Across the Saharan region of North Africa, oasis territories constitute the dominant form of human settlement. In Algeria, the Sahara is undergoing rapid urban and agricultural expansion, resulting in significant spatial and demographic transformations and increased environmental pressures on oasis systems. Despite these critical dynamics, existing studies have addressed oasis sustainability only superficially, lacking quantitative, territory-scale indicators that integrate both spatial and demographic dimensions. As a result, preserving oasis territories has become a critical challenge for national economic and industrial development. Spatial planning and demographic balance are key drivers for oasis landscape sustainability. This study focuses on the Tolga oasis territory, one of the largest in North Africa, to investigate the spatial and demographic relationships among the built environment, urban perimeters, population dynamics, and palm grove areas. The methodology combines: (1) historical cartographic analysis using georeferenced maps from 1900 to 2020 processed in QGIS (RMSE < 5 m); (2) GIS-based digitization of built-up areas (BuA) and palm grove areas (PGA) across four reference periods (1900, 1940, 1980, 2020); (3) polynomial regression modeling for urban perimeter vs. inter-oasis distance; and (4) least squares method for the population-palm tree correlation. Using spatial and statistical analyses, the results indicate that the built-up area should remain below a threshold ratio of 0.05 relative to the cultivated area to maintain the oasis landscape. Strong polynomial correlations (0.5876 <= R-2 <= 0.974) confirm the structural link between urban perimeter growth and inter-oasis distance, outperforming linear regression (mean Delta R-2 = +0.226). In addition, a strong correlation is identified between population size and palm tree abundance, as expressed by the relationship PT = 1.6376 Po + 755,050, where P denotes population size (F-statistic = 178.4; p < 0.01; N = 24; 95% CI of slope = +/- 0.24). Adopting a territorial-scale approach, this study proposes novel quantitative indicators, including ratio and formula-based models that can be integrated into Saharan territorial planning strategies to support sustainable oasis development.

ABSTRACT Urban flooding is one of the most frequent and destructive hazards, with disproportionate impacts in vulnerable urban neighborhoods. Although community resilience is critical for reducing flood impacts and supporting recovery, the integrated quantitative assessment of interactions between household, collective, and institutional factors remains limited, particularly in vulnerable urban contexts. This study aims to explore how household preparedness, residential context, collective dynamics, and institutional communication interact to shape community resilience to flooding in vulnerable urban neighborhoods of Antananarivo, Madagascar. It adopts an integrative community resilience framework combining process, collective, and structural dimensions. The analysis is based on a standardized household survey of 400 households across 20 flood‐prone neighborhoods, using household typology and quantitative analyses. Results show that demographic and socio‐economic characteristics play a limited role in shaping collective resilience. In contrast, household preparedness, neighborhood configuration, and the perceived quality of institutional communication emerge as the main determinants of community preparedness and collective response. Although the household typology identifies distinct vulnerability profiles, these profiles do not translate into significantly different collective dynamics, underscoring the central role of organizational and relational mechanisms in shaping community resilience. These findings call for multi‐level flood resilience strategies that prioritize institutional communication, community intermediaries, and participatory governance mechanisms over predominantly infrastructure‐centered approaches in resource‐constrained urban contexts.

Land take - the conversion of greenfield land into built-up areas - poses critical challenges for sustainable urban development. Addressing this issue requires understanding the balance between outward urban expansion and inward urban densification. This study employs a Multinomial Logistic Regression-based Cellular Automata (MNL-CA) model to simulate two different future scenarios of urban development till 2050 in Wallonia, Belgium - a region experiencing rapid urbanisation. The model simulates two contrasting built-up demand scenarios. The first scenario is density-based, which follows a linear extrapolation of historical trends in built-up demand into the future. Conceptually, this scenario represents a gradual slowdown of urban expansion, indicating a shift to densification. The second applies a stress-test scenario in which urban expansion continues steadily through 2050. The results reveal that under the density-based scenario referred to as Business-As-Usual (BAU), expansion rates decrease sharply, stabilising at approximately 0 hectares per day by 2040. This indicates a shift toward compact urban forms, albeit accompanied by a continuous decline in overall demand. By contrast, the growth-based scenario or Growth-As-Usual (GAU) produces ongoing expansion at around 2.5 hectares per day in 2050, highlighting the risks of uncontrolled land consumption. Spatial metrics further demonstrate that the density-based scenario fosters compact and contiguous development, whereas the growth-based scenario results in fragmented urban forms that challenge the resilience of spatial planning policies. By evaluating both scenarios, our model provides a framework for stress-testing long-term development strategies, enabling more robust assessments for sustainable land-use policy.

