HawaSpatial Pro

Health & Areal Weighted Analysis (HAWA) Platform

Author: Abdisalam Hassan Muse
Affiliation: Amoud University | Global Development Intelligence
Status: Production Ready (Validated across 6 Nations)

???? Global Intelligence for the SDGs

HawaSpatial Pro is a professional-grade, multi-sectoral R package designed to bridge the gap between complex Bayesian spatial modeling and actionable policy insights. It provides a robust framework for sub-national monitoring of development indicators, specifically optimized for large-scale, complex household surveys.

???? Validated & Stress-Tested

The platform has been rigorously validated using datasets from Somaliland, Ethiopia, Kenya, Somalia, Bangladesh, and Afghanistan. It is engineered to handle the most common global health and development data architectures:

  • DHS: Demographic and Health Surveys (USAID)
  • MICS: Multiple Indicator Cluster Surveys (UNICEF)
  • IHBS: Integrated Household Budget Surveys (World Bank)
  • SPA/MIS: Service Provision Assessment & Malaria Indicator Surveys

???? Universal SDG Alignment

HawaSpatial Pro supports the monitoring of the United Nations Sustainable Development Goals (SDGs). It has been successfully tested against indicators for 7 different SDGs, enabling precision targeting for Poverty (SDG 1), Hunger (SDG 2), Health (SDG 3), Education (SDG 4), Gender (SDG 5), Water (SDG 6), and Inequality (SDG 10).

???? Key Features

1. 16 ESDA Diagnostic Methods

Comprehensive Exploratory Spatial Data Analysis including:

  • Clustering: Global & Local Moran’s I, LISA Cluster Maps, and Getis-Ord Gi*.
  • Smoothing: Empirical Bayes (EBS) Smoothing for small-area estimation.
  • Diagnostics: Spatial Correlograms, Join Count Analysis, and Local Geary’s C.

2. Bivariate Spatial Analysis

Advanced visualization of the geographic relationship between two variables (e.g., Wealth Index vs. Stunting Rates) using 3x3 quantile color grids.

3. Bayesian Multilevel Engine (INLA)

A sophisticated 4-stage modeling framework using Integrated Nested Laplace Approximations (INLA):

  • Stage 0: Null Model (ICC Calculation).
  • Stage 1: Individual-level predictors.
  • Stage 2: Community/Contextual-level predictors.
  • Stage 3: Full Model with Bayesian Random Effects.

4. Advanced Geostatistics

  • Ordinary Kriging: Surface interpolation for continuous spatial prediction.
  • Spatial Lag Models (SAR): Analysis of spatial residuals and neighborhood context.

???? Installation

You can install the development version of HawaSpatial from GitHub with:

# 1. Install remotes if not already installed
if (!require("remotes")) install.packages("remotes")

# 2. Install INLA (Required engine - not on CRAN)
install.packages("INLA", repos = c(getOption("repos"), INLA = "https://inla.r-inla-download.org/R/stable"), dep = TRUE)

# 3. Install HawaSpatial Pro
remotes::install_github("Abdisalammuse/HawaSpatial")

???? Quick Start

To launch the interactive HawaSpatial Pro dashboard:

library(HawaSpatial)

# Launch the Intelligence Platform
HawaSpatial::run_app()

???? Technical Engine

HawaSpatial Pro leverages a high-performance ecosystem:

  • INLA: Fast Bayesian inference for latent Gaussian models.
  • sf & spdep: Modern spatial geometry processing and weight matrix construction.
  • biscale: Bivariate mapping logic.
  • gstat & spatialreg: Geostatistical interpolation and spatial autoregressive modeling.
  • bs4Dash: Real-time reactive UI framework.

?????? Contact & Support

Abdisalam Hassan Muse
Specialist in Health Intelligence & Bayesian Spatial Statistics
GitHub Profile
Amoud University

Disclaimer: HawaSpatial Pro is intended for research and policy monitoring. Users should ensure they have the legal right to use survey datasets (DHS/MICS) according to their respective terms of use. ```