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Starting from a well defined and demarcated area in space, the Corgo River, in the region of Trás-os-Montes and Alto Douro, this work seeks through Information Technologies (IT), Digital Humanities and online tools and applications (software) to provide digital images of space and water resources, anthropic elements related to water and various natural features. Based on these available resources and a set of historical cartographic documents, we provide a realistic view of the cultural and natural water landscape and how augmented reality can help archaeology interpret this landscape and its historical transformations.

Managing the rapid growth of deprived areas (commonly known as slums, informal settlements, etc.) in cities of Low- to Middle-Income Countries (LMICs) demands detailed and consistent information that is often unavailable. Recent Earth Observation (EO) mapping approaches with supervised classification models overlook the diversity of deprived areas and require resource-intensive training sets. In this study, we analyse the potential of unsupervised machine learning (ML) models to capture intra-urban diversity of deprived areas in São Paulo, using solely open geodata. We provide a workflow of characterising deprivation at a city scale with a disaggregated approach, offering scalability and transferability potential. First, we extract a pool of spatial features from open geospatial datasets to characterise the morphological and environmental conditions of the study area. After input preparation, we train and optimise a k-means model, including a coupled feature importance tool. Four cluster types emerged with different deprivation aspects such as higher and lower accessibility to services and infrastructure, sparser and denser occupation; regular and complex morphology; flat and steep terrain. This alternative methodology to capture diversity of deprived areas with open EO-based features can inform locally targeted, thus more efficient, urban policies and interventions.

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 research aims to integrate Remote Sensing Imagery (RSI) and Street View Images (SVI) for slum mapping. Jakarta city is the study area representing the challenge of distinguishing between slum and non-slum kampungs, and these kampungs accommodate approximately 60% of the population of Jakarta. This research compares the mapping results obtained by four DL networks: FCN-DK6 used only RSI, a VGG16 used only SVI, and two networks combined RSI and SVI (FCN-DK6-i and Modified FCN-DK6). Further, the Modified FCN-DK6 network was explored by integrating SVI at each convolutional layer, i.e., Modified FCN-DK6_1, Modified FCN-DK6_2, Modified FCN-DK6_3, Modified FCN-DK6_4, and Modified FCN-DK6_5. Experimental results demonstrate that combining RSI and SVI improves the accuracy, depending on how and at what level in the FCN network they are integrated. The Modified FCN-DK6_2 outperforms the rest in Modified FCN-DK6 experiments and FCN-DK6-i.

Cultural heritage is under tremendous pressure in the rapidly growing and transforming cities of the global south. Historic cities and towns are often faced with the dilemma of having to preserve old monuments while responding to the pressure of adapting itself to a modern lifestyle, which often results in the loss of cultural heritage. Indian cities such as Delhi possess a rich legacy of tangible heritage, traditions, and arts, which are reflected in their present urban form. The creation of temporal 3D models of such cities not only provides a platform with which one can experience the past, but also helps to understand, examine, and improve its present deteriorating state. However, gaining access to historical data to support the development of city-scale 3D models is a challenge. While data gaps can be bridged by combining multiple data sources, this process also presents considerable technical challenges. This paper provides a framework to generate LoD-2 (level-of-detail) 3D models of the present (the 2020s) and the past (the 1970s) of a heritage mosque surrounded by a dense and complex urban settlement in Shahjahanabad (Old Delhi) by combining multiple VHR (very high resolution) satellite images. The images used are those of Pleiades and Worldview-1 and -3 (for the present) and HEXAGON KH-9 declassified spy images (for the past). The chronological steps are used to extract the DSMs and DTMs that provide a base for the 3D models. The models are rendered, and the past and present are visualized using graphics and videos. The results reveal an average increase of 80% in the heights of the built structures around the main monument (mosque), leading to a loss in the visibility of this central mosque.

In this editorial linked to the thematic issue on “Gaming, Simulations, and Planning: Physical and Digital Technologies for Public Participation in Urban Planning,” we explore how urban planning has been, arguably, slow on the uptake of modern technologies and the move towards the next media revolution: The Metaverse is now on the horizon. By artfully pushing technological, cultural, and social boundaries in creating virtual environments, games and gaming technologies have presented interesting opportunities and challenges for the planning profession, theory, and education over the years. This thematic issue documents a wide range of innovative practices in planning enabled by games and gaming technologies. It attempts to open discussions about the way we conceptualize and treat new media and technologies in planning. By providing a wide range of examples, from non-digital games to gamified systems, interactive simulations and digital games, the issue shows that the lack of adoption of these practices has less to do with their technical possibilities and more to do with the way we understand tools and their added value in the dominant narratives of planning. As we note at the end, planning should be at the forefront of these technologies, not embracing technologies for technologies sake but because it should, as a profession, be leading the way into these new environments.

Environmental health inequalities in cities of the Global South are hardly studied up to now, though they are expected to increase due to rapid urbanization and motorization taking place in many of these cities. In this study, environmental inequalities in the city of Kathmandu, Nepal for the years 2013 and 2021 are analyzed. The goal of the study is to determine the degree of environmental inequalities and their changes over time. The study examines horizontal and vertical inequalities in access to drinking water sources, air and noise pollution exposure, and health effects based on self-reported household data. Results show statistically significant environmental inequalities between neighborhoods in Kathmandu regarding access to basic water infrastructure, air and noise pollution exposure, and resulting health effects. Inequalities between socio-economic groups are not significant. Over time, the perceived exposure to air pollution has increased, mainly due to increased motorized traffic, whereas vertical inequalities persisted. While inequalities between socio-economic groups have not increased, the high socioeconomic group reveals more concerns and awareness about environmental burdens than the low socioeconomic group. In conclusion, given the intertwined horizontal and vertical inequalities, any planning response action needs to consider the population's vulnerability to target interventions to the most affected areas.

Public spaces are considered a vital ingredient of prosperous cities as they support their sense of community, identity and culture. Understanding their relevance, the Mexican government has developed new regulatory frameworks. In 2016, the first federal legislation in Mexico was created and combined the concepts of urban development, public space, and social participation. This change affected national and local regulations. Acknowledging that public spaces are anchored to the institutional and governance context, we based our explorative research from a theoretical perspective on the Contextual Interaction Theory (CIT) and its five governance elements. Considering the regulatory and institutional framework for public spaces in Mexico, this research aims to understand how the different actors perceive the current conditions of public spaces in Mexico City. This research adopted a case study approach, and we conducted eight semi-structured interviews with stakeholders that participate in the planning process of public spaces in Mexico City. Through these interviews and revision of secondary sources, we found that all interviewed actors agree that the regulatory framework of public spaces differs with the application in practice, with legal gaps and lack of clarity in responsibilities which brings confusion among practitioners and developers of public space projects. The implementation of the public spaces policy is affected by the lack of continuity of public space projects and the discrepancies between the efforts to promote inclusive and safe public spaces, and a negative perception of society on the current conditions of public spaces.