This article examines social vulnerability (SV) as a necessary component of landslide risk assessment in the urban area of Quito, Ecuador. Landslide susceptibility identifies where slope instability is more likely, but it does not explain which populations have fewer resources to anticipate, cope with, or recover from such events. Using 2010 census-tract data, principal component analysis (PCA) was applied to derive interpretable factors of SV. The most robust factor—structural socioeconomic precariousness—combines precarious occupational conditions, lack of access to social security or private insurance, and limited access to new technologies. This factor was combined with a previously developed landslide susceptibility map (LSM) based on events recorded between 2005 and 2017 and aggregated to census tracts. The Comparative Environmental Risk Index (CERI) was then used to interpret whether socially vulnerable groups are disproportionately located in areas of higher landslide susceptibility. Results reveal a comparatively safer and socially advantaged populations axis from the center-north toward the eastern valleys, while high-risk and socially vulnerable areas concentrate in the south and selected peripheral zones. The study provides a historical and methodological baseline and contributes a quantitative, spatial, urban approach to landslide risk inequity in an Andean city.

Rising temperatures driven by climate change and rapid urbanization have heightened the need for effective urban cooling strategies. Green roofs, as nature-based solutions, can offer a practical alternative contribution in dense urban areas by improving thermal comfort. However, accurately modeling their microclimatic impact remains difficult due to a large variety in green roof design parameters and limited validation of available tools. In addition, few green roof modeling studies account for urban morphology at the block scale. This study addresses these gaps by validating the green roof module of the Solene-microclimat urban microclimate model with a comparison against measurements and by conducting a local sensitivity analysis of the green roof parameters. The green roof model outcomes were compared to measurements and found to depict physical processes correctly. A local sensitivity analysis investigates how substrate and vegetation parameters as well as irrigation factors affect pedestrian level thermal comfort across nine representative urban morphological archetypes in Li & egrave;ge, Belgium, during a hot summer day. Results show that while irrigation and vegetation characteristics influence outdoor thermal comfort, substrate properties have minimal impact. Well-irrigated, tall, and dense green roofs were found to reduce average pedestrian air temperature by up to 1.4 degrees C, compared to average scenario green roof. Additionally, green roofs are most effective in improving pedestrian comfort when green roofs are clustered and located near walkable pedestrian areas. The compact mid-rise + low-rise archetype is found to maximize the benefits of green roofs.

Mosques play a central role in the organization of Muslim communities and the structuring of urban spaces. This study explores the socio-spatial elements associated with mosque locations in Belgium and highlights the disparities in their geographical coverage, particularly the lack of mosques in certain areas, at different scales. A binary logistic regression model was used. The results confirm that the presence of Muslim populations is a variable strongly associated with mosque presence. In addition, a tendency for mosques to cluster has been observed, with mosques tending to be located close to pre-existing mosques. Located in residential areas, close to facilities such as schools and shopping centers, they contribute to the transformation of these areas into veritable urban ecosystems, where religious practices, community activities, and economic dynamics converge, redefining physical and symbolic centralities. The model developed in this study also identified areas lacking mosques, mostly located on the periphery of historic urban centers, often urbanized at the end of the 19th century and the beginning of the 20th century. This article provides an analytical framework for better understanding the local dynamics of mosque locations in urban environments. It reveals new perspectives for urban planning and the socio-spatial comprehension of Islamic places of worship.

