Geographic analytics

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Geographic analytics or geographic analytics is an analytical approach of strategic management and data analytics in order to be able to efficiently make geographical decisions (location decisions, regional activities), such as choosing the location of a new warehouse or defining a marketing campaign for a specific region. Data, information and framework conditions are visualized on maps in order to derive recommendations for action.

Compared to geographic information systems ( GIS ), which primarily aim to represent information on maps (descriptive analytics), geographic analytics also focuses on decision-making based on data visualization on the map (prescriptive analytics).

background

Purely mathematical approaches that are used in data analysis to support management decisions often have the disadvantage that they require extensive data. This is usually associated with a great deal of effort in terms of data provision, data cleaning and data interpretation. Furthermore, framework conditions that are difficult to grasp can occur in practice, which make the theoretically optimal solution impractical.

  • Example [logistics]: If the optimal location for a warehouse location in a logistic network is to be determined, geographical focus models (so-called center-of-gravity analyzes) are used. To minimize overall costs, these models use transport volume data, customer locations, cost data, etc. to determine the optimal location. However, framework conditions often arise that cannot be formulated mathematically from the outset or only poorly: regulatory obstacles, transport infrastructure, limits and other physical framework conditions.

In practice, such framework conditions are often only recognized at the end of the data analysis. The general conditions must then be taken into account in the model and the model recalculated. In the worst case, a completely new approach to analysis has to be chosen. This delays the decision-making process and is usually associated with a lot of effort.

Application and purpose

In geographic analysis, the data analysis starts with the visualization of essential basic data on a map. By integrating technical experts, an attempt is made to work out essential framework conditions and focal points of the question under consideration using the visualization.

As a result, the solution space, i.e. the number of possible solutions for the data analysis, is restricted. In addition, framework conditions, but also data errors, are recognized in the early stages of the analysis. Only then are traditional data analysis methods used to determine the optimal solution.

The geographic analytics approach means that less data is required for the overall analysis and the time required is significantly reduced. In addition, impractical or incorrect solutions are identified and excluded even before the purely data-based analysis.

Areas of application

Geographical analytics is used in the context of data analysis to support management decisions that contain a geographical component, for example location decisions, marketing campaigns, placement of service or customer centers, etc.

Example industries:

Example: Planning the location of a distribution center in order to keep the logistic costs when distributing the products to customers to a minimum

Example: Opening a new store in a strategically favorable location for commuters

Example: Planning the locations of test wells in order to develop a new oil field at the lowest possible cost

Example cross-sectional areas:

Example: Developing a regional marketing campaign for a new product

Example: Traffic planning / planning of traffic expansion in a large city in order to reduce traffic jam times

Example: Planning the placement of ATMs in order to achieve the highest possible degree of coverage in a region

Concept emergence

The term and the methodology of Geographic Analytics were first described in 2013 by Jozo Acksteiner and Claudia Trautmann in the Supply Chain Management Review Magazine .

Individual evidence

Web links