN’ gives 36 station in filtered list
5. Station name more than 10 letters; ‘Vishakapatnam Junction’
6. ‘Anand Nagar - Gujarat’ … ‘Anand Vihar - West Bengal’

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problems ?
Text input
Audio input

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problems ?
Text input
Audio input
1. 22 major languages, 1600 dialects
2. Great regional influence; ‘Mumbai - Bambai’ … ‘Vadodara - Baroda’
3. Pronunciation varies drastically from person to person
4. Audio based IVRS not liked; unavoidable options, mistakes

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proposed solution

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proposed solution:
Map-based interaction
for inputting
Source & Destination

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proposed solution:
Map-based interaction
for inputting
Source & Destination
Map of India divided into 3 X 3 grid,
mapped onto number pad of mobile
phone

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working of map-based solution:

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working of map-based solution:

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working of map-based solution:

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working of map-based solution:

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working of map-based solution:

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working of map-based solution:
1. Normally 4 levels of zoom
2. Total stations that can be catered > 5000
3. If density of stations high, 5 zoom & if less, 3 zoom levels

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user study:

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user study:
Found,
1. People travel yearly to their native places
2. Singles / Workers between home & work town
3. Few travel to new tourist or unknown places

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user study:
Found,
1. People travel yearly to their native places
2. Singles / Workers between home & work town
3. Few travel to new tourist or unknown places
Also, stated;
“We try to find or ask some information if we
have to go to a new place; like which state,
nearest junction station, stations to have
famous food, etc.”
“We have rough idea of the location of our
native place and if we are shown map, we will
be able to figure out where exactly it is.”

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user testing:
with paper map: 15 people

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user testing:
with paper map: 15 people
find native place, home, work town station

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user testing:
with paper map: 15 people
find native place, home, work town station

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user testing:
with paper map: 15 people
find native place, home, work town station
rarely or once visited station

35. atishpatel06@gmail.com
user testing:
with paper map: 15 people
find native place, home, work town station
rarely or once visited station

36. atishpatel06@gmail.com
user testing:
with paper map: 15 people
find native place, home, work town station
rarely or once visited station
never visited station, with some hints

37. atishpatel06@gmail.com
user testing:
with paper map: 15 people
find native place, home, work town station
rarely or once visited station
never visited station, with some hints

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user testing:
with Nokia 6300 prototype
Semi-literate people could not create or write
name of their home town stations in English or
native language; but, they could easily recognize
their already written station name.
Also, this map based navigation was little difficult
to understand at first go. But, when the users
reached 1st or 2nd zoom in level, then the
navigation model was completely clear to them
and they started using it confidently.

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user testing:
map-based vs textual method:

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user testing:
map-based vs textual method:
5 illiterates, 10 semi-literates, 5 literates

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user testing:
map-based vs textual method:
5 illiterates, 10 semi-literates, 5 literates
first attempt

42. atishpatel06@gmail.com
user testing:
map-based vs textual method:
5 illiterates, 10 semi-literates, 5 literates
first attempt

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user testing:
map-based vs textual method:
5 illiterates, 10 semi-literates, 5 literates
first attempt
second attempt

44. atishpatel06@gmail.com
user testing:
map-based vs textual method:
5 illiterates, 10 semi-literates, 5 literates
first attempt
second attempt

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user testing:
map-based vs textual method:
Non-mobile phone savvy & semi-literate people, very
strongly preferred map-based method over text
inputting.
This was because of ease in understanding map-based
inputting compared to three tap textual input model.

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refining design:
solve confusion

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refining design:
solve confusion

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refining design:
solve confusion
20px

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refining design:
solve confusion, overload

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refining design:
solve confusion, overload
Load Information on map step by step:
1st level - States demarcated , Capital cities and only Trunk routes
2nd level - Major stations and Other routes
3rd level - All stations with major stations bold
4th level - Stations present in grid in form of list

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other areas

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other areas
Health Care Sector

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other areas
Health Care Sector
Governance

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other areas
Health Care Sector
Governance
Farming, etc.

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thank you 
Prof. Anirudha Joshi for guidance
Prof. Athavankar, Prof. Sreekumar, Prof. Ravi Poovaiah for inputs
Friends/Batch-mates of ACE and IDC
Users and Participants of User Studies
Images: www.flickr.com, www.google.com , www.wikipedia.com

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