Search Results (51)

The abandonment of farmland in Europe is a significant issue due to its environmental, socio-economic, and landscape consequences. This tendency mainly impacts marginal and inner areas, located far from large urban districts, because of biophysical and/or socio-economic factors. Although European and national regulations try to turn the fragility of these territories into an opportunity for sustainable development, many of these areas, especially in southern Europe, continue to suffer socio-economic disparities. For this reason, it is necessary to consider regional and district-wide initiatives that can economically revitalize marginal areas while safeguarding their natural capital. Alternative cropping systems, capable of optimizing the quality of some food crops, can play an essential role in the economic development of populations living in marginal areas. These areas, represented by inland zones often abandoned due to the difficulty of applying mechanized agriculture, can represent an opportunity to rediscover sustainable and profitable practices. Among the high-value crops, saffron (Crocus sativus L.), “red gold” and “king of spices”, stands out for its potential. Indeed, thanks to the use of tuff tubs, a more eco-sustainable choice compared to the plastic pots already mentioned in the literature, it is possible to improve the quality of this spice. Furthermore, Crocus sativus L. not only lends itself to multiple uses but also represents a valid opportunity to supplement agricultural income. This is made possible by its high profitability and beneficial properties for human health, offering a way to diversify agricultural production with positive economic and social impacts. It is known that the saffron market in Italy suffers from competition from developing countries (Iran, Morocco, India) capable of producing saffron at lower costs than European countries, thanks to the lower cost of labor. Therefore, this study seeks to identify marginal areas that can be recovered and valorized through an eco-sustainable cultivation system with the potential to enhance the quality of this spice, making it unique and resilient to competition. Specifically, this paper is organized on a dual scale of investigation: (a) at the local level to demonstrate the economic-ecological feasibility of saffron cultivation through the adoption of an alternative farming technique on an experimental site located in Tricarico (Basilicata—Southern Italy, 40°37′ N, 16°09′ E; 472 m. a.s.l.) that, although fertile, is not suitable for mechanized cropping systems; (b) at the regional level through a spatially explicit land suitability analysis to indicate the possible location where to export saffron cultivation. The final map, obtained by combining geo-environmental variables, can be considered a precious tool to support policymakers and farmers to foster a broad agricultural strategy founded on new crop management systems. The adoption of this alternative agroecological system could optimize the use of land resources in the perspective of increasing crop productivity and profitability in marginal agricultural areas. Full article

The 2030 Agenda for Sustainable Development, approved by the international community, represents a global strategic guide with 17 Sustainable Development Goals (SDGs) and 169 interconnected targets, aimed at promoting equitable economic, social and environmental development. In this context, innovation and research can play a crucial role in supporting policies for the achievement of SDGs, especially at local and regional levels. This article reports what was developed in the Puglia Region according to a double action. On the one hand, the creation of an IT platform to improve collaboration between legislative institutions and research centres facilitates the collection and transfer of data as best practices to support political decisions. On the other hand, practical experimentation on issues related to regional development is conducted, with particular attention to sustainability-oriented partnerships, legislative needs, and knowledge of the territory. The new information system, based on a geo-database and developed entirely with open source software, collects regional regulatory data on SDGs, cooperation projects, and technical and scientific documents contributing to the knowledge of the application of plans and programmes applied to the territory. This tool, in addition to mapping relevant projects, represents an important resource for monitoring progress and supporting sustainable development policies, facilitating the sharing of information between local, national, and European actors. Full article

