Search Results (1,126)

Urban blight and litter are twin issues that significantly affect the quality of life in city neighborhoods. This paper investigates the relationship between blight and litter, commonly overlooked in urban studies literature. We measure the prevalence of blight and litter across block groups in our mapping with a focus on socioeconomic factors, including income levels, crime rates, and land use types (industrial, commercial, and residential) for our case study, Memphis, Tennessee. Using statistical and spatial analytics, as well as data from the Memphis Data Hub and the City of Memphis, we show the prevalence of blight and litter across block groups. GIS was used to map neighborhood-specific blighted structures and their spatial connection to litter accumulation. We also explore the distribution of blight and litter across different land uses. A Pearson correlation value of 0.639 suggests a strong positive relationship between blight and litter at the block group level. Spatial clustering is assessed by Global Moran’s Iand Local Moran’s I, identifying neighborhood-level hotspots. The block group is used as the unit of analysis to capture micro-spatial variation and to enable meaningful equity-based insights at the neighborhood level. Our mapping offers practical insights into urban revitalization strategies in deference to per capita income, crime rate, and land use. The findings contribute to urban policy discussions by promoting the joint consideration of blight and litter, helping guide future community-based interventions aimed at alleviating the negative impacts of blight and litter, particularly in disadvantaged neighborhoods. Full article

The Ordos section of the Yellow River Basin represents a typical semi-arid zone in northern China. Due to dual pressures from natural drivers and human activities, this region is at the forefront of desertification. Therefore, rapidly and accurately identifying desertification and analyzing its evolutionary trends plays a vital role in desertification control. Using six-phase Landsat imagery (2000–2023) of Ordos City, this study extracted NDVI and Albedo to construct a fitting model, thereby analyzing desertification severity, spatial distribution patterns, and evolutionary dynamics. Through integrated analysis trends in meteorological and anthropogenic data, key driving factors of desertification processes were further investigated. Conclusions: (1) By 2023, the area of extremely severe and severe desertification reduction accounted for 12.67% of the total study area, the proportion of no desertification area increased by 11.27%, and the expansion of desertification was effectively curbed. (2) Desertification intensification cluster near residential zones and grazing lands, while improved areas concentrate in the western and southern of Mu Us Sandy Land vicinity. (3) Spatial autocorrelation analysis revealed statistically significant clustering patterns across the study area, predominantly characterized by distinct low–low and high–high aggregations. (4) Wind speed, temperature, and pastoral activities were major factors contributing to desertification. These research findings provided references for the ecological restoration and sustainable development of semi-arid areas in the Yellow River Basin. Full article

This study examines the impact of catchment characteristics and design on the performance of urban lakes in terms of water quality and stormwater harvesting potential. Two urban lake systems in Western Sydney, Australia, were selected for comparison: Wattle Grove Lake, a standalone constructed lake, and Woodcroft Lake, part of an integrated wetland–lake system. Both systems receive runoff from surrounding residential catchments of differing sizes and land uses. Over a one-year period, continuous monitoring was conducted to evaluate water quality parameters, including turbidity, total suspended solids (TSS), nutrients (total nitrogen and total phosphorus), pH, dissolved oxygen, and biochemical oxygen demand. The results reveal that the lake with an integrated wetland significantly outperformed the standalone lake in terms of water quality, particularly in terms of turbidity and total suspended solids (TSS), achieving up to 70% reduction in TSS at the outlet compared to the inlet. The wetland served as an effective pre-treatment system, reducing pollutant loads before water entered the lake. Despite this, nutrient concentrations in both systems remained above the thresholds set by the Australian and New Zealand Environment and Conservation Council (ANZECC) Guidelines (2000), indicating persistent challenges in nutrient retention. Notably, the larger catchment area and shallow depth of Wattle Grove Lake likely contributed to higher turbidity and nutrient levels, resulting from sediment resuspension and algal growth. Hydrological modelling using the Model for Urban Stormwater Improvement Conceptualisation (MUSIC) software (version 6) complemented the field data and highlighted the influence of catchment size, hydraulic retention time, and lake depth on pollutant removal efficiency. While both systems serve important environmental and recreational functions, the integrated wetland–lake system at Woodcroft demonstrated greater potential for safe stormwater harvesting and reuse within urban settings. The findings from the study offer practical insights for urban stormwater management and inform future designs that enhance resilience and water reuse potential in growing cities. Full article

