Geographies

Indonesia is one of the world’s most seismically active countries, experiencing frequent earthquakes that make assessing regional resilience essential for disaster risk reduction. This study dynamically evaluates socioeconomic resilience in 28 regencies/municipalities affected by destructive earthquakes between 2016 and 2022. Resilience was quantified using the Adjusted Mazziotta–Pareto Index (AMPI) at three periods: pre-event (T₀), during the event (T₁), and post-event (T₂). Index changes were interpreted as resistance (Δ₁ = T₁ − T₀), recovery (Δ₂ = T₂ − T₁), and adaptive capacity (Δ₃ = T₂ − T₀). Results show substantial regional differences in resilience trajectories: some areas experienced only minor declines during the earthquake, while others were heavily affected but recovered quickly. Cluster analysis revealed distinct typologies, including consistently high-resilience regions, rapid-recovery regions, and persistently vulnerable regions. These disparities are associated with variation in economic capacity, social vulnerability, labor market conditions, and access to health services. Overall, the findings highlight the value of a multidimensional, time-sensitive approach to measuring socioeconomic resilience. The study advances an AMPI-based temporal measurement framework and offers policy insights for development planning and disaster mitigation. Full article

The southern coast of Java, Indonesia, is situated along the active Sunda subduction margin where raised coastal landforms may record the combined influence of relative sea-level change, wave processes, sedimentation, and vertical land motion. This study presents field-based RTK-GNSS topographic profiles from four coastal sites: Pantai Ajah, Kalijali, Kulon Progo, and Wingko. Profiles were used to locate terrace treads, risers, slope breaks, residual topographic highs, and possible raised coastal surfaces. The results show spatially varying coastal morphology. Pantai Ajah has a marked riser and probable terrace tread at about 7–8.5 m elevation. Kalijali shows a lower terrace-like surface at about 4–5 m, an upper surface at about 7–9 m, and a higher local topographic high at about 12–13 m. Kulon Progo is characterized by a low-elevation coastal surface that is only weakly expressed in the topography, whereas Wingko contains a distinct slope break and a broad landward surface at approximately 5–6.5 m elevation. Across the four profiles, broad low-gradient surfaces recur within two elevation ranges, approximately 4–6.5 m and 7–9 m. These ranges are treated as provisional morphometric groupings rather than correlated or coeval terrace levels. Higher isolated elevations are described as ridge-like or residual topographic highs whose origin and age remain unresolved. No direct chronological or sedimentological constraints are currently available, so correlations with Holocene or older sea-level highstands are only tentative. The results show the usefulness of RTK-GNSS profiling for the documentation of local coastal terrace morphology and for the identification of priority sites for future dating, sedimentological analysis, and coastal-hazard assessment. Full article

Rapid urbanization is reshaping land surface conditions and local thermal environments in fast-growing coastal cities. This study examines how Land Use Land Cover (LULC) transformation influenced seasonal land surface temperature (LST) dynamics in Chattogram City Corporation (CCC), Bangladesh, over 2004–2024. Multi-temporal Landsat imagery was analyzed using a Random Forest classifier, and spectral indices (NDVI, NDBI, NDBaI, MNDWI) were derived to characterize surface biophysical conditions. Built-up land expanded by 27.71 km², largely replacing agricultural and vegetated areas. Summer mean LST rose from 36.08 °C to 36.50 °C, while winter LST rose from 25.25 °C to 26.97 °C. Only the winter warming trend is statistically significant; the summer change falls within the ±1–2 °C retrieval uncertainty of Landsat-derived LST. The summer–winter thermal gap consequently narrowed from 10.83 °C to 9.53 °C, indicating that urbanization-driven warming in this tropical coastal city is disproportionately concentrated in the cool dry season. Partial correlation and multiple regression analyses confirm that built-up intensity (NDBI) is the dominant driver of surface warming, while vegetation (NDVI) exerts a consistent cooling influence. Water bodies showed contrasting seasonal trends, with winter extent declining alongside a slight summer increase. These findings highlight the critical role of vegetation and water bodies in moderating urban heat and provide data-driven insights for climate-responsive planning in rapidly urbanizing coastal cities. Full article