The augmentation of clean energy usage is necessary to achieve Australia’s net zero emission target. New South Wales has proposed renewable energy zones (REZs) to align with this target and aims to retire coal-based energy production plants. Site selection criteria for REZs are more focused on technical and economic benefits rather than social and environmental considerations. This study aims to create a comprehensive suitability of optimal locations for renewable energy production that will minimize social disruption and maximize production rates, aligning with Sustainable Development Goal 7. To approach this gap, this study adopted a GIS-based multi-criteria evaluation method to combine understated variables such as agriculture and farmland, Aboriginal heritage, and natural conservation. Literature reviews have helped to weigh criteria based on their priority to social, economic, and environmental benefits, supporting both government targets and community concerns. This research explores the use of flexible criteria weighing in the context of a collaborative geo-design framework. This evidenced-based approach aims to assist planners in advocating for current legislation and developing strategies for coordination among private and government agencies, environmental groups, and stakeholders to minimize land-use conflicts. This study found that conducting land suitability analysis using a geo-design framework offers a collaborative strategy that can assist in mapping areas of significance for preserving community and environmental integrity. Such a collaborative approach can improve stakeholder participation in the decision-making process, with a particular focus on reaching net-zero emissions. Full article

Urban Vitality (UV) is a critical indicator for measuring sustainable urban development and quality. It reflects the dynamic interactions and supply–demand coordination within urban systems, especially concerning the human–land relationship. This study aims to quantify the UV of Luohe City, China, for the year 2023, analyze its spatial characteristics, and investigate the driving patterns of socio-ecological land functions on UV intensity and heterogeneity under different scenarios. Utilizing multi-source data, including human mobility data from Baidu Location-Based Services (LBSs), Landsat-9, MODIS, and diverse geo-information datasets, we conducted factor screening and comprehensive assessments. Firstly, Self-Organizing Maps (SOMs) were employed to identify typical activity patterns, and the Urban Vitality Index (UVI) was calculated based on Human Mobility Intensity (HMI) data. Subsequently, a framework for quantity–quality–structure assessments weighted and aggregated sub-indicators to evaluate the Land Social Function (LSF) and Land Ecological Function (LEF). Following the screening process, a Multi-scale Geographically Weighted Regression (MGWR) was applied to analyze the scale and driving relationships between UVI and the land assessment sub-indicators. The results were as follows: (1) The UV distribution in Luohe City was highly uneven, with high vitality areas concentrated within the built-up regions. (2) UV showed significant correlations with both LSF and LEF. The influence of LSF on UV was stronger than that of LEF, with the effectiveness of LEF relying on the well-established provisioning of LSF. (3) Artificial Surface Ratio (ASR) and Corrected Night Lights (LERNCI) were identified as key drivers of UV across multiple scenarios. Under the weekend scenario, the Green Space Ratio (GSR) and the Vegetation Quality (VQ) notably enhanced the attractiveness of human activities. (4) The impacts of drivers varied at the urban, township, and street scales. The analysis focuses on factors with significant bandwidth changes across multiple scenarios: VQ, Remote-Sensing-based Ecological Index (RSEI), GSR, ASR, and ALSI. This study underscores the importance of socio-ecological land functions in enhancing urban vitality, offering valuable insights and data support for urban planning. Full article

Cities will face increasing challenges due to the impacts of global climate change, particularly in the form of cyclonic events, necessitating a deeper understanding and the establishment of effective response mechanisms at both institutional and citizen levels. In this research, we tested the efficiency of crowdsourcing in fostering participatory resilience and improving urban management. The main aim was to design novel and accurate proactive response strategies and mitigate the adverse effects of cyclonic wind events through volunteerism, citizen science, and urban science. To achieve this goal, as a case study, the municipality of Soledad, Colombia was used. This research employed a two-phase methodological approach: (i) initially evaluating the spatial distribution of emergency response resources, and (ii) developing a geo-referenced survey to map, systematize, and categorize data and outcomes. A total of three hundred and seventy-eight residents across five neighborhoods in Soledad, which have experienced a high frequency of atmospheric wind phenomena over the past two decades, were surveyed. The results indicate that the crowdsourcing mechanism effectively enhanced the empirical understanding of atmospheric wind events in Soledad, facilitating the establishment of a geo-referenced volunteer network for real-time responses. Additionally, this study shed light on previously undocumented challenges, in terms of reducing the number of people affected, and the actions that would lead to improved urban development to reduce the impacts of cyclonic events, emphasizing the significance of citizen science in the social construction of risk and disaster risk reduction (DDR) efforts. Full article