As China’s urbanization deepens, the spatial structure of residential areas and land use patterns has undergone profound transformations, with the differentiation of housing prices emerging as a key indicator of urban spatial dynamics and socioeconomic stratification. This study examines the spatial and temporal evolution of residential housing prices in Qingdao’s main urban area over a 20-year period, using data from three representative years (2003, 2013, and 2023) to capture key stages of change. It employs Local Indicators of Spatial Association (LISA) spatial and temporal path and leap analyses, as well as Geographically and Temporally Weighted Regression (GTWR) modeling. The results show that Qingdao’s housing price patterns exhibit distinct spatiotemporal heterogeneity, characterized by multi-level transitions, leapfrog dynamics and strong spatial dependence. The urban center and coastal zones demonstrate positive synergistic growth, while some inland and peripheral areas show negative spatial coupling. Evident is the spatial restructuring from a monocentric to a polycentric pattern, driven by shifts in industrial layout, policy incentives, and transportation infrastructure. Key driving factors, such as community attributes, locational conditions, and amenity support, show differentiated impacts across regions and over time. Business agglomeration and educational resources are primary positive drivers in central districts, whereas natural environments and commercial density play a more complex role in peripheral areas. These findings provide empirical evidence to inform our understanding of housing market dynamics and offer insights into urban planning and the design of equitable policies in transitional urban systems. Full article

In 2024, significant increases in surface temperature were recorded in Batam City and Bintan Regency, marking the highest levels observed in regional climate monitoring. The rapid conversion of vegetated land into residential and industrial areas has been identified as a major contributor to the acceleration of local climate warming. Climatological analysis also revealed extreme temperature fluctuations, underscoring the urgent need to understand spatial patterns of temperature distribution in response to climate change and weather variability. This research uses a Cellular Automata–Artificial Neural Network (CA−ANN) approach to model spatial and temporal changes in land surface temperature across the Riau Islands. To overcome the limitations of single-model predictions in a geographically diverse and unevenly developed region, Landsat satellite imagery from 2014, 2019, and 2024 was analyzed. Surface temperature data were extracted using the Brightness Temperature Transformation method. The CA−ANN model, implemented via the MOLUSCE platform in QGIS, incorporated additional environmental variables, such as rainfall distribution, vegetation density, and drought indices, to simulate future climate scenarios. Model validation yielded a Kappa accuracy coefficient of 0.72 for the 2029 projection, demonstrating reliable performance in capturing complex climate–environment interactions. The projection results indicate a continued upward trend in surface temperatures, emphasizing the urgent need for effective mitigation strategies. The findings highlight the essential role of remote sensing and spatial modeling in climate monitoring and policy formulation, especially for small island regions susceptible to microclimatic changes. Despite the strengths of the CA−ANN modeling framework, several inherent limitations constrain its application, particularly in the complex and heterogeneous context of tropical island environments. Notably, the accuracy of model predictions can be limited by the spatial resolution of satellite imagery and the quality of auxiliary environmental data, which may not fully capture fine-scale microclimatic variations. Full article

Digital twins play a crucial role in linking data with practical solutions. They convert raw measurements into actionable insights, enabling spatial planning that addresses environmental challenges and meets the needs of local communities. This paper presents the development of a digital geospatial twin for a residential district in Burgas, the largest port city on Bulgaria’s southern Black Sea coast. The aim is to provide up-to-date geospatial data quickly and efficiently, and to merge available data into a single, accurate model. This model is used to test three scenarios for revitalizing coastal functions and improving a waterfront urban park in collaboration with stakeholders. The methodology combines aerial photogrammetry, ground-based mobile laser scanning (MLS), and airborne laser scanning (ALS), allowing for robust 3D modeling and terrain reconstruction across different land cover conditions. The current topography, areas at risk from geological hazards, and the vegetation structure with detailed attribute data for each tree are analyzed. These data are used to evaluate the strengths and limitations of the site concerning the desired functionality of the waterfront, considering urban priorities, community needs, and the necessity of addressing contemporary climate challenges. The carbon storage potential under various development scenarios is assessed. Through effective visualization and communication with residents and professional stakeholders, collaborative development processes have been facilitated through a series of workshops focused on coastal transformation. The results aim to support the design of climate-neutral urban solutions that mitigate natural risks without compromising the area’s essential functions, such as residential living and recreation. Full article

