Lecture 4 Trade Area Delimitation and Analysis
Trade Area Conceptualization (1):
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Refers to the spatial extent (or distribution) of
customers around an individual stores or a
network of stores.
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can be viewed as a contiguous area (or polygon)
around a store (supply point) that contains the
majority of the customers or potential customers
(demand points).
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also known as market area or customer
catchment area.
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Trade Area Conceptualization (2):
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Also viewed as the way of mapping the
confines of interaction between a set of store
locations and the customers that patronize
them.
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Interaction can be measured in different ways:
number of customers,
number of transactions
dollar value of transactions
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It has a spatial dimension and geographical
boundaries, though boundaries are not always
clear
Trade Area Conceptualization (3):
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Trade Areas vary in size and shape.
Factors that affect trade area size and shape are:
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Store size (attractiveness)
Settlement patterns (residential density)
Transportation network
Barriers to movement
Presence of competitors (which provide alternative
locations and intervening opportunities)
Can be used to provide information for trade area
analysis
 characteristics of consumers/customers
 screen development potential
 assess existing stores performance,
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Can be conceptualized and defined in different ways.
Who are concerned with trade
area delimitation/analysis?
Who are concerned with trade area
delimitation/analysis?
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Retailers/ commercial service providers
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Commercial property developers
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Real estate department of retail chains
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Leasing companies
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Location analysts working for the above
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Marketing firms who do advertisement for businesses
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Educators who train students in the profession of marketing
geography, retail geography, and business geography
Three approaches to trade area
delimitation:

Spatial Monopoly (Deterministic)
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Market Penetration (Probabilistic)
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Dispersed Market (Customer profiling)
Deterministic approach has the
following characteristics:

Makes a clear-cut assumption about the spatial
dimension of the trade area

Trade areas are polygons, each has definite
boundaries; they do not overlap
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Assumes all customers come from this area;
(those living outside are excluded from
consideration)
Probabilistic approach has the following
characteristics:
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Makes no clear-cut assumption about the spatial
dimension of the traded areas
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Trade areas are not polygons, with no definite
boundaries; they overlap
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Assign persons (households, CT etc.) to stores
partially, with the assumption that people do not
always go to the closer store
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Treat trade areas as the surface of probabilities:
primary (60%) and secondary (60-80%) etc.
Dispersed Market (also known as
Customer Profiling) has the following
characteristics:

The supplier is often highly specialized. (e.g.,
specializing one or two lines of imported
furniture, selling a narrow selection of books, or
serving a widely scattered ethnic group.)
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There is no obvious spatial concentration of
customers; customers are widely dispersed.
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Distance decay relationship is weak
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Trade area is defined through customer profiling
(i.e., age, income, ethnicity and life style.)
Two types of data for trade area analysis:
Secondary data : the most commonly used are census data
◦ less expensive; and need less effort to acquire
◦ can be used to identify potential customers, but many of these
potential customers do not necessarily patronize the store. So, the
demographic profiles produced are not real customer profile report.
Primary data: compiled by retailers.
◦ collected at POS (either based on credit card transactions or by sales
associates asking postal codes and phone numbers)
◦ Through customer data analysis, retailers develop a customer profile
consisting of demographic, social and economic attributes.
◦ They can also use this profile to search for suitable sites in new
markets.
User defined trade area
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Also called “rules of thumb”. It is hand-drawn around a given
store, from which the analyst believes the majority of customers
are attracted.
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Relies on the level of experience and expertise of the person who
defines the trade area. It assumes that the person has knowledge
of customer base and how far they travel.
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It is highly subjective, not scientific.
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Quality can be improved, if limited customer spotting data are
available and used as reference.
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Usually used to define trade area for a single store
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There are two types of such trade areas:
◦ Unconstrained trade area that do not follow census
geographies (but may follow physical barriers)
◦ Boundary constrained
User Defined Trade Area
Free-hand
DA confined
Census tract confined
FSA confined
Circular trade area
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The easiest, quickest and least expensive
method
Trade area defined as a circle using pre-defined
radius (usually walking distance or driving
distance)
Assuming the transport surface is uniform, and
the store is equally accessible from all directions
Competition is not a major factor
Adjacent trade areas may overlap or not overlap,
depending on distances between stores and predefined radius.
Circular Trade Area
Percentage of Customers
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Percentage of customers uses customer
data.
The analytical tools is the “customer spotting”
map.
This simply is a map of distribution of
customers around a given store.
Boundaries are drawn to include the CTs/DAs
that contain a given percentage of customers.
Usually, distance is used to select the closest
60% and 80% of the customers to the store
location.
Travel Distance/travel time
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This method uses travel distance or driving time to define
trade area.
can be 5km or 10km. Can also be 20 minutes or 30 minutes.
Distance and travel time are influenced by the characteristics
of the road network (such as speed limit, one-way street,
number of lanes, road capacity, etc.)
The map may look like a spider’s web
The trade area is irregular shaped
Market Penetration
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Divides the area into grids (200x200m, 500x500m, or using DA)
Place the same grid over the customer spotting map
Count the number of spotted customers in each cell
Divide the number of customers in each cell by the cell’s total
population
The ratio or percentage is regarded as a measure of market
penetration
If sales are known from the customer data, the number of
customers can be translated into sales, and sales can be divided
by total disposable income in the cell to develop a ratio.
Outward from the store location, the number of cells is
counted until 60% or 80% of the customers or sales are
reached.These cells form the primary and secondary trade
areas.
With this method, there may be some holes which have no
data or no customers; or some outliers which have a significant
number of customers. It is the analyst’s decision to include
them or exclude them.
Thiessen Polygon
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a geometric procedure for delimiting
theoretical trade areas for a network of
stores
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assumes the stores are similar in size and
sell similar products for similar price;
consumers purchase products from the
closest store.
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most suitable for delimiting trade areas of
chain stores.
Thiessen polygon
Thiessen polygon
Reilley’s Law
Reilley’s Law
A
B
A-B
Sherway Garden
Yorkdale
3.38 cm (6.8km)
Sherway
Eaton
3.10cm (6.7km)
Yorkdale
Fairview
2.25cm (4.7km)
Yorkdale
Eaton
2.05cm (4.4km)
Fairwview
Eaton
3.06cm (6.3km)
Fairview
Scarborough
1.7cm (3.2km)
Scarborough
Eaton
3.44cm (7.6km)
B-A
Reilley’s Law
Statistically-Calculated Probabilistic Method;
The Huff Model (1)
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Huff model is useful in the following ways:
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Generate customer volume estimate for existing stores
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Generate customer volume estimate for proposed new
stores
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Answer such strategic questions:
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What would happen to my trade area if my store expand by
50%?
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What would happen to my trade area if one of my stores is
close?
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What would happen to my trade area if an existing
competitor were to leave the market?
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What if a competitor introduces a new store in the market?
Map the probability surface
Estimate sales potential
Statistically-Calculated Probabilistic Method;
The Huff Model (2)
Huff model requires the following data:
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A list of stores (shopping centers), their locations and
attributes (attractiveness)
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A list of building block areas (CT or DA) with
demographic and social economic data (market size and
purchasing power)
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A matrix of distance, driving time, travel costs between
each building block and each store
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A sample data set (for calibrating parameters/weights) .
Statistically-Calculated Probabilistic Method;
The Huff Model (3)
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The challenge is to estimate the parameters.
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There are two ways to estimate them:
1. To make an ‘educated guess”.This is used when sample data are not available. It
depends on the experience and knowledge of the local market. Usually several
guesses are made for experiment to find out which one generates better results.
2. To statistically estimate or calibrate the model. Often, it includes using a number
of different non-linear models. This requires the use of sample data. Several
parameters are experimented, and a measure of goodness of fit is produced.
Calculations are undertaken to estimate the direction and amounts each of the
parameters should change to improve the fit. Each change is then entered into
the model, and the model is re-run until the best values that give rise to the best
fit to the sample data are found.
St. James Town example
St. James Town example
Sales potential estimate
S. P. = No. of HH * average HH income
* % of income spent on consumer goods
* probability
Example:
S.P. in building 1 at supermarket A:
=567 * $26,700 * 0.3 * 0.93
=$4.22 million
Comparison of Thiessen, Reiley’s and Huff
Factors
Thiessen
Reiley’s
Huff
Quality of transport
system
Yes
(d-time; travel time
reflect quality of
transport system)
Yes
(d=time)
Attractiveness
Yes
Yes
Types of goods
Competition
Yes (λ)
Yes
Transport barriers
Yes
Yes
Yes (d=time)
Yes
Accuracy of sale
estimate
Low
Low
High
Comments
Good for chain stores
(similar size,
identical goods and
price);
No major barriers;
Simple to use
Good for different sized
stores;
Consider barriers;
Relatively simple to use
Good for different sized
stores;
Consider barriers;
Complicated to use