The document provides a comprehensive overview of geocoding and its associated challenges in India. It discusses the processes of geocoding, types of spatial data, issues with addressing systems in developing nations, and specific challenges faced in India due to unstructured data and inconsistent geocoding practices. The document also includes examples of various address formats and highlights the importance of accurate geospatial data for effective geocoding.

1. The World of Geocoding and challenges in India
Dr. Nishant Sinha

2. Discussion
• Introduction to Geocoding
• All about spatial data and real world data
• Addressing system of developing nation
• Addressing system in India
• Data sources and standardization
• Data arrangement
• Geocoding
• Challenges
• Steps to overcome challenges
• Q & A

3. Introduction to Geocoding

4. What is Geocoding ?
Geocoding is the process of transforming a description of a location (such as a pair of
coordinates, an address, or a name of a place) to a location on the earth's surface
Source Definition Possible Problems
Environmental
Sciences Research
Institute (1999)
The process of matching tabular data that
contains location information such as street
addresses with realworld coordinates.
Limited to coordinate output
only.
Harvard University
(2008)
The assignment of a numeric code to a
geographical location.
Limited to numeric code output
only.
Statistics Canada (2008) The process of assigning geographic identifiers
(codes) to map features and data records.
Limited input range.
U.S. Environmental
Protection Agency
(2008)
The process of assigning latitude and longitude
to a point, based on street addresses, city, state
and USPS ZIP Code.
Limited to coordinate output
only.

5. What is Geocoding ?
Geocoding (verb) is the act of transforming
aspatial locationally descriptive text into a valid
spatial representation using a predefined process.
A geocoder (noun) is a set of inter-related
components in the form of operations, algorithms,
and data sources that work together to produce a
spatial representation for descriptive locational
references.
A geocode (noun) is a spatial representation of a
descriptive locational reference.
To geocode (verb) is to perform the process of
geocoding.

6. Geocoding ………
Some points to ponder…….
• Does geocoding refer to a specific
computational process of transforming
something into something else, or simply
the concept of a transformation?
• Is a geocode a real-world object, simply an
attribute of something else, or the process
itself?
• Is a geocoder the computer program that
performs calculations, a single component
of the process, or the human who makes
the decisions?

7. Spatial real world data

8. What is there in
Spatial data….
Location information
• The fundamental primitive is the
point, a 0-dimensional (0-D) object
that has a position in space but no
length.
• A line is a 1-D geographic object
having a length and is composed
of two or more 0-D point objects.
• A polygon is a geographic object
bounded by at least three 1-D line
objects or segments with the
requirement that they must start
and end at the same location (i.e.,
node)

9. Spatial Data
• Spatial data often referred to as layers
• Layers represent features on, above, or
below the surface of the earth
• Data layers are of 2 major types
• Vector data represent features as discrete
points, lines, and polygons.
• Raster data represent the landscape as a
rectangular matrix of square cells.
The Real World
Vector Data
Raster Data

10. Spatial Data
• Vector
• TAB Files
• Shape Files
• CAD (AutoCAD DXF & DWG)
• Raster Vector
• Grids
• Images
• Digital Elevation Models (DEMs)

11. Geocoding and Spatial Data
• Most geocoding applications work with
vector-based GIS data.
• The key aspects from a geocoding
perspective :
• Determine and record the locations of
these objects on the surface of the Earth,
and
• Calculate distance because many
geocoding algorithms rely on one or more
forms of linear interpolation.

12. Addressing system of developing nation

13. Addressing System
• Addresses are one of the fundamental means by which
people conceptualize location in the modern world
• Addresses are of 2 Types
• Relative input data
• Examples of these types of data include “Across the street from Togo’s”
and “The northeast corner of Vermont Avenue and 36th Place.”
• Absolute input data
• Example House no-xxxx, ABC Street, JJJJJ Locality, XYZ City, QQ
State, DEF Country, ###### Postal Code

14. Addresses
• Come in a variety of formats
• Address Components
• Address (house or building) number
• Prefix direction
• Prefix type
• Street name
• Street type
• Suffix direction
• Zone

15. What is street addressing?
• Street addressing is an exercise that makes it
possible to identify the location of a plot or
dwelling on the ground, that is, to “assign an
address” using a system of maps and signs that
give the numbers or names of streets and
buildings
• Street addressing provides an opportunity to
• Create a map of the city that can be used by different
municipal units
• Conduct a systematic survey that collects a
significant amount of information about the city and
its population, and
• Set up a database on the built environment

16. History of street addressing across some countries
• Before 1728, no street names were indicated in Paris,
except in very rare cases, such as “Rue Saint-
Dominique, formerly des Vaches” (1643)
• In Belgrade, changes to street names are frequent
• The name China itself (country of the center), which
makes reference to this principle, and in the name of
the capitals, Beijing (capital of the North) and Nanjing
(capital of the South). Names of the provinces are also
strongly influenced by these references to
geographical direction
• Aside from a few main thoroughfares, streets in Japan
do not have names. In fact, the city districts (ku) are
divided into neighborhoods (chome) that group
together several dozen houses and thus form a block.

