Built-up Surface Research Articles

ASSESSING URBAN HEAT ISLAND IMPACT AND IDENTIFYING VULNERABILITY ZONES THROUGH GEOSPATIAL AND GEO-STATISTICAL TECHNIQUES

An urban heat island emerged due to micro urban temperature variations are also referred to as urban heat islands or urban hot spots. The high-rise buildings along the roads form "Urban Canyons" that inhibit reflected radiation from the built-up surface. Urban heat island develops over the cities due to man-made activity and the landscape. An understanding of the urban heat island and its formation is not only helpful in understanding urban thermal characteristics but also helps in understanding human comfort. A geospatial technique has the ability to acquire updated and cost-effective data over large regions. For urban climatology studies, remote sensing and geographic information systems are an important source of information and an effective methodology. Since 1971, the city of Krishnanagar and its vicinity have been witnessing rapid urban growth. Due to its dense population, urban climate and rapid urban expansion, they cause environmental degradation. Appraisal and Impact of urbanization on micro-climate in the Krishnanagar city complex based on satellite derived parameters. For the years 1995, 2007 and 2018, several satellite image analysis approaches such as NDVI, NDWI and NDVI were computed. Significant differences in land surface temperature were observed between 1995 and 2007, as compared to 2007 and 2018.

Advances on the Global Human Settlement Layer by joint assessment of Earth Observation and population survey data

ABSTRACT The Global Human Settlement Layer (GHSL) project fosters an enhanced, public understanding of the human presence on Earth. A decade after its inception in the Digital Earth 2020 vision, GHSL is an established project of the European Commission’s Joint Research Centre and an integral part of the Copernicus Emergency Management Service. The 2023 GHSL edition, a result of rigorous research on Earth Observation data and population censuses, contributes significantly to understanding worldwide human settlements. It introduces new elements like 10-m-resolution, sub-pixel estimation of built-up surfaces, global building height and volume estimates, and a classification of residential and non-residential areas, improving population density grids. This paper evaluates the key components of the GHSL, including the Symbolic Machine Learning approach, using novel reference data. These data enable a comparative assessment of GHSL model predictions on the evolution of built-up surface, building heights, and resident population. Empirical evidence suggests that GHSL estimates are the most accurate in the public domain today, e.g. achieving an IoU of 0.98 for the water class, 0.92 for the built-up class, and 0.8 for the non-residential class at 10 m resolution. At 100 m resolution, we find that the MAE of built-up surface estimates corresponds to 6% of the grid cell area, the MAE for the building height estimates is 2.27 m, and we find a total allocation accuracy of 83% for resident population. This paper consolidates the theoretical foundation of the GHSL and highlights its innovative features for transparent Artificial Intelligence, facilitating international decision-making processes.

Simulation advances with EnviBatE- A case study on urban heat island mitigation in Singapore

Urban morphology and surface thermal properties are two key factors that determine the local-level urban heat island effect. Among different strategies that help in mitigating this effect, applying high reflective cool coating on urban surfaces is one of the most effective ways for changes in both the daytime and night-time urban air temperatures. In this paper, advances in the EnviBatE microclimate model for district-level numerical simulations are presented and the results are compared against full-scale experimental measurements conducted in Singapore. Key aspects of the EnviBatE tool along with their mathematical models are demonstrated. The experimental site consists of two side-by-side street canyons located at an industrial estate in the west of Singapore: one canyon with conventional and the other with cool surfaces for side-by-side comparison. The results suggest that the drop in street-level canyon air temperature varies from 1 to 4 °C with the application of the cool surface coating on roofs, walls and roads in comparison with conventional canyon under various scenarios. By analysing the effects of the three types of built-up surfaces separately, the cool roads scenario appears to have the greatest impact on canyon air temperature (at 3 m above ground), followed by the cool roofs scenario. The cool walls scenario produces negligible cooling impact. The advantages of the cool coating application on urban surfaces in reducing air temperature are statistically analysed and demonstrated. The pedestrian comfort in terms of the Universal Thermal Climate Index (UTCI) is proposed to be integrated with EnviBatE and discussed using the simulation results.

