A GIS Based Approach to Manage Spatial Distribution and Location of Financial Services: A Case Study of ATM Services

Transcription

1 Jurnal f Bangladesh Institute f Planners ISSN Vl. 9, 2016 (Printed in April 2018), pp , Bangladesh Institute f Planners A GIS Based Apprach t Manage Spatial Distributin and Lcatin f Financial Services: A Case Study f ATM Services Md. Sadat Khan * Nurshad Yesmin ** Abstract Lcatin cnvenience is an imprtant factr when custmers select a financial institutin. T stay cmpetitive, banks usually attempt t increase cnvenience (be as clse as pssible t custmers) by expanding their branch r ATM netwrks. Using GIS in the arena f finance can be very supprtive and cst effective t facilitate lcatin based decisins fr financial institutins. Fr business expansin planning, banks need lcatin-relevant data, and cst effective site analysis t select a new bank branch r ATM lcatin cnfidently and reliably in a shrten-time cycle. When banks plan t pen new branches, they need t cnsider the data such as the cncentratin f cmmercial areas, traffic patterns, wrkplaces r living places f custmers whse demgraphics and purchase behavir match banks target custmer prfiles. In this research, GIS-based apprach has been develped t identify suitable lcatin fr an ATM netwrk in Perspective f Bangladesh. The newly established banks thse have nt set up ATM netwrk yet r have very small scale ATM services netwrk can expand their banking services thrugh acquiring partners frm existing ATM netwrk f ther Banks and simultaneusly can cnstruct new ATMs in the mst suitable unserved areas. This is hw these banks can expand their ATM netwrk mre rapidly with a less establishment cst. Als banks, thse have large ATM netwrk with sme unserved areas, can expand their existing netwrk by reaching t the custmers cnvenience lcatins. The paper recmmends that the analysis using GIS t find mst suitable lcatins fr ATM is better applicable fr ther cities and twns. Intrductin Lcatin cnvenience is an imprtant factr when custmers select a financial institutin (Mylnakis, et al. 1998; Driscll 1999). A custmer may find a bank cnvenient, if it has a branch r an Autmated Teller Machine (ATM) near his / her residence r wrkplace. T stay cmpetitive, banks usually attempt t increase cnvenience (be as clse as pssible t custmers) by expanding their bank and / r ATM netwrks. Basically, such expansins culd be dne in at least tw ways (Birkin, et al. 2002): by building branches/ installing ATMs in new lcatins r by acquiring an existing (e.g. cmpetitr, partner, etc.) netwrk. The frmer ptin is likely t be expensive and time cnsuming, s many banks resrt t acquiring r partnering already established ATM and / r branch netwrks (Farhan, 2007). Gegraphic Infrmatin System (GIS) is widely used and very helpful tl fr decisin making. In particular, if it invlves making a decisin related t lcatin, it requires GIS. GIS makes lcatinal analysis very easy. It makes the analysis simple and precise if the inputs are crrect (Khan, 2013). Using GIS in the arena f finance can be very * Assistant Directr (Urban Planning), RMSU, LGED, Chittagng ** Lecturer, Faculty f Business Administratin, USTC, Chittagng

