Search Results (77)

To promote the sustainable use of land resources and improve air pollution control, this study investigates the spatiotemporal dynamics of industrial land development and the heterogeneity of PM_2.5 concentrations across regions. Based on national land transaction data and PM_2.5 raster datasets, the analysis employs Moran’s I, a hot and cold spot analysis, and multivariate linear regression to examine how the transaction frequency, transaction area, and total transaction price of industrial land influence PM_2.5 concentrations in 286 cities from 2010 to 2021. The study focuses on quantifying the impact of industrial land development on PM_2.5 concentrations. The main findings are as follows: (1) the frequency of industrial land transactions varies significantly across regions, with clear intra-regional differences. The transaction area and total transaction price decrease in the following order: “East-West-Central-North-East” and “East-Central-West-North-East”, respectively. (2) The spatial clustering of PM_2.5 concentrations has intensified, with hot spots concentrated in Eastern and Central cities. Cold spots are distributed in bands along the Southern coast and scattered patterns in Heilongjiang Province. (3) The influence of industrial land development on PM_2.5 concentrations has generally weakened nationwide, with the strongest effects observed in the Eastern region. Among the development indicators, the impact of the transaction area is increasing, while those of the transaction frequency and total price are declining, showing clear regional disparities. Therefore, integrating sustainable development principles into the adjustment of the industrial land market is essential for effective air pollution prevention. Full article

Under China’s dual land ownership system, the use rights of urban land (state-owned) and rural land (collective-owned) are not equal. Understanding the roles of ownership type and government decision-making behaviors in the formation of land prices is crucial for further reform to promote “equal rights and equal prices” for urban and rural land. This paper analyzed the impact of ownership type on construction land prices using micro-level land transaction data from Wujin District, Changzhou City, from 2015 to 2021 and investigated the role of government decision-making behaviors such as spatial planning and supply plan in this relationship. The results show that collective ownership has a negative impact on land prices, and the development of collective-owned construction land has a positive impact on the prices of adjacent land. In addition, the boundary of downtown areas determined by spatial planning enhances the negative impact of collective ownership on land prices, thus widening the price gap between state and collective-owned land within the downtown areas. Furthermore, the proportion of collective-owned construction land in the annual land supply determined by the land supply plan strengthens the negative impact of collective ownership on land prices, meaning that an increase in the supply of collective-owned construction land leads to further downward pressure on land prices. This study can provide insights for policy making aiming to achieve “equal rights and equal prices” for land with different ownership type in China and in other countries with a dual land ownership system. Full article

Positional accuracy in cadastral data is fundamental for secure land tenure and efficient land administration. However, many land administration systems (LASs) experience difficulties to meet accuracy standards, particularly when data come from various sources or historical maps, leading to disruptions in land transactions. This study investigates the use of unsupervised clustering algorithms to identify and characterize systematic spatial error patterns in cadastral maps. We compare Fuzzy c-means (FCM), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Gaussian Mixture Models (GMMs) in clustering error vectors using two different case studies from Greece, each with different error origins. The analysis revealed distinctly different error structures: a systematic rotational pattern surrounding a central random-error zone in the first, versus localized gross errors alongside regions of different discrepancies in the second. Algorithm performance was context-dependent: GMMs excelled, providing the most interpretable partitioning of multiple error levels, including gross errors; DBSCAN succeeded at isolating the dominant systematic error from noise. However, FCM struggled to capture the complex spatial nature of errors in both cases. Through the automated identification of problematic regions with different error characteristics, the proposed approach provides actionable insights for targeted, cost-effective cadastral renewal. This aligns with fit-for-purpose land administration principles, supporting progressive improvements towards more reliable cadastral data and offering a novel methodology applicable to other LASs facing similar challenges. Full article

Information consumption has been reshaping the modes of human living and production, and driving the transformation of production and trade activities traditionally dependent on land resources, thus influencing urban land green use efficiency (ULGUE). Based on the panel data of 281 prefecture-level cities in China from 2011 to 2023, this study employs the national Information Consumption Pilot Policy (ICPP) as a quasi-natural experiment and utilizes a double machine learning model to assess the ICPP’s impacts on ULGUE. According to the results of the causal mediating effect analysis, the ICPP has improved ULGUE through three mediating mechanisms: expanding the scale of digital transactions, nurturing future industrial developments, and promoting green consumption behaviors. Moreover, in light of the results of the heterogeneity analysis, the ICPP’s impacts on ULGUE vary significantly. Such variation can primarily be attributed to differences in urban resource endowments, disparities in transportation infrastructure development, and variations in geographical location. Specifically, the ICPP has produced more prominent impacts on enhancing land green use efficiency in resource-based cities, cities with high-speed rail access, and coastal cities. Therefore, the government should proactively establish an urban information consumption environment, enhance the role of digital transactions, strategize future industrial developments, encourage green consumption behaviors, and differentiate local policies to effectively promote the continuous improvement of ULGUE. Full article