Rainfall variability plays a significant role in modulating both aquatic and socioeconomic systems in the Eastern Himalayas. Among the longitudinal ranges of the Himalayas, the Greater Himalaya is considered the most vulnerable due to its high climatic sensitivity and steep topography. Given this importance, the present study investigates the spatial patterns of temperature and precipitation, their trends, and the occurrence of extreme events over Eastern Greater Himalaya (EGH) during 1981–2025. High-resolution gridded datasets for temperature (0.1° × 0.1°) from ERA5-Land and precipitation (0.05° × 0.05°) from CHIRPS are used. The significance and magnitude of trends are quantified using the Mann–Kendall test and Sen’s slope, respectively. The temperature in EGH is increasing at ~0.27 °C per decade, with the most pronounced increase during winter (>0.4–0.5 °C per decade). Temperature extremes show a clear shift toward warmer conditions, with an increasing frequency of warm days and nights (TX90p: ~0.29; TN90p: ~0.64), along with rising intensity (TXx: ~0.007 °C/year; TNn: ~0.04 °C/year) and a decline in cold extremes. Precipitation exhibits high spatial variability (<500 mm to >2000 mm) but weak and statistically not significant long-term trends. The monsoon contributes the highest precipitation (~1200–2000 mm), with a shift toward drier conditions after 2010–2015. Extreme precipitation indices show declining trends in daily maximum (RX1day: ~5–140 mm), maximum 5-day (RX5day: ~8.4–240 mm), total (PRCPTOT: ~−0.23 mm/year) and intensity of (SDII: ~−0.041 mm/day) precipitation. This research indicates a significant climate shift in EGH with rapid warming along with uncertain precipitation patterns, which may have implications for water resources, aquatic systems, and dependent communities. Full article

Monitoring dynamic post-catastrophic landscapes necessitates unsupervised classification approaches capable of incorporating newly emerging landscape-cover states without relying on predefined classes. Within this framework, the temporal correspondence of independently derived spectral clusters presents a critical methodological challenge. This study compared different temporal correspondence approaches for multi-temporal Sentinel-2 imagery of the post-catastrophic floodplain landscape of Khortytsia Island (Ukraine) from 2021 to 2026. In addition to existing temporal cluster correspondence methods based on centroid distance, Mahalanobis distance, Linear Discriminant Analysis, and Random Forest, geometrically oriented approaches employing the elongation and principal-axis orientation of spectral point clouds were evaluated. A series of tests assessed correspondence accuracy, robustness to seasonal and interannual drift, graph connectivity, and consensus structure among different temporal correspondence solutions. The results demonstrated that geometrically oriented approaches preserved temporal correspondence among landscape-cover states with high stability despite phenological and interannual variability. In particular, axis-based correspondence more effectively maintained separation between corresponding and competing clusters amid progressive temporal divergence. Consensus analysis revealed that disagreement among methods was concentrated in ecotonal and actively transforming zones, indicating areas of increased landscape instability. This study shows that the geometry of spectral trajectories contains valuable information for temporal correspondence and provides a promising foundation for monitoring dynamic post-catastrophic landscape systems. Full article

