City Scale Research Articles

Spatial-temporal variation, driving mechanism and management zoning of ecological resilience based on RSEI in a coastal metropolitan area

Urbanization results in drastic land use/cover change (LUCC) and ecological resilience (ER) issues, which has become an important and urgent question of regional sustainability. This study applied the remote sensing ecological index (RSEI) to represent the ER, and further combined ordinary least squares (OLS), and the Google Earth Engine (GEE) platform to analyze the spatiotemporal variations of ER and LUCC at the regional and city scales from 1990 to 2020 in Hangzhou Bay Metropolitan Area (HBMA), explore the relationship between ER and LUCC, and apply for urban resilience management zoning. The results demonstrated that the ER in HBMA showed a characteristic of “slightly worse overall and improved locally”, the mean value of which decreased from 0.508 in 1990 to 0.502 in 2020. Specifically, the ER in Shanghai, Shaoxing, and Ningbo increased, and others decreased. ER exhibited a very obvious spatial aggregation feature of “high value in the southwest and low value in the northeast”. The spatiotemporal dynamic feature of LUCC from 1990 to 2020 depicted that the expansion of construction land was at the expense of occupying cropland. The LUCC degree and population have significant negative effects on the ER. The ER management zoning of HBMA could be divided into five zones, namely the ecological core protection area, ecological monitoring area, ecological optimization area, ecological restoration area, and ecological potential governance area. Results of this study could provide a more comprehensive understanding of ER change in metropolitan areas and the implication of resilience management was provided to improve the adaptation and sustainable development of the coastal urban area.

Geospatial analysis of the territory for solving urban planning problems

The purpose of the study is to conduct a geospatial analysis of the territory, taking into account urban planning characteristics, for the location of a shopping center. The study characterizes the factors that turn cities into diverse and complex forms of settlements that are constantly changing. The process of urban settlement, which is reflected in their space, is analyzed. It is determined that spatial factors lead to differences in the scale and shape of cities, types of development and methods of spatial planning. The necessity of studying the spatial structure of cities, which has the characteristics of a relatively complete theoretical justification and wide coverage, is established. It is determined that the process of spatial development of cities is multidimensional and complex, which is manifested in spatial planning, transformation of many functions and expansion of the territory. The direction of research on the spatial analysis of the selected city territory is determined. Various characteristics of the site are considered, including views, transport links, access and other constraints that may affect the future development of each approach to the shopping center. A spatial analysis of the shopping center location was conducted. This analysis determines the transport accessibility of the shopping center, the accessible distance to residential development, service areas, etc. It also determines which parts of the territory are suitable for a particular type of use. The results are obtained regarding the location of the shopping center in the city. The estimated number of regular visitors to the projected shopping center is determined, provided that the level of its attractiveness is the same as that of its competitors. The increase in the level of attractiveness of the projected shopping center in comparison with competitors is calculated. Based on the results of the geospatial analysis, the stages of design and survey work were developed to draw up a large-scale topographic basis for the development area.

Multi-scale analysis on sustainability and driving factors based on three-dimensional ecological footprint: A case study of the Yangtze River Delta region, China

Accurate assessment of the sustainable development level is crucial to providing reasonable and effective management recommendations. However, analysis with a single scale can only reflect the sustainable status at a specific level. In this study, a multi-scale analysis of sustainability was conducted via the three-dimensional Ecological Footprint (EF) model in the Yangtze River Delta (YRD). Moreover, driving factors of sustainability were analyzed using the Multi-scale Geographically Weighted Regression model (MGWR). The conclusions include: (1) the development of YRD showed an unsustainable trend from 2014 to 2021, with per capita EF decreasing from 5.769 ha/cap to 5.283 ha/cap. The provincial sustainability is ranked as follows, from being the most sustainable to the least: Anhui Province > Jiangsu Province > Zhejiang Province > Shanghai City. (2) Based on dual indicators of EF depth and size, 41 cities were categorized into nine groups. The dominant type is always Medium size-Low depth (ML) and High size-Low depth (HL) type cities from 2014 to 2021. From the perspective of city scale, the sustainability of five cities weakened, while seven cities showed improvements. (3) Output value of primary industry (OVPI) and population (POP) were always the dominant factors affecting EF, but showed positive and negative correlations respectively. OVPI changes from north-south symmetric type to central symmetric type, while POP changes from central symmetric type to north-south symmetric type. This paper aims to comprehensively assess the sustainable development status from agglomeration-province-city perspectives, and its findings can provide a reference for similar regions with unbalanced development status between ecology and production.

