This document describes a study that used GIS techniques to analyze the density and suitable locations for new ATM centers in Margao city, Goa, India. Primary and secondary data on existing ATM locations, customer details, land use/cover, and roads were collected and analyzed in GIS. Density analysis identified areas of very high to very low ATM density. Site suitability analysis using weighted overlay considered factors like land use, road access, density patterns, and customer data to identify optimal new sites. The findings suggest locations like Doverlim, PowerHouse, and Fatorda have potential for new ATM centers to better serve areas with many customers and residents but low existing ATM access.

1. BY
MR. SUYOG PRAMOD PATWARDHAN
MR. PRASAD VIVEK GANDHI
UNDER THE GUIDENCE OF
Mr. Vishal. R. Malave
ASSISTANT PROFESSOR
IN
POST GRADUATE DEPARTMENT OF GEOINFORMATICS
Paravtibai Chowgule College Margao, Goa

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Introduction
Aims and objective
Data base and Methodology
Study region
Limitations
Density analysis of ATM centers
Site suitability analysis
Findings
Conclusion
references

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GIS plays vital role in decision making process
Location convenience is very important in the service
sector.
Time, cost of transport, convenient place, service
provided by consumers, suitability of sites etc. are crucial
factors in service sector.
Suitability analysis used to give best sites for new ATM
sites
Margao is commercial capital of Goa.
Density of existing ATM centers and new sites for
proposing ATM mapped.

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To assess the density of ATM centers in Margao
city
To give site suitability for new ATM center with
the help of GIS

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Data based on primary and secondary form
Primary data collected from GPS points of all
ATM centers, customer details, card holders of
each banks
Secondary data based on satellite images,
Toposheet, research articles and magazines etc

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GPS data points imported to GIS software
Sample survey methods for each banks using
questioner format. Questions such as
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Number of customer
Number of Account Holder
Number of Account Holder with ATM
Approximately percentage of Card holders of that area

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Vector operations are as follows used in this work
Digitization
of land use
and land
cover
Prepare Land
use and land
cover Map
Import all
GPS data
Digitization
of Road and
Settlement
Prepare
Exisiting ATM
centers map
Mosaic and
Georeference
of satellite
image
Clip image
with village
boundery
Final map of
ATM, Roads,
Settlement
and Land use
and Cover
Georeference
and Digitizing
ward boundry

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Raster operation for site suitability analysis are
as follows
Extract by
Mask of
satellite
Image
Weighted
Overlay
analysis of
Slope, ATM,
Road and
Landuse
weighted
Overlay
Index
Multple
Ring Buffer
of ATM
Reclassify
of All
raster
Layer
Selecting
optimum
New sites
For ATM
Kernel
Density
Estimation
Euclidean
distance of
Road
Final
output
Map

9. Introduction to Margao
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Commercial Capital of goa
Covering nearly 24 sq.km area
More service sector
Nearness to tourist places, better
transport and communication
facilities creates scope for
banking activities
Nearly 25-30 banks having 52
ATM centers
Market area having more density
of ATM centers

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Difficult to get customer data from banks
Very few works done in India
2011 census data not yet published thus used
2001 census data for demographic factors
Each banks have different policies to construct
the New ATM

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Density of each ATM points
calculate
Kernel density estimation
used
calculates the density of
features in a neighborhood
around those features.
To estimate density categories
given such as
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Very High Density
High Density
Medium Density
Low Density
Very Low Density

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Distance from first class to second class is nearly
300-400Mt
Market area and KTC area shows highest density
and power house , Dowerlim, Fatorda shows the
lowest density

14. Number of Customer
30000
25000
20000
15000
10000
5000
0

15. Percentage of customer using ATM
120
% of ATM holders
100
80
60
40
20
0

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Site suitability analysis used to give new sites for the
ATM centers
For site suitability following methods are used
 Multiple ring buffer
 Reclassification
 Slope
 Distance
 Density
 Weighted overlay
 Conditional operators using CON
 Optimal site selection from settlement and road buffer

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The purpose of reclassification is to create new
raster layer by changing the attributes value of
the cell of the input layer.
This usually takes one of the following forms that
used either logical or arithmetic operators
Ascending the values to classes or range of old
value with the purpose of reducing the number of
the classes in the original input layer or to group
value into categories in a new classification.

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Weighted Overlay is a technique for applying a common
measurement scale of values to diverse and dissimilar
inputs to create an integrated analysis.
Geographic problems often require the analysis of many
different factors.
For instance, choosing the site for a new housing
development means assessing such things as land
cost, proximity to existing services, slope, and flood
frequency.
Within a single raster layer, you must usually prioritize
values.

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For example, a value of 1 represents slopes of 0 to 5 degrees, a
value of 2 represents slopes of 5 to 10 degrees, and a value of 3
represents slopes of 10 to 15 degrees.
If slope is a criteria in finding a new site, for example, and your
evaluation scale is from 1 to 9 by 1, you might give a scale value
of 9 to the input value of 1 (the most suitable areas with least
steep slopes), a scale value of 6 to the input value of 2 (the
second most suitable slopes), and a scale value of 3 to the input
value of 3 (the least suitable, steepest slopes).
If it was decided that slopes greater than 15 degrees would not
be considered, all input values greater than 3 would be assigned
a scale value of restricted to exclude them.

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Site selection based on following criteria
 Influence of Land use and Land cover such as Barren
land, Settlement, Open Land etc
 Distance from the Road ( 30-50 Mt)
 Density pattern of existing ATM
 Settlement
 Number of bank customer and Number of card
holders
 Buffering from settlement and intersecting Road

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With the help of site suitability analysis we can give best
suitable sites for recreational sites
SBI, HDFC, ICICI, kotak mahindra, union bank having
highest number of ATM
BOI having more customer nearly 20000-30000 in
Fatorda, Aquem area but no ATM centers
BOM and IDBI banks also having low frequency of ATM
Doverlim and Power House ,ravanpond, Pajifond area
having very low frequency of ATM centers though this
area having highest population

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Location convenience is an important factor
when customers select a financial institution.
Doverlim, PowerHouse, Sonsodo, Gogol, Fatorda
area have potential for constructing the new
sites for the ATM centers
BOI, BOM, IDBI banks have great potential to
settled the New ATM centers in
Gogol, Fatorda, Powerhouse and Dowerlim area
because of highest number of customer and
population

29. 
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BooksGeographic Information Systems and Science by Longely Paul A, Goodchild Mike
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Websiteswww.anastasia-fp6.org/.../BNSC%20presentations%20-%20C%20Swiftbr...
https://www.tenders.gov.au/?category...closed...ATM.
ec.europa.eu/transport/.../2012_10_23_atm_master_plan_ed2oct2012.pdf
www.esri.com/industries/banking
www.instantsiteintelligence.com/.../WhitePaper-MarketForte-GISinBanki
www.cjrs-rcsr.org/archives/24-3/macdonald.pdf
www.pbinsight.com/files/resource-library/resource.../yankee-group.pdf
www.saudigis.org/.../SaudiGISArchive/2ndGIS/.../15_E_BilalFarhan_US...
financialservices.gov.in/GIS/Usermanual.pdf
www.gisdevelopment.net/application/business/ma03075pf.htm

30. THNKING YOU
ANY ????