Sensing the dependable surficial signatures of temporal groundwater variations in arid coastal regions through geospatial techniques with respect to microclimate changes

Reports on Groundwater level variations and quality changes have been a critical issue, especially in arid regions. An attempt has been made in this study to determine the surface manifestations of groundwater variations through processing imageries for determining the changes in land use, Normalized Differential Building Index (NDBI), Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), along with Groundwater level (GWL) and Electrical conductivity (EC). Decadal variation between these parameters for 2013 and 2023 shows that the average water level had increased by 1.03amsl, while the EC values of groundwater decreased by 418 μS/cm. The decrease in EC values indicates freshwater recharge, promoting natural vegetation, thus reducing the LST values by 3.28 °C. In addition, urban landscaping and relatively lesser emissivity from built-up surfaces than the sandy desert have further reduced the LST. The interrelationship of the parameters indicates that an increase in LST correlates with an increase in NDBI and with less significant changes in NDVI. The lowering of the LST along the coastal regions was inferred to be due to the influence of Sea breeze, adjacent moisture from the ocean, shallow water level, and the shadow effect of the buildings. Further, the increase in water level was mainly attributed to the recent increase in rainfall and the extreme event in 2018. The higher EC in the lesser NDBI regions is attributed to the anthropogenic contamination from agriculture and landfill leachates. Though there was an increase in NDBI, the LST of the region was inferred to be reduced mainly due to the increase in water level and reduction of emission from desert sand by recent urban developments.

Land use as key element for urban heat island inception in small cities. Case study: Barlad City, Romania

The study investigates the thermal characteristics of the urban area of Bârlad city, Romania, in relation with urban land use (LU) and land cover (LC). This was done using Land Surface Temperature (LST) data provided by Moderate Resolution Imaging Spectroradiometer (MODIS) and mobile measurements conducted during stable atmospheric conditions. To identify important factors that influence Surface Urban Heat Island (SUHI) and Canopy Layer Urban Heat Island (CLUHI) two types of LU/LC classifications were used. At the annual level, during the day, the highest LST values are observed in the most densely built-up areas of the city, with values exceeding 18°C. The lowest LST instead are observed in the northern area of the city, with prevailing natural areas, where LST values reach 17.4°C. During the night, the urban area maintains constantly high LST above 4°C, while the lowest LST values are observed in the forested area in the eastern part of the city, where mean LST drops close to 3°C. The results of this study highlight the significant impact that LU/LC has on the inception of SUHI in small cities. Generally, built-up surfaces from Urban Atlas (UA) and Local Climate Zones (LCZ) classifications, are warmer than natural land cover types, both during day and night, with at least 0.5 to 1.5°C, which is enough to outline a clearly delimited SUHI especially during summer days. During stable atmospheric conditions the CLUHI exhibits large intensities emphasized by local cooling processes manifested especially during thermal inversion conditions.

Multi-Dimensional Urban Flooding Impact Assessment Leveraging Social Media Data: A Case Study of the 2020 Guangzhou Rainstorm

In the contexts of global climate change and the urbanization process, urban flooding poses significant challenges worldwide, necessitating effective rapid assessments to understand its impacts on various aspects of urban systems. This can be achieved through the collection and analysis of big data sources such as social media data. However, existing literature remains limited in terms of conducting a comprehensive disaster impact assessment leveraging social media data. This study employs mixed-methods research, a synergy of statistical analysis, machine learning algorithms, and geographical analysis to examine the impacts of urban flooding using the case of the 2020 Guangzhou rainstorm event. The result show that: (1) analyzing social media content enables monitoring of the development of disaster situations, with varied distributions of impact categories observed across different phases of the urban flood event; (2) a lexicon-based approach allows for tracking specific sentiment categories, revealing differential contributions to negative sentiments from various impact topics; (3) location information derived from social media texts can unveil the geographic distribution of impacted areas, and significant correlations are indicated between the waterlogging hotspots and four predisposing factors, namely precipitation, proportion of built-up surfaces, population density, and road density. Consequently, this study suggests that collecting and analyzing social media data is a reliable and feasible way of conducting rapid impact assessment for disasters.

