Monika Kuffer
Biographic Data
| ID | 3562571 |
|---|---|
| NAME | Monika Kuffer |
| GIVEN NAMES | Monika |
| FAMILY NAME | Kuffer |
| SIGNATURE | KUFFER M |
| AFFILIATIONS | University of Twente |
| ORCID | 0000-0002-1915-2069 |
| VERIFIED | Yes |
| TOTAL WORKS | 21 |
| TOTAL CITATIONS | 28 |
| AUTHOR COUNT | 21 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2016 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 4 |
Data Are Power: Addressing the Power Imbalance Around Community Data with the Open-Access Data4HumanRights Curriculum
Data4HumanRights’ training materials have been developed as open-source and tailored to limited-resource settings, where community data collectors often live and work. Access to training on data collection, analysis, and visualisation to support the advocacy of vulnerable groups is essential, particularly in the context of increasing human rights challenges such as land rights, adequate housing, conflicts, and climate justice. This paper provides…
Using comparative approaches to model deprivation in Antananarivo, Madagascar: A multidimensional analysis using principal components analysis and weighting system across meso and macro scales
Toward 3D hedonic price model for vertically developed cities using street view images and machine learning methods
Towards a scalable and transferable approach to map deprived areas using Sentinel-2 images and machine learning
African cities are growing rapidly and more than half of their populations live in deprived areas. Local stakeholders urgently need accurate, granular, and routine maps to plan, upgrade, and monitor dynamic neighborhood-level changes. Satellite imagery provides a promising solution for consistent, accurate high-resolution maps globally. However, most studies use very high spatial resolution images, which often cover only small areas and are cost …
Do informal settlements contribute to sprawl in Sub-Saharan African cities
Cities in Sub-Saharan Africa are recognized as drivers of development but also as internally disconnected and sprawling. Informal or “unplanned” settlements are often suggested to hamper sustainable growth, while commonly being labeled “sprawl” by default. This notion is addressed in the present paper, which aims to analyze empirically whether informal settlements do contribute more to sprawl than planned areas, in the setting of Sub-Saharan Afri…
A Global Estimate of the Size and Location of Informal Settlements
Slums are a structural feature of urbanization, and shifting urbanization trends underline their significance for the cities of tomorrow. Despite their importance, data and knowledge on slums are very limited. In consideration of the current data landscape, it is not possible to answer one of the most essential questions: Where are slums located? The goal of this study is to provide a more nuanced understanding of the geography of slums and their…
EO + Morphometrics: Understanding cities through urban morphology at large scale
Earth Observation (EO)-based mapping of cities has great potential to detect patterns beyond the physical ones. However, EO combined with the surge of machine learning techniques to map non-physical, such as socioeconomic, aspects directly, goes to the expense of reproducibility and interpretability, hence scientific validity. In this paper, we suggest shifting the focus from the direct detection of socioeconomic status from raw images through im…
Bringing economic complexity to the intra-urban scale: The role of services in the urban economy of Belo Horizonte, Brazil
This study explores the formation of economic complexity within a city from the Global South, during 2011–2019. It proposes an expanded interpretation of the Economic Complexity Index (ECI) to be applied at the intra-urban context of Belo Horizonte, Brazil, focusing on three different spatial levels of analysis (i.e., local, neighbourhood, and community levels). By applying the index to these three levels, instead of regional or national administ…
Mapping Deprived Urban Areas Using Open Geospatial Data and Machine Learning in Africa
Reliable data on slums or deprived living conditions remain scarce in many low- and middle-income countries (LMICs). Global high-resolution maps of deprived areas are fundamental for both research- and evidence-based policies. Existing mapping methods are generally one-off studies that use proprietary commercial data or other physical or socio-economic data that are limited geographically. Open geospatial data are increasingly available for large…
The Missing Millions in Maps: Exploring Causes of Uncertainties in Global Gridded Population Datasets
Gridded population datasets model the population at a relatively high spatial and temporal granularity by reallocating official population data from irregular administrative units to regular grids (e.g., 1 km grid cells). Such population data are vital for understanding human–environmental relationships and responding to many socioeconomic and environmental problems. We analyzed one very broadly used gridded population layer (GHS-POP) to assess i…