Land serves as a crucial repository of resource elements, and enhancing the green use efficiency of urban land (GUEUL) is essential for attaining sustainable development. Based on 296 cities in China from 2006 to 2022, this study explored the relationship between land system innovation and GUEUL by integrating multi-source data, ArcGIS analysis, the EBM-DEA model, and the DID model, and elucidating the temporal trend and spatial utilization characteristics of GUEUL in China. Based on the natural experimental scenario of the pilot reform of China’s urban construction land use approval system, this study finds through in-depth analysis of the double-difference model that the vertical transfer of land approval authority has fundamentally optimized the development pattern of GUEUL, and that this positive impact is mainly reflected in two dimensions: on the one hand, it reduces the systematic transaction costs, and on the other hand, it enhances the density of industrial spatial agglomeration. Second, the lower the initial level of infrastructure and the lower the degree of dependence on land finance, the more significant the decentralization of land approval power in the promotion of GUEUL. Currently, China is undergoing a swift phase of urbanization and industrialization, and this study provides policy support for improving the comprehensive efficiency of green land use and promoting high-quality and sustainable development of the region. Full article

While the literature has investigated the associations between urban environments and COVID-19 infection, most studies primarily focused on urban density factors and early outbreaks, often reporting mixed results. We examined how diverse urban factors impact COVID-19 cases across 229 administrative districts in South Korea during Pre-Omicron and Post-Omicron periods. Real-time big data (Wi-Fi, GPS, and credit card transactions) were integrated to capture dynamic mobility and economic activities. Using negative binomial regression and random forest modeling, we analyzed urban factors within the D-variable framework: density (e.g., housing density), diversity (e.g., land-use mix), design (e.g., street connectivity), and destination accessibility (e.g., cultural and community facilities). The results revealed the consistent significance of density and destination-related factors across analytic approaches and transmission phases, but specific factors of significance varied over time. Residential and population densities were more related in the early phase, while employment levels and cultural and community facilities became more relevant in the later phase. Traffic volume and local consumption appeared important, though their significance is not consistent across the models. Our findings highlight the need for adaptive urban planning strategies and public health policies that consider both static and dynamic urban factors to minimize disease risks while sustaining urban vitality and health in the evolving pandemic. Full article

Investigating the experience and improvement measures for China’s distinctive land supply participation in macro-control processes holds significance for full utilization of land policy. However, the spatial heterogeneity and its theoretical and comprehensive analysis of drivers are still poorly revealed. This paper uses spatial analysis methods and micro-scale big data on land transactions to depict the spatiotemporal heterogeneity of land supply, and analyses its driving mechanisms via an endogenous–exogenous factor framework and regression models. Land supply experienced fluctuating “growth–decline–growth” trends in 2001–2021, spatially showed a large cluster in the east, a small cluster in the center and scattering in the west, with the gravity center relocating southwest, and formed a multi-core, hierarchical, circular structure of high density in core cities, density in peripheral cities and sparseness in districts. Endogenously, total land resources and road accessibility facilitated land supply, while topographic relief and urban proximity showed inhibitory effects; land supply positively correlated with land finance dependence, officials’ appraisal pressure, local government competition and officials’ corruption but negatively related with fiscal tax revenues and fiscal transparency; construction land indicators directly determined land supply, while the intensity of use control restricted the conversion of arable land and weakened land supply. Exogenously, urbanization, industrialization, capital investment, technological innovation and marketization level promoted land supply, while the substitution of human capital reduced the demand for land; economic fluctuations showed non-significant relationships with land supply. Differentiated impacts of multiple factors on land supply pattern are emphasized and should be integrated into formulating land policy and optimizing land allocation. Full article

Land and real estate have long been regarded as stable investments, with property prices steadily rising, underscoring the need for accurate predictive models to capture the varying rates of price growth across different locations. This study leverages a decade-long dataset of 83,527 apartment transactions in Vienna, Austria, to train machine learning models using XGBoost. Unlike most prior research, the extended time span of the dataset enables predictions for multiple future years, providing a more robust long-term prediction. The primary objective is to examine how spatial factors can enhance real estate price predictions. In addition to transaction data, socio-demographic and geographic variables were collected to characterize the neighborhoods surrounding each apartment. Ten models, each varying in the number of input years, were trained to predict the price per square meter. The model performance was assessed using the mean absolute percentage error (MAPE), offering insights into their predictive accuracy for both short-term and long-term predictions. This study underscores the importance of distinguishing between newly built and existing apartments in real estate price modeling. By splitting the dataset prior to training, predictive models focusing solely on newly built properties achieved an average reduction of about 6% in MAPE. The best-performing models achieved an average MAPE of 15% for one-year-ahead predictions and maintained a MAPE below 20% for predictions up to three years ahead, demonstrating the effectiveness of leveraging spatial features to enhance real estate price prediction accuracy. Full article

