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This paper is situated within the discussion of mega-urbanization, a particular urbanization process that entails a large-scale agglomeration. In this paper, our focus is on urbanization in Java, Indonesia's most dynamic region. We add to the literature by investigating the change and prediction of the land use/land cover (LULC) of mega-urbanization in Java. This research uses a vector machine approach to support the classification of land cover change dynamics, cellular automata-Markov (CA Markov), and the Klassen typology technique. This paper indicates that major metropolitan areas are still expanding in terms of built-up areas, generating a larger urban agglomeration. However, attention should be also given to the urbanization process outside existing metropolis' boundaries given that more than half of the built-up land coverage in Java is located in non-metropolitan areas. In terms of future direction, the projection results for 2032 show that the Conservative scenario can reduce and slow down the increase in built-up land on the island of Java. On the other hand, the Spatial Plan (RTRW) scenario facilitates a rapid increase in the LULC of built-up land from 2019. The urban spatial dynamics in Java raises challenges for urban and regional planning as the process is taking place across multiple administrative authorities. [ABSTRACT FROM AUTHOR]

To achieve the goals of "carbon peaking and carbon neutrality", this paper puts forward the connotation and measurement method for the carbon emission intensity of urban industrial land and conducts an empirical study with the Yangtze River Economic Belt (YREB) as an example. We defined the carbon intensity of urban industrial land as the industrial carbon emissions per unit area of land, which is a spatial mapping of urban industrial economic development and carbon spillover and a key indicator for urban and territorial spatial planning oriented towards the "dual carbon" goal. Findings: The carbon emission density of industrial land in the YREB varied greatly between cities and exhibited significant positive spatial autocorrelation. In addition, the geographical pattern and spatio-temporal evolution model of the urban industrial land carbon emission density had a very complex driving mechanism, and different factors had significant synergistic effects. Therefore, it is suggested that while striving towards the goal of "dual carbon", the government should incorporate the carbon emission density indicator of urban industrial land into the urban and territorial spatial planning system, and based on the threshold of the medium suitable density, they should design differentiated management policies according to concrete urban policies and encourage cooperation among cities to jointly promote carbon emission management of urban industrial land. In policy design, emphasis should also be placed on highlighting the interactive effects of foreign direct investment, fiscal expenditure, and the number of patent authorizations as well as constructing a combination of policies centered around them to better leverage the impacts of globalization, government intervention, and innovation. [ABSTRACT FROM AUTHOR]

With the sustainable and coordinated development of cities, the formulation of urban street policies requires multiangle analysis. In regard to the existing street research, a large number of studies have focused on specific landscapes or accessibility of streets, and there is a lack of research on the multiple functions of streets. Recent advances in sensor technology and digitization have produced a wealth of data and methods. Thus, we may comprehensively understand streets in a less labor-intensive way, not just single street functions. This paper defines an index system of the multiple functions of urban streets and proposes a framework for multifunctional street measurement. Via the application of deep learning to Baidu Street View (BSV) imagery, we generate three functions, namely, landscape, traffic, and economic functions. The results indicate that street facilities and features are suitably identified. According to the multifunctional perspective, this paper further classifies urban streets into multifunctional categories and provides targeted policy recommendations for urban street planning. There exist correlations among the various street functions, and the correlation between the street landscape and economic functions is highly significant. This framework can be widely applied in other countries and cities to better understand street differences in various cities. [ABSTRACT FROM AUTHOR]

Geocoding converts unstructured geographic text into structured spatial data, which is crucial in fields such as urban planning, social media spatial analysis, and emergency response systems. Existing approaches predominantly model geocoding as a geographic grid classification task but struggle with the output space dimensionality explosion as the grid granularity increases. Furthermore, these methods generally overlook the inherent hierarchical structure of geographical texts and grids. In this paper, we propose a hierarchy-aware geocoding model based on cross-attention within the Seq2Seq framework, incorporating S2 geometry to model geocoding as a task for generating grid labels and predicting S2 tokens (labels of S2 grids) character-by-character. By incorporating a cross-attention mechanism into the decoder, the model dynamically perceives the address contexts at the hierarchical level that are most relevant to the current character prediction based on the input address text. Results show that the proposed model significantly outperforms previous approaches across multiple metrics, with a median and mean distance error of 41.46 m and 93.98 m, respectively. Furthermore, our method achieves superior results compared to others in regions with sparse data distribution, reducing the median and mean distance error by 16.27 m and 7.52 m, respectively, suggesting that our model has effectively mitigated the issue of insufficient learning in such regions. [ABSTRACT FROM AUTHOR]

Land use allocation (LUA) is of prime importance for the development of urban sustainability and resilience. Since the process of planning and managing land use requires balancing different conflicting social, economic, and environmental factors, it has become a complex and significant issue in urban planning worldwide. LUA is usually regarded as a spatial multi-objective optimization (MOO) problem in previous studies. In this paper, we develop an MOO approach for tackling the LUA problem, in which maximum economy, minimum carbon emissions, maximum accessibility, maximum integration, and maximum compactness are formulated as optimal objectives. To solve the MOO problem, an improved non-dominated sorting genetic algorithm III (NSGA-III) is proposed in terms of mutation and crossover operations by preserving the constraints on the sizes for each land use type. The proposed approach was applied to KaMavota district, Maputo City, Mozambique, to generate a proper land use plan. The results showed that the improved NSGA-III yielded better performance than the standard NSGA-III. The optimal solutions produced by the MOO approach provide good trade-offs between the conflicting objectives. This research is beneficial for policymakers and city planners by providing alternative land use allocation plans for urban sustainability and resilience. [ABSTRACT FROM AUTHOR]

It is important to capture passengers' public transit behavior and their mobility to create profiles, which are critical for analyzing human activities, understanding the social and economic structure of cities, improving public transportation, assisting urban planning, and promoting smart cities. In this paper, we develop a generative adversarial machine learning network to characterize the temporal and spatial mobility behavior of public transit passengers, based on massive smart card data and road network data. The Apriori algorithm is extended with spatio-temporal constraints to extract frequent transit mobility patterns of individual passengers based on a reconstructed personal trip dataset. This individual-level pattern information is used to construct personalized feature vectors. For regular and frequent public transit passengers, we identify similar transit mobility groups using spatio-temporal constraints to construct a group feature vector. We develop a generative adversarial network to embed public transit mobility of passengers. The proposed model's generator consists of an auto-encoder, which extracts a low-dimensional and compact representation of passenger behavior, and a pre-trained sub-generator containing generalization features of public transit passengers. Shenzhen City is taken as the study area in this paper, and experiments were carried out based on smart card data, road network data, and bus GPS data. Clustering analysis of embedding vector representation and estimation of the top K transit destinations were conducted, verifying that the proposed method can profile passenger transit mobility in a comprehensive and compact manner. [ABSTRACT FROM AUTHOR]

The visual quality and spatial distribution of architectural styles represent a city's image, influence inhabitants' living conditions, and may have positive or negative social consequences which are critical to urban sensing and designing. Conventional methods of identifying architectural styles rely on human labor and are frequently time-consuming, inefficient, and subjective in judgment. These issues significantly affect the large-scale management of urban architectural styles. Fortunately, deep learning models have robust feature expression abilities for images and have achieved highly competitive results in object detection in recent years. They provide a new approach to supporting traditional architectural style recognition. Therefore, this paper summarizes 22 architectural styles in a study area which could be used to define and describe urban architectural styles in most Chinese urban areas. Then, this paper introduced a Faster-RCNN general framework of architectural style classification with a VGG-16 backbone network, which is the first machine learning approach to identifying architectural styles in Chinese cities. Finally, this paper introduces an approach to constructing an urban architectural style dataset by mapping the identified architectural style through continuous street view imagery and vector map data from a top-down building contour map. The experimental results show that the architectural style dataset created had a precision of 57.8%, a recall rate of 80.91%, and an F1 score of 0.634. This dataset can, to a certain extent, reflect the geographical distribution characteristics of a wide variety of urban architectural styles. The proposed approach could support urban design to improve a city's image. [ABSTRACT FROM AUTHOR]

Pedestrian evacuation is an important measure to ensure disaster safety in central retail districts, the efficiency of which is affected by the synergy of road network planning indices. This paper established the typical forms of central retail district (CRD) road networks in terms of block form, network structure and road grade, taking China as an example. The experiment was designed using the orthogonal design of experiment (ODOE) method and quantified the evacuation time under different road network planning indices levels through the Anylogic simulation platform. Using range and variance analysis methods, the synergy of network density, network connectivity, road type and road width on pedestrian evacuation efficiency were studied from the perspectives of significance, importance and optimal level. The results showed that the type of CRD will affect the importance of network planning indices, and that the network connectivity is insignificant (P 0.477/0.581) in synergy; networks with wide pedestrian primary roads (30.1~40.0 m), medium secondary roads (3.1~5.0 m/side) and high density (11.0~13.0 km/km2) have the highest evacuation efficiency. From the perspective of evacuees, this paper put forward urban design implications on CRD road networks to improve their capacity for disaster prevention and reduction. [ABSTRACT FROM AUTHOR]

As a hot research topic in urban geography, spatiotemporal interaction analysis has been used to detect the hotspot mobility patterns of crowds and urban structures based on the origin-destination (OD) flow data, which provide useful information for urban planning and traffic management applications. However, existing methods mainly focus on the detection of explicit spatial interaction patterns (such as spatial flow clusters) in OD flow data, with less attention to the discovery of underlying crowd travel demands. Therefore, this paper proposes a framework to discover the crowd travel demands by associating the dynamic spatiotemporal interaction patterns and the contextual semantic features of the geographical environment. With urban functional zones (UFZs) as the basic units of human mobility in urban spaces, this paper gives a case study in Wuhan, China, to detect and interpret the human mobility patterns based on the characteristics of spatiotemporal interaction between UFZs. Firstly, we build the spatiotemporal interaction matrix based on the OD flows of different UFZs and analyze the characteristics of the interaction matrix. Then, hotspot poles, defined as the local areas where people gather significantly, are extracted using the Gi-statistic-based spatial hotspot detection algorithm. Next, we develop a frequent interaction pattern mining method to detect the frequent interaction patterns of the hotspot poles. Finally, based on the detected frequent interaction patterns, we discover the travel demands of crowds with semantic features of corresponding urban functional zones. The characteristics of crowd travel distance and travel time are further discussed. Experiments with floating car data, road networks, and POIs in Wuhan were conducted, and results show that the underlying travel demands can be better discovered and interpreted by the proposed framework and methods in this paper. This study helps to understand the characteristics of human movement and can provide support for applications such as urban planning and facility optimization. [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]

Integrating autonomous vehicles (AVs) into urban areas poses challenges for transportation, infrastructure, building, environment, society, and policy. This paper goes beyond the technical intricacies of AVs and takes a holistic, interdisciplinary approach by considering the implications for urban design and transportation infrastructure. Using a complex methodology encompassing various software types such as Simulation of Urban Mobility (SUMO 1.17.0) and STREETMIX, the article explores the results of a simulation that anticipates the implementation of AVs through different market penetration scenarios. We investigate how AVs could enhance the efficiency of transportation networks, reducing congestion and potentially increasing the throughput. However, we also acknowledge the dynamic nature of the scenarios, as new mobility patterns emerge in response to this technological shift. Furthermore, we propose innovative urban design approaches that could harness the full potential of AVs, fostering the development of sustainable and resilient cities. By exploring these design strategies, we hope to provide valuable guidance for urban planners and policymakers as they navigate the challenges and opportunities presented by the integration of these advanced technologies. [ABSTRACT FROM AUTHOR]

The rapid development of the economy promotes the increasing of interactions between cities and forms complex networks. Many scholars have explored the structural characteristics of urban spatial interaction networks in China and have conducted spatio-temporal analyzes. However, scholars have mainly focused on the perspective of static networks and have not understood the dynamic spatial interaction patterns of Chinese cities. Therefore, this paper proposes a research framework to explore the urban dynamic spatial interaction patterns. Firstly, we establish a dynamic urban spatial interaction network according to monthly migration data. Then, the dynamic community detection algorithm, combined with the Louvain and Jaccard matching method, is used to obtain urban communities and their dynamic events. We construct event vectors for each urban community and use hierarchical clustering to cluster event vectors to obtain different types of spatial interaction patterns. Finally, we divide the urban dynamic interaction into three urban spatial interaction modes: fixed spatial interaction pattern, long-term spatial interaction pattern, and short-term spatial interaction pattern. According to the results, we find that the cities in well-developed areas (eastern China) and under-developed areas (northwestern China) mostly show fixed spatial interaction patterns and long-term spatial interaction patterns, while the cities in moderately developed areas (central and western China) often show short-term spatial interaction patterns. The research results and conclusions of this paper reveal the inter-monthly urban spatial interaction patterns in China, provide theoretical support for the policy making and development planning of urban agglomeration construction, and contribute to the coordinated development of national and regional cities. [ABSTRACT FROM AUTHOR]

In this paper, we take the position that cities gain to be represented as three-dimensional spaces populated by scores of micro-scale-built spaces (buildings, rooms, passageways, squares, etc.). Effective algorithms that evaluate place-based accessibility in built structures while considering the indoor spaces' complexity at a fine granularity are essential for indoor–outdoor seamless urban planning, navigation, way findings, and supporting emergencies. We present a comprehensive set of spatial modeling options and visualizations of indoor accessibility for an entire built structure based on various notions of travel impedance. Notably, we consider the metric length of the paths and their cognitive complexities due to topologic, syntactic, or integrated intricacy within our approaches. Our work presents a comprehensive selection of indoor accessibility analysis with a detailed implemental discussion that can be applied as a solid foundation for smart city applications or seamless urban research and planning. The analysis and visualization techniques presented in this paper can be easily applied to analyze and visualize built interior geographic spaces to study accessibility differentials in cities with vast vertical expansion aimed at achieving (or at avoiding) specific accessibility outcomes. [ABSTRACT FROM AUTHOR]

Recent research indicates that information and communication technologies (ICTs) can support social participation in the planning, design and maintenance of public spaces (PDMPS), specifically to create comprehensive knowledge among different stakeholders. However, critics point out that the use of ICTs by planners and decision-makers often ignores the needs of local residents. For this research, we inquired how ICTs can support social participation in PDMPS. Our case study combines a literature review and 21 semi-structured interviews with government officials, non-governmental organisations, academics and architecture/urban planning consultancy companies in Mexico to understand how different stakeholders use ICTs to improve the quality of public spaces. We developed an approach that facilitates the analysis of ICT aspects related to hardware and software supporting social participation in PDMPS. The findings show that Mexico has a base of digital tools requiring technical capacities and spatial literacy at various stages of PDMPS, and ICTs are seen as an opportunity to engage with residents. However, in practice, our interviewees mentioned that ICTs are rarely implemented to support participatory processes due to high costs, a lack of political support and the insufficient technical expertise of technical staff to engage with residents using ICTs. The paper closes with recommendations and suggestions for future research on how ICTs can better support participatory processes in PDMPS. [ABSTRACT FROM AUTHOR]

With the acceleration of urbanization, climate problems affecting human health and safe operation of cities have intensified, such as heat island effect, haze, and acid rain. Using high-resolution remote sensing mapping image data to design scientific and efficient algorithms to excavate and plan urban ventilation corridors and improve urban ventilation environment is an effective way to solve these problems. In this paper, we use unmanned aerial vehicle (UAV) tilt photography technology to obtain high-precision remote sensing image digital elevation model (DEM) and digital surface model (DSM) data, count the city's dominant wind direction in each season using long-term meteorological data, and use building height to calculate the dominant wind direction. The projection algorithm calculates the windward area density of this dominant direction. Under the guidance of K-means, the binarized windward area density map is used to determine each area and boundary of potential ventilation corridors within the threshold range, and the length and angle of each area's fitted elliptical long axis are calculated to extract the ventilation corridors that meet the criteria. On the basis of high-precision stereo remote sensing data from UAV, the paper uses image classification, segmentation, fitting, and fusion algorithms to intelligently mine potential urban ventilation corridors, and the effectiveness of the proposed method is demonstrated through a case study in Zhuji City, Zhejiang Province. [ABSTRACT FROM AUTHOR]

Urban planning and function layout have important implications for the journeys of a large percentage of commuters, which often make up the majority of daily traffic in many cities. Therefore, the analysis and forecast of traffic flow among urban functional areas are of great significance for detecting urban traffic flow directions and traffic congestion causes, as well as helping commuters plan routes in advance. Existing methods based on ride-hailing trajectories are relatively effective solution schemes, but they often lack in-depth analyses on time and space. In the paper, to explore the rules and trends of traffic flow among functional areas, a new spatiotemporal characteristics analysis and forecast method of traffic flow among functional areas based on urban ride-hailing trajectories is proposed. Firstly, a city is divided into areas based on the actual urban road topology, and all functional areas are generated by using areas of interest (AOI); then, according to the proximity and periodicity of inter-area traffic flow data, the periodic sequence and the adjacent sequence are established, and the topological structure is learned through graph convolutional neural (GCN) networks to extract the spatial correlation of traffic flow among functional areas. Furthermore, we propose an attention-based gated graph convolutional network (AG-GCN) forecast method, which is used to extract the temporal features of traffic flow among functional areas and make predictions. In the experiment, the proposed method is verified by using real urban traffic flow data. The results show that the method can not only mine the traffic flow characteristics among functional areas under different time periods, directions, and distances, but also forecast the spatiotemporal change trend of traffic flow among functional areas in a multi-step manner, and the accuracy of the forecasting results is higher than that of common benchmark methods, reaching 96.82%. [ABSTRACT FROM AUTHOR]

With the rapid development of urbanization, fire risk factors have increased greatly, indicating a higher requirement for urban firefighting security. Fire rescue capabilities can be effectively improved by the scientific layout of fire stations, and therefore, the optimal spatial arrangement of fire stations has practical implications for urban safety. In this paper, a method for planning the locations of urban fire stations is presented, taking into account the fire risk points of interest (POIs) data, road networks and fire station planning principles. The combined model method is validated against the nearest facility point model, and the service area model is proposed for the coverage of POIs and regional areas of planned new sites. The efficacy of the model is demonstrated through an improvement in the coverage of crosspoints of the regional area and points of interest (POIs), with increases of 10.20% and 12.43%, respectively. We applied the combined model method to Fengdong New Town, Shaanxi Province, China. A total of 11 new potential sites were proposed to improve the efficiency of spatial coverage, and subsequently, the coverage rate of the POIs and regional area reached 97.66% and 84.80%, respectively. This study provides application guidelines for the decision-making of fire services and the allocation of firefighting resources. [ABSTRACT FROM AUTHOR]

Urban planners have been long interested in understanding how urban structure and activities are mutually influenced. Human mobility and economic activities naturally drive the formation of road network structure and the accessibility of the latter shapes the patterns of movement flow across urban space. In this paper, we perform an exploratory study on the relationship between the street network structure and the intensity of human movement in urban areas. We focus on two cities and we utilize a dataset of geo-tagged tweets that can form a proxy to urban mobility and the corresponding street networks as obtained from OpenStreetMap. We apply three network centrality measures, including closeness, betweenness and straightness centrality, calculated at a global or local scale, as well as under mixed or individual transportation mode (e.g., driving, biking and walking) with its directional accessibility, to uncover the structural properties of urban street networks. We further design an urban area transition network and apply PageRank to capture the intensity of human mobility. Our correlation analysis indicates different centrality metrics have different levels of correlation with the intensity of human movement. The closeness centrality consistently shows the highest correlation (with a coefficient around 0.6 ) with human movement intensity when calculated at a global scale, while straightness centrality often shows no correlation at the global scale or weaker correlation ρ ≈ 0.4 at the local scale. The correlation levels further depend on the type of directional accessibility and of various types of transportation modes. Hence, the directionality and transportation mode, largely ignored in the analysis of road networks, are crucial. Furthermore, the strength of the correlation varies in the two cities examined, indicating potential differences in urban spatial structure and human mobility patterns. [ABSTRACT FROM AUTHOR]

Taxi travel flow patterns and their interday stability play an important role in the planning of urban transportation and public service facilities. Existing studies pay little attention to the stability of the travel flow patterns between days, and it is difficult to consider the impact of dynamic changes in daily travel demand analysis when supporting related decision making. Taxi trajectory data have been widely used in urban taxi travel-pattern analysis. This paper uses the taxi datasets of Shenzhen and New York to analyze and compare the interday stability of the taxi travel spatial structure and the flow volume based on the improved Levenshtein algorithm and geographic flow theory. The results show that (1) interday differences in taxi travel flow are obvious in both spatial structure and flow volume, high-frequency origin–destination (OD) trips are relatively stable; (2) the ODs between the central urban area and surrounding areas exhibit high traffic volume and high interday stability, and the ODs starting or ending at an airport exhibit high traffic stability; (3) one week's data can describe 86% of the overall travel structure and 84% of travel flow in Shenzhen, and one week's New York data can describe 73% of travel structure and 76% of travel flow. There are differences in the travel patterns of people in different cities, and the representativeness of datasets in different cities will be different. These findings can help to better understand the outcomes of taxi travel patterns derived from a relatively short period of data to avoid potential misuse in related decision making. [ABSTRACT FROM AUTHOR]

With the advancement of urbanization, the contradiction in the man–land relationship becomes more and more difficult to ignore. Investigation of the change in urban land use, population distribution and its mechanism can provide powerful assistance for urban planning. Since the changes in urban land use and population distribution is a complex process with spatial heterogeneity, the current methods for describing them are still lacking in both interpretability and spatial differences. In this paper, we combine the expansion phenomena of urban land use and population distribution with the heat equation to understand the mechanism. The particle swarm optimization (PSO) algorithm is used to identify the diffusion coefficient to obtain the diffusion law in the city's development. In this way, the diffusion coefficient identified from the data is directly associated with urban changes. The mechanism of changes in urban land use and population distribution can be explained with the diffusion equation and the diffusion coefficient. Our model is first validated on land use and land cover data, followed by further refinement of the spatial differences in the artificial impervious surface data. The experiment's results imply that by applying the model to the population data, the model's generalization ability has been significantly improved. [ABSTRACT FROM AUTHOR]

As cities continue to grow, the functions of urban areas change and problems arise from previously constructed urban planning schemes. Hence, the actual distribution of urban functional areas needs to be confirmed. POI data, as a representation of urban facilities, can be used to mine the spatial correlation within the city. Therefore it has been widely used for urban functional area extraction. Previous studies are mostly devoted to mining POI linear location relationships and do not comprehensively mine POI spatial information, such as spatial interaction information. This results in less accurate modeling of the relationship between POI-based and urban function types. In addition, they all use Euclidean distance for proximity assessment, which is not realistic. This paper proposes an urban functional area identification method that considers the nonlinear spatial relationship between POIs. First, POI adjacency is determined according to road network constraints, which forms the basis of a co-occurrence matrix. Then, a Global Vectors (GloVe) model is used to train POI category vectors and the feature vectors for each basic research unit are obtained using weighted averages. This is followed by clustering analysis, which is realized by a K-Means++ algorithm. Lastly, the functional areas are labeled according to the POI category ratio, enrichment factors, and mobile phone signal heat data. The model was tested experimentally, using core areas of Zhengzhou City in China as an example. When the results were compared with a Baidu map, we confirmed that making full use of nonlinear spatial relationships between POIs delivers high levels of identification accuracy for urban functional areas. [ABSTRACT FROM AUTHOR]

Understanding public perceptions of images of urban public spaces can guide efforts to improve urban vitality and spatial diversity. The rise of social media data and breakthroughs in deep learning frameworks for computer vision provide new opportunities for studying public perceptions in public spaces. While social media research methods already exist for extracting geo-information on public preferences and emotion analysis findings from geodata, this paper aims at deep learning analysis by building a VGG-16 image classification method that enhanced the research content of images without geo-information. In this study, 1940 Flickr images of the Haihe River in Tianjin were identified in multiple scenes with deep learning. The regularized VGG-16 architecture showed high accuracies of 81.75% for the TOP-1 and 96.75% for the TOP-5 and Grad-CAM visualization modules for the interpretation of classification results. The result of the present work indicate that images of the Haihe River are dominated by skyscrapers, bridges, promenades, and urban canals. After using kernel density to visualize the spatial distribution of Flickr images with geodata, it was found that there are three vitality areas in Haihe River. However, the kernel density result also shows that judging spatial visualization based solely on geodata is incomplete. The spatial distribution can be used as an assistant function in the case of the under-representation of geodata. Collectively, the field of how to apply computer vision to urban design research was explored and extended in this trial study. [ABSTRACT FROM AUTHOR]

Buildings are important entity objects of cities, and the classification of building shapes plays an indispensable role in the cognition and planning of the urban structure. In recent years, some deep learning methods have been proposed for recognizing the shapes of building footprints in modern electronic maps. Furthermore, their performance depends on enough labeled samples for each class of building footprints. However, it is impractical to label enough samples for each type of building footprint shapes. Therefore, the deep learning methods using few labeled samples are more preferable to recognize and classify the building footprint shapes. In this paper, we propose a relation network based method for the recognization of building footprint shapes with few labeled samples. Relation network, composed of embedding module and relation module, is a metric based few-shot method which aims to learn a generalized metric function and predict the types of the new samples according to their relation with the prototypes of these few labeled samples. To better extract the shape features of the building footprints in the form of vector polygons, we have taken the TriangleConv embedding module to act as the embedding module of the relation network. We validate the effectiveness of our method based on a building footprint dataset with 10 typical shapes and compare it with three classical few-shot learning methods in accuracy. The results show that our method performs better for the classification of building footprint shapes with few labeled samples. For example, the accuracy reached 89.40% for the 2-way 5-shot classification task where there are only two classes of samples in the task and five labeled samples for each class. [ABSTRACT FROM AUTHOR]

Energy is a fundamental need of modern society and a basis for economic and social development, and one of the major Sustainable Development Goals (SDG), particularly SDG7. However, the UN's SDG Report 2021 betrays millions of people living without electricity and one-third of the world's population deprived of using modern energy cooking services (MECS) through access to electricity. Achieving the SDG7 requires standard approaches and tools that effectively address the geographical, infrastructural, and socioeconomic characteristics of a (rural) municipality of Nepal. Furthermore, Nepal's Constitution 2015 incorporated a federal system under the purview of a municipality as the local government that has been given the mandate to ensure electricity access and clean energy. To address this, a methodology is developed for local government planning in Nepal in order to identify the optimal mix of electrification options by conducting a detailed geospatial analysis of renewable energy (RE) technologies by exploring accessibility and availability ranging from grid extensions to mini-grid and off-grid solutions, based on (a) life cycle cost and (b) levelized cost of energy. During energy assessment, geospatial and socio-economic data are coupled with household and community level data collected from a mobile survey app, and are exploited to garner energy status-quo and enable local governments to assess the existing situation of energy access/availability and planning. In summary, this paper presents a geo-enabled municipal energy planning method and a comprehensive toolkit to facilitate sustainable energy access to local people. [ABSTRACT FROM AUTHOR]

Urban change detection is an important part of sustainable urban planning, regional development, and socio-economic analysis, especially in regions with limited access to economic and demographic statistical data. The goal of this research is to create a strategy that enables the extraction of indicators from large-scale orthoimages of different resolution with practically acceptable accuracy after a short training process. Remote sensing data can be used to detect changes in number of buildings, forest areas, and other landscape objects. In this paper, aerial images of a digital raster orthophoto map at scale 1:10,000 of the Republic of Lithuania (ORT10LT) of three periods (2009–2010, 2012–2013, 2015–2017) were analyzed. Because of the developing technologies, the quality of the images differs significantly and should be taken into account while preparing the dataset for training the semantic segmentation model DeepLabv3 with a ResNet50 backbone. In the data preparation step, normalization techniques were used to ensure stability of image quality and contrast. Focal loss for the training metric was selected to deal with the misbalanced dataset. The suggested model training process is based on the transfer learning technique and combines using a model with weights pretrained in ImageNet with learning on coarse and fine-tuning datasets. The coarse dataset consists of images with classes generated automatically from Open Street Map (OSM) data and the fine-tuning dataset was created by manually reviewing the images to ensure that the objects in images match the labels. To highlight the benefits of transfer learning, six different models were trained by combining different steps of the suggested model training process. It is demonstrated that using pretrained weights results in improved performance of the model and the best performance was demonstrated by the model which includes all three steps of the training process (pretrained weights, training on coarse and fine-tuning datasets). Finally, the results obtained with the created machine learning model enable the implementation of different approaches to detect, analyze, and interpret urban changes for policymakers and investors on different levels on a local map, grid, or municipality level. [ABSTRACT FROM AUTHOR]

Extracting buildings and roads from remote sensing images is very important in the area of land cover monitoring, which is of great help to urban planning. Currently, a deep learning method is used by the majority of building and road extraction algorithms. However, for existing semantic segmentation, it has a limitation on the receptive field of high-resolution remote sensing images, which means that it can not show the long-distance scene well during pixel classification, and the image features is compressed during down-sampling, meaning that the detailed information is lost. In order to address these issues, Hybrid Multi-resolution and Transformer semantic extraction Network (HMRT) is proposed in this paper, by which a global receptive field for each pixel can be provided, a small receptive field of convolutional neural networks (CNN) can be overcome, and the ability of scene understanding can be enhanced well. Firstly, we blend the features by branches of different resolutions to keep the high-resolution and multi-resolution during down-sampling and fully retain feature information. Secondly, we introduce the Transformer sequence feature extraction network and use encoding and decoding to realize that each pixel has the global receptive field. The recall, F1, OA and MIoU of HMPR obtain 85.32%, 84.88%, 85.99% and 74.19%, respectively, in the main experiment and reach 91.29%, 90.41%, 91.32% and 84.00%, respectively, in the generalization experiment, which prove that the method proposed is better than existing methods. [ABSTRACT FROM AUTHOR]

The demand for participant engagement in urban planning shows a great need for tools that enable communication between stakeholders and make planning processes more transparent. So far, common methods use different tools and platforms independently. This prevents the full potential for effective, efficient, and creative collaboration from being realized. Hence, this paper presents an approach that combines different participation settings (off-site, on-site, and online) by using an interactive touch table and an additional screen, as well as virtual reality (VR) and augmented reality (AR) devices, and synchronizing them in real-time. To fulfill the collaboration requirements, three major technical aspects are addressed in the concept and prototype implementation: Firstly, the demands for various settings and devices require a uniform and cross-device interaction concept. Secondly, all changes in the course of the participation (e.g., adding, manipulating, or removing objects) must be synchronized across all devices in real-time, with very low latency. Thirdly, the various states should be saved persistently during the collaboration process. Detailed empirical usability studies are still pending; however, pretests indicate that the concept is appreciated, and the transferability to other planning processes is given. [ABSTRACT FROM AUTHOR]

Human mobility is closely associated with places. Due to advancements in GPS devices and related sensor technologies, an unprecedented amount of tracking data has been generated in recent years, thus providing a new way to investigate the interactions between individuals and places, which are vital for depicting individual's characteristics. In this paper, we propose a framework for mining individual similarity based on long-term trajectory data. In contrast to most existing studies, which have focused on the sequential properties of individual's visits to public places, this paper emphasizes the essential role of the spatio-temporal interactions between individuals and their personally significant places. Specifically, rather than merely using public geographic databases, which include only public places and lack personal meanings, we attempt to interpret the semantics of places that are significant to individuals from the perspectives of personal behavior. Next, we propose a new individual similarity measurement that incorporates both the spatio-temporal and semantic properties of individual's visits to significant places. By experimenting on real-world GPS datasets, we demonstrate that our approach is more capable of distinguishing individuals and characterizing individual features than the previous methods. Additionally, we show that our approach can be used to effectively measure individual similarity and to aggregate individuals into meaningful subgroups. [ABSTRACT FROM AUTHOR]

Address matching continues to play a central role at various levels, through geocoding and data integration from different sources, with a view to promote activities such as urban planning, location-based services, and the construction of databases like those used in census operations. However, the task of address matching continues to face several challenges, such as non-standard or incomplete address records or addresses written in more complex languages. In order to better understand how current limitations can be overcome, this paper conducted a systematic literature review focused on automated approaches to address matching and their evolution across time. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed, resulting in a final set of 41 papers published between 2002 and 2021, the great majority of which are after 2017, with Chinese authors leading the way. The main findings revealed a consistent move from more traditional approaches to deep learning methods based on semantics, encoder-decoder architectures, and attention mechanisms, as well as the very recent adoption of hybrid approaches making an increased use of spatial constraints and entities. The adoption of evolutionary-based approaches and privacy preserving methods stand as some of the research gaps to address in future studies. [ABSTRACT FROM AUTHOR]

Drawing on 185 cities in the northeastern region of China, this paper improves the radiation model by incorporating the accessibility index to characterize the asymmetric process of economic linkages before HSR in 2007 and after HSR in 2016. Then social network analysis is utilized to examine the impact of HSR on the spatial structure of economic networks, including nodal centrality and community structures. Finally, spatial econometric models are employed to explore the driving factors of nodal centrality in economic networks and some policy implications are proposed. The major findings of this paper are the following. First, HSR services can weaken the core-peripheral inequality of economic linkages and a corridor economy is evident in northeastern China. Second, HSR services have significantly improved the out-degree centrality of prefecture-level cities but have slightly decreased the in-degree centrality of Liaoning. Third, there was a slight decline of coherence in the economic network after the construction of HSR and the within-modular connections were strengthened by HSR. Four, the spatial error model (SEM) is more desirable for explaining the distribution of in-degree centrality. GDP, fixed asset investment, education, population, and fiscal expenditure are important contributors to the in-degree centrality in economic networks. These findings give significant insights into city system planning, integrated transport and land use development, formulating regional poles and the coordinated development across administrative boundaries in northeastern China. [ABSTRACT FROM AUTHOR]

Web-based participatory mapping technologies are being increasingly harnessed by local governments to crowdsource local knowledge and engage the public in urban planning policies as a means of increasing the transparency and legitimacy of planning processes and decisions. We refer to these technologies as "geoparticipation". Current innovations are outpacing research into the use of geoparticipation in participatory planning practices. To address this knowledge gap, this paper investigates the objectives of web-based geoparticipation and uses empirical evidence from online survey responses related to 25 urban planning projects in nine countries across three continents (Europe, North America, and Australia). The survey adopts the objectives of the Spectrum for Public Participation that range from information empowerment, with each category specifying promises about how public input is expected to influence decision-making (IAP2, 2018). Our findings show that geoparticipation can leverage a 'middle-ground' of citizen participation by facilitating involvement alongside consultation and/or collaboration. This paper constitutes a pilot study as a step toward more robust and replicable empirical studies for cross-country comparisons. Empowerment (or citizen control) is not yet a normative goal or outcome for web-based geoparticipation. Our evidence also suggests that information is pursued alongside other objectives for citizen participation, and therefore functions not as a "low-hanging fruit" as portrayed in the literature, but rather as a core component of higher intensities of participation. [ABSTRACT FROM AUTHOR]

It has been suggested that the method of constructing an urban spatial structure typically follows a forward process from planning and design up to expression, as reflected in both graphic and text descriptions of urban planning. Although unorthodox, the original status structures can be extracted and constructed from an existing urban land-use map. This approach not only provides the methodological foundation for urban spatial structure evolution and allows for a comparative and quantitative analysis between the existing and planned conditions, but also lays a theoretical basis for failure in scientific decision making during the planning phase. This study attempts to achieve this by identifying the city centre (a typical element of the urban spatial structure) from urban land use data. The city centre is a special region consisting of several units with particular spatial information, including geometric attributes, topological attributes, and thematic attributes. In this paper, we develop a methodology to support the delineation of the city centre, considering these factors. First, using commercial land data, we characterise the city centre as units based on a series of indicators, including geometric and thematic attributes, and integrate them into a composite index of "urban centrality"; Second, a graph-based spatial clustering method that considers both topological proximity and attribute similarity is designed and used to identify the city centre. The precise boundary of the city centre is subsequently delimited using a shape reconstruction method based on the cluster results. Finally, we present a case study to demonstrate the effectiveness and practicability of the methodology. [ABSTRACT FROM AUTHOR]

Due to the incomprehensive and inconsistent description of spatial and temporal information for city data observed by sensors in various fields, it is a great challenge to share the massive, multi-source and heterogeneous interdisciplinary instant point observation data resources. In this paper, a spatio-temporal enhanced metadata model for point observation data sharing was proposed. The proposed Data Meta-Model (DMM) focused on the spatio-temporal characteristics and formulated a ten-tuple information description structure to provide a unified and spatio-temporal enhanced description of the point observation data. To verify the feasibility of the point observation data sharing based on DMM, a prototype system was established, and the performance improvement of Sensor Observation Service (SOS) for the instant access and insertion of point observation data was realized through the proposed MongoSOS, which is a Not Only SQL (NoSQL) SOS based on the MongoDB database and has the capability of distributed storage. For example, the response time of the access and insertion for navigation and positioning data can be realized at the millisecond level. Case studies were conducted, including the gas concentrations monitoring for the gas leak emergency response and the smart city public vehicle monitoring based on BeiDou Navigation Satellite System (BDS) used for recording the dynamic observation information. The results demonstrated the versatility and extensibility of the DMM, and the spatio-temporal enhanced sharing for interdisciplinary instant point observations in smart cities. [ABSTRACT FROM AUTHOR]

This paper investigates the effect of four driving forces, including elevation, slope, distance to drainage and distance to major roads, on urban expansion in five Saudi Arabian cities: Riyadh, Jeddah, Makkah, Al-Taif and Eastern Area. The prediction of urban probabilities in the selected cities based on the four driving forces is generated using a logistic regression model for two time periods of urban change in 1985 and 2014. The validation of the model was tested using two approaches. The first approach was a quantitative analysis by using the Relative Operating Characteristic (ROC) method. The second approach was a qualitative analysis in which the probable urban growth maps based on urban changes in 1985 is used to test the performance of the model to predict the probable urban growth after 2014 by comparing the probable maps of 1985 and the actual urban growth of 2014. The results indicate that the prediction model of 2014 provides a reliable and consistent prediction based on the performance of 1985. The analysis of driving forces shows variable effects over time. Variables such as elevation, slope and road distance had significant effects on the selected cities. However, distance to major roads was the factor with the most impact to determine the urban form in all five cites in both 1985 and 2014. [ABSTRACT FROM AUTHOR]

Urbanization is leading us to a more chaotic state where healthy living becomes a prime concern. The high-rise buildings influence the urban setting with a high shadow rate on surroundings that can have no positive impact on the general neighborhood. Nevertheless, shadows are the main factor of defeatist virtual settings, they are expensive to render in real-time. This paper investigates how the amount of sunlight varies by season and how seasons can indicate the time of year to understand how shadows vary in length at different times of the day and how they change over the seasons. We propose a novel efficient (fast and scalable) algorithm to calculate a 2.5D cast-shadow map from a given LiDAR-derived Digital Surface Model (DSM). We present a proof-of-concept demonstration to examine the technical practicability of the introduced algorithm. Tensor-based techniques such as singular value decomposition, tensor unfolding are examined and deployed to represent the multidimensional data. The proposed method exploits horizon mapping ideas and extends the method to a modern graphics algorithm (Bresenham's line drawing algorithm) to account for the DSM's underlying surface geometry. A proof-of-concept is developed utilizing Python's TensorFlow library, exploring data flow graphs and the tensor data structure. The heavy computer graphics algorithm used in this paper is parallelized using PySpark. Results explicate significant enhancements in overall performance while preserving accuracy at negligible variations. [ABSTRACT FROM AUTHOR]

The principal aim of this paper is to present the capabilities of newly developed GIS tools for measurement analysis of urban spatial layouts, using the square grid method. The study of urban morphology and metrology is a multistage process, which involves the metrological analysis of town plans. The main research step is the determination of measurement modules of town layouts, using the square grid. By using GIS software, the authors developed a new tool, named HGIS Tools, which allow to create any number of modular grids with the selected cell size that corresponds to urban units of distance and surface area. When investigating a large number of towns and cities, this offers a significant improvement of the research procedure. The paper presents a test of the tool's potential on the example of regular medieval towns from the area of the former Teutonic Order state (currently the territory of Poland), diversified in terms of size, genesis and morphometrics. The obtained results confirmed that HGIS Tools allowed to determine the hypothetical measurement module of the layout of the cities studied. The results were consistent with the analyses of other authors carried out with the traditional grid-square methods. The test of the HGIS Tools showed their significant potential in conducting morphometric analyses of spatial arrangements of urban spatial layout on a larger scale. [ABSTRACT FROM AUTHOR]

The rapid expansion of cities brings in new challenges for the urban firefighting security, while the increasing fire frequency poses serious threats to the life, property, and safety of individuals living in cities. Firefighting in cities is a challenging task, and the optimal spatial arrangement of fire stations is critical to firefighting security. However, existing researches lack any consideration of the negative effects of the spatial randomness of fire outbreaks and delayed response time due to traffic jams upon the site selection. Based on the set cover location model integrated with the spatiotemporal big data, this paper combines the fire outbreak point with the traffic situation. The presented site selection strategy manages to ensure the arrival of the firefighting task force at random simulated fire outbreak points within the required time, under the constraints of the actual city planning and traffic situation. Taking Nanjing city as an example, this paper collects multi-source big data for the comprehensive analysis, including the full data of the fire outbreak history from June 2014 to June 2018, the traffic jam data based on the Amap, and the investigation data of the firefighting facilities in Nanjing. The regularity behind fire outbreaks is analyzed, the factors related to fire risks are identified, and the risk score is calculated. The previous fire outbreak points are put through the clustering analysis, the spatial distribution probability at points in each cluster is calculated according to the clustering score, and the random fire outbreak points are generated via the Monte Carlo simulation. Meanwhile, the objective emergency response time is set as five minutes. The average vehicle speed for each road in the urban area is calculated, and the actual traffic network model is built to compute the travel time from massive randomly-distributed simulated fire points. The problem is solved by making the travel time for all simulated demand points below five minutes. At last, the site selection result based on our model is adjusted and validated, according to the planned land use. The presented method incorporates the view of the spatiotemporal big data and provides a new idea and technical method for the modification and efficiency improvement of the fire station site selection model, contributing to a service cover ratio increase from 58% to 90%. [ABSTRACT FROM AUTHOR]

Automatic curb detection is an important issue in road maintenance, three-dimensional (3D) urban modeling, and autonomous navigation fields. This paper is focused on the segmentation of curbs and street boundaries using a 3D point cloud captured by a mobile laser scanner (MLS) system. Our method provides a solution based on the projection of the measured point cloud on the XY plane. Over that plane, a segmentation algorithm is carried out based on morphological operations to determine the location of street boundaries. In addition, a solution to extract curb edges based on the roughness of the point cloud is proposed. The proposed method is valid in both straight and curved road sections and applicable both to laser scanner and stereo vision 3D data due to the independence of its scanning geometry. The proposed method has been successfully tested with two datasets measured by different sensors. The first dataset corresponds to a point cloud measured by a TOPCON sensor in the Spanish town of Cudillero. The second dataset corresponds to a point cloud measured by a RIEGL sensor in the Austrian town of Horn. The extraction method provides completeness and correctness rates above 90% and quality values higher than 85% in both studied datasets. [ABSTRACT FROM AUTHOR]

The Right to Build is defined by textual elements that determine what an owner can build on a parcel. Such regulations contain elements that can influence the development of territories. Expressed through legal texts, their effects on the territory are difficult to assess because of the documents' complexity and of the diversity of urban configurations. In this paper, we present a generic and extendable model to represent such regulations. This model is based on (1) a representation of geographical concepts (attributes, features and relations) mentioned in regulations and (2) rules formalized with Object Constraints Language (OCL). We also propose an implementation that allows the handling of formalized rules in order to check if a building configuration proposal respects urban regulations. Many applications are possible in order to assist in the conception of such regulations, land acquisition strategy or territorial evolution studies, in this article, we notably describe a future application dedicated to assist building permit surveyors. [ABSTRACT FROM AUTHOR]

Urban road traffic spatio-temporal characters reflect how citizens move and how goods are transported, which is crucial for trip planning, traffic management, and urban design. Video surveillance camera plays an important role in intelligent transport systems (ITS) for recognizing license plate numbers. This paper proposes a spatio-temporal visualization method to discover urban road vehicle density, city-wide regional vehicle density, and hot routes using license plate number data recorded by video surveillance cameras. To improve the accuracy of the visualization effect, during data analysis and processing, this paper utilized Internet crawler technology and adopted an outlier detection algorithm based on the Dixon detection method. In the design of the visualization map, this paper established an urban road vehicle traffic index to intuitively and quantitatively reveal the traffic operation situation of the area. To verify the feasibility of the method, an experiment in Guiyang on data from road video surveillance camera system was conducted. Multiple urban traffic spatial and temporal characters are recognized concisely and efficiently from three visualization maps. The results show the satisfactory performance of the proposed framework in terms of visual analysis, which will facilitate traffic management and operation. [ABSTRACT FROM AUTHOR]

With the gradual emergence of the separation and dislocation of urban jobs-housing space, rational planning of urban jobs-housing space has become the core issue of national land-spatial planning. To study the existing relationship between workspaces and living spaces, a new method to identify jobs-housing space is proposed, which not only considers the static spatial distribution of urban public facilities but also identifies the jobs-housing space by analyzing the real mobility characteristics of people from a humanistic perspective. This method provides a new framework for the identification of urban jobs-housing space by integrating point-of-interest (POI) and trajectory data. The method involves three steps: Firstly, based on the trajectory data, we analyze the characteristics of the dynamic flow of passengers in the grid and construct the living factors and working factors to identify the distribution of jobs-housing space. Secondly, we reclassify the POIs to calculate the category ratios of different types of POIs in the grid to identify the jobs-housing space. Finally, an OR operation is performed on the results obtained by the two methods to obtain the final recognition result. We selected Haikou City as the experimental area to verify the method proposed in this paper. The experimental results show that the recognition accuracy of the travel flow model is 72.43%, the POI quantitative recognition method's accuracy is 74.94%, and the accuracy of the method proposed in this paper is 85.90%, which is significantly higher than the accuracy of the previous two methods. Therefore, the method proposed here can serve as a reference for subsequent research on urban jobs-housing space. [ABSTRACT FROM AUTHOR]

Technological advances have enabled new sources of geoinformation, such as geosocial media, and have supported the propagation of the concept of smart cities. This paper argues that a city cannot be smart without citizens in the loop, and that a geosocial sensor might be one component to achieve that. First, we need to better understand which facets of urban life could be detected by a geosocial sensor, and how to calibrate it. This requires replicable studies that foster longitudinal and comparative research. Consequently, this paper examines the relationship between geosocial media content and socio-demographic census data for a global city, London, at two administrative levels. It aims for a transparent study design to encourage replication, using Term Frequency—Inverse Document Frequency of keywords, rule-based and word-embedding sentiment analysis, and local cluster analysis. The findings of limited links between geosocial media content and socio-demographic characteristics support earlier critiques on the utility of geosocial media for smart city planning purposes. The paper concludes that passive listening to publicly available geosocial media, in contrast to pro-active engagement with citizens, seems of limited use to understand and improve urban quality of life. [ABSTRACT FROM AUTHOR]

Sustainable development is a key component in urban studies. Earth Observation (EO) can play a valuable role in sustainable urban development and planning, since it represents a powerful data source with the potential to provide a number of relevant urban sustainability indicators. To this end, in this paper we propose a conceptual list of EO-based indicators capable of supporting urban planning and management. Three cities with different typologies, namely Basel, Switzerland; Tel Aviv, Israel; and Tyumen, Russia were selected as case studies. The EO-based indicators are defined to effectively record the physical properties of the urban environment in a diverse range of environmental sectors such as energy efficiency, air pollution and public health, water, transportation and vulnerability to hazards. The results assess the potential of EO to support the development of a set of urban environmental indicators towards sustainable urban planning and management. [ABSTRACT FROM AUTHOR]

Urban systems involve a multitude of closely intertwined components, which are more measurable than before due to new sensors, data collection, and spatio-temporal analysis methods. Turning these data into knowledge to facilitate planning efforts in addressing current challenges of urban complex systems requires advanced interdisciplinary analysis methods, such as urban informatics or urban data science. Yet, by applying a purely data-driven approach, it is too easy to get lost in the 'forest' of data, and to miss the 'trees' of successful, livable cities that are the ultimate aim of urban planning. This paper assesses how geospatial data, and urban analysis, using a mixed methods approach, can help to better understand urban dynamics and human behavior, and how it can assist planning efforts to improve livability. Based on reviewing state-of-the-art research the paper goes one step further and also addresses the potential as well as limitations of new data sources in urban analytics to get a better overview of the whole 'forest' of these new data sources and analysis methods. The main discussion revolves around the reliability of using big data from social media platforms or sensors, and how information can be extracted from massive amounts of data through novel analysis methods, such as machine learning, for better-informed decision making aiming at urban livability improvement. [ABSTRACT FROM AUTHOR]

Social media apps provide analysts with a wide range of data to study behavioral aspects of our everyday lives and to answer societal questions. Although social media data analysis is booming, only a handful of prominent social media apps, such as Twitter, Foursquare/Swarm, Facebook, or LinkedIn are typically used for this purpose. However, there is a large selection of less known social media apps that go unnoticed in the scientific community. This paper reviews 110 social media apps and assesses their potential usability in geospatial research through providing metrics on selected characteristics. About half of the apps (57 out of 110) offer an Application Programming Interface (API) for data access, where rate limits, fee models, and type of spatial data available for download vary strongly between the different apps. To determine the current role and relevance of social media platforms that offer an API in academic research, a search for scientific papers on Google Scholar, the Association for Computing Machinery (ACM) Digital Library, and the Science Core Collection of the Web of Science (WoS) is conducted. This search revealed that Google Scholar returns the highest number of documents (Mean = 183,512) compared to ACM (Mean = 1895) and WoS (Mean = 1495), and that data and usage patterns from prominent social media apps are more frequently analyzed in research studies than those of less known apps. The WoS citation database was also used to generate lists of themes covered in academic publications that analyze the 57 social media platforms that offer an API. Results show that among these 57 platforms, for 26 apps at least some papers evolve around a geospatial discipline, such as Geography, Remote Sensing, Transportation, or Urban Planning. This analysis, therefore, connects apps with commonly used research themes, and together with tabulated API characteristics can help researchers to identify potentially suitable social media apps for their research. Word clouds generated from titles and abstracts of papers associated with the 57 platforms, grouped into seven thematic categories, show further refinement of topics addressed in the analysis of social media platforms. Considering various evaluation criteria, such as provision of geospatial data or the number (i.e., absence) of currently published research papers in connection with a social media platform, the study concludes that among the numerous social media apps available today, 17 less known apps deserve closer examination since they might be used to investigate previously underexplored research topics. It is hoped that this study can serve as a reference for the analysis of the social media landscape in the future. [ABSTRACT FROM AUTHOR]

In recent years, with the development of various types of public transportation, they are also more and more closely connected. Among them, subway transportation has become the first choice of major cities. However, the planning of subway stations is very difficult and there are many factors to consider. Besides, few methods for selecting optimal station locations take other public transport in to consideration. In order to study the relationship between different types of public transportation, the authors collected and analyzed the travel data of subway passengers and the passenger trajectory data of taxis. In this paper, a method based on LeaderRank and Gaussian Mixed Model (GMM) is proposed to conduct subway station locations selection. In this method, the author builds a subway-passenger traffic zone weighted network and a station location prediction model. First, we evaluate the nodes in the network, then use the GPS track data of taxis to predict the location of new stations in future subway construction, and analyze and discuss the land use characteristics in the prediction area. Taking the design of the Beijing subway line as an example, the suitability of this method is illustrated. [ABSTRACT FROM AUTHOR]

During the period of rapid urbanization, the urban fringe area is the area where urban expansion occurs first, and land use change is the most active. Studying its evolution laws and characteristics is of great significance to urban planning and urban expansion, and the primary task of fringe area research is the spatial recognition and boundary division of urban fringe area. The previous methods for defining urban fringe areas are mainly divided into qualitative division based on experience and quantitative division based on indicators constructing. This research avoids the construction of index systems and the selection of mathematical models and improves the objectivity of the experiment. Based on the existing methods, this research considers the correlation between the difference of industrial distribution within cities and the urban spatial structure and spatial distribution of urban elements and considers the distance decay law of urban density. The urban fringe area in this research is defined as the distinction region of the service and manufacturing industry extending outward from the inside of the city. First, calculate the POI density of service industry and manufacturing industry. Then look for the inflection point where its density value drops sharply and get the isoline of that point. The range within the isoline is that the industry extends outward from the inner city and has reached the saturation state. Two types of industries can determine two isolines, and the belt region between those isolines is the urban fringe area. We use the urban fringe area identified from the impervious surface data to verify the result. The comparative results show that the identification method of urban fringe area based on POI works effectively, and it can successfully identify the multi-center urban core area. The method mentioned in this paper provides a new idea from the perspective of industrial activities in identifying and defining the belt region of urban fringe area. [ABSTRACT FROM AUTHOR]

The delimitation of permanent basic farmland is essentially a multi-objective optimization problem. The traditional demarcation methods cannot simultaneously take into account the requirements of cultivated land quality and the spatial layout of permanent basic farmland, and it cannot balance the relationship between agriculture and urban development. This paper proposed a multi-objective permanent basic farmland delimitation model based on an immune particle swarm optimization algorithm. The general rules for delineating the permanent basic farmland were defined in the model, and the delineation goals and constraints have been formally expressed. The model introduced the immune system concepts to complement the existing theory. This paper describes the coding and initialization methods for the algorithm, particle position and speed update mechanism, and fitness function design. We selected Xun County, Henan Province, as the research area and set up control experiments that aligned with the different targets and compared the performance of the three models of particle swarm optimization (PSO), artificial immune algorithm (AIA), and the improved AIA-PSO in solving multi-objective problems. The experiments proved the feasibility of the model. It avoided the adverse effects of subjective factors and promoted the scientific rationality of the results of permanent basic farmland delineation. [ABSTRACT FROM AUTHOR]

In order to take full advantage of the landscape resources in the city's featured landscape planning, and mutually integrate ecological green land with city space, this paper takes the mountainous city, Qianxi County, as the research subject to conduct an ecological sensitivity analysis with the GIS space analytical method, while adopting the Analytic Hierarchy Process (AHP) method to find a landscape resource assessment system for Qianxi County. Based on the analysis of the mountainous city landscape pattern characteristics and ecological adaptability, the paper combines with the landscape planning practice in Qianxi County and starts from the ecological pattern construction and urban landscape resource assessment to expound the methodological guidance function of the GIS-AHP analytical method for the mountainous city landscape planning. This method helps recognize the characteristics of the city landscape resources in an all-sided way that protects the city landscape, improves the use-value of the mountainous city landscape resources, integrates the city land area with the water area landscape's green land and builds an ecological, cultural, and habitable mountainous city featured landscape pattern. [ABSTRACT FROM AUTHOR]

A quantitative analysis of rural development is required to comprehend the spatial differentiation of a rural area and promote rural sustainable development under the pressure of urbanization and industrialization, especially areas with dramatic changes in rural socioeconomic development of China and other developing countries. Taking Wuhan as the case study, this paper developed an index system including rural settlement, land, industry and human settlement environment for evaluating the level of rural development. Then, using the exploratory spatial data analysis, the principal component analysis and the cluster analysis, this paper analyzes the spatial differentiation and correlation and categorizes the types of rural development. The results are as follows. (1) The spatial differentiation of the level of rural development in Wuhan City's new urban districts is obvious and the areas with a high level of rural development are mainly distributed at the intersection of the new and central urban areas and gradually decrease outward. (2) There is a significant spatial agglomeration of the developed rural areas and the structure of the spatial change in these areas resembles a certain continuity, specifically a circle of "central heat surrounding cold". (3) Rural development in the new urban areas can be divided into the following five types: the ecological leisure type, the traditional farming type, the balanced development type, the industrial-and-agricultural mixed type and the industrial promotion type. The corresponding development path is proposed in combination with different types of rural development to provide a theoretical basis and decision-making reference for rural revitalization. [ABSTRACT FROM AUTHOR]