China has experienced fast urbanization with a growing urban population, which has inevitably led to the adoption of a vertical housing style with high-rise buildings. However, how people subjectively perceive the vertical dimension (3D) in the property market is neither adequately documented nor well understood. The 3D perception helps us to understand a myriad of social and psychological effects of living in high-rise buildings. We organized and conducted semi-structured expert interviews, focus groups, and the circulation and compilation of questionnaires in Xi’an, China, to investigate how different stakeholders in the property market perceive 3D. The results show that: (1) real estate developers do not adjust property prices for specific 3D factors, and the local government does not consider 3D in housing policies; (2) the current status of 3D modeling in Xi’an is still in the embryonic state; (3) 3D factors are highly valued by buyers but not well-understood by real estate developers and local government. In addition, 3D factors score higher than horizontal (2D) factors (1.12 to 0.88). Gender and age groups do not influence housing preferences concerning 2D and 3D factors. These findings provide valuable insights for real estate developers concerning pricing policies and the local government concerning housing policies. In the future, 3D perceptions and factors should be prioritized in order to improve urban infrastructure and ensure the increased availability of, and fair public access to welfare related to 3D in urban areas.

The idea of a walkable city refers to the extent to which the built environment encourages people to walk by establishing comfortable pedestrian routes, which allows people to connect to numerous services with reasonable effort and time. Walkability is currently regarded as a “good to know about” rather than a “must-have” factor for sustainable development. A combination of walkability with a standard design strategy, such as generative design, may result in a more efficient way of planning a walkable city. Interestingly, the sole indicator taken into account for walkability in the generative design domain is “distance to amenities”, while in reality, other parameters, such as the comfort factor, could also influence walkability. Therefore, in this research, we developed a workflow based on the generative design, which considers the comfort dimension in combination with distance to amenities and street-level greeneries. We also included the human perspective, given that walkability is always personal. This research successfully generated three different scenarios of walkability-optimal urban plans, where the highest walkability is 82.43 (very walkable). Furthermore, the baseline scenario of two different locations also aligns with people’s perspectives when compared. In addition, we found that the inclusion of a temporal dimension, enhanced 3D-related indicators, and constraints should benefit future research.

Defining and measuring progress in adaptation are important questions for climate adaptation science, policy, and practice. Here, we assess the progress of urban adaptation planning in 327 European cities between 2005 and 2020 using three ‘ADAptation plan Quality Assessment’ indices, called ADAQA-1/ 2/ 3, that combine six plan quality principles. Half of the cities have an adaptation plan and its quality significantly increased over time. However, generally, plan quality is still low in many cities. Participation and monitoring and evaluation are particularly weak aspects in urban adaptation policy, together with plan ‘consistency’. Consistency connects impacts and vulnerabilities with adaptation goals, planned measures, actions, monitoring and evaluation, and participation processes. Consistency is a key factor in the overall quality of plans. To help evaluate the quality of plans and policies and promote learning, we suggest incorporating our ADAptation plan Quality Assessment indices into the portfolio of adaptation progress assessments and tracking methodologies. Validerad;2023;Nivå 1;2023-04-14 (joosat);Funder: JPI Urban Europe Grant, NWO (438.21.445); María de Maeztu excellence accreditation (MDM-2017-0714), funded by MCIN/AEI/10.13039/501100011033/; Basque Government (BERC 2022-2025 programme); National Biodiversity Future Centre (CN000033), funded by the Italian National Recovery and Resilience Plan (PNRR Mission 4, Component 2, Investment 1.4)Licens fulltext: CC BY License

By implementing nature-based solutions (NBS), cities generate value for their residents, such as health and wellbeing. We estimate the aggregate social value to urban residents of 85 NBS projects implemented across Europe and find that the majority yield attractive social returns on investment. We offer a new metric to support investments for NBS by public and private actors for whom social value creation to residents is a core objective.

To answer the question if the Geohealth domain requires a body of knowledge (BoK), we need a general understanding of concepts associated with this field. Two years ago, the United Nation (UN) committee of experts on global geospatial information management identified "semantic- and ontology-linked data" as something that "will become essential to support the next generation of autonomous systems" (UN-GGIM, 2020). The term ontology is closely related to BoK. Unlike data models, ontologies are independent of application; they are generic, can be used in different ways and have clear advantages but they are challenging to create and even more difficult to maintain. The brief description below summarizes what ontologies are, why they are needed to support linked data, what the role of the semantic web is, what is already going on within the Geohealth domain on ontologies and how a BoK can assist. ispartof: Geospat Health vol:17 issue:2 ispartof: location:Italy status: Published online

Urban population distribution maps are vital elements for monitoring the Sustainable Development Goals, appropriately allocating resources such as vaccination campaigns, and facilitating evidence-based decision making. Typically, population distribution maps are derived from census data from the region of interest. Nevertheless, in several low- and middle-income countries, census information may be unreliable, outdated or unsuitable for spatial analysis at the intra-urban level, which poses severe limitations in the development of urban population maps of adequate quality. To address these shortcomings, we deploy a novel framework utilizing multisource Earth Observation (EO) information such as Sentinel-2 and very-high-resolution Pleiades imagery, openly available building footprint datasets, and deep learning (DL) architectures, providing end-to-end solutions to the production of high quality intra-urban population distribution maps in data scarce contexts. Using several case studies in Sub-Saharan Africa, namely Dakar (Senegal), Nairobi (Kenya) and Dar es Salaam (Tanzania), our results emphasize that the combination of DL and EO data is very potent and can successfully capture relationships between the retrieved image features and population counts at fine spatial resolutions (100 meter). Moreover, for the first time, we used state-of-the-art domain adaptation methods to predict population distributions in Dar es Salaam and Nairobi (R2 = 0.39, 0.60) that did not require national census or survey data from Kenya or Tanzania, but only a sample of training locations from Dakar. The DL architecture is based on a modified ResNet-18 model with dual-streams to analyze multi-modal data. Our findings have strong implications for the development of a new generation of urban population products that are an output of end-to-end solutions, can be updated frequently and rely completely on open data.

Border artificial intelligence (AI)—biometrics-based AI systems used in border control contexts—proliferates as common tools in border securitization projects. Such systems classify some migrants as posing risks like identity fraud, other forms of criminality, or terrorism. From a human rights perspective, using such risk framings for algorithmically facilitated evaluations of migrants’ biometrics systematically calls into question whether these kinds of systems can be built to be trustworthy for migrants. This article provides a thought experiment; we use a bottom-up responsible design lens—the guidance-ethics approach—to evaluate if responsible, trustworthy Border AI might constitute an oxymoron. The proposed European AI Act only limits the use of Border AI systems by classifying such systems as high risk. In parallel with these AI regulatory developments, large-scale civic movements have emerged throughout Europe to ban the use of facial recognition technologies in public spaces to defend EU citizens’ privacy. The fact that such systems remain acceptable for states’ usage to evaluate migrants, we argue, insufficiently protects migrants’ lives. In part, we argue that this is due to regulations and ethical frameworks being top-down and technology driven by focusing more on the safety of AI systems than on the safety of migrants. We conclude that bordering technologies developed from a responsible design angle would entail the development of entirely different technologies. These would refrain from harmful sorting based on biometric identifications but would start from the premise that migration is not a societal problem.