17. Street Addressing System Some common Practice
• Sequential alternating numbering
systems
• Decametric numbering system
• Codification of intersections
• Combined addressing system
Segment Distance (m) No. left side No. right side
1 0–10 1 2
2 10–20 3 4
3 20–30 5 6
4 30–40 7 8
Sequential alternating
numbering systems
Codification of
intersections
Combined addressing
system

18. Addressing system in India

19. Addressing System in India
• Spatial data capturing based on demand rather
than homogeneous capture
• Streets with no names or unstructured addresses
• Absence of consistent and accurate dataset
throughout the area being geocoded
• Presence of slum-like areas that change frequently
and are not street addressable
• Non-existence of reference datasets or GIS data
infrastructure
• Lack of hierarchical data structure beyond tehsil
• Absence of standardized geocoding algorithm
• Non-existence of approach to validate assumptions
made in the geocoding algorithm

20. Sample Addresses varieties of India
Address with Person Name
c/o Yashwant S.Prabhu , 318, C - Wing, Suyog Co.Housing Society Ltd, T. P.S. Road & III Link Road, Vazira, Borivali, West Mumbai, Maharashtra, 400092
c/o Late Esmail Bagani, Y/2/122, Satghara Road, PO- Badartala, PS – Nadial, Kolkata, West Bengal, 700044
Address with Building names
White C/403, Aamrpali Appt, opp. GHB complex, Ankur Road, Ahmedabad, Gujarat, 380013
13/9, Daksha Bldg, Vallabh Baug Lane, Ghatkopar, Mumbai, Maharashtra, 400077
Address with House No
299/15, Padmavati Vikar Mandal, Shahibaug, Ahmedabad, Gujarat, 380001
NO88, Srinivasa Nagar, 2NS Main Road, Kolathur, Chennai, Tamil Nadu 600099
Address with Street Name
1304, Cornation Road, Bargarpet, Kolar, Bangalore, Karnataka, 560000
20K, Dhakuria Station Road, Dhakuria PS: Jadavpur, Kolkata, West Bengal, 700031
Address with POI
BMC Software, Next Muttha Chamber, Senapati Bapat Road, Pune, Maharashtra, 411016
Life Style International Pvt. Ltd., Near Payal Cinema Complex, Gurgaon, Haryana, 122001

21. India Dynamics
• 35 States & UTs, ~650 Districts , ~6000 Tehsil/Town, more than
30k postcodes
• 21 regional , two official (Hindi & English) languages
• 3.2m sq KM Area, 1.2 billion population
• Administrative Hierarchy
• State >>District>>Tehsil/Town>> Ward>>Locality/Village>>Sub
locality>>Block/Pocket
• Addressing Pattern
• Near Govt. Hopital, Zirapur , District Rajgarh (MP)
• 351 Ground Floor , Shakti khand 3 , Near St. Teresa School ,
Indirapuram, Ghaziabad 201010 U.P.
• 9 Mansarovar Colony Opp. 3/686, Kala kuan Housing Board Alwar
301001
• 9/19/98/19-D Flat No. # 303 Hitech City Madhapur Hyderabad
• 176 Devi Nagar New Sanganer Road Sodala , Jaipur Rajasthan
Indian Postal Code

22. The History of Postal System in India
• Long colonial realm – British, Mughal,
Portuguese…….
• Britain's involvement in the postal services of
India began in the eighteenth century
• Warren Hastings (Governor General of
British India from 1773-1784) opened the
posts to the public in March 1774
• Main purpose of the postal system had been
to serve the commercial interests of the East
India Company and to serve Govt. orders

23. The History of Postal System in India

24. The History of Postal System in India

25. Data sources and standardization

26. India Geocoding Data
27
Streets
• NH
• SH
• Local roads
Place of
Interest
• Banks
• Retails
• Hospital
• Other landmarks
Administrative
• State
• District
• Town
• Locality
• Sub locality
Geography
• Block
• Locality
• Town
• Postcode
Address Point
• House no
• Building name

27. Reference datasets
• The reference dataset is the underlying
geographic database containing
geographic features that the geocoder can
use to generate a geographic output.
• This dataset stores all of the information the
geocoder knows about the world and
provides the base data from which the
geocoder calculates, derives, or obtains
geocodes. Interpolation algorithms
Type Example
Vector line file U.S. Census Bureau’s TIGER/Line (United
States Census Bureau 2008c)
Vector polygon file Los Angeles (LA) County Assessor Parcel
Data (Los Angeles County Assessor 2008)
Vector point file Australian Geocoded National Address File
(G-NAF) (Paull 2003)

