GIS and Remote Sensing in Urban Poverty and Inequality Mapping Course

Introduction

Urban poverty and inequality remain pressing challenges in rapidly growing cities across the globe. Traditional data collection methods often fail to capture the spatial complexity of deprivation, leaving critical gaps in policymaking. Geographic Information Systems (GIS) and remote sensing have revolutionized how poverty and inequality can be mapped, analyzed, and addressed.

This course introduces participants to advanced geospatial methods that help identify, measure, and visualize urban poverty dynamics. It demonstrates how satellite imagery, socio-economic data, and spatial analytics can be integrated to provide actionable insights for development planning.

The program emphasizes practical applications of GIS and remote sensing in monitoring informal settlements, mapping access to services, and identifying spatial inequalities in health, education, housing, and employment. By doing so, it bridges the gap between spatial technology and urban development policies.

Learners will gain exposure to innovative techniques such as machine learning, spatial regression, and geospatial big data analysis. These tools enhance the ability to track multidimensional poverty, understand inequality patterns, and propose evidence-based interventions.

Case studies from diverse cities will be used to illustrate how urban poverty and inequality mapping supports sustainable development goals (SDGs), inclusive governance, and targeted resource allocation. Participants will also explore challenges such as data limitations, ethical considerations, and participatory approaches.

By the end of the course, participants will be empowered to apply GIS and remote sensing to design effective strategies for poverty alleviation and urban inclusion, aligning with global and national policy frameworks.

Who Should Attend

• Urban and regional planners working on development and poverty reduction.
• Government officials in ministries of housing, urban development, and planning.
• NGOs and international organizations addressing inequality and slum upgrading.
• Social scientists and development researchers interested in spatial inequality.
• GIS and remote sensing professionals seeking new applications in urban development.
• Public health experts mapping spatial inequalities in health access.
• Data scientists and policy analysts focused on socio-economic monitoring.

Duration

10 days

Course Objectives

• Provide a deep understanding of how GIS and remote sensing contribute to mapping urban poverty and inequality patterns.
• Equip participants with skills to analyze spatial data on housing, health, education, and infrastructure for equity assessment.
• Train learners in integrating socio-economic indicators with geospatial datasets for multidimensional poverty analysis.
• Enhance capacity to apply remote sensing imagery in monitoring informal settlements and slum growth dynamics.
• Develop expertise in using spatial regression and econometric models for poverty and inequality research.
• Strengthen practical skills in open-source and proprietary GIS tools for urban poverty mapping.
• Introduce machine learning and big data analytics for detecting hidden patterns of urban inequality.
• Provide case-based learning from cities implementing GIS-driven poverty reduction strategies.
• Foster ability to design poverty mapping projects that inform policy, SDG reporting, and inclusive governance.
• Enhance understanding of participatory GIS approaches that involve communities in poverty data collection.
• Build capacity to evaluate the ethical and political dimensions of poverty and inequality mapping.
• Prepare participants to communicate spatial inequality findings effectively to policymakers and stakeholders.

Course Outline

Module 1: Introduction to Urban Poverty and Inequality Mapping

• Definitions and dimensions of urban poverty and inequality.
• Role of spatial data in urban poverty analysis.
• SDGs and global urban development frameworks.
• Challenges of traditional poverty measurement.

Module 2: Fundamentals of GIS and Remote Sensing in Poverty Studies

• Key GIS and remote sensing concepts.
• Sources of socio-economic and spatial datasets.
• Remote sensing imagery for urban mapping.
• Integrating geospatial and census data.

Module 3: Remote Sensing of Informal Settlements

• Identifying slums using satellite imagery.
• Monitoring settlement growth and density.
• Case studies of slum mapping initiatives.
• Linking settlement data with social indicators.

Module 4: Multidimensional Poverty Mapping

• Frameworks for multidimensional poverty analysis.
• Combining income, health, and education indicators.
• Spatial inequality indices and mapping tools.
• Case applications in urban environments.

Module 5: Access to Services and Spatial Inequality

• Mapping access to health, education, and housing.
• Transportation networks and urban accessibility analysis.
• Water, sanitation, and energy inequality mapping.
• Identifying service gaps in marginalized communities.

Module 6: Spatial Statistics and Regression Models

• Introduction to spatial econometrics.
• Regression models for inequality analysis.
• Hotspot detection and clustering of poverty.
• Practical exercises in spatial modelling.

Module 7: Machine Learning and Big Data in Poverty Analysis

• Remote sensing and AI for settlement detection.
• Using machine learning to classify socio-economic conditions.
• Integrating mobile data and social media for inequality mapping.
• Case examples of AI in urban poverty studies.

Module 8: Participatory GIS and Community Mapping

• Role of communities in poverty data collection.
• Participatory GIS approaches for inclusion.
• Tools for citizen-driven mapping initiatives.
• Linking participatory maps with official statistics.

Module 9: Gender and Social Dimensions of Inequality

• Mapping gendered dimensions of poverty.
• Child and youth vulnerability analysis.
• Disability and inequality in access to services.
• Intersectional analysis of urban poverty.

Module 10: Health and Environmental Inequalities

• Spatial mapping of health disparities.
• Environmental justice and urban exposure risks.
• Pollution and waste management inequality.
• Climate vulnerability and poverty intersections.

Module 11: Monitoring SDGs Using GIS and Remote Sensing

• SDG 1 (No Poverty) and SDG 10 (Reduced Inequalities).
• Geospatial tools for SDG reporting.
• Indicators for sustainable urban development.
• Examples of SDG-based urban mapping projects.

Module 12: Ethical and Policy Dimensions

• Ethical concerns in poverty data collection.
• Data privacy and sensitivity in inequality studies.
• Policy implications of spatial inequality evidence.
• Global and national case study comparisons.

Module 13: Case Studies of Urban Poverty Mapping

• Poverty mapping in African cities.
• Inequality mapping in Asian megacities.
• Latin American experiences in urban slum monitoring.
• Lessons from global best practices.

Module 14: Tools and Platforms for Poverty Mapping

• QGIS, ArcGIS, and open-source tools.
• Google Earth Engine for large-scale poverty mapping.
• Specialized urban poverty mapping software.
• Integration with dashboards and decision-support tools.

Module 15: Project Design and Implementation

• Steps in designing an urban poverty mapping project.
• Data collection, analysis, and reporting methods.
• Building partnerships with stakeholders.
• Resource mobilization for project sustainability.

Module 16: Project and Practical Application

• Hands-on project on urban poverty and inequality mapping.
• Application of learned techniques on real datasets.
• Group presentations and peer evaluations.
• Expert mentorship and final feedback.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue

The training will be held at our Upskill Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment

Unless otherwise agreed between the two parties payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.