Increasing flood risk due to rapid urbanization and climate change calls for improved integration between flood risk management and spatial planning processes to enhance the resilience of cities, including in Egypt. Although much work has been conducted on flood impact in Egypt, the gap in integrating flood risk assessment with spatial planning practices has not been discussed in academia. In practice, flood risk assessment is not mandatory for local-level spatial planning projects in Egypt, resulting in increased flood risk. This study examines the causes of this gap and proposes possible solutions that increase flood resiliency. A mixed-method approach was utilized based on a questionnaire survey with local stakeholders in academic and professional categories. The results reveal reasons for the gap, including issues related to the realization of the role of spatial planning in flood risk reduction, lack of coordination between the authorities responsible for developing the flood hazard maps and the spatial planning authority, availability and accessibility of the required data, and subjectivity of conducted flood analyses. Four key recommendations pertain to building an operational framework for integrating flood risk assessment in spatial planning, improving stakeholder awareness and collaboration, strengthening risk communication, and improving both quality and access to data. These measures will help to overcome the identified difficulties and enhance the integration between spatial planning and flood risk assessment, effectively increasing their flood resilience.

The consensus nowadays is that there is a need to adapt to increasingly occurring climate impacts by means of adaptation plans. However, only a minority of European cities has an approved climate adaptation plan by now. To support stakeholder dialogue and decision-making processes in climate adaptation planning, a detailed spatial information and evidence base in terms of a climate impact assessment is needed. This article aims to compare the climate impact assessment done in the context of two regional climate change adaptation planning processes in a Dutch and a German region. To do so, a comparison of guidelines and handbooks, methodological approaches, available data, and resulting maps and products is conducted. Similarities and differences between the two approaches with a particular focus on the input and output of such analysis are identified and both processes are assessed using a set of previously defined quality criteria. Both studies apply a similar conceptualisation of climate impacts and focus strongly on issues concerning their visualisation and communication. At the same time, the methods of how climate impacts are calculated and mapped are quite different. The discussion and conclusion section highlights the need to systematically consider climatic and socio-economic changes when carrying out a climate impact assessment, to focus on a strong visualisation of results for different stakeholder groups, and to link the results to planning processes and especially funding opportunities., Urban planning;6(3)

The National Program of Systematic Land Registration aims to register all land property in Romania by 2023. The goal has proven difficult to achieve, as by June 2022 only 4% of the localities in the country were completed. The aim of this research is to find the similarities and differences between the fit for purpose principles of land administration for the spatial framework and the practices in The Romanian National Program of Systematic Land Registration. This is the first study that analyzes the Romanian land registration program through the lens of the fit for purpose concept. The research shows that there are similarities with the fit for purpose spatial framework principles for the use of aerial images and the participatory nature of the process. However, The National Program of Systematic Land Registration uses almost exclusively fixed boundaries and does not have different standards of accuracy and procedures for incremental improvement. Registering the parcels earlier in the process, flexibility for accuracy, and technical standards and regulations for incremental improvement of the system can speed up the registration in a fit for purpose manner.

Contemporary land administration (LA) systems incorporate the concepts of cadastre and land registration. Conceptually, LA is part of a global land management paradigm incorporating LA functions such as land value, land tenure, land development, and land use. The implementation of land-related policies integrated with well-maintained spatial information reflects the aim set by the United Nations to deliver tenure security for all (Sustainable Development Goal target 1.4, amongst many others). Innovative methods for data acquisition, processing, and maintaining spatial information are needed in response to the global challenges of urbanization and complex urban infrastructure. Current technological developments in remote sensing and geo-spatial information science provide enormous opportunities in this respect. Over the past decade, the increasing usage of unmanned aerial vehicles (UAVs), satellite and airborne-based acquisitions, as well as active remote sensing sensors such as LiDAR, resulted in high spatial, spectral, radiometric, and temporal resolution data. Moreover, significant progress has also been achieved in automatic image orientation, surface reconstruction, scene analysis, change detection, classification, and automatic feature extraction with the help of artificial intelligence, spatial statistics, and machine learning. These technology developments, applied to LA, are now being actively demonstrated, piloted, and scaled. This Special Issue hosts papers focusing on the usage and integration of emerging remote sensing techniques and their potential contribution to the LA domain.

Background. Soil-transmitted helminths (STH) are parasitic diseases with significant public health impact. Analysis is generally based on cross-sectional prevalence surveys; outcomes are mostly aggregated to larger spatial units. However, recent research demonstrates that infection levels and spatial patterns differ between STH species and tend to be localized. Methods. Incidence data of STHs including roundworm (Ascaris lumbricoides), whipworm (Trichuris trichiura) and hookworms per primary health facility for 2008 were linked to spatially delineated primary health center service areas. Prevalence data per district for individual and combined STH infections from the 2008 nationwide survey in Rwanda were also obtained. Results. A comparison of reported prevalence and incidence data indicated significant positive correlations for roundworm (R2 = 0.63) and hookworm (R2 = 0.27). Weak positive correlations were observed for whipworm (R2 = 0.02) and the three STHs combined (R2 = 0.10). Incidence of roundworm and whipworm were found to be focalized with significant spatial autocorrelation (Moran’s I > 0: 0.05–0.38 and p ≤ 0.03), with (very) high incidence rates in some focal areas. In contrast, hookworm incidence is ubiquitous and randomly distributed (Moran’s I > 0: 0.006 and p = 0.74) with very low incidence rates. Furthermore, an exploratory regression analysis identified relationships between helminth infection cases and potential environmental and socio-economic risk factors. Conclusions. Findings show that the spatial distribution of STH incidence is significantly associated with soil properties (sand proportion and pH), rainfall, wetlands and their uses, population density and proportion of rural residents. Identified spatial patterns are important for guiding STH prevention and control programs.

Sustainability challenges in socio-environmental systems (SES) are inherently multiscale, with global-level changes emerging from socio-environmental processes that operate across different spatial, temporal, and organisational scales. Models of SES therefore need to incorporate multiple scales, which requires sound methodologies for transferring information between scales. Due to the increasing global connectivity of SES, upscaling – increasing the extent or decreasing the resolution of a modelling study – is becoming progressively more important. However, upscaling in SES models has received less attention than in other fields (e.g., ecology or hydrology) and therefore remains a pressing challenge. To advance the understanding of upscaling in SES, we take three steps. First, we review existing upscaling approaches in SES as well as other disciplines. Second, we identify four main challenges that are particularly relevant to upscaling in SES: 1) heterogeneity, 2) interactions, 3) learning and adaptation, and 4) emergent phenomena. Third, we present an approach that facilitates the transfer of existing upscaling methods to SES, using two good practice examples from ecology. To describe and compare these methods, we propose a scheme of five general upscaling strategies. This scheme builds upon and unifies existing schemes and provides a standardised way to classify and represent existing as well as new upscaling methods. We demonstrate how the scheme can help to transparently present upscaling methods and uncover scaling assumptions, as well as to identify limits for the transfer of upscaling methods. We finish by pointing out research avenues on upscaling in SES to address the identified upscaling challenges.