28. Reference dataset types
• Linear-Based Reference Datasets
• Roads, Ferries
• Polygon-Based Reference datasets
• Administrative Boundaries, Postal Codes
• Point-Based Reference Datasets
• POIs
Source Description Coverage Cost
Tele Atlas
(2008c),
NAVTEQ
(2008)
Building footprints, parcel footprints Worldwide,
but sparse
Expensive
County or
municipal
Assessors
Building footprints, parcel footprints U.S., but
sparse
Relatively
inexpensive but
varies
U.S. Census
Bureau
Census Block Groups, Census
Tracts, ZCTA, MCD, MSA,
Counties, States
U.S. Free
Name Description Coverage
U.S. Census Bureau’s TIGER/Line files (United
States Census Bureau 2008c)
Street centerlines U.S.
NAVTEQ Streets (NAVTEQ 2008) Street centerlines Worldwide
Tele Atlas Dynamap, MultiNet (Tele Atlas
2008a, c)
Street centerlines Worldwide
Supplier Product Description Coverage
Government GeoNames (United
States National
Geospatial-Intelligence
Agency 2008)
Gazetteer of geographic
features
World,
excepting
U.S.
Academia Alexandria Digital
Library (2008)
Gazetteer of geographic
features
World

29. The geocoding algorithm
• Geocoding Algorithm performs two basic tasks
• Feature matching,
• Feature interpolation,

30. Input data processing
Address normalization
• Address normalization organizes and
cleans input data to increase its
efficiency for use and sharing
Address standardization
• Address standardization converts an
address from one normalized format into
another. It is closely linked to
normalization and is heavily influenced
by the performance of the normalization
process.
Sample Address
3620 South Vermont Avenue, Unit 444, Los Angeles, CA 90089-0255
3620 S Vermont Ave, #444, Los Angeles, CA 90089-0255
3620 S Vermont Ave, 444, Los Angeles, 90089-0255
3620 Vermont, Los Angeles, CA 90089

31. Output data
• The last component of the geocoder is the actual
output data, which are the valid spatial
representations derived from features in the
reference dataset.
• Data can have many different forms and formats, but
each must contain some type of valid spatial
attribute.
• The most common format of output is points
described with geographic coordinates (latitude,
longitude).
• Alternate forms can include multi-point
representations such as polylines or polygons.
• These geocoder outputs, while in the same format
and produced through the same process, do not
represent data at the same geographic resolution
and must be differentiated.

32. Feature matching - Algorithm
• The matching algorithms are
• Noninteractive matching algorithms (i.e., they are
automated and the user is not directly involved).
• Interactive matching algorithms
• Classifications of matching algorithms
• Two main categories:
• Deterministic
• Probabilistic

33. Deterministic matching
• Ease of implementation
• These algorithms are created by
defining a series of rules and a
sequential order in which they
should be applied. Like- “Match all
attributes of the input address to
the corresponding attributes of the
reference feature.”
• Attribute relaxation
• Attribute relaxation, the process of
easing the requirement that all street
address attributes must exactly
match a feature in the reference data
source to obtain a matching street
feature, often is applied to create
these less restrictive rules.
Preferred attribute relaxation order with resulting ambiguity, relative magnitudes of ambiguity and
spatial error, and worst-case resolution, passes 1 – 4
Relaxed
Attribute
Ambiguity
Relative Exponent and
Magnitude of Ambiguity
Relative
Magnitude of
Spatial Error
Worst-
Case
Resolutio
n
none none (0) none certainty of
address location
single
address
location
number multiple
houses on
single street
(0) # houses on street length of street single
street
pre single
house on
multiple
streets
(1) # streets with same name and
different pre
bounding area of
locations
containing same
number house on
all streets with the
same name
USPS
ZIP
Codepost (1) # streets with same name and
different post
type (1) # streets with same name and
different type
number,
pre
multiple
houses on
multiple
streets
(2) # houses on street * # streets
with same name and different pre
bounding area of
all
streets with the
same
name
number,
type
(2) # houses on street * # streets
with same name and different type
number,
post
(2) # houses on street * # streets
with same name and different post

34. Probabilistic matching
• Probabilistic matching has its roots in the
fields of probability and decision theory
• Employed in geocoding processes since the
outset (e.g., O’Reagan and Saalfeld 1987,
Jaro 1989).
• The exact implementation details can be
quite messy and mathematically
complicated, but the concept in general is
quite simple.

35. Attribute weighting
• Attribute weighting is a form of
probabilistic feature matching in which
probability based values are associated
with each attribute, and either subtract
from or add to the composite score for
the feature as a whole.

