UNIVERSITY OF ALBERTA INTEGRATING A DESKTOP GEOGRAPHIC INFORMATION SYSTEM (GIS) WlTH A LOCATION-ALLOCATION MODEL ROD SCHATZ O A thesis submitted to the FacuIty of Graduate Studies and Resemh in partial fiil filIrnent of the requirements for the degree of Master of Science. Deparbnent of Earth and Atrnospheric Sciences Edmonton, Alberta Spring 2000 National Library Bibliothèque nationale 1+1 ,canada du Canada ~uisitionsand Acquisitions et Bibiiographic SeMces seMces bibliographiques The author has granted a non- L'auteur a accordé une Licence non exclusive licence allowing the exclusive permettant à la National Liiof Canada to Bibliothèque nationale du Canada de reproduce, loan, distriiute or sell reproduire, prêter, distritbuer ou copies of this thesis in microforni, vendre des copies de cette thèse sous paper or electronic formats. la fonne de microfiche/nlm, de reprodnction sur papier ou sur format électronique. The author retains ownership of the L'auteur conserve la propriété du copyright in this thesis. Neither the droit d'auteur qui protège cette thèse. thesis nor substantid extracts fiom it Ni la thèse ni des extraits substmtieIs may be printed or otherwise de celle-ci ne doivent être imprimés reproduced without the author's ou autrement reproduits sans son permission. autorisation. Abstract This research integrates a Geographic information System (GIS), Relational Database Management System (RBDMS)and Location-Allocation (LA) into a system that alleviates data handling problems in locational analysis. Custom developed hnctions are incorporated into an ArcViewtB GIS add-on module known as ArcViewls@ Location-Allocation (ARCLA) modelling extension. ARCLA simplifies many of the tedious data promsing tasks such as data query, display and manipulation. ARCLA works in conjunction with a RDBMS to provide real world data sets to use with LA models and socio-economic data analysis. The RDBMS stores the 1996 Census of Population data for al1 Census Metropolitan Areas (CMA) in Canada. The use of this system demonstrates that GIS is very beneficial ?O LA modelling. Using this systern, the effects of data sumgation emr are investigated for fifieen mehopolitan Canadian cities. The sumgation error results reveal that surrogation error is always present, albeit in some instances it may be minimal. Dedication This thesis is dedicated to my parents for their wonderful support during rny lengthy academic pursuh and to my fiancée, Stacy, who has always ken a wonderful source of encouragement and counsel. Acknowledgments 1 would like to thank a number of people who provided assistance during the development of this research study. Heidi Grainger and Stacy Grainger for their fantanic editorial skills. I would also like to congratulate Stacy for the success of her own dissertation in the Fall of 1998. AI Muirhead and Danen Wolchyn of Harrison Muirhead Systems Inc. for their guidance with many of technical database issues encountered during the implementation of this research. Arndt Buhlmann for his wonderhl listening skills as 1 passed ideas by him during the many stages of this research. Alex Miller of ESRI Canada for donating the Programming with Avenue (advanced course) notes, which proved to be a trernendous resource during the developrnent of the ArcView Extension. GDS & Associates Systems Inc.. my present employer, for their trernendous understanding and support to ensure that I was able to finish this research in a timely manner. Without the assistance of everyone mentioned above this research would not have corne to miition. TABLE OF CONTENTS Chapter Page 1. Introduction 1 Thesis Outiine 3 II. Background to the Study S Classic Location-allocation rnodels 5 Real world data structure 9 Geographic Information Systems and rnodel integration 10 Relation of this Study to the Literature 13 111. Research Problem 15 Research Problem 15 GIS Fundamentals 15 Location-Allocation Mode1 18 Location-Allocation Study 19 Limitations of this research 19 VI. 1996 Census of Population Data 21 Database Fundarnentals 21 1996 Census of Population Data 26 Census of Population - Attribute Data 27 Development of a Census of Population Attribute Database 28 1996 Spatial Centroid Data 35 Census of Population - Spatial Databases 36 Census of Population - Attribute Data Quality, Sampling, Weighting And Randorn Rounding 40 Census of Population - Spatial Data Quality 41 CMA Spatial Databases - Metadata 42 Sumrnary 42 V. Integtaong GIS and Location-Allocation Modeb ArcView@ GIS Overview Arcvie- V iews ArcV iedTables Arcvie* Charts Arcvie* Layouts ArcVieiv43 Scripts Conceptual Integration Design Location - Al location Files ARCLA Basics Census Attribute Database Integration Creating the Demand Nodes File Creating the Potential Facilities Nodes File Creating the Location-Allocation Parameter Fi les Executing the Location - Allocation Program Mapping Location-Allocation Solutions in Arcvie- ARCLA Utilities Enhancing ARCLA Summary VI. Location-Allocation Analysis The Location-AIlocation Mode1 Location-Allocation Problem Setup GIS Analysis Sunugation Ermr s-w W. Summary and Discussion First Objective Second Objective Data Analysis Section Applications of the GIS/LA System Conclusion Further Discussion VIII. Bibliography Appendix 1 Appendix 2 Appendix 3 Appendlx 4 Appendix 5 Appendix 6 Appendix 7 Appendix 8 LIST OF TABLES Tables Page Table 4-1: Demopphic variables covered by each Nation Table 32 Table 4-2: Primary CMA regions with their respective population and EA zone values 38 Table 4-3: Secondary CMA regions with their respective population and EA zone values 39 Table 5-1: Location-allocation program input files specifics 5 1 Table 5-2: Location-allocation program solution files spec i fics 53 Table 6-1 : Population breakdowns for the fifleen selected Canadian cities 69 Table 6-2: Location-allocation program set up statistics 70 LIST OF FIGURES Figures Page Figure 4-1: Organization of data as records in a table 22 Figure 4-2: Relational join and query results 24 Figure 4-3: Sample entity relationship diagram for a Customer Order database 25 Figure 4-4: EAuid lookup table - screen capture of a small section of the table 3 1 Figure 4-5: Entity relationship diagram' for the final 1996 Census Attribute Database design 34 Figure 4-6: City of Edmonton - EA zones with representative points (centroids) 36 Figure 4-7: Digital Cartographie File for Victoria, British Columbia at the EA level 37 Figure 4-8: CMA region for Vancouver, British Columbia at the EA level 40 Figure 5-1 ArcView's Graphical User Interface 45 Figure 5-2: Conceptual Inregration design between the Census Attribute Database, ArcView and the location-allocation program 49 Figure 5-3: ArcView's location-allocation extension ARCLA interface 54 Figure 5-4: Demonstration of the final output fiom the ARCLA extension 63 Figure 6-1 : Senior's population per capita in Edmonton 72 Figure 6-2: Children's population per capita in Edmonton 72 Figure 6-3: Total population distribution in Edmonton 73 Figure 64: Final facility allocations for seven preschool children facilities (less then six years of age) in Edmonton 74 Figure 6-5: Final facility allocations for seven senior facilities (63 years of age and oIder) in Edmonton 75 Figure 6-6: Final facility allocations for seven total population facilities in Edmonton 76 Figure 6-7: Cost-E ffe~tivenessGraph for Edmonton 77 Figure 64: Location Error - children and seniors (proper population) demographic grwps compareci to the totai population (surrogate data) for Edmonton 79 Figure 6-9: Sunogation Ermr - seniors (proper population) demographic group compareci to the total population (surrogate data) for al1 fifieen cities 80 Figure 69: Smogation Emr - children (proper population) demographic group compared to the total population (smgate data) for dl fifteen cities 81 Chapter 1. Introduction Over the last 30 yem, location analysts have developed rnethods and models to solve complex problems concerned with detennining the optimal location of facilities. These models, often temed location-allocation (LA) models, simultaneously locate several facilities and allocate demands, expressed as weights at nodes, to them. The overall intention of LA models is to optimally serve the demands at each node, which in tum are allocated to a facility. The complexity of these models has Ied the location analysis community to model problems about real world situations using simplified means. One such means, employed by many of these practitioners, is to simpliQ their real world surroundings by using randomly generated data sets to represent random locations and randorn demand weights. Even today, many in the LA community continue to use randomly generated data sets in place of red world conditions to create, formuiate and test new location models. To truly understand a location model, it is argued that it must be tested with objective, non-structured data, since these models are designed to predict the real worid scenarios. Many of these researchers do not utilize real world data sets as the data possess an inhennt structure, which can bias the results of their research. An example of such structure is the dense concentration of people closer to the central business district (CBD), and the thinning out of people towards the municipal boundary of many North American cities. Srudies that use real world data typically only perform analysis on one city and this can bias their results because of the city's inherent structure. Moreover, real world data involves the time consuming tasks of data