Search Results (89)

The Sonora Estuarine Biocultural Corridor (CBES) is made up of six coastal wetlands with mangrove forest, internationally certified as Ramsar Sites. Four are part of indigenous territories whose inhabitants have serious development lags and low water security. Five are within one or more of six irrigation districts of national relevance. The objective is to learn about the socio-environmental problems of the CBES, focused on the issue of water, as well as community proposals for solutions. Intercultural, mixed methodology approach. Prospecting visits were carried out in the six estuaries of the CBES, and 84 semi-structured interviews were conducted with experts from all social sectors who know the problems of the CBES in three (out of six) estuaries associated with indigenous territories. The main problem is centered on the issue of water: they receive contaminated water from agroindustry, aquaculture, and the municipal service; the fresh water of the rivers is almost nil, rainfall has decreased while the heat increases, and marine and terrestrial biodiversity decreases. This affects the food and economic security of the local population and generates conflicts between the different productive activities. A multisectoral organization that integrates the six estuaries would improve community wellbeing and, in turn, climate resilience. Full article

As global urbanization accelerates, communities have emerged as key areas where social conflicts and public safety risks clash. Traditional crime prevention models experience difficulties handling dynamic crime hotspots due to data lags and poor spatiotemporal resolution. Therefore, this study proposes a hybrid model combining Informer and Spatiotemporal Graph Convolutional Network (ST-GCN) to achieve precise crime prediction at the community level. By employing a community topology and incorporating historical crime, weather, and holiday data, ST-GCN captures spatiotemporal crime trends, while Informer identifies temporal dependencies. Moreover, the model leverages a fully connected layer to map features to predicted latitudes. The experimental results from 320,000 crime records from 22 police districts in Chicago, IL, USA, from 2015 to 2020 show that our model outperforms traditional and deep learning models in predicting assaults, robberies, property damage, and thefts. Specifically, the mean average error (MAE) is 0.73 for assaults, 1.36 for theft, 1.03 for robbery, and 1.05 for criminal damage. In addition, anomalous event fluctuations are effectively captured. The results indicate that our model furthers data-driven public safety governance through spatiotemporal dependency integration and long-sequence modeling, facilitating dynamic crime hotspot prediction and resource allocation optimization. Future research should integrate multisource socioeconomic data to further enhance model adaptability and cross-regional generalization capabilities. Full article

Background: The integration of mining and urban spaces in coal-resource-based cities holds significant implications for urban transformation and sustainable development. However, existing research lacks an in-depth analysis of its characteristics and driving factors. Methods: This study takes the central urban area of Huaibei City as a case, utilizing historical documents, POI data, and spatial analysis methods to explore the evolution patterns and influencing factors of mining–urban spatial integration. Standard deviation ellipse analysis was employed to examine historical spatial changes, while a binary logistic regression model and principal component analysis were constructed based on 300 m × 300 m grid units to assess the roles of 11 factors, including location, transportation, commerce, and natural environment. Results: The results indicate that mining–urban spatial integration exhibits characteristics of lag, clustering, transportation dominance, and continuity. Commercial activity density, particularly leisure, dining, and shopping facilities, serves as a core driving factor. Road network density, along with the areas of educational and residential zones, positively promotes integration, whereas water surface areas (such as subsidence zones) significantly inhibit it. Among high-integration areas, Xiangshan District stands as the most economically prosperous city center; Lieshan–Yangzhuang mining area blends traditional and modern elements; and Zhuzhuang–Zhangzhuang mining area reflects the industrial landscape post-transformation. Conclusions: The study reveals diverse integration patterns under the synergistic effects of multiple factors, providing a scientific basis for optimizing spatial layouts and coordinating mining–urban development in coal-resource-based cities. Future research should continue to pay attention to the dynamic changes of spatial integration of mining cities, explore more effective integrated development models, and promote the rational and efficient use of urban space and the sustainable development of cities. Full article