Integrating Remote Sensing and Street View Imagery for Mapping Slums
Mapping slums is vital for monitoring the Sustainable Development Goal (SDG) indicators. In the absence of reliable data, Remote Sensing (RS)-based approaches, particularly the Deep Learning (DL) methods, have gained recognition and high accuracies for slum mapping. However, using RS alone has its limitation in complex urban environments. Previous studies showed the added value of combining ground-level information with RS. Therefore, this resear…
Toward 3D Property Valuation—A Review of Urban 3D Modelling Methods for Digital Twin Creation
Increasing urbanisation has inevitably led to the continuous construction of buildings. Urban expansion and densification processes reshape cities and, in particular, the third dimension (3D), thus calling for a technical shift from 2D to 3D for property valuation. However, most property valuation studies employ 2D geoinformation in hedonic price models, while the benefits of 3D modelling potentially brought for property valuation and the general…
“Domains of deprivation framework” for mapping slums, informal settlements, and other deprived areas in LMICs to improve urban planning and policy: A scoping review
The majority of urban inhabitants in low- and middle-income country (LMIC) cities live in deprived urban areas. However, policy efforts and the monitoring of global goals and agendas such as the United Nation's Sustainable Development Goals (SDGs) and UN-Habitat New Urban Agenda are hindered by the unavailability of statistical and spatial data at metropolitan, city and sub-city scales. Deprivation is a complex and multidimensional concept, and p…
Identifying degrees of deprivation from space using deep learning and morphological spatial analysis of deprived urban areas
Many cities in low- and medium-income countries (LMICs) are facing rapid unplanned growth of built-up areas, while detailed information on these deprived urban areas (DUAs) is lacking. There exist visible differences in housing conditions and urban spaces, and these differences are linked to urban deprivation. However, the appropriate geospatial information for unravelling urban deprivation is typically not available for DUAs in LMICs, constituti…
On the knowledge gain of urban morphology from space
Urbanization processes are manifested by the change in the physical morphology of cities. Gaining knowledge about cities through their morphology is naturally linked to the capability of remote sensing (RS) that can monitor city forms with a synoptic view. Yet, our knowledge of the urban form does not linearly increase with the increase of image data. Thus, the role, challenges and potentials of RS in deriving knowledge about urban morphology are…
Urban poverty maps - From characterising deprivation using geo-spatial data to capturing deprivation from space
Most earth observation (EO) approaches only yield a binary delineation of deprived/non-deprived areas – an oversimplified characterisation with little information inferred regarding the diversity of intra-urban deprivation. In this study, we attempt to explore the potential of using VHR EO-based data to predict the degrees of intra-urban deprivation in Nairobi, Kenya. This involves a two-step workflow of characterising and predicting a continuous…
Towards user-driven earth observation-based slum mapping
Earth observation (EO) capabilities to produce up-to-date geographical information on slums over large areas supporting urban planning and evidence-based policymaking are largely acknowledged. Most EO studies typically use a data-driven approach without an understanding of end-user requirements. This study addresses this gap by aligning EO methods with societal needs and concerns using a user-driven approach in Accra, Ghana. By carrying out in-si…
Spatial Information Gaps on Deprived Urban Areas (Slums) in Low-and-Middle-Income-Countries: A User-Centered Approach
Routine and accurate data on deprivation are needed for urban planning and decision support at various scales (i.e., from community to international). However, analyzing information requirements of diverse users on urban deprivation, we found that data are often not available or inaccessible. To bridge this data gap, Earth Observation (EO) data can support access to frequently updated spatial information. However, a user-centered approach is urge…
The Spatial Dimension of Covid-19: The Potential of Earth Observation Data in Support of Slum Communities with Evidence from Brazil
