Urban Land Research Articles

What determines the synergy among urban systems? Evidences from the Yangtze River economic belt over 20 years

China's urbanization has reshaped landscapes, economies, and societies nationwide at unprecedented paces, yet inconsistent march among these processes has resulted in insufficient and unbalanced urban development. Here, we constructed a Population-Land-Economic-Social urbanization (PLESU) system with multiple elements and interaction flows, and explored the spatiotemporal dynamics and influencing factors of the coupling coordination degree (CCD) among PLESU system in 110 prefectural-level cities of Yangtze River Economic Belt (YREB) from 2000 to 2020. Results indicated that the CCD evolution exhibited significant path dependence during the past two decades, with considerable room for improvement. A key finding was that the low-quality development of land urbanization and social urbanization subsystems was the main factor for the lack of synergy among PLESU system, with land finance dependence and municipal investment confirmed to have significant impact on this. Furthermore, results also highlighted narrowing inter-regional differences in the CCD among downstream, midstream and upstream, revealing clustering of CCD in urban agglomerations, yet differing in their internal differentiation patterns and drivers. Exploring the synergy among urban systems will raise policymakers’ awareness of the necessity for embracing a holistic approach toward better sustainability by considering the insufficient and unbalanced nature of urbanization.

Mapping and Assessing Urban Agriculture in São Paulo: Tackling Socio-Economic and Environmental Issues through Nature-Based Solutions

Inefficient urban transformations pose multifaceted challenges. In this context, urban agriculture (UA) can address environmental and socio-economic issues. Despite recent UA policies in São Paulo, the lack of data hampers comprehensive evaluations, highlighting the need for robust indicators to enhance environmental sustainability. This article assesses São Paulo’s UA potential using an Ecosystem Services (ES)-based approach, combining ES spatial mapping and a producer survey at 49 UA sites. Results show natural habitats and anthropic areas within urbanised land, especially in the south, with high habitat quality scores. Nine ES were identified: food supply, commercialisation, income generation, waste mediation, lifecycle maintenance, soil formation, leisure/social, well-being, and education. Utilising mixed methods, this study provides innovative insights into ES related to UA in São Paulo, offering valuable input for urban policies and planning.

Mapping the Time-Series of Essential Urban Land Use Categories in China: A Multi-Source Data Integration Approach

Accurate, detailed, and long-term urban land use mapping is crucial for urban planning, environmental assessment, and health evaluation. Despite previous efforts, mapping essential urban land use categories (EULUCs) across multiple periods remains challenging, primarily due to the scarcity of enduring consistent socio-geographical data, such as the widely used Point of Interest (POI) data. Addressing this issue, this study presents an experimental method for mapping the time-series of EULUCs in Dalian city, China, utilizing Local Climate Zone (LCZ) data as a substitute for POI data. Leveraging multi-source geospatial big data and the random forest classifier, we delineate urban land use distributions at the parcel level for the years 2000, 2005, 2010, 2015, 2018, and 2020. The results demonstrate that the generated EULUC maps achieve promising classification performance, with an overall accuracy of 78% for Level 1 and 71% for Level 2 categories. Features derived from nighttime light data, LCZ, Sentinel-2 satellite imagery, and topographic data play leading roles in our land use classification process. The importance of LCZ data is second only to nighttime light data, achieving comparable classification accuracy to that when using POI data. Our subsequent correlation analysis reveals a significant correlation between POI and LCZ data (p = 0.4), which validates the rationale of the proposed framework. These findings offer valuable insights for long-term urban land use mapping, which can facilitate effective urban planning and resource management in the near future.