Enhancing building segmentation by deep multiview classification for advancing sustainable urban development

Accurate building segmentation plays a crucial role in a wide range of applications such as urban planning, monitoring, and mapping. Different deep learning models were employed for building segmentation. However, these models analyze images from a single view. Given the limitations of single-view building segmentation models, our research aims to enhance accuracy by proposing a novel multi-view U-Net deep model for accurate building segmentation that incorporates multiple views of the images. We employ two pre-trained convolutional neural network architectures, MobileNetV2 and ResNet50, to extract features representing two different views of our images. By fusing these features, our proposed method effectively captures complementary information, leading to enhanced segmentation accuracy. To further improve the model’s performance, we incorporate skip connections and up-convolutional layers to ensure fine-grained feature propagation. Our experimental results on a large building dataset demonstrate a significant improvement in segmentation accuracy 91% compared to state-of-the-art methods, highlighting the effectiveness of our multiview fusion approach. The experimental results enhance the benefits of creating different views by adopting the novel concept proposed in this paper. This research has the potential to redefine the landscape of building segmentation in applications such as urban planning and mapping. We also conducted a test on a large study area (city scale of Belval–Luxembourg). This demonstrates the capabilities of our method and its efficiency in segmenting satellite images from a large extent area and reinforces its potential for real-world applications.

Healing Applications in Hospital Interiors: Ceramic Art

Healthcare facilities include complex partnerships that accommodate different types of users to meet the needs of the healthcare sector and require the collaboration of many disciplines to meet these needs. Hospitals, which contain the outputs of different fields of expertise from city scale to industrial product scale, are fed by the fields of engineering, architecture, and interior architecture in terms of space. Interior design, on the other hand, is divided into theoretical and practical specializations related to building types. The fact that health buildings have significant differences from other buildings due to their function and the obligations that would be met is of great importance in terms of the benefit to be provided to the public by the studies to be carried out in the field. Hospital buildings and spaces are constructed and designed according to the standards determined by the shade's laws, regulations, or guidelines where they are to be built. In this context, the study aims to provide a public contribution with art outputs that would positively affect the recovery of the user for the spaces that are generally open to the use of users in hospital interiors by researching the standards in the framework of international standards. However, since traditional and modern art contents have different spatial needs; ceramic art outputs, which are included in traditional art, focus on the specific evaluation of the research with its visual and tactile character. Using qualitative research methods; hospital construction guidelines and literature research were carried out by data collection and inductive methods. The research aims to make theoretical and practical contributions to the healing space with ceramic artworks/objects in the field of architecture and design.

High-resolution estimation of industrial water use in Beijing-Tianjin-Hebei based on multi-source data

Understanding changes in industrial water use at finer spatial scales is critical for regional water allocation and sustainability. This study employs a top-down method based on multi-source data to estimate the industrial water use at 1 km × 1 km resolution in the Beijing-Tianjin-Hebei (BTH) region during 2000–2019. Statistical data from municipalities and prefectures are used to assess the accuracy of simulated industrial water use. During the validation period, all accuracy validation indices (R, NSE, |RE|, RMSE) have achieved good results, indicating that the method is credible. In addition, industrial water use at the county level in BTH has a strong spatial correlation with 99% confidence. Spatiotemporal change pattern mainly depends on the industrial scale and technical level of prefecture-level cities, and the economic environment will also contribute to changes in the annual industrial water use in BTH. The method provides a reference for high-resolution estimation of industrial water use and can also be applied to regional water management and allocation.