Esse estudo, cujo objetivo é identificar barreiras e incentivos para o uso da bicicleta na cidade de São Paulo, a partir da consulta a stakeholders e da adaptação de um método de análise multicritério e multiatores (MAMCA), envolveu planejadores e representantes de organizações não governamentais. Os resultados, obtidos por meio de workshops e consultas, mostraram que os atores envolvidos não possuíam leituras da realidade tão distintas entre si, embora o grupo de influenciadores tenha se mostrado mais crítico que o de planejadores. A infraestrutura cicloviária destacou-se como essencial para atrair ciclistas. A ausência de uma cultura da bicicleta também recebeu atenção. Contudo, mais significativo foi o fato de que o grupo de planejadores ignorou a importância da segurança de trânsito para o uso da bicicleta. Finalmente, a avaliação de cenários demonstrou que, para ambos os grupos, as medidas adotadas por São Paulo são pouco efetivas quando comparadas com outros casos.

Homogeneous fine-grained patterns of urban form represent identifiable areas in cities and allow their classification. Urban morphology uses the concepts of “morphological period,” “urban tissue,” or “character areas” to link fine-grained homogeneity of form to historical origins and the social and economic characters associated with them. However, identifying such fine-grained spatial patterns is a labor-intensive, specialist operation, thus limiting replicability and scalability. Therefore, comprehensive urban form classification has rarely been conducted at a very large scale, hindering our understanding of how form contributes to social, economic, and environmental urban dynamics. With expanding capacity in geo-computation, urban analytics, and Earth Observation (EO) technology, new numerical approaches to the large-scale and detailed description of urban form have recently emerged. However, limitations due to availability, quality, and consistency of data still apply. We present an integrated approach to extra-large-scale urban form analysis that combines a novel Urban MorphoMetrics (UMM) method for the generation of rich and unsupervised urban form taxonomies with advanced EO feature-extraction techniques. UMM utilizes extremely parsimonious input information to generate a comprehensive set of urban form characters for three morphometric elements (buildings, streets, and plots), over six categories (dimension, shape, spatial distribution, intensity, connectivity, and diversity) and three scales (small, medium, and large). All characters are measured at the building level and clustered into distinct homogeneous urban types, thus creating a comprehensive taxonomy of urban form. UMM is applicable across cases, allowing individual type profiling and cross-case comparison. We illustrate UMM outputs across a range of case studies covering formal and informal urban areas in sharply different geographical and cultural contexts worldwide. The results demonstrate an encouraging ability to map urban form in cities in ways that relate to historical origins, land uses, and other validating geographies.The method also shows a pathway to address varying degrees of availability, quality, and consistency of input data, which is commonly poor, for example, in informal settlements. Explorations of ways to resolve this issue include integrating UMM with EO. The latter offers a way to generate globally consistent input data from freely accessible repositories, hence ensuring full control of quality and consistency. Thus, we show that our first efforts to combine UMM and EO data into an integrated UMM+EO process are suitable for use at a global scale.

The past decade has seen a gradual but steady increase in the planning scholars’ interest in outlining a functional place for games in planning. A wide range of games for and about urban planning is developed and tested, from data-driven games that rely on extensive modelling techniques and aim to reduce the cost and risk of real-world scenario testing, to those that seek to educate their players about the complex nature of political and social issues. Despite the increasing interest in strengthening communications between planning and game studies, the current state is an amalgam of confusion and optimism about games’ role and added value. To shed light on why such confusions emerge, the article reflects on the nature and outcomes of communications between urban planning and games studies and explores games’ historical and current conceptions in planning. By adopting concepts from the work of Holbrook on interdisciplinary communications, the article explores how game studies’ concepts are rendered useful in planning and how planning theory has dealt with untranslatability and incommensurability of concepts in the processes of establishing and sustaining communications with game studies.

Agricultural land is an indispensable resource for agrarian communities worldwide. There is a growing awareness that the world’s arable land supplies are limited and finite. For the last five decades, the Government of Ethiopia (GoE) and other development organisations have carried out land preservation uptakes intended to curb the effects of land degradation and improve agricultural productivity through various soil and water conservation practices (SWCPs). The study assessed the sustainability, drivers, and constraints of SWCPs in Dessie Zuria and Kutaber Woredas of South Wollo. We used the exploratory case study approach, involving qualitative and quantitative methods. Catastrophic weather conditions, the presence of the soil and water conservation program, and declining soil fertility were the top drivers influencing the program’s implementation. Based on the identified farm-level indicators and views of the respondents, physical measures were more sustainable than biological and mixed methods. The prominent factors hurdling the intervention were lack of tenure security, risk of rodent infestation, and losing a sense of ownership. Training community members on the importance of land preservation, amending the existing rigid land tenure policy, incorporating indigenous SWCPs, broadening the scale and extent of community participation, and enforcing laws and bylaws are recommended for the upcoming interventions. The finding has implications for land preservation and food security actors working to scale up evidence-based sustainable land management practices to the broader area.

The ongoing fast and unprecedented urbanisation, strongly affecting cities in the Global South, is casting urban development into challenging dynamics, involving, for example, the construction of new or expansion of existing informal settlements lacking the most basic services. This informal urban development is insufficiently understood due to limited resources, leading to an absence of data to support its urban analysis and, consequently, obstacles to address social equity, sustainability, and resilience through urban planning and design practices. Although the potential of Earth Observation (EO) has widely been recognized for mapping different urban patterns, the surge of big data and data science, along with usually costly high resolution datasets are marginalizing the open science of interpretable urban morphology with the abstract relationship between mapped socioeconomic patterns and image features. In this work, an "EO + Morphometrics" framework is proposed to combine open EO data with open tools for explicit, reliable and consistent measurements of urban form and thus to understand development patterns based on urban morphology. More specifically, a reengineered convolutional neural network is applied to freely available Google Earth imagery to extract building footprints for entire cities. Morphometrics then uses this information to measure hundreds of metrics of the built environment and output homogenous urban form types. This two-step method is applied to a few fast-growing sub-Saharan cities. To test whether specific urban types correspond to socioeconomic patterns, we compare the outcomes with local delineations of informal settlements. Morphological types, characterised by a compact/organic urban fabric, seem to be predominantly contained in the boundaries of informal settlements. We argue that "EO + Morphometrics" paves the way for deriving a generalizable understanding of urban form in challenging contexts. This information can, in turn, be used for further analysis (for example, with socioeconomic data) and inform local planning and interventions.