The marginal effect of landscapes on urban land surface temperature within local climate zones based on optimal landscape scale

The rising urban land surface temperature (LST) has undoubtedly caused an imminent threat to human habitat. Landscape patterns are among the most significant factors related to LST. However, the marginal effects and thresholds of landscape at optimal grid scale on LST within diverse local climate zones (LCZs) are poorly understood. This study employed the boosted regression trees (BRT) model to evaluate the relative influences and marginal effects of built-up, water, and green landscape metrics on LST at the optimal grid scale across various LCZs. The findings reveal that: (1) The spatial spread trend of LST closely matches the expansion trend of built-up landscapes. (2) Significant variations in average summer LST were observed among LCZs, with LCZ8 (large low-rise) exhibiting the highest temperatures and LCZ11 (dense trees) the lowest. (3) At the optimal scale of 1000 m, the DIVISION metric for water landscapes significantly influenced LCZ6 (open low-rise), LCZ8, and LCZ14 (low plants). (4) In LCZ6 and LCZ8, effective heat mitigation requires increasing water area to 50% and optimizing the water body separation index to 0.88. These findings suggest that heterogeneous landscape heat mitigation strategies should be considered across diverse LCZs in urban areas.

Land price dynamics in response to high-speed rail network characteristics: An empirical analysis

High-speed railway (HSR) has become the backbone of China's rapid and comprehensive urban transportation network system. However, the impact of the network characteristics of HSR on urban land prices has not been studied in sufficient detail. To fill this gap, this study focuses on both the Yangtze River Delta urban agglomeration and the Beijing-Tianjin-Hebei urban agglomeration and explores the linkage relationship between the HSR network and urban land prices. For this analysis, social network analysis and fixed effects model are used. The results show that the overall HSR network density of these two urban agglomerations still has room for improvement. Affected by regional proximity and the direction of HSR lines, HSR presents a spatial pattern of a multi-center–multi-tier network. The “siphon effect” is the main effect of the HSR network characteristics on land prices. However, the weakening degree of spatial spillover effect on siphon effect of the HSR network in the Yangtze River Delta urban agglomeration is stronger than that in the Beijing-Tianjin-Hebei urban agglomeration. Local governments should fully utilize the spatial effects of HSR and enhance the level of coordinated development among urban agglomerations.

Luxury Effect, Heritage Effect, and Land Use Hypotheses Revealing Land Cover Distribution in Hainan Island, China

Land cover analysis is a key method used to understand past land use patterns and explore the driving forces and processes behind them. This study focuses on land cover in 18 counties and cities of Hainan Island, delving into the driving factors of land cover in specific areas of Hainan Island, including the southern, northern, eastern, western, inland, and coastal regions. The effects of socio-economic factors, landscape pattern, and urban functional units on land cover are considered, and three hypotheses are proposed to explain the observed trends. The results indicate that house prices are positively correlated with construction area, woodlands land, and urban green space, thereby supporting the luxury effect hypothesis on land cover. In addition, construction age was negatively correlated with the woodlands area, confirming the role of the inverse legacy effect hypothesis in land cover. Other relationships between urban functional units and land cover emphasized the importance of the land use hypothesis in land cover planning. These results will help decision-makers and managers to better understand the current drivers of land cover, allowing for a more scientific basis when planning and managing urban land cover.

Integrating geographic knowledge into deep learning for spatiotemporal local climate zone mapping derived thermal environment exploration across Chinese climate zones

The Local Climate Zone (LCZ) scheme representing urban structure and land use pattern is essential for urban heat island (UHI) research. Fine-grained LCZ mapping considering spatial and temporal heterogeneity can provide a more precise characterization of surface thermal properties, thereby enabling a comprehensive analysis and understanding of spatiotemporal trends in climate change research. However, data-driven deep learning-based methods have limitations in coping with the complex urban landscapes of the real-world scenarios, including the spectral similarity of different LCZ and the geospatial heterogeneity of urban LCZ categories. In this study, we constructed a geographic knowledge base for enhanced LCZ characterization with the consideration of prior surface spatial information, including a set of spectral indices and urban morphological parameters (UMPs). Then, we integrated the explicable geographic knowledge base into a learnable deep learning framework in an end-to-end manner for accurate LCZ mapping by fusing multi-source heterogeneous data with a multi-level fusion strategy. The constructed Chinese Climate Zone Time Series LCZ (CClimate-TLCZ) dataset derived from Landsat-8 data with a 30 m spatial resolution, covering 18 representative cities in China, were used to evaluate the proposed framework. The experimental results demonstrate that the proposed framework achieved optimal outcomes across 18 cities, with an average overall accuracy exceeding 94 %, which is more than 20 % higher than that obtained by the standard WUDAPT method. Furthermore, the analysis of LCZ mapping-driven land surface temperature (LST) and surface UHI (SUHI) applications shows that cities within the same climate zone have similar LST distribution patterns, while significant heterogeneity exist between different zones. The annual consistency of LST patterns within each climate zone supports the validity of the LCZ classification scheme and accurate LCZ mapping for urban thermal environment studies. From 2016 to 2022, SUHI intensity initially increases and then decreases, indicating improvements in the urban thermal environment. These findings underscore the critical role of precise LCZ mapping in urban climate resilience and sustainable urban planning.

