ISPRS Int. J. Geo-Inf., Volume 12, Issue 3 (March 2023) – 53 articles

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. Full article

Physical activity (PA) plays a vital role in children’s physical and mental health. The built, natural, and socio-demographic environmental variables affect children’s PA behaviors in various ways. However, few studies focus on systematically measuring the environmental spatiality to enhance PA research. We propose a Physical activity Access Disparity (PAD) index for children. This study aims to design, test, and apply an integrated approach to the children’s PAD index. We adopt five dimensions of “access” to healthcare to measure the children’s PAD index for the United States (US) and the state of Georgia at the county level. The PAD index sorts 18 environmental measures with 23 variables into accessibility, availability, accommodation, affordability, and acceptability (5 As) for children’s PA. We use the self-organizing map (SOM) method to measure how the 5 As affect the PAD index values. According to the result, the children’s PAD index’s ranking normalizes from 0 to 1 and identifies “play oases” to “play deserts” in the US and Georgia using diverse 5 As combinations. The children’s PAD index shows Low disparity in the north and coastal region and High disparity in Deep South states in the US. Moreover, the PAD index shows Low disparity and High disparity in the north and south of Georgia. The PAD index provides a valuable tool for researchers and policymakers to analyze disparity in children’s “access” to the PA environment. The flexible parameters and the weighing scheme also extend the method’s generality and allow users to customize the PAD index based on local preferences and conditions. Full article

With the rapid development of information technology, virtual reality and gesture interaction have been gradually applied in the research and development of map navigation systems. Traditional visualization methods are no longer suitable for this novel interactive map. This research offers a dynamic visualization plan for a virtual reality (VR) navigation map focusing on natural gesture interaction to give examples for creating similar systems. The principal work is composed of two experiments. The first experiment focuses on designing map navigation gestures (moving, rotating, and zooming). Heuristic experiments are used to collect users’ subjective preferences and design suggestions about gestures. The second experiment is designed as a behavioral study to investigate which types of gestures and visualizations, among those obtained from the heuristic experiment in the first part, yield higher performance in our specific scenario. This result offers a practical VR map dynamic display approach through experimental validation. It also provides the basis for a human factor and technology support for future investigations. Full article

Lane line detection is a fundamental and critical task for geographic information perception of driverless and advanced assisted driving. However, the traditional lane line detection method relies on manual adjustment of parameters, and has poor universality, a heavy workload, and poor robustness. Most deep learning-based methods make it difficult to effectively balance accuracy and efficiency. To improve the comprehensive perception ability of lane line geographic information in a natural traffic environment, a lane line detection algorithm based on a mixed-attention mechanism residual network (ResNet) and row anchor classification is proposed. A mixed-attention mechanism is added after the backbone network convolution, normalization and activation layers, respectively, so that the model can focus more on important lane line features to improve the pertinence and efficiency of feature extraction. In addition, to achieve faster detection speed and solve the problem of no vision, the method of lane line location selection and classification based on the row direction is used to detect whether there are lane lines in each candidate point according to the row anchor, reducing the high computational complexity caused by segmentation on a pixel-by-pixel basis of traditional semantic segmentation. Based on TuSimple and CurveLane datasets, multi-scene, multi-environment, multi-linear road image datasets and video sequences are integrated and self-built, and several experiments are designed and tested to verify the effectiveness of the proposed method. The test accuracy of the mixed-attention mechanism network model reached 95.96%, and the average time efficiency is nearly 180 FPS, which can achieve a high level of accuracy and real-time detection process. Therefore, the proposed method can meet the safety perception effect of lane line geographic information in natural traffic environments, and achieve an effective balance between the accuracy and efficiency of actual road application scenarios. Full article

Based on the data of existing warehouse supermarkets in Liaoning Province, China, spatial autocorrelation analysis, kernel density analysis, composite correlation coefficient analysis and other methods have been adopted to analyze their spatial-temporal evolution pattern to reflect the general law of the development of China’s existing warehouse supermarkets and fill the gap in this research field. The results show that the spatial distribution of warehouse supermarkets in Liaoning Province is extremely uneven, and areas with high nuclear density are distributed along the “Shenyang-Dalian” line belonging to the aggregation distribution. The Lorentz curve shows a downward trend with a large degree of spatial imbalance, that is, the regional concentration of warehouse supermarkets is high. Through global and local autocorrelation analysis, the regions with similar development levels of warehouse supermarkets in Liaoning Province tend to gather together, and the spatial distribution has a strong correlation. The distribution of warehousing supermarkets in Liaoning Province is affected by traffic location conditions, economic conditions, population quantity and population density, the number of urban functional areas, policy conditions and the role of the government, especially by economic conditions. Full article