During the past years, unmanned aerial vehicles (UAVs) gained importance as a tool to quickly collect high-resolution imagery as base data for cadastral mapping. However, the fact that UAV-derived geospatial information supports decision-making processes involving people’s land rights ultimately raises questions about data quality and accuracy. In this vein, this paper investigates different flight configurations to give guidance for efficient and reliable UAV data acquisition. Imagery from six study areas across Europe and Africa provide the basis for an integrated quality assessment including three main aspects: (1) the impact of land cover on the number of tie-points as an indication on how well bundle block adjustment can be performed, (2) the impact of the number of ground control points (GCPs) on the final geometric accuracy, and (3) the impact of different flight plans on the extractability of cadastral features. The results suggest that scene context, flight configuration, and GCP setup significantly impact the final data quality and subsequent automatic delineation of visual cadastral boundaries. Moreover, even though the root mean square error of checkpoint residuals as a commonly accepted error measure is within a range of few centimeters in all datasets, this study reveals large discrepancies of the accuracy and the completeness of automatically detected cadastral features for orthophotos generated from different flight plans. With its unique combination of methods and integration of various study sites, the results and recommendations presented in this paper can help land professionals and bottom-up initiatives alike to optimize existing and future UAV data collection workflows.

Geospatial data is urgently needed in decision-making processes to achieve Sustainable Development Goals (SDGs) at global, national, regional and local scales. While the advancement of geo-technologies to obtain or produce geospatial data has become faster and more affordable, many countries in the global south still experience a geospatial data scarcity at the rural level due to complex geographical terrains, weak coordination among institutions and a lack of knowledge and technologies to produce visualised geospatial data like maps. We proposed a collaborative spatial learning framework that integrates the spatial knowledge of stakeholders to obtain geospatial data. By conducting participatory mapping workshops in three villages in the Deli Serdang district in Indonesia, we tested the framework in terms of facilitating communication and collaboration of the village stakeholders while also supporting knowledge co-production and social learning among them. Satellite images were used in digital and non-digital mapping workshops to support village stakeholders to produce proper village maps while fulfilling the SDGs&rsquo, emphasis to make geospatial data available through a participatory approach.

Land administration constitutes the socio-technical systems that govern land tenure, use, value and development within a jurisdiction. The land parcel is the fundamental unit of analysis. Each parcel has identifiable boundaries, associated rights, and linked parties. Spatial information is fundamental. It represents the boundaries between land parcels and is embedded in cadastral sketches, plans, maps and databases. The boundaries are expressed in these records using mathematical or graphical descriptions. They are also expressed physically with monuments or natural features. Ideally, the recorded and physical expressions should align, however, in practice, this may not occur. This means some boundaries may be physically invisible, lacking accurate documentation, or potentially both. Emerging remote sensing tools and techniques offers great potential. Historically, the measurements used to produce recorded boundary representations were generated from ground-based surveying techniques. The approach was, and remains, entirely appropriate in many circumstances, although it can be timely, costly, and may only capture very limited contextual boundary information. Meanwhile, advances in remote sensing and photogrammetry offer improved measurement speeds, reduced costs, higher image resolutions, and enhanced sampling granularity. Applications of unmanned aerial vehicles (UAV), laser scanning, both airborne and terrestrial (LiDAR), radar interferometry, machine learning, and artificial intelligence techniques, all provide examples. Coupled with emergent societal challenges relating to poverty reduction, rapid urbanisation, vertical development, and complex infrastructure management, the contemporary motivation to use these new techniques is high. Fundamentally, they enable more rapid, cost-effective, and tailored approaches to 2D and 3D land data creation, analysis, and maintenance. This Special Issue hosts papers focusing on this intersection of emergent remote sensing tools and techniques, applied to domain of land administration.

As a leading cause of morbidity and premature mortality, obesity has become a major global public health problem. It is therefore important to investigate the spatial variation of obesity prevalence and its associations with environmental and behavioral factors (e.g., food environment, physical activity), to optimize the targeting of scarce public health resources. In this study, the geographic clustering of obesity in the Netherlands was explored by analyzing the local spatial autocorrelation of municipal-level prevalence rates of adulthood obesity (aged ≥19 years) in 2016. The potential influential factors that may be associated with obesity prevalence were first selected from five categories of healthrelated factors through binary and Least Absolute Shrinkage and Selection Operator (LASSO) regressions. Geographically Weighted Regression (GWR) was then used to investigate the spatial variations of the associations between those selected factors and obesity prevalence. The results revealed marked geographic variations in obesity prevalence, with four clusters of high prevalence in the north, south, east, and west, and three clusters of low prevalence in the north and south of the Netherlands. Lack of sports participation, risk of anxiety, falling short of physical activity guidelines, and the number of restaurants around homes were found to be associated with obesity prevalence across municipalities. Our findings show that effective, region-specific strategies are needed to tackle the increasing obesity in the Netherlands.

Geographic information-based planning support tools implemented in a maptable have the potential to mediate collaborative spatial planning processes. However, available tools for a maptable either lack advanced analytical functions or have usability shortcomings. Given these limitations, this research aims to conceptualize an interactive planning support tool intended to fully exploit maptable capabilities while providing spatial analytical functions to better support planning and decision-making processes with a larger group of participants. To do so, we conducted a literature review of reported maptable-based applications and semi-structured interviews with identified intended user groups of such applications, and derived Agile user stories. We identified a) principal spatial analyses, b) must-have functionalities, c) required support for individual contributions, and d) preferred space-time settings for group work collaboration, and based on that conceptualized an interactive tool for a maptable. By involving the intended users in the conception of the tool we revealed a discrepancy between the understanding of scholars and developers with respect to what users need and what they do. Intended user groups require tailored but straightforward instruments, rather than complicated or time-consuming models. Our research has laid down the foundation for future maptable tool development to support collaborative planning processes.

This working paper examines the development and design of the Observatorio Metropolitano de Agua para Lima-Callao (MWO): a co-produced digital observatory for water and data justice. The metropolitan city of Lima and Callao (hereafter referred to as Lima) faces a water security challenge, which results from the combination of scarce water resources with inefficient and unequal distribution. The lack or limited water access has been a crucial factor shaping inequality patterns in the city. Amongst the challenges for improving water distribution are the difficulties around mapping water needs and the lack of adequate and comprehensive data for all citizens since a significant part of the population is excluded from formal government datasets. Those living in subservice areas, who do not have access to administrative or political structures, and migrants are examples of such exclusion from formal government sources for evidence-based policy and implementation. The purpose of the MWO is to contribute to a fairer distribution of water resources amongst urban residents within Lima. By combining collaborative design approaches with the theory-informed data justice guidelines, we have developed a prototype of the MWO with participants from three districts in Lima. The MWO aims to challenge the hegemonic representation of Lima’s water infrastructure and help communicate the experiences and views of residents currently overlooked. In addition, the MWO teaches us how to design digital platforms according to the principles of data justice in practice.