The urban settlement of migrants and their families is an important aspect of new urbanization. Affordable housing, a key measure to improve their living conditions, can advance their urbanization goals. Based on the China Migrants Dynamic Survey (CMDS) data and land transaction data of cities, this study employs a complementary log–log model to estimate the effect of public rental housing (PRH) on the long-term settlement intention (LTSI) of migrants and delves into the intrinsic effect mechanism through the mediating effect. The results show that: (1) Living in PRH can significantly improve the LTSI of migrants who rent. A series of robustness tests and endogeneity tests support the validity of this conclusion; (2) The visualization of a heterogeneity analysis shows that PRH has a greater influence on the LTSI of first-generation migrants and urban–urban migrants. As the city class of the destination decreases, the effect of PRH gradually diminishes; (3) A mechanism analysis suggests that a sense of identity plays a mediating role in PRH affecting the LTSI of the migrants, particularly in first-tier cities. This paper enriches the literature related to the field of housing security programs, provides policy references for enhancing the LTSI of the migrants, and promotes the development of urbanization. Full article

The continuous reduction in peri-urban agricultural land due to spatial urban expansion forces subsistence farmers to seek arable land through different land access strategies. Among these, land rental transactions are crucial for accessing arable land across different regions. This study aimed to examine factors affecting land rental transactions in the peri-urban areas of the East Gojjam Administrative Zone in Ethiopia. Data were collected from 353 household heads of peri-urban areas, who were affected by expropriation. A total of 350 valid responses were analyzed using descriptive and inferential statistics and an econometrics model. The results indicated that 58% of the respondents participated in both renting and renting out land, which underlines the importance of land rental transactions in the peri-urban areas. Specifically, 60% of female-headed households were engaged in land rental transactions, with 14% renting in and 46% renting out land. In contrast, 38% of the male-headed respondents rented land, while only 19% rented out land. The model result identified sex, landholding size, number of oxen, participation in off-farm activities, and extension service as significant determinant variables for renting land. Households made land rental agreements both orally and in written documents, with oral agreements being more prevalent. Transaction dues were conducted through sharecropping and fixed rents, with sharecropping being the most common method. Thus, land rental transactions play pivotal roles to support the livelihoods of peri-urban subsistence farmers. Full article

In the ambitiously evolving construction industry of Kuwait, characterised by its vision 2035 and rapid technological integration, there exists a pressing need for advanced analytical frameworks. The pressing need for advanced analytical frameworks in the Kuwait Construction Market arises from the necessity to identify inefficiencies, predict market trends, and enhance decision-making processes. For instance, these frameworks can be used to detect anomalies in investment patterns, forecast the impact of economic changes on project timelines, and optimise resource allocation by analysing labour and material supply data. By leveraging deep learning techniques, such as autoencoder neural networks, stakeholders can gain deeper insights into the market’s complexities and improve strategic planning and operational efficiency. This research paper introduces a deep learning approach utilising an autoencoder neural network to analyse the complexities of the Kuwait Construction Market and identify data irregularities. The construction sector’s significant investment influx and project expansion make it an ideal candidate for deploying sophisticated analytical techniques to detect anomalous patterns indicating inefficiencies or unveiling potential opportunities. Our approach leverages the capabilities of autoencoder architectures to delve into and understand the prevalent patterns in market behaviours. This analysis involves training the autoencoder on historical market data to learn the normal patterns and subsequently using it to identify deviations from these learned patterns. This allows for the detection of anomalies that may lead to operational or financial consequences. We elucidate the mathematical foundations of autoencoders, highlighting their proficiency in managing the complex, multidimensional data typical of the construction industry. Through training on an extensive dataset—comprising variables like market sizes, investment distributions, and project completions—our model demonstrates its ability to pinpoint subtle yet significant anomalies. The outcomes of this study enhance our understanding of deep learning’s pivotal role in construction and building management. Empirically, the model detected anomalies in transaction volumes of lands and houses, highlighting unusual spikes that correlate with specific market activities. These findings demonstrate the autoencoder’s effectiveness in anomaly detection, emphasising its importance in enhancing operational efficiency and strategic planning in the construction industry. Full article