Quantifying traffic congestion is a critical task for transportation planning and research. Numerous metrics have been developed, mainly focusing on changes in vehicle speeds, their extents, and travel time. In this study, new metrics are presented using the Hägerstrand’s space-time cube that has been studied from time geography perspectives since the 1960s. Particularly, the product of distance and time, i.e., distanceTime, is proposed as a base metric to measure traffic congestion amounts. Using the base metric such as mileHours, metrics of weighted congestion and normalized congestion amounts were also developed. New metrics were applied to six metropolitan areas and their vicinities in the United States (Atlanta, Chicago, Washington, D.C. and Baltimore, Dallas and Fort Worth, Los Angeles, and New York), and congestion amounts were calculated and compared. The Google Traffic Layer API was used to obtain traffic congestion datasets for six months (April–September 2022), and GIS (geographic information systems) was used for delineating road features and traffic intensity levels. Among the six areas, New York and its vicinity showed the largest congestion when only heavy congestion was used. Los Angeles and its vicinity showed the largest congestion when all congestion levels were considered. This study shows that the proposed metrics are very effective in summarizing traffic amounts and broadly applicable for further analyses of traffic congestion phenomena by associating various other factors, such as weekdays, months, or gas prices. The new metrics developed in this research may help transportation researchers and practitioners by providing them with a set of metrics applicable to summarizing congestion amounts by synthesizing congestion intensity, extent, and duration. Full article

Under the background of intensified human activities and global climate warming, the frequency and intensity of flood disasters have increased, causing many casualties and economic losses every year. Given the difficulty of mountain shadow removal from large-scale watershed flood monitoring based on Sentinel-1 SAR images and the Google Earth Engine (GEE) cloud platform, this paper first adopted the Support Vector Machine (SVM) to extract the water body information during flooding. Then, a function model was proposed based on the mountain shadow samples to remove the mountain shadows from the flood maps. Finally, this paper analyzed the flood disasters in the middle and lower basin of the Yangtze River (MLB) in 2020. The main results showed that: (1) compared with the other two methods, the SVM model had the highest accuracy. The accuracy and kappa coefficients of the trained SVM model in the testing dataset were 97.77% and 0.9521, respectively. (2) The function model proposed based on the samples achieved the best effect compared with other shadow removal methods with a shadow recognition rate of 75.46%, and it alleviated the interference of mountain shadows for flood monitoring in a large basin. (3) The flood inundated area was 8526 km², among which, cropland was severely affected (6160 km²). This study could provide effective suggestions for relevant stakeholders in policy making. Full article

Point symbols on a scanned topographic map (STM) provide crucial geographic information. However, point symbol recognition entails high complexity and uncertainty owing to the stickiness of map elements and singularity of symbol structures. Therefore, extracting point symbols from STMs is challenging. Currently, point symbol recognition is performed primarily through pattern recognition methods that have low accuracy and efficiency. To address this problem, we investigated the potential of a deep learning-based method for point symbol recognition and proposed a deep convolutional neural network (DCNN)-based model for this task. We created point symbol datasets from different sources for training and prediction models. Within this framework, atrous spatial pyramid pooling (ASPP) was adopted to handle the recognition difficulty owing to the differences between point symbols and natural objects. To increase the positioning accuracy, the k-means++ clustering method was used to generate anchor boxes that were more suitable for our point symbol datasets. Additionally, to improve the generalization ability of the model, we designed two data augmentation methods to adapt to symbol recognition. Experiments demonstrated that the deep learning method considerably improved the recognition accuracy and efficiency compared with classical algorithms. The introduction of ASPP in the object detection algorithm resulted in higher mean average precision and intersection over union values, indicating a higher recognition accuracy. It is also demonstrated that data augmentation methods can alleviate the cross-domain problem and improve the rotation robustness. This study contributes to the development of algorithms and the evaluation of geographic elements extracted from STMs. Full article