2 126 Jurnal f Bangladesh Institute f Planners, Vl. 9, 2016 supprtive and cst effective t facilitate lcatin based decisins fr financial institutins. Objectives and Methdlgy The main bjective f this research is t determine the existing lcatin f ATM services f all banks and find ut the service area f ATMs using GIS analysis. It is necessary t understand the cncepts f GIS, Banking Services, ATM Service, and Lcatin Analysis. Different text and dcument related t GIS and banking issue help t develp the idea f it. An elabrate literature survey was carried ut t have a basic understanding n the relatin between the uses f GIS technlgy and Banking service sectr issue. This includes thesis papers, related bks, seminar papers, articles, and jurnals as the imprtant surces f literature review. A selected prtin f Old Dhaka City Crpratin (21 full wards and 1 partial ward) area has been selected t carry ut this research. A significant prtin f greater DMDP area within the study area has already develped with significant number f ATM bths f different banks at different lcatins. In this research, an extensive GIS database f the selected area has been used t identify existing physical features pattern and land uses f the area. GIS database, which has been used in this research, was prepared by RAJUK under Detail Area Plan (DAP) fr DMDP area (590 square miles). Identificatin f existing lcatins f all ATMs services can be dne by using GPS and by using high regulatin satellite image. In this research, satellite based image has used t identify the lcatin f all ATMs within the study area due t lack f time and financial cnstrain. Questiner survey has been cnducted t identify the mst preferable service area f an ATM. Fr questinnaire survey, sample has been cllected thrugh randm sampling methd. And ttal 30 samples have been cllected frm ATM users within the study area t satisfy the central limit therem (CLT). The cllected data frm questinnaire survey has been analyzed t explre mst preferable service area f an ATM. Statistical sftware (SPSS and MS Excel) have been used t make quantitative analysis n cllected data frm questinnaire survey. Different Spatial data, like vectr data and raster data have been analyzed thrugh develping a GIS Mdel. Finally, sme practical implicatins have been shwn. Applicatin f GIS in Bank Industry There have been s many ways t define GIS. An earlier definitin f GIS is "a special case f infrmatin systems where the database cnsists f bservatins n spatially distributed features, activities, r events, which are definable in space as pints, lines r areas. A GIS manipulates data abut these pints, lines, and areas t retrieve data fr in particular queries and analyses" (Dueker, 1979). GIS prvides acuminus insight f financial services business t knw custmers purchasing habits and demands, s that it fixes n the target custmers, the target market and the distributin f advertising resurces, afterward prvides custmers with value-added prducts and services. Gegraphic mdels integrate with banking mdels can ffer tangible benefits t the banking sectr. Adding spatial lcatins t bank s custmer database via gegraphical tls can btain answers t cmpete effectively. Thus GIS is becming a critical tl in tday s banking envirnment (Fu, 2007). The imprtance f lcatin cannt be ignred as argued:

3 A GIS Based Apprach t Manage Spatial Distributin and Lcatin f Financial Services 127 "Yu can be the best retailer in the wrld, but if yu set up yur shp in the wrng place, yu will never d much business. If yu perate frm the wrng prperties, yu start with yur hands tied behind yur back?-yu shuld always g where yur custmer is" By Gerge Davies (Davies, 1991). GIS can help t understand hw a ptential new branch shuld be perfrmed. Much infrmatin fr bank seeking lcatin are needed, fr example land csts, building availability and suitability, cnstructin csts, lcal and state taxes, lcal and state develpment incentives, availability and cst f energy, transprtatin csts t custmers, the lcatin and market areas f cmpetitrs, and the availability f ther infrastructure such as telecmmunicatins, sewer, and water, even the quality f life (Jafrullah, et al. 2003). Using GIS, this varius infrmatin can be identified and integrated easily. In additin, this infrmatin can als be displayed in map frmats t demnstrate that the sites meet specified criteria. Fr prviding an ATM machine t an area, banks need t think different spatial issues. Fr example, if establishing an ATM in a residential area, then the density f demgraphy shuld be taken int accunt; r establishing an ATM in a cmmercial area, then the cncentratin f cmmercial land use shuld be cnsidered. Als banks have t attach imprtance t cncentratin f debit/credit card hlders f an area, rute fr taking mney t the ATM etc. These all can be dne with GIS withut dubt. Accrding t Thulasi (2007), banks shuld take int accunt different spatial cmpnents t establish a new bank branch, fr example Land value, r sci-ecnmic cnditin t make a general idea f the area fr establishing branch. The cmmercial land use f the area. Lcatins f the residential area and business area. The rad netwrk. Banks can analyze the perfrmance by using GIS. Spatial cmpnents can ffer greater advantages fr mnitring the branches perfrmance. The spatial distributin f the custmers can help bank t draw ptential custmer znes. GIS may help banks t mnitr branches in defining a trade area arund the branch, measuring the market ptential within the trade area, and finding ut the nearby cmpetitrs (Jafrullah, et al. 2003). One f the majr services in banking system is t prvide lan service t the custmers, especially in real estate lan. Banks have t take int accunt credit risks fr making decisin regarding if they prvide real estate lan t the custmers r nt. Banks make sanctin f lans by evaluating the value f the land, plt, r huse. In ding this activity, banks have t analyze the prperty r plt like current land valuatin, the size f the huse and s n (Fu, 2007). After prviding a lan, it is better if bank can lcate the accunt hlders r lan takers in rder t ffer ther services in the future. Bank is nw spending a lt f mney n this task (DelFin Analytics, 2005). GIS can make this jb easily by prfiling and finding custmers. Banks need t prduce a list f accunt hlders r lan takers, and each in the list shuld have an address. Here GIS play a rle fr assigning a spatial identifier t each