Auditing and Rating Sustainability of Mediterranean Buildings, Neighbourhoods and Cities

Sustainability rating systems for the built environment involve a multitude of indicators that are based on different types of data. This work capitalizes on an existing multicriteria assessment method and supporting decision-making tool at building and neighbourhood scale, to develop an enhanced method and tool at city scale. The main sustainability issues at building and city scales include site and infrastructure, energy and resources consumption, environmental loadings, climate change, environmental quality, water, waste, transportation, services, social aspects, economy and governance. Approximately 300 indicators distributed among the different scales are used to describe and quantify the various facets of sustainability. Specifically, the building scale includes a pool of 80 indicators of which 17 are key performance indicators (KPIs), the neighbourhood scale has 133 indicators of which 14 are KPIs and the city scale has a total of 99 indicators of which 10 are KPIs that were new additions to the existing method. The emphasis in this paper is given on elaborating the key performance indicators for cities and demonstrating their applicability through a case study. The common method and tools provide a flexible assessment system for local authorities and stakeholders to develop and assess sustainability plans.

Implementation of programs for the development of operational processes of the HR management system of government agencies in Western countries

Today, the problem of digitalization of HR management system operational processes is the lack of automated HR management programs for government agencies, lack of resources, staff reduction, data management, and change management. Many countries have faced problems with the digitalization of HR processes in government agencies. To a greater or lesser extent, such structural problems of the labor market can be found in all Western countries. The development of technologies and information systems creates new opportunities for managing and improving the processes of government agencies. The specifics of public administration require effective personnel management on a country or city scale, which makes it necessary to implement effective technological solutions. With this in mind, today's workforce data from many sources provides an opportunity to manage civil servants with an evidence-based HR policy. Thus, governments are increasingly able to recruit, deploy, train, motivate, and retain their employees scientifically and analytically based on objective HR data. As a result of the analysis, the authors found that most Western countries collect HR data to analyze, interpret and actively use it to make better management decisions, as well as to develop and implement HR policies. The purpose of the article is to highlight the problem of digitalization of HR processes in government agencies and to demonstrate the analysis of HR data from Western countries in tabular forms. The article demonstrates the results of implementing information collection of HR data for effective HR management on a country or city scale. The authors analyze and demonstrate in infographics the growing possibility of collecting human resources data as a basis for improving public services, service delivery and continuous performance management. The authors analyze and present in the infographic data from surveys of civil service employees in Western countries, information on employee engagement and satisfaction with work and the working environment in government agencies.

Satellite A-DInSAR pattern recognition for seismic vulnerability mapping at city scale: insights from the L’Aquila (Italy) case study

ABSTRACT L’Aquila downtown (Central Italy) is situated in a highly seismic region, making it susceptible to numerous historical and recent earthquakes. Among these, the earthquake of Mw 6.3 on 6 April 2009, and the one of Mw 6.7 on 2 February 1703, caused severe damage or complete destruction of the majority of buildings in the historical center. An integrated statistical analysis of A-DInSAR and seismic related building damage data is illustrated. By comparing the seismic damage maps from the 2009 and 1703 earthquakes with the A-DInSAR map produced with Cosmo-SkyMed descending orbit images (acquired between 2010 and 2021), a correlation between post-seismic deformations (in terms of average velocity) and building damage intensity has been identified. Furthermore, ground and building velocities have been separately examined, in order to evaluate the impact of building features and reconstruction efforts on ground deformations. The geostatistical analysis revealed a widespread subsidence motion (until −2 mm/year) across the whole study area. Notably, neighboring points did not exhibit consistent deformation velocities, indicating a lack of spatial correlation. Additionally, Cluster Analysis has allowed recognition of recurring subsidence/uplift trends, which, in terms of shape of curve displacement vs. time, appears independent on building damage intensity or reconstruction interventions. Our results pave the way for a novel utilization of long-term series of satellite SAR data in high-risk seismic zones, serving as a valuable tool to map the most susceptible areas and mitigate seismic risk.