Many events such as large-scale activities and traffic accidents could cause an increase in vehicle density in an area, which makes the evacuation of vehicles important. However, the existing evacuation methods are not efficient limit to multi-vehicles sequences or destinations. In this paper, we introduce a novel dynamic weighted road network model for route planning. Based on the model, the route planning algorithm can obtain higher search efficiency while avoiding congested roads. For multi-vehicles evacuation, we propose a spatial diversity theory to evaluate the overlaps of routes between vehicles to be evacuated and those already evacuated. To verify the efficiency and effectiveness of our model, we conducted experiments on real road network. The results showed that our methods and algorithms can provide more reasonable paths and manage the process more efficiently. Full article

The application of AR to explore augmented map representation has become a research hotspot due to the growing application of AR in maps and geographic information in addition to the rising demand for automated map interpretation. Taking the AR map as the research object, this paper focuses on AR map tracking and registration and the virtual–real fusion method based on element recognition. It strives to establish a new geographic information visualization interface and application model. AR technology is applied to the augmented representation of 2D planar maps. A step-by-step identification and extraction method of unmarked map elements are designed and proposed based on the analysis of the characteristics of planar map elements. This method combines the spatial and attribute characteristics of point-like elements and line-like elements, extracts the color, geometric features, and spatial distribution of map elements through computer vision methods, and completes the identification and automatic extraction of map elements. The multi-target image recognition and extraction method based on template and contour matching, and the line element recognition and extraction method based on color space and area growth are introduced in detail. Then, 3D tracking and registration is used to realize the unmarked tracking and registration of planar map element images, and the AR map virtual–real fusion algorithm is proposed. The experimental results and results of an analysis of stepwise identification and extraction of unmarked map elements and map virtual–real fusion reveal that the stepwise identification of unmarked map elements and map model virtual–real fusion studied in this paper is effective. Through the analysis of map element step-by-step recognition efficiency and recognition rate, it is proved that the element step-by-step method in this paper is fast, its recognition efficiency meets the AR real-time requirements, and its recognition accuracy is high. Full article

Mobile videos contain a large amount of data, where the information interesting to the user can either be discrete or distributed. This paper introduces a method for fusing 3D geographic information systems (GIS) and video image textures. For the dynamic fusion of video in 3DGIS where the position and pose angle of the filming device change moment by moment, it integrates GIS 3D visualization, pose resolution and motion interpolation, and proposes a projection texture mapping method for constructing a dynamic depth camera to achieve dynamic fusion. In this paper, the accuracy and time efficiency of different systems of gradient descent and complementary filtering algorithms are analyzed mainly by quantitative analysis method, and the effect of dynamic fusion is analyzed by the playback delay and rendering frame rate of video on 3DGIS as indicators. The experimental results show that the gradient descent method under the Aerial Attitude Reference System (AHRS) is more suitable for the solution of smartphone attitude, and can control the root mean square error of attitude solution within 2°; the delay of video playback on 3DGIS is within 29 ms, and the rendering frame rate is 34.9 fps, which meets the requirements of the minimum resolution of human eyes. Full article

Concerns about the expanding human population’s adequate supply of food draw attention to the field of Food Security. Future-focused analysis and processing of agricultural data not only improve planning capabilities in this field but also enables the required precautions to be taken beforehand. However, given the breadth and number of these regions, field research would be an expensive and time-consuming endeavour. With the advent of remote sensing and optical sensors, it is now possible to acquire diverse data remotely, quickly, and inexpensively. This study investigated the limitations and capabilities of remote sensing data application in the field of planning Food Security. As a result, Sentinel 2 and Shuttle Radar Topography Mission (SRTM) data were used to estimate winter wheat yields with a high degree of accuracy (98.03%) using the Mamatkulov technique and the MEDALUS model, which was both free and widely available. This method can make it possible to make predictions about the productivity of newly created crop fields or for which we do not have information about the productivity of previous years, without the need to wait for building regression models or any field studies. Considering the outcome, wide-range and larger analyses on this topic can be carried through. Full article