4 128 Jurnal f Bangladesh Institute f Planners, Vl. 9, 2016 accunt hlder r lan takers recrd such as a pstal cde, address r census tract and s n, then accunt hlders r lan takers can be identified in the map. Als GIS can help t calculate the distance and present the rute frm bank r branch t the client s address. This prcess is knwn as ge-cding. But fr this, the database has t be updated regularly (Fu, 2007). Experiences f Using GIS in Banks: Glbal Perspective The usage f GIS in business areas has been increased in the western cuntries in recent years (Grimshaw, 1994 and Lngley at el, 1995). Banks in Canada use GIS in many kinds f applicatins. They use GIS t select new branch sites, identify risks fr clsing a branch, perfrm targeted marketing campaigns, navigate custmers tward their spatial lcatins, gain better understanding int markets served (Hugh, 2001). Accrding t Hugh (2001), in 1998, Canadian evaluates the cuntry s largest banking institutins mergers by using GIS. His paper fcuses n the ways that GIS based methdlgies are applied in the merger evaluatin prcess, particularly in defining market prcess and estimate market share. The research testifies hw GIS can be used fr helping a financial plan fr cmpetitins related t merger attempts. His research indicates that it is pssible t use GIS t define markets thrugh a cmbinatin f ppular GIS and database sftware. Fr example, inspectins f branch lcatins and municipality lcatins can be cnverted int a cmputer prgram executed in a cuple a minutes (Hugh, 2001). Panigrahi et al. (2003) reprts n hw t use GIS tls fr simplifying the cllectin management system in banks and financial service rganizatins. Their paper prpses that an integrated GIS apprach enables banks t lcate current defaulters, identify the best lcatin fr cllectin bxes and ATMs in varius znes, and identify ptential defaulters frm existing custmers wh have availed lans and s n. The steps in the methdlgies fllwed in this reprt are: First prepare the city map and rad map f the city, Then use GPS t lcate ATM centers, Lcate custmers using Ge-cding which is a mdule develped by using Map Objects and VB, subsequently they classify defaulters based n types f credit cards, husing lans and persnal lans and s n, Finally, they analyze the defaulters t find the lcatin f the defaulters and learn infrmatin frm the defaulters, fr instance, methds f cllecting mney t chse lcatin f the ATM. In the analysis f the defaulters, fr example the cncentratin areas f a particular type f custmer can be identified using GIS easily. Als GIS can help t select different types f defaulters depending n the distance frm an ATM. Finally, they cnclude GIS is "an effective tl in any discipline with relevance t space, it can be used t identify the lcatin f ATM centers, lcatin f custmers, classificatin f defaulters, and s n" (Panigrahi, et al. 2003). The banks wh dn t want t lse mney frm payments f defaulters shuld have prper strategic planning and gd peratin managements that the cllectin management system fllws thrugh the latest technlgy like GIS fr revlutin f cllectin prcess (Panigrahi, et al. 2003).

5 A GIS Based Apprach t Manage Spatial Distributin and Lcatin f Financial Services 129 Lcatin f the Study Area Finding Suitable Lcatins fr New ATMs A selected prtin f Old Dhaka City Crpratin area has been selected t carry ut this research. A significant prtin f greater DMDP area under the study area is develped with significant number f ATM bths f different banks. The selected wards f ld Dhaka City Crpratin are ward number 32, 36, 39,40 (Partial), 49, 50, 51, 52, 53, 54, 56, 57, 59, 61, 62, 63, 64, 67, 68, 69, 70 and 71 (Ttal 22 wards). Ttal size f the study area is abut 10, square meters r abut 100 square kilmeter. Spatial Data Cllectin Fr building a GIS applicatin system, there, first need t have a GIS based database system. This database system includes basic spatial database and integrated database f all kinds f bank netwrk & services. T implement data cllectin prcesses, generally a bank shuld fllw fllwing digital survey methds: Physical feature survey t cllect detail physical feature data like- structure, Land use survey t cllect land use data f the study area, A tpgraphic survey t cllect land elevatin data (if needed), Alng with digital survey, database can als be prepared frm any secndary surce, thrugh: Scan f the had map dcument (Must have a spatial reference system fr the data), Digitize f the map dcument & creating attribute data, GPS Survey t take grund cntrl pint (GCP) fr making spatial crrectin. In this research, spatial database have been cllected frm DAP (Detail Area Plan) prject f RAJUK. Under this prject, extensive and detail GIS database has been prepared fr greater DMDP area including Dhaka City. Als spatial data fr existing ATMs lcatin f the study area have been prcessed 1 frm satellite image. Fr having limitatins in term f time and financial supprt, Ggle Earth s data has been used instead f ding GPS survey t cllect lcatin data f every ATM within the study area. Spatial Sketch f the Study Area The selected study area is the ne f the cmpact and busiest prtin f the Dhaka City. Ttal area f the study area is abut square meters. The predminant pattern f land use within the study area is educatin and research use (abut 25 % f ttal land use), and the secnd and third fcus land use patter are Circulatin Netwrk (22.90 %) and Mixed use (19.56 %) cnstitutively. Land use pattern f the study area is shwn in Figure 1. 1 After cllecting.kmz file frm Ggle Earth, Data fr all ATMs lcatin has been cnverted t shapefile by using ArcGIS 10 sftware.