The COVID-19 health emergency is impacting all of our lives, but the living conditions and urban morphologies found in poor communities make inhabitants more vulnerable to the COVID-19 outbreak as compared to the formal city, where inhabitants have the resources to follow WHO guidelines. In general, municipal spatial datasets are not well equipped to support spatial responses to health emergencies, particularly in poor communities. In such critic…
Need for an Integrated Deprived Area "Slum" Mapping System (Ideamaps) in Low- and Middle-Income Countries (LMICs)
Ninety percent of the people added to the planet over the next 30 years will live in African and Asian cities, and a large portion of these populations will reside in deprived neighborhoods defined by slum conditions, informal settlement, or inadequate housing. The four current approaches to neighborhood deprivation mapping are largely siloed, and each fall short of producing accurate, timely, and comparable maps that reflect local contexts. The …
Slums from Space—15 Years of Slum Mapping Using Remote Sensing
The body of scientific literature on slum mapping employing remote sensing methods has increased since the availability of more very-high-resolution (VHR) sensors. This improves the ability to produce information for pro-poor policy development and to build methods capable of supporting systematic global slum monitoring required for international policy development such as the Sustainable Development Goals. This review provides an overview of slu…
Need for an Integrated Deprived Area "Slum" Mapping System (Ideamaps) in Low- and Middle-Income Countries (LMICs)
Ninety percent of the people added to the planet over the next 30 years will live in African and Asian cities, and a large portion of these populations will reside in deprived neighborhoods defined by slum conditions, informal settlement, or inadequate housing. The four current approaches to neighborhood deprivation mapping are largely siloed, and each fall short of producing accurate, timely, and comparable maps that reflect local contexts. The …
Mapping Deprived Urban Areas Using Open Geospatial Data and Machine Learning in Africa
Reliable data on slums or deprived living conditions remain scarce in many low- and middle-income countries (LMICs). Global high-resolution maps of deprived areas are fundamental for both research- and evidence-based policies. Existing mapping methods are generally one-off studies that use proprietary commercial data or other physical or socio-economic data that are limited geographically. Open geospatial data are increasingly available for large…
Toward 3D hedonic price model for vertically developed cities using street view images and machine learning methods
A Global Estimate of the Size and Location of Informal Settlements
Slums are a structural feature of urbanization, and shifting urbanization trends underline their significance for the cities of tomorrow. Despite their importance, data and knowledge on slums are very limited. In consideration of the current data landscape, it is not possible to answer one of the most essential questions: Where are slums located? The goal of this study is to provide a more nuanced understanding of the geography of slums and their…
Spatial Information Gaps on Deprived Urban Areas (Slums) in Low-and-Middle-Income-Countries: A User-Centered Approach
Routine and accurate data on deprivation are needed for urban planning and decision support at various scales (i.e., from community to international). However, analyzing information requirements of diverse users on urban deprivation, we found that data are often not available or inaccessible. To bridge this data gap, Earth Observation (EO) data can support access to frequently updated spatial information. However, a user-centered approach is urge…
Slums from Space—15 Years of Slum Mapping Using Remote Sensing
The body of scientific literature on slum mapping employing remote sensing methods has increased since the availability of more very-high-resolution (VHR) sensors. This improves the ability to produce information for pro-poor policy development and to build methods capable of supporting systematic global slum monitoring required for international policy development such as the Sustainable Development Goals. This review provides an overview of slu…
The Spatial Dimension of Covid-19: The Potential of Earth Observation Data in Support of Slum Communities with Evidence from Brazil
The COVID-19 health emergency is impacting all of our lives, but the living conditions and urban morphologies found in poor communities make inhabitants more vulnerable to the COVID-19 outbreak as compared to the formal city, where inhabitants have the resources to follow WHO guidelines. In general, municipal spatial datasets are not well equipped to support spatial responses to health emergencies, particularly in poor communities. In such critic…
Need for an Integrated Deprived Area "Slum" Mapping System (Ideamaps) in Low- and Middle-Income Countries (LMICs)