Gridded gross domestic product (GDP) data are a crucial land surface parameter for many geoscience applications. Recently, machine learning approaches have become powerful tools in generating gridded GDP data. However, most machine learning approaches for gridded GDP estimation seldom consider the geographical properties of input variables. Therefore, in this study, a geographically weighted stacking ensemble learning approach was developed to generate gridded GDP data. Three algorithms—random forest, XGBoost, and LightGBM—were used as base models, and the linear regression in stacking ensemble learning was replaced by geographically weighted regression to locally fuse the three predictions. A case study was conducted in China to demonstrate the effectiveness of the proposed approach. The results showed that the proposed GDP downscaling approach outperformed the three base models and traditional stacking ensemble learning. Meanwhile, it had good predictive power on county-level GDP test data with R² of 0.894, 0.976, and 0.976 for the primary, secondary, and tertiary sectors, respectively. Moreover, the predicted 1 km gridded GDP data had a high accuracy (R² = 0.787) when evaluated by town-level GDP data. Hence, the proposed GDP downscaling approach provides a valuable option for generating gridded GDP data. The generated 1 km gridded GDP data of China from 2020 are of great significance for other applications. Full article

The aim of the article is to prepare a model for making available metadata and digital objects of the new Globe Virtual Collection for the Map Collection of the Faculty of Science of Charles University. The globes are special cartographic documents; therefore, they are also described in a special way. The article deals with the digitization, visualization and accessibility of an old globe by Josef Jüttner from 1839, which comes from the depository of one of the most important central European collections. A simple model for a new virtual processing of the globe collection at Charles University is presented. SfM-MVS photogrammetry was chosen for digitization of the globe. The basic elements of the copperplate were set as basic parameters for image acquisition. Contrast, density, black line, line, dash and dot patterns and their complex use were observed for a good graphic design of the globe. Other parameters included a closer determination of the users for whom the resulting product is intended, as well as the profile of the users’ behavior on the site so far. New metadata were extracted from the bibliographic description. The virtual 3D globe was integrated into the database using the Cesium JavaScript library. Metadata and a 3D model of the globe were linked together and made available to the general public on the Globe page of the Map Collection of the Faculty of Science of Charles University. A comparison of web browsers was performed focusing on the loading time of the 3D model on the website. New graphic elements were identified with the new processing. It was possible to read the factual information written on the globe. Different possibilities and limitations of metadata description, photogrammetric methods and web presentation are described. This good practice can be applied by other virtual map collections. Full article

Based on Ctrip’s ‘tourism digital footprint’, the spatial pattern of tourism flows in the Chengdu–Chongqing Economic Circle from 2018 to 2021 is explored, social network analysis and spatial visualisation of tourism information data are conducted, and factors affecting the network structure of tourism flows are analysed using linear weighted regression methods. The results show that tourism flows in the Chengdu–Chongqing Economic Circle show a significant ‘dual core’ polarisation effect. At the end of 2019, as a turning point, the density value of the tourism flow network shows an irregular inverted ‘U’ distribution. Kuanzhai Alley, Hong Ya Dong and Chunxi Road have irreplaceable competitive advantages in the tourism flow network. The density of highways, the number of star-rated hotels and the regional GDP per capita are positively correlated with the effective size of the structural hole of the administrative unit. Finally, based on the research results, countermeasures are proposed to optimise the tourism development of the Chengdu–Chongqing Economic Circle. Full article

Compactification of cities, i.e., the opposite of urban sprawl, has been increasingly presented in the literature as a possible solution to reduce the carbon footprint and promote the sustainability of current urban environments. Compact environments have higher concentrations of interaction opportunities, smaller distances to them, and the potential for increased active mode shares, leading to less transport-related energy consumption and associated emissions. This article presents a GIS-based quantitative methodology to estimate on how much can be gained in that respect if vacant spaces within a city were urbanized, according to the municipal master plan, using four indicators: accessibility, active modal share, transport energy consumption, and a 15-minute city analysis. The methodology is applied to a case study, in which the city of Coimbra, Portugal, and a compact version of itself are compared. Results show the compact layout improves all indicators, with averages per inhabitant improving by 20% to 92%, depending on the scenario assumed for cycling, and is more equitable. Full article

