Geospatial Data Visualisation
At NZGA, we specialize in geospatial data visualization, a powerful tool for displaying and communicating complex information in a clear and concise manner. Data visualization is a fundamental aspect of spatial data science, integral throughout the analysis process. It aids in exploring, interpreting, and effectively communicating findings. Advantages of Geospatial Data Visualization are:
Understanding Data: Data visualization, beyond just mapping, helps researchers comprehend their data. For example, it can reveal spatial outliers, such as finding a point in the ocean while analyzing a desert species.
Data Exploration: Techniques like histograms, scatter plots, and temporal charts assist in exploring data distribution, variable relationships, and temporal trends. For instance, scatter plots can visualize the relationship between variables, crucial in predictive analysis.
Data Quality Assessment: Visual exploration allows researchers to assess data quality and refine research questions. Histograms can help determine if data follows a normal distribution.
Analytical Insight: In spatial analysis, data visualization goes beyond decoration; it's a necessity. It combines 2D maps, 3D scenes, and various charts to evaluate and interpret results. This aids in understanding underlying algorithms.
Multivariate Analysis: Complex relationships involving multiple variables often require more than maps. Integrating charts into the workflow helps summarize and gain deeper insights into analysis results.
Automated Tools: Analysis tools like ArcGIS automatically generate charts as part of their output, recognizing their importance in conveying results.
Effective Communication: To make analyses useful, effective communication is vital. Data visualization simplifies the communication of complex findings to stakeholders and decision-makers, enabling informed decision-making.
In summary, data visualization in spatial data science encompasses various techniques, from mapping to charts and histograms, facilitating data understanding, exploration, and effective communication. It's a requirement for comprehending analysis results and making data-driven decisions in spatial data science.