Does self-containment of spatial scale and land use function contribute to mitigate urban heat island effects? Lessons from new towns in Shanghai

The concept of self-containment in new towns has been widely discussed from social and economic perspectives. However, localized interpretations within the context of China's development, particularly regarding climate adaptability and urban heat island (UHI) mitigation, are scarce. To fill this gap, our research analyzed self-containment from the perspectives of urban spatial scale and land use function. Focusing on Shanghai’s five new towns, we empirically demonstrated how self-containment influenced the UHI effects from 2005 to 2020, employing the Geodetector method. The findings reveal that during the daytime, the intensity of UHI in new towns decreased, serving as vital connectivity nodes of UHI within the region. Conversely, during the nighttime, both the intensity and area of UHI showed an increasing trend. The research confirmed that expanding the urban scale and functional diversity are effective strategies for mitigating the UHI. Based on these findings, we offer practical suggestions for the development of new towns: Increase population size while ensuring coordination with development scale; enhance mixed-use functions in large-scale development projects like university towns and industrial parks; and be vigilant of potential functional decline in central areas and increasing thermal impact due to new town development. Overall, this study enriches our understanding of self-containment in Chinese new towns and provides valuable insights for mitigating UHI in other similar contexts.

Analisis penerimaan dan penyetoran Pajak Bumi dan Bangunan Perdesaan dan Perkotaan (PBB-P2) di Desa Tombasian Atas Kecamatan Kawangkoan Barat

The Rural and Urban Land and Building Tax (or PBB-P2) is a crucial source of revenue for local governments in Indonesia. This research will analyze the revenue collection and payment of PBB-P2 in Tombasian Village, Kawangkoan Barat Subdistrict. The research methodology employed a qualitative approach, gathering data through interviews with tax officials and secondary data analysis. The findings clearly show a low level of public awareness regarding tax obligations, which is hurting tax revenue. Public understanding of tax policies and the quality of tax services significantly influence the payment of PBB-P2. The study provides recommendations to local governments to enhance public awareness campaigns, empower tax officials, and improve services to the community, thereby increasing revenue collection and tax payment of PBB-P2. These measures will undoubtedly lead to an increase in local tax revenue, which will support the development of Tombasian Village.

LCZ-based city-wide solar radiation potential analysis by coupling physical modeling, machine learning, and 3D buildings

Addressing climate change and urban energy problems is a great challenge. Building Integrated Photovoltaics (BIPV) plays a pivotal role in energy conservation and carbon emission reduction. However, traditional approaches to assessing solar radiation on buildings with physical models are computing-intensive and time-consuming. This study presents a hybrid approach by integrating physical model-based solar radiation calculation and machine learning (ML) for city-wide building solar radiation potential (SRP) analysis. By considering urban morphology, land cover, and meteorological characteristics, local climate zones (LCZs) are classified. The SRP of representative LCZs is precisely evaluated using computing-intensive physical models integrated with 3D building models. A ML model is then developed to effectively predict the SRP of building roofs and facades throughout the city. An experiment was conducted in Shenzhen, China to validate the presented approach. The results demonstrate that Shenzhen has a total annual building solar radiation of 3.28∗1011kwh. Luohu District exhibits the highest SRP density. The LCZ-based analysis highlights that compact low-rise LCZs offer greater SRP for roofs, while compact high-rise LCZs do so for facades. Moreover, BIPV could cut CO2 emission by up to 41.85 million tons annually. Notably, solar PV installation only on rooftops in Shenzhen could meet 87.81% of the city's electricity department's carbon reduction goal. This study provides an alternative for city-wide SRP estimation by combining physical modeling and ML and offers valuable insights for data-driven and model-driven urban planning and management in low-carbon cities.