Ninety percent of the people added to the planet over the next 30 years will live in African and Asian cities, and a large portion of these populations will reside in deprived neighborhoods defined by slum conditions, informal settlement, or inadequate housing. The four current approaches to neighborhood deprivation mapping are largely siloed, and each fall short of producing accurate, timely, and comparable maps that reflect local contexts. The …
Towards user-driven earth observation-based slum mapping
Earth observation (EO) capabilities to produce up-to-date geographical information on slums over large areas supporting urban planning and evidence-based policymaking are largely acknowledged. Most EO studies typically use a data-driven approach without an understanding of end-user requirements. This study addresses this gap by aligning EO methods with societal needs and concerns using a user-driven approach in Accra, Ghana. By carrying out in-si…
Spatial Information Gaps on Deprived Urban Areas (Slums) in Low-and-Middle-Income-Countries: A User-Centered Approach
Routine and accurate data on deprivation are needed for urban planning and decision support at various scales (i.e., from community to international). However, analyzing information requirements of diverse users on urban deprivation, we found that data are often not available or inaccessible. To bridge this data gap, Earth Observation (EO) data can support access to frequently updated spatial information. However, a user-centered approach is urge…
The Missing Millions in Maps: Exploring Causes of Uncertainties in Global Gridded Population Datasets
Gridded population datasets model the population at a relatively high spatial and temporal granularity by reallocating official population data from irregular administrative units to regular grids (e.g., 1 km grid cells). Such population data are vital for understanding human–environmental relationships and responding to many socioeconomic and environmental problems. We analyzed one very broadly used gridded population layer (GHS-POP) to assess i…
Integrating Remote Sensing and Street View Imagery for Mapping Slums
Mapping slums is vital for monitoring the Sustainable Development Goal (SDG) indicators. In the absence of reliable data, Remote Sensing (RS)-based approaches, particularly the Deep Learning (DL) methods, have gained recognition and high accuracies for slum mapping. However, using RS alone has its limitation in complex urban environments. Previous studies showed the added value of combining ground-level information with RS. Therefore, this resear…
Toward 3D Property Valuation—A Review of Urban 3D Modelling Methods for Digital Twin Creation
Increasing urbanisation has inevitably led to the continuous construction of buildings. Urban expansion and densification processes reshape cities and, in particular, the third dimension (3D), thus calling for a technical shift from 2D to 3D for property valuation. However, most property valuation studies employ 2D geoinformation in hedonic price models, while the benefits of 3D modelling potentially brought for property valuation and the general…
“Domains of deprivation framework” for mapping slums, informal settlements, and other deprived areas in LMICs to improve urban planning and policy: A scoping review
The majority of urban inhabitants in low- and middle-income country (LMIC) cities live in deprived urban areas. However, policy efforts and the monitoring of global goals and agendas such as the United Nation's Sustainable Development Goals (SDGs) and UN-Habitat New Urban Agenda are hindered by the unavailability of statistical and spatial data at metropolitan, city and sub-city scales. Deprivation is a complex and multidimensional concept, and p…
Identifying degrees of deprivation from space using deep learning and morphological spatial analysis of deprived urban areas
Many cities in low- and medium-income countries (LMICs) are facing rapid unplanned growth of built-up areas, while detailed information on these deprived urban areas (DUAs) is lacking. There exist visible differences in housing conditions and urban spaces, and these differences are linked to urban deprivation. However, the appropriate geospatial information for unravelling urban deprivation is typically not available for DUAs in LMICs, constituti…
On the knowledge gain of urban morphology from space
Urbanization processes are manifested by the change in the physical morphology of cities. Gaining knowledge about cities through their morphology is naturally linked to the capability of remote sensing (RS) that can monitor city forms with a synoptic view. Yet, our knowledge of the urban form does not linearly increase with the increase of image data. Thus, the role, challenges and potentials of RS in deriving knowledge about urban morphology are…
Urban poverty maps - From characterising deprivation using geo-spatial data to capturing deprivation from space