6 130 Jurnal f Bangladesh Institute f Planners, Vl. 9, 2016 Surce: Develped by Authr, 2014 Figure 1: Existing Land use Pattern f the Study Area Surces: Field Survey, 2014 Figure 2: Cnvenient and Ratinal Distance fr ATM Service.

7 A GIS Based Apprach t Manage Spatial Distributin and Lcatin f Financial Services 131 Existing ATMs Lcatin and Their Service Area within the Study Area It has mentined earlier that lcatin data fr all existing ATM has been cllected thrugh using satellite image available in Ggle Earth. There are a ttal f 176 ATM bths cvering the study area. The highest numbers f ATM bth are under Dutch Bangla Bank Limited t ensure sund financial service and brad netwrk cnnectin. In term f ATM service prviding strength, within the study area there are n ther bank can develp as like as DBBL except BRAC Bank Limited. After deriving data frm satellite image in.kmz frmat, these data has been cnverted int pint file (shape file) with detail attribute data fr each and every ATM bth. T find the mst preferable distance r the service area f ATM, a questinnaire survey was dne amng 30 respndents. Frm the survey; it has been fund that (see figure 12), the mst preferable distance fr the availability f an ATM bth is within 200 meter (72% f the respndents) frm their existing standing. The secnd mst preferred distance is 100 meter (20% f the respndents). Existing lcatin and the service area (200 meter and 100 meter buffer) f all ATM are shwn in Figure 3. Surce: Develped by Authr, 2014 Figure 3: Existing Lcatin and Service Area f all ATMs within the Study Area.

8 132 Jurnal f Bangladesh Institute f Planners, Vl. 9, 2016 Finding New Lcatin fr ATM Services within the Study Area The brief wrkflw f this GIS analysis is Step 1: Input Datasets, Step 2: Derive Datasets, Step 3: Reclassify Datasets and Step 4: Weight and Cmbine Datasets. The inputs in this datasets are Land use, Rad Netwrk and Existing ATMs. Then, there need t derive Land use in raster dataset, Distance t existing ATMs and distance t rads, then reclassify these derived datasets t a cmmn scale Finally, weight them accrding t a percentage influence and cmbine them t prduce a map displaying suitable lcatins fr new ATM. Figure 4 shws the GIS analysis prcess at a glance. Landuse Step 1 ATMs Rads Cnverting Int Raster Step 2 Find Distance Find Distance Step 3 Reclassify Reclassify Reclassify Step 4 Weight & Cmbine Datasets Surce: Develped by Authr, 2014 Figure 4: GIS Wrking Prcess t Find Suitable Lcatins fr New ATM. Deriving Dataset fr Analysis Deriving data frm the input datasets is the next step in the suitability mdel. Here need t derive the fllwing: Cnverting Land use Vectr Data t Raster Data, Distance frm Existing ATMs, and Distance frm Rad Netwrk (3m. t 30m.).

9 A GIS Based Apprach t Manage Spatial Distributin and Lcatin f Financial Services 133 Cnverting Land Use Vectr Data t Raster Data As here, need t make peratins with all raster datasets fr the suitability analysis, first f all, land use database f the study area shuld be cnverted int raster frmat frm vectr frmat (plygn). By using Cnversin Tl (Plygn t Raster) frm Arc Tlbx f ArcGIS 10, the land use plygn has been cnverted t raster (Figure 5). Surce: Develped by Authr, 2014 Figure 5: Plygn t Raster Cnversin f Land use. Input Datasets Output Datasets Surce: Develped by Authr, 2014 Figure 6: Deriving Distance Data frm Existing ATMs.

