No-Code Wind Farm Site Selection Workflow in GIS | Tutorial

Multi-Criteria Evaluation (MCE), also known as Multi-Criteria Decision Analysis (MCDA), is a systematic decision-making framework used in GIS for suitability analysis that involves identifying a decision objective, defining a decision frame (alternatives), establishing criteria/factors (such as elevation, slope, size, and cost) that determine suitability, applying constraints that limit the search space, standardizing all factors to a common scale, assigning weights to reflect relative importance, and combining these elements using decision rules (such as weighted linear combinations, boolean operations, or fuzzy logic) to produce a suitability index that identifies the optimal decision set.

The final phase combines all prepared criteria into a comprehensive suitability model: (1) Using Raster Calculator to multiply each criterion's rescaled score by its assigned weight (e.g., elevation=0.3, stream distance=0.3, slope=0.2, land cover=0.2); (2) Summing all weighted criteria to produce the final suitability index; (3) Understanding that the output represents a relative index with no physical units; (4) Visualizing results using graduated colors and optionally reclassifying into discrete categories. This demonstrates how multi-criteria decision analysis synthesizes multiple factors into actionable spatial information.

Spatial Multi-Criteria Decision Analysis (SMCDA) is a GIS-based methodology that combines multiple spatial criteria into a single suitability map by assigning standardized scores (typically 0-9) to each criterion layer, then aggregating them through mathematical operations like weighted sum or equal weighting to identify optimal locations for decision-making purposes such as site selection, land use planning, or resource allocation.

Weight assignment determines factor importance in suitability analysis. Weights can derive from expert consultation (professional judgment) or statistical methods like linear regression (coefficient estimation). Common practice assigns decimal weights summing to 1.0. For proximity factors like hospital distance, optimal scoring balances multiple objectives: being too close creates redundancy; being too far reduces accessibility. Intermediate distances receive maximum scores while extremes receive zero. This weighted approach transforms qualitative decision-making into quantitative spatial analysis, ensuring transparent and reproducible site selection processes.

Multicriteria spatial analysis is a decision-making methodology that combines multiple spatial criteria and constraints to identify optimal locations or assess vulnerability. The approach uses GIS software like QGIS to process spatial data through systematic steps: defining the problem and criteria, collecting and preparing spatial data, reclassifying data for comparability, assigning weights to criteria, combining factors to generate synthesis maps, and evaluating results. Three primary analytical methods exist: Analytic Hierarchy Process (AHP) for pairwise comparison-based weighting, Weighted Linear Combination for direct weight assignment, and Fuzzy Logic for handling uncertainty through value intervals. Applications span site selection (landfills, hospitals), risk assessment (erosion, flooding), agricultural suitability evaluation, conservation planning, and urban development prioritization.

Vector overlay operations combine spatial layers: union (all areas with combined attributes), clip (extract within study area), intersect (common areas only), and erase (remove specified areas). Buffer analysis creates zones around features at specified distances, with single-layer and multi-ring buffers. These operations transfer attributes and are essential for proximity analysis, site selection, and understanding spatial relationships between features.

Spatial data analysis studies place characteristics and relationships to determine relationships, detect patterns, assess trends, and make predictions. Spatial overlay combines multiple layers covering the same area, requiring identical coordinate systems and raster cell sizes. The methodology evaluates feature overlaps using boolean logic through a systematic workflow: define problems, identify layers, reclassify data, weight layers, combine, and analyze. Common vector tools include Append, Buffer, Clip, Dissolve, Erase, Identity, Intersect, Union, Symmetrical Difference, and Update. The common feature problem causes sliver polygons when identical features appear differently across layers. Proximity analysis uses buffers to identify nearby features, with types including simple (dissolved boundaries), compound (maintained boundaries), nested (multiple distances), and variable distance buffers. Spatial interpolation estimates unknown values using known measurements: Nearest Neighbor assigns nearest observation values, Fixed Radius averages within specified distance, Natural Neighbor weights by area sizes, and Inverse Distance Weighting gives closer points more weight. Global interpolation uses all points while local interpolation samples nearby points within proximity.

Geoprocessing encompasses spatial analysis operations including proximity (finding nearby features) and overlay (combining layers to identify intersections). QGIS organizes tools into three categories: Data Management (join by location), Research (select by location), and Geoprocessing (buffers and overlays). Buffer and union are primary tools capable of performing 80-90% of spatial operations. Set theory concepts form the mathematical foundation: Intersection (A AND B), Union (A OR B), Symmetrical Difference (A XOR B), and Difference (A minus B). These same logical operations apply to both attribute-based SQL queries and spatial operations. The main overlay operations include: Intersect (unique combinations within intersecting areas), Union (all unique combinations), Clip (cookie cutter effect), and Symmetrical Difference (erase tool). The buffer tool generates areas around features at specified distances, with a dissolve option to prevent double-counting in connected networks.

This section covers vector geoprocessing operations. The Buffer tool creates zones around features: (1) Vector > Geoprocessing Tools > Buffer, (2) Select input layer, (3) Enter distance, (4) Run tool. For lines, buffer creates parallel lines; for points, circles; for polygons, expanded areas. Vector overlay operations: (1) Difference - subtracts overlay from input (input minus overlay), (2) Intersection - keeps only overlapping features, (3) Union - combines all features including overlaps. These operations are fundamental for spatial analysis, such as finding areas within multiple zones or combining datasets. The output maintains attribute information from both input layers.

Advanced geospatial analysis employs three key operations: overlay analysis identifies spatial relationships between layers (e.g., finding trailheads outside city boundaries using geopandas.overlay() with 'difference' operation); buffering creates zones around features at specified distances (using buffer() method with meter-based CRS); spatial join finds intersections between buffered zones and other features (using sjoin()). These operations enable proximity analysis to answer questions like finding trailheads within one mile of transit stops.

Optimal wind power plant locations must satisfy multiple constraints: minimum elevation (above 1500m), minimum average wind speed (above 3 m/s), exclusion of protected areas (national parks, forests, federal lands), avoidance of urban zones and airports, and proximity to electrical grid infrastructure (within 30 km).

When siting wind farms, developers prioritize two main factors: wind resource quality and grid connectivity. Wind speed is critically important because power output is proportional to the cube of velocity, making site selection highly sensitive to wind conditions. Proximity to transmission infrastructure is equally crucial because building new transmission lines is expensive. Additional considerations include environmental impacts, species populations, forest fragmentation concerns, and watershed protection. Approximately 80% of existing and projected wind turbines in Pennsylvania are located in forested areas, particularly along ridgelines and Appalachian fronts where elevation changes create increased wind speeds.

Wind farm site selection requires consideration of three main factors: (1) Wind speed - areas with average wind speeds of 25-40 km/h for most of the year are most suitable for wind turbine installation, (2) Geographic suitability - turbines should be installed away from areas prone to natural disasters like earthquakes and typhoons, and away from areas that would obstruct air traffic, (3) Grid connectivity - commercial wind turbines typically have capacities of 1.5-3 MW, so proximity to power grid infrastructure is essential for efficient power transmission.

Wind speed is the single most important factor when determining suitable locations for wind farms. Higher average wind speeds result in significantly greater power generation potential. In New Brunswick, areas with higher average wind speeds are shown on maps in orange to red colors. All three NB Power wind facilities are located in these high-wind areas. Elevating turbines above ground level reduces interference from trees and other obstacles, resulting in faster, cleaner wind flow.

The selection of locations for wind farms depends on multiple factors, with the average wind speed in the region being a critical consideration. Higher average wind speeds generally indicate better potential for wind energy generation.