What is Multi-Criteria Overlay Analysis with QGIS?

Multi-criteria overlay Analysis MCOA is a strong tool in geographic information systems GIS for decision-making and spatial analysis. When several criteria which can be factors or constraints are combined and placed on the same map, it is easy to determine areas suitable for a certain use. There are many freely available tools for MCOA in QGIS, which is user friendly and easy to learn for novices and experts.

Steps Involved in MCOA with QGIS:

Data Collection: You obtain other spatial data layers such as the land use layer, slope, road-network layer, water body layer, or any other layer depending on the study area.
Data Preparation: Rasterize all the data layers, and make them homogeneous at least concerning the grade as well as the extent of cell sizes.
Criteria Weighting: In the case of criteria this has entailment of establishing order of priority of the criteria in so far as the level of importance. This can be done for instance by a tool like the Analytical Hierarchical Process (AHP).
Reclassification: Overlap conventional the data layers by quantifying them on the same scale (for instance on a scale of one to ten to compare.
Overlay Analysis: Other operations would be done on the map such as weighted sum so that one gets a map of the composite suitability map from the weighted layers.
Result Interpretation: Once the map has been obtained the next step one is required to follow is the step where one is supposed to look at the map and determine which of the criteria are best met.

Applications:

Urban Planning: List the successful accomplishment of among other aspects; the identification and establishment of areas that are desirable for the commencement of structures.
Environmental Management: Suitability modeling in wildlife conservation: The case of application.
Agriculture: Prediction of areas of the globe where different sorts of crops should be grown.

How Can I Assign Weights to Different Criteria?

Weighting is a very sensitive part of MCOA because it seeks to compare the studied criteria and determine the weight or significance of each factor in the analysis. In QGIS, weights can attached using a weighted overlay approach most of which employ the pairwise comparison technique such as the AHP. Here's a simple approach:

Identify Criteria: Also, complete the following concerning your decision:
Pairwise Comparison: Rank each criterion on the face value against all the other criteria to find out how they compete.
Assign Weights: If required transmute the results of comparison into numerical scales, more often into weights adding up to 1 (or 100%).
Apply Weights: Each criterion has its map layer, and during the overlay process, the map layer of each criterion should be multiplied by the weight of the criterion.

There are simply algebraic operations such as the Raster Calculator and the Weighted Sum tool through which actions such as the integration of the weighted criteria within a composed map can be carried out.

What if Some Criteria are Qualitative Rather Than Quantitative?

Movement of the qualitative criteria however becomes difficult in the MCOA framework because they are not anchored on a quantitative framework. However, QGIS allows for the integration of qualitative data by converting these criteria into a quantitative format using several methods: Qualitative data can be incorporated into QGIS by transforming these criteria into quantitative by several functions:

Categorical Reclassification: Exemplify each of the performance indicators in a qualitative criterion by numbers. For instance, it is possible to give values concerning types of land known as urban, agricultural, and forest and the rest.
Expert Judgment: Integrate other studies to place subsets of the qualitative factor in order from the scale that would express the factor’s importance.

The fact that such values can be converted just enables other quantitative attributes to be keyed into the weighted overlay analysis.

What if Some Criteria are Both Quantitative and Qualitative?

In cases where criteria are both quantitative and qualitative, you can integrate them into MCOA by applying a hybrid approach: When criteria are both quantitative and qualitative, one can exercise several options to incorporate into MCOA – by applying several measures:

Quantify Qualitative Data: Earlier noted, qualitative data should be measured by putting them on a quantitative scale; that is Numerical values.
Normalize Quantitative Data: Considering that all the quantitative data is measured in the same units as each other and most of the time in the range between 0 and 1.
Combine Criteria: Integrate a weighted method on top of it to set the Quantitative and Qualitative data side by side as a single analysis.

Fortunately, as has been explained earlier, QGIS does not have a problem dealing with multiple data types at the same time, therefore the above-mentioned criteria can be used concurrently for comprehensive analysis.

What if Some Criteria Have Missing Data or Uncertainty?

If there is or is likely to be any doubt in criteria or lack of data, this may reduce the effectiveness of MCOA. QGIS provides several strategies to address these challenges: To handle these challenges QGIS has several approaches, which are explained below.

Data Imputation: There are there are some techniques known as interpolation or imputation which can be used for estimation of missing value from available value.
Sensitivity Analysis: Perform sensitivity analysis to understand the relationship between variability in specific factors and results. This helps in identifying the extent of the data that must be considered as most vital.
Fuzzy Logic: Vagueness is controlled by the use of fuzzy logic and for the criteria you can indicate their rationale in terms of probability or degree of truth.

Can You Show Me an Example of Using Fuzzy Logic for Uncertain Data in QGIS?

This shows that the use of fuzzy logic is therefore useful in the management of uncertainty in spatial data analysis. In QGIS, you can implement fuzzy logic by following these steps: The following is a brief guide to using fuzzy logic in QGIS:

Define Fuzzy Membership Functions: Find out how each of the criteria will be translated into a fuzzy membership value which is, usually in the range of 0 to 1.
Apply Fuzzy Operations: This involves propositions where there are logical operators such as conjunction & AND, disjunctions & OR, and negations NOT which shows many possibilities of the made decision.
Fuzzy Overlay: The fuzzy overlay analysis has to be done in such a manner that the appropriate map of evaluation criteria should be overlaid on the map to get the final map of a suitable area.

Example: For instance; let you research the viability of prospecting the land for cultivation through soil fertility and water in most instances known as isifiable. Therefore, from the membership functions of the criteria for instance a sigmoid function for soil quality and a linear function or distance to water sources there is a combination of the criteria through the fuzzy logic which results in a refined suitability map because the conception of the map involves an uncertainty factor.

Can You Recommend Any Other GIS Software for Fuzzy Logic Analysis?

While QGIS is a robust tool for fuzzy logic analysis, other GIS software also offers powerful capabilities: While QGIS is a robust tool for fuzzy logic analysis, other GIS software also offers powerful capabilities:

ArcGIS: ArcToolBox, especially with the help of the extension Spatial Analyst, offers quite several specialized tools for fuzzy logic and MCOA, such as fuzzy membership and overlay.
GRASS GIS: Proprietary and cost-free, its flexible Geographical Information System (GIS) tools are fully developed and extensive for fuzzy logical applications.
IDRISI: Specific to raster-based GIS analysis, IDRISI has a large endowment of fuzzy logic and is widely employed in environments and suitability modeling.

Many of these SWs such as QGIS offer the capabilities to handle the above sources of uncertainty and conduct sound spatial analysis to support decision making.

Conclusion

Multi-criteria overlay Analysis with QGIS is a highly effective and flexible tool for spatial decision-making. To ensure effective rating of criterion weights, dealing with qualitative and quantitative information, managing missing or uncertain values, and applying fuzzy definitions it is possible to produce more or less accurate and sophisticated suitability maps. For the new, aspiring, GIS analyst as well as for the tested and seasoned geospatial professional; QGIS and some other tools and resources provide the firepower to look into spatial challenges and opportunities with the right approaches.

Video Tutorial Multi-Criteria Overlay Analysis