This article examines how Spanish rural cinema functions as a media dispositif—understood, following the Foucauldian concept as operationalised in media studies, as an apparatus of heterogeneous practices, discourses and institutional arrangements that produces and governs representations—for the construction and circulation of territorial imaginaries, thereby participating in the media ecosystem of public debates on depopulation, migration and multicultural coexistence. Through a diachronic and sociodemographic comparison of La piel quemada (Josep Maria Forn, 1967) and Suro (Mikel Gurrea, 2022)—two feature films set in the province of Girona (Catalonia, Spain) but separated by 55 years and opposing migratory pressures—the study analyses how audiovisual fiction produces, challenges and renegotiates the dominant sociodemographic imaginaries of two distinct historical conjunctures: the developmentalist boom of late Francoism and the neo-rural turn of the early twenty-first century. A qualitative chronotopic methodology is applied to 18 systematically selected sequences from both films, organised around three contextualising axes (historical, territorial, sociodemographic). The central original contribution is the concept of the media–geographic diptych as an instrument for diachronic comparative analysis of rural cinema as a media form. Secondary contributions include evidence that the two media texts analysed construct an imaginary of structural continuity of xenophobia in the rural territorial discourse of Girona and a theoretical proposal on cinematic multilingualism as an under-theorised territorial media practice. The discussion situates these findings within current debates on rural cosmopolitanism, overtourism and the media construction of la España vaciada, and offers preliminary evidence that audiovisual fiction occupies a differentiated position in the media ecology of territorial public discourse that merits systematic attention in future studies. Full article

Ensuring a high quality of life for citizens is a fundamental objective for every city, with public safety representing one of the most critical challenges to social sustainability and equitable urban development. This study analyzes the spatiotemporal dynamics of violent crime in Mexico City across pre-pandemic, pandemic, and post-pandemic periods (2019–2023) to evaluate how COVID-19 mobility restrictions were associated with changes in crime patterns. Using publicly available crime reports, we applied Seasonal-Trend decomposition using Loess (STL) and Moran’s I to examine four violent crimes: homicide, robbery, kidnapping, and rape. The results reveal crime-specific pandemic-related patterns. While robbery showed a sustained decline, its spatial clustering intensified significantly, with Moran’s I increasing from 0.22 to 0.59, indicating highly localized risk zones. Conversely, rape exhibited a steady increase that appeared unaffected by lockdown measures, while maintaining significant spatial autocorrelation. Likewise, Cuauhtémoc borough persisted as the main urban hotspot across all phases. Overall, crime did not decline uniformly during the pandemic; instead, mobility restrictions reshaped the geographic distribution and concentration of specific offenses. This research contributes to the understanding of crime dynamics during and after a public health emergency. Full article

Precise identification and monitoring of small agricultural water bodies are essential for sustainable water resources management in arid and semi-arid regions, where even limited water losses can significantly affect agricultural productivity and local water security. However, the accurate detection of small ponds remains a major challenge in remote sensing, to address this challenge, this study proposes an integrated three-step framework that combines high-resolution remote sensing imagery, machine and deep learning techniques, and hydrological analysis to identify agricultural ponds and quantify their evaporation losses in Bastam, Iran. In the first step, a dedicated annotated dataset comprising 1061 RGB satellite images, each with a spatial size of 256 × 256 pixels and a ground resolution of 0.5 m, was developed for model training and evaluation. Using this dataset, three deep learning models BiSeNet, UNet3+, and SegNet and four traditional supervised classifiers Maximum Likelihood, Neural Network, Mahalanobis Distance, and Minimum Distance were implemented and compared for pond detection. The results demonstrated that deep learning models consistently outperformed conventional classifiers in delineating small agricultural ponds. Among all evaluated methods, BiSeNet achieved the highest segmentation performance, with an IoU of 82.08%, an F1-score of 90.15%, a precision of 91.86%, and a recall of 88.50%. Among the conventional classifiers, Maximum Likelihood combined with a 5 × 5 spatial kernel produced the best performance, achieving an IoU of 76.90%, an F1-score of 86.93%, a precision of 90.87%, and a recall of 83.32%, whereas simpler classifiers such as Minimum Distance showed only marginal improvements after kernelization. In the final step, the detected ponds were used to estimate evaporation losses through the Meyer method. The hydrological analysis revealed a clear periodic pattern in evaporation and a cumulative water loss of 388,636.7 m³ over a nine-month period, highlighting the considerable impact of evaporation on the efficiency of small agricultural water storage systems in dry environments. Based on these findings, practical mitigation strategies, including evaporation-reducing chemical surface films and floating covers, are discussed as potential options for reducing water loss. Overall, the proposed framework demonstrates the clear advantage of deep learning for the accurate identification of small agricultural ponds and provides an integrated methodological basis for monitoring water bodies and evaluating associated evaporation losses. The study offers a practical and transferable approach for supporting agricultural water management and improving water-use efficiency in arid and semi-arid regions. Full article