Investigating the Impact of Heat Stress and Green Space Accessibility for At-Risk Communities

This work addresses the urban heat island issue and its impact on vulnerable populations in cities. Urban heat islands refer to the phenomenon where temperatures in built-up areas exceed those of surrounding rural regions due to the absorption and retention of heat by built-up surfaces and the lack of vegetation. The study focuses on Milan and uses data from Daytime Near-Surface Air Temperature estimates and the Normalized Difference Vegetation Index to assess accessibility to green spaces during an extreme heat event. The goal is to establish a practical approach for assessing urban areas that are particularly vulnerable to extreme heat and have low accessibility to possible ‘urban cool islands’ to inform climate-proof urban planning, design, and policies and promote equitable access to green spaces. The methodology involves constructing a 3x3 table containing nine classes based on matrix logic, representing different degrees of thermal perception-based accessibility to green. The results of this study could be used to prioritize interventions to increase thermal comfort and achieve a more sustainable urban environment.

Spatially explicit accuracy assessment of deep learning-based, fine-resolution built-up land data in the United States.

Geospatial datasets derived from remote sensing data by means of machine learning methods are often based on probabilistic outputs of abstract nature, which are difficult to translate into interpretable measures. For example, the Global Human Settlement Layer GHS-BUILT-S2 product reports the probability of the presence of built-up areas in 2018 in a global 10 m × 10 m grid. However, practitioners typically require interpretable measures such as binary surfaces indicating the presence or absence of built-up areas or estimates of sub-pixel built-up surface fractions. Herein, we assess the relationship between the built-up probability in GHS-BUILT-S2 and reference built-up surface fractions derived from a highly reliable reference database for several regions in the United States. Furthermore, we identify a binarization threshold using an agreement maximization method that creates binary built-up land data from these built-up probabilities. These binary surfaces are input to a spatially explicit, scale-sensitive accuracy assessment which includes the use of a novel, visual-analytical tool which we call focal precision-recall signature plots. Our analysis reveals that a threshold of 0.5 applied to GHS-BUILT-S2 maximizes the agreement with binarized built-up land data derived from the reference built-up area fraction. We find high levels of accuracy (i.e., county-level F-1 scores of almost 0.8 on average) in the derived built-up areas, and consistently high accuracy along the rural-urban gradient in our study area. These results reveal considerable accuracy improvements in human settlement models based on Sentinel-2 data and deep learning, as compared to earlier, Landsat-based versions of the Global Human Settlement Layer.

Measuring the extent and impact of urban expansion in an agricultural-urbanized landscape in Central Iran.

The unplanned urban expansion is a major environmental challenge in Iran resulting in vast degradation of agricultural lands. Focusing on an agricultural-urbanized landscape in Central Iran, the spatial pattern of built-up expansion was assessed from Landsat data processed in 1992 (TM), 2002 (ETM+), 2012 (TM), and 2022 (OLI). Multi-year crop NDVI was also used as a proxy for cropland suitability to assess the intensity of the urban growth impact. Results showed that (1) the area of built-up surfaces increased almost up to double than that of 1992 and passed 36% (413.42 km2) by 2022, (2) the region experienced a coalescence-diffusion transition phase with decreasing spatial connectivity of newly developed patches with old ones, (3) the most suitable croplands were lost in the middle period (2002-2012) when urban patches started to diffuse, and (4) a significantly positive spatial (Spearman's) relationship (r (22100) = 0.181, p = 0.000) was found between the area and quality of croplands which further highlights the high importance of cropland protection in the region.

Integrated Approach for the Study of Urban Expansion and River Floods Aimed at Hydrogeomorphic Risk Reduction

Urbanization in flood-prone areas is a critical issue worldwide. The historical floods, the urban expansion in terms of building footprint, the extent and construction period of inundated buildings with reference to two representative floods (5–6 November 1994 and 24–25 November 2016), and the ground effects and dynamics of these events were investigated in the cities of Garessio, Ceva, and Clavesana, along the Tanaro River (NW Italy). An integrated approach based on historical data analysis, photograph interpretation, field surveys, and GIS investigations was adopted, and novel metrics for quantitative analysis of urbanization and flood exposure at the individual-building scale were introduced. The considered cities were hit by damaging floods several times over the last centuries and experienced an increase in built-up surface after the mid-19th century, especially between the 1930s and 1994. The 1994 and 2016 high-magnitude floods highlighted that urban expansion largely occurred in flood-prone areas, and anthropogenic structures conditioned flood propagation. One of the rare Italian cases of the relocation of elements exposed to floods is documented. This research aims to emphasize the relevance of information on past floods and urbanization processes for land planning and land management and the need for land use planning for flood control to forbid new urban expansion in potentially floodable areas. The outcomes represent an essential knowledge base to define effective and sustainable management measures to mitigate hydrogeomorphic risk.