It is acknowledged by International declarations and policy guidance documents that cultural heritage (CH) can contribute directly to many of the Sustainable Development Goals (SDGs), including resilience and adaptation to climate change (SDG 13). CH can support climate change action as it conveys local knowledge that builds resilience for change through mitigation and adaptation. Moreover, the vulnerability of the built environment to climate change possesses inherent resilient properties that allow it to resist damage. The integration of policies and practices of CH conservation into the wider framework of sustainable urban development entails the application of a landscape approach that (i) responds to local cultural contexts and value systems, (ii) integrates distinct theoretical perspectives to address the complex layering of the spatial, mental, and functional process-related dimensions of the landscape, and (iii) addresses policies and governance concerns at international and local levels (Ginzarly et al., 2019). Yet, the application of a landscape approach to CH conservation in the context of climate change is faced with different challenges.First, while at the turn of the twenty-first century the concept of CH has extended from monuments to cultural landscapes and cities as living heritage, assessment processes have been slow to evolve and address the interdisciplinary nature of heritage (Déom & Valois, 2020). Second, there is a challenge around assessing the vulnerability of CH to climate change and integrating its vulnerability status into the broader context of sustainable urban development. This challenge is imposed by the lack of a framework that addresses landscapes rather than heritage sites in isolation (Cook et al., 2021).To address the above-mentioned challenges, this presentation presents a landscape people-centered conceptual framework for resilient CH that is applicable at the city scale (i) to map how different stakeholder groups value heritage in the context of climate change, (ii) using social networks as a tool to engage communities and get access to information about heritage values, and (iii) assess the vulnerability of urban heritage and its associated values to climate change.The conceptual framework is structured around four prominent themes: (1) the city is a living heritage that encompasses the physical, mental, and digital heritage landscapes; (2) digitally mediated heritage practices provide new prospects for digitally-enabled forms of co-creation of heritage values; (3) longitudinal records on social media serve as a data source for the assessment of heritage values and their vulnerability to change; and (4) online communities contribute to communities’ disaster resilience. ReferencesCook, I., Johnston, R., & Selby, K. (2021). Climate Change and Cultural Heritage: A Landscape Vulnerability Framework. The Journal of Island and Coastal Archaeology, 16(2–4), 553–571.Déom, C., & Valois, N. (2020). Whose heritage? Determining values of modern public spaces in Canada. Journal of Cultural Heritage Management and Sustainable Development, 10(2), 189–206.Ginzarly, M., Houbart, C., & Teller, J. (2019). The Historic Urban Landscape approach to urban management: A systematic review. International Journal of Heritage Studies, 25(10), 999–1019.

This study evaluates the comparability of aggregated mobile phone data (MPD) derived from passive network signalling events and traditional travel survey data for urban transport planning, using the province of Liège as a case study. Our analysis demonstrates that while MPD captures a higher density of origin–destination (OD) connections, it cannot fully replicate all flows observed in surveys, underscoring the need for a complementary approach between the two data sources. Key mobility indicators, including average trip rates, hourly trip volumes, and structural patterns in daily OD matrices, show strong alignment. This structural similarity is rigorously quantified using a Mean Structural Similarity Index with a distance decay effect. Furthermore, Kolmogorov-Smirnov tests confirm comparable trip length distributions between the sources. While MPD-based population estimates closely match official 3:00 AM census counts, discrepancies in specific zones highlight potential pitfalls for real-time population mapping. Our findings confirm that MPD provides a robust and valuable complement to traditional surveys, particularly in contexts with limited or infrequent survey data. The study offers critical insights for integrating MPD into urban policy planning, emphasizing its utility for validation and its caveats for population estimation.

This article contributes to ongoing discussions on the role of grassroots mobilizations on social media in building community resilience and tackling specific challenges during disaster events. It investigates the use of two social media platforms, Facebook and Twitter, now referred to as X, during the 2021 flood in Belgium, both in the immediate aftermath of the crisis and in the short-term. First, it analyzes the activities of Facebook community groups established post-crisis over a six-month period. Then, it examines tweets related to the 2021 flood in Belgium. Finally, it complements the social media data with online interviews conducted with Facebook groups administrators. The analytical framework employs (1) a social media data-driven quantitative and qualitative text analysis using topic modeling and sentiment analysis, and (2) an inductive thematic analysis of interviews. The findings highlight the different roles played by the two social media platforms. Facebook served as an effective platform to mobilize and organize local communities for immediate and practical support, while Twitter served as a platform for broader global engagement and advocacy. The convergence of results from diverse data sources provides comprehensive insights into the effectiveness and challenges of leveraging social media for community resilience in the aftermath of disaster events.

Flood mapping is essential to urban resilience, but it often relies on traditional hydrological models, which are poorly suited to data-scarce contexts due to their complexity and input requirements. This study, conducted in Antananarivo, Madagascar, evaluates alternative approaches combining remote sensing (Pleiades and Sentinel-1), simplified hydrological modeling (Fast Flood Simulation—FFS), and multicriteria analysis (MCA), with validation from field observations. The results show contrasting performances: FFS (10 cm threshold) detected 45

Policies are a deliberate system that defines action and guides short-term decisions in pursuing a goal. Policy is a fundamental instrument of governance which is extensively used worldwide. However, not all policies are created equally. Contemporary literature is littered with examples of policy failures, and a large research emphasis is dedicated to co-creating ‘good’ policy. This challenge of developing good policy is exacerbated when we consider the rapidly evolving risks related to climate change. The evolving risks can make it difficult to define valid policy goals over the longer term. Furthermore, stakeholders are increasingly needed across policy and practice to overcome siloed working and co-create transdisciplinary risk management policies, considering both long-term strategic objectives and short- to medium-term operational solutions. Risk management policy instruments are relevant across spatial scales and engage with policies from other disciplines (urban planning, heritage conservation, environmental management). The article presents an innovative tool called the policy matrix to address challenges faced by policy experts. The policy matrix capitalises upon the co-creative research of the Organigraphs technique defined by Durrant et al. (2021) to co-create disaster risk management governance maps. The article compares two policy matrices developed as part of a Horizon 2020-funded project called SHELTER. In its simplest form, a policy matrix arranges risk management policy instruments around an issue depending on their scale of implementation and disciplinary lens. This allows stakeholders to see all the policy instruments considered relevant to some specific issues. It can further provide stakeholders a robust platform to critique those policies. By way of example, providing them with a tool to clearly “measure” the links between these policies, to identify policy gaps in thematic/operationalisation, or, from a practical perspective, to provide a tool for experts to review the level of participation in the design of these policies or the effectiveness of these policies in practice.

Geothermal energy from mine water can transform former mining regions into renewable energy hubs, supporting 5th generation heat networks. Wallonia, with its rich coal mining history, is well-suited for this approach. A 2019 study identified strong geothermal potential in the Couchant de Mons, Charleroi, and Liege basins, leading to three feasibility studies. These highlighted both opportunities and limitations of geothermal mine water projects, depending on demand and subsurface conditions. The most promising site in the Liege basin was selected for a pilot project to showcase how abandoned flooded mines can drive the energy transition, offering sustainable energy and storage while revitalizing post-mining areas in Wallonia.

PurposeCommunity is hard to define, but understanding what constitutes a community is crucial for effective decision-making. This article presents the idea of the community mosaic model. The model offers a flexible and operative tool for researchers, policymakers, and practitioners to identify and explore communities.Design/methodology/approachA two-phase systematic literature review established the theoretical definition of community. Following this, the three parts of the community mosaic model were conceptualised. Finally, the mosaic model was tested with a working example of a community association called the La Brouck collective in Belgium.FindingsThe article outlines a unique operative tool called the community mosaic model and how it can be applied in practice. Furthermore, the article highlights theoretical insights from the approach and how it can inform practice and policy around Disaster Risk Management (DRM).Practical implicationsThe community mosaic model helps operationalise the term "community". It provides DRM experts (and potentially beyond) with a tool to identify community groups and explore why they exist in each context. The model could facilitate the development of fit-for-purpose engagement strategies, map adaptive governance structures, and inform policy, potentially enhancing community-based disaster risk management (CBDRM).Social implicationsThe article could help to empower community-based disaster risk management.Originality/valueThe article defines an original operative tool as the community mosaic model and provides a working example of its application based on real-world experiences. Also, the researchers revised the proposed lifecycle stages of community groups to include the stage "decay".

The impact of different global and local variables in urban development processes requires a systematic study to fully comprehend the underlying complexities in them. The interplay between such variables is crucial for modelling urban growth to closely reflects reality. Despite extensive research, ambiguity remains about how variations in these input variables influence urban densification. In this study, we conduct a global sensitivity analysis (SA) using a multinomial logistic regression (MNL) model to assess the model’s explanatory and predictive power. We examine the influence of global variables, including spatial resolution, neighborhood size, and density classes, under different input combinations at a provincial scale to understand their impact on densification. Additionally, we perform a stepwise regression to identify the significant explanatory variables that are important for understanding densification in the Brussels Metropolitan Area (BMA). Our results indicate that a finer spatial resolution of 50 m and 100 m, smaller neighborhood size of 5 × 5 and 3 × 3, and specific density classes—namely 3 (non-built-up, low and high built-up) and 4 (non-built-up, low, medium and high built-up)—optimally explain and predict urban densification. In line with the same, the stepwise regression reveals that models with a coarser resolution of 300 m lack significant variables, reflecting a lower explanatory power for densification. This approach aids in identifying optimal and significant global variables with higher explanatory power for understanding and predicting urban densification. Furthermore, these findings are reproducible in a global urban context, offering valuable insights for planners, modelers and geographers in managing future urban growth and minimizing modelling.

The sovereignty challenges of public data in the face of the private sector lead local authorities to implement data management strategies. The study is based on a comparative analysis of the digital strategies of four urban local authorities between France and Belgium. These two countries are characterized by two distinct territorial organization models: a unitary system and a federal system. The results reveal an awareness of sovereignty issues by the actors who develop strategies to address them. In Belgium, the Walloon region benefits from a great capacity for action visible through several regional initiatives, while in France, the Grand Est region has limited autonomy. The comparative analysis highlights a different distribution of management, regulatory, and facilitator roles among the territorial levels of each country, which raises the question of the relevant scope for the management of public data, supported by a European legal framework. Finally, the cross-border context of the study raises the question of sharing cross-border public data. This constitutes a major challenge whose lack of response is likely to be exploited by large digital companies.

Rapid population growth and global urbanization pose socio-economic challenges, causing inequalities in Global South (GS) cities, such as the proliferation of deprived areas. This study aims to develop methods for mapping and characterizing urban deprivation in GS cities using the IDEAMAPS framework, focusing on household, area, and area-connect levels. The methodology employs 23 indicators across five domains, with a weighting system applied at macro (agglomeration of Antananarivo) and meso (Urban Commune of Antananarivo- CUA) scales, alongside Principal Component Analysis (PCA) and population-weighted analysis. A variable reduction process assessed the impact of simplifying indicators while retaining explanatory power. Results demonstrated significant spatial contrasts in deprivation between central and peripheral areas and eastern and western neighborhoods. The equal weighting system provided an intuitive overview, showing that 53% of neighborhoods were privileged at the macro scale, while 15% were highly deprived. At the meso scale, 27% of neighborhoods were highly deprived, emphasizing the importance of finer spatial scales to uncover localized disparities. PCA reduced data complexity and identified key deprivation dimensions but remains sensitive to outliers. Population- weighted analysis revealed the misalignment between deprivation level and population density, highlighting the need for targeted interventions in densely populated neighborhoods. Variable reduction confirmed model robustness but underscored the importance of retaining critical variables. This study highlights the need for accurate, multi-scale assessments to inform policies addressing urban inequalities. Future research should integrate advanced spatial techniques, temporal dynamics, and additional indicators, such as governance and environmental hazards, to refine deprivation analyses and guide inclusive urban policies.

Improving residential energy efficiency is essential for optimizing energy consumption. This article analyzes the electricity and natural gas consumption of a benchmark multi-family housing model in Algiers, based on data from 295 residential units collected over three consecutive years (2022, 2023, and 2024). A comprehensive approach combining data visualization, statistical analysis, a clustering approach, a tariff structure assessment, and an energy performance index is applied to assess residential energy-consumption trends. The findings reveal opposing trends between electricity and natural gas consumption. The electricity demand increased steadily (+15% from 2022 to 2024), particularly in the third trimester (summer), where 40% of the housing unit consumption exceeded 1000 kWh per trimester, indicating a growing reliance on air conditioning. In contrast, natural gas consumption declined significantly, with winter usage dropping by more than 20%, suggesting improved heating efficiency, better thermal insulation, and/or milder weather conditions. The clustering analysis also highlights a shift toward more homogenous consumption profiles, with fewer outliers and a narrower interquartile range, indicating greater energy efficiency across households. The results underscore the need for adaptive energy pricing policies and targeted household awareness programs. They further suggest that incentive-based measures, particularly during peak summer periods, could mitigate demand spikes and enhance energy system resilience. The energy benchmarking approach developed in this study can support decision-makers in adjusting tariff structures according to household energy profiles to improve overall energy efficiency.

In Belgium, floods are acknowledged as one of the most frequent natural disasters, posing serious threats to people's lives and property. Growing evidence suggests flood risks will intensify in the coming decades, driven by climate change, population growth, and evolving land use patterns at the catchment scale. These compounding factors make improved flood risk understanding and management an urgent priority. The impact of floods on the transportation system primarily stems from road interruptions, which significantly affect travel demand. Exploiting mobile phone data collected by providers makes it possible to geolocate mobile phone users over time to derive time-dependent crowding maps. By intersecting these maps with flood inundation data, we can quantify human exposure to flood risks. This integrated approach enables a detailed analysis of both the spatial extent of floods and temporal changes in population movement patterns during flood events. In this context, we propose a sensitivity analysis based on mobile phone data collected from pre- and post-flood calendar periods in the Vesdre catchment area (Wallonia, Belgium). In light of the floods that occurred in July 2021, mobile phone data collected in 2018 and 2022 have been processed and compared. Meanwhile, we investigate the impact of the transportation infrastructure disruptions on mobility within the Vesdre catchment area and apply a Tobit regression model to analyze the significant parameters. As a result, we observe a decrease in interaction between the valley and the plateau, except between urban centers in the valley and neighboring residential communes in the heights, along with a general decline in mobility in the most affected communes. This suggests that the flooding has incited people to get further away from the river. Besides, we find that the parameter representing the number of out-of-service transportation infrastructures per kilometer is significant in the 2022 flow regression.

Several sources related that the electricity sector emits almost a quarter of greenhouse gases each year in the world. It is therefore one of the important sectors to take into account to limit global warming. Indian Ocean cities produce significant CO2 emissions during electricity consumption. Their volume and accuracy remain practically unknown and untested. Indeed, until now, there is no methodology suggested by the researchers to evaluate Fossil Fuel carbon dioxide (FFCO2) emission, and electricity consumption in this region. Aware of these crucial problems, this study was carried out to assess and analyse CO2 emissions coming from Electricity consumption (called Scope2) in 111 cities located in the Indian Ocean from 55 Power plants between 2015 and 2022 (08 years) and in four sectors (Residential, Commercial, Industrial, and On-road). To carry out a good comparison, all the data were grouped into three categories, before the lockdown measures due to COVID-19 (2015–2018); During the COVID-19-induced lockdown period (2019–2020); and after the lockdown period (2021–2022). The results showed that the CO2 emission difference is significant in the residential and commercial sectors. It was observed that CO2 emissions increased in 2019–2022 in the residential, industrial, and on-road sectors whereas, simultaneously during the same period, it decreased in the commercial sector. During the three periods, the CO2 emissions rate was the highest in the residential sector (around 52%), and the least on-road (around 1%). The significant difference in the commercial sector suggests a decrease in electricity consumption during the peak of the pandemic due to reduced business activities. Businesses adapted to new operating conditions, such as reduced hours or enhanced energy efficiency measures, which also contributed to the change in consumption patterns.

In the face of climate change, cultural heritage (CH) is vulnerable to risks, yet it is a powerful source of resilience. As efforts in climate change adaptation and disaster risk reduction progress, CH plays a crucial role in strengthening communities’ capacity to recover and adapt. International frameworks such as the Hangzhou Declaration and the Sendai Framework emphasise the integration of CH into people-centred strategies for strengthening community resilience. This paper develops a people-centred conceptual framework that explores the intersection of CH, community resilience, and digital tools, drawing on emerging theories in heritage and resilience studies, particularly in relation to digital practices. The framework advocates for more inclusive and locally contextualised practices by engaging communities in the co-construction of heritage values and enhancing multivocality through digital platforms. It highlights the transformative role of digitally mediated heritage practices, from digitisation and crowdsourcing to active community participation in crisis response. Despite the growing potential of digital tools, significant challenges remain, such as data bias, unequal access, and the need for a more holistic approach that overcomes both traditional rigid differentiation and the split between tangible and intangible heritage, as well as between heritage by designation and heritage by appropriation. This study offers future directions for developing more resilient heritage practices, focusing on the equitable inclusion of diverse community voices in shaping CH preservation and resilience strategies.