Determining the areas most suitable for urban land use while minimizing impact on natural areas. The case of the Machachi Valley, Ecuador

Due to the impact of urban growth on the environment, especially in developing countries, decision-making tools are needed to help locate the ideal areas for urban use. This study aims to identify the areas most suitable for urban use, minimizing their impact on forest land use in an area with high urban sprawl. As a new development, the study also considers the connectivity between forest-use patches and the loss of forest area as decision variables. The methodology used to determine these areas was AHP (Analytic Hierarchy Process) with GIS (Geography Information System). Twenty experts evaluated the criteria, considering forest conservation in addition to urban suitability. Socio-economic, physical, and environmental criteria and sub-criteria were scored with values from 1 to 5, with 5 indicating the maximum potential of the pixel to accommodate urban use. The study area was the Machachi Valley and the conurbation of Quito in Ecuador. The results indicate that the most suitable urban areas are located in the buffer that surrounds the initial urban use, and the land along the Pan-American Highway. The most vulnerable forest zone was in the south center of Quito and on the hillsides of the Pasochoa volcano. The results were also compared with maps that estimate the evolution of the land uses in this valley in the coming years. Based on this comparison, maintenance of current trends will result in a significant loss of native forest and fragmentation of forest patches in the lower valley. The information provided by maps of suitable urban use is a useful tool to protect the natural resources that land use policies should take into consideration.

Research on Urban Resilience from the Perspective of Land Intensive Use: Indicator Measurement, Impact and Policy Implications

Land intensive use reflects the spatial structure, agglomeration characteristics, and internal mechanisms of urban economic, social, and ecological system development, significantly impacting urban resilience. Based on panel data from 287 cities in China from 2010 to 2020, this paper measures the levels of land intensive use and urban resilience, and empirically examines the impact mechanism of land intensive use on urban resilience through baseline regression and panel quantile regression. The results reveal that: (1) During the study period, China’s urban land intensive use level has significantly improved. The land intensive use level shows a trend of “the strong become stronger, and the weak are always weak” and “high in the east and low in the west” spatial differentiation, while the urban resilience level showed a trend of accelerated “catching up” of low-resilience cities towards high-resilience cities and “high in the east and low in the west” spatial differentiation as well. (2) Land intensive use significantly promotes effect on urban resilience, and the effect depends on different conditions. (3) Among all dimensions of land intensive use, both land input intensity and land use benefits significantly promote urban resilience, while land use intensity shows an insignificant effect. (4) The impact of land intensive use on urban resilience demonstrates significant scale heterogeneity and geographic regional heterogeneity. Based on these findings, the paper proposes relevant policy suggestions for land intensive use aimed at improving urban resilience, offering guidance for promoting high-quality land use and sustainable urban resilience development.

Land Surface Temperature Anomalies as Indicators of Urban Land Cover Change—A Study of Two Indian Cities

Rapid urbanisation over the years has led to the loss of natural land cover, thereby affecting Land Surface Temperature (LST) distribution in urban areas. This study aims to analyse LST anomalies (calculated as the deviation from the normal) over selected Indian cities and check if critical land cover changes can be identified. LST from Landsat Thermal Infrared (TIR) images acquired in March, April and May from 1988 to 2020 were used to estimate LST anomalies. Positive LST anomalies were observed mainly over barren and impervious areas; however, some areas showed a negative anomaly where the barren lands were converted to vegetated areas. The study has demonstrated that while some developed areas exhibit a positive anomaly indicative of significant changes or development, there are instances where the conversion of barren land to developed (i.e. built up) areas has resulted in a negative anomaly. Developed areas that are closer to the water creek or mangroves were associated with lower anomaly values indicating the cooling effect of the water body and vegetation. Conversely, the core urban areas generally exhibited higher LST values with positive anomalies indicating a warming effect. These findings can be used by city planners to identify hotspot areas and develop more effective strategies and policies to address the challenges of urban heat. They also highlight the regions that require infrastructural resources and policy changes to reduce the temperature.