Complete neighbourhoods, places where residents can meet their daily needs on foot, have become a central component of healthy and sustainable urban planning. Yet most assessment frameworks are calibrated for dense metropolitan environments, leaving rural and peri-urban municipalities without operational tools suited to their territorial needs. This article presents MilieuxVie, an open-source, browser-based interactive mapping application developed for the Laurentides health region of Québec (76 municipalities, 11 land-based unorganised territories, 2 indigenous territories and 4 aquatic administrative units; 93 territorial units in total; ~680,000 inhabitants). The tool evaluates the spatial accessibility of 12 service categories drawn from the Vivre en Ville (2026) complete-neighbourhood framework and OpenStreetMap data, using 2026 residential parcels from the provincial property assessment roll as origin points and weighting results by number of dwelling units. Three adaptive radius tiers (dense, intermediate, rural), based on residential dwelling-unit density (dwellings per km² of residentially designated urban land), scale the distance standards to settlement density. Because thresholds are scaled to settlement density, scores express context-relative service proximity rather than a uniform pedestrian standard and should not be read as directly comparable absolute accessibility across rural, peri-urban, and urban settings. A dedicated urban perimeter mode further disaggregates analysis to sub-municipal built-up zones, aligning the tool with Québec’s provincial Government land-use planning guidelines (GLPG). Gap analysis outputs identify which service types fall below the 70% coverage target, helping elected officials and planners identify where to focus further analysis. Results illustrate the scope of accessibility deficits across the region and highlight the analytical limits of uniform distance thresholds when applied beyond metropolitan contexts. Scores differ significantly across different settings (Kruskal–Wallis p = 0.006); the adaptive radius tiers narrow but do not close the structural gap, with rural municipalities scoring significantly lower than dense ones. The tool is freely available and requires no software installation, making it directly deployable by local planning offices. Full article

Mountain tourism destinations are increasingly challenged by climate change, environmental degradation, and the need to balance economic development with the long-term conservation of natural resources. This study evaluates alternative pathways for the sustainable development of mountain tourism in Central Serbia by applying an integrated multi-criteria decision-making framework that combines conventional and fuzzy approaches to account for uncertainty in expert judgments. A set of economic, environmental, social, developmental, and governance-related criteria was used to assess different tourism development models and identify those with the greatest potential to support long-term sustainability. The findings indicate that development strategies emphasizing climate adaptation, environmental protection, tourism diversification, and active participation of local communities provide the most promising basis for sustainable mountain tourism development. The study also highlights the importance of diversifying tourism products beyond winter-based activities through nature-based forms of tourism, including geotourism, which builds upon the region’s rich geoheritage, geomorphological diversity, and cultural landscapes while strengthening destination resilience to climate change. The proposed evaluation framework provides a practical decision-support tool that can be adapted to other mountain regions facing similar environmental and developmental challenges. Full article

