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Kevin Byrne Offering a Team Study of GIS in Business, 2009
Kevin Byrne Offering a Team Study of GIS in Business, 2009
Kevin Byrne Offering a Team Study of GIS in Business, 2009
Kevin Byrne Offering a Team Study of GIS in Business, 2009
Kevin Byrne Offering a Team Study of GIS in Business, 2009
Kevin Byrne Offering a Team Study of GIS in Business, 2009
Kevin Byrne Offering a Team Study of GIS in Business, 2009
Kevin Byrne Offering a Team Study of GIS in Business, 2009
Kevin Byrne Offering a Team Study of GIS in Business, 2009
Kevin Byrne Offering a Team Study of GIS in Business, 2009
Kevin Byrne Offering a Team Study of GIS in Business, 2009
Kevin Byrne Offering a Team Study of GIS in Business, 2009
Kevin Byrne Offering a Team Study of GIS in Business, 2009
Kevin Byrne Offering a Team Study of GIS in Business, 2009
Kevin Byrne Offering a Team Study of GIS in Business, 2009
Kevin Byrne Offering a Team Study of GIS in Business, 2009
Kevin Byrne Offering a Team Study of GIS in Business, 2009
Kevin Byrne Offering a Team Study of GIS in Business, 2009
Kevin Byrne Offering a Team Study of GIS in Business, 2009
Kevin Byrne Offering a Team Study of GIS in Business, 2009
Kevin Byrne Offering a Team Study of GIS in Business, 2009
Kevin Byrne Offering a Team Study of GIS in Business, 2009
Kevin Byrne Offering a Team Study of GIS in Business, 2009
Kevin Byrne Offering a Team Study of GIS in Business, 2009
Kevin Byrne Offering a Team Study of GIS in Business, 2009
Kevin Byrne Offering a Team Study of GIS in Business, 2009
Kevin Byrne Offering a Team Study of GIS in Business, 2009
Kevin Byrne Offering a Team Study of GIS in Business, 2009
Kevin Byrne Offering a Team Study of GIS in Business, 2009
Kevin Byrne Offering a Team Study of GIS in Business, 2009
Kevin Byrne Offering a Team Study of GIS in Business, 2009
Kevin Byrne Offering a Team Study of GIS in Business, 2009
Kevin Byrne Offering a Team Study of GIS in Business, 2009
Kevin Byrne Offering a Team Study of GIS in Business, 2009
Kevin Byrne Offering a Team Study of GIS in Business, 2009
Kevin Byrne Offering a Team Study of GIS in Business, 2009
Kevin Byrne Offering a Team Study of GIS in Business, 2009
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Kevin Byrne Offering a Team Study of GIS in Business, 2009

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  • 1. GIS In Business As Topic for Week 3’s Facilitation Seminar in Saint Mary University’s Advanced Modeling Course ______________________________ Kevin Byrne, Dan Haglund, and Lisa Schickendanz September 21, 2009  
  • 2. Goal 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.  
  • 3. Our process • Concept mapped the universe of Business GIS • Concentration basics derived from map • Our core presentation in 3 parts: A, B, C (statement clusters exemplified and demonstrated • Speed challenge  
  • 4. 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 migration. 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.  
  • 5. 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.  
  • 6. 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.  
  • 7. 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)  
  • 8. 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 physics.” http://en.mimi.hu/gis/gravity_model.html  
  • 9. “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.”  
  • 10. “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.”  
  • 11. “The gravity model can be used to estimate traffic flows, migration between two areas, and the number of people likely to use one central place.” http://en.wikipedia.org/wiki/Gravity_model_of_migration  
  • 12. Gravity Model http://geography.about.com/library/weekly/aa031601a.htm Geography 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 Literature 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 Gravitation 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 features. 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
  • 13. 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 commercial centers. 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 status quo. 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 Name Email subscribe 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
  • 14. Case example • Retail Trade Analysis, Donald Segal • Gravity Model • Drive Time Analysis • POS-Based Analysis  
  • 15. Retail Trade Area Analysis: Concepts and New Approaches By Donald B. Segal Spatial Insights, Inc. 8221 Old Courthouse Road Suite 203 Vienna, VA 22182 USA
  • 16. 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.
  • 17. 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.
  • 18. 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.
  • 19. 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.
  • 20. 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.
  • 21. 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 characteristics. 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.
  • 22. 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  
  • 23. Community Tapestry ™ ™ Handbook Industrious Urban Fringe Connoisseurs
  • 24. E 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
  • 25. Software • 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.
  • 26. Workflow for ESRI’s Community Vision 123456789 123456789 123456789 123456789 123456789 123456789 Customer Geocoding Data Demographic Community Data Tapestry Data ESRI Data Demographics Geocode Tapestry Customer Data with Appends Customer Market Tapestry Profile Potential Data Identifying Core and Developmental Customers PDF Rep ort Adobe Executive Summary Reports and Maps ESRI Data Analysts
  • 27. C: Challenge Scenario • See handout • Half hour in your team • Prizes!  
  • 28. GIS in Business Seminar Teaching Team – “Classmate Challenge” For September 22, 2009 Scenario: Met Council has issued an RFP for GIS planning and consulting. Their goal: 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. Their givens: • 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. Their requirements: • 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 Team’s plan: • Presented verbally, Powerpoint not needed. • Must include one or more maps. • 10 minute duration. Their decision: 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).

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