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Recent advances in knowledge graphs show great promise to link various data together to provide a semantic network. Place is an important part in the big picture of the knowledge graph since it serves as a powerful glue to link any data to its georeference. A key technical challenge in constructing knowledge graphs with location nodes as geographical references is the matching of place entities. Traditional methods typically rely on rule-based matching or machine-learning techniques to determine if two place names refer to the same location. However, these approaches are often limited in the feature selection of places for matching criteria, resulting in imbalanced consideration of spatial and semantic features. Deep feature-based methods such as deep learning methods show great promise for improved place data conflation. This paper introduces a Semantic-Spatial Aware Representation Learning Model (SSARLM) for Place Matching. SSARLM liberates the tedious manual feature extraction step inherent in traditional methods, enabling an end-to-end place entity matching pipeline. Furthermore, we introduce an embedding fusion module designed for the unified encoding of semantic and spatial information. In the experiment, we evaluate the approach to named places from Guangzhou and Shanghai cities in GeoNames, OpenStreetMap (OSM), and Baidu Map. The SSARLM is compared with several classical and commonly used binary classification machine learning models, and the state-of-the-art large language model, GPT-4. The results demonstrate the benefit of pre-trained models in data conflation of named places. [ABSTRACT FROM AUTHOR]

In response to the call of the New Urban Agenda—Habitat III for a reinvigoration of long-term and integrated planning towards sustainable urban development, this paper presents an empirical comparative study of planning practices based on the "satellite city" and "new town" concepts in Tokyo and Shanghai to examine from a long-term perspective how well they have guided polycentric urban development at a time of massive population growth. We aim to deliver evidence-based contributions to boost the knowledge transfer between the Global North and the Global South. The paper adopts a multi-dimensional framework for the comparative analysis, including a review of long-term urban development policies and an inspection of the population distribution and extent of built-up areas using time-specific categorizations to map the spatiotemporal changes based on GHSL data. The comparative analysis shows that urban plans in Tokyo and Shanghai based on satellite cities and new towns as steering instruments for polycentric urban growth management have not lived up to the original aspirations. In fact, the intended steering of population distribution has essentially failed, despite the practical steps undertaken. [ABSTRACT FROM AUTHOR]

OpenStreetMap (OSM) road networks provide public digital maps underlying many spatial applications such as routing engines and navigation services. However, turning relationships and time restrictions at OSM intersections are lacking in these maps, posing a threat to the accuracy and reliability of the services. In this paper, a new turn information detection method for OSM intersections using the dynamic connection information from crowdsourced trajectory data is proposed to address this problem. In this solution, the OSM intersection structure is extracted and simplified and crowdsourced trajectories are projected onto OSM road segments using an improved Hidden Markov Model (HMM) map matching method that explicitly traces the turning connections in road networks. Optimal path analysis increases the turning support related to short road segments. On this basis, this study transforms complex turning identification scenarios into the simple analyses of traffic connectivity. Furthermore, a voting strategy is used to identify and calculate turning time restrictions. The experimental results, using trajectory data from three cities in China, show that the turning relationships can be detected at a precision of 90.71% with a recall of 96.55% and an F1-value of 93.54% in Shanghai. For Wuhan, the precision is 95.33% and the recall is 95.00%, with an F1-value of 95.16%. The precision and recall when identifying turning time restrictions both reach 90% in Xiamen. These results demonstrate the effectiveness of the proposed turning detection method. [ABSTRACT FROM AUTHOR]