Impact of Industrial Intelligence on China’s Urban Land Green Utilization Efficiency

Against the backdrop of the fourth technological revolution, industrial intelligence (INDI) represented by industrial robots has rapidly developed. This evolution provides favorable opportunities for precise decision-making in pollution control and achieving China’s “dual carbon” goals. Previous studies have mainly discussed the economic effects of INDI from the perspective of the labor market. This study shifts its focus to examining the impact of INDI on the land green utilization efficiency (LGUE) in cities. Using the panel data of Chinese cities spanning 2009–2021, this study empirically tests the effect and transmission mechanism of INDI on LGUE. We find that urban INDI significantly enhances LGUE. In terms of its transmission mechanism, INDI drives improvements in urban LGUE through technological progress, energy structure optimization, and industrial structure upgrading. Urban infrastructure construction and financial agglomeration level can further strengthen the positive impact of INDI on LGUE. In addition, the improvement in LGUE due to INDI is more significant in non-resource-based and large-sized cities than resource-based and small and medium-sized cities. Therefore, each region should enhance the integration of intelligent technology with traditional industrial manufacturing. Doing so is essential to establish comprehensive assessment indicators that balance environmental protection and economic growth, strengthen regional information infrastructure construction, ensure steady financial flow, and support green development initiatives across regions.

Spatial effect of urban morphology on land surface tempature from the perspective of local climate zone

Urban expansion results in the intrusion of artificial surfaces into natural environments, giving rise to phenomena such as urban heat islands and heightened temperatures within inhabited areas. This study examined the dynamic evolution of urban morphology and land surface temperature through the lens of local climate zone. Furthermore, the spatial relationship between urban morphology indices and land surface temperature was analyzed within the context of loops. The investigation yielded several noteworthy findings: (1) LCZ8, LCZ6, and LCZD were the most significant transfer-in and transfer-out classes within the study area. (2) The heat contribution of residential LCZs (LCZ 2,3,5) decreased over time, ranging from 0.415 to 0.171, while that of industrial LCZs (LCZ 8,10) increased within the range of 0.348–0.553. (3) Urban heat island mitigation can be achieved by regulating building height and surface fraction. Establishing a judicious threshold for reducing LST is imperative for sky view factor and impervious surface fraction factors. Surface albedo exhibited a substantial inhibitory effect in 2020. The observation of urban morphological changes and long-term local climate zone considerations integrate the responsibility of mitigating urban heat islands into urban planning.

Refined Land Use Classification for Urban Core Area from Remote Sensing Imagery by the EfficientNetV2 Model

In the context of accelerated urbanization, assessing the quality of the existing built environment plays a crucial role in urban renewal. In the existing research and use of deep learning models, most categories are urban construction areas, forest land, farmland, and other categories. These categories are not conducive to a more accurate analysis of the spatial distribution characteristics of urban green space, parking space, blue space, and square. A small sample of refined land use classification data for urban built-up areas was produced using remote sensing images. The large-scale remote sensing images were classified using deep learning models, with the objective of inferring the fine land category of each tile image. In this study, satellite remote sensing images of four cities, Handan, Shijiazhuang, Xingtai, and Tangshan, were acquired by Google Class 19 RGB three-channel satellite remote sensing images to establish a data set containing fourteen urban land use classifications. The convolutional neural network model EfficientNetV2 is used to train and validate the network framework that performs well on computer vision tasks and enables intelligent image classification of urban remote sensing images. The model classification effect is compared and analyzed through accuracy, precision, recall, and F1-score. The results show that the EfficientNetV2 model has a classification recognition accuracy of 84.56% on the constructed data set. The testing set accuracy increases sequentially after transfer learning. This paper verifies that the proposed research framework has good practicality and that the results of the land use classification are conducive to the fine-grained quantitative analysis of built-up environmental quality.