This study analyses the Master Plan for Analysis, Anticipation and Reaction to Natural Disasters of the Valencian Community (Spain) from the perspective of geographical risk literacy. The research adopts a qualitative methodology based on documentary analysis and conceptual assessment of public policy. The corpus includes Valencian institutional documents, Spanish civil protection and education regulations, international disaster risk reduction frameworks and the scientific literature on geography education, risk perception, social vulnerability and school preparedness. The findings show that the plan can strengthen institutional coordination, public communication and preventive culture through its preventive orientation, phased structure, official sources, attention to vulnerable groups, updated cartography and promotion of drills. However, its social effectiveness will depend on transforming protocols and guides into verifiable citizen capacities: identifying hazards, recognizing local exposure, interpreting warnings and maps, selecting safe routes, preparing households and rehearsing decisions in realistic contexts. The article proposes complementing the plan with a geographical literacy programme for territorial resilience based on local risk sheets, school atlases, evaluated drills, community mediation, accessible viewers and public monitoring indicators. The discussion places the Valencian case within international debates on disaster risk reduction, anticipatory governance, people-centred early warning systems and school safety. Because no empirical implementation data are yet available, the contribution is framed as an analytical and design-oriented framework requiring subsequent validation through pilots, surveys, interviews and evaluated drills. Full article

Indigenous knowledge and associated indigenous resource management practices are at the root of sustainable land and marine management. Typically, they point to the necessity of maintaining biodiversity and of ensuring the sustenance of social and economic systems, which benefit the well-being of indigenous communities. Conscious of these core attributes, the Government of Indonesia has enabled formal access for indigenous communities to forests for their livelihoods. Nonetheless, meeting the sustainable development goals through such forest management and conservation in Indonesia is threatened by various competing interests and power imbalances. These lead to the disproportionate conversion of naturally vegetated areas, as well as the inability of communities to benefit from economic opportunities. Moreover, the Government of Indonesia has insufficiently regulated the utilisation of indigenous knowledge to conserve the forest areas. This creates a policy design and implementation gap which is not properly understood or addressed. In this conceptual article, we posit that applying cultural space methodology fills the gaps. This methodology combines cultural space and land administration concepts and connects people to land and marine space. This article discusses how and why using the methodology proves to be effective for agricultural and maritime communities in Indonesia and helps to reform the administration capacities of the territories. It identifies and assesses people and land/marine space relationships by the existence of (1) knowledge, practices, and/or objects that represent the relationship, (2) the social, economic, and environmental function of space for the community, and (3) administration of the forest and conservation areas. The methodology also provides a procedure to convert information on the interrelation of the indigenous community, its cultural space in the forest and conservation areas, and indigenous knowledge into geospatial information and data that represent the cultural space unit as a geographic feature. Full article

Platform-mediated short-term rental (STR) markets concentrate intensely in heritage urban cores, yet the temporal stability of this concentration remains poorly understood. This study examines spatial dynamics of STR concentration in Dubrovnik’s UNESCO-listed historic core across five biennial cross-sections (2017–2025) using complete administrative eVisitor registration data and an H3 hexagonal grid at resolution 11. Global and local spatial autocorrelation (Moran’s I, LISA, Getis-Ord Gi*), Emerging Hot Spot Analysis for spatiotemporal typologies, and bivariate LISA to distinguish capacity saturation from fragmentation were applied. Results demonstrate structural persistence: Moran’s I remained highly significant (p < 0.001) across all periods including COVID-19 disruption (range 0.417–0.467, CV = 4.4%), despite 53% supply growth and only 3.7% spatial expansion. Consecutive hotspots dominated typological classification, indicating active consolidation. Capacity analysis revealed concordant High–High patterns (56.8% of significant cells) with zero High–Low associations, confirming saturation not fragmentation. Findings support spatial lock-in in STR markets: concentration persists because locational advantages are properties of place rather than market volume, requiring spatially differentiated regulation rather than aggregate supply controls. Full article

