APPLICATIONS OF BUSINESS GEOMATICS

Business geomatics is concerned with the testing of theories, and the discovery of patterns and regularities, that explain and predict both spatially and aspatially referenced information. Prior to 1990, in practice this duality was often ignored, even in site location analysis, in favour of more conventional aspatial multivariate modeling. In recent years, business geomatics has evolved to take into account both referential features. These technologies have facilitated the linking of geo-referenced databases (fusion), and hence promoted data mining - the search for regularities and associations through independent databases which are connected (that is, made relational) via spatial and aspatial identifiers.

The information involved in business geomatics relates to the consumer service sector of the economy. This includes retail activities, personal services, consumer related FIRE services (that is, retail aspects of finance, insurance, and real estate), restaurants, and entertainment facilities. In Canada, this sector of activities provides jobs for about 5.3 million people, or about 43% of the labour force, working in 1.2 million locations. As it is individuals and household units that provide the demand (market), and commercial enterprises, or units (stores-outlets) that provide the supply, and each of these has a spatial location, it is vitally important that analytical work be undertaken at the highest level of geo-referenced aggregation possible - ideally involving unit record data relating to individuals or households.

BUSINESS GEOMATICS FOR MARKETING APPLICATIONS

The figure above illustrates the ways in which elements (examples only) of a business-oriented spatial data warehouse (the businesses involve the consumer service sector), which includes supply-side and demand-side databases at various levels of aggregation often fused with proprietary client data, that may be utilized in various types of management and strategic planning activities. These activities may include such activities as site screening and selection, store portfolio segmentation, network planning, and so forth. Whatever the management objective, the information contained within the extensive spatial data warehouse has to be massaged or manipulated in some way for it to be useful in the management exercise. This is the function of a decision-support system, which provides modeling and analytical tools, along with visualization capabilities to enhance the business decision process.

MARKETING APPLICATIONS

Many business organizations in Canada have already adopted the basic GIS approach to support marketing decisions. Hernandez and Biasiotto (2001) note that about 60 per cent of major retailers have adopted GIS. Yet, for many, application is limited to rudimentary market mapping. The development of systematic scientific approaches to decision support have not really occurred. There remains significant potential to develop more rigorous business geomatic-based approaches to decision support. Applications include, for example customer relationship management, network planning, site selection, marketing planning, and strategic business development.

Source: MacEachren & Kraak, 1998

Source: MacEachren & Kraak, 1998

The Canadian Government have invested 'seed' funds to support the development of business geomatics and promote technological innovations through public-private partnerships. The GEOIDE mandate is to encourage greater use of geomatics in decision-making and information-dissemination (diffusion), while at the same time supporting basic research in acquisition, transformation, and data management technologies and promoting new application fields (especially in the service sector). These objectives of intensifying activities at the end of the geomatics information life course, and placing greater emphasis on new fields within the industry, are aimed at strengthening the role of the industry in the Canadian economy.

INNOVATION IN THE NEW ECONOMY

The new economy is characterized by a global business environment that is both knowledge and technology driven. Corporations throughout the world are transforming their business models and organizational structures to exploit and capitalize on technology-enabled competitive opportunities. Those that are unhindered by 'conventional wisdom' and traditional approaches to decision-making will represent the leaders in the new economy. Recent advances in decision-support theory, that reflect new (and near future) advances in information processing are playing an increasingly integral role within these newly emerging business models. Globalization, corporate concentration and competition have formalized the decision-making activities of many organizations. As accountability and risk have increased, so has the need for decisions to be based on both qualitative insights and, critically, structured quantitative analysis. Most businesses today have realized that improving the way in which they interpret their data is the key to making better business decisions. Corporations are investing substantial amounts of capital in collecting, developing and maintaining voluminous data resources. The widespread development of data warehousing technologies and associated large-scale relational databases has provided the platform for the handling of business data. Despite these technological advances, many corporations remain 'data rich and information poor', for the process of knowledge-creation has been constrained due to a lack of techniques that facilitate the analysis and visual display of spatial business data.

SPATIAL TECHNIQUES AND TECHNOLOGIES

Currently, a myriad of non-spatial statistical and data mining techniques are typically used to analyze and interpret data, such as deductive multivariate statistics, neural networks and genetic algorithms. In terms of spatial analysis, developments in geomatics (in particular, geographical information science) have augmented traditional databases with spatial data handling capabilities. Most GIS, however, are based on old technologies that are rigidly two-dimensional and are limited in terms of analysis and visual display of data. Conventional techniques provide decision support as a series of static snap-shots, and do not allow the decision maker (user) to explore, visualize or dynamically interact with the data. Geovisualization (or visual spatial data mining) is emerging as new area of geomatics research, fuelled by significant improvements in computer processing, data storage and graphical rendering, that enables modeling and simulation applications to come alive with such features as dynamic mapping and 3D data representation. Geovisualization provides the theoretical constructs and computer-based techniques to enhance visual display and analysis of multi-dimensional data. The primary goals are to facilitate interactive visual exploration of data, support searches for the unknown and enhance decision support through acceleration of the knowledge-creation process.