Seasonal dynamics of land surface temperature and urban thermal comfort with land use land cover pattern in semi-arid Indian cities: Insights for sustainable Urban Management

The cities in semi-arid and arid regions generally exhibit distinctive land use land cover (LULC) pattern and seasonal variation in urban thermal comfort (UTC) and land surface temperature (LST). This study examines the seasonal variation in LST and UTC and quantify the importance of LULC pattern in influencing LST in eight Indian semi-arid cities using Landsat 8 datasets. Random forest regression (RFR) model has been applied to quantify the influence of LULC in determining LST. The study shows that bare soil and open spaces in outskirts of selected cities have comparatively high LST than the core of cities. The mean LST varies seasonally from 12.84 °C in Udaipur to 26.18 °C in Jaipur in winter and summer. Analysis of UTC shows that autumn and winter seasons have better UTC than spring and summer seasons. RFR analysis shows a robust correlation (R2 > 0.85) between LULC and LST across all cities. Notably, built-up areas, open land, and vegetation cover exert the greatest influence on LST. Study suggests that targeted LULC planning may effectively address UTC problem in semi-arid cities and enhance sustainability. Study may help in understanding the seasonality thermal environment and assist in mitigating UHI and maintain UTC in these cities.

Effect of Climate Change on Wildlife

Climate change is a global catastrophe that disrupts the balance of ecosystem by altering the climatic measures. Human being are not the only living species on earth that is affected by climate change. All the inhabitants on Earth are dependent on suitable living conditions for their survival. All human and non-human species on Earth either directly or indirectly relying on each other. Wildlife is the key indicator of climatic changes in the environment as they are part of various biological and ecological cycles in the atmosphere. Over the century, the atmospheric temperature has increased around 1.1°C. Changes in climatic conditions is affecting the terrestrial and aquatic biomes in plenty of ways. Limiting basic living conditions of animals by destruction of their natural habitation and its transformation into urban lands which intensifies the human-wildlife interactions. Migration of temperature sensitive species to the areas of higher altitude. High temperatures have increased the occurrence of wild-land fires, floods and droughts five times more than past 50 years. Animals naturally respond to altering climatic conditions by exhibiting changes in their general and reproductive behavior, growth patterns, increased zoonotic diseases. All these above cited effect put them to the status of endangered species and ultimately to the extinction. Strategic approaches i.e., protection, management, planning and evaluation of natural biomes to conserve the Earth’s biodiversity must be opted to avoid the extinction of wild species.

Implications of peri-urban land reform programs on urban land markets: a case study of Harare, Zimbabwe

Zimbabwe implemented the Fast-Track Land Reform Program (FTLRP) in the year 2000. This program has had far-reaching implications on urban development, especially on urban land markets in cities and towns. This paper highlights the implications of the land reform program on urban land markets, using Harare as a case study. The paper is based on document review (policy, academic and development literature) and interviews with key informants with experience and knowledge of the land reform and its associated implications on urban land markets. Our analysis demonstrates that the land reform process in peri-urban spaces was complex and largely driven by political interests. The land reform process distorted the urban planning processes, leading to disparities in the land markets. Such disparities include informal and uncontrolled processes of accessing land and, ultimately, rapid development of informal settlements in the city. Political elites and land barons took advantage of the land reform program extorting money from desperate home-seekers. We conclude that the distortion of urban land markets by politically driven land reform processes negatively affects sustainable urban development. Overall, the paper makes a valuable contribution to the growing literature on land reforms and peri-urban development in Africa, demonstrating the complex and multidimensional implications of land reform programs on peri-urban land markets. Based on the study findings, the paper provides a set of policy recommendations aimed at improving the effectiveness and equity of land reform programs in African peri-urban areas.

Monthly electricity consumption data at 1 km × 1 km grid for 280 cities in China from 2012 to 2019

High spatio-temporal resolution estimates of electricity consumption are essential for formulating effective energy transition strategies. However, the data availability is limited by complex spatio-temporal heterogeneity and insufficient multi-source feature fusion. To address these issues, this study introduces an innovative downscaling method that combines multi-source data with machine learning and spatial interpolation techniques. The method’s accuracy showed significant improvements, with determination coefficients (R2) increasing by 30.1% and 33.4% over the baseline model in two evaluation datasets. With this advanced model, we estimated monthly electricity consumption across 1 km x 1 km grid for 280 Chinese cities from 2012 to 2019. Our dataset is highly consistent with officially released electricity consumption of different industries (Pearson correlation coefficients within 0.83 - 0.91). Moreover, our data can reflect the electricity consumption patterns of different urban land uses compared to other datasets. This study bridges a significant gap in fine-grained electricity consumption data, providing a robust foundation for the development of sustainable energy policies.