Assessment of regional digital divide in Türkiye

Technological advancement has not been equally distributed. It has differed from country to country, region to region, and even among individuals. As a result, the digital divide has emerged as an umbrella term to represent these disparities. Recently, the research focus has shifted to the outcomes of information and communication technology usage or tangible benefits (the third-level digital divide). As an emerging economy, Türkiye must overcome the digital divide to maximise tangible benefits. Thus, this research starts by determining the digital divide indicators for Türkiye and goes further to examine the digital divide between regions in Türkiye. The main aim is to present a comprehensive index for the regional scale that is currently missing in the literature. To do so, this research starts with exploring the digital divide indicators. Then, with the help of principal component analysis (PCA), a new index is formulated for Türkiye. The result maps indicate digital inequalities both at regional and city scales, yet inequalities are more remarkable at the city scale. Increasing the diversity of technology usage, focusing on gender equality, expanding R&D expenditures, and supporting initiatives, especially ICT initiatives, will assist Türkiye in reducing digital inequalities.

Urban park attributes as predictors for the diversity and composition of spontaneous plants − A case in Beijing, China

Urban biodiversity homogenization and native species habitat loss are becoming increasingly severe. The overuse of lawn grasses and cultivars in green spaces is one of the most important drivers. Scholars have asserted that spontaneous vegetation provides various ecological benefits and should be widely incorporated in planting design. Factors influencing spontaneous plant diversity have mainly been studied in specific habitats or at the city scale, and findings surrounding the attributes of urban parks that support the diversity of spontaneous plants are lacking. This study aimed to provide a perspective on conserving spontaneous plants through inferring the urban park attributes associated with plant diversity. Twenty-two urban park samples in Beijing were examined to identify the correlation between various park variables and spontaneous vegetation. This study recorded 199 spontaneous plant species, with large historic parks containing the most number of species. Park size was a remarkable predictive variable for the diversity of spontaneous plants. Species richness and diversity in large parks were significantly higher than in small parks; not only in total richness due to the species–area relationship but also in richness and diversity at the community level independent of plot number. Microhabitat heterogeneity and the percentage of green area had the strongest positive association with the richness of spontaneous plants. In contrast, the maintenance intensity, perimeter–area ratio, and fragmentation of green area, deciduous forest, and open lawn patches had negative associations with spontaneous plant richness. Redundancy analysis demonstrated that park area, maintenance intensity, microhabitat heterogeneity, and deciduous forest patches were the most remarkable variables predicting species composition. The correlations of variables related to water and open lawn patches with spontaneous plant diversity were more evident in summer. The predicted variables were also more significantly correlated with the diversity of exotic species in summer than in spring. Perennial herbs were more sensitive to the evaluated park attributes. This study elucidates how spontaneous plants are associated with the attributes of urban parks in Beijing, with critical implications for other large cities.

Modelling Radiative Coolers for the Built Environment in the Urban Context

AbstractRadiative Coolers (RCs) represent an emerging technology that has the potential to significantly reduce urban heat and improve indoor/outdoor thermal comfort in the built environment. Broadband and Selective Radiative Coolers (BRCs and SRCs, respectively) are the two main types of RCs that have been proposed for application in the built environment. Being both typically characterized by high solar reflectance, the former emits thermal radiation within the overall infrared spectrum, whilst the latter emits thermal radiation mainly within the Atmospheric Window (AW) waverange. A variety of both types of RCs have been developed and tested in both in‐lab and in‐field experimental campaigns. Yet, all experiments comprise small scale specimens that do not represent real‐life scale components of the built environment. In addition, no meso scale assessments have been performed with respect to the RCs' performance at a city scale. Here, for the first time, the thermo‐optical performance of both BRCs and SRCs is introduced and investigated in a multilayer Urban Canopy Model (UCM) coupled with extended Weather Research and Forecasting model (WRF) and we simulated 20 different scenarios representing the vast majority of urban layouts. The outcomes show that both types of RCs maintain lower surface temperature and air temperature at 2 m height inside the canyon, compared to conventional roofs. In addition, a city scale application of RCs has been found capable of decreasing ambient temperature up to 1.6°C as potentially experienced by pedestrians.

