Marketing GIS as Spatial Decision Support Systems
Geographic Information System is software to connect a location with the description of that location. Being a decision support system it does not just give you any single dimension of a map but explains it by unveiling many layers of information in different aspects. These digital maps are not like conventional paper maps; their intelligence is accredited to Geographical information System technology. It is a user friendly software system which can help in making the processing information easy to get in an effective way.
Moreover it can be handled by any novel user. It serves the information like the location of spot, length of the road, the distance and area being covered is saved in digital format as layers of information. A sort of heap of data is generated as layers of information and is displayed on user’s query. This heap consists of different geographical features like rivers, roads, etc. The layers can be displayed or hidden as per user’s desire to control the amount of information about an area (ESRI, 2008).
Organizations and firms are helped in an effective manner so as to provide them with better decision support. Geographic data in a discrete form is collected and organized properly and
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There are a number of techniques which help in integrating the data based on preferences of locations. Every technique either provides with spatial domination or approaches to penetrate in market such as concentric ring approach or drive time/distance polygons. This has to be kept in mind that how a store dominates an area, relation of trade area to the store and no visits from outside the area. This bound leads to the formation of a geographical ring to prepare a profile capturing market.
Using GIS the data used in making profiles can be extracted or aggregated (Dramowisz, 2005). Market penetration approach supposes a competition that the spatial variation between the households is because they prefer different stores. This approach exemplify Huff trade area model that defines probability surface as a representative of customer backing possibility. The basic food for thought of the Huffman model is selecting one out of various stores in the same area.
Spatial interaction model involves the attraction, competition, and distance parameters on which probability surface bases upon. Probability surface can be made up to regions of customer backing probability. These probability surfaces are used as weights to make the market profile. Integration of data on consumer’s preferences for selecting a retail site is modeled in huff model by analyzing trade area in a more complex way. The complexity is generated when the data of customer’s preferences is used and information of shopping routes is measured (Dramowisz, 2005).
The procedure begins by taking multiple predictor variables. Estimation is then done by least squares methods. Predictor variables have destination, origin, and distance between the both. In this way modeling of weighing consumer’s shopping preference can be done. For example if we have three stores in an area which have same retail chain. Each of the stores makes polygons both of primary trade areas and secondary trade areas. Three datasets of destinations, origins and scenarios are required then (Dramowisz, 2005).