Many are the suggestions on how to develop a new land information system or evaluate how successful is the existing system. However, guidance about how the created system can be kept up to date is shallowly mentioned in the literature while it is the key feature for its sustainability. This paper is part of an extensive research done regarding the updating of land information systems that were created using unconventional approaches during systematic land registration. For these systems, huge database are created in a short period during the initial registration. To minimize uncertainties that may be in the updating phase, a framework was developed and presented in this paper. A refined traditional approach for system design was used in the development of this framework. The design requirements were extracted from literature and they were refined basing on the responses of land experts from nine case-study countries. These refined requirements were used to develop the updating framework and its validation was done by experts in the use of unconventional approaches in land registration. The developed framework explains what to consider in the updating to ensure the registration of changes in land records. The framework is composed of ���dimension��� which is a group of parameters related to each other; ���parameters��� of land information system that are worthy to consider in the updating and the ���requirements��� describing how to design these parameters to ensure that changes in land records are being registered., African Journal on Land Policy and Geospatial Sciences, Vol 5, No 1: Special Issue - January 2022

Akatsi North District, Ghana was selected as a study site for the HABITABLE project based on a number of selection criteria assembled by the project consortium. This report is intended to give project partners a brief overview of the most salient climate change and migration issues currently characterizing the wider context of Ghana, as well as the most urgent issues within Akatsi North District. Both Ghana and Akatsi North are facing a number of changing environmental dynamics and climate change-related threats. In Akatsi North District, those changes may contribute to increased migration rates. While employment, education and family reunions have been previously reported as the main drivers of migration throughout Ghana, one aim of HABITABLE and Work Package 2 in particular, is to further examine local perceptions of migration drivers and add to the body of knowledge in this area. This report aims to take a step in that direction by bringing together and summarizing a number of relevant issues as background information for project partners. The report concludes that due to its ongoing and observable environmental changes as well as increasing migration rates, Akatsi North is a relevant and appropriate study site for the HABITABLE project.

Vegetation maps have been made since centuries. The vegetation cover was represented as homogeneous mapping units (polygons), representing different vegetation types, where each type consists a combination of different plant species (floristic composition). More recent, with the use of satellite imagery, the polygons have been replaced by pixels with similar content as the polygon maps. In both approaches, field-observations were linked to the mapping units (polygons or pixels) often resulting in a complex of different vegetation types per mapping unit. In our new approach field data (sample points) on presence and abundance of individual grass species are spatially extrapolated based on a set of environmental layers, using the species distribution modelling approach (SDM). When combined, each pixel will contain its own set of information about the vegetation structure and its floristic composition. This new methodology (SPiCla) results in a very accurate and detailed vegetation map at pixel level, allowing extraction of very detailed, accurate and easy to update spatial information on e.g., forage production and quality (palatability) for rangelands management. As no exact boundaries exist, but only gradients, we introduced fuzzy accuracy. The resolution mainly depends on the resolution of (or one of) the environmental layers used, scale of interest and workability. The methodology is generic and applicable to any other region in the world.

In the past two decades, Earth observation (EO) data have been utilized for studying the spatial patterns of urban deprivation. Given the scope of many existing studies, it is still unclear how very-high-resolution EO data can help to improve our understanding of the multidimensionality of deprivation within settlements on a city-wide scale. In this work, we assumed that multiple facets of deprivation are reflected by varying morphological structures within deprived urban areas and can be captured by EO information. We set out by staying on the scale of an entire city, while zooming into each of the deprived areas to investigate deprivation through land cover (LC) variations. To test the generalizability of our workflow, we assembled multiple WorldView-3 datasets (multispectral and shortwave infrared) with varying numbers of bands and image features, allowing us to explore computational efficiency, complexity, and scalability while keeping the model architecture consistent. Our workflow was implemented in the city of Nairobi, Kenya, where more than sixty percent of the city population lives in deprived areas. Our results indicate that detailed LC information that characterizes deprivation can be mapped with an accuracy of over seventy percent by only using RGB-based image features. Including the near-infrared (NIR) band appears to bring significant improvements in the accuracy of all classes. Equally important, we were able to categorize deprived areas into varying profiles manifested through LC variability using a gridded mapping approach. The types of deprivation profiles varied significantly both within and between deprived areas. The results could be informative for practical interventions such as land-use planning policies for urban upgrading programs.

This study aims to help bring the domestic food transport network into focus for The State of Food and Agriculture 2021 – Making agrifood systems more resilient to shocks and stresses. Transport infrastructure and logistics, not least domestic food transport networks, are an integral part of agrifood systems, and play a fundamental role in ensuring physical access to food at the local level, as well as in producing non-food agricultural output. The flow of food from farm to fork is vulnerable to various shocks; however, the resilience of this flow has rarely been studied. This study aims to fill that gap; in doing so, it develops a spatial analysis framework that realistically characterizes the physical transport network, and uses this framework to then analyse the network’s ability to transport enough food to meet demand. The analysis builds on a preliminary spatial workflow and on evaluated resilience metrics to analyse the structure of transport networks in the context of national food transport network resilience. For a total of 90 countries, it considers road, river and rail transport infrastructure, along with trade ports, border crossings and their respective import and export quantities. It then measures food transport network resilience for each country through three main indicators: proximity-based resilience, relative detour cost and alternative route availability. Findings show that where food is transported more locally and where the network is denser, disturbances have a much lower impact. This is mostly the case for high-income countries, as well as for densely populated countries like China, India, Nigeria and Pakistan. Conversely, low-income countries have much lower levels of transport network resilience, although some exceptions exist. A simulation of the impact of localized 1-in-10-year flooding events in Mozambique, Nigeria and Pakistan is also used to capture the effect of potential disruptions to food transport networks for crops in the three countries. The simulation illustrates the loss of network connectivity that results when links become impassable, potentially affecting millions of people.

The continuous urbanisation in most Low-to-Middle-Income-Country (LMIC) cities is accompanied by rapid socio-economic changes in urban and peri-urban areas. Urban transformation processes, such as gentrification as well as the increase in poor urban neighbourhoods (e.g., slums) produce new urban patterns. The intersection of very rapid socio-economic and demographic dynamics are often insufficiently understood, and relevant data for understanding them are commonly unavailable, dated, or too coarse (resolution). Traditional survey-based methods (e.g., census) are carried out at low temporal granularity and do not allow for frequent updates of large urban areas. Researchers and policymakers typically work with very dated data, which do not reflect on-the-ground realities and data aggregation hide socio-economic disparities. Therefore, the potential of Earth Observations (EO) needs to be unlocked. EO data have the ability to provide information at detailed spatial and temporal scales so as to support monitoring transformations. In this paper, we showcase how recent innovations in EO and Artificial Intelligence (AI) can provide relevant, rapid information about socio-economic conditions, and in particular on poor urban neighbourhoods, when large scale and/or multi-temporal data are required, e.g., to support Sustainable Development Goals (SDG) monitoring. We provide solutions to key challenges, including the provision of multi-scale data, the reduction in data costs, and the mapping of socio-economic conditions. These innovations fill data gaps for the production of statistical information, addressing the problems of access to field-based data under COVID-19.

Although the management of livestock numbers within the bounds of carrying capacity of African rangelands is a way to manage risks, both scientists and practitioners, caution against a momentary and local use of carrying capacity as a management indicator. Carrying capacity should be seen in wider spatial and temporal/seasonal context as well as in a social and economic context. Given the large numbers of conservancies across Kenya, and its Maasai Mara region in particular, with many more landowner members, it is difficult for conservancies’ managers to contextualize phenomena such as carrying capacity and market price over space and time. We report the results of an investigation in the Maasai Mara rangelands, into functional characteristics a tool for spatial-temporal carrying capacity assessment and livestock markets prices monitoring should have to provide relevant management information to conservancy managers and conservancy members. A scalable web-application called the Mara Rangeland Information System, or MARIS, was developed, which assesses, at 23 meter resolution and 10 day historic or 1-day near-future intervals, both grassland dry matter production, and consumption by 19 wildlife and livestock species, as well as rangeland carrying capacity. MARIS facilitates managers to develop scenarios by varying input variables of either grass production or consumption, or by drawing different management blocks on a carrying capacity map assessing different management practices under scenarios of rainfall. Managers can relate the carrying capacity scenarios to offtake prices at different markets that MARIS monitors over time. After testing MARIS in 6 workshop iterations across the whole development process, Maasai Mara rangeland managers concluded that the prototype is ready for pilot use in management plan development.

Intense exploitation of land in the vertical direction has brought up complex legal relations between different types of spatial units with various characteristics (e.g., land, marine, air, underground parcels, and infrastructure objects). Therefore, the use of 3D models is required to clearly represent real property and associated Rights, Restrictions and Responsibilities (RRRs), deriving both from Private and Public Law. The latter are either not registered to cadastral systems (i.e., in The Netherlands very few have been registered like the private natural beauty areas, as they came with tax benefits), or are recorded to individual, thematic registries. Public Law Restrictions (PLRs) impose significant impact on ownership rights and land management, thus requiring to be systematically organized and registered. This brings out issues of identifying which types of PLRs need to be registered (based on land administration policies that apply in each country/ jurisdiction), selecting and “spatializing” them (in 2D/3D/nD). Within the field of land administration, the ISO 19152:2012 Land Administration Domain Model (LADM) plays predominant role in standardizing legal relations between parties (people) and spatial units (land). LADM is currently under revision with its second edition widening its scope as a multipart standard comprising 6 Parts. The revision of LADM stimulates discussion on new concepts that could be included at the Edition II, and possibilities of refining the existing ones. In this context, the paper investigates the option to model PLRs into the multipart standard and investigates how to optimally categorize them based on the LADM Edition II Parts. The paper builds on previous work by the authors and aims to propose a flexible framework to model PLRs at conceptual level in the context of LADM Edition II. To validate the modelling proposal, two case studies of PLRs are studied. The first one relates to the restrictions imposed on land parcels crossed by the Trans Adriatic Pipeline (TAP) in Northern Greece, and they refer to the establishment of protection zones, where construction and agricultural restrictions apply. The second case study refers to land use restrictions in the vicinity of an archaeological site in the municipality of Patras, in southern Greece. Those use cases were selected because of their generic character that may apply to other countries/ jurisdictions, regardless of legal framework differences.

The timely and effective dissemination of property values is an essential part of a transparent and efficient property valuation system as property values are required in several land administration processes, such as land acquisition, taxation, transaction, consolidation, readjustment and transformation. In the last decade, a web-based data-sharing system has been increasingly used for dissemination of property values. The 3D visualization of valuation units may be required in order to better communicate with users and provide more effective and efficient dissemination, however, none of those systems share valuation information associated with 3D representation (legal or physical) of property (valuation) units. The objective of this paper is to reveal the requirements, opportunities and challenges for web-based 3D visualization and dissemination of property valuation information. To deliver this objective, the requirements for U(ser), D(ata), and V(isualization) are investigated. The general public/property owners and professionals are determined as main user groups in this research. For each of the groups specific data and 3D visualization requirements are discussed and a number of suggestions are provided for developing an effective dissemination of property valuation information. These requirements includes the visualization of multi-part properties (e.g. condominium unit, storage unit, car parking), thematic mapping of valuation information, and aggregation of valuation units into valuation units groups. Furthermore, the capabilities of the LADM Valuation Information Model as a schema for storage, and the features and functionalities of 3D visualization platforms (geoweb viewers) in terms of better value dissemination (e.g. altering visual variables, solution for occlusion, visualizing below surface properties) are briefly investigated. Lastly, an initial prototype is developed and presented.

In the past decade, a lot of effort is put into applying digital innovations to building life cycles. 3D Models have been proven to be efficient for decision making, scenario simulation and 3D data analysis during this life cycle. Creating such digital representation of a building can be a labour-intensive task, depending on the desired scale and level of detail (LOD). This research aims at creating a new automatic deep learning based method for building model reconstruction. It combines exterior and interior data sources: 1) 3D BAG, 2) archived floor plan images. To reconstruct 3D building models from the two data sources, an innovative combination of methods is proposed. In order to obtain the information needed from the floor plan images (walls, openings and labels), deep learning techniques have been used. In addition, post-processing techniques are introduced to transform the data in the required format. In order to fuse the extracted 2D data and the 3D exterior, a data fusion process is introduced. From the literature review, no prior research on automatic integration of CityGML/JSON and floor plan images has been found. Therefore, this method is a first approach to this data integration.

Summary While current climate and environmental efforts are mostly focused on the necessary and urgent CO2 emissions cuts, energy transition and climate adaptation, other not least pressing issues such as air quality start reaching the political agenda (European Commission, 2019). In this paper, we address air pollution from the social welfare perspective and bring together two strings of literature on the productivity of green urban infrastructure for ambient air quality and the monetisation of air quality improvements. Based on the EU air quality data by country and city, we identify those regions, which would benefit most from improved urban green infrastructure to improve air quality performance in Europe. We review a set of academic literature on the impact of urban green combating urban air pollution, and provide a synthesis review of social externality costs connected to urban air pollution. We extrapolate, in a stylized manner, the effect of increasing urban vegetation throughout the EU and in a selection of EU cities. Our estimates show that additional 1m2 of green cover throughout the EU cities would lead to 65,9 mln EUR of benefit per year, split about equally between the benefits of improved air quality and carbon sequestration, equivalent to 976 euros per ha per year for air pollutants and 1024 euros per ha per year for carbon abatement.

Despite the significant and explicit focus on the United Nations Sustainable Development Goals (SDGs), much of the world’s land rights remain unrecorded and outside formal government systems. Blame is often placed on land administration processes that are considered slow, expensive, and expertise-dependent. Fit-For-Purpose Land Administration (FFPLA) has been suggested as an alternative, time and cost-effective approach. Likewise, the UN endorsed Framework for Effective Land Administration (FELA) demands attention to worldwide tenure insecurity by directly linking it to responsible land administration. Implementation of FFPLA and FELA is country-context dependent, and there are now many lessons of execution from various jurisdictions. Undertaken in 2022, this study synthesizes a review of experiences to provide a further update on the best global FFPLA implementation practices and inform approaches for future FFPLA projects. A systematic review is adopted as the research methodology, and contemporary articles from the internationally recognized land administration discourse are examined. The studies focus on FFPLA implementation practices and innovative approaches for delivering land tenure security. A checklist is developed, based on the FELA strategic pathways and the FFPLA fundamental framework principles and characteristic elements, to identify best implementation practices. Success stories across the globe show that the FFPLA characteristic elements and the FELA pathway goals are achieved through effective execution of the FFPLA framework key principles. As a result, the study identified successful FFPLA implementation practices in Asia and Africa, which can be synthesized and extended to realize tenure security in rapidly urbanizing areas. However, further study is necessary to determine the efficacy, practicability, innovativeness, and transferability of the best practices to other land administration scenarios.

Digital twins (DTs) have been found useful in manufacturing, construction, and maintenance. Adapting DTs to serve cities, the question arises of what an urban digital twin should contain and how it should be orchestrated to serve a city’s dynamical ecosystem, along with how to enhance the efficiency of the city. We are aligning with the commonplace idea that the main advantage of using DTs is economical as, for example, DTs can improve the planning of activities thus saving money and time. But how can they be useful for a city? Instead of looking at the DTs as solutions in search of problems to be solved, we start from city needs. Our approach is two-fold. We start by briefly reviewing existing possibilities for meeting some specific needs, but keep the focus on identifying and attempting to close the gap between the needs arising from everyday city functions and the latest DT techniques useful for meeting those needs. DTs are technically different and serve different applications, yet they share a common identity and name, as well as several technical similarities. Adopting computer science terminology, we see a back-end city DT as the container of all information, while any single front-end, visualized or used either by humans or robots, offers a limited but meaningful representation of the DT for a specific application. Alas, there are multiple open questions regarding the realization and benefits of such back-end DT. Nevertheless, we discuss how the back-end DT (or any specific DT) could be updated autonomously from sensor data using artificial intelligence techniques, and how the front-ends could be used for large benefits to the entire city ecosystem.

Land administration is established to manage the people-to-land relationship. However, it is believed that 70% of the land in developing countries is unregistered. In the case of Ecuador, the government has an ambitious strategy to implement a national cadaster on the full territory in a short time period. Therefore, the objective of this study was the assessment of land administration in Ecuador based on the fit-for-purpose approach as an assessment framework. A literature review was performed on the topic of land administration, including guidelines for improvement and assessment frameworks. The basic concept of fit-for-purpose land administration was reviewed with the three frameworks, which are: spatial, legal, and institutional. Interviews and focus group discussions were performed in Ecuador for collecting primary and secondary data about land administration in this country. Results from these activities are presented and discussed using the structure of the basic concept of fit-for-purpose land administration with the three frameworks. It was found that during the field data collection precise land survey of fixed boundaries was performed and around 55–60 attributes per parcel were collected as a part of the field land survey in Ecuador. Based on the findings, discussions were developed, and a score table was created identifying which principles should be addressed if rapid mapping and land registration are desired by the government of Ecuador to be implemented on the whole territory in a short time period. Finally, the paper ends with conclusions and recommendations.

Mozambique started a massive land registration program to register five million parcels and delimitate four thousand communities. The results of the first two years of this program illustrated that the conventional methods utilized for the land tenure registration were too expensive and time-consuming and faced several data quality problems. The purpose of this research was to conceptualize, develop and test a country-specific Fit For Purpose Land Administration (FFPLA) approach for Mozambique, denominated as FFPLA-MOZ, intertwining three pillars: people, processes, and technology, to solve the constraints faced in systematic registrations. Such a contextualized approach needed to be: (i) in line with legislation, (ii) appropriate to the circumstances and needs of the systematic registration, (iii) cost-effective, (iv) based on available technology, and (v) fit to establish a sound and sustainable land administration system. By connecting people, processes, and technology, the FFPLA-MOZ approach achieved several benefits, including cost and time reduction, increased community satisfaction, and improved quality of work and data. The FFPLA-MOZ approach also supported a more robust community engagement through a more participatory land registration, denominated community-based crowdsourcing. Initial observations indicated that strong leadership and commitment were of extreme importance to ensure change management, capacity development, and project delivery for the success of these initiatives. The research only focused on the registration of land under good faith and customary occupations, as well as community delimitations. The next stages should focus on other land management activities and integrate other cadastres.

This study investigated the role of participatory village maps in strengthening the Musrenbang, an annual multi-stakeholder public consultation forum to discuss development issues and plans in Indonesia. We evaluated the Musrenbang in five villages in Deli Serdang District after conducting participatory mapping workshops to produce village maps to inform the Musrenbang process. Our results show that communication between Musrenbang participants improved because the maps provided a clear definition of the village administrative area, geospatial data as resources for participation, transparency, and a dynamic deliberative process. Collaboration was also evident as the maps enabled participants to exchange knowledge, experience social learning, and have greater influence on the decision-making process. Despite the benefits, some issues impeded the optimal use of the village maps to support the participatory process in the Musrenbang. The maps could not completely overcome the power disparities between Musrenbang participants. Certain actors still dominated the implementation of the Musrenbang, making the deliberative process inaccessible to and less inclusive of some local stakeholders. Several improvements are urgently needed to optimise the use of participatory village maps and enhance Musrenbang implementation.

Open Science (OS) is an umbrella term comprising principles to increase the transparency of research. Besides Open Access to scientific articles, these principles contain public availability of reusable methods (e.g., code and tools), data, and educational materials. This document outlines a plan to achieve the transition towards OS. ITC’s Strategic Plan for OS 2021-2025 - Towards an Open Future contains five initiatives: 1.OS at ITC aims to provide guidelines and OS capacity development to address the obstacles ITC researchers encounter when doing OS. 2.The ITC Knowledge Hub will provide services and tools to access, create, and publish open research, including scientific results based on qualitative/quantitative analyses using computational workflows. 3.Open Educational Resources will be addressed by exploring options to realise Open Educational Resources at ITC and providing lecturers with guidelines and support to create them. 4.The OS Community Twente serves as an inter-disciplinary, bottom-up community to promote, learn, share, and discuss OS practices. 5.Research & Funding aims to address challenges in OS through innovative developments and user studies. A further output is to generate funding to realise the ambitious aims presented in the plan. For a successful OS transition, the initiatives aim to address the Rewards & Recognition system, valorise Sharing & Collaboration, develop OS Knowledge & Skills, and foster Cultural change & Societal impact.

Urban blight functions inversely to city development and often leads to cities’ deterioration in terms of physical beauty and functionality. While the underlying causes of urban blight in the context of the global north are mainly known in the literature to be population loss, economic decline, deindustrialisation and suburbanisation, there is a research gap regarding the root causes of urban blight in the global south, specifically in prime areas. Given the differences in the property rights regimes and economic growth trajectories between the global north and south, the underlying reasons for urban blight cannot be assumed to be the same. This study, thus, employed a qualitative method and case study approach to ascertain in-depth contextual reasons and effects for urban blight in a prime area, East Legon, Accra-Ghana. Beyond economic reasons, the study found that socio-cultural practices of landholding and land transfer in Ghana play an essential role in how blighted properties emerge. In the quest to preserve cultural heritage/identity, successors of old family houses (the ancestral roots) do their best to stay in them without selling or redeveloping them. The findings highlight the less obvious but relevant functions that blighted properties play in the city core at the micro level of individual families in fostering social cohesion and alleviating the need to pay higher rents. Thus, in the global south, we conclude that there is a need to pay attention to the less obvious roles that so-called blighted properties perform and to move beyond the default negative perception that blighted properties are entirely problematic.