Climate change is thought to be responsible for the spread of various vector-borne diseases. The current study was conducted to evaluate the impact of different temperature and relative humidity regimes on the developmental stages of the yellow fever mosquito, Aedes aegypti (Diptera: Culicidae). The study also evaluated the impact of larval density on the survival of Ae. aegypti. In addition, the association between vector larval abundance, dengue incidence, and climatic factors were elucidated during 2016–2019 in three populated districts of Punjab, Pakistan, i.e., Lahore, Rawalpindi, and Multan. The results of the study revealed that at 10 °C and 35 °C, egg hatching and adult emergence were significantly reduced, regardless of the relative humidity. In contrast, at 20 °C and 30 °C, the rates of egg and adult survival increased with higher relative humidity. In addition, a density-dependent response was observed regarding larval survival of Ae. aegypti. Moreover, larval incidence was positively correlated with the number of dengue patients, Tmax, RH, and precipitation at Lahore (0.55, 0.23, 0.29, and 0.13), Rawalpindi (0.90, 0.30, 0.21, and 0.14), and Multan (0.05, 0.27, and 0.13) respectively, except in Multan, where a negative correlation (−0.09) with precipitation was observed. The inflow of patients had a positive correlation with the occurrence of a larval population, relative humidity, and precipitation at Lahore, Rawalpindi, and Multan districts, with the scale values of 0.55, 0.25, and 0.16; 0.90, 0.22, and 0.03; and 0.05, 0.06, and 0.03, respectively. In addition, a forecast model, ARIMA, predicted that there was a higher rate of larval occurrence in Rawalpindi, followed by Lahore. This study concluded that the role of precipitation > 200 mm prior to a 1–2-month lag, a 20–30 °C temperature range, and an RH exceeding 60% lead to the occurrence of larvae and dengue case spikes. This study will help to reinforce dengue surveillance and control strategies in Pakistan and to establish early management strategies based on changing climatic factors. Full article

Rural areas are undergoing a transformation, shifting from traditional agriculture to green and leisure industries, driven by urban–rural imbalances and environmental challenges. This transition, however, presents the growing conflicts between preserving spatial features and promoting industrial development. Based on the unique rural spatial typology of polders, this study integrated theories from cultural, landscape, ecological, economic, and social perspectives to construct a conceptual framework of the interactive relationship between spatial features (SFs) and industrial development (ID). Then, an evaluation index system was constructed to measure the current status of SFs and ID, using data from field surveys, satellite imagery, and 2020 yearbooks, with the Gaochun Polder District, Nanjing (China), as the case study. Next, the coupling coordination degree (CCD) model and a scenario analysis based on orthogonal design were applied to assess the coherence and development between SFs and ID, and to identify strategies for optimizing rural industrial development. The results show that (1) the current SFs and ID are in the break-in and basic coordination stage, with ID lagging behind SFs, and (2) the 25 scenarios generated through orthogonal design were categorized into three groups: high-level coordination with synchronization between the two systems, high-level coordination but ID lagging behind SFs, and basic coordination where ID lags behind SFs. To achieve a high-level coordination with synchronization, specific strategies were proposed to enhance agricultural input–output benefits, improve agricultural scale–quality benefits, and, overall, protect the SF system while making minor adjustments to the village dwelling subsystem. Therefore, the findings provide recommendations for traditional polder villages to optimize their rural industries while preserving the distinctive SFs of the rural cultural landscape. Full article

As one of the most important urban public spaces, the design and management of streets have shifted from “two-dimensional plan” to “three-dimensional space”, and higher requirements have been put forward for the scale and precision of urban design. The core research question of this research is how to refine street spatial quality measurement and evaluation based on multitemporal street view images, while providing basic data and corresponding decision support for updates and renovations. “One Garden and Twelve Fangs” in Jinan old city is the core area of the Jinan Commercial Port District. It integrates diverse cultural elements of tradition and modernity, local and foreign, and is of great significance to the cultural inheritance and urban development of Jinan. Nowadays, there is a lack of vitality, lagging development, and shorting of high-quality living service facilities here. How to enhance the overall vitality of the region and drive regional social value is an urgent problem that needs to be solved at present. This research takes the old city area of Jinan as the research scope, constructs a street space quality evaluation model through street view images and machine learning, and establishes the connection between quantitative research on street space quality and urban renewal practice. In this research, the standard system will be supplemented and improved, and the practicality of the application will be enhanced through more refined evaluation models. The evaluation indicators include walkability, green visibility, enclosure, openness, imaginability, coordination, extreme boundary area, and interface transparency. This article provides a feasible framework and paradigm for measuring the quality of large-scale and high-precision street spaces through the combination of big data and artificial intelligence, effectively bridging the gap between spatial quantification research and urban renewal practices. Full article

With the increasing frequency of extreme rainfall events, enhancing urban drainage systems’ regulation capacity is crucial for mitigating urban flooding. Existing studies primarily analyze infrastructure impacts on peak flow delay but often lack a systematic exploration of time-lag mechanisms. This study introduces the time-lag parameter, using the hysteresis curve of the water level–flow rate relationship to quantify drainage system dynamics. An SWMM-based drainage model was developed for the Rongdong area of Xiong’an New District to evaluate the independent roles of green, gray, and blue infrastructures in peak flow reduction and time-lag modulation. The results indicate that green infrastructure extends the horizontal width and reduces the vertical height of the hysteresis curve, prolonging time lag and making it effective for small-to-medium rainfall. Gray infrastructure enhances drainage efficiency by compressing the hysteresis curve horizontally and increasing its vertical height, facilitating rapid drainage but offering limited peak reduction. Blue infrastructure, by lowering outlet water levels, improves drainage capacity and reduces time lag, demonstrating adaptability across various rainfall scenarios. This study systematically quantifies the role of each infrastructure type in time-lag regulation and proposes a collaborative optimization strategy for urban drainage system design. Full article

Under China’s rural revitalization strategy, peri-urban villages function as pivotal nodes in urban–rural integration. Existing policy research predominantly emphasizes macro-level land and industrial policies, neglecting their spatial development effects on peri-urban villages. This study addresses the gap by constructing a policy quantification framework and employing a Vector Autoregression (VAR) model to analyze policy impacts on rural spatial development, focusing on peri-urban villages in Dalian’s main districts from 2004 to 2023. The results indicate a fluctuating yet upward trend in policy effectiveness. Initial supply-side policies prioritized infrastructure development, whereas subsequent demand-side policies significantly enhanced living conditions, underscoring the necessity of adaptive policy strategies. The rural revitalization construction index exhibited notable spatial heterogeneity, evolving from clusters near industrial zones to expansion into areas like the Jinzhou District, aligned with urban growth patterns. Granger causality analysis confirmed the strong influence of policy interventions, with the first-order lag VAR model offering reliable predictions of short- and long-term policy effects. Initially, the construction index was entirely self-driven (100%), but its reliance on self-influence waned to 69.8% over time, highlighting a transition toward greater policy-driven development. Full article

Against the backdrop of rapid urbanization, associated environmental problems, including low resource consumption, severe pollution emissions, and low environmental awareness, have become salient. The key to achieving sustainable development in Ya’an lies in accelerating the development of new urbanization while ensuring the preservation of existing ecological advantages. Firstly, this study constructs evaluation index systems for new urbanization and the ecological environment using the Population–Economic–Spatial–Social (PESS) and Pressure–State–Response (PSR) models, respectively. Then, the entropy weight model is used to calculate weights for each secondary indicator of the new urbanization and ecological environment systems. The coupling coordination degree (CCD) and relative development degree (RDD) models are applied to analyze spatial and temporal changes in new urbanization and the ecological environment in Ya’an from 2011 to 2021. Finally, spatial autocorrelation and geographically weighted regression (GWR) models are combined to analyze the factors influencing coupling coordination degree differences among Ya’an’s districts and counties. The results indicate the following: (1) From 2011 to 2021, the CCD of Ya’an shifted from basically balanced to highly balanced, and RDD shifted from new urbanization lag to systematic balanced. (2) The CCD of Ya’an varies significantly among regions, and the spatial differentiation of the effects of different factors has different characteristics. Full article

With the implementation of the rural revitalization strategy, rural e-commerce has become an essential means of promoting rural economic development and increasing farmers’ income. However, the development of rural e-commerce varies significantly among different regions. Based on the perspective of “three rural areas”, this study constructs a rural e-commerce development capability measurement system centered on readiness, utilization, and influence. It adopts a panel vector autoregressive model to identify key influencing factors. Through the exploratory spatiotemporal data analysis (ESTDA) method, the spatiotemporal dynamic characteristics of rural e-commerce development capacity and the interaction relationship between provinces and regions are revealed. The study shows that (1) China’s rural e-commerce development capacity gained significant improvement from 2011 to 2022, but provincial polarization is evident, with eastern and central provinces leading and western and marginal provinces lagging; the rural e-commerce development capacity shows a decreasing dynamic pattern from the east to the central and western to the northeastern regions. (2) The eastern region has active rural e-commerce development, stable spatial structure, and provincial solid correlation, which creates a significant linkage effect. The western region shows strong internal spatial dependence, the district cross-regional interaction and linkage effect are beginning to emerge, and the northeastern low-development provinces are challenging to leap to a higher level in the short term; (3) the spatiotemporal interaction network of rural e-commerce development among several provinces and regions shows a positive synergistic relationship, and it is an essential consideration for the high-quality development of rural e-commerce to strengthen regional cooperation and realize complementary advantages. The study results provide a theoretical basis for formulating differentiated regional e-commerce development policies, which can help enhance regional synergy and narrow the regional development gap. Full article

Developing operation strategies for district cooling systems with chilled water storage is challenging due to uncertain fluctuations of cooling demand in actual operations. To address this issue, this paper developed an adaptive operation strategy and performed its validations by modeling and simulating a commercial cooling system in Shanghai using OpenModelica. Firstly, the originally designed operation strategy of the cooling system was evaluated by simulation but was found unable to meet the statistically averaged ideal cooling requirements due to the early exhaustion of stored chilled water at about 5:30 PM. Then, to build foundations for adaptive operation strategy development, a newly designed operation strategy was established by increasing the operation time of base load chillers in the valley and flat electricity price periods. The new strategy proved numerically sustainable in satisfying the ideal cooling demand. Moreover, to realize the strategy’s adaptability to actual cooling load fluctuations, an adaptive operation strategy was developed by tracking the target stored chilled water mass curve that was calculated by implementing the newly designed strategy. The simulation results verify that the adaptive operation strategy enables good adaptability to representative cooling load fluctuation cases by automatically and periodically adjusting the operation status of base load chillers. The adaptive operation strategy was then further widely numerically tested in hundreds of simulation cases with different cooling load variations. The time-lagging problem resulting in strategy failures was found in numerical tests and was addressed by slightly modifying the adaptive strategy. Results indicate that the adaptive operation strategy enables adaptability to deal with cooling demand fluctuations as well as allowing low cooling supply economic costs and power grid-friendly characteristics. This study provides theoretical support to strategy design and validations for district cooling system operations. Full article

While socioeconomic gradients in regional health inequalities are firmly established, the synergistic interactions between socioeconomic deprivation and climate vulnerability within convenient proximity and neighbourhood locations with health disparities remain poorly explored and thus require deep understanding within a regional context. Furthermore, disregarding the importance of spatial spillover effects and nonlinear effects of covariates on childhood stunting are inevitable in dealing with an enduring issue of regional health inequalities. The present study aims to investigate the spatial inequalities in childhood stunting at the district level in Pakistan and validate the importance of spatial lag in predicting childhood stunting. Furthermore, it examines the presence of any nonlinear relationships among the selected independent features with childhood stunting. The study utilized data related to socioeconomic features from MICS 2017–2018 and climatic data from Integrated Contextual Analysis. A multi-model approach was employed to address the research questions, which included Ordinary Least Squares Regression (OLS), various Spatial Models, Machine Learning Algorithms and Explainable Artificial Intelligence methods. Firstly, OLS was used to analyse and test the linear relationships among selected variables. Secondly, Spatial Durbin Error Model (SDEM) was used to detect and capture the impact of spatial spillover on childhood stunting. Third, XGBoost and Random Forest machine learning algorithms were employed to examine and validate the importance of the spatial lag component. Finally, EXAI methods such as SHapley were utilized to identify potential nonlinear relationships. The study found a clear pattern of spatial clustering and geographical disparities in childhood stunting, with multidimensional poverty, high climate vulnerability and early marriage worsening childhood stunting. In contrast, low climate vulnerability, high exposure to mass media and high women’s literacy were found to reduce childhood stunting. The use of machine learning algorithms, specifically XGBoost and Random Forest, highlighted the significant role played by the average value in the neighbourhood in predicting childhood stunting in nearby districts, confirming that the spatial spillover effect is not bounded by geographical boundaries. Furthermore, EXAI methods such as partial dependency plot reveal the existence of a nonlinear relationship between multidimensional poverty and childhood stunting. The study’s findings provide valuable insights into the spatial distribution of childhood stunting in Pakistan, emphasizing the importance of considering spatial effects in predicting childhood stunting. Individual and household-level factors such as exposure to mass media and women’s literacy have shown positive implications for childhood stunting. It further provides a justification for the usage of EXAI methods to draw better insights and propose customised intervention policies accordingly. Full article

Water demand forecasting is essential for ensuring a reliable water supply in any water utility. It involves making accurate predictions for both short- and long-term water needs. Many traditional time series forecasting methods are presently used; however, recent machine learning techniques have grown in popularity for their robustness and accuracy. Random forest is an emerging machine learning algorithm which was used to forecast short-term water demand for ten district metered areas in Italy. Our predictions on test datasets using the trained model yielded correlations as high as 0.98. Important explanatory variables affecting model performance included consumption patterns represented by the seven-day water demand lag. In this paper, we present a reliable application of the random forest algorithm for short-term water demand forecasting. Full article

Accurate prediction of the heat load in district heating systems is challenging due to various influencing factors, substantial transmission lag in the pipe network, frequent fluctuations, and significant peak-to-valley differences. An autoencoder—grey wolf optimization—gated recurrent unit (AE-GWO-GRU)-based heat load prediction method for district heating systems is proposed, employing techniques such as data augmentation, lag feature extraction, and input feature extraction, which contribute to improvements in the model’s prediction accuracy and heat load control stability. By using the AE approach to augment the data, the issue of the training model’s accuracy being compromised due to a shortage of data is effectively resolved. The study discusses the influencing factors and lag time of heat load, applies the partial autocorrelation function (PACF) principle to downsample the sequence, reduces the interference of lag and instantaneous changes, and improves the stationary characteristics of the heat load time series. To increase prediction accuracy, the GWO algorithm is used to tune the parameters of the GRU prediction model. The prediction error, measured by RMSE and MAPE, dropped from 56.69 and 2.45% to 47.90 and 2.17%, respectively, compared to the single GRU prediction approach. The findings demonstrate greater accuracy and stability in heat load prediction, underscoring the practical value of the proposed method. Full article

As a new concept for systematically evaluating ecosystem services, Gross Ecosystem Product (GEP) provides an effective means to comprehensively reveal the overall status of the ecosystem, the impact of economic activities on the ecological environment, and the effectiveness of ecological protection efforts. GEP accounting has been conducted in various regions; however, GEP’s application in natural reserves still requires further exploration. Taking the Qinling Mountains as the research area, this paper aims to assess the relationship between GEP and economic development on the basis of the GEP accounting system. The results indicated that: (1) From 2010 to 2020, GEP tended to increase continuously and exhibited a distribution pattern with high value regions in the east and west, and low value regions in the north and south. (2) Over the years, the coupling coordination degree between GEP and GDP was in a consistent upward trend. In 2020, a good coupling coordination state between GEP and GDP was achieved in most districts and counties. (3) With the relative development between GEP and GDP, the social economy of most districts and counties lagged behind GEP in 2010. The number of districts and counties lagging in GEP in 2020 increased, while the number of regions with a balanced development of GEP and GDP was still relatively discouraging. (4) In general, elevation, contagion, temperature, population density, and precipitation were the main drivers of coupling coordination degree between GEP and GDP. If the relationship between economic development and ecological environmental protection can be reasonably balanced, it will further promote the sustainable development of nature reserves, and provide a scientific basis for sustainable policy-making in other similar areas. Full article