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This commentary article explores the impact of ChatGPT on Urban Planning Ph.D. programs within universities in the United States. Our survey reveals a high awareness of Artificial Intelligence tools (e.g. ChatGPT) among faculty yet highlights a cautious approach to integrating ChatGPT into curricula and preliminary exam settings. While some educators recognize ChatGPT's potential to enhance research efficiency and language proficiency, especially for international students, concerns about academic integrity and the over-reliance on AI are prevalent. The commentary underscores the need for a balanced approach, advocating for ChatGPT's use as a supplementary tool while maintaining critical thinking in doctoral preliminary exams. [ABSTRACT FROM AUTHOR]

Digital Twins (DTs) are increasingly recognized for their potential to improve efficiency and decision-making in various domains of the built environment. Despite their promise, challenges like cost, complexity, interoperability, and data integration remain. This paper introduces a novel interactive visual analytics system that tackles these issues, using a case study of simulating class distribution and campus building capacity at a large public university. The system leverages enrollment data, converting it into a spatial-temporal format for interactive exploration and analysis of class distribution and resource utilization. Through case studies, we demonstrate the system's effectiveness, adaptability, and real-world applicability, highlighting its role in practical DT implementation for built environments. [ABSTRACT FROM AUTHOR]

With the rapid development of computer technology, urban informatics, as a new discipline in the field of urban planning, has gradually attracted academic attention. The rise of urban informatics puts new pressures on urban planning, but it also provides a new perspective of analysis. This paper is a summary of a panel discussion among scholars in urban informatics held at the 2020 International Association for China Planning(IACP).In this context, the panel outlines the definition of urban informatics, and the difference between urban informatics and urban analytic and computing, and found that urban informatics pays more attention to end user. This indicates that urban informatics has been more than a supporting role in urban planning or design, and is increasingly integrated with urban planning. The panel also discusses the connotation of urban informatics and its wide application in practice, and illustrates with examples. At the same time, the team identifies the difficulties of its development mainly reflected in the two aspects of resources and talents, and the learners of urban planning discipline have natural advantage in learning urban informatics. Finally, the panel discusses how to improve teaching, and concludes that the promotion of good cases, discipline integration, training data thinking rather than focusing too much on methods and other concepts. All in all, this panel's report contributes to the wider discussion about the role of urban informatics plays in urban planning. [ABSTRACT FROM AUTHOR]

Polymyxin resistance is conferred by MCR‐1 (mobile colistin resistance 1)‐induced lipopolysaccharide (LPS) modification of G− bacteria. However, the peptide MSI‐1 exerts potent antimicrobial activity against mcr‐1‐carrying bacteria. To further investigate the potential role of MCR‐1 in improving bacterial virulence and facilitating immune evasion, and the immunomodulatory effect of peptide MSI‐1, we first explored outer membrane vesicle (OMV) alterations of mcr‐1‐carrying bacteria in the presence and absence of sub‐MIC MSI‐1, and host immune activation during bacterial infection and OMV stimulation. Our results demonstrated that LPS remodelling induced by MCR‐1 negatively affected OMV formation and protein cargo by E. coli. In addition, MCR‐1 diminished LPS‐stimulated pyroptosis but facilitated mitochondrial dysfunction, further aggravating apoptosis in macrophages induced by OMVs of E. coli. Similarly, TLR4‐mediated NF‐κB activation was markedly alleviated once LPS was modified by MCR‐1. However, peptide MSI‐1 at the sub‐MIC level inhibited the expression of MCR‐1, further partly rescuing OMV alteration and attenuation of immune responses in the presence of MCR‐1 during both infection and OMV stimulation, which can be exploited for anti‐infective therapy. [ABSTRACT FROM AUTHOR]

Urban digital twins (UDTs) have been identified as a potential technology to achieve digital transformative positive urban change through landscape architecture and urban planning. However, how this new technology will influence community resilience and adaptation planning is currently unclear. This article: (1) offers a scoping review of existing studies constructing UDTs, (2) identifies challenges and opportunities of UDT technologies for community adaptation planning, and (3) develops a conceptual framework of UDTs for community infrastructure resilience. This article highlights the need for integrating multi-agent interactions, artificial intelligence, and coupled natural–physical–social systems into a human-centered UDTs framework to improve community infrastructure resilience. [ABSTRACT FROM AUTHOR]

In this work, crystal structure, ferromagnetic martensitic transition, and magnetocaloric effect were investigated for Ni35Co15-xVxMn35Ti15 (x = 1, 2, 3) all-d-metal Heusler alloy ribbons. All three samples crystalize pure B2 cubic phase in austenite at room temperature. With increasing V content, the appearance of thermal hysteresis of thermomagnetic curves, together with the negative values of Arrott plots, proves the nature of first-order transition. And normal/reverse martensitic transition temperature (Tt/Tt') monotonously decreases from 236 (248) K (x = 1) to 82 (110) K (x = 3) and the Curie temperature in austenite (TCA) also decreases. Obvious magnetic-field-induced martensitic transition can also be seen with external magnetic field. Maximum magnetic entropy change values are 17.2, 18.7, 9.6 J/kgK with the magnetic field change of 50 kOe, respectively. The refrigerant capacity (RC) and relative cooling power (RCP), as two important parameters to evaluate the refrigeration performance, can reach 194.7 (240.8), 244.3 (300.0), 162.4 (201.6) J/kg with the magnetic field change of 50 kOe, respectively. [ABSTRACT FROM AUTHOR]

This research presents a sophisticated framework for the precise downscaling of population data from census blocks to individual residential units, employing an integration of housing unit characteristics. The aim was to devise and substantiate a thorough methodology for the distribution of households within specific residential buildings. Utilizing the Microsoft Building Footprint dataset, LiDAR remote sensing, and Point of Interest (POI) data, a detailed inventory of residential structures was compiled. A quadratic programming model and Monte Carlo Simulation techniques were applied independently for the strategic allocation of households to these buildings. For validation, this study conducted a comparative analysis between the two methods. The outcomes revealed that the quadratic programming model provided superior precision and detail in population data compared to the Monte Carlo Simulation technique. Consequently, the quadratic programming model significantly enhances the granularity of population distribution data, offering a valuable tool for more informed decision-making. • Development of a framework for accurately downscaling population data to individual residential units. • Integration of housing unit characteristics, Microsoft Building Footprint dataset, LiDAR, and POI data. • Utilization of quadratic programming model and Monte Carlo Simulation for household allocation. • Comparison between the two methods shows the quadratic programming model's superior precision. • Enhancement of population distribution data granularity for more informed decision-making. [ABSTRACT FROM AUTHOR]

ATS interacts with the SPSR via cation exchange. The ATS@SPSR suspension eye drops had a sustained release property and improved stability of ATS in vitro. As a result, ATS@SPSR suspension eye drops prolonged the ocular mean residence time of ATS in the tear fluid and enhanced the bioavailability of ATS in the tear fluid and aqueous humor of New Zealand rabbits. [Display omitted] Atropine sulfate (ATS) eye drops at low concentrations constitute a limited selection for myopia treatment, with challenges such as low ophthalmic bioavailability and inadequate stability. This study proposes a novel strategy by synthesizing ophthalmic sodium polystyrene sulfonate resin (SPSR) characterized by a spherical shape and uniform size for cationic exchange with ATS. The formulation of ATS@SPSR suspension eye drops incorporates xanthan gum and hydroxypropyl methylcellulose (HPMC) as suspending agents. In vitro studies demonstrated that ATS@SPSR suspension eye drops exhibited sustained release characteristics, and tropic acid, its degradation product, remained undetected for 30 days at 40 °C. The ATS levels in the tear fluids and aqueous humor of New Zealand rabbits indicated a significant increase in mean residence time (MRT) and area under the drug concentration–time curve (AUC 0-12 h) for ATS@SPSR suspension eye drops compared to conventional ATS eye drops. Moreover, safety assessment confirmed the non-irritating nature of ATS@SPSR suspension eye drops in rabbit eyes. In conclusion, the cation-responsive sustained-release ATS@SPSR suspension eye drops enhanced the bioavailability and stability of ATS, offering a promising avenue for myopia treatment. [ABSTRACT FROM AUTHOR]

[Display omitted] Controlled release drug delivery systems of eye drops are a promising ophthalmic therapy with advantages of good patient compliance and low irritation. However, the lack of a suitable drug carrier for ophthalmic use limits the development of the aforementioned system. Herein, the crosslinked cyclodextrin organic framework (COF) with a cubic porous structure and a uniform particle size was synthesized and applied to solidify vitamin A palmitate (VAP) by using the solvent-free method. The VAP@COF suspension eye drops were formulated by screening co-solvents, suspending agents, and stabilizing agents to achieve a homogeneous state and improve stability. According to the in vitro release study, the VAP@COF suspension exhibited a controlled release of VAP within 12 h. Both the ex vivo corneal contact angle and in vivo fluorescence tracking indicated that the VAP@COF suspension prolonged the VAP residence time on the ocular surface. This suspension accelerated the recovery of the dry eye disease (DED) model in New Zealand rabbits. Furthermore, the suspension was non-cytotoxic to human corneal epithelial cells and non-irritation to rabbit eyes. In summary, the particulate COF is an eye-acceptable novel carrier that sustains release and prolongs the VAP residence time on the ocular surface for DED treatment. [ABSTRACT FROM AUTHOR]

Despite extensive research on the relationship between tourism and agriculture, the specific impact of tourism on agriculture's low-carbon transition has not been thoroughly investigated. This study analyzes the effects of tourism agglomeration on agricultural carbon intensity across 30 Chinese provinces from 2001 to 2020. It is framed within the context of rural digitalization, with a particular emphasis on the integration of agro-tourism and the total factor productivity of agriculture. Utilizing spatial econometric models, we find that tourism agglomeration hinders the low-carbon transition in agriculture by influencing carbon intensity both directly and indirectly. At the national level, the impact of tourism agglomeration follows an inverted-U curve with respect to agro-tourism integration and carbon intensity. At the regional level, the effects vary, with weaker indirect influences in major grain-producing areas. Furthermore, rural digitalization appears to lessen the adverse impacts of tourism on carbon intensity. This study also identifies significant spatial spillover effects from tourism agglomeration. The findings suggest that provinces with high tourist influx should enhance investments in climate-smart agricultural practices and technologies to counteract these negative impacts. Moreover, integrated governance of tourism and agriculture is essential for achieving carbon neutrality in both sectors. • Tourism agglomeration impedes agricultural decarbonization in China. • Tourism agglomeration has a significant and mixed influence on ACEI. • Agro-tourism integration is crucial to agricultural productivity and ACEI. • Rural digitalization is a mediator between tourism agglomeration and ACEI. • The influence of tourism agglomeration on ACEI has spatial spillover effects. [ABSTRACT FROM AUTHOR]

Coastal communities are increasingly vulnerable due to sea level rise and population growth. Managed retreat is commonly recognized as a strategy that yields multifaced benefits in community adaptation. However, limited studies have explored the cumulative effects of sea level rise, population migration, and managed retreat on the community resilience. This study presents a parcel-level land use change model to analysis land-based flood mitigation strategies in Galveston County, Texas. The developed model integrates a Gradient Boosting Decision Tree with a flood risk model and diverse datasets. Our model results reveal the spatial patterns of urban development in Galveston under different relocation policies and the compounding impacts of sea level rise and population growth. Our findings illustrate that elevating the first floors of buildings can significantly mitigate flood risks and associated relocation costs. The private adaptation measure, together with government-led buyout policies, could foster a shift toward more resilient urban development and yield a more affordable relocation strategy. Our findings emphasize the need for a multidisciplinary approach in building resilient coastal communities, particularly in the face of escalating climate risks in local communities. • We developed a parcel-level land use change model to simulate managed retreat in Galveston, TX. • Sea level rise and population change trends are projected from historical data. • Results show the compounding impacts of sea level rise and population growth under relocation policies. • Our findings illustrate that elevating the first floors of buildings can significantly reduce flood risks and associated relocation costs. [ABSTRACT FROM AUTHOR]

These case studies use relatively smaller datasets than the giant social media and mobile phone data sets we might be used to seeing under the heading of "social spatial networks". A spatial social network (SSN) can be defined as a set of agent-based connections that are embedded in geographic space. Third, the exploratory nature of these works suggests that institutional structures, such as research ethics, pedagogy, and data infrastructure, should be strengthened to maintain a sustainable agenda for spatial social network research. First, historical social networks and historical geospatial data provide a rich view into the humanistic side of mapping and GIScience. [Extracted from the article]

In the waves of the ongoing coronavirus disease 2019 (COVID-19) pandemic, each U.S. state governor has been leading efforts to curb the spread of the virus within his or her state. Twitter has been widely used for crisis communications by governors. Their Twitter usage patterns can largely reflect how state governments responded to the COVID-19 crisis from both geographical and social network perspectives. Through spatial–temporal analysis, network analysis, and text mining, we identified several important usage patterns, such as (1) tweet quantities positively correlated with the pandemic severity; (2) most governors started to mention COVID-19 weeks before the first reported case in their states; (3) COVID-19-related hashtags were commonly used to organize information; (4) feedback was frequently provided by at-mentioning or retweeting other accounts; and (5) governors were networked for crisis communication, driven by demographics, pandemic severities, geographic closeness, cooperation, and party affiliations among states. The current usage patterns are generally consistent with the criteria for effective crisis communications on Twitter (listening, informing, feedback, and connections). Some actionable approaches for governors to improve Twitter crisis communications are also discussed. This exploratory study provides a guide for other agencies and officials to develop future crisis communication plans through leveraging social media for social good. [ABSTRACT FROM AUTHOR]

It is important to reconstruct the hidden network structure from the infection status change of an information propagation process for evidence‐based spatial decision‐making. Unlike previous work, we not only consider the heterogeneity of the propagation agents, but also incorporate the heterogeneity of the text contents of information within the propagation process. In addition, the infection status is no longer restricted to the binary type (infected or not), and we allow the number of pieces of information texts to be counted which represents the degree of infection. The resulting model is a network‐based multivariate recurrent event model, in which the interactions between different types of text, between different agents, between agents and text types, and their mutual impacts on the whole propagation process can be comprehensively investigated. On that basis, a nonparametric mean‐field equation is derived to govern the propagation process, and a compressive sensing algorithm is provided to infer the hidden spatial propagation network from the infection status data. Finally, the proposed methodology is tested through synthetic data and a real data set of information diffusion on Twitter. [ABSTRACT FROM AUTHOR]

We describe the use of network modeling to capture the shifting spatiotemporal nature of the COVID-19 pandemic. The most common approach to tracking COVID-19 cases over time and space is to examine a series of maps that provide snapshots of the pandemic. A series of snapshots can convey the spatial nature of cases but often rely on subjective interpretation to assess how the pandemic is shifting in severity through time and space. We present a novel application of network optimization to a standard series of snapshots to better reveal how the spatial centres of the pandemic shifted spatially over time in the mainland United States under a mix of interventions. We find a global spatial shifting pattern with stable pandemic centres and both local and long-range interactions. Metrics derived from the daily nature of spatial shifts are introduced to help evaluate the pandemic situation at regional scales. We also highlight the value of reviewing pandemics through local spatial shifts to uncover dynamic relationships among and within regions, such as spillover and concentration among states. This new way of examining the COVID-19 pandemic in terms of network-based spatial shifts offers new story lines in understanding how the pandemic spread in geography. [ABSTRACT FROM AUTHOR]

The connectedness between cities has become one of the most widely discussed topics in urban and regional research in the mobile and big data era. One problem identified is the asymmetric city connectivity, partially due to data availability. We present a data-driven approach based on location and toponym (place name) extracted from social media data, to assess the asymmetric connectivity between cities. The assumption is that a higher frequency of occurrences of the name of city i in posts located in city j would imply that the city i is more influential than other cities upon city j. In addition, we've developed a group of measurements such as the relatedness index, impact index, link strength index, dependence index, and structure similar index to characterize such interactions. This framework of connectivity measurements can also be used to support smart planning taking into account the evolving interplay among cities. The space-time structure of urban systems in China is examined as the case study. [ABSTRACT FROM AUTHOR]

The rapid urbanization in China has been associated with a growing hunger for energy consumption and steadily-increasing CO2 emissions. In this paper, an integrated system dynamics model composed of four sub-models is developed to simulate the urbanization and energy consumption in China from 1998 to 2050. Three scenarios are provided: accelerated economic development, emission reduction constraint, and low-carbon oriented. The result reveals that rapid economic growth and sufficient energy supply will foster China's urbanization in all three scenarios. Under the low carbon transition scenario, China's urbanization rate is expected to reach 76.41% in 2050, both reducing carbon emissions and promoting eco-friendly development. All three scenarios witness a dramatic growth of residential energy consumption and a steady increase of industrial energy consumption. China still has a long way to achieve the low-carbon transition goal. China should promote renewable resources and energy, pursue a low-carbon lifestyle, and reduce energy intensity over the next few decades. [ABSTRACT FROM AUTHOR]

In order to examine how Twitter can be a significant indicator of how suicidal thoughts occur and spread, we used content analysis to analyze 4524 Twitter messages. Although expressions of "wanting to die" occurred frequently, most of the tweets appeared to be non-threatening. In addition, discussions about suicide on Twitter mostly involved sharing suicide prevention resources with fellow users. This finding suggests that Twitter could offer an efficient way to circulate suicide awareness materials across geographical bounds. [ABSTRACT FROM AUTHOR]

• By improving INNOV, EDEX and degree of openness, the interaction of labor quality and production technology can be better. • Provinces with a moderate CDD should make efforts to evaluate new technology and decide how much subsidies will be given. • Improving the degree of population concentration of the central and western provinces with very low CDD can increase CDD. Effective factor inputs and sustainable industrial development are essential for supply-side structural reforms, which are mainly based on the coordinated development of labor quality and production technology. This study explores the coordinated development of labor quality and production technology, using provincial data from China. The results show an interaction between labor quality and production technology. However, China's coordinated development of labor quality and production technology is low because of issues including its innovativeness, education expenditure, technology value, and the openness of the economy. [ABSTRACT FROM AUTHOR]

Homeowners' Associations (HOAs) dictate landscape structure and management through legally enforceable land covenants at the neighborhood scale in the USA. Determining the location and spatial extent of HOAs is critical for examining its influence. However, such analysis is confounded by the lack of spatial data at the appropriate unit for such analysis. The purpose of this paper is to develop and realize an open source implementation to automate land parcel classification, which is an initial step towards the goal of determining the impact of HOAs on urban land management. Using Maricopa County, Arizona as a testbed, we found that parcel merging processes reduce the number of subdivisions from 26,042 to 17,269, such that boundaries better align with neighborhood units to which rule sets like land covenants apply. Moreover, after an initial training period, this process was completed in just over 7 hours. This research is an important first step in enabling a number of analysis including determining the location and spatial extent of HOAs regionally and, eventually, nationally and determining proposed links between HOAs and land management outcomes. [ABSTRACT FROM AUTHOR]

AbstractProblem, research strategy, and findingsTakeaway for practiceThe integration of a artificial intelligence (AI) into urban planning presents potential ethical challenges, including concerns about bias, transparency, accountability, privacy, and misinformation. As planners rely more on AI for decision making, the potential for these systems to perpetuate biases, obscure decision-making processes, and infringe on privacy becomes more pronounced, potentially undermining public trust and excluding marginalized communities. We reviewed existing literature on AI ethics in urban planning, examining biases, transparency, accountability, and privacy issues. Our methodology synthesized findings from various studies, reports, and theoretical frameworks to highlight ethical concerns in AI-driven urban planning. Recommendations for ethical AI implementation emphasize transparency, inclusive data sets, public engagement, and robust ethical guidelines. Our research identified critical ethical concerns in AI-driven urban planning. Bias in AI systems can lead to unequal outcomes, disproportionately affecting marginalized communities. Transparency issues arise from the black box nature of AI, complicating understanding and trust in AI-driven decisions. Privacy concerns are heightened due to extensive data collection and potential misuse, raising the risk of surveillance and data breaches. Limitations include the availability of specific literature focused on AI ethics for urban planning and the evolving nature of AI technologies, suggesting a need for ongoing research and adaptive strategies. Human oversight and continuous monitoring are essential to ensure ethical practices, with an emphasis on community engagement and public education to foster trust and inclusivity.Urban planners should adopt a proactive approach to mitigate ethical risks associated with AI. Ensuring transparency, involving diverse community groups, and maintaining robust data privacy measures are crucial. Prioritizing public engagement and education will help to demystify AI technologies and build public trust. Addressing these ethical concerns allows planners to leverage AI’s potential while safeguarding equity, privacy, and accountability in urban development. [ABSTRACT FROM AUTHOR]

The concept of a location's irreplaceability pertains to its unique characteristics that make it challenging for other locations to replace in terms of providing services and meeting needs. While scholars have shown great enthusiasm for studying location irreplaceability, accurately measuring it and understanding the effects of urban form have not been thoroughly explored through empirical research. This study selects Shenzhen, China, as the study area. First, the I‐index, which focuses on geographic flow, is utilized to measure the location's irreplaceability using mobile phone data. Subsequently, the factors related to urban form are analyzed from the perspectives of demography, building capacity, public services, and the location's conditions. Finally, a geographically and temporally weighted regression model (GTWR) is constructed to explore the spatiotemporal relationships between irreplaceability and urban form. The study demonstrates that the I‐index, considering flow volume and distance, can effectively measure the location's irreplaceability. The results of GTWR indicate that the location's irreplaceability and its influencing factors exhibit spatiotemporal heterogeneity. This study advances our understanding of the relationships between location irreplaceability and urban form both conceptually and methodologically. The findings have practical implications for urban design and planning, including urban space optimization, facility equity, and the alleviation of traffic congestion. [ABSTRACT FROM AUTHOR]

Various methods have been developed to investigate the geospatial information, temporal component, and message content in disaster-related social media data to enrich human-centric information for situational awareness. However, few studies have simultaneously analyzed these three dimensions (i.e. space, time, and content). With an attempt to bring a space-time perspective into situational awareness, this study develops a novel approach to integrate space, time, and content dimensions in social media data and enable a space-time analysis of detailed social responses to a natural disaster. Using Markov transition probability matrix and location quotient, we analyzed the Hurricane Sandy tweets in New York City and explored how people's conversational topics changed across space and over time. Our approach offers potential to facilitate efficient policy/decision-making and rapid response in mitigations of damages caused by natural disasters. [ABSTRACT FROM AUTHOR]

Extracting spatial network structure (SNS) from large-scale origin-destination flow data is an important approach for understanding interregional association patterns and interaction laws. Currently, the extraction of SNS primarily relies on complex network clustering or aggregated statistics with predefined regional constraints. However, these methods often overlook one or more fundamental principles essential for ensuring correctness and accuracy: 1) Aggregation of spatially proximate nodes is necessary when strong interactions exist, whereas separation is preferred in the absence of such interactions. 2) It is crucial to maintain strong interactions between non-spatially proximate nodes. 3) Ultimately, nodes within each group should exhibit spatial continuity. To address these challenges, a multi-hierarchical SNS extraction method is proposed, which focuses on raw node aggregating and generalization, measurement of interaction volume and strength between node groups and strategies for node/edge filtering. The effectiveness and value of the proposed method are demonstrated through a case study using city population migration data. Furthermore, the method provides a general approach for extracting SNSs from any origin-destination flow dataset that includes locations and weights, facilitating effective flow map generalization through aggregation of origin destination (OD) flow data. [ABSTRACT FROM AUTHOR]

Urban vibrancy describes the attraction, diversity and accessibility of a place and exhibits spatio-temporal variability. The relationships between urban vibrancy and land-use configurations are significant for governments, planners and residents. To date, it is challenging for traditional census datasets to support real-time analysis with detailed spatial and temporal granularity. This article takes advantage of emerging crowdsourcing data and adopts social media check-ins over a 24-h period as a proxy for urban vibrancy. A framework that incorporates kernel density estimation (KDE), geographically and temporally weighted regression (GTWR) and the Herfindahl-Hirschman index (HHI) is proposed to explore the spatio-temporal distribution characteristics of vibrancy and the spatio-temporal relationships with the influential factors. The results show that the evolution of vibrancy is influenced by various factors that are heterogeneous over space and time. With a new perspective and deeper understanding of the varying spatio-temporal relationships between vibrancy and point of interest (POI)-based configurations, this study can offers meaningful implications for policy makers and planners regarding the improvement of resource utilization and the rational design of neighbourhoods. [ABSTRACT FROM AUTHOR]

With the advent of Information and Communication technology (ICT) in modern age, the statement of “death of distance” has received numerous discussions. This article contributes a new empirical study to the debate of “death of distance” by considering the effect of spatial autocorrelation in the estimation of distance decay effect with the incorporation of network autocorrelation in spatial econometric modeling. This work is based on a city-level dataset from China's largest social networking site called Weibo. The findings are shown as following. First, the coefficient value of network autocorrelation term (0.007, significant at 0.01 level) suggests that the city-level online social links are spatially dependent. In other words, these social connections are not randomly distributed across space but tend to form spatial clusters where neighboring links are more similar. Second, controlling spatial autocorrelation in the data, a distance decay effect on the formation of online social links is unveiled with a much smaller scaling exponent of the distances (i.e., 0.276) as compared to those (e.g., 2.0, 1.8, 1.45, 1.06, 1.03, 0.4, and 0.5) in existing studies. This research provides a useful modeling framework to analyze the real-world driving forces that characterize the patterns of social interactions in virtual space and thus advance our understanding in the connection of virtual and real spaces. [ABSTRACT FROM AUTHOR]

This paper presents a parallel varied density-based clustering algorithm with optimized data partition (PVDB). First, we improve the partition with reduced boundary points algorithm using shared nearest neighbour (SNN) methods and propose the reachable partition with reduced boundary points algorithm. Second, we introduce a layered grouping grid structure and propose an efficient k nearest neighbour (kNN) search method. This method enhances the efficiency of kNN searches and determines whether kNNs are in their own partitions. Third, we propose a new merging strategy for connecting clusters in different partitions, based on the reachable point concept. Meanwhile, the strategy avoids connecting clusters with varying densities by SNN as occurs with SNN-based clustering methods. Our algorithm is implemented and compared with DBSCANMR and GriDBSCAN using the MapReduce paradigm and shows better varied density clustering capability and scalability. In addition, varied applications show our algorithm's capability of discerning spatial patterns and extending to many fields. [ABSTRACT FROM AUTHOR]

Social media analytics has become prominent in natural disaster management. In spite of a large variety of metadata fields in social media data, four dimensions (i.e. space, time, content and network) have been given particular attention for mining useful information to gain situational awareness and improve disaster response. In this article, we review how existing studies analyze these four dimensions, summarize common techniques for mining these dimensions, and then suggest some methods accordingly. We then propose a schema to categorize the gathered articles into 15 classes and facilitate the generation of data analysis tasks. We find that (1) a large part of studies involve multiple dimensions of social media data in their analyses, (2) there are both separate analyses for each dimension and simultaneous analyses for multiple dimensions and (3) there are fewer simultaneous analyses as dimensions increase. Finally, we suggest research opportunities and challenges in fusing social media data with authoritative datasets, i.e. census data and remote-sensing data. [ABSTRACT FROM PUBLISHER]

News articles capture a variety of topics about our society. They reflect not only the socioeconomic activities that happened in our physical world, but also some of the cultures, human interests, and public concerns that exist only in the perceptions of people. Cities are frequently mentioned in news articles, and two or more cities may co-occur in the same article. Such co-occurrence often suggests certain relatedness between the mentioned cities, and the relatedness may be under different topics depending on the contents of the news articles. We consider the relatedness under different topics assemantic relatedness. By reading news articles, one can grasp the general semantic relatedness between cities; yet, given hundreds of thousands of news articles, it is very difficult, if not impossible, for anyone to manually read them. This paper proposes a computational framework which can ‘read’ a large number of news articles and extract the semantic relatedness between cities. This framework is based on a natural language processing model and employs a machine learning process to identify the main topics of news articles. We describe the overall structure of this framework and its individual modules, and then apply it to an experimental dataset with more than 500,000 news articles covering the top 100 US cities spanning a 10-year period. We perform exploratory visualizations of the extracted semantic relatedness under different topics and over multiple years. We also analyze the impact of geographic distance on semantic relatedness and find varied distance decay effects. The proposed framework can be used to support large-scale content analysis in city network research. [ABSTRACT FROM PUBLISHER]

Desertification land occupies vast area of world, which is a big threat to eco-environmental safety and brings huge economic losses. Wind erosion is a key process and ecological problem of land desertification in arid and semi-arid area, where unreasonable land use is one of major causes. Therefore, optimizing land use is a substantially effective approach to eco-environmental safety and development of sandy area. Yuyang County, located in Mu Us Desert of semi-arid sandy area and farming-pastoral zone in North China, was selected as a typical study area. The principles and methods on environmentally friendly land use planning surrounding urban area for combating soil wind erosion were constructed, and a 'tri-circle' land use paradigm around Yulin City was worked out. It was predicted that by the year of 2020, farmland will be only 10 percent of total area of Yuyang County, which are able to meet the need of food and meat. The proportion of ecological land, productive land and living land would be 2.27:12.09:85.64, which will lead to a reasonable land use system for ecological security and social-economic sustainable development of the region. It shows that this research will help construct the sustainable land use system under ecological security, and ensure habitat environmental safety in urban area. [ABSTRACT FROM AUTHOR]

Accessibility to parks could be an important determinant of housing prices. This article applies the gravity model to calculate accessibility based on park classification in Shenzhen, China. Unlike most traditional studies that use the ratio method and nearest distance (including straight-line distance and network distance) to measure accessibility to given facilities, in this study, we use gravity-based accessibility by park type. Then, we explore the relationships between accessibility to parks and housing prices using a hedonic price model. In addition, we apply a geographical detector method to assess the association between housing price and related factors. The results indicate the following conclusions: (1) compared to traditional methods, the gravity model provides a more effective and objective measure of accessibility to parks because it considers distance decay effects, supply, and demand; (2) it is necessary and important to investigate the effects of the accessibility to different park types on housing prices; and (3) geographical detector models can efficiently detect correlations and interactions among housing prices and related factors. [ABSTRACT FROM AUTHOR]

Housing is among the most pressing issues in urban China and has received considerable scholarly attention. Researchers have primarily concentrated on identifying the factors that influence residential property prices and how such mechanisms function. However, few studies have examined the potential factors that influence housing prices from a big data perspective. In this article, we use a big data perspective to determine the willingness of buyers to pay for various factors. The opinions and geographical preferences of individuals for places can be represented by visit frequencies given different motivations. Check-in data from the social media platform Sina Visitor System is used in this article. Here, we use kernel density estimation (KDE) to analyse the spatial patterns of check-in spots (or places of interest, POIs) and employ the Getis-Ord method to identify the hot spots for different types of POIs in Shenzhen, China. New indexes are then proposed based on the hot-spot results as measured by check-in data to analyse the effects of these locations on housing prices. This modelling is performed using the hedonic price method (HPM) and the geographically weighted regression (GWR) method. The results show that the degree of clustering of POIs has a significant influence on housing values. Meanwhile, the GWR method has a better interpretive capacity than does the HPM because of the former method’s ability to capture spatial heterogeneity. This article integrates big social media data to expand the scope (new study content) and depth (study scale) of housing price research to an unprecedented degree. [ABSTRACT FROM AUTHOR]

Restrictions in the USA on registered sex offenders ( RSOs) are examined from the spatial aspects. The long history of various restrictions imposed by government, particularly local ones, is covered in the introduction. Spatial aspects, such as delineation of zones from which certain activities or certain people are excluded is the focus. Then the nature of restrictions on RSOs is considered at the state, county and municipal level. Typical of restrictions are that RSOs are prohibited from moving into residence within a prescribed distance of certain features in a community. The distances are typically 1,000 feet but are quite variable. Typical proscribed venues are schools, parks and day care centers, but there can be many others such as bus stops. Spatial aspects of these restrictions, such as how offender locations are geocoded and represented and how proscribed venues are delineated is analyzed. Specific details and theoretical concerns related to the many problematic issues with RSO restrictions is presented. In particular questions of their constitutionality and efficacy are raised. The paper concludes with a discussion of the implications of RSO restrictions for the discipline of geography in general and for the evolution of increasingly precise methods of spatial analysis in particular. [ABSTRACT FROM AUTHOR]

This paper introduces an exploratory space-time analysis tool for determining the two components of a spatial redistribution process: ( i) the shock, which is the moment that triggers a spatial redistribution process; for example, a new policy, a war, an earthquake, etc.; and ( ii) the duration of the regime fade, which is the time between the shock and the moment in which a new regime emerges as a better representation of the spatial distribution of the attribute. Two examples are provided: the first uses China's provincial per capita GDP between 1978 and 2008, and the second uses state level housing price and unemployment rate data for the US between 2002 and 2012. [ABSTRACT FROM AUTHOR]

Borrowing methods from epidemiology, studies of spatiotemporal regularities of crime have been booming in various industrialized countries. However, few such attempts are empirical studies using crime data in developing countries due to a lack of data availability. Utilizing a recent burglary dataset in Wuhan, the fourth largest city in China, current research applied the sequential kernel density estimation and the space–time K -function methods to analyze the spatiotemporal changes of hotspots of residential burglaries. The results show that, both spatial and spatiotemporal clustering exists. The hotspots were relatively stable over time. The space–time clustering, however, shows significant concentrations both in space and over time. In addition, analytic results show significant effects of distance decay in terms of occurrences of burglary incidents along the spatial and temporal dimensions. Moreover, findings from the research provide critical information on the space–time rhythm of crime, and therefore can be utilized in crime prevention practice. Finally, the implications of the findings and limitations are discussed. [ABSTRACT FROM AUTHOR]

A dramatic urbanization process might severely impact the ecosystem service functions in resource-based regions, affecting the quality of life for the residents. Taking Huaibei, a mineral resource-based city in China during the period of 1990–2011 as a case study, this paper evaluates its level of urbanization and analyzes the relationship between the level of urbanization and ecosystem service values, using Xie's ecosystem unit service value table. The results show that Huaibei's level of urbanization has progressed gradually during the study period while ecosystem service values increased at the earlier time period but dropped in later time. Moreover, the relationship between the level of urbanization and ecosystem service values over time exhibits an irregular inverse “U” shape. From 1990 to 2002, Huaibei's ecosystem service value rose with the growing urbanization level, while it declined from 2003 to 2011. This indicates the possible effect that urbanization weakens the study area's ecosystem service function by altering ecosystem structure, ecological habitat and ecosystem circulation process. This paper provides the evidence why a coordinated development of urbanization and ecosystem service function is needed towards the sustainable management of mineral resources area. [ABSTRACT FROM AUTHOR]

Older pedestrians are vulnerable to outdoor falling while walking on streets/sidewalks, but few studies have examined the role of the street environment and tree canopy cover over streets in relation to pedestrian falls among the elderly. We used spatial analysis to examine the association between tree canopy cover over streets and pedestrian falls reported to Emergency Medical Service (EMS) providers from March 2013 to February 2020 among adults aged 65 and older living in urban areas of Marin County, CA. Tree canopy cover over streets was measured using 1-m resolution of tree canopy within street polygons. After controlling for socioeconomic status and built environments, we found an inverse association between tree canopy cover over streets and elderly pedestrian fall rates at the census block level. Specifically, with a 10-percentage point increase in tree canopy cover over streets of a block, we expected to see about an 11.2% decrease in the elderly pedestrian fall rate. We found that the inverse relationship between tree canopy cover over streets and elderly pedestrian falls was only significant during the leaf-on season in the spring and summer. Finally, sub-analysis found that the relationship between tree canopy cover over streets and elderly pedestrian falls was stronger in low-income areas, compared to high-income areas. Planting street trees is a potential evidence-based intervention to prevent pedestrian falls. However, special attention must go beyond the quantity of tree canopy cover over streets to consider biophysical factors and social conditions. • We explored spatial distribution of elderly pedestrian falls in urban areas. • Tree canopy over streets was associated with reduced elderly pedestrian fall rate. • This relationships was significant during leaf-on season. • Such protective effects were more beneficial in low-income areas. • Planting street trees could be an effective to prevent pedestrian falls. [ABSTRACT FROM AUTHOR]

Abstract: From 2002 to 2011, West Nile virus mosquitoes (WNV) has been ever-present in traps across Harris County, TX which contains the city of Houston. Disease-positive trap locations have peaked twice, from 2002 to 2006 and then again from 2009 onwards. This paper will examine fine scale spatial and temporal patterns in disease-positive mosquito traps for the Houston area across this time frame, using three different analytical approaches: kernel density, spatial filtering and SaTScan. The purpose of this paper is twofold. Firstly, to identify spatial and space-time clusters of WNV in order to spatially prioritize subsequent research for causative associations. Secondly, to compare the effectiveness of three methods that vary in complexity and ease of use in order to suggest a transferable methodology for mosquito control and environmental health departments across the United States with only lower level GIS skillsets. This paper also illustrates a successful ongoing academic and mosquito control collaboration with the Harris County Public Health Services Mosquito Control Division's (MCD) program. [Copyright &y& Elsevier]

Comparative space-time thinking lies at the heart of spatiotemporally integrated social sciences. The multiple dimensions and scales of socioeconomic dynamics pose numerous challenges for the application and evaluation of public policies in the comparative context. At the same time, social scientists have been slow to adopt and implement new spatiotemporally explicit methods of data analysis due to the lack of extensible software packages, which becomes a major impediment to the promotion of spatiotemporal thinking. The proposed framework will address this need by developing a set of research questions based on space-time-distributional features of socioeconomic datasets. The authors aim to develop, evaluate, and implement this framework in an open source toolkit to comprehensively quantify the changes and level of hidden variation of space-time datasets across scales and dimensions. Free access to the source code allows a broader community to incorporate additional advances in perspectives and methods, thus facilitating interdisciplinary collaboration. Being written in Python, it is entirely cross-platform, lowering transmission costs in research and education. [ABSTRACT FROM AUTHOR]

China's economic reforms and unprecedented growth have generated many fascinating issues for scholarly research. An understanding of urbanization and land use change in China is required for appropriate strategies and policies to facilitate future sustainable development. This paper reviews the literature on urbanization, land use and sustainable development in China with a focus on land use change. We argue that land use and environmental research are embedded in the complex economic-geographical processes and multiple trajectories of development and urbanization in China. This paper highlights the important role of space-time modeling in a multi-disciplinary setting in the study of urbanization, land use and sustainable development. It also points out potential areas for future research. [ABSTRACT FROM AUTHOR]

A Coarse-Grained Parallel Genetic Algorithm (CGPGA) is utilized to search for near-optimal solutions for land use allocation optimization problems in the context of multiple objectives and constraints. Plans are obtained based on the trade-off among three spatial objectives including ecological benefit, accessibility and compatibility. The Multi-objective Optimization of Land Use model integrates these objectives with the fitness function assessed by reference point method (goal programming). The CGPGA, as the first coupling in land use allocation optimization problems, is tested through the experiments with one processor, two processors and four processors to pursue near-optimal land use allocation scenarios and the comparison to these experiments based on Generic Genetic Algorithm (GGA), which clearly shows the robustness of the model we proposed as well as its better performance. Furthermore, the successful convergent (near-convergent) case study utilizing the CGPGA in Tongzhou Newtown, Beijing, China evinces the capability and potential of CGPGA in solving land use allocation optimization problems with better efficiency and effectiveness than GGA. [ABSTRACT FROM AUTHOR]

Abstract: Many questions regarding local economic growth lay at the intersection of the disciplines of geography and economics, that is, the space-time interface. This paper attempts to show how space-time analysis can help identify communities that are at risk from economic contractions. Exploratory data analysis is illustrated to provide insights on the temporal and spatial patterns of economic growth. Then this paper offers a space-time analysis of employment rates in Ohio counties over the time period of 1969–2007. [Copyright &y& Elsevier]

Abstract: This paper studies the relationship between homicide rate and socioeconomic factors at community area level in Chicago from 1960 to 1995. Most of prior studies of social disorganization theory are based on cross-sectional spatial regression or longitudinal studies. This research integrates space and time in testing social disorganization theory. First, exploratory spatial data analysis (ESDA) is used to examine dynamic spatial patterns of these indicators. This investigation justifies the estimation of homicide rates across community areas through panel-data models that extend to include spatial lag and spatial error autocorrelation. [Copyright &y& Elsevier]

A schematic diagram depicting possible mechanism underlying the antimicrobial effects of MSI-1 against drug-resistant S. aureus , including MRSA, VISA and VRSA. MSI-1 could interact with CrtN to suppress its enzyme activity to reduce STX production at sub-MIC levels, thus attenuating local injury and systemic infection in vivo. (Figure partially created using BioRender, https://biorender.com/). [Display omitted] • MSI-1 interacted with CrtN to suppress its enzyme activity to reduce STX production. • The assistance of sub-MICs MSI-1 could sensitize pigmented S. aureus to immune clearance. • MSI-1 had potent activity against drug-resistant S. aureus, including MRSA and VRSA. • MSI-1 can be developed as a new antibacterial agent to resist refractory S. aureus infection. Development of novel therapeutic strategies and antibacterial agents against antibiotic-resistant Staphylococcus aureus (S. aureus) is urgent. In this study, antibacterial activities and possible mechanisms of peptide MSI-1 against multiple drug-resistant S. aureus were investigated. Results demonstrated that MSI-1 had potent bacteriostatic activity and bactericidal efficiency against S. aureus , including methicillin-resistant S. aureus (MRSA), vancomycin-intermediate S. aureus (VISA) and vancomycin-resistant S. aureus (VRSA), with minimum inhibitory concentrations (MICs) ranging from 4 to 16 μg/mL and bactericidal times from 2–12 h. MSI-1 exhibited a low incidence of developing resistance and additive effects with vancomycin to overcome MRSA and VRSA. Moreover, MSI-1, even at sub-MIC concentrations, inhibited staphyloxanthin (STX) production of S. aureus. This inhibitory effect was unique and effectively sensitized S. aureus to host immune defense. In terms of its modes of action, MSI-1 disrupted the cell membrane of S. aureus by binding to negatively-charged lipoteichoic acid to exert a direct bactericidal effect. Interestingly, MSI-1 interacted with 4,4′-diapophytoene desaturase (CrtN) of S. aureus via ionic bonds, hydrogen bonds, and Pi-Pi or Pi-alkyl interactions, and alanine substitution of the key amino acids contributed to these interactions weakened this STX production inhibition. Thus, in a MRSA-induced skin infection in mice and MRSA/VRSA-induced systemic infection in Galleria mellonella, MSI-1 alleviated staphylococcal scalded skin syndrome to promote mouse skin wound repair and mitigated staphylococcus infection-induced immune melanization to enhance G. mellonella survival. Collectively, MSI-1 has potent antibacterial activity against drug-resistant S. aureus by affecting bacterial viability and exerting its anti-virulence effects. It can be developed as a new antibacterial agent to resist refractory S. aureus infection. [ABSTRACT FROM AUTHOR]

Interests are growing in the distributional impact of natural catastrophes. Motivated by the increasing frequency and intensity of extreme weather events, the present study proposes the use of structural path analysis (SPA) to identify the paths that render certain populations more vulnerable to an economic shock. The analysis proceeds in three main stages. The first stage employs geographic information systems (GIS) maps to identify economic activities disrupted by virtue of their geo-location in directly affected areas. The direct sectoral impact is subsequently fed into a social accounting matrix (SAM) model in the second stage to estimate the ripple effects generated by the network of economic linkages. SPA is deployed in the final stage to determine the paths through which the impact ripples from the shock's origin to households. Because SAM disaggregates households into distinct categories, the multiplier decomposition procedure enables SPA to identify the specific links that transmit the shock to every household category. The approach is employed to examine the New York City case in the backdrop of Superstorm Sandy and compare it with post-Katrina New Orleans. The results suggest that while relatively well-off New Yorkers may have suffered the most from Sandy's path of destruction, the New Orleans poor appeared most vulnerable in Katrina's aftermath. • We employ SAM-based structural path analysis to identify channels of shock transmission to vulnerable populations. • While the well-off suffered the most from Superstorm Sandy, the poor appeared most vulnerable in Katrina's aftermath. • Ex-ante inequality, as well as geographic and sectoral distribution of the shock, contribute to the ex-post outcome. • The results highlight the vital distinction between ex-ante vulnerability and ex-post vulnerability. [ABSTRACT FROM AUTHOR]

This study investigated stated trip changes related to smartphone usage among the residents in nine small and rural communities in Texas, based on a 419-respondent online survey. The survey results showed that most participants always used smartphones to perform tasks. After controlling for socioeconomic status, perceived adaptability to technology, and population density, smartphone usage was generally positively associated with the stated impact on daily trip frequency. High-income participants were less likely to report a positive impact of smartphone usage on work-related trips. Compared to younger adults, smartphone use among older adults (aged 50 years or older) was less likely to influence trip-making. Based on the study findings, policy recommendations about smartphone-based information provision systems and information accessibility were discussed. The findings could help policymakers and practitioners design planning protocols that encourage sustainable travel and build the next-generation small and rural communities. [ABSTRACT FROM AUTHOR]

Topical ocular sustained-release drug delivery systems represent an effective strategy for the treatment of ocular diseases, for which a suitable carrier has yet to be sufficiently developed. Herein, an eye-compatible sodium polystyrene sulfonate resin (SPSR) was synthesized with a uniform particle size of about 3 μm. Ligustrazine phosphate (LP) was adsorbed to SPSR by cation exchange to form LP@SPSR. LP@SPSR suspension eye drops were further developed using the combination of Carbopol 934P and xanthan gum as suspending agents. The LP@SPSR suspension showed a sustained release in vitro , which was consistent with the observed porcine corneal penetration ex vivo. Pharmacokinetics in tear fluid of rabits indicated that LP@SPSR suspension led to prolonged ocular retention of LP and a 2-fold improved the area under the drug concentration-time curve (AUC 0-t). Pharmacokinetics in the aqueous humor of rabbits showed 2.8-fold enhancement in the AUC 0-t compared to LP solution. The LP@SPSR suspension exhibited no cytotoxicity to human corneal epithelial cells, nor irritation was observed in rabbit eyes. Thus, the LP@SPSR suspension has been validated as a safe and sustained release system leading to enhanced ophthalmic bioavailability for treating ocular diseases. [Display omitted] • Compared with commercial product, this study synthesized a spherical sodium polystyrene sulfonate resin (SPSR) with a uniform particle size of 3 μm for ophthalmic use. • Ligustrazine phosphate (LP), an anti-inflammatory drug, was cation exchanged by SPSR to form LP@SPSR, which achieved a sustained release property. • LP@SPSR suspension eye drops markedly enhanced the ophthalmic bioavailability of LP in rabbit. [ABSTRACT FROM AUTHOR]

Unlike the large body of research on investigating interactions among cities using survey data, the social media-based city interaction study has received much less exploration. Based on geographical studies of social media content in China, we develop a few indices quantifying various levels of geographical awareness among cities. (1) We find that the geographical awareness proxy by the social media-based indices can measure interactions among cities. Specifically, the geographical awareness among cities follows gravitational law and is highly correlated with mobility flows. (2) The spatial in-awareness index (SIAI) is an appropriate index indicating a city's ranking in the urban hierarchy (3) the spatial out-awareness rate (SOAR) can indicate the interactions from a focal city to other cities. Our findings also show that SOAR can predict the number of people infected during a pandemic in a city system. Once the origin city or hotspots of the outbreak and the number of infected persons within those cities are known, we can use the social media-based SOAR index to predict number of cases for other else cities in the urban system. With this information, governments can properly and efficiently deliver medical equipment and staff to cities where large populations are infected. • Develops social media-based geographical awareness indices: such as spatial out-awareness rate (SOAR) and in-awareness index (SIAI). • Using an econometric model, the study shows that geographical awareness among cities follows gravitational law with a decay function parameter of 0.308 • Use mobility flow data to verify that the social media-based indices can measure interactions among cities. • Shows that SIAI is an appropriate index for indicating a city's ranking in the urban hierarchy • SOAR can indicate the interactions from a focal city to other cities and predict the number of people infected during a pandemic. [ABSTRACT FROM AUTHOR]

Street view imagery such as Google Street View is widely used in people's daily lives. Many studies have been conducted to detect and map objects such as traffic signs and sidewalks for urban built-up environment analysis. While mapping objects in the horizontal dimension is common in those studies, automatic vertical measuring in large areas is underexploited. Vertical information from street view imagery can benefit a variety of studies. One notable application is estimating the lowest floor elevation, which is critical for building flood vulnerability assessment and insurance premium calculation. In this article, we explored the vertical measurement in street view imagery using the principle of tacheometric surveying. In the case study of lowest floor elevation estimation using Google Street View images, we trained a neural network (YOLO-v5) for door detection and used the fixed height of doors to measure doors' elevation. The results suggest that the average error of estimated elevation is 0.218 m. The depthmaps of Google Street View were utilized to traverse the elevation from the roadway surface to target objects. The proposed pipeline provides a novel approach for automatic elevation estimation from street view imagery and is expected to benefit future terrain-related studies for large areas. [ABSTRACT FROM AUTHOR]

Urban green spaces (UGSs) have potential significance for urban ecosystems, as they provide environmental benefits and benefits for residents' physical activity and mental health. Objective assessments of UGSs are necessary for optimizing the allocation of green space public resources and providing a basis for social equality. Previous studies have adopted a few indicators to evaluate UGS provision but have neglected multidimensional interaction characteristics. This study proposes a framework that integrates principal component analysis and the spatial clustering method to identify geographically homogeneous subregions with similar characteristics, including dimensions of accessibility, ecological function, quality, and quantity. Shenzhen, China, was selected as a case study, and the results indicate the following: (1) the multiple dimensions of UGSs are unevenly distributed, and it is necessary to evaluate UGSs from multiple dimensions; and (2) there are significant disparities in UGSs among different clusters that can be summarized to guide the improvement of green space equity. It is necessary to increase the number of community parks and optimize the green view index in more densely populated areas and the outskirts. Our study provides an intuitive and comprehensive evaluation framework for the distribution of UGSs and identifies their characteristics in different subregions, which is valuable for space quality improvement and reasonable zoning planning. [ABSTRACT FROM AUTHOR]