Retail Trade Area Analysis Using the Huff ModelWord文件下载.docx

1、The Microsoft Virtual Earth Platform is an integrated set of services that combines unique birds eye, aerial, and satellite imagery with best-of-breed mapping, location and search functionality. It enables businesses to deliver innovative solutions and breakthrough customer experiences. Learn more a

2、t IntroductionThe concept of a retail trade area has been used by analysts and practitioners in retail site evaluation and other market studies for a very long time. In fact, retail trade area analysis and site evaluation are complementary procedures. Retail trade area analysis focuses on locating a

3、nd describing the target market. This knowledge is critical for both marketing and merchandising purposes, as well as for choosing new retail locations. In site evaluation, trade area analysis is combined with many operational requirements of the retail chain （Jones, Simmons 1993）. It is much easier

4、 to analyze trade areas and produce market profiles using GIS. The majority of GIS software includes functionality for extracting and aggregating data at various levels of geography. As a result, trade area analysis became one of the most popular areas of GIS applications in analyzing business probl

5、ems. The most common definition of a retail trade area is used for the purpose of this article. According to this definition, a retail trade area is “that area, typically around the store, from which the store derives most of its patronage” （Lea 1998b, p.140）.Retail trade area analysis was a very po

6、pular theme during the time Business Geographics magazine was published （1993-2001）. A couple dozen articles were written on this subject by researchers and GIS consultants specializing in the retail sector. A variety of techniques on how to delimit and analyze trade areas were discussed, along with

7、 their advantages and disadvantages. These techniques range from simple ones, such as an application of rings, to more sophisticated, such as utilizing probabilistic trade area surfaces （Gross 1997, Hooper 1997, Lea 1998a, Peterson 1997, Simmons 1998）. All techniques represent either the spatial mon

8、opoly or market penetration approaches to analyzing trade areas （Jones, Simmons 1993）. The concentric rings method, drive time/distance polygons or Thiessen （Voronoi） polygons are examples of this type of approach. These methods are easy to conceptualize and use. However, they assume that a store ha

9、s a monopoly over the area that all households in the trade area relate to the store and no households outside the trade area visit the store. Once the trade area is delimited geographically as a ring, Thiessen or other type of polygon, it is easy to prepare a market profile by extracting and aggreg

10、ating data using GIS software. Although the methods representing the spatial monopoly approach are commonly used, they involve a lot of simplification because they do not account for the existence of competing stores. Therefore, they should be utilized only if no better alternatives exist （Lea 1998c

11、）. The market penetration approach assumes that there is a spatial variation in the proportion of households served by a store due to competition. The best example of this type of approach is the Huff trade area model. The trade area is conceptualized as a probability surface, which represents the l

12、ikelihood of customer patronage. This model provides an answer to a basic question: What is the probability that a customer will decide to shop at a particular store, given the presence of competing stores? The creation of probability surface is based on a spatial interaction model that takes into a

13、ccount such variables as distance, attractiveness and competition. The probability surface can be contoured to produce regions of patronage probability, which can then be further used as weights in the preparation of market profile.The intent of this article is to raise the awareness of the Huff mod

14、el within the GIS community.Introduction to the Huff ModelThe Huff model was introduced by David Huff in 1963 （Huff 1963）. Its popularity and longevity can be attributed to its conceptual appeal, relative ease of use, and applicability to a wide range of problems, of which predicting consumer spatia

15、l behavior is the most commonly known. The probability （Pij） that a consumer located at i will choose to shop at store j is calculated according to the following formula （Huff 2003）.Where: Aj is a measure of attractiveness of store j, such as square footage Dij is the distance from i to j is an attr

16、activeness parameter estimated from empirical observations is the distance decay parameter estimated from empirical observations n is the total number of stores including store j. The quotient received from dividing by is known as the perceived utility of store j by a consumer located at i. The para

17、meter is an exponent to which a stores attractiveness value is raised, and enables the user to account for nonlinear behavior of the attractiveness variable. The parameter models the rate of decay in the drawing power of the store as potential customers are located further away from the store. Incre

18、asing the exponent would decrease the relative influence of a store on more distant customers. Examples Four examples included in this article use the Huff model for the following purposes.1. Trade area analysis for a single site using a single variable for site attractiveness 2. Trade area analysis

19、 for a single site using multiple variables for site attractiveness 3. Comparison of potential revenue for two sites 4. Modeling a market scenario more complex trade area analysis involving the use of customer spotting data, information on shopping trips and model calibration. Examples 1-3 are simpl

20、e applications of the model. They do not involve parameter estimation and do not utilize customer spotting data. They are based on the defaults implemented in GIS software that include straight line distance calculation and the most typical values for an attractiveness parameter （the value of 1）, an

21、d for the distance decay parameter （the value of 2）. Even with the default values used for parameters, this method is superior to the methods based on the spatial monopoly concept.Trade area analysis for a single site using a single variable for site attractivenessTwo data sets were used in this exa

22、mple: shopping centers with their characteristics, and small census units with some related data. The purpose of this example was to create a market profile for a single mall. The Gross Leasable Area （GLA） was used as an attractiveness variable. The patronage probability surface （a grid） was created

23、 for a selected mall （Figure 1）. A potential customer is assumed to be located at every grid cell. The probability of a customer patronizing a selected mall is positively related （directly proportional） to the attractiveness of the mall and negatively related （inversely proportional） to the distance

24、 between the mall and the customer, given the presence of all competing malls.Figure 1. Customer patronage probability map for Micmac Mall, Halifax-Dartmouth, Nova Scotia, using Gross Leasable Area （GLA） as an attractiveness variable. （Click for larger image） The patronage probability surface was co

25、nverted to regions of probability. It is possible to select any number of regions. For this example, ten regions of probability were chosen. The regions are delineated using contours shown as white lines in Figure 1. The data was then extracted for each region from underlying census polygons （Table

26、1）. The numbers in Table 1 include all households in the study area. Table 1. Market profile （unweighted data） for regions of patronage probability. （Click for larger image） Table 1 was then summarized to show the totals for each variable （Table 2）.Table 2. Market profile （unweighted data） for regio

27、ns of patronage probability - summary. （Click for larger image） Now the values of probabilities came into play. They were used as weights for scaling down the numbers from Table 1, to simply make them more realistic. Each of the first four columns in Table 1 （Population, Dwellings, Families and Hous

28、eholds）, representing absolute values, was multiplied （weighted） by the midpoint value of every probability region. For example, the midpoint value for the 0 0.1 region equals to 0.05. Table 3 presents more realistic market area profile that is based on weighted data. The last two columns in Table 1

29、 were not weighted because they represent relative values and therefore are not included in Table 3.Table 3. Market profile （weighted data） for regions of patronage probability. （Click for larger image） Table 3 was then summarized to show the totals for each variable （Table 4）.Table 4. Market profil

30、e （weighted data） for regions of patronage probability - summary. （Click for larger image） The comparison of tables 2 and 4 allows for stating that the actual number of households patronizing this mall will be less than 10% of all households located in the study area. This number was determined by c

31、alculating the proportion of weighted number of households （10,203） in the total number of households （110,810）.Trade area analysis for a single site using multiple variables for site attractivenessThe difference between Examples 1 and 2 is that in Example 2 more than one variable was used as an att

32、ractiveness index. “The value of the model depends on the ability to incorporate a number of different measures of store attractiveness” （Jones, Simmons 1993, p.345）. If more variables are included, it is easier to understand a variation in patronage patterns. In addition to the GLA, in this example the number of stores in each mall and the number of parking spaces were als