Visualize proximities between objects as distances in a spatial map. 

Handle mixed data types—numeric, ordinal, and nominal. 

Provide nonlinear, optimally transformed mappings for maximum dimensionality reduction. 

Represent both objects and variables in a joint space, known as a biplot. 

Metric and nonmetric data 

Optionally transformed proximities 

A wide range of models and constraints 

Euclidean distances for numeric variables 

Chi-squared distances for nominal variables 

A custom distance function for ordinal variables 

Attributes: Additional object information that shapes the spatial configuration. 

Properties: Supplementary variables that help interpret the configuration. 

Deriving proximities 

Shaping the configuration 

Interpreting the space 

Use Case: Product Placement & Market Perception 

Application: PROXMAP visualizes how consumers perceive different products, helping brands position items optimally in the market. 

Example: Mapping snack brands based on taste, price, and packaging to identify competitive clusters and white space opportunities. 

Use Case: Customer Segmentation 

Application: Analyse customer usage patterns and preferences to uncover natural groupings for targeted service offerings. 

Example: Segmenting users based on call frequency, data usage, and churn risk to tailor retention strategies. 

Use Case: Patient Profiling & Treatment Mapping 

Application: Map patient profiles based on symptoms, diagnoses, and treatment responses to personalize care plans. 

Example: Visualizing proximity among patients with chronic conditions to identify common treatment pathways. 

Use Case: Risk Profiling & Investment Strategy 

Application: Group clients or assets based on risk tolerance, investment behavior, or financial goals. 

Example: Mapping mutual funds by performance metrics and volatility to guide investor recommendations. 

Use Case: Student Performance Clustering 

Application: Identify patterns in academic performance, learning styles, or engagement levels. 

Example: Mapping students based on test scores, attendance, and participation to design personalized learning interventions. 

Use Case: Supplier & Product Quality Analysis 

Application: Visualize relationships among suppliers or product lines based on quality metrics and delivery performance. 

Example: Mapping suppliers to identify clusters of high reliability versus high risk. 

Use Case: Concept Testing & Brand Mapping 

Application: Use consumer feedback to map perceptions of new product concepts or brand attributes. 

Example: Visualizing how test audiences relate new product ideas to existing market offerings. 

Use Case: Community Profiling & Policy Impact 

Application: Map communities based on socio-economic indicators, service access, or policy outcomes. 

Example: Identifying clusters of neighbourhoods with similar needs to allocate resources more effectively. 

Proximities: Histogram 

Initial Analysis: Scree Plot, Configuration, Stress, Biplot 

Model Criteria: Scree, Iterations, Stress 

Model Configuration: Spatial Map, Minimum Spanning Tree, Neighbours, Threshold Graph 

Model Diagnostics: Transformation, Residuals, Fit, Shepard Diagram