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Spatial Clustering and Regionalisation
| Short Description | Cluster analysis is a statistical method used in urban health research to identify distinct groups or clusters of areas based on their health characteristics. |
|---|---|
| Data | PointPolygon |
| Suggested tools | GeodaPythonR |
| Category | Spatial Analysis |
| Variable | BivariableMultivariable |
Overview
Spatial Clustering and Regionalization, involves grouping adjacent spatial units into larger regions based on their similarity in specific criteria, while also considering the contiguity of geographical areas. Unlike clustering, regionalization aims at creating larger spatial entities that are internally homogeneous but distinct from each other. This approach is useful for defining functional regions for health service delivery, policy-making, and resource allocation. It can help in designing health service zones or districts that are based on actual patterns of health service use, population distribution, and health needs rather than arbitrary administrative boundaries.
Description
- Cluster Algorithm Selection: Choose a suitable clustering algorithm. Popular options include K-means clustering, hierarchical clustering (agglomerative or divisive), or density-based clustering (e.g., DBSCAN).
- Determining the Number of Clusters: Determine the optimal number of clusters using techniques such as the elbow method, silhouette coefficient, or hierarchical clustering dendrogram.
- Performing the Cluster Analysis: Apply the selected clustering algorithm to the standardized data using the chosen distance metric and the determined number of clusters.
- Interpreting and Visualizing the Clusters: Analyze and interpret the resulting clusters to identify meaningful patterns. Visualize the clusters using techniques like scatter plots, heatmaps, or geographic mapping.
- Definition of Criteria: Similar to clustering, the process starts with the selection of relevant variables that reflect the objectives of the regionalization, such as optimizing healthcare delivery or targeting interventions.
- Contiguity Constraint: Unlike in clustering, regionalization requires the additional step of defining contiguity constraints to ensure that the resulting regions are geographically contiguous.
- Algorithm Selection: Choosing a regionalization algorithm that can handle the criteria and constraints defined. Algorithms used in regionalization include the p-regions problem approach, spatially constrained clustering, and iterative spatial clustering.
- Optimization and Validation: The process involves optimizing the division of spatial units into regions to meet predefined criteria, such as minimizing within-region variance while maximizing between-region variance. Validation is crucial to ensure that the regions are meaningful and useful for the intended applications.
Applications on Urban Health and Wellbeing
Identifying Health Hotspots: Spatial clustering can be used to identify hotspots of diseases, pollution, or accidents within an urban area. This information is crucial for targeting public health interventions and resources more effectively.
Resource Allocation: By regionalising urban areas based on health indicators (e.g., access to healthcare facilities, incidence of certain health conditions), policymakers can tailor health services and resource allocations to the specific needs of each region, ensuring that underserved areas receive appropriate support.
Tutorial