Most earth observation (EO) approaches only yield a binary delineation of deprived/non-deprived areas – an oversimplified characterisation with little information inferred regarding the diversity of intra-urban deprivation. In this study, we attempt to explore the potential of using VHR EO-based data to predict the degrees of intra-urban deprivation in Nairobi, Kenya. This involves a two-step workflow of characterising and predicting a continuous…
EO + Morphometrics: Understanding cities through urban morphology at large scale
Earth Observation (EO)-based mapping of cities has great potential to detect patterns beyond the physical ones. However, EO combined with the surge of machine learning techniques to map non-physical, such as socioeconomic, aspects directly, goes to the expense of reproducibility and interpretability, hence scientific validity. In this paper, we suggest shifting the focus from the direct detection of socioeconomic status from raw images through im…
Bringing economic complexity to the intra-urban scale: The role of services in the urban economy of Belo Horizonte, Brazil
This study explores the formation of economic complexity within a city from the Global South, during 2011–2019. It proposes an expanded interpretation of the Economic Complexity Index (ECI) to be applied at the intra-urban context of Belo Horizonte, Brazil, focusing on three different spatial levels of analysis (i.e., local, neighbourhood, and community levels). By applying the index to these three levels, instead of regional or national administ…
Mapping Deprived Urban Areas Using Open Geospatial Data and Machine Learning in Africa
Reliable data on slums or deprived living conditions remain scarce in many low- and middle-income countries (LMICs). Global high-resolution maps of deprived areas are fundamental for both research- and evidence-based policies. Existing mapping methods are generally one-off studies that use proprietary commercial data or other physical or socio-economic data that are limited geographically. Open geospatial data are increasingly available for large…
Towards a scalable and transferable approach to map deprived areas using Sentinel-2 images and machine learning
African cities are growing rapidly and more than half of their populations live in deprived areas. Local stakeholders urgently need accurate, granular, and routine maps to plan, upgrade, and monitor dynamic neighborhood-level changes. Satellite imagery provides a promising solution for consistent, accurate high-resolution maps globally. However, most studies use very high spatial resolution images, which often cover only small areas and are cost …
Do informal settlements contribute to sprawl in Sub-Saharan African cities
Cities in Sub-Saharan Africa are recognized as drivers of development but also as internally disconnected and sprawling. Informal or “unplanned” settlements are often suggested to hamper sustainable growth, while commonly being labeled “sprawl” by default. This notion is addressed in the present paper, which aims to analyze empirically whether informal settlements do contribute more to sprawl than planned areas, in the setting of Sub-Saharan Afri…
A Global Estimate of the Size and Location of Informal Settlements
Slums are a structural feature of urbanization, and shifting urbanization trends underline their significance for the cities of tomorrow. Despite their importance, data and knowledge on slums are very limited. In consideration of the current data landscape, it is not possible to answer one of the most essential questions: Where are slums located? The goal of this study is to provide a more nuanced understanding of the geography of slums and their…
Data Are Power: Addressing the Power Imbalance Around Community Data with the Open-Access Data4HumanRights Curriculum
Data4HumanRights’ training materials have been developed as open-source and tailored to limited-resource settings, where community data collectors often live and work. Access to training on data collection, analysis, and visualisation to support the advocacy of vulnerable groups is essential, particularly in the context of increasing human rights challenges such as land rights, adequate housing, conflicts, and climate justice. This paper provides…
Using comparative approaches to model deprivation in Antananarivo, Madagascar: A multidimensional analysis using principal components analysis and weighting system across meso and macro scales
Toward 3D hedonic price model for vertically developed cities using street view images and machine learning methods
Geography (18 works) · Land Use and Ecosystem Services (15 works) · Computer Science (14 works) · Cartography (12 works) · Remote-Sensing Image Classification (8 works) · Data science (7 works) · Economics (7 works) · Impact of Light on Environment and Health (7 works) · Population (7 works) · Remote sensing (7 works)