The continuous nature of space and time is a fundamental tenet of many scientific endeavors. That digital representation imposes granularity is well recognized, but whether it is possible to address space completely remains unanswered. This paper argues Hales’ proof of Kepler’s conjecture on the packing of hard spheres suggests the answer to be “no”, providing examples of why this matters in GIS generally and considering implications for spatio-temporal GIS in particular. It seeks to resolve the dichotomy between continuous and granular space by showing how a continuous space may be emergent over a random graph. However, the projection of this latent space into 3D/4D imposes granularity. Perhaps surprisingly, representing space and time as locally conjugate may be key to addressing a “smooth” spatial continuum. This insight leads to the suggestion of Face Centered Cubic Packing as a space-time topology but also raises further questions for spatio-temporal representation. Full article

Recent developments in Web Service and Semantic Web technologies have shown great promise for the automatic chaining of geographic information services (GIService), which can derive user-specific information and knowledge from large volumes of data in the distributed information infrastructure. In order for users to have an informed understanding of products generated automatically by distributed GIServices, provenance information must be provided to them. This paper describes a three-level conceptual view of provenance: the automatic capture of provenance in the semantic execution engine; the query and inference of provenance. The view adapts well to the three-phase procedure for automatic GIService composition and can increase understanding of the derivation history of geospatial data products. Provenance capture in the semantic execution engine fits well with the Semantic Web environment. Geospatial metadata is tracked during execution to augment provenance. A prototype system is implemented to illustrate the applicability of the approach. Full article

The analysis of individuals’ movement behaviors is an important area of research in geographic information sciences, with broad applications in smart mobility and transportation systems. Recent advances in information and communication technologies have enabled the collection of vast amounts of mobility data for investigating movement behaviors using trajectory data mining techniques. Trajectory clustering is one commonly used method, but most existing methods require a complete similarity matrix to quantify the similarities among users’ trajectories in the dataset. This creates a significant computational overhead for large datasets with many user trajectories. To address this complexity, an efficient clustering-based method for network constraint trajectories is proposed, which can help with transportation planning and reduce traffic congestion on roads. The proposed algorithm is based on spatiotemporal buffering and overlapping operations and involves the following steps: (i) Trajectory preprocessing, which uses an efficient map-matching algorithm to match trajectory points to the road network. (ii) Trajectory segmentation, where a Compressed Linear Reference (CLR) technique is used to convert the discrete 3D trajectories to 2D CLR space. (iii) Spatiotemporal proximity analysis, which calculates a partial similarity matrix using the Longest Common Subsequence similarity indicator in CLR space. (iv) Trajectory clustering, which uses density-based and hierarchical clustering approaches to cluster the trajectories. To verify the proposed clustering-based method, a case study is carried out using real trajectories from the GeoLife project of Microsoft Research Asia. The case study results demonstrate the effectiveness and efficiency of the proposed method compared with other state-of-the-art clustering-based methods. Full article

Electronic Navigational Chart (ENC) data are essential for safe maritime navigation and have multiple other uses in a wide range of enterprises. Charts are relied upon to be as accurate and as up-to-date as possible by the vessels moving vast amounts of products to global ports each year. However, cartographic generalization processes for updating and creating ENCs are complex and time-consuming. Increasing the efficiency of the chart production workflow has been long sought by the nautical charting community. Toward this effort, approaches must consider intended scale, data quality, various chart features, and perform consistently in different scenarios. Additionally, supporting open-science initiatives through standardized open-source workflows will increase marine data accessibility for other disciplines. Therefore, this paper reviews, improves, and integrates available open-source software, and develops new custom generalization tools, for the semi-automated processing of land and hydrographic features per nautical charting specifications. The robustness of this approach is demonstrated in two areas of very different geographic configurations and the effectiveness for use in nautical charting was confirmed by winning the first prize in an international competition. The presented rapid data processing combined with the ENC portrayal of results as a web-service provides new opportunities for applications such as the development of base-maps for marine spatial data infrastructures. Full article

