Spatial Analysis Beyond Numbers

Demystifying Geographically Weighted Regression (GWR) and Multiscale Geographically Weighted Regression (MGWR) for Policy and Research

MEMO is a project focused on gathering and communicating migration data to communicate trends in migration to improve global governance around it.

Keywords: Geographically Weighted Regression, Multiscale Geographically Weighted Regression, Geospatial Insights for Research, Spatial Heterogeneity, Policy Decisions Topics: Predictive and prescriptive analytics, Business Intelligence and data visualization, Decision-Making

Abstract: In today’s day and age, where the world is more connected than ever, it is easy to overlook the influence of local nuances in geospatial data. A one size fits all model can miss critical details hidden in spatial variability. Therefore, it is important to understand the weight of each independent variable affected by its geographical significance, especially in decision making for policy makers and researchers. The goal is to analyze spatial heterogeneity by tailoring the regression coefficients to geographic contexts. Geographically Weighted Regression (GWR) and its evolution, Multiscale Geographically Weighted Regression (MGWR), enable professionals to move beyond static, global models and embrace region-specific dynamics, ensuring more informed, actionable insights. This paper simplifies and demystifies the mechanics of GWR and MGWR, contrasting their methodological underpinnings and practical applications. By bridging theory with practice, to enhance the relevance to diverse fields like immigration policies and migration studies, urban planning, environmental management, and public policy.

This paper, as part of the MEMO project, uses real-world data to illustrate the mechanics and applications of GWR and MGWR. The MEMO project has leveraged geospatial data to understand complex migration trends in Ghana, incorporating multiple driving variables such as rainfall, temperature, and socio-political factors. These variables are inherently spatial, with their influence varying across regions. This information was reported using standard dashboards and interactive choropleth maps along with a continued development of Multi-surface interactive environments.

While global models like Ordinary Least Squares (OLS) regression provide broad insights, they often overlook spatial heterogeneity—the way relationships between variables change across locations. To bridge this gap, Geographically Weighted Regression (GWR) and Multiscale Geographically Weighted Regression (MGWR) build on top of the OLS model and offer powerful frameworks for analyzing localized dynamics. Ensuring we have devices for our researchers, and later policy makers, to understand and interpret these models will enhance the delivery of MEMO results.

GWR adapts regression models to local contexts by calculating unique regression coefficients for each location, uncovering region-specific relationships. For instance, in Ghana, rainfall might be a stronger predictor of migration in the northern regions, while temperature or political stability may dominate in others. MGWR advances this approach by modeling variables at multiple spatial scales. For example, while socio-political stability operates at a national scale, rainfall’s impact is more localized. By integrating MEMO’s Ghana data into simplified visualizations, this paper explains how these coefficients are calculated and how these models can be modified as per users needs. This offers policymakers and researchers an accessible guide to spatial analysis catering to their needs. Whether applied to migration studies, urban planning, or environmental management, GWR and MGWR empower stakeholders to make decisions rooted in local realities, ensuring targeted and effective solutions.