Over the past two decades, there has been increasing research on the use of artificial intelligence (AI) and geographic information technologies for monitoring and mapping varying phenomena on the Earth’s surface. At the same time, there has been growing attention given to the ethical challenges that these technologies present (both individually and collectively in fields such as critical cartography, ethics of AI and GeoAI). This attention has produced a growing number of critical commentaries and articles as well as guidelines (by academic, governmental, and private institutions) that have been drafted to raise these ethical challenges and suggest potential solutions. This paper presents a review of 16 ethical guidelines of AI and 8 guidelines of geographic information technologies, analysing how these guidelines define and employ a number of ethical values and principles (e.g., autonomy, bias, privacy, and consent). One of the key findings from this review is the asymmetrical mentioning of certain values and principles within the guidelines. The AI guidelines make very clear the potential of AI to negatively impact social and environmental justice, autonomy, fairness and dignity, while far less attention is given to these impacts in the geographic information guidelines. This points to a need for the geo-information guidelines to be more attentive to the role geographic information can play in disempowering individuals and groups. Full article

Mapping cultural ecosystem services (CES) in river basins is crucial for spatially identifying areas that merit conservation due to their significant CES contributions. However, precise quantification of the appropriate area of mapping units, which is the basis for CES assessment, is rare in existing studies. In this study, the optimal area threshold of mapping units (OATMU) identification, consisting of a multi-dimensional indicator framework and a methodology for validation, was established to clarify the boundary and the appropriate area of the mapping units for CES. The multi-dimensional indicator framework included geo-hydrological indicator (GI), economic indicator (EI) and social management indicator (SMI). The OATMU for each indicator was determined by seeking the inflection point in the second-order derivative of the power function. The minimum value of the OATMU for each indicator was obtained as the OATMU for CES. Finally, the OATMU for CES was validated by comparing it with the area of administrative villages in the river basin. The results showed the OATMU for CES was 3.60 km². This study adopted OATMU identification, with easy access to basic data and simplified calculation methods, to provide clear and generic technical support for optimizing CES mapping. Full article

Environmental noise control is a major health and social issue. Numerous environmental policies require local authorities to draw up noise maps to establish an inventory of the noise environment and then propose action plans to improve its quality. In general, these maps are produced using numerical simulations, which may not be sufficiently representative, for example, concerning the temporal dynamics of noise levels. Acoustic sensor measurements are also insufficient in terms of spatial coverage. More recently, an alternative approach has been proposed, consisting of using citizens as data producers by using smartphones as tools of geo-localized acoustic measurement. However, a lack of calibration of smartphones can generate a significant bias in the results obtained. Against the classical metrological principle that would aim to calibrate any sensor beforehand for physical measurement, some have proposed mass calibration procedures called “blind calibration”. The method is based on the crossing of sensors in the same area at the same time, which are therefore supposed to observe the same phenomenon (i.e., measure the same value). The multiple crossings of a large number of sensors at the scale of a territory and the analysis of the relationships between sensors allow for the calibration of the set of sensors. In this article, we propose to adapt a blind calibration method to data from the NoiseCapture smartphone application. The method’s behavior is then tested on NoiseCapture datasets for which information on the calibration values of some smartphones is already available. Full article

