Poverty is a challenge facing all countries and the international community as a whole. Promoting development, narrowing the rural-urban gap, eliminating poverty and achieving common prosperity are ...all ideals that humanity constantly pursues. Despite the great achievements have been made in poverty alleviation over the past three decades, the extent of poverty in rural China is still high. It is both necessary and urgent to fully understand the root and status quo of China's poverty and establish a scientific poverty relief system. Based on high resolution poverty data, this study systematically examined the status quo and spatial distribution characteristics of poverty in rural China and its driving mechanism. The results showed that the distribution of the Chinese rural poor exhibits a distinct spatial agglomeration feature. Poverty is mainly concentrated in the remote deep rocky mountainous areas, border areas and minority areas of central and western China and gradually gathers towards the southwestern region. The “islanding effect” may well appear in China's poverty-stricken regions in the future. The proportion of poor people in the northwestern and southeastern regions of the Hu Huanyong line was 16.4% and 83.6%, respectively, indicating the uneven distribution of the rural poor. Furthermore, further investigations indicated that suffering from illness is the greatest contributor to current individual or transient poverty in rural China. The lack of natural endowments, poor geographic conditions and fragile ecological environment are the main driving forces behind persistent poverty. Ultimately, we proposed that China implement a scientific, differentiated, sustainable, targeted and problem-oriented poverty alleviation strategy that can lift the country's rural poor population out of poverty by 2020 as scheduled.
•We examine the spatio-temporal patterns and driving forces of poverty in rural China.•The distribution of Chinese rural poor exhibits a distinct spatial agglomeration.•The “islanding effect” may well appear in China's poverty-stricken regions in the future.•Suffering from illness is the greatest contributor to transient poverty in rural China.•Some people-oriented anti-poverty measures and suggestions are proposed.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
The integration of natural and social sciences in multidisciplinary research may be the key to improving public policies for urban greening. Analyses on the distribution of urban forests in relation ...to income are scarce for cities in developing countries, which is a crucial problem in view of the problematic and unplanned urbanization process common in these cities. Our study aims to fill this gap, focusing on the mega-city of Sao Paulo. We analyzed the spatial correlation between tree cover and per capita income and the interaction of these variables with other land covers (lawn, buildings and shadows). The city's land cover was estimated from a digital infrared orthoimage and Exploratory Spatial Data Analysis techniques. We found that Sao Paulo presents two patterns for the spatial correlation between tree cover and income, which may represent other mega-cities with similar characteristics. When considering the entire area of the city, the spatial correlation is negative, result contrary to most of the literature and strongly influenced by the presence of reminiscent forests in the peripheral regions, where lower-income population lives. When considering only more urbanized areas, the spatial correlation is positive, indicating an uneven distribution of urban forest that favors the wealthier neighborhoods.
•The city of São Paulo has an uneven distribution between tree cover and income.•Was found a negative correlation, influenced by the presence of reminiscent forests in the peripheral and lower-income regions.•Once the reminiscent forests were disregarded, was found a positive spatial correlation.•Vertical areas of Sao Paulo city are forested area, with high infrastructure and accessible to the upper classes.•Sprawled areas of Sao Paulo city have less infrastructure, are little forested and inhabited by lower classes.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•Relationships among water, food, and energy in 30 regions in china were investigated.•New model describes the internal structure of the food, water, and energy system.•New model determines the ...efficiency value of each subsystem.•Food subsystem is the main reason for low efficiency across the entire system.•Efficiency of water and energy subsystems is relatively high.
