This project is part of the Developing Data Products course within the Johns Hopkins University Data Science Specialization on Coursera. The project utilizes R Markdown and the Leaflet library to transform geographic coordinates into an interactive data product. By leveraging spatial visualization, we aim to:
Exploratory spatial analysis of the Leaflet visualization reveals significant geomorphological divergence between the two study regions. By operationalizing the landmark coordinates, we observe the following categorical patterns:
Florida (Green Clusters): The distribution across sites like Siesta Key and Bahia Honda exhibits a consistent low-elevation, barrier-island morphology. This region is characterized by a uniform shallow-shelf coastline, which the model identifies as highly optimized for high-temperature recreational use.
California (Red Clusters): The Pacific dataset reflects high topographic heterogeneity. The distribution spans anthropogenic infrastructure (Santa Monica Pier), sensitive ecological preserves (La Jolla Cove), and localized geological anomalies, such as the unique formations at Pfeiffer Beach. # {r layered_map, out.width=‘100%’, height=‘600px’}
The visualization effectively quantifies the contrast between Florida’s homogeneous coastal profile and California’s multifaceted coastal architecture, integrating both rugged natural coves and developed urban hubs. This spatial distribution highlights the latent environmental variance between the Atlantic/Gulf and Pacific coastal systems.
To further validate the regional divergence noted in our analysis, adding a Summary Statistics table provides a quantitative foundation for our spatial visualization.
| Region (State) | N (Markers) | Mean Latitude | Mean Longitude | Latitudinal Spread | Longitudinal Spread |
|---|---|---|---|---|---|
| CA | 4 | 33.9460 | -118.6913 | 3.5522 | 4.6300 |
| FL | 4 | 26.0399 | -81.4933 | 2.6059 | 2.4178 |
N(Markers): Confirms a balanced sample size (per region) for the comparative analysis.
Latitudinal/Longitudinal Spread: Quantifies the topographic heterogeneity discussed in your analysis. A larger spread in California (CA) coordinates mathematically supports the “diverse coastal topography” mentioned in your insights, whereas a tighter grouping in Florida (FL) suggests a more “uniform shallow-shelf” distribution.
This document was generated in RStudio using the Leaflet for R package. The analysis ensures a reproducible workflow by integrating data processing and visualization in a single source file. Including this table ensures our project meets the Computational Reproducibility standards of the Johns Hopkins University Data Science Specialization.