With population ageing being a notable demographic phenomenon, aging in place is an efficient model to accommodate the mounting aging needs. Based on the community scale, this study takes the 15-min community-life circle as the basic research unit to investigate the imbalanced distribution of pension resources and its influencing factors in downtown Shanghai. We obtained six types of elderly care facilities data from the Shanghai elderly care service platform and utilized the Gaussian 2-step Floating Catchment Area method to calculate the accessibility of 6-type elderly care facilities. Then, we used the Entropy Weight Method to calculate the comprehensive accessibility of elderly care facilities. The Getis–Ord Gi* method was adopted to analyze the overall distribution, identifying the well-developed and the under-developed areas. To explore the influencing factors of the distribution, this paper obtained multi-source data to construct a total of 17 indicators and established a Random Forest model to identify the feature importance. With the selected eight factors, the Geographically Weighted Regression (GWR) model was applied to study the spatial heterogeneity of influencing factors, and the model showed a good performance with the AdjR2 being 0.8364. The findings of this research reveal the following: (1) The distribution of six types of elderly care facilities is extremely uneven, with obvious spatial aggregation characteristics. Amongst the seven administrative regions, Huangpu District has the best accessibility to pension resources, while the resources in the other six regions are highly inadequate. (2) Essential influencing factors of the comprehensive accessibility of community-based elderly care facilities are accessibility of nursing institutions (positive), hotel density (positive), catering density (negative), education density (positive) and medical density (negative), while "rents", "plot ratio" and "building density" have little impact on comprehensive accessibility. (3) The results of GWR revealed that the eight indicators are heterogeneous in space, all of which have bidirectional effects on comprehensive accessibility. By investigating the spatial distribution patterns and influencing factors of pension resources in Shanghai, this research could further contribute to establishing a sound community-based elderly care service system that improves older adults' quality of life and promotes social fairness and justice. [ABSTRACT FROM AUTHOR]

The academic research on the spatial distribution of pension institutions is mostly from the perspective of constructing or improving spatial analysis methods. It is not considered that with the development of social science and technology, the facilities and services of elderly care institutions will develop in the direction of intelligence. Exploring the intelligence level and spatial distribution of Shanghai's elderly care institutions has important practical significance for improving and optimizing the service facilities and resource allocation of Shanghai's pension institutions. The spatial scale and cluster distribution of pension institutions in Shanghai are described by means of standard deviation ellipse, kernel density analysis, spatial autocorrelation analysis, and spatial hotspot analysis. The Gini coefficient of intelligent bed is proposed to describe the comprehensive allocation of resources of pension institutions. Additionally, correlation analysis is used to explore the spatial fairness distribution of pension institutions in Shanghai. The results show that the development of pension institutions in various districts of Shanghai is uneven; the distribution of pension institutions is concentrated in the central urban area; the intelligent facilities, service resources, and the number of beds of pension institutions in the suburbs are better than those in the central urban area. Based on the analysis results, policy suggestions are put forward, such as optimizing the allocation of bed resources in pension institutions and focusing on building a more equitable and rationally structured smart pension institution. [ABSTRACT FROM AUTHOR]

The urban ecological environment is related to human health and is one of the most concerned issues nowadays. Hence, it is essential to detect and then evaluate the urban ecological environment. However, the conventional manual detection methods have many limitations, such as the high cost of labor, time, and capital. The aim of this paper is to evaluate the urban ecological environment more conveniently and reasonably, thus this paper proposed an ecological environment evaluation method based on remote sensing and a projection pursuit model. Firstly, a series of criteria for the urban ecological environment in Shanghai City are obtained through remote sensing technology. Then, the ecological environment is comprehensively evaluated using the projection pursuit model. Lastly, the ecological environment changes of Shanghai City are analyzed. The results show that the average remote sensing ecological index of Shanghai in 2020 increased obviously compared with that in 2016. In addition, Jinshan District, Songjiang District, and Qingpu District have higher ecological environment quality, while Hongkou District, Jingan District, and Huangpu District have lower ecological environment quality. In addition, the ecological environment of all districts has a significant positive spatial autocorrelation. These findings suggest that the ecological environment of Shanghai has improved overall in the past five years. In addition, Hongkou District, Jingan District, and Huangpu District should put more effort into improving the ecological environment in future, and the improvement of ecological environment should consider the impact of surrounding districts. Moreover, the proposed weight setting method is more reasonable, and the proposed evaluation method is convenient and practical. [ABSTRACT FROM AUTHOR]

The increasing popularity of intercity commuting is affecting regional development and people's lifestyles. A key approach to addressing the challenges brought about by intercity commuting is analyzing its determinants. Although spatial nonstationarity seems inevitable, or at least worth examining in spatial analysis and modeling, the global perspective was commonly employed to explore the determinants of intercity commuting flows in previous studies, which might result in inaccurate estimation. This paper aims to interpret intercity commuting flows from Suzhou to Shanghai in the Yangtze River Delta region. For this purpose, mobile signaling data was used to capture human movement trajectories, and multi-source big data was used to evaluate social-economic determinants. Negative binomial (NB) regression and spatially weighted interaction models (SWIM) were applied to select significant determinants and identify their spatial nonstationarity. The results show that the following determinants are significant: (1) commuting time, (2) scale of producer services in workplace, (3) scale of non-producer services in residence, (4) housing supply in residence, (5) year of construction in residence, and (6) housing price in residence. In addition, all six significant determinants exhibit evident spatial nonstationarity in terms of significance scope and coefficient level. Compared with the geographically weighted regression (GWR), SWIM reveals that the determinants of intercity commuting flows may manifest spatial nonstationarity in both residence and workplace areas, which might deepen our understanding of the spatial nonstationarity of OD flows. [ABSTRACT FROM AUTHOR]

Accurate subway passenger flow prediction is crucial to operation management and line scheduling. It can also promote the construction of intelligent transportation systems (ITS). Due to the complex spatial features and time-varying traffic patterns of subway networks, the prediction task is still challenging. Thus, a hybrid neural network model, GCTN (graph convolutional and comprehensive temporal neural network), is proposed. The model combines the Transformer network and long short-term memory (LSTM) network to capture the global and local temporal dependency. Besides, it uses a graph convolutional network (GCN) to capture the spatial features of the subway network. For the sake of the stability and accuracy for long-term passenger flow prediction, we enhance the influence of the station itself and the global station and combine the convolutional neural networks (CNN) and Transformer. The model is verified by the passenger flow data of the Shanghai Subway. Compared with some typical data-driven methods, the results show that the proposed model improves the prediction accuracy in different time intervals and exhibits superiority in prediction stability and robustness. Besides, the model has a better performance in the peak value and the period when passenger flow changes quickly. [ABSTRACT FROM AUTHOR]

Understanding solar radiation in urban street spaces is crucial for comprehending residents' environmental experiences and enhancing their quality of life. However, existing studies rarely focus on the patterns of urban street solar radiation over time and across different urban and suburban areas. In this study, street view images from the summers of 2013 and 2019 in Shanghai were used to calculate solar radiation in urban street spaces. The results show a general decrease in street solar radiation in 2019 compared to 2013, with an average drop of 12.34%. The decrease was most significant in October (13.47%) and least in May (11.71%). In terms of solar radiation data gathered from street view sampling points, 76.57% showed a decrease, while 23.43% showed an increase. Spatially, solar radiation decreased by 79.66% for every additional 1.5 km from the city centre. In summary, solar radiation generally shows a decreasing trend, with significant variations between different areas. These findings are vitally important for guiding urban planning, optimising green infrastructure, and enhancing the urban ecological environment, further promoting sustainable urban development and improving residents' quality of life. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak followed by the strict citywide lockdown in Shanghai has sparked negative emotion surges on social media platforms in 2022. This research aims to investigate the spatial–temporal heterogeneity of a unique emotion (helplessness) and its built environment determinants. First, we scraped about twenty thousand Weibo posts and utilized their sentiments with natural language processing (NLP) to extract helplessness emotion and investigated its spatial–temporal variations. Second, we tested whether "helplessness" was related with urban environment attributes when other real estate economic and demographic variables were controlled using the ordinary least squares (OLS) model. Our results confirmed that helplessness emotion peaked in early April when the lockdown started. Second, residents in neighborhoods characterized by higher rents and property management fees, higher population density, lower housing prices, lower plot ratios, or surrounded by less tree view and higher perceived visual complexity, are found to exhibit higher degree of "helplessness". This study provides an effective data-driven framework to utilize social media data for public sentiments monitoring. The helplessness emotion identified is a unique mental distress under strict quarantine measures, which expands the growing literature of urban governance in the post-pandemic era. Decision makers should pay attention to public opinions and design tailored management measures with reference to civic emotion dynamics to facilitate social sustainability and resilience in face of future crises. [ABSTRACT FROM AUTHOR]

Dockless bike-sharing (DBS) is a green and flexible travel mode, which has been considered as an effective way to address the first-and-last mile problem. A two-level process is developed to identify the integrated DBS–metro trips. Then, DBS trip data, metro passenger data, socioeconomic data, and built environment data in Shanghai are used to analyze the spatiotemporal characteristics of integrated trips and the correlations between the integrated trips and the explanatory variables. Next, multicollinearity tests and autocorrelation tests are conducted to select the best explanatory variables. Finally, a geographically and temporally weighted regression (GTWR) model is adopted to examine the determinants of integrated trips over space and time. The results show that the integrated trips account for 16.8% of total DBS trips and that departure-transfer trips are greater than arrival-transfer trips. Moreover, the integrated trips are concentrated in the central area of the city. In terms of impact factors, it is found that GDP, government count, and restaurant count are negatively correlated with the number of integrated trips, while house price, entropy of land use, transfer accessibility index, and metro passenger flow show positive relationships. In addition, the results show that the GTWR model outperforms the OLS model and the GWR model. [ABSTRACT FROM AUTHOR]

As a driving force for regional development, innovation holds an increasing position in regional competitiveness, and a reasonable and coordinated innovation network structure can promote high-quality regional development. Utilizing the modified gravity model and social network analysis method, an innovation network composed of 27 cities in the Yangtze River Delta urban agglomeration from 2010 to 2021 was studied. The following conclusions were founded: (1) The innovation development level in the Yangtze River Delta urban agglomeration was constantly improving, and the innovation development level generally showed a spatial pattern of high in the southeast and low in the northwest. (2) The intensity and density of innovation network correlations in urban agglomerations were increasing, and the centrality of network nodes had an obvious hierarchical characteristic. The innovation network had a significant core–periphery spatial structure, with core cities that had higher centrality, such as Shanghai, Nanjing, and Hangzhou, playing the role of "intermediaries" and "bridges", while cities with lower centrality, such as Anhui and cities in northern Jiangsu, generally played the role of "periphery actors" in the network. (3) The spatial correlation network of innovation of the Yangtze River Delta urban agglomeration could be divided into four blocks, namely, main benefit, broker, two-way spillover, and net spillover, and the spillover effect among them had obvious gradient characteristics of hierarchy. [ABSTRACT FROM AUTHOR]

Earlier studies have examined various factors that may contribute to the contagion rate of COVID-19, such as urban climatic and socioeconomic characteristics. However, there is a lack of studies at the township level detailing the spatiotemporal settings of built environment attributes, especially in the context of lockdown as a response to the global Omicron outbreak. In this study, we extended the existing literature by relating the initial-stage Omicron pandemic conditions with more comprehensive measures of the built environment, including density, diversity, design, distance to transit, and destination accessibility. The variations from the confirmed clusters of COVID-19 and asymptomatic infected cases before, during, and after the lockdown throughout the Omicron outbreak were identified geographically using GIS methods in 218 township-level divisions across Shanghai during the lockdown period. We also compared the regression results of the ordinary least-squares regression, geographically weighted regression, and geographically and temporally weighted regression. Our results show that (1) among all the built environment variables, metro line length, walking accessibility, hotel and inn density, and population exhibited positive significance in influencing pandemic prevalence; (2) spatial and temporal variations were evident in the association between accessibility, mobility, density-related built environment variables, and COVID-19 transmission across three phases: pre-lockdown, during lockdown, and post-lockdown. This study highlights the importance of targeted public health interventions in densely populated areas with high demand for public transit. It emphasizes the significance of transportation network layout and walking accessibility in controlling the spread of infectious diseases in specific urban contexts. By considering these factors, policymakers and stakeholders can foster urban resilience and effectively mitigate the impact of outbreaks, aligning with the objectives of the 2030 UN Sustainable Development Goals. [ABSTRACT FROM AUTHOR]

The development of the HSR station area is the result of the combined effect of the three elements of transport, place, and industry. This study introduces the industrial dimension and constructs the node-place-industry model to empirically analyze the development potential of station areas along the Hunan section of the Beijing–Guangzhou and the Shanghai–Kunming high-speed railway lines. The results show that (1) the development of the three spatial elements of the station area is mostly out of sync, and the node value has the highest fit with the integrated potential value of the station area; (2) there is a significant correlation between the magnitude of the combined potential of the station area and the site location, station class and time of development; (3) according to the results of the cluster analysis, it was found that most of the stations were in a state of disequilibrium, and the main reason was that the functional value of the place did not match with the value of industrial aggregation. In particular, the introduction of the industry dimension extends the NP model and establishes a tessellated analytical framework for station type classification, providing an interesting assessment tool for the sustainable development of transport hub areas. [ABSTRACT FROM AUTHOR]

Historical geographic data play an important supporting role in the study of long-term geographic studies, such as climate change, urban expansion and land-use and land-cover change. These data vary in source, format and accuracy and are widely found in historical documents, old maps, produced vector data, aerial photographs, old photographs, etc. The complex nature of data makes it difficult for researchers to organize, store and manage in a unified manner. Thus, GIS practitioners and social scientists will collectively face the challenge of integrating historical data into spatial databases. Herein, we introduced the concept of a multi-level spatial grid, selecting Shanghai as the study area, to construct the Shanghai historical geographic database and give the conceptual model and processing method. The experiment was performed using the China Historical Geographic Information System (CHGIS), which showed the historical evolution of Shanghai more conveniently. Meanwhile, we simulated one million rows of historical geographic data in Shanghai and compared the retrieval efficiency of the encoding method with the latitude–longitude and geometric object indexing methods, which demonstrated that our method was very effective. This research is important for the construction of a historical urban database, which can better preserve historical resources and promote urban culture with information science and technology. [ABSTRACT FROM AUTHOR]

Building façade colors play an important role in influencing urban imageability, attraction and citizens' experience. However, the relations between street functions and the building façade color distribution, color harmony and color diversity have not been thoroughly examined. We obtained the dominant colors of building façades in Changning District, Shanghai, utilizing Baidu street view images, image semantic segmentation technology and the K-means algorithm. The variations in building façades' dominant colors, color harmony and diversity across different types of functional streets were examined through logistic regression and ANOVA analyses. The results indicate that, compared to industrial streets, red hues are more common in science education streets, residential streets and mixed functional streets. Business streets are more likely to have hues of green, red and red–purple. Residential streets' saturation is overall higher than that of industrial streets. In business streets, the medium–high value occurs less frequently than other streets. Moreover, we found that the street building façade colors in industrial streets were more harmonious and less diversified than that in other functional streets. This study has implications for urban color planning practices. Color harmony and color diversity should be well considered in future planning. The role of street functions should also be addressed in building façade color planning, to improve existing planning frameworks as well as related strategies. [ABSTRACT FROM AUTHOR]

Transit-oriented development (TOD) is among the most feasible strategies for relieving urban issues caused by the unbalanced development of transportation and land use. This study proposes a multiobjective TOD land use design framework for the optimization of the land use layout in station catchments. Given the high density and diverse development in Chinese megacities, a planning model that considers nonlinear impacts on ridership, land use efficiency, quality of life, and the environment is constructed. The model applies fine-grained geo-big data to fill gaps in the empirical and statistical data and improve practicability. A genetic multiobjective optimization approach without reliance on objective weighting is used to generate alternative land use schemes. A metro station in Shanghai is applied as a case study. The results indicate that the proposed ridership objective outperforms the commonly used linear function, and the optimization method has superior extreme optima and convergence to baseline models. We also discuss the consistencies and conflicts in the objectives and provide a balanced land use scheme considering local policies. This work provides suggestions for sustainable urban design with coordinated land use and transportation. [ABSTRACT FROM AUTHOR]

Numerous Global Positioning System connected vehicles are collecting extensive data remotely in cities, enabling data-driven infrastructure planning. To truly benefit from this emerging technology, it is important to combine telematics and map data to make it easier to extract and mine useful information from the data. By performing map matching, data points that cannot be accurately located on the road network can be projected onto the correct road segment. As an important means of remote data processing, it has become an important pre-processing step in the field of data mining. However, due to the various errors of location devices and the complexity of road networks, map matching technology also faces great challenges. In order to improve the efficiency and accuracy of the map matching algorithm, this study proposes an offline method for low-frequency trajectory data map matching based on vehicle trajectory segmentation. First, the trajectory is segmented based on the vehicle's travel direction. Then, the comprehensive probability of the corresponding road segment is calculated based on the spatial probability and the directional probability of each road segment around the location. Third, the k candidate matching paths under consideration are selected based on the comprehensive probability evaluation. Finally, the shortest path planning and the probability calculation of the different candidate paths are combined to find the optimal matching path. The experimental results on the real trajectory dataset in Shanghai and the road network environment show that the proposed algorithm has better accuracy, efficiency, and robustness than other algorithms. [ABSTRACT FROM AUTHOR]