In the context of economic boom and climate change, monitoring the spatio-temporal dynamics of forest fragmentation induced by disturbances and understanding its corresponding associated factors are critical for developing informed forest management strategies. In this study, based on multi-temporal Landsat images acquired from 1999 to 2020, a SVM classifier was first applied to produce high-accuracy land cover maps in Xinyu City. Next, morphological spatial pattern analysis (MSPA) was implemented to characterize the spatio-temporal patterns of forest fragmentation by producing maps of seven fragmentation components, including the core, islet, perforation, edge, bridge, loop, and branch. Then, both natural and human factors responsible for the observed forest fragmentation dynamics were analyzed using the geo-detector model (GDM). The results showed that over the past two decades, Xinyu City experienced a process of significant forest area loss and exacerbating forest fragmentation. The forest area decreased from 1597.35 km² in 1999 to 1372.05 km² in 2020. The areal ratio of core patches decreased by 8.49%, and the areal ratio of edge patches increased by 5.98%. Spatially, the trend of forest fragmentation exhibited a progressive increase from the southern and northern regions towards the central and eastern areas. Large-scale forest core patches were primarily concentrated in the northwestern and southwestern regions, while smaller core patches were found in the eastern and central areas. Notably, human activities, such as distance from the roads and land use diversity, were identified as significantly associated with forest fragmentation. The interaction effect of these factors had a greater impact on forest fragmentation than their individual contributions. In conclusion, Xinyu City possesses the potential to further alleviate forest fragmentation by employing the regional differentiation development strategies: (1) intensive development in the northwest and southern regions; (2) high-density development in the western, northwestern, and southern regions, and (3) conservation development in the southwest, northeast, and east-central regions, thus aligning with the path of local social advancement. Full article

This study aimed to further explore the concept of health vulnerability in Argentina, including environmental pollution in 2010. To this end, we developed a geo-referenced database of PM2.5 concentrations and emissions data from the national emissions inventory to analyze possible correlations with the demographic, activity, education, and health data from the 2010 national census. In addition, to provide a more complete picture of health vulnerability in Argentina, an extended index (SVI + PM2.5) was constructed and mapped, including PM concentration. We obtained data for annual PM2.5 values emissions and air concentrations in Argentina from public sources (GEEA-AEIv3.0M for emissions and the Atmospheric Composition Analysis Group V5.GL.03 dataset for surface PM2.5). We evaluated health vulnerability using the “Sanitary Vulnerability Index” (SVI). PM2.5 emissions are concentrated in urban and intensive agricultural areas of Argentina. PM2.5 air concentrations were acceptable (≤10 µg/m³) in only 15% of the Argentinean territory. The newly developed SVI + PM2.5 index showed that exposure to particulate material significantly increases the vulnerability shown by SVI in almost all census blocks. These results indicate that the new SVI + PM2.5 index might help identify populations that are at risk because of social issues or air pollution. Full article

This paper explores recent transformations in Islamic pilgrimage patterns in Israel/Palestine. The meanings assigned to traditional Muslim sites, and the conduct and practices of the dwindling number of pilgrims who visit them, are the struggling victims of strategic socio-political erasures caused by dramatic geo-political changes. Since 1948, the hegemony of the State of Israel has perversely politicized sacred Islamic sites beyond their traditional religious functions. Muslim pilgrims, for their part, engage in rituals that have become a counterweight to Israeli ethnocratic imperatives. The reconstruction of an Islamic pilgrimage map presents a shared imaginative landscape as lieux de mémoire that undergird political and social resistance. The dogged survival of Islamic pilgrimage comprises a counterweight to state power. Muslims fight to affirm Palestinian identity, reclaim heritage spaces as anchors for identity, and actively engage with land claiming. Full article

Recreational ecosystem services (RESs) are the diverse recreational opportunities provided by nature to humans, which contribute to the improvement of public health and social well-being. The use of online social media is an efficient method for quantifying public perceptions of recreational ecosystem services (RESs) delivered by a given landscape. With the continuously changing demand for nature-focused outdoor recreational activities since COVID-19, camping has become the fastest-growing outdoor leisure activity in megacities and a key indicator for how people perceive and value the RESs provided by the landscape. Such unexpected changings triggered by COVID-19 have further led to an imbalance between demand and supply, which results in fierce conflicts in urban green space management. This study presents a spatial pattern analysis of how people perceive RESs in a megacity-scale case study of Beijing using geo-tagged camping notes posted on Little Red Book (LRB). We employed these camping notes in the context of a megacity to (i) map public camping behaviors patterns in urban green spaces, (ii) evaluate spatial clusters of high/low RESs, and (iii) investigate the relationship between RESs, local landscape features, and gender through correspondence analysis. Our results show that considerable spatial clustering of camping behaviors was observed in both suburban and urban green spaces. However, suburbs revealed a substantially higher RES value than central urban areas. In addition, water bodies were discovered to have remarkably low RES, while grassland and urban forests were found to have a close link with higher RES. In addition, significant gender preferences have been discovered, where female visitors prefer to camp in grassland, and male visitors favor bare ground and urbanized regions. Our findings would assist decision-makers in optimizing urban green space planning and management, adapting to fast-changing public camping demands in the context of the post-COVID-19 era. Findings also contribute to the literature by applying spatial analysis of social media data to understand public outdoor recreation activities and perceived value for megacities’ green space management. Full article