Does urban growth mean the loss of greenness? A multi-temporal analysis for Chinese cities

Urban growth is recognized as the conversion of vegetated surface to built-up surface. However, there is still no consensus about the urbanization-induced dynamic of vegetation greenness in view of existing literatures. In this study, we aimed to empirically investigate whether urban growth mean the loss of vegetation greenness. We selected 340 Chinese cities as the study areas, relied on consistent multi-temporal remotely sensed data and adopted linear regression analysis, annual growth area, Tail-Sen slope and Mann-Kendall models. Results show that although vegetation greening generally lagged behind urban growth in the monitoring period, a tendency of their consistent speeding up can be observed over time. By categorizing four forms and four trends of vegetation greenness dynamics related to urban growth, we revealed the diversity of Chinese cities. The former focused on the velocity of urban growth and vegetation greenness dynamics within newly urbanized area in three phases, i.e., 2003–2008, 2008–2013 and 2013–2018. The latter focused on the interannual trends of vegetation greenness dynamics among the previously existing and newly urbanized areas. The key finding is that, in over 85 % of the cities, we measured an increase of vegetation greenness along with urban growth. In addition, our detailed results allow quantifying the impact of urbanization in Chinese cities on vegetation protection and sustainable development.

TOWARDS A PAN-EU BUILDING FOOTPRINT MAP BASED ON THE HIERARCHICAL CONFLATION OF OPEN DATASETS: THE DIGITAL BUILDING STOCK MODEL - DBSM

Abstract. This paper presents a hierarchical conflation process applied to open datasets for the creation of a seamless pan-European map of building footprints in vector format, named Digital Building Stock Model – DBSM. The objective is the sequential addition of input components (which currently include OpenStreetMap, Microsoft GlobalML Building Footprints, European Settlement Map), taking into account their limitations, and aiming at the highest level of completeness possible, for planning and evaluating energy transition scenarios at the EU level. The results indicate how DBSM compares robustly against cadastral data from Estonia, used as reference area. The comparison of DBSM with GHS-BUILT-S, a 10 metres resolution grid with worldwide coverage that encodes the built-up surface in each pixel as derived from Sentinel-2 imagery for the year 2018, reveals a relative overestimation of the latter, factored by 0.68 at the EU scale for a sound match.

Intensity Analysis for Urban Land Use/Land Cover Dynamics Characterization of Ouagadougou and Bobo-Dioulasso in Burkina Faso

Ouagadougou and Bobo-Dioulasso remain the two major urban centers in Burkina Faso with an increasing trend in human footprint. The research aimed at analyzing the Land Use/Land Cover (LULC) dynamics in the two cities between 2003 and 2021 using intensity analysis, which decomposes LULC changes into interval, category and transition levels. The satellite data used for this research were composed of surface reflectance imagery from Landsat 5, Landsat 7 and Landsat 8 acquired from the Google Earth Engine Data Catalogue. The Random Forest, Support Vector Machine and Gradient Tree Boost algorithms were employed to run supervised image classifications for four selected years including 2003, 2009, 2015 and 2021. The results showed that the landscape is changing in both cities due to rapid urbanization. Ouagadougou experienced more rapid changes than Bobo-Dioulasso, with a maximum annual change intensity of 3.61% recorded between 2015 and 2021 against 2.22% in Bobo-Dioulasso for the period 2009–2015. The transition of change was mainly towards built-up areas, which gain targeted bare and agricultural lands in both cities. This situation has led to a 78.12% increase of built-up surfaces in Ouagadougou, while 42.24% of agricultural land area was lost. However, in Bobo-Dioulasso, the built class has increased far more by 140.67%, and the agricultural land areas experienced a gain of 1.38% compared with the 2003 baseline. The study demonstrates that the human footprint is increasing in both cities making the inhabitants vulnerable to environmental threats such as flooding and the effect of an Urban Heat Island, which is information that could serve as guide for sustainable urban land use planning.