This article argues that there is a “substance” within places that is essential for understanding local space and its spatial processes but remains a research lacuna in human geography. It consists of geographical entities which, through relationships with the place, participate significantly in processes within it, e.g., production of its space, configuring the nature of its space, and transforming the nature of the place. I first address the issue of a geographical entity and its agency that is attributed to it under the posthuman paradigm and how it may be viewed relationally, highlighting commonalities and differences between human geography and sociology, why entitativity does not defy relationality, the nature of relationality of geographical entities, and how it may be studied practically. Three geographical entities with different natures and geographical contexts are illustrated: the spatiality of an urban military base; the nature of a rural space created and transformed by an industrial compound; and the transformed nature of a place by an intentional community. The discussion highlights the merit of geographical entities as significant “substances” for understanding local space and the various issues and questions that surface when engaging with them. The conclusion dwells upon the value of studying geographical entities for human geography and for geography in general. Full article

Coffee production in Peru plays a crucial socio-economic role, supporting over 200,000 families and contributing significantly to export income. However, the spatial variation in coffee farming across ecological and socio-economic regions remains poorly understood. This study examines spatial patterns of coffee farm clustering in three Peruvian mountainous regions (Moyobamba, Tingo María, and Tocache) using descriptive statistics, geospatial visualization, and unsupervised clustering techniques. Farm-level reports and government geospatial records covering 2019–2023 were analyzed to evaluate cultivation area, altitude, and spatial distribution. Kernel density mapping, Moran’s I spatial autocorrelation, and Local Indicators of Spatial Association (LISA) were applied to identify statistically significant clustering patterns, while regression analysis and DBSCAN clustering were used to evaluate spatial trends and production hotspots. Moran’s I indicated moderate spatial clustering (0.34, p < 0.001), while regression analysis showed a weak negative association between altitude and cultivation area (β = −1.144 × 10⁻⁴, adjusted R² = 0.023). Results suggest that measured environmental variables explain only a limited proportion of spatial variation in coffee production, indicating that additional unmeasured factors, potentially including socio-economic influences, may contribute to observed clustering patterns. These findings highlight the value of spatial analysis for understanding production heterogeneity and for supporting regionally adapted agricultural planning strategies. Full article

Access to safe and reliable water resources is one of the most critical global challenges of the twenty-first century. In developing regions, such as Palestine, ensuring adequate water access has become increasingly difficult due to rapid population growth and the intensifying impacts of climate change, which place additional pressure on limited water resources. In this context, alternative water sources, such as rooftop rainwater harvesting (RWH), represent a promising option for enhancing water resilience. This study assesses the impacts of climate change on water resilience in the West Bank by evaluating the future performance of rooftop RWH systems. The potential effects of climate change on optimal storage capacity, system reliability, and deficit ratio are assessed spatially across different West Bank governorates. Monthly rainfall projections for the period 2021–2100 were obtained under three climate change scenarios (SSP126, SSP370, and SSP585). The results indicate that climate change is expected to negatively affect both optimal storage capacity and system reliability, with the most pronounced impacts occurring under the high-emission scenarios. However, the magnitude of these impacts varies spatially among governorates. At the West Bank level, the reliability of the RWH system decreases from 38% to 34% when shifting from the low-emission scenario to the high-emission scenario. Additionally, the optimal storage capacity and the harvested volume will decrease from 65 m³ to 58 m³. This study is the first in this area to use the mass balance approach to predict the future impact of climate risk on the storage capacity of rooftop rainwater harvesting systems. Overall, the findings provide a comprehensive assessment of the feasibility of rooftop RWH as a mitigation and adaptation measure to climate risks and its potential role in enhancing water resilience in the West Bank. This study presents the full framework for assessing the reliability of rainwater harvesting systems under conditions of climate uncertainty. Full article

In Brazil, approximately 16.4 million people (8.1% of the population) live in informal settlements (favelas), with Rio de Janeiro among the most heavily affected. This situation results from rapid rural–urban migration and unplanned urbanization, leading to persistent land tenure conflicts, exemplified by the decades-long struggle in the Santa Luzia favela. This study demonstrates how participatory geospatial methodologies can support land regularization while preventing displacement. Unlike conventional participatory mapping studies that often prioritize community empowerment over technical precision or, conversely, state-led cadastres that prioritize accuracy over local participation, this study integrates two complementary frameworks: counter-cartographies (to redress power asymmetries) and fit-for-purpose land administration (to ensure minimal technical standards for tenure security). Through a university–community collaboration, a low-cost cadastral survey of Santa Luzia was conducted using remotely piloted aircraft photogrammetry to generate high-resolution orthoimagery (2 cm ground sample distance), GIS vectorization integrated with resident interviews and local knowledge, and spatial analysis compliant with local technical standards. The findings demonstrate three specific innovations: (1) methodological: volunteer students and community residents co-produced cartography achieving 2 cm precision, meeting legal requirements for land regularization without expensive professional surveys; (2) participatory: unlike purely community-led mapping that may lack legal enforceability or top-down systems that exclude local knowledge, this model embeds participatory data collection within Brazil’s Social Interest Regularization (REURB-S) framework, ensuring both grassroots legitimacy and state recognition; and (3) policy-making: the project operationalizes counter-cartographies not as symbolic resistance but as a legally compliant pathway to tenure security, offering a transferable model for democratizing land administration in informal settlements while challenging exclusionary urban planning. Full article

Land management is a relevant problem in rural areas all over the world, conditioning the planning decisions and the applicability of planning instruments. This study evaluates the limitations of cadastral data in representing land fragmentation and management patterns in wild-fire-prone landscapes, using Alferce (Portugal) as a case study with broader international relevance. Similar challenges—fragmented ownership, incomplete land registries, and increasing wildfire risk—affect many regions worldwide, particularly across the Mediterranean basin and other fire-prone rural landscapes. A mixed-methods approach combines cadastral data with field data from 23 landowners producing two datasets: cadaster-only and ownership-enhanced. Fragmentation is assessed using Simmons and Januszewki indices, supported by spatial analysis (Kernel Density and Moran’s I). Results show that cadastral data alone significantly overestimates fragmentation. While parcel-based analysis suggests a highly fragmented landscape, incorporating ownership information reveals more aggregated management structures. The 23 landowners manage 1247 ha (≈13% of the area), forming a “keystone” group with strong potential for coordinated land management and fire prevention. Higher fragmentation is associated with population centers. These findings demonstrate that cadastral units do not reflect functional management units and considerations about property fragmentation are biased by the lack of information about the owners, a key theoretical contribution with implications beyond Portugal. For policymakers, integrating ownership data and targeting key land managers can improve land use planning and wildfire mitigation and, overall, the sustainability of the territory. Despite limitations (small sample), the approach is transferable to other regions facing similar structural constraints. Full article

Property taxation serves as a critical instrument for fiscal efficiency and equitable distribution, yet implementation faces significant challenges including valuation inaccuracies, insufficient administrative capacity, and diminished public trust. Indonesia’s Land and Building Tax (PBB-P2) utilizes the Sales Value of Taxable Objects (NJOP) as an administrative proxy for market value, which frequently deviates from actual land prices. These disparities create horizontal inequities, diminish local revenue potential, and generate taxpayer resistance, especially in decentralized regions with constrained technical resources. This research presents the Spatially Adaptive Land Value Determination (SALAD) model as a comprehensive framework for enhancing property tax governance and modernizing fiscal cadastre systems. Unlike conventional mass appraisal methods, SALAD integrates spatial zoning, assessment ratio analysis, land-use characteristics, and the Index of Developing Villages (IDM) with socio-economic indicators including purchasing power and community fiscal behavior. The model incorporates structured social validation to improve public acceptance. Field validation in Lebak Regency employed mixed-methods design with surveys of 75 respondents across 20 villages and interviews with village heads and tax officials. Results demonstrate that transparency, fairness, and visible public benefits are essential for community support. Validation indices vary significantly by IDM category (ANOVA: F = 4.23, p = 0.03 for economic; F = 3.81, p = 0.04 for institutional), confirming that the SALAD model’s adaptive mechanism is empirically grounded. Full article