36. String comparison algorithms
• Character-level equivalence,
• Essence-level equivalence
• This allows for minor misspellings in the input address to be handled,
returning reference features that “closely match” what the input may have
“intended.”.
• Word stemming
• Phonetic algorithms or the Soundex Algorithm

37. Soundex Algorithm
• It has existed since the late 1800s and originally was
used by the U.S. Census Bureau.
• The algorithm is very simple and consists of the
following steps:
• Keep the first letter of the string
• Remove all vowels and the letters y, h, and w,
unless they are the first letter
• Replace all letters after the first with numbers
based on a known table
• Remove any numbers which are repeated in a row
• Return the first four characters, padded on the right
with zeros if there are less than four
Original Porter Stemmed Soundex
Running Ridge run ridg R552 R320
Runs Ridge run ridg R520 R320
Hawthorne
Street
hawthorn street H650 S363
Heatherann
Street
heatherann street H650 S363

38. Challenges of India Geocoding

39. Geocoding Challenges
• Unavailability of geospatial data
• Data if available is
• Unstructured
• Incomplete
• Inaccurate
• Lack precision
• Does not have official name or region
developed because of anthropogenic
pressure
• “General" geocode no longer sufficient; the
"most accurate" geocode required
• Inconsistent use of base maps and geocoding
services within and across programs and
agencies
Data
Inconsistency
Erroneous
data
Frequent data
changes
requests
Coverage
New data and
modified data
in every
vintages

40. Some Samples of real postal deliveries

41. Classes of geocoding failures with an example of true address “Maulsari BnB, 142 Sunder Nagar, Near Delhi Public School, New Delhi, Delhi 110003, India”
Class Geocoded Problem Example
1 No Failed to geocode because the input data are incorrect. Maulsari BnB, 142 Sunder Nagar, New Delhi, 110003, India
2 No Failed to geocode because the input data are incomplete. Maulsari BnB, Sunder Nagar, Near Delhi Public School, New Delhi, 110003, India
3 No Failed to geocode because the reference data are incorrect. 140 Sunder Nagar, New Delhi, Delhi 110003, India
4 No Failed to geocode because the reference data are incomplete. Street segment does not exist in reference data
5 No Failed to geocode because the reference data are temporally incompatible. Street segment name has not been updated in the reference data
6 No Failed to geocode because of combination of one or more of 1-5.
Maulsari BnB, 142 Sunder Nagar, Near Delhi Public School, New Delhi, Delhi 110003, India where
the reference data has not been updated to include Near Delhi Public School, New Delhi, Delhi
address segment
7 Yes Geocoded to incorrect location because the input data are incorrect.
Maulsari BnB, 142 Sunder Nagar, Near Delhi Public School, New Delhi, Delhi 110003, India was
(incorrectly) relaxed and matched to Delhi Public School, New Delhi, 110003, India
8 Yes Geocoded to incorrect location because the input data are incomplete.
Maulsari BnB, 142 Sunder Nagar, Near Delhi Public School, New Delhi, Delhi 110003, India was
arbitrarily (incorrectly) assigned to 145 Sunder Nagar, New Delhi, Delhi 110003, India
9 Yes Geocoded to incorrect location because the reference data are incorrect. Sunder Nagar, New Delhi, Delhi 110003, India
10 Yes Geocoded to incorrect location because the reference data are incomplete. Street segment geometry is generalized straight line when the real street is extremely curvy
11 Yes Geocoded to incorrect location because of interpolation error. Interpolation (incorrectly) assumes equal distribution of properties along street segment
12 Yes Geocoded to incorrect location because of drop back error. Drop back placement (incorrectly) assumes a constant distance and direction
13 Yes
Geocoded to incorrect location because of combination of one or more of 7-
12.
The address range for 140-150 is reversed to 150-140 and dropback of length 0 is used

42. Geocoding
Impacts
A good geocoder vs A Bad Geocoder

43. Data Issues

44. Steps to address challenges

45. Overcome the challenge
• Standardization of addresses by governing bodies
• Integrating addressing data models from variety of addressing system to
develop region specific data models
• Utilizing multiple data sources for data completeness
• Inclusion of local landmarks in geocode process to they form integral part
of Indian address system
• Embed locational awareness and intelligence within geocoding data
models
• Multi Lingual phonetic support
• Large test set to address different kinds of geocoder irregularities
• Validation methodology to confirm geocode results
• Changes in map policies for consistent capturing
• Homogeneous information dissemination by governing bodies in regard to
spatial data
• Standards protocols developed for input/reference data correction
• Address normalization
• Address standardization

46. Features to address challenges
• Sub locality Features
• Inclusion of Local Landmarks
• POI Level Geocoding
• Bank Dictionaries
• Focus on Local Geography and Social Settings

47. ……. Questions?