A novel index for assessing the rural population hollowing at fine spatial resolutions based on Tencent social media big data: A case study in Guangdong Province, China

Evaluating the rural population hollowing (RPH) caused by the massive out-migration of rural laborers is essential for China’s rural land use policy formulation and rural revitalization. Existing studies mainly assessed the RPH based on survey data or statistical data. The former is mainly based on selected local villages and towns, and cannot be replicated on a large scale, while the latter is usually at the county or city scale and cannot reveal the cyclical migration of rural labors at fine spatial resolutions such as townships. To fill this gap, this paper proposed a social media data based-rural population hollowing (SM-RPH) index, which was calculated by Tencent User Density big data on holidays and non-holidays and was used to characterize the "migratory bird" process of rural laborers. Taking Guangdong Province, China as the case, this study employed the proposed novel index to evaluate the RPH at the county and township scales. This was integrated with a GWR model to explore how the important contributing factors of RPH vary across the province. The results showed a trend of transition from the cold spot areas in the core regions of PRD to the hot spot areas around the PRD. Additionally, this study revealed that distance to the nearest prefecture-level city, total nighttime lighting per capita, fiscal expenditure per unit of GDP and road network density were dominant factors in affecting rural population hollowing, and the influence of these four variables vary considerably throughout the province. Further, k-means cluster was used to group the hollowing towns according to the local coefficients of the influencing factors, and policy zoning was suggested to provide differentiated countermeasures and suggestions for rural revitalization. This methodological framework can be easily applied to other regions, and is expected to be used to evaluate the return characteristics of migrant workers in real time to help monitor the progress of rural revitalization in China and provide decision support for policy makers.

City Scale Traffic Monitoring Using WorldView Satellite Imagery and Deep Learning: A Case Study of Barcelona

Accurate traffic data is crucial for a range of different applications such as quantifying vehicle emissions, and transportation planning and management. However, the availability of traffic data is geographically fragmented and is rarely held in an accessible form. Therefore, there is an urgent need for a common approach to developing large urban traffic data sets. Utilising satellite data to estimate traffic data offers a cost-effective and standardized alternative to ground-based traffic monitoring. This study used high-resolution satellite imagery (WorldView-2 and 3) and Deep Learning (DL) to identify vehicles, road by road, in Barcelona (2017–2019). The You Only Look Once (YOLOv3) object detection model was trained and model accuracy was investigated via parameters such as training data set specific anchor boxes, network resolution, image colour band composition and input image size. The best performing vehicle detection model configuration had a precision (proportion of positive detections that were correct) of 0.69 and a recall (proportion of objects in the image correctly identified) of 0.79. We demonstrated that high-resolution satellite imagery and object detection models can be utilised to identify vehicles at a city scale. However, the approach highlights challenges relating to identifying vehicles on narrow roads, in shadow, under vegetation, and obstructed by buildings. This is the first time that DL has been used to identify vehicles at a city scale and demonstrates the possibility of applying these methods to cities globally where data are often unavailable.

Multi-scale analysis of urbanization and gross primary productivity during 2000–2018 in Beijing, China

Urban vegetation experiences multiple natural and human impacts during urbanization, including land conversion, local environmental factors, and human management, which may bring positive or negative impacts on vegetation gross primary productivity (GPP) at multiple scales. In this study, we analyzed the spatial-temporal changes of GPP and three urbanization factors: land urbanization (impervious surface coverage), population urbanization (Population), and economic urbanization Gross domestic product (GDP) at city-district-grid scales in Beijing during 2000–2018. Overall, both GPP and three urbanization factors showed an increased trend. The relationships between GPP and urbanization factors exhibit diverse characteristics at multiple scales: unlike the linear relationship observed at city scale, the relationships at district and grid scales all demonstrated nonlinear relationship, even a U shape between GPP and population/GDP. Furthermore, the positive impact of urbanization on GPP increased and offset the negative impact of land conversion from 9.9% in 2000 to 35% in 2018, indicating that urban management and climate during urbanization effectively promote vegetation photosynthesis and neutralize the negative impact of urban area expansion. Our findings highlight the increased growth offset by urbanization on vegetation and the importance of analysis at a finer scale. Understanding these urbanization types’ impact on vegetation is pivotal in formulating comprehensive strategies that foster sustainable urban development and preserve ecological balance.

Identification of Pollution Sources in Urban Wind Environments Using the Regularized Residual Method

The scale of cities is increasing with continuous urban development. Effective methods, such as the source term estimation (STE) method, must be established for identifying the sources of air pollution in cities to prevent economic losses and casualties caused by pollutant leakage. Herein, methods for optimizing sensor configuration and identifying pollution sources are discussed, and an STE method based on the regularized minimum residual method is proposed. Urban wind environments were simulated using a computational fluid dynamics (CFD) model, and the results were compared with experimental data pertaining to the wind tunnel of an architectural ensemble to verify the model’s accuracy. The sensor layout was optimized using the simulated annealing (SA) algorithm and adjoint entropy, and the relationship between sensor responses and potential pollution sources was established using the CFD model. Pollutant concentrations measured using sensors were combined with the regularization method to extrapolate the pollution source strength, and the regularized minimum residual method was used to obtain the locations of the real pollution sources. The results show that compared with the Bayesian methods, the proposed method can more accurately identify pollution sources (100%), with a smaller source strength error of 2.01% for constant sources and one of 2.62% for attenuation sources.

An Integrated Approach for 3D Solar Potential Assessment at the City Scale

The use of solar energy has shown the fastest global growth of all renewable energy sources. Efforts towards careful evaluation are required to select optimal locations for the installation of photovoltaics (PV) because their effectiveness is strongly reliant on exposure to solar irradiation. Assessing the shadows cast by nearby buildings and vegetation is essential, especially at the city scale. Due to urban complexity, conventional methods using Digital Surface Models (DSM) overestimate solar irradiation in dense urban environments. To provide further insights into this dilemma, a new modeling technique was developed for integrated 3D city modeling and solar potential assessment on building roofs using light detection and ranging (LiDAR) data. The methodology used hotspot analysis to validate the workflow in both site and without-site contexts (e.g., trees that shield small buildings). Field testing was conducted, covering a total area of 4975 square miles and 10,489 existing buildings. The results demonstrate a considerable impact of large, dense trees on the solar irradiation received by smaller buildings. Considering the site’s context, a mean annual solar estimate of 99.97 kWh/m2/year was determined. Without considering the site context, this value increased by 9.3% (as a percentage of total rooftops) to 109.17 kWh/m2/year, with a peak in July and troughs in December and January. The study suggests that both factors have a substantial impact on solar potential estimations, emphasizing the importance of carefully considering the shadowing effect during PV panel installation. The research findings reveal that 1517 buildings in the downtown area of Austin have high estimated radiation ranging from 4.7 to 6.9 kWh/m2/day, providing valuable insights for the identification of optimal locations highly suitable for PV installation. Additionally, this methodology can be generalized to other cities, addressing the broader demand for renewable energy solutions.

Influence of Natural and Social Economic Factors on Landscape Pattern Indices—The Case of the Yellow River Basin in Henan Province

The Yellow River Basin holds significance as a vital ecological shield and economic hub within China. Adapting land utilization practices and optimizing landscape patterns are of paramount significance in preserving the ecological equilibrium of the Yellow River Basin while fostering high-quality economic development. In this study, we selected the Yellow River Basin in Henan Province as our research area. We use a land use transition matrix and FRAGSTATS 4.2 software to analyze changes in land use and landscape patterns within the study area from 1990 to 2020. Furthermore, Geographical Detector is employed to explore the impact of different natural and social economic factors that have influenced the progress of the landscape surface pattern in the study area. Finally, to identify the zonal aggregation effects of primary components in connection with landscaping feature indices at the city dimension, we use bivariate local spatial autocorrelation. The results are as follows: (1) In terms of land use change characteristics, the area of cultivated land, grassland, shrubs, and bare land shows a decreasing tendency, the area of construction land and forest land shows an increasing tendency, and the water area fluctuates and changes. Most of the cultivated land is shifted to construction land, followed by forest land, construction land, and cultivated land mainly transferred from grassland. (2) At the level of type in terms of shifting landscape patterns, cultivated land, forest land, water, and construction land have a more complex landscape shape, reduced fragmentation, and better natural connectivity. At the overall level, the overall landscape pattern indices are relatively stable, with more patch types and a more balanced distribution. (3) The findings regarding influencing factors reveal that the primary industry output value, population, secondary industry output value, and temperature are the principal driving forces behind the progress of the landscape surface pattern. The main drivers have changed over time in different regions. As indicated by the findings from bivariate local spatial autocorrelation analysis, at the city scale, the leading cause of landscape fragmentation in Luoyang is the primary industry output value, while in Xinxiang, landscape fragmentation is primarily driven by the secondary industry output value and temperature. In this study, we introduce the bivariate local spatial autocorrelation method to analyze the clustering effects of key influencing factors and landscape patterns at the city scale. This is crucial for the harmonized growth of land use planning and the urban economy in the Yellow River Basin.

District-scale surface temperatures generated from high-resolution longitudinal thermal infrared images

This paper describes a dataset collected by infrared thermography, a non-contact, non-intrusive technique to acquire data and analyze the built environment in various aspects. While most studies focus on the city and building scales, an observatory installed on a rooftop provides high temporal and spatial resolution observations with dynamic interactions on the district scale. The rooftop infrared thermography observatory with a multi-modal platform capable of assessing a wide range of dynamic processes in urban systems was deployed in Singapore. It was placed on the top of two buildings that overlook the outdoor context of the National University of Singapore campus. The platform collects remote sensing data from tropical areas on a temporal scale, allowing users to determine the temperature trend of individual features such as buildings, roads, and vegetation. The dataset includes 1,365,921 thermal images collected on average at approximately 10-second intervals from two locations during ten months.

URBAN CATALYZE IN RESIDENTIAL AREAS IN BAGHDAD CITY

Urban Catalyze is the process that catalyzed the largest scale in cities by introducing appropriate variables as well as encouraging the continuous and progressive reform and development of the urban fabric. Sustainable Urban Catalyze is a strategy that aims to promote urban development in a sustainable and balanced manner between current and future needs, so this approach includes improving the sustainability of cities in general and residential areas in particular through the development of green infrastructure and placemaking, as well as enhancing community participation and improving the quality of life for residents. The research diagnosed its research problem in a lack of knowledge on “role of sustainable urban catalyzes in the development of residential areas in the city of Baghdad”. The research aims to clarify the characteristics, patterns, levels and indicators of sustainable urban catalyze that may contribute positively to catalyzed residential areas to be more efficient in achieving the requirements of their occupants. Its hypothesis was that “the characteristics of sustainable urban catalyze have a positive dimension in catalyzing and developing sustainable urban design in the residential area”. The research finds that the characteristics of sustainable urban catalyze in residential areas can be achieved by adopting its indicators (resource use efficiency, biodiversity and environment, sustainable transport, sustainable economy, sustainable social and cultural life, and finally sustainable urban management), as these indicators contribute to achieving a balance between the economic, social and environmental dimensions of sustainable urban development of the residential area, so that cities and residential areas are built that combine economic, social, and environmental aspects, which enhance their sustainability and attractiveness as place to live and work