Industrial land is essential for supply-side structural reforms, particularly in the Chengdu–Chongqing area, Western China’s most densely populated and industrially robust region. This area, a pivotal hub linking Southwest China with South Asia and Southeast Asia, is critical for the national strategic layout and regional economic restructuring. Despite its substantial industrial foundation as an old industrial base, internal developmental stagnation has led to an irrational industrial land use structure. This paper analyzed land transaction data from the China Land Market Network (2010–2021) using methods such as kernel density estimation, the standard deviation ellipse method, and Global Moran’s I index. The analysis focuses on the spatiotemporal evolution of industrial land marketization and its driving factors in 44 cities within the Chengdu–Chongqing economic circle. The findings aim to enhance the strategic implementation of national policies and regional economic optimization, suggesting intensified development efforts in key cities and promoting integrated growth in potential areas like Suining and Ziyang to foster a conducive environment for high-quality regional development. Full article

Recent advancements in deep learning have spurred the development of numerous novel semantic segmentation models for land cover mapping, showcasing exceptional performance in delineating precise boundaries and producing highly accurate land cover maps. However, to date, no systematic literature review has comprehensively examined semantic segmentation models in the context of land cover mapping. This paper addresses this gap by synthesizing recent advancements in semantic segmentation models for land cover mapping from 2017 to 2023, drawing insights on trends, data sources, model structures, and performance metrics based on a review of 106 articles. Our analysis identifies top journals in the field, including MDPI Remote Sensing, IEEE Journal of Selected Topics in Earth Science, and IEEE Transactions on Geoscience and Remote Sensing, IEEE Geoscience and Remote Sensing Letters, and ISPRS Journal Of Photogrammetry And Remote Sensing. We find that research predominantly focuses on land cover, urban areas, precision agriculture, environment, coastal areas, and forests. Geographically, 35.29% of the study areas are located in China, followed by the USA (11.76%), France (5.88%), Spain (4%), and others. Sentinel-2, Sentinel-1, and Landsat satellites emerge as the most used data sources. Benchmark datasets such as ISPRS Vaihingen and Potsdam, LandCover.ai, DeepGlobe, and GID datasets are frequently employed. Model architectures predominantly utilize encoder–decoder and hybrid convolutional neural network-based structures because of their impressive performances, with limited adoption of transformer-based architectures due to its computational complexity issue and slow convergence speed. Lastly, this paper highlights existing key research gaps in the field to guide future research directions. Full article

Measuring the impact of land use regulation on the land market involves identifying and classifying relevant impact factors related to the land market. The objective of this study was to identify land market impact factors in the context of the introduction of land use regulation in Nepal. Through a combination of desktop review and the incorporation of stakeholder perspectives, the paper presents a new approach for determining land market impact factors due to land use regulation where both generic and country issues are considered. A desktop review was carried out to identify a preliminary set of impact factors, which were reclassified through intuitive analysis based on the degree of thematic closeness. Perspective-based impact factors were identified through the qualitative analysis of primary data collected through semi-structured interviews with the Nepalese land market stakeholders. These independently derived impact factors were compared with the desktop literature review impact factors, resulting in 14 land market impact factors across four dimensions, including transaction cost, valuation, mortgage availability, taxation, and compensation across the economic dimension; lot size, subdivision restrictions, and coordination across the institutional dimension; awareness, expectation, and proximity across the social dimension; and risk reduction, quality of residential land, and suitability of zoning classification across the environmental dimension. There was significant overlap and commonality across factors identified from both the literature review and semi-structured interviews. The land market impact factors determined in this study may be adapted and generalized across other countries and could be utilized to better understand the impacts of land policy decisions on urban planning and development. Further research is recommended on the process to operationalize the use of these factors to quantify the impact of land use regulation on different land markets. Full article

South Korea’s Particulate Matter (PM) concentration is among the highest among Organization for Economic Cooperation and Development (OECD) member countries. However, many studies in South Korea primarily focus on housing characteristics and the physical built environment when estimating apartment prices, often neglecting environmental factors. This study investigated factors influencing apartment prices using transaction data for Seoul apartments provided by the Ministry of Land, Infrastructure, and Transport (MOLIT) in 2019. For this purpose, the study compared and analyzed a traditional hedonic price model with a machine learning-based random forest model. The main findings are as follows: First, the evaluation results of the traditional hedonic price model and the machine learning-based random forest model indicated that the random forest model was found to be more suitable for predicting apartment prices. Second, an importance analysis using Explainable Artificial Intelligence (XAI) showed that PM is more important in determining apartment prices than access to education and bus stops, which were considered in this study. Finally, the study found that areas with higher concentrations of PM tend to have higher apartment prices. Therefore, when proposing policies to stabilize apartment prices, it is essential to consider environmental factors. Furthermore, it is necessary to devise measures such as assigning PM labels to apartments during the home purchasing process, enabling buyers to consider PM and obtain relevant information accordingly. Full article