GIS is a system used to analyze specific regions or subregions, useful for various applications like city planning, disaster management, environmental studies etc. It allows us to visually understand patterns and relationships in geographic data.
Church and Murray (2009) mentioned a digital representation approximation problem in GIS called: selective inclusion. Selective inclusion refers to the fact that not all possible data about an area can or will be included in a GIS database.
Selective Inclusion could be due to reasons such as:
- Spatial Sampling: GIS data is often conceived of in terms of layers, which can represent various geographical features such as roads, rivers, vegetation, etc. However, it might not be feasible to include all possible layers or features within a layer due to constraints on resources or the scope of the analysis.
- Temporal Dimension: Geographical data can change over time. For instance, the vegetation of an area can change with seasons or a new road may be constructed. Some GIS data may only represent a cross-section of time rather than reflecting these changes.
- Resource Limitations: Time, budget, and knowledge constraints might mean that not all data can be acquired or created for a GIS database
They suggest that despite the reality of selective inclusion, best practices in GIS use would ensure that relevant features and layers are included in the system.
Selective inclusion can have a variety of outcomes, some of which include:
- Data Quality and Accuracy: Because selective inclusion entails selecting which data to include, it may result in a lack of representativeness or accuracy if some crucial geographic features or time periods are skipped. This might have an impact on the accuracy of the study and the conclusions that are made.
- Bias: Results may be affected if the decision-makers' prejudices are taken into account when choosing which data to include. In statistics, this is frequently referred to as "sampling bias."
- Analysis constraint: Strict inclusion may restrict the range of queries the GIS can address. For instance, the system wouldn't be able to allow analysis concerning a particular feature if data on that feature weren't present in the GIS.
- Scalability and Efficiency: By concentrating exclusively on the most pertinent data, selective inclusion, on the other hand, can make GIS more controllable and effective. This can be particularly essential when dealing with enormous geographic areas and complex data.
- Cost and Time Effectiveness: Resources are needed for data collection, management, and analysis. GIS can be made more time and money efficient by carefully including data, concentrating resources on the most pertinent and important data.
Church, Richard L. & Murray, Alan T. 2008. Business Site Selection, Location Analysis and GIS. Print ISBN:9780470432761. DOI:10.1002/9780470432761. John Wiley & Sons, Inc.