Spatiotemporal Characterization Of Land Surface Temperature In Relation Landuse/Cover: A Spatial Autocorrelation Approach

Abstract The land use and land cover (LULC) characteristics of Ghaziabad have experienced dynamic changes because of the city’s ongoing industrialization and urbanisation processes. These shifts can be directly attributed to human actions. These shifts can be directly attributed to human actions. Thermal variation in the study area necessitates LULC analysis. Landsat and Sentinel satellite data for 2011 and 2021 were used to map LULC, estimate land surface temperature (LST) and analysis spatial autocorrelation among the variables using ArcGIS software and the Google Earth Engine (GEE) cloud platform. A sharp descent is observed in the cropland while built-up area has increased during the study period. With the increase in the built-up surface in the area, the ambient temperatures have also increased from 18.70 °C in 2011 to 21.81 °C in 2021 leading to urban heat island effect. At all spatial scales, spatial autocorrelation is a characteristic property of most ecological parameters. The spatial clustering of LST in an ecosystem can play a crucial role in determining the dynamics of LULC.The Moran’s, I show that there is a considerable level of spatial autocorrelation in the values of LST and highly clustered pattern for both the years. Monitoring and understanding the surface thermal environment is crucial to discerning the causes of climate change.

STUDY OF WEAR OF THE BUILDING-UP ZONE OF MARTENSITE-AUSTENITIC AND SECONDARY HARDENING STEELS OF THE Cr-Mn-Ti SYSTEM

The work deals with wear of the building-up zone of martensite-austenitic and secondary hardening steels of the Cr-Mn-Ti system (partially alloyed with Mo, B, and V). Additional alloying of the studied steels with titanium in the amount of 2…5% supported avoiding cleavages along the building-up zone. It was determined that there is a 7…15 µm wide parent metal zone next to the weld line. The influence of temperature on the weld toughness of the building-up zone was assessed according to the criterion of Malkin and Tetelman. The microhardness of the surface layers of built-up layers and the range of its dispersion are close to the results of laboratory tests, where ТСт ~ 823 K. This approves the formation of structure of metal of contact volumes with the collective effect of heating, plastic deformation and diffusion. It was experimentally approved that the change of microhardness in the sub-surface layers at the wear stage is justified simultaneously with the processes of mutual diffusion of friction pair materials, selective oxidation and thermodiffusion redistribution of the hardening phase under the influence of temperatures and deformations. The diffusion coefficient in the deformed sub-surface layer, which is up to some dozen micrometers thick, is one of the values providing the kinetics of growing of nuclear cracks and development of destruction cracks. The received results provide an opportunity to use martensite-austenitic and secondary hardening steels of the Cr-Mn-Ti system with built-up surface for the hot forming tools.

The ‘Deluge’ of 25 October 1822 in Genoa, Italy

The ‘Deluge’ of 25 October 1822 in Genoa, Italy

SPATIO-TEMPORAL ANALYSIS OF LAND COVER CHANGES OF DHAKA CITY IN BANGLADESH

The objective of this study was to identify the Spatio-temporal variation in landcover of Dhaka city, the capital of Bangladesh. The study was conducted to highlight the issues of changing land-cover of mega cities of Bangladesh and its future environmental consequences. Land cover changes and temporal maps for the selected study areas were developed by using multi-temporal information data sets by Multispectral Scanner (MSS), Thematic Map (TM), Enhanced Thematic Mapper (ETM), Enhanced Thematic Mapper Plus (ETM+),or Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) for the years 1993, 2000, 2010 and 2015. The results of this study highlighted the rapid urbanization in Dhaka which caused significant decline in vegetation cover and increase of built-up or paved surfaces. The land-cover classified images highlighted, that in 1993 only 22% per cent area of the study area was covered with built-up surface. However, in 2015, it increased up to 48%. This substantial decline in vegetation cover and upsurge in built-up area of Dhaka city may put petrifying effect on local environments.

