Data Analysis and Visualization
These guidelines aim to help institutions to effectively communicate information through GIS data analysis and visualizations. Key recommendations include:

Data Analysis
i.    Geoprocessing: 

 

 Verify the spatial relationships to avoid overlaps, gaps, or invalid geometries. 

 Ensure all spatial datasets have the same coordinate system or project them to a common one for accurate analysis. 

 Convert data between different formats (e.g., vector to raster) based on analysis requirements. 

 

 ii.    Spatial Analysis: 

 

 Utilize buffering and proximity analysis to study the relationships between features and their surroundings. 

 Combine spatial datasets and aggregate data to analyze relationships and summarize information. 

 Use appropriate spatial interpolation methods to estimate values at unsampled locations. 

 Apply network analysis to model movement and connectivity within spatial networks. 

 

 iii.    Statistical Modelling: 

 

 Split the dataset into training and testing subsets for model training and evaluation. 

 Clearly define the dependent variable and select relevant independent variables for the model. 

 Choose appropriate statistical models based on the nature of the data. (e.g., linear regression, logistic regression). 

 Assess model performance using appropriate metrics (e.g., accuracy, RMSE, R-squared) and cross-validation techniques. 

 Interpret the results of the statistical model in the context of the research objectives and data patterns. 

 Validate the model on unseen data to ensure its generalizability and avoid overfitting. 

 

  

Data Visualization
i.    Map Design Principles: 

 

 Simplicity - Maps should be visually clean, focusing on the most important information while removing unnecessary elements. 

 Legibility - Ensure that the map's text, labels, and symbols are easy to read and understand, even when zoomed in or out. 

 Balance - Maintain a visual balance on the map by evenly distributing elements, avoiding overcrowding in any particular area. 

 Contrast - Use contrasting colors and symbols to make the map visually appealing and enhance the distinction between different features. 

 Hierarchy - Arrange map elements in a clear hierarchy, with more important or prominent features receiving greater visual emphasis. 

 Consistency - Maintain a consistent design style, color scheme, and symbology throughout the map to create a coherent visual experience. 

 

 ii.    Use of Symbology: 

 

 Color - Use color wisely to convey meaning, such as warm colors for higher values and cool colors for lower values. 

 Size - Use different symbol sizes to represent varying magnitudes or quantities, ensuring that the size variation is easily distinguishable. 

 Shape—Differentiate between various features using distinct symbol shapes, especially when dealing with multiple datasets on the same map. 

 Iconography—Utilize appropriate icons to represent specific features or categories, making the map more visually informative. 

 Labels—Provide clear and concise labels for important features or regions to enhance map readability 

 Transparency—Use transparency for overlapping elements to prevent visual clutter and improve comprehension.