GIS In Business As Topic for
Week 3’s Facilitation Seminar
in Saint Mary University’s
Advanced Modeling Course
Kevin Byrne, Dan Haglund, and
September 21, 2009
To provide an overview of Business GIS in
terms of concepts, key themes, and
applications that afford classmates various
forms of learning, media, and challenges.
• Concept mapped the universe of Business
• Concentration basics derived from map
• Our core presentation in 3 parts: A, B, C
(statement clusters exemplified and
• Speed challenge
Concentration basics: ten statements derived from
our concept map become a small part of the
universe of Business GIS we hope to cover…
1. GIS in Business requires theories and models such the gravity model of
2. GIS in Business has interesting “best practice methods” such a scenario
descriptions that are written in plain English in order to benefit commercial
clients and users.
3. Theories require methods that gather, organize and analyze good data.
4. GIS in Business defines and achieves its goals via good data.
5. Good data is worked on a) using critical thinking, and b) by software tools
that are proprietary or non-proprietary (free).
6. Proprietary software is ESRI’s ArcGIS and is used by business GIS
analysts with its extensions such as a) Business Analyst, b) Spatial Analyst,
and c) Network Analyst.
7. Non-proprietary software used by business GIS analysts is exemplified by
Social Explorer, GeoDa, and others, along with 3rd party free extensions to
ArcGIS such as those presented in week 2 of Advanced Modeling.
8. Good non-proprietary data is sourced by and from institutions like the U.S.
Census Bureau that house a) terrabytes of shapefiles and GDB’s such as
TIGER files, and b) terrabytes of demographic tables.
9. Good proprietary data pertaining to demography is exemplified by ESRI’s
Community Tapestry methods and files.
10. Lastly, all the above can be supported and exemplifed via case examples
and speed challenges that we herewith offer to our classmates.
Our facilitation-seminar offers three
clusters of statements:
• A: Theory, models, and a case example
(1, 3, 9, and 10, as lecture and case)
• B: Census scales and sources,
demographic data sources, and other
sources and free software (4, 5, and 10,
as lecture and software demos)
• C: Locational scenario (2, 6, 7, 8, and 10,
as speed challenge)
A: Gravity theory and model
“A methodology used in the geography,
engineering and social sciences to model the
behavior of populations. The underlying
assumption of the gravity model is that the
influence of populations on one another is
inversely proportional to the distance between
them. This approach is analogous to the view
of gravitational attraction from Newtonian
“Newton's law states that: ‘Any two bodies
attract one another with a force that is
proportional to the product of their masses
and inversely proportional to the square of the
distance between them.’”
“When used geographically, the words
'bodies' and 'masses' are replaced by 'towns'
and 'populations' respectively.”
“The gravity model of migration is therefore
based upon the idea that as the size of one or
both of the towns increases, there will also be
an increase in movement between them. The
farther apart the two towns are, however, the
movement between them will be less. This
phenomenon is known as distance decay.”
“The gravity model can be used to estimate
traffic flows, migration between two areas,
and the number of people likely to use one
Gravity Model http://geography.about.com/library/weekly/aa031601a.htm
Gravity Model Email Print
Predict The Movement of People and Ideas Between Two Places
Elsewhere on the Web
by Matt T. Rosenberg • Additional Gravity Models and
For decades, social scientists have been using a modified version of Isaac Newton's • Newton's Law of Gravitation
• Reilly's Law of Retail
Law of Gravitation to predict movement of people, information, and commodities
between cities and even continents.
The gravity model, as social scientists refer to the modified law of gravitation, takes into account the population
size of two places and their distance. Since larger places attract people, ideas, and commodities more than
smaller places and places closer together have a greater attraction, the gravity model incorporates these two
The relative strength of a bond between two places is determined by multiplying the population of city A by the
population of city B and then dividing the product by the distance between the two cities squared.
The Gravity Model
Thus, if we compare the bond between the New York and Los Angeles metropolitan areas, we first multiply their
1998 populations (20,124,377 and 15,781,273, respectively) to get 317,588,287,391,921 and then we divide
that number by the distance (2462 miles) squared (6,061,444). The result is 52,394,823. We can shorten our
math by reducing the numbers to the millions place - 20.12 times 15.78 equals 317.5 and then divide by 6 with
a result of 52.9.
Now, let's try two metropolitan areas a bit closer - El Paso (Texas) and Tucson (Arizona). We multiply their
populations (703,127 and 790,755) to get 556,001,190,885 and then we divide that number by the distance
(263 miles) squared (69,169) and the result is 8,038,300. Therefore, the bond between New York and Los
Angeles is greater than that of El Paso and Tucson!
How about El Paso and Los Angeles? They're 712 miles apart, 2.7 times farther than El Paso and Tucson! Well,
Los Angeles is so large that it provides a huge gravitational force for El Paso. Their relative force is 21,888,491, a
surprising 2.7 times greater than the gravitational force between El Paso and Tucson! (The repetition of 2.7 is
simply a coincidence.)