Gully erosion is a worldwide threat with numerous environmental, social, and economic impacts. The purpose of this research is to evaluate the performance and robustness of six machine learning ensemble models based on the decision tree principle: Random Forest (RF), C5.0, XGBoost, treebag, Gradient Boosting Machines (GBMs) and Adaboost, in order to map and predict gully erosion-prone areas in a semi-arid mountain context. The first step was to prepare the inventory data, which consisted of 217 gully points. This database was then randomly subdivided into five percentages of Train/Test (50/50, 60/40, 70/30, 80/20, and 90/10) to assess the stability and robustness of the models. Furthermore, 17 geo-environmental variables were used as potential controlling factors, and several metrics were examined to evaluate the performance of the six models. The results revealed that all of the models used performed well in terms of predicting vulnerability to gully erosion. The C5.0 and RF models had the best prediction performance (AUC = 90.8 and AUC = 90.1, respectively). However, according to the random subdivisions of the database, these models exhibit small but noticeable instability, with high performance for the 80/20% and 70/30% subdivisions. This demonstrates the significance of database refining and the need to test various splitting data in order to ensure efficient and reliable output results. Full article

The emergence of the novel coronavirus (COVID-19) generated a need to quickly and accurately assemble up-to-date information related to its spread. In this research article, we propose two methods in which Twitter is useful when modelling the spread of COVID-19: (1) machine learning algorithms trained in English, Spanish, German, Portuguese and Italian are used to identify symptomatic individuals derived from Twitter. Using the geo-location attached to each tweet, we map users to a geographic location to produce a time-series of potential symptomatic individuals. We calibrate an extended SEIRD epidemiological model with combinations of low-latency data feeds, including the symptomatic tweets, with death data and infer the parameters of the model. We then evaluate the usefulness of the data feeds when making predictions of daily deaths in 50 US States, 16 Latin American countries, 2 European countries and 7 NHS (National Health Service) regions in the UK. We show that using symptomatic tweets can result in a 6% and 17% increase in mean squared error accuracy, on average, when predicting COVID-19 deaths in US States and the rest of the world, respectively, compared to using solely death data. (2) Origin/destination (O/D) matrices, for movements between seven NHS regions, are constructed by determining when a user has tweeted twice in a 24 h period in two different locations. We show that increasing and decreasing a social connectivity parameter within an SIR model affects the rate of spread of a disease. Full article

The image of a city represents the sum of beliefs, ideas, and impressions that people have of that city. Mostly, city images are assessed through direct or indirect interviews and cognitive mapping exercises. Such methods consume more time and effort and are limited to a small number of people. However, recently, people tend to use social media to express their thoughts and experiences of a place. Taking this into consideration, this paper attempts to explore city images through social media big data, considering Colombo, Sri Lanka, as the testbed. The aim of the study is to examine the image of a city through Lynchian elements—i.e., landmarks, paths, nodes, edges, and districts—by using community sentiments expressed and images posted on social media platforms. For that, this study conducted various analyses—i.e., descriptive, image processing, sentiment, popularity, and geo-coded social media analyses. The study findings revealed that: (a) the community sentiments toward the same landmarks, paths, nodes, edges, and districts change over time; (b) decisions related to locating landmarks, paths, nodes, edges, and districts have a significant impact on community cognition in perceiving cities; and (c) geo-coded social media data analytics is an invaluable approach to capture the image of a city. The study informs urban authorities in their placemaking efforts by introducing a novel methodological approach to capture an image of a city. Full article