This research is a pilot study to develop a maritime traffic control system that supports the decision-making process of control officers, and to evaluate the usability of a prototype tool developed in this study. The study analyzed the movements of multiple vessels through automatic identification system (AIS) data using one of the existing methodologies in GIScience, the RElative MOtion (REMO) approach. The REMO approach in this study measured the relative speed, delta-speed, and the azimuth of each vessel per time unit. The study visualized the results on electronic navigational charts in the prototype tool developed, V-REMO. In addition, the study conducted a user evaluation to assess the user interface (UI) of V-REMO and to future enhance the usability. The general usability of V-REMO, the data visualization, and the readability of information in the UI were tested through in-depth interviews. The results of the user evaluation showed that the users needed changes in the size, position, colors, and transparency of the trajectory symbols in the digital chartmap view of V-REMO for better readability and easier manipulation. The users also indicated a need for multiple color schemes for the spatial data and more landmark information about the study area in the chartmap view. Full article

In recent years, with the widespread use of location-aware handheld devices and the development of wireless networks, trajectory data have shown a trend of rapid growth in data volume and coverage, which has led to the prosperous development of location-based services (LBS). Spatio-temporal range query, as the basis of many services, remains a challenge in supporting efficient analysis and calculation of data, especially when large volumes of trajectory data have been accumulated. We propose a Hilbert-GeoSOT spatio-temporal meshing and coding method called HGST to improve the efficiency of spatio-temporal range queries on massive trajectory data. First, the method uses Hilbert to encode the grids obtained based on the GeoSOT space division model, and then constructs a unified time division standard to generate the space–time location identification of trajectory data. Second, this paper builds a novel spatio-temporal index to organize trajectory data, and designs an adaptive spatio-temporal scaling and coding method based on HGST to improve the query performance on indexed records. Finally, we implement a prototype system based on HBase and Spark, and develop a Spark-based algorithm to accelerate the spatio-temporal range query for huge trajectory data. Extensive experiments on a real taxi trajectory dataset demonstrate that HGST improves query efficiency levels by approximately 14.77% and 34.93% compared with GeoSOT-ST and GeoMesa at various spatial scales, respectively, and has better scalability under different data volumes. Full article

The past decade has witnessed an increasing frequency and intensity of disasters, from extreme weather, drought, and wildfires to hurricanes, floods, and wars. Providing timely disaster response and humanitarian aid to these events is a critical topic for decision makers and relief experts in order to mitigate impacts and save lives. When a disaster occurs, it is important to acquire first-hand, real-time information about the potentially affected area, its infrastructure, and its people in order to develop situational awareness and plan a response to address the health needs of the affected population. This requires rapid assembly of multi-source geospatial data that need to be organized and visualized in a way to support disaster-relief efforts. In this paper, we introduce a new cyberinfrastructure solution—GeoGraphVis—that is empowered by knowledge graph technology and advanced visualization to enable intelligent decision making and problem solving. There are three innovative features of this solution. First, a location-aware knowledge graph is created to link and integrate cross-domain data to make the graph analytics-ready. Second, expert-driven disaster response workflows are analyzed and modeled as machine-understandable decision paths to guide knowledge exploration via the graph. Third, a scene-based visualization strategy is developed to enable interactive and heuristic visual analytics to better comprehend disaster impact situations and develop action plans for humanitarian aid. Full article

Understanding the space–time pattern of the transmission locations of COVID-19, as well as the relationship between the pattern, socioeconomic status, and environmental factors, is important for pandemic prevention. Most existing research mainly analyzes the locations resided in or visited by COVID-19 cases, while few studies have been undertaken on the space–time pattern of the locations at which the transmissions took place and its associated influencing factors. To fill this gap, this study focuses on the space–time distribution patterns of COVID-19 transmission locations and the association between such patterns and urban factors. With Hong Kong as the study area, transmission chains of the four waves of COVID-19 outbreak in Hong Kong during the time period of January 2020 to June 2021 were reconstructed from the collected case information, and then the locations of COVID-19 transmission were inferred from the transmission chains. Statistically significant clusters of COVID-19 transmission locations at the level of tertiary planning units (TPUs) were detected and compared among different waves of COVID-19 outbreak. The high-risk areas and the associated influencing factors of different waves were also investigated. The results indicate that COVID-19 transmission began with the Hong Kong Island, further moved northward towards the New Territories, and finally shifted to the south Hong Kong Island, and the transmission population shows a difference between residential locations and non-residential locations. The research results can provide health authorities and policy-makers with useful information for pandemic prevention, as well as serve as a guide to the public in the avoidance of activities and places with a high risk of contagion. Full article