While the gravity model was created to anticipate migration between cities (and we can expect that more people
1 of 3 9/19/09 1:12 PM
Gravity Model http://geography.about.com/library/weekly/aa031601a.htm
migrate between LA and NYC than between El Paso and Tucson), it can also be used to anticipate the traffic
between two places, the number of telephone calls, the transportation of goods and mail, and other types of
movement between places. The gravity model can also be used to compare the gravitational attraction between
two continents, two countries, two states, two counties, or even two neighborhoods within the same city.
Some prefer to use the functional distance between cities instead of the actual distance. The functional distance
can be the driving distance or can even be flight time between cities.
The gravity model was expanded by William J. Reilly in 1931 into Reilly's law of retail gravitation to calculate the
breaking point between two places where customers will be drawn to one or another of two competing
Opponents of the gravity model explain that it can not be confirmed scientifically, that it's only based on
observation. They also state that the gravity model is an unfair method of predicting movement because its
biased toward historic ties and toward the largest population centers. Thus, it can be used to perpetuate the
Try it out for yourself! Use the How Far is It? site and city population data to determine the gravitational
attraction between two places on the planet.
S ub sc riib e t o t he N ew slle tt er
Su bs cr be to th e Ne ws et te r
New posts to the Geography forums:
Featuers associated with rocks?
Photos of Cameron's Corner - Outback Aus
Ridge where the west commences
Articles by Date | Articles by Topic
2 of 3 9/19/09 1:12 PM
• Retail Trade Analysis, Donald Segal
• Gravity Model
• Drive Time Analysis
• POS-Based Analysis
Retail Trade Area Analysis: Concepts and New Approaches
Donald B. Segal
Spatial Insights, Inc.
8221 Old Courthouse Road
Vienna, VA 22182 USA
Figure 1. Location of store, showing 1, 3, and 5 mile radii. The dots indicate the
locations of demographic samples. Red colored dots fall within the 5-mile radius.
Note that samples located across the river would be included in the 5-mile
demographic summaries for this site.
Figure 2a. Gravity based patronage probability model showing the theoretical
store trade area. The blue – green – yellow – red progression represents zones of
increasing patronage probability.
Figure 2b. Gravity based patronage probability model showing the locations of
demographic sample sites. Blue colored dots fall within the patronage
probability zones. Green colored dots indicate the locations of sample sites that
fall within the 5-mile radius but are not within the patronage probability zones.
Figure 3a. Drive time analysis showing areas that can be reached within 5, 10
and 15-minute drive times.
Figure 3b. Drive time analysis showing the location of demographic samples.
The blue colored dots represent the demographic sample sites that fall within a
10-minute drive time. Green colored dots represent demographic sample sites
that fall within the 5-mile radius, but fall outside of the 10-minute drive time
polygon. Conversely, red colored dots that fall within the 15-minute drive time
polygon represent demographics that would not be included using a traditional
5-mile radius approach.
In order to alleviate this limitation, Spatial Insights, Inc. has developed a radial
filter based trend surface modeling application, know as TrendMap, which
models trade areas directly from customer level POS data. The TrendMap
analysis provides a very accurate and precise measure of the spatial distribution
and characteristics of store trade areas. Because customer level POS data is
used, the effects of logistical barriers are automatically accounted for.
TrendMap uses a unique radial filter based algorithm that evaluates either the
density of points, the sum, or average attribute value calculated from all points
that fall within the specified radius.
Figure 4. Map showing the location of customers.
Figure 5. Color thematic trade area map showing concentration of revenue.
This map was produced by summarizing the customer revenue data according
to the block groups within which the customer locations fall. Colors ranging from
blue – green – yellow – red represent the progression from low to high revenue.
Figure 6a. Revenue based trade area map produced using TrendMap. Colors
ranging from blue – green – yellow – red represent the progression from low to
high revenue. The TrendMap analysis clearly shows discrete pockets of
customer/revenue strength. Note how the “hotspots” identified using the
TrendMap analysis are small and discrete, and are not constrained by pre-
existing census geographic boundaries.
Summary and Conclusions:
A number of traditional GIS based trade area analysis techniques have been
reviewed. Use of the radial ring method assumes that the store trade area is
circular, and this method does not account for logistical barriers or the effects of
competitors. Trade areas based on drive time analysis offer a more realistic view
of the trade area, particularly for a convenience store scenario. However, the
availability and accuracy of road networks upon which the analysis is based
may limit drive time analysis. Drive time analysis is of limited utility when
attempting to model trade areas of destination stores that draw from specific
demographics. Gravity modeling is a sophisticated technique, which can
account for the effects of competitors and is appropriate for convenience
scenarios. Small differences in the gravity model parameters can have a large
effect on the resulting trade area.
A new approach, which makes extensive use of customer based POS data, was
introduced. This method uses a circular filter to produce a trend surface map,
which accurately and precisely delineates the trade area extent and
A comparative analysis of the summary demographics calculated using each of
these methods was presented. The results of the comparative analysis show
significant differences between each of the methods. These differences would
have obvious implications regarding the development of demographic profiles,
merchandising, and site suitability modeling.
B: Census scales and sources,
demographic data sources, and other
sources and free software
• Census “scale”
• U.S. Census Bureau
• Census sources and data acquisition
• Other sources
• Demonstration: Scott Co. and Metro Counties
• Demonstration: use of Network Analyst extension
• Free: SocialExplorer.com
Industrious Urban Fringe Connoisseurs
SRI’s segmentation system, Community Tapestry, provides a robust, powerful portrait of the
65 U.S. consumer markets. To provide a broader view of these 65 segments, ESRI combined
them into 12 LifeMode groups based on lifestyle and lifestage composition. For instance,
Group L1, High Society, consists of the seven most affluent segments whereas Group L5, Senior
Styles, includes the nine segments with a high presence of seniors.
L1 High Society L7 High Hopes
L2 Upscale Avenues L8 Global Roots
L3 Metropolis L9 Family Portrait
L4 Solo Acts L10 Traditional Living
L5 Senior Styles L11 Factories and Farms
L6 Scholars and Patriots L12 American Quilt
Community Tapestry’s 65 segments are also organized into 11 Urbanization groups to highlight
another dimension of these markets. These 11 groups are based on geographic and physical
features such as population density, size of city, location in or outside a metropolitan area, and
whether or not it is part of the economic and social center of a metropolitan area. For example,
U1, Principal Urban Centers I, includes eight segments that are mainly in densely settled cities
within a major metropolitan area. The “I” or “II” appearing after each group name designates the
relative affluence within the group, with I being more affluent than II.
U1 Principal Urban Centers I U7 Suburban Periphery I
U2 Principal Urban Centers II U8 Suburban Periphery II
U3 Metro Cities I U9 Small Towns
U4 Metro Cities II U10 Rural I
U5 Urban Outskirts I U11 Rural II
U6 Urban Outskirts II
• ArcGIS Business Analyst—This wizard-driven desktop software allows you to perform quick and
easy analyses. Community Tapestry integrates seamlessly into the existing data and the applications.
• Segmentation Module—A wizard-driven, optional add-on for ArcGIS Business Analyst can be
used to understand consumer behavior; create target profiles of different consumer types; and
generate reports, maps, and charts that show detailed data about consumers.
• Community Coder—Community Tapestry is included in this geocoding software, now
completely integrated into ArcGIS, along with interactive features such as the ability to sort the
Tapestry Segmentation Area Profile by segment name, customers, penetration, base, or segment
index. A report called Customer Geographic Complete by ZIP Code, State, County,
and Core-Based Statistical Area allows you to list the top user-specified areas.
• Community Sourcebook•America with ArcReader—Community Tapestry segmentation data
at the census tract geography level is included.
Workflow for ESRI’s Community Vision
Data Tapestry Data
with Appends Customer Market
Tapestry Profile Potential Data
Identifying Core and
Reports and Maps
ESRI Data Analysts
C: Challenge Scenario
• See handout
• Half hour in your team
GIS in Business Seminar Teaching Team – “Classmate Challenge”
For September 22, 2009
Met Council has issued an RFP for GIS planning and consulting.
To contract with a three-person GIS team that will identify three existing “big box” retail
locations in the Twin Cities most suitable for the addition of a second story of housing.
• Likely candidates will be the Targets and/or Wal-Marts in the Twin Cities, though
Byerlys, Lunds, Rainbows, or Cub are possibilities.
• Any big box retail candidate in the Twin Cities will have the
engineering/architectural capabilities for a second story.
• Budget: still TBD.
• A big box retail candidate selected must have a food department.
• A big box retail candidate selected must have square footage between roughly
140,000 and 200,000. (The mean square footage of a Target SuperCenter is
174,000, example the one in Roseville is close to that.)
• A big box retail candidate selected must be located within 2500 meter buffer of
residents who fall into both high and low median HH income groups as verified
by Census 2000 data using tracts as the level of granularity.
• A big box retail candidate selected must be suitably located adjacent to a bus
line. The GIS team is asked to define (briefly) a “suitable” proximity criteria.
• A team approach
Team’s timeframe to produce a plan: half hour
• Presented verbally, Powerpoint not needed.
• Must include one or more maps.
• 10 minute duration.
Met Council has engaged Mr. John Ebert, Associate Director of M.Sc. GIS Program at
Saint Mary’s U, to judge presentations and award the contract.
Available shapefiles for Minnesota:
bus_routes_l, county_ctu_2000, shopping_centers, tl_2008_27_tract00 (tract
shapefiles), and TR_HHOLD (midwest-west US household census data in .DBF format).