Assessing the relationship between morphology and mapping accuracy of built-up areas derived from global human settlement data

ABSTRACT It is common knowledge that the level of landscape heterogeneity may affect the performance of remote sensing based land use/land cover classification. While this issue has been studied in depth for land cover data in general, the specific relationship between the mapping accuracy and morphological characteristics of built-up surfaces has not been analyzed in detail, an urgent need given the recent emergence of a variety of global, fine-resolution settlement datasets. Moreover, previous studies typically rely on aggregated, broad-scale landscape metrics to quantify the morphology of built-up areas, neglecting the fine-grained spatial variation and scale dependency of such metrics. Herein, we aim to fill this knowledge gap by assessing the associations between localized (focal) landscape metrics, derived from binary built-up surfaces and localized data accuracy estimates. We tested our approach for built-up surfaces from the Global Human Settlement Layer (GHSL) for Massachusetts (USA). Specifically, we examined the explanatory power of landscape metrics with respect to both commission and omission errors in the multi-temporal GHS-BUILT R2018A data product. We found that the Landscape Shape Index (LSI) calculated in focal windows exhibits, on average, the highest levels of correlation to focal accuracy measures. These relationships are scale-dependent, and become stronger with increasing level of spatial support. We found that thematic omission error, as measured by Recall, has the strongest relationship to measures of built-up surface morphology across different temporal epochs and spatial resolutions. The results of our regression analysis (R2 > 0.9), estimating accuracy based on landscape metrics, confirmed these findings. Lastly, we tested the generalizability of our findings by regionally stratifying our regression models and applying them to a different version of the GHSL (i.e. the GHS-BUILT-S2) and a different study area. We observed varying levels of model transferability, indicating that the relationship between accuracy and landscape metrics may be sensor-specific, and is heavily localized for most accuracy metrics, but quite generalizable for the Recall measure. This indicates that there is a strong and generalizable association between morphological properties of built-up land and the degree to which it is “undermapped.”

Heat Stress and Its Negative Impact on City and its Inhabitants: A Case Study of Bhubaneswar

Abstract: This paper aims to analyse how rapid urbanization helps in increasing urban temperature and how urban population suffering due to rise in temperature. Heat stress happens when the body's capability of controlling its inner temperature begins to fail. Temperature rise is of the main reason led to warmness stress. This heat stress related issues are mostly being seen among urban workers and urban poor. Usually, urban areas experience higher temperature than its peri urban & rural areas. These higher temperatures have negative human health impact like respiratory disorder, cardiovascular and heat stroke as well. Therefore, it is important to lower heat risk from urban environment. Deaths & Health hazards due to heat stress can be avoid easily. We just need to identify the high heat risk areas and with the help of proper planning and prevention we can prevent health related issues. Rapid Urbanization & increase of built-up surface is one of the major causes increasing urban heat.

Land surface temperature and human thermal comfort responses to land use dynamics in Chittagong city of Bangladesh

Intense urbanization alters the microclimate and ecology of cities by converting naturally vegetated and permeable surfaces into impervious built-up surfaces. These artificial impermeable surfaces re-balance the surface energy budget by storing solar heat due to their higher thermal conductivity, and consequently, increase the Land Surface Temperature (LST). The higher LST affects the city dwellers' Human Thermal Comfort (HTC). To address these issues, unlike most prior research, we assess not only the influence of Land Use and Land Cover (LULC) alterations on summer LST but also on winter LST in Chittagong City of Bangladesh between 1993 and 2020 by using Remote Sensing (RS) and Geographic Information Systems (GIS). Additionally, the study evaluates the impact of LULC changes on the HTC during summer as LST substantially affects HTC in summer. The LULC analysis shows an increase in built-up area by 204% from bare lands, vegetated lands, lowlands, and water bodies between 1993 and 2020. In contrast, bare lands were converted from naturally vegetated surfaces, followed by lowlands and water bodies because of anthropogenic activities. The LSTs of Chittagong city, derived from remote sensing data, show a strong upward trend, with summer (winter) ranges of 20.62–34.07 °C (7.50–27.52 °C), 22.82–37.62 °C (14.92–29.32 °C), and 22.32–43.52 °C (17.08–31.83 °C) for 1993, 2007, and 2020, respectively. Between 1993 and 2020, the spatial mean winter and summer LSTs increased by 4.04 °C and 6.45 °C, respectively, or 0.15 °C and 0.24 °C per year. Chittagong had the highest mean LST in built-up areas for all the years. In addition, the study area's HTC gradually shifted to intense heat stress. The summer LST strongly correlated with normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI) and normalized difference water index (NDWI) while winter LST exhibited poor correlation with these indices.