Climate change intensifies the challenge of elevated temperatures in dense urban areas, notably in Seoul, South Korea. This study investigates the effects of land use and urban form on summer air temperatures by leveraging Seoul’s city-wide Smart Seoul Data of Things sensor network. Using spatial regression models and temperature data collected during July and August 2021, the analysis identifies key environmental factors associated with urban heat dynamics. The results show that medium- and high-density residential areas, industrial zones, and roads consistently increase temperatures, while greenery, taller buildings, and greater urban porosity contribute to cooling effects. The findings highlight the need for urban planning strategies that expand green spaces, promote vertical development with attention to ventilation, and reconfigure built environments to enhance thermal comfort. This study provides robust empirical insights and offers evidence-based recommendations for climate-responsive urban planning and policies in Seoul and similar high-density cities worldwide. Full article

In the paper, using the three-statement financial modelling methodology as applied to a representative development project, we aim to analyse, ex ante, the industry-level impact of transition to mandatory escrow schemes in residential and mixed-use construction in Tashkent city (due to be implemented in Uzbekistan from 2026). Modelling single-milestone escrow plans against the current steep-discount advance-based system of off-plans as a baseline, the model accounts for salient institutional features of the Tashkent city development market, including land auctioning, full-cycle Value-added tax (VAT) accounting, and Tax loss carryforward provisions. It also incorporates a framework for demand-driven residual valuations for the development land element. Our findings indicate practically unchanged cashflow profitability of developers on the market in question. Around 30% p.a. in nominal Free-cashflow-to-equity based IRRs expressed in the national currency, provided that the transition to the greater use of leverage in funding unfolds as expected. The disappearance of steep off-plan discounts while the transition to escrows unfolds will be countervailed by the reliance on costly loans from escrow banks. Absent the greater use of leverage, the IRR (FCFE) profitability of the developers is expected to decline by some 5%. For the apartment buyers, this is effectively equivalent to increasing property transaction prices on the primary market in line with their headline asking amounts. Thus-generated economic surplus will be partially captured by the developers and partially passed through to escrow banks, increasing their gross profits by up to $50M, p.a. due to their new role in financing Tashkent city residential developments that are still largely equity-driven. Apart from this effect, we find only a moderate financial leverage influence on developers’ profitability due to the high-interest-rate environment prevailing in Uzbekistan. We also find a demand-driven pressure on land auction prices suggested by increasingly back-loaded alterations in project cashflow profiles. This study also purports to make a material contribution to the evolving body of literature on financial modelling of apartment and mixed-use property developments by offering a flexible three-statement modelling framework with innovative endogenised equity management features. Full article

Hong Kong faces critical construction challenges, including workforce aging, land shortages, and near-capacity waste disposal. Modular Integrated Construction (MiC) offers a promising solution. As Hong Kong has just recently adopted the MiC, quantitative studies that explore the actual performance differences between MiC projects and conventional on-site construction projects in Hong Kong are lacking. To fill this knowledge gap, this study utilizes an extended life cycle assessment–Life Cycle Performance Assessment to conduct on-site investigations and case studies on a MiC pilot residential project and a conventional on-site construction residential project in Hong Kong from multiple dimensions: cost, time, safety, and environment. The assessment indicators include five types of greenhouse gas emissions, cost performance, schedule performance, and safety-level index. This study found that the greenhouse gas emissions of the MiC project during the entire construction period were reduced by approximately 21.60% compared to traditional on-site construction projects. The most significant part of the greenhouse gas emissions of the two methods was the embodied emissions of construction materials, accounting for 83.11% and 87.17%. Compared with the conventional construction project, the factors that actively promote the reduction of greenhouse gas emissions in the MiC project are the embodied greenhouse gas emissions of building materials, the transportation of construction waste, and the resource consumption of equipment. In addition, there is no significant difference in the safety performance index of the two construction methods, but MiC projects have more efficient schedule performance management. Surprisingly, the cost control of MiC projects is not as good as that of conventional construction projects, which differs from existing research results in other regions. Full article

In river-connected lake regions, both land use and hydrological regime changes may affect the ecosystem services; however, few studies have attempted to elucidate their complex influences. In this study, the spatiotemporal dynamics of eight ecosystem services (crop production, aquatic production, water yield, soil retention, flood regulation, water purification, net primary productivity, and habitat quality) were investigated through remote-sensing images and the InVEST model in the Dongting Lake Region during 2000–2020. Results revealed that crop and aquatic production increased significantly from 2000 to 2020, particularly in the northwestern and central regions, while soil retention and net primary productivity also improved. However, flood regulation, water purification, and habitat quality decreased, with the fastest decline in habitat quality occurring at the periphery of the Dongting Lake. Land-use types accounted for 63.3%, 53.8%, and 40.3% of spatial heterogeneity in habitat quality, flood regulation, and water purification, respectively. Land-use changes, particularly the expansion of construction land and the conversion of water bodies to cropland, led to a sharp decline in soil retention, flood regulation, water purification, net primary productivity, and habitat quality. In addition, crop production and aquatic production were higher in cultivated land and residential land, while the accompanying degradation of flood regulation, water purification, and habitat quality formed a “production-pollution-degradation” spatial coupling pattern. Furthermore, hydrological fluctuations further complicated these dynamics; wet years amplified agricultural outputs but intensified ecological degradation through spatial spillover effects. These findings underscore the need for integrated land-use and hydrological management strategies that balance human livelihoods with ecosystem resilience. Full article

Carbon flow tracking and spatial pattern optimization at the scale of urban functional zones are key scientific challenges in achieving carbon neutrality. However, due to the complexity of carbon metabolism processes within urban functional zones, related studies remain limited. To address these scientific challenges, this study, based on the “source–sink–flow” ecosystem services framework, develops an integrated analytical approach at the scale of urban functional zones. The carbon balance is quantified using the CASA model in combination with multi-source data. A network model is employed to trace carbon flow pathways, identify critical nodes and interruption points, and optimize the urban spatial pattern through a low-carbon land use structure model. The research results indicate that the overall carbon balance in Hangzhou exhibits a spatial pattern of “deficit in the center and surplus in the periphery.” The main urban area shows a significant carbon deficit and relatively poor connectivity in the carbon flow network. Carbon sequestration services primarily flow from peripheral areas (such as Fuyang and Yuhang) with green spaces and agricultural functional zones toward high-emission residential–commercial and commercial–public functional zones in the central area. However, due to the interruption of multiple carbon flow paths, the overall carbon flow transmission capacity is significantly constrained. Through spatial optimization, some carbon deficit nodes were successfully converted into carbon surplus nodes, and disrupted carbon flow edges were repaired, particularly in the main urban area, where 369 carbon flow edges were restored, resulting in a significant improvement in the overall transmission efficiency of the carbon flow network. The carbon flow visualization and spatial optimization methods proposed in this paper provide a new perspective for urban carbon metabolism analysis and offer theoretical support for low-carbon city planning practices. Full article

This article presents a spatial analysis of projects selected by city residents and implemented in five successive editions (2015–2019) of the participatory budgeting in Częstochowa, Poland. The study examines the relationship between the type of hard projects (small investments in public infrastructure and landscaping) and the pre-existing characteristics of the land use of each district. Kernel density estimation and Spearman correlation analysis were used. The highest spatial density occurred in projects related to the modernization of roads and sidewalks, recreation, and greenery, indicating a relatively high number of proposals within or near residential areas. Key correlations included the following: (1) greenery projects were more common in districts lacking green areas; (2) recreational infrastructure was more frequently chosen in areas with significant water features; (3) street furniture projects were mostly selected in districts with sparse development, scattered buildings, and postindustrial sites; (4) educational infrastructure was often chosen in low-density, but developing districts. The selected projects often reflect local deficits in specific land use or public infrastructure, but also stress the predestination of the recreational use of waterside areas. Full article

Urban heat islands (UHIs) are a growing sustainability challenge impacting public health, energy use, and climate resilience, especially in hot, arid cities like San Antonio, Texas, where land surface temperatures reach up to 47.63 °C. This study advances a data-driven, interdisciplinary approach to UHI mitigation by integrating Machine Learning (ML) with physical and socio-demographic data for sustainable urban planning. Using high-resolution spatial data across five functional zones (residential, commercial, industrial, official, and downtown), we apply three ML models, Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM), to predict land surface temperature (LST). The models incorporate both environmental variables, such as imperviousness, Normalized Difference Vegetation Index (NDVI), building area, and solar influx, and social determinants, such as population density, income, education, and age distribution. SVM achieved the highest R² (0.870), while RF yielded the lowest RMSE (0.488 °C), confirming robust predictive performance. Key predictors of elevated LST included imperviousness, building area, solar influx, and NDVI. Our results underscore the need for zone-specific strategies like more greenery, less impervious cover, and improved building design. These findings offer actionable insights for urban planners and policymakers seeking to develop equitable and sustainable UHI mitigation strategies aligned with climate adaptation and environmental justice goals. Full article

While the explosive growth in e-commerce stresses urban logistics systems, city planners lack of fine-grained data in order to anticipate and manage the resulting freight flows. Using a three-stage analytical approach combining descriptive zonal statistics, hotspot analysis and different regression modeling from univariate to geographically weighted regression, this study integrates one year of parcel deliveries from a leading courier with open spatial layers of land-use zoning, census population, mobile-signal activity and household income to model last-mile demand across different land use types. A baseline linear regression shows that residential population alone accounts for roughly 30% of the variance in annual parcel volumes (2.5–3.0 deliveries per resident) while adding daytime workforce and income increases the prediction accuracy to 39%. In a similar approach where coefficients vary geographically with Geographically Weighted Regression to capture the local heterogeneity achieves a significant raise of the overall R² to 0.54 and surpassing 0.70 in residential and institutional districts. Hot-spot analysis reveals a highly fragmented pattern where fewer than 5% of blocks generate more than 8.5% of all deliveries with no apparent correlation to the broaden land-use classes. Commercial and administrative areas exhibit the greatest intensity (1149 deliveries per ha) yet remain the hardest to explain (global R² = 0.21) underscoring the importance of additional variables such as retail mix, street-network design and tourism flows. Through this approach, the calibrated models can be used to predict city-wide last-mile demand using only public inputs and offers a transferable, privacy-preserving template for evidence-based freight planning. By pinpointing the location and the land uses where demand concentrates, it supports targeted interventions such as micro-depots, locker allocation and dynamic curb-space management towards more sustainable and resilient urban-logistics networks. Full article

This study investigates the heavy metal contamination in urban and peri-urban soils of Thessaloniki, Greece, over a two-year period (2023–2024). A total of 208 composite soil samples were systematically collected from 52 sites representing diverse land uses, including high-traffic roadsides, industrial zones, residential neighborhoods, parks, and mixed-use areas, with sampling conducted both after the wet (winter) and dry (summer) seasons. Soil physicochemical properties (pH, electrical conductivity, texture, organic matter, and calcium carbonate content) were analyzed alongside the concentrations of heavy metals such as Cd, Co, Cr, Cu, Mn, Ni, Pb, and Zn. A pollution assessment employed the Geoaccumulation Index (I_geo), Contamination Factor (Cf), Pollution Load Index (PLI), and Potential Ecological Risk Index (RI), revealing variable contamination levels across the city, with certain hotspots exhibiting a considerable to very high ecological risk. Multivariate statistical analyses (PCA and HCA) identified distinct anthropogenic and geogenic sources of heavy metals. Health risk assessments, based on USEPA models, evaluated non-carcinogenic and carcinogenic risks for both adults and children via ingestion and dermal contact pathways. The results indicate that while most sites present low to moderate health risks, specific locations, particularly near major transport and industrial areas, pose elevated risks, especially for children. The findings underscore the need for targeted monitoring and remediation strategies to mitigate the ecological and human health risks associated with urban soil pollution in Thessaloniki. Full article