Purpose - The present research has three major aims: to examine the concept of geographic information in business application through a critical review of different definitions and conceptualization that, from several views, literature and business applied sectors present; to individuate a logical framework to support the decomposition of spatial analysis models used to support business decision making, and a conceptualization scheme to help the user/analyst to gain insight into geographic representation inherent in location intelligence applications; finally, to apply the framework proposed to some common location intelligence problem statements to evaluate its meaningfulness. Design/methodology/approach – This research critically reviews existing literature of business application of Geographic Information Systems; it adopts the Beguin-Thisse framework of geographic space to focus on how is representation of geography included in spatial analysis techniques and models used to afford location intelligence problems. The logical framework proposed is then applied to some analytical business approaches: trade area analysis models, retail location models, location allocation models, and spatial allocation models. Findings – This research has identified a logical framework, named geo-element mapping chart (GEMC), to support mapping and making practical evidence of the “geographic dimension” (distance, direction, connectivity, and shape) inside spatial analysis models used to explore some specific business problems. The general conclusions are that traditional spatial analysis approaches simplifies its representation of geography, using principally the “classic distance dimension”. The GEMC has showed that other dimensions, such as connectivity and shape, can be present in some models, but their practical conceptualization and successive implementation for more insightful spatial modelling approaches require multidisciplinary competencies and computational expertise. Research implications/limitations – The idea on which the framework proposed (GEMC) is based is that, for business applications, every spatial analysis models can be decomposed using some elementary model building blocks, which, subsequently, can contains in their definition a “geographic dimension” or represent an element of the geographic space upon which conceptually the model works. The GEMC has been applied only to some case studies, therefore its implementation need to be extended to evaluate other modelling contexts, such as spatial statistics and spatial econometrics, to provide more general considerations and coclusions. Practical implications – Understanding the use and the value of geography and geographic information in business decision making, i.e. the GEMC major purpose, can support further developments of specific GIS-based support tools and related spatial analysis techniques. The development of a framework to decompose models and then to make evidence of the representation of geographic elements and dimensions inherent in the problem, can support a more useful management of spatial analytical models, helping a potential user to build new location intelligence models by reusing existing modelling approaches with their “geographical meaning”, and facilitating a more intelligence model selection in a complex problem solving environment (such as Knowledge Based Spatial Decision Support Systems and Knowledge Based Planning Support Systems). In other words, the generalization of the GEMC application to other spatial analysis approaches used to model different location intelligence problem, could potentially help to build a kind of “library” (model library) of different approaches used to model several geographic component, inherent in business problems, that have in the spatial dimension an important variable of their definition and for their effective solution. Originality/value – This research organizes and proposes a framework of integration of the different definitions related to the use of geographic information and Geographic Information Systems in the business sector. It attempts to formalize and test in some specific contexts a logical approach to evaluate geographic representation in spatial analysis models used to support decision making processes. The GEMC is intended to be a flexible approach to highlight where geography comes into play during spatial models formulation. The dissertation offers an original applied examination of some issues that have an impact on many aspect of location intelligence applications. By adopting the notion of GEMC, this research provides a detailed analysis of some methodologies used to model specific spatial business problems. The author is not aware of this logical approach having being applied elsewhere in research or application.

Mastrodonato, S.L. (2009). Geographic representation in location intelligence problems analysis: the geo-element mapping chart.

Geographic representation in location intelligence problems analysis: the geo-element mapping chart

MASTRODONATO, STEFANO LUIGI
2009-08-31

Abstract

Purpose - The present research has three major aims: to examine the concept of geographic information in business application through a critical review of different definitions and conceptualization that, from several views, literature and business applied sectors present; to individuate a logical framework to support the decomposition of spatial analysis models used to support business decision making, and a conceptualization scheme to help the user/analyst to gain insight into geographic representation inherent in location intelligence applications; finally, to apply the framework proposed to some common location intelligence problem statements to evaluate its meaningfulness. Design/methodology/approach – This research critically reviews existing literature of business application of Geographic Information Systems; it adopts the Beguin-Thisse framework of geographic space to focus on how is representation of geography included in spatial analysis techniques and models used to afford location intelligence problems. The logical framework proposed is then applied to some analytical business approaches: trade area analysis models, retail location models, location allocation models, and spatial allocation models. Findings – This research has identified a logical framework, named geo-element mapping chart (GEMC), to support mapping and making practical evidence of the “geographic dimension” (distance, direction, connectivity, and shape) inside spatial analysis models used to explore some specific business problems. The general conclusions are that traditional spatial analysis approaches simplifies its representation of geography, using principally the “classic distance dimension”. The GEMC has showed that other dimensions, such as connectivity and shape, can be present in some models, but their practical conceptualization and successive implementation for more insightful spatial modelling approaches require multidisciplinary competencies and computational expertise. Research implications/limitations – The idea on which the framework proposed (GEMC) is based is that, for business applications, every spatial analysis models can be decomposed using some elementary model building blocks, which, subsequently, can contains in their definition a “geographic dimension” or represent an element of the geographic space upon which conceptually the model works. The GEMC has been applied only to some case studies, therefore its implementation need to be extended to evaluate other modelling contexts, such as spatial statistics and spatial econometrics, to provide more general considerations and coclusions. Practical implications – Understanding the use and the value of geography and geographic information in business decision making, i.e. the GEMC major purpose, can support further developments of specific GIS-based support tools and related spatial analysis techniques. The development of a framework to decompose models and then to make evidence of the representation of geographic elements and dimensions inherent in the problem, can support a more useful management of spatial analytical models, helping a potential user to build new location intelligence models by reusing existing modelling approaches with their “geographical meaning”, and facilitating a more intelligence model selection in a complex problem solving environment (such as Knowledge Based Spatial Decision Support Systems and Knowledge Based Planning Support Systems). In other words, the generalization of the GEMC application to other spatial analysis approaches used to model different location intelligence problem, could potentially help to build a kind of “library” (model library) of different approaches used to model several geographic component, inherent in business problems, that have in the spatial dimension an important variable of their definition and for their effective solution. Originality/value – This research organizes and proposes a framework of integration of the different definitions related to the use of geographic information and Geographic Information Systems in the business sector. It attempts to formalize and test in some specific contexts a logical approach to evaluate geographic representation in spatial analysis models used to support decision making processes. The GEMC is intended to be a flexible approach to highlight where geography comes into play during spatial models formulation. The dissertation offers an original applied examination of some issues that have an impact on many aspect of location intelligence applications. By adopting the notion of GEMC, this research provides a detailed analysis of some methodologies used to model specific spatial business problems. The author is not aware of this logical approach having being applied elsewhere in research or application.
A.A. 2008/2009
Geoinformazione
18.
GIS; spatial analysis; location intelligence; model management system
Settore ICAR/06 - Topografia e Cartografia
English
Tesi di dottorato
Mastrodonato, S.L. (2009). Geographic representation in location intelligence problems analysis: the geo-element mapping chart.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/1061
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