Urban rail transit is an essential part of the urban public transportation system. The reasonable spatial data visualization of urban rail transit stations can provide a more intuitive way for the majority of travelers to arrange travel plans and find destinations. The map service of rail transit stations generated by data visualization has gradually become indispensable information guidance in the rail transit system. The existing map service icons block each other when the scale changes, and new stations cannot be displayed dynamically when users drag the map. This paper uses filtering and sorting methods to dynamically query and visualize the relatively more important transportation stations within the users’ visible range, so as to solve the above problems and provide people with better transportation services. Our method introduces three constraints: spatial diversity, time-sharing passenger flow analysis and whether it is a transit station, and calculates the scores of constraint relationships of feature objects to evaluate stations. On the basis of the skyline query, the scores of feature objects are combined and sorted to obtain an ordered object set of the most interesting k points(top-k POIs), and the rail transit stations are dynamically retrieved and visualized. Before sorting POIs, we filter out POIs that need to be fitted, so that only the k most representative POIs in the currently visible range are displayed. When the map scale changes, the displayed POIs are updated. Finally, through the statistics of efficiency calculation of this method under different scales and centers, combined with users’ evaluations, it was proved that our method could better display critical information and improve user experience. Full article

The development of the county economy in China is a complicated process that is influenced by many factors in different ways. This study is based on multi-source big data, such as Tencent user density (TUD) data and point of interest (POI) data, to calculate the different influencing factors, and employed a multiscale geographically weighted regression (MGWR) model to explore their spatial non-stationarity impact on China’s county economic development. The results showed that the multi-source big data can be useful to calculate the influencing factor of China’s county economy because they have a significant correlation with county GDP and have a good models fitting performance. Besides, the MGWR model had prominent advantages over the ordinary least squares (OLS) and geographically weighted regression (GWR) models because it could provide covariate-specific optimized bandwidths to incorporate the spatial scale effect of the independent variables. Moreover, the effects of various factors on the development of the county economy in China exhibited obvious spatial non-stationarity. In particular, the Yangtze River Delta, the Pearl River Delta, and the Beijing-Tianjin-Hebei urban agglomerations showed different characteristics. The findings revealed in this study can furnish a scientific foundation for future regional economic planning in China. Full article

Street greenness visibility (SGV) is associated with various health benefits and positively influences perceptions of landscape. Lowering the barriers to SGV assessments and measuring the values accurately is crucial for applying this critical landscape information. However, the verified available street view imagery (SVI) data for SGV assessments are limited to the traditional top-down data, which are generally used with download and usage restrictions. In this study, we explored volunteered street view imagery (VSVI) as a potential data source for SGV assessments. To improve the image quality of the crowdsourced dataset, which may affect the accuracy of the survey results, we developed an image filtering method with XGBoost using images from the Mapillary platform and conducted an accuracy evaluation by comparing the results with official data in Shinjuku, Japan. We found that the original VSVI is well suited for SGV assessments after data processing, and the filtered data have higher accuracy. The discussion on VSVI data applications can help expand useful data for urban audit surveys, and this full-free open data may promote the democratization of urban audit surveys using big data. Full article

Environmental and health deterioration due to the increasing presence of air pollutants is a pressing topic for governments and organizations. Institutions such as the European Environment Agency have determined that more than 350,000 premature deaths can be attributed to atmospheric pollutants. The measurement of trace gas atmospheric concentrations is key for environmental agencies to fight against the decreased deterioration of air quality. NO${}_2$, which is one of the most harmful pollutants, has the potential to cause diseases such as Chronic Obstructive Pulmonary Disease (COPD). Unfortunately, not all countries have local atmospheric pollutant monitoring networks to perform ground measurements (especially Low- and Middle-Income Countries). Although some alternatives, such as satellite technologies, provide a good approximation for tropospheric NO${}_2$, these do not measure concentrations at the ground level. In this work, we aim to provide an alternative to ground sensor measurements. We used a combination of ground meteorological measurements with satellite Sentinel-5P observations to estimate ground NO${}_2$. For this task, we used state-of-the-art Machine Learning models, linear regression models, and feature selection algorithms. From the results obtained, we found that a Multi-layer Perceptron Regressor and Kriging in combination with a Random Forest feature selection algorithm achieved the lowest RