City Clusters Research Articles

Etnomatematika “Tabut Bengkulu” Terhadap Kemampuan Berpikir Kritis Siswa Kelas IV SDN Gugus 10 Kota Bengkulu

This study aimed to determine the effect of the application of the model-based Discovery Learning etnomatematika toward critical thinking ability in mathematics of fourth grade students of SDN Force 10 Bengkulu City. This research is quantitative. The method used is quasy experiment with the research design matching only pretest-posttest control group design. The sampling technique using cluster random sampling. The population in this study is that SDN Force 10 fourth grade students at SDN 58 for grade control and fourth grade students at SDN 38 for the experimental class. The instrument used in this study is the critical thinking skills test sheet in the form of a description, amounting to 5 questions. Making the test twice, ie before and after the learning took place in the experimental class and control. Based on the calculation of the t-test posttest results of students, where thitung =2,10 and ttabel = 2.00. Because thitung > tabel, show that there are differences in resultsposttest between the experimental class and control class. The conclusion of this studythere is a significant effect of useetnomatematika discovery learning models based on critical thinking skills in mathematicsstudents of SDN 10 Bengkulu City Cluster.

Mining and analysis of public sentiment during disaster events: The extreme rainstorm disaster in megacities of China in 2021

Cities are concentrated areas of population that are vulnerable to the impact of natural disasters. Owing to the impact of climate change and extreme weather incidents in recent years, many cities worldwide have been affected by sudden disasters, especially floods, causing many casualties. Social media plays an important role in the communication and sharing of information when physical communication is limited in emergency situations. However, obtaining and using public sentiment during major disasters to provide support for emergency disaster relief is a popular research topic. In the summer of 2021, China's inland plains experienced extremely serious rainstorms. The rainfall on July 20 in the capital city of Zhengzhou, Henan Province, the most population province in China, reached 201.9 mm/h, causing extremely serious consequences. This case study examines people's sentiment about this event through datamining of Chinese Weibo social media during the extreme rainfall period. The six most concerned types of public response topics and 14 subcategory topics were determined from 2,124,162 Weibo messages. “Asking for help” and “public sentiment” dominated the main topics, reaching almost 66%, with a relatively even distribution of secondary categories, but with “appeal for assistance” taking the top spot. Topics changed cyclically with work and rest, but these areas seemed to lag behind coastal areas in their responses to the storm in the same time. The topics were centred around Zhengzhou and distributed in China's major city clusters, such as the Beijing-Tianjin-Hebei agglomerations, Yangtze River Delta, and Pearl River Delta regions. Community-level disaster relief information was also discovered, which showed that high building power outages, basement flooding, tunnel trapping, and drinking water shortages were common topics in specific inner urban regions. This detailed information will contribute to accurate location-based relief in the future. Based on this lesson, a series of measures for urban flood reduction are proposed, including disaster prevention awareness, infrastructure building, regulation mechanisms, social inclusivity, and media dissemination.

Research on the socioeconomic factors that influence the development of voluntary, non-remunerated blood donation in China-A correlation and regression analysis based on data from 2012 to 2018.

Previous research has shown that socioeconomic factors, such as income and education, are associated with blood donation behavior. This study aims to investigate the factors that influence blood donation behavior using a large database of blood donation records and to provide further empirical evidence and insights into the factors that influence blood donation behavior and to identify potential strategies to increase blood donation rates in China. This study employed correlation analysis and regression analysis to investigate the impact of socioeconomic factors on blood donation rates in 36 major cities in China. The study also used the K-Means clustering algorithm to identify the socioeconomic factors that influence blood donation behavior at the per capita level. Additionally, the study conducted correlation analysis between the donor's age groups and the socioeconomic factors. The indices of economy and education were determined by correlation analysis. At the city level, overall economic output and university student populations were positively correlated with total blood donation times. However, at the per capita level, this linear relationship disappeared. By applying the K-Means clustering algorithm, the cities were divided into subgroups where the linear relationship reemerged. The study also found that the number of college students had a linear relationship with blood donors between the ages of 18-34. City characteristics and demographics significantly impact blood donation rates in different ways. A universal policy may have limited effect given the heterogeneity across cities. Tailored interventions targeting specific city clusters and education levels are needed to promote blood donation behavior, especially among youth and students. Investment in education and targeted education on blood donation appear particularly impactful. A combined approach integrating social and economic policies will likely be most effective.

The impacts of Brewer-Dobson and Hadley circulation on tropospheric ozone variations over three city clusters in China

The positive trend of tropospheric column ozone (TCO) in China has been confirmed by numerous observations, particularly in the rapidly developing city clusters such as Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD) and the Pearl River Delta (PRD). An unignorable but poorly known function may be played by the Brewer-Dobson circulation (BDC) and the Hadley circulation (HC), both of which are in their strengthening phase. This study discusses their connection to TCO in these three city clusters based on a novel and powerful causal analysis method Convergent Cross Mapping (CCM). It revealed that both BDC and HC exerted substantial causal effects on the variations of the TCO in BTH, the YRD, and the PRD. The upwelling moving northward in the summer substantially lowered TCO in the PRD, contributing to the difference in the TCO annual cycle characteristics between northern and southern China. Besides, CCM distinguished that hemispheric HC impacts TCO greatly more than total HC, and the most essential branches of BDC were stratospheric or shallow branches. The BDC and HC indices determined by CCM were used for building a multiple linear regression model, which assessed their importance for long-term changes in regional TCO. The intensity of BDC is the most important influencing factor in each region, particularly in the YRD. BTH is mainly affected by the intensity of the two meridian circulations. The influence of the widths of meridional circulation upwellings on TCO in the PRD cannot be ignored. Furthermore, TCO displayed significant positive trends during 2005–2020, with rates of 2.3 ± 0.4, 2.5 ± 0.4, and 2.7 ± 0.5 DU/decade in BTH, the YRD, and the PRD, respectively.

Study on Spatial and Temporal Characteristics and Influencing Factors of Carbon Emissions in the Urban Agglomeration of the Middle Reaches of the Yangtze River

The industrial transfer of heavy industries such as non-metallic mineral manufacturing, metal smelting and manufacturing from the eastern coast of China to the central region is beneficial to the economic development of the central region on the one hand, but increases carbon emissions in the central region on the other hand. In February 2022, the National Development and Reform Commission approved the “14th Five-Year Plan for the Development of the Urban Agglomeration in the Middle Reaches of the Yangtze River”. This indicates that the urban agglomeration of the middle reaches of the Yangtze River is an important region for implementing green development in the central area. The spatial and temporal evolution of carbon emissions and influencing factors in this region are the foundation for achieving carbon peaking and the carbon neutrality goal. This paper calculates the total carbon emissions of the cities in the urban agglomeration of the middle reaches of the Yangtze River and uses models such as spatial autocorrelation, geographically weighted regression, and Geodetector to explore the spatial–temporal pattern of carbon emissions. The results show the following: (1) The total carbon emissions of the middle reaches of the Yangtze River urban agglomeration showed fluctuations during 2010–2020, and the carbon emission reduction effect is unstable. Additionally, the carbon emissions of the middle reaches of the Yangtze River city cluster show obvious spatial variability, but the high carbon emission area is always concentrated in Wuhan, and this remains unchanged. (2) In 2010, 2014 and 2017, population size was the most important factor affecting carbon emission divergence, and in terms of interaction, the interaction between energy intensity and GDP and urbanization is the reason for the increasing carbon emissions. (3) The influence of population size on carbon emissions decreases from north to south, the influence of energy intensity on carbon emissions shows a spread from the most influential region in the northwest to the centre and then to the northeast, and the GDP per capita has little influence on the difference of carbon emissions spatial distribution.

Assessing Transboundary Impacts of Energy-Driven Water Footprint on Scarce Water Resources in China: Catchments under Stress and Mitigation Options.

The energy supply chains operating beyond a region's jurisdiction can exert pressure on the availability of water resources in the local area. In China, however, there is a lack of transboundary assessments that investigate the effects of energy consumption on water stress within and across river basins. In this study, we therefore investigate transboundary impacts on scarce water resources that are induced by energy demands (i.e., electricity, petroleum, coal mining, oil and gas extraction, and gas production). We develop a bottom-up high spatial resolution water inventory and link it to a 2017 multiregional input-output (MRIO) table of China to analyze supply chain scarce water use at provincial and river basin levels. We find that the energy-driven water footprint accounts for 21.6% of national water usage, of which 35.7% is scarce water. Nonelectric power energy sectors contribute to around half of the nation's scarce water transfer. We identify three sets of catchments whose water resources are stressed by energy demand, i.e., (a) from the northern Hai River Basin to the eastern part of the Yellow River Basin and the Huai River Basin, (b) the northern area of the Northwest Rivers, and (c) the developed coastal city clusters in the Yangtze River Basin and the Pearl River Basin. We then evaluate the impacts of eight mitigation options, which may potentially shift around half of the moderate- or high-stress areas in the Hai River Basin and the Northwest Rivers to low to moderate (or even low) stress. We highlight the need for transboundary collaboration to sustain water-constrained energy demand and to develop targeted measures to mitigate stress on water resources within a river basin.

Monitoring Urban Expansion (2000–2020) in Yangtze River Delta Using Time-Series Nighttime Light Data and MODIS NDVI

The Yangtze River Delta Urban Agglomeration (YRDUA), which is located in the convergence zone of “The Belt and Road Initiative”, is one of the regions with the best urbanization foundations in China. Referring to the four five-year plans (China’s national economic plan), this study aimed to investigate the spatiotemporal patterns of urban expansion in the YRDUA from 2000 to 2020. To conduct a long-term analysis of urbanization, an extended time series (2000–2020) of a nighttime light (NTL) dataset was built from the multi-temporal Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) data (2000–2013), and Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) data (2014–2020); data from these sources are crucial to understanding the urbanization processes in the region in order for more effective decision making to take place. The support vector machine (SVM) method was used to extract urban clusters from the extended time-series NTL data and MODIS NDVI products. The evolution of the urban expansion intensity was detected at city scales, and the inequality of urban growth was demonstrated using the Lorenz curve and Gini coefficient. Finally, a quantitative relationship between urban NTL intensity and socio-economic data was built to explore the main factors that control urban intensity. The results indicated that the urban extents extracted from time-series NTL data were consistent with those extracted from Landsat data, with an average overall accuracy (OA) of 89%. A relatively fast urbanization pace was observed during the 10th five-year plan (from 2000 to 2005), which then declined slightly in the 11th five-year plan (from 2006 to 2010). By the 12th and 13th five-year plan (from 2011 to 2020), urban clusters in all cities tended to grow steadily. Urban expansion has presented a radial pattern around the main cities, with sprawl inequality across cities. The results further revealed that the primary factors controlling NTL brightness were gross domestic product (GDP), total fixed asset investment, tertiary industry, gross industrial output, urban area, and urban permanent residents in city clusters, but the same driving factors had a different contribution order on the NTL intensity across cities. This study provides significant insight for further urbanization study to be conducted in the YRDUA region, which is crucial for sustainable urban development in the region.

Water Cycle Health Assessment Using the Combined Weights and Relative Preference Relationship VIKOR Model: A Case Study in the Zheng-Bian-Luo Region, Henan Province

Both the natural and social water cycles form part of the regional water cycle, and the assessment of the health of the social water cycle provides useful recommendations for resource allocation, urban planning, and development. The Zheng-Bian-Luo region (Zhengzhou, Kaifeng, Luoyang city cluster in China) is used as an example in this study. The three-level “goal criterion index” is used to develop a water cycle index system based on deeper knowledge of the notion of the social water cycle. The system has four criterion layers that measure water quantity, utility, quality, and ecology, in addition to 22 index levels regarding the total water resources and drinking water compliance rate. By using this as a foundation, the minimum information entropy principle was applied to couple AHP (Analytic Hierarchy Process) and EFAST (Extended Fourier Amplitude Sensitivity Analysis) in order to calculate the comprehensive weights of the evaluation indicators and build a VIKOR (Intuitionistic Fuzzy Multi-attribute Decision Making Method) model of the relative preference relationship of the fused weights. This model was then compared to the conventional VIKOR model and the FCE (Fuzzy Comprehensive Evaluation Method) method in order to reflect on the objectivity of the evaluation results. The primary barriers preventing the improvement of water cycle health in the Zheng-Bian-Luo region were determined in this study by using the barrier degree model. The findings demonstrate that over the past 11 years, the overall water cycle health in the Zheng-Bian-Luo region has developed toward a healthy trend and that the water cycle health level in the region has gradually improved from the initial sub-pathological state to a healthy state. The results also demonstrate compliance with domestic drinking water sources, comprehensive water consumption per capita, the water consumption of CNY 10,000 of industrial-added value, the water consumption of CNY 10,000 of GDP, and the water consumption of CNY 10,000 for water. The primary barrier to the Zheng-Bian-Luo region’s improvement in water health is the water consumption ratio. The findings of this study can serve as a scientific foundation for creating a balanced urban water cycle and achieving long-term development in the area.

Study on the Spatiotemporal Evolution Characteristics and Influencing Factors on Green Building Development of City Clusters in the Yangtze River Delta Region in China

This paper explores the spatiotemporal evolution characteristics and spatial correlation patterns of green building development differences in 41 cities in the Yangtze River Delta region from 2012 to 2020 by means of the gravity center analysis model and spatial autocorrelation analysis. In addition, it further clarifies the impact factors of the spatial differentiation pattern of green building development in combination with GeoDetector based on four dimensional factors of population and economy, market environment, policy, and other factors. The results showed that: (1) According to the analysis of the number of green buildings in each city from 2012 to 2020 and the natural discontinuity method, the development pattern of green buildings in the Yangtze River Delta region city clusters shows an imbalance, being highly concentrated in the eastern coastal areas with Suzhou (1) and Shanghai as the core. The overall trajectory of the center of gravity shows the development from southeast to northwest. (2) The global Moran’s I of green buildings in the Yangtze River Delta region city clusters is greater than 0, and all passed the significance test (Z > 1.96, p < 0.01), indicating that the green buildings in the Yangtze River Delta region city clusters show typical spatial aggregation characteristics. By using the local LISA index, it is found that in the H-H spatial autocorrelation distribution pattern with Suzhou (1) and Shanghai as the core, the core city has a strong attraction ability and relatively low radiation ability. (3) Based on the explanatory power mean, the main driving factors of the spatial differentiation pattern of green building development in the Yangtze River Delta region city clusters are education level (0.6656), technical level (0.6269), and the gross domestic product (0.6091). The factor interaction shows a two-factor enhancement and nonlinear enhancement effect, and there is neither a weakening nor an independent relationship.

Housing networks in urban China: A panel VAR model with Bayesian stochastic search

This paper analyzes housing networks in China established through the inter-city information spillover. Unlike conventional methods that either fail to identify the city-level linkage or impose arbitrary restrictions, we investigate both the direction and magnitude of cross-city housing spillover in a data-driven manner by employing a panel VAR framework with Bayesian stochastic search. Through this, significance of the pair-linkages is examined from both perspectives of return and shock with insignificant ones being detected. Using a monthly dataset of housing prices covering 70 major China's cities over 2005–2021, our results confirm the presence of positive housing spillover that 11.68 % and 12.92 % of the total city-paired linkages are found to exist in terms of return and shock, respectively. With a particular focus on China's key urban agglomerations, housing spillovers exist not only across city-tiers in forms of ‘top-down’ and ‘bottom-up’ but also within the same tier in a ‘peer learning’ pattern, and the spillover magnitude of the latter is shown to be greater than that of the former. Despite the above common pattern, housing networks also demonstrate heterogeneous features across city clusters. Additional analyses reassure robustness of our findings that possess implications for clear comprehension of regional housing price dynamics.

What Type of Energy Structure Improves Eco-Efficiency? A Study Based on Statistical Data of 285 Prefecture-Level Entities in China

Increasing environmental pollution, resource depletion, and climate change have led to policymakers paying increased attention to the environmental and ecological impacts of economic activities. To establish which type of energy structure is most conducive to improving eco-efficiency, we use the super-efficiency data envelopment analysis (DEA) model to quantify the relationship between the two, based on the panel data of 285 prefecture-level cities in China from 2005 to 2016. The heterogeneity and spatial spillover effect on different types of cities are further discussed. Our findings suggest that energy structure optimization by reducing the proportion of coal energy is beneficial to improving ecological efficiency. However, the effect is nonlinear, showing an inverted U-shaped nonlinear change. The influence of energy structure optimization on ecological efficiency has a stronger effect on its improvement of resource-based and old industrial cities. Moreover, it has an obvious “local–neighborhood” spatial spillover effect. Additionally, the energy structure could be improved according to local conditions in different regions, such as the level of economic development, industrial structure, and resource endowment conditions. Furthermore, regional cooperation and coordination should be strengthened and consolidated, along with the positive spatial effects of high eco-efficiency cities. Especially in city clusters and metropolitan areas, the strengthening of cross-city cooperation in emission trading, environmental governance, and compensation is vital.

Spatial association network of economic resilience and its influencing factors: evidence from 31 Chinese provinces

The spatial correlation pattern of economic resilience is an important proposition for China’s sustainable economic development. This paper measures the economic resilience of 31 provinces in China from 2012 to 2020, and explores the spatial correlation of economic resilience from the overall, group and individual perspectives and its influencing factors. The results show that first, a tightly ordered hierarchy of economic resilience formed in each province of China after 2016. Among them, Jiangsu, Shandong, Guangdong, Hubei, and Shaanxi are the most important clustering points and radiation centers in the spatial correlation framework of economic resilience. Second, being adjacent to marginal and core provinces will maintain the province’s centrality index category to the greatest extent, while being adjacent to sub-core and general provinces leads the province to gain more opportunities for upward transfer. Third, the essence of the interprovincial economic resilience subordination linkage in China is manifested in the aggregation of city clusters or economic circles. The northern economic resilience linkage system with the Bohai Rim as the core contains more provinces but is less stable. Provinces located in the Yangtze River Delta region are the opposite. Fourth, the proximity of geographical location and the difference in human capital level drive the formation of spatial association networks, while the difference in external openness and the difference in physical capital inhibit the formation of networks.

Application of Artificial Light in the Interior Design of Green Lake City Cluster East Asia Residential House in Tangerang

The current residence is a primary need for humans, lighting is one of the requirements for humans to carry 
 out an activity, therefore lighting is also needed that supports human needs in living activity. In human 
 activities that require lighting, lighting itself is divided into two types, namely natural lighting and artificial 
 lighting. Natural lighting is lighting that comes from the sun as a source of light, while artificial lighting is 
 lighting that is produced by light sources other than natural light. Along with the times, artificial lighting has 
 various types of lamps, lighting procedures, lighting color display and color temperature. So that artificial 
 lighting is needed in order to achieve the ideal visual view for activities in every room.

Clustering Cities over Features Extracted from Multiple Virtual Sensors Measuring Micro-Level Activity Patterns Allows One to Discriminate Large-Scale City Characteristics.

The impact of micro-level people's activities on urban macro-level indicators is a complex question that has been the subject of much interest among researchers and policymakers. Transportation preferences, consumption habits, communication patterns and other individual-level activities can significantly impact large-scale urban characteristics, such as the potential for innovation generation of the city. Conversely, large-scale urban characteristics can also constrain and determine the activities of their inhabitants. Therefore, understanding the interdependence and mutual reinforcement between micro- and macro-level factors is critical to defining effective public policies. The increasing availability of digital data sources, such as social media and mobile phones, has opened up new opportunities for the quantitative study of this interdependency. This paper aims to detect meaningful city clusters on the basis of a detailed analysis of the spatiotemporal activity patterns for each city. The study is carried out on a worldwide city dataset of spatiotemporal activity patterns obtained from geotagged social media data. Clustering features are obtained from unsupervised topic analyses of activity patterns. Our study compares state-of-the-art clustering models, selecting the model achieving a 2.7% greater Silhouette Score than the next-best model. Three well-separated city clusters are identified. Additionally, the study of the distribution of the City Innovation Index over these three city clusters shows discrimination of low performing from high performing cities relative to innovation. Low performing cities are identified in one well-separated cluster. Therefore, it is possible to correlate micro-scale individual-level activities to large-scale urban characteristics.

Location matters: High-speed railway (HSR) stations in city evolution

This study examines the nexus between high-speed rail (HSR) stations and city evolution from an urban science perspective. We analyzed 1627 HSR stations built from 1976 to 2021 in 533 Chinese and European cities. Node or activity clusters in cities are identified through the street network. Then, the urban forms of Chinese and European cities are compared and the Zipf's law is tested. We found that activity clusters in Europe tend to be more contiguous and clustered, suggesting a clear downtown. In Chinese cities, these clusters are more scattered, suggesting polycentric urban development. When the Zipf's law is applied to the size distribution of activity clusters, the rank-size rule is supported both at the aggregate and country levels. Generally, it takes roughly two decades for an HSR station to be able to mature in attracting related development, such as residence and businesses, to complete the exploration process of city evolution. Finally, contributory factors for the integration of HSR stations with the urban fabrics are examined. Apart from the size of the city, economic power, station features and the spatial pattern of activity clusters, the relative location of the HSR station in relation to the urban form matters. Our study contributes both to the HSR and urban science literature, and has practical implications for locational decisions about HSR stations.

Multiple source apportionments, secondary transformation potential and human exposure of VOCs: A case study in a megacity of China

The Central Plain City Cluster represented by Zhengzhou is one of the most haze-polluted regions, especially for the heating period. As critical precursors of Secondary Organic Aerosol (SOA) and Ozone, the ambient Volatile Organic Compounds (VOCs) emitted from anthropogenic activities have aroused widespread concern. This study provided a revealing insight on the concentration level, chemical composition, multiple source apportionments (SAs), SOA formation potential (SOAp) and exposed health risk of VOCs before and during heating periods at a megacity site. The average concentrations of TVOCs during clear days before heating, pollution days before heating, clear days during heating, and pollution days during heating were 37.81 ± 17.21, 54.48 ± 17.83, 39.27 ± 16.38 and 63.98 ± 22.42 ppbv, respectively. Compared with the before heating period, the concentration of TVOCs increased by 3.9% and 17.4% on clear days and polluted days during the heating period, respectively. Among them, alkanes were the most abundant species of VOCs, followed by halocarbons and alkenes. The multiply SAs methods were applied to identify potential sources, and the contributions of source structures were reasonable and credible by comparing PMF and emission inventory. Vehicular exhaust was the largest contributor during the non-heating period (33.2%), while for the heating period, combustion source contributed the most to VOCs emission (24.2%). In terms of the contribution to SOAp formation, aromatics were the most abundant VOCs species, and combustion (29.4%) was the major source, followed by solvent usage (26.5%) and vehicular exhaust (24.6%). The non-carcinogenic risks of VOCs sources were significantly lower than the acceptable safety threshold, while all the specific VOCs sources posed carcinogenic risks except for biogenic emission. The ways of analysis and assessment in this study can highlight the need for more effective control strategies related to VOCs anthropogenic activities during the heating period in megacities of Central China.

Investigating the causes of O3 pollution in the western rim of sichuan basin, southwestern China

The western rim of the Sichuan Basin (SCB), located between the Qinghai-Tibetan Plateau (QTP) and the Chengdu-Chongqing city cluster, is a mountainous region renowned for its rich biodiversity. Previous atmospheric modeling studies have revealed high O3 exposures for ecosystems in this region. To better understand the levels and causes of O3 pollution at different elevations, we conducted a year-long observation of air pollutants and meteorological conditions at the top and the base of Mount Emei. Results showed that O3 concentrations were higher at the top (Jinding: 3077 m) than at the foot (Leshan: 460 m), likely due to different O3 sources at the sites. O3 at Leshan was mainly generated by O3 photochemical production within the SCB. In contrast, O3 at Jinding was mainly transported from other regions and exhibited higher concentrations in the evening than in the daytime. Five major O3 transport pathways were identified for Jinding, including three in the QTP and its adjacent regions, one in the SCB, and one in the Yunnan-Guizhou Plateau. Furthermore, both sites can be affected by air pollutants from the spring biomass-burning in the southern and southeastern Asia. Although certain O3 exposure indices for ecosystem protection exceeded their empirical critical loads at both sites, others did not. In addition, our results revealed significant discrepancies between the observations and previous modeling results, which might be attributed to the complex topography and coarse horizontal resolution of the model. Further investigations are recommended to understand air pollution-ecosystem interactions in the western rim of the SCB.

Study on Population-Innovation Coordination and Spatial Layout Based on the Context of Aging

As a major area for the docking and integration of China's 21st Century Maritime Silk Road, the innovation-driven development of the Bay Area city cluster can provide support for the continuous enhancement of China's economic innovation and competitiveness. Population vitality is the prerequisite and foundation of the country's innovative development, but China is facing a very serious aging situation. The process of population aging in the Guangdong-Hong Kong-Macao Greater Bay Area has also become an important factor limiting its rapid development. This study intends to analyze the aging population data and urban innovation and other related indices of the Greater Bay Area urban agglomerations through OLS, entropy method and Moran index, and use ArcGIS to visualize the trends, laws and characteristics of urban development, so as to provide scientific and reasonable data reference and support for the upper-level decision of urban planning.

Data-Driven Multi-Scale Study of Historic Urban Environments by Accessing Earth Observation and Non-Destructive Testing Information via an HBIM-Supported Platform

ABSTRACT Digital analytical tools combined with 3D documentation are used incrementally in building rehabilitation in the conservation state analysis process. In the last decade, due to the current advancements in the Architecture Engineering Construction (AEC) industry, the application of BIM methods in heritage building conservation started becoming more attractive for specialists and practitioners. In light of the latest concepts in data management at city level, as a result of the discussion about smart city representations, the use of a shared digital environment that caters to technical studies related to conservation analysis, building provenance, structural changes, and urban context transformations can lead to reduced time, improved quality, and lowered cost of city management for all domain experts and city stakeholders. This paper explores the benefits of multi-scale and discipline digitization for the restoration of heritage buildings, highlighting the potential impact of innovative data integration, methods, and workflows on architectural renovation and energy upgrades. Specifically, it focuses on the integration of conservation information for heritage buildings and large-scale environmental analysis data for historic clusters in modern cities.

Combining LSTM and PLUS Models to Predict Future Urban Land Use and Land Cover Change: A Case in Dongying City, China

Land use is a process that turns a piece of land’s natural ecosystem into an artificial one. The mix of plant and man-made covers on the Earth’s surface is known as land cover. Land use is the primary external force behind change in land cover, and land cover has an impact on how land use is carried out, resulting in a synergistic interaction between the two at the Earth’s surface. In China’s Shandong Peninsula city cluster, Dongying is a significant coastal port city. It serves as the administrative hub for the Yellow River Delta and is situated in Shandong Province, China’s northeast. The changes in its urban land use and land cover in the future are crucial to understanding. This research suggests a prediction approach that combines a patch-generation land use simulation (PLUS) model and long-term short-term memory (LSTM) deep learning algorithm to increase the accuracy of predictions of future land use and land cover. The effectiveness of the new method is demonstrated by the fact that the average inaccuracy of simulating any sort of land use in 2020 is around 5.34%. From 2020 to 2030, 361.41 km2 of construction land is converted to cropland, and 424.11 km2 of cropland is converted to water. The conversion areas between water and unused land and cropland are 211.47 km2 and 148.42 km2, respectively. The area of construction land and cropland will decrease by 8.38% and 3.64%, respectively, while the area of unused land, water, and grassland will increase by 5.53%, 2.44%, and 0.78%, respectively.