Herein, we investigated the relationship between water, food, and energy (W–E–F) in different regions of China to provide suggestions for sustainable and stable regional development of these resources. Based on the system theory, we constructed three relevant subsystems and analyzed the action mechanism between them. Furthermore, we constructed a network based on the super-efficiency data envelopment analysis model for a W–E–F input–output index system. This model was applied to 30 areas in China from the year 2005 to 2015 for measuring W–E–F coupling efficiency. We conducted a spatial correlation analysis to measure the results. This study demonstrated that the overall regional and interprovincial W–E–F coupling efficiency was low, with food subsystem as the main reason for low efficiency in the entire system and key reason for regional differences. When comparing the three subsystems, the efficiency value of the water resources subsystem showed little change over time. In 2015, the water resource efficiency in most regions reached an effective and effective marginal state. The efficiency of the energy subsystem and the food subsystem is on the rise. Overall, the food subsystem's efficiency is low, and in 2015, the food efficiency of several regions in northwest China was between 0.2–0.4 Spatial agglomeration was detected in W–E–F coupling efficiency in various regions of China. From a local spatial differentiation perspective, the proportion of the low–low (LL) space-related agglomeration pattern and high–high (HH) agglomeration was substantial. The LL agglomeration was primarily concentrated in the western and central underdeveloped areas, and a relatively stable space distribution of HH agglomeration occurred mainly in the eastern region.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Accurate land valuation is necessary for tax purposes, land resources allocation, real estate management and urban development and planning. Since various factors from different domains affect land ...prices through non-linear relationships, automating the land valuation process on a large scale is a complex task. Advanced technologies in big data analysis and artificial intelligence have demonstrated superior capabilities in knowledge extraction in such cases. Accordingly, this paper develops and compares the performance of four Automated Valuation Models (AVMs) based on machine learning and deep learning techniques utilizing physical, geographical, socio-economic, environmental, legal and planning factors in Melbourne Metropolitan, Australia. According to the results, the eXtreme Gradient Boosting (XGBoost) method outperforms other algorithms of Support Vector Regression (SVR), random forest and Deep Neural Network (DNN). This method has achieved the coefficient of determination (R2) of 0.862, Mean Absolute Percentage Error (MAPE) of 0.139, and normalized Root Mean Square Error (nRMSE) of 0.281. The achieved high accuracy is due to incorporating a wide range of driving factors and applying innovative feature selection and hyperparameter tuning procedures evaluating various possible feature sets and hyperparameters. Accordingly, this paper can contribute to research, governmental and industry-based activities in terms of developing AVMs for mass land valuation.
•Automated land valuation models using machine learning and deep learning•Comprehensive feature set and hybrid feature selection process for robust model performance•Spatial mapping of land values in large urban areas•Contribution to fair housing policies and urban development by accurate land valuation•Practical insights for housing affordability strategies in terms of land management
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The circular economy pattern is the key to solve the contradiction between industrial development and resource protection. Based on the principle of the “3Rs” of the circular economy (reduction, ...reuse, and resource utilization), and combined with the characteristics of industrial and provincial circular economy development from the perspective of material flow analysis, this study constructs an evaluation index system of industrial circular economy in China's provinces. A combination of the global entropy and the coefficient of variation methods is used to calculate the comprehensive index of industrial circular economy development. Furthermore, this study analyzes the development of China's provincial industrial circular economy from the two dimensions of time and space. The results show that China's industrial circular economy developed rapidly during the Eleventh and Twelfth Five-Year Plan periods. Resource output, resource consumption, and resource reuse and waste disposal have improved over time, but the outlook for emissions of major pollutants is not optimistic. China's industrial circular economy has significant spatial correlation and obvious regional differences, since the development of the circular economy requires a large amount of capital input, and the capacity of capital input fundamentally depends on the regional economic situation. Therefore, the spatial agglomeration pattern of China's industrial circular economy is roughly the same as that of China's economic development. Eastern China has the fastest development, followed by the central and northeast regions, while the western region, especially the northwest, lags the other regions.
Display omitted
•China's industrial circular economy is developing rapidly.•This study analyzes China's circular economy from two dimensions: time and space.•Pollutant treatment index is important for evaluating circular economy development.•Material flow analysis is used to evaluate development of China's circular economy.•China's circular economy has significant spatial differences.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The outbreak and spread of COVID-19 remind us again of the devastating attack that human-to-human transmitted respiratory infectious diseases (H-HRIDs) bring to global economics and public health. ...Predicting the trend of H-HRIDs yields important suggestions for making response strategies. Existing methods predict the future trend of H-HRIDs based on the prophase epidemiological data. However, it is also crucial to predict the potential outbreak trend of H-HRIDs in regions without prophase epidemiological data based on those with epidemiological data available. This prediction can provide constructive pharmaceutical strategies and non-pharmaceutical interventions such as pre-allocating the limited preventions to regions with different potential trends, especially at the early stage of an H-HRID. In this paper, transform the problem of predicting the early-stage trend of an H-HRID in new regions without prophase epidemiological data into the problem of learning regions’ geospatial features via a Contrastive learning-based Hierarchical Graph Convolutional Neural Network (CHGCN). Specifically, CHGCN first generates training data from regions with epidemiological data available via a Relaxed Graph Edit Distance (RGED) algorithm. CHGCN then uses a constructive learning schema to learn region embeddings, where regions with similar geospatial features and H-HRID trends exhibit similar embeddings. Once trained, CHGCN searches for the region with epidemiological data available that has the highest embedding similarity to the given query region without prophase epidemiological data, so as to achieve early-stage H-HRID trend prediction for the query region. Experimental results on COVID-19 demonstrate that the proposed CHGCN achieves state-of-the-art performance in predicting the early-stage trend of H-HRIDs, compared with the baselines.