10 134 Jurnal f Bangladesh Institute f Planners, Vl. 9, 2016 Deriving Distance frm Exiting ATMs It is preferable t lcate the new ATM away frm existing ATMs. S, here need t derive a dataset f distance frm existing ATMs. Spatial Analyst tl is an ideal tl fr calculating Euclidean Distance frm an input feature (Pint data existing ATMs). Figure 7 is shwing the input and utput dataset fr Euclidean Distance analysis f existing ATMS. Multiple Ring Buffer frm Existing Rads Cnverting Multiple Ring Buffer int Raster Surce: Develped by Authr, 2014 Figure 7: Multiple Ring Buffer Analysis and Cnverting int Raster. Deriving Distance frm Rad Netwrk One f mst suitable and preferable site fr lcatin ATM is site clseness t rad access. In this regard, Land parcel (within 3-30 meter frm rad) is cnsidered fr lcatinal mdel. By using Multiple Ring Buffer analysis tl fr Arc GIS, the area within 3 meter t 30 meter frm the rad has been derived. And later n, the Multiple Ring Buffer (Ranging- 3 meter t 30 meter frm the edge f rad) is cnverted int raster frmat t derive desired data f distance (Figure 8). Reclassificatin f Datasets By Deriving dataset, here all required datasets are available t run further analysis fr suitability mdel. The next step is t cmbine them t find ut where the ptential lcatins can be fund. In rder t cmbine the datasets, they must first be set t a cmmn scale. That cmmn scale will shw hw suitable a particular lcatin (each cell) is fr establishing a new ATM. Here need t reclassify each data set t a cmmn scale, within the range 1-10.

11 A GIS Based Apprach t Manage Spatial Distributin and Lcatin f Financial Services 135 Surce: Develped by Authr, 2014 Figure 8: Reclassificatin Analysis fr Desired Data Using Arc GIS 10. The reclassificatin parameters fr land use, distance frm ATMs and distance frm rads has been shwn in Table 1 and Figure 8. Analysis has been dne by using the Spatial Analyst Tl f Arc GIS where the value has been classified accrding t unique value (fr land use and rad) and equal interval (fr existing ATMs). In the Land use reclassificatin, the incmpatible land uses (like water bdy, vacant land, circulatin netwrk and restricted use) are exclude frm the analysis by giving n data value. Table 1: Reclassificatin Parameter f Land use, Distance frm ATMs and Distance frm Rads. Reclassificatin Parameter f Land use (Raster) Parameter Land use Histrical Land use Gvernment Educatin & Research Recreatinal Cmmunity Activity Residential Service Activity Manufacturi ng & Prcessing Mixed use Cmmercial Reclassificatin Parameter f Distance frm Rad Parameter Distance frm Rad (In meter) 30 meter 27 meter 24 meter 21 meter 18 meter 15 meter 12 meter 9 meter 6 meter 3 meter Reclassificatin Parameter f Distance frm Existing ATM The Mst Nearest Cell = 1, T The Mst Lng Distanced Cell = 10 (Ranging Parameter 1-10) Surce: Develped by Authr, 2014

12 136 Jurnal f Bangladesh Institute f Planners, Vl. 9, 2016 Weighting and Cmbining Datasets and Finding New Suitable Lcatin After applying a cmmn scale t the datasets, where higher valued are given t thse attributes that are cnsidered mre suitable within each datasets, nw datasets are ready t cmbine t find the mst suitable lcatins. This is mst preferable t lcate a new ATM away frm the existing ne. This is als nticeable that the need f financial transactin is mstly affected by the land use pattern f that area, as we have seen mst financial service pints and institutins are agglmerate within cmmercial area rather that thers land uses like residential use. Als gd accessibility is required fr the cnvenience f custms f cmmercial and service rganizatins. Cnsidering all these factrs, the influence pattern is given t each layer (Table 2). Table 2: Influence Pattern f Each Layer fr Weighting and Cmbining. Layer Influence Pattern* Reclass f Distance ATMs 0.5 (50%) Reclass f Distance Rads 0.1 (10 %) Reclass f Land use 0.4 (40%) Ttal 1 (100%) *Each Percentage is Divided by 100 t nminalize the values Surce: Develped by Authr, 2014 The weighting and cmbining analysis f spatial datasets are dne with the Raster Calculatr tls frm Spatial Analyst (Figure 9). After ding analysis with Raster Calculatr, it creates a new raster map having cell value f 1-8, where value 8 dnates mst preferable lcatin t value 1 fr least preferable lcatin t establish new ATMs within the study Area. Surce: Develped by Authr, 2014 Figure 9: Perfrming Weighting and Cmbining Analysis with Raster Calculatr.

13 A GIS Based Apprach t Manage Spatial Distributin and Lcatin f Financial Services 137 Figure 10 shws the suitable lcatins fr new ATMs after running lcatin mdel. By cnsidering the mst suitable lcatin (Red Cell) and secnd mst suitable lcatin (Blue Cell), there still 36 new ATM Bths can be installed t cver the unserved area. The new lcatins fr ATMs are prpsed by cnsidering structural density and use within the study area which is als shwn in Figure 10. Surce: Develped by Authr, 2014 Figure 10: Mst Suitable Lcatins fr New ATMs within the Study Area. Implicatin f Lcatin Suitability Analysis fr ATM Services Banks expand their ATM netwrks basically t keep existing custmers r acquire new nes. Expanding an ATM netwrk can be dne essentially in tw ways: building new lcatins (rganic grwth) r by partnering with a retailer. The first ptin is likely t be cstly and can take a lng time, s the latter may be a better ptin. Chsing the right partner is nt an easy decisin t make (Farhan, 2007). Frm the findings f the suitability mdel, there are tw imprtant implicatins fr ATM netwrk develpment.

14 138 Jurnal f Bangladesh Institute f Planners, Vl. 9, 2016 Fr newly establish bank like Bangladesh Develpment Bank Ltd., Unin Bank Ltd., NRB Cmmercial Bank Ltd., NRB Bank Ltd., Mdhumti Bank Ltd. and all ther banks thse have nt establish ATM netwrk yet r have very small scale ATM services, they can expand their banking services thrugh acquiring partners frm existing ATM netwrk develped by ther banks and simultaneusly can cnstruct new ATMs in the mst suitable unserved area. By this way, these banks can expand their ATM netwrk mre rapidly with less establishment cst. Thse banks, which have already large ATM netwrk with sme unserved area, can strengthening their existing netwrk by reaching t the custmer s cnvenience lcatin. These type f analysis fr finding mst suitable lcatins fr ATM is mre applicable fr ther divisinal twns, district twns r municipal twns, like Chittagng, Khulna, Rajshahi, Barisal ect. Cnclusin GIS was instrumental thrughut this research. As it is shwn in this paper, GIS was used fr presenting spatial data, prcessing spatial data (e.g. perfrming spatial queries and buffers t determine unserved areas), preparing the data fr the spatial ptimizatin mdel and finally shwing the slutins f the ptimizatin mdel n the map t assure that the mdel is applied prperly, especially fr display purpses (i.e. mapping). Fr banks, wh want t be successful in retail banking business in Bangladeshi cmpetitive envirnment, it is crucial t make the ratinal distributin f bank branches r ATM netwrks accrding t different custmers attributes and demands. This type f suitability mdel als can be designed fr all ther urban areas f Bangladesh t disperse ATM services all ver the cuntry. A Strategic Plan can be prepared fr all urban areas if pssible fr the whle cuntry by giving pririty in several phases (like 1st Phase ATM develpment, 2 nd Phase Develpment) fr cnstructing new ATMs. Besides finding lcatin fr ATMs, Als suitable lcatins fr bank branch establishment r Mbile Banking Agent/ Pint establishment can be fund by develping GIS based mdel. This research has several limitatins regarding time and data availability, but this type f mdeling can give mre realistic results, if we can incrprate density data and incme data f any lcality in raster frmat. References Birkin, M., Clarke, G. and Clarke, M Retail Intelligence and Netwrk Planning, England, Jhn Wiley. Davies, G What Next?, Arrw, Lndn. DelFin Analytics Lk Up fr Inspiratin, The Hindu Business Line, in ubusinessline.cm/ew/2005/09/26/stries/ htm (accessed n March 10, 2014). Driscll, J Bank Wars: Episde 2. The Branches Strike Back', Bank Marketing, Vl. 31 N. 12, pp Dueker, K. J Land Resurce Infrmatin Systems: A Review f Fifteen Years Experience, GePrcessing, Vl.1, pp

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