Location data analysis

Project description

Location data is often unstructured and difficult to process into useful insights. In the Netherlands, biodiversity data lacks a common structure and automated pipelines for generating visuals.

Context

Biodiversity and urban green space planning increasingly rely on spatial data. However, a lack of standardization and tooling hampers effective data analysis and interpretation.

Results

This study successfully developed and demonstrated a geospatial dashboard to analyze and visualize greenery distribution within urban neighborhoods. By integrating a modular system architecture with GeoJSON-based spatial data, the dashboard provides an intuitive platform for representing complex greenery metrics.

The core contribution includes a novel grid-based algorithm that models greenery coverage using key statistical parameters—mean and standard deviation—and translates these into visually meaningful spatial patterns. The algorithm’s ability to adjust greenery clustering through a spread multiplier offers flexibility in representing real-world variability.

The React-based visualization component facilitates rapid, clear comparison across neighborhoods, making green-deficient areas and spatial disparities readily identifiable. These insights have direct implications for urban planning, enabling policymakers to design targeted greening strategies and monitor their potential impact via scenario simulations.

Future work may include integrating live data streams, extending the grid resolution, and incorporating additional environmental factors to enhance the dashboard’s analytical power and applicability. Overall, this work lays a foundation for data-driven urban greenery assessment and supports informed decision-making to promote sustainable and equitable urban environments.