•We predict a region’s potential trend of early-stage respiratory infectious disease.•We collect data from NYC & SF containing geospatial features and COVID-19 case rates.•We propose RGED to generate (dis)similar regions by editing geospatial features.•We propose a trend prediction model by investigating geospatial features.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Identifying vulnerable regions to non-technical losses allows more assertive combat against them. In this context, this paper presents a spatiotemporal methodology composed of two modules, spatial ...and temporal, to assist distribution companies in action planning to decrease the rates of non-technical losses by region. The spatial module contains a neighborhood structure based on the similarity among small regions named “neighborhood by the similarity of attributes”, which improves the characterization of non-technical losses actions performed by end-consumers. That neighborhood structure is incorporated as an input parameter into a hierarchical spatial autoregressive regression model to represent the relationships between inhabitants. On the other hand, the temporal module uses a linear mixed-effects model to consider future values that are subject to the actions of consumers or distribution companies. The proposed methodology is applied to a medium-sized city with approximately 200,000 inhabitants, considering the inspections carried out by a Brazilian distribution utility. The proposal identified the future non-technical loss state in all the city’s regions with values greater than 69% of the success rate in identifying NTL to residential, commercial, and industrial consumer classes.
•The study of geographic space was incorporated into detecting non-technical losses.•A spatial module to characterize non-technical losses actions performed by consumers.•A spatial autoregressive regression model to represent inhabitants’ relationships.•A linear mixed-effects model for consumer-driven future values.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Based on UN 2030 Sustainable Development Goals (SDGs), this study selects 43 indicators from three dimensions to construct an evaluation index system for sustainable land use (SLU). Thirty provincial ...administrative regions in China from 2002 to 2018 are chosen to comprise the study area. Real-coded accelerating genetic algorithm–projection pursuit techniques and an exploratory spatial data analysis model are used to study the spatiotemporal differences in the SLU levels of the provinces in China. Results show the following: (1) During the study period, the SLU level in China demonstrated an overall upward trend of fluctuations, along with obvious regional differences. (2) The spatial pattern of the SLU level in China was high in the eastern region and low in the western region in the east–west direction; for the north–south direction, an inverted U-shaped distribution was noted. (3) The main types of local spatial autocorrelation are H–H and L–L types, and both have evident spatial clustering characteristics. (4) The realization of Goal A is the main driving force for the improvement of the SLU level, whereas the failure to realize Goals B and C effectively is the main bottleneck in the improvement of the SLU level.
•A new evaluation index system for SLU based on SDGs in China was established.•Mathematical and spatial techniques are applied in research on SLU level.•The SLU level in China has spatial–temporal difference.•Policy suggestions for improving the SLU level by achieving SDGs were proposed.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Analysis of changes over time in the spatial structure of housing prices provides reliable information to infer latent patterns and processes of urban growth. Although the 2008 financial recession ...had a negative impact on building activity in Europe - and especially in southern Europe - the overall impact of the global crisis on the spatial structure of housing prices has been occasionally investigated at the local scale. The present study compares the spatial structure of transaction prices under economic expansion (2003–2009) and recession (2009–2015) in Rome (central Italy), evaluating the combined influence of demography, land-use and territorial factors. Transaction prices increased significantly during expansion and decreased less sharply during recession. The highest housing prices were recorded in downtown Rome. Convergence and divergence in the level of housing prices between urban and rural areas were observed respectively during economic expansion and recession. The empirical results of this study indicate that the 2008 financial crisis influenced considerably the spatial structure of housing prices in Rome, weakening the urban-rural divide and consolidating the gap between wealthy and poor neighbourhoods.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
There is a vast literature on analyzing univariate spatial data by modeling, inference, and prediction in the past decades. While statistical modeling and inference with multivariate spatial data ...have been well developed, spatial prediction using multivariate spatial data called cokriging is relatively less investigated. Cokriging is usually considered to be superior to kriging, but there are not many theoretical studies that investigate how good cokriging is over kriging or when cokriging does not better than kriging. In this work, we provide explicit conditions on the covariance parameters and sampling schemes under an intrinsic coregionalization covariance model, with which cokriging does not better than kriging. Simulation studies and real data examples are also introduced to support our theoretical findings.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
