Spatial Analytics and Decision Analysis

Our experienced team leverages GIS technology to provide actionable insights for decision-making in various sectors.

Multi-Criteria Evaluations

We employ sophisticated methodologies to assess and prioritize spatial scenarios based on multiple criteria, facilitating informed decision-making processes.

Our MCE analysis consists of the following steps:

1. Criteria Identification: Identify and define the criteria relevant to the decision-making process. These criteria may include factors such as land use, slope, proximity to infrastructure, environmental sensitivity, geological, geophysical, and economic factors.
2. Data Acquisition: Gather spatial data representing each criterion from various sources, such as satellite imagery and hyperspectral data, digital elevation models, land cover maps, and other available datasets.
3. Data Preprocessing: Prepare and standardize the data layers to ensure compatibility and consistency. This may involve resampling, reprojecting, or reclassifying the data to a common spatial resolution and coordinate system.
4. Weighting: Assign relative weights to each criterion based on its importance in the decision-making process. The weighting process may involve consultation with stakeholders or experts to ensure that diverse perspectives are considered.
5. Overlay Analysis: Overlay the weighted data layers in a GIS environment to generate composite maps that represent the combined suitability or desirability of different locations. Various techniques, such as weighted summation, fuzzy logic, or analytical hierarchy process (AHP), may be used to combine the layers.
6. Sensitivity Analysis: Assess the sensitivity of the results to changes in criteria weights or data inputs to understand the robustness and reliability of the analysis.
7. Decision Support: Use the results of the GIS multi-criteria evaluation to support decision-making processes, such as site selection, exploration targetting, land use planning, environmental impact assessment, and infrastructure development.
8. Foundational Dataset: The result of our MCE is a foundational dataset which enables further spatial investigation including suitability analysis and least-cost path routing.

Suitability Analysis

We evaluate the suitability of study areas for specific purposes, such as site selection for infrastructure and development projects or land use planning. Integrating various spatial datasets and criteria, suitability analysis provides a systematic and transparent framework for evaluating geographic areas, enabling stakeholders to identify optimal locations that meet their objectives while minimizing potential trade-offs.

Least Cost Path Routing

GIS least cost path routing is a spatial analysis technique used to determine the optimal path or route between two locations across a landscape, considering factors such as terrain, land cover, and other impedance factors. The goal is to identify the path that requires the least amount of effort or cost to traverse from the origin to the destination.

Benefits

• Informed Decision-Making: Our spatial analytics enable stakeholders to make well-informed decisions by considering multiple factors simultaneously.
• Cost and Time Efficiency: By identifying optimal routes and suitable locations, we help minimize costs and streamline project implementation processes.
• Risk Mitigation: Through comprehensive analyses, we identify and mitigate potential risks associated with spatial decisions, enhancing project success and resilience.

At Aurora Geosciences, we are committed to delivering tailored Spatial Analytics and Decision Analysis solutions that address the unique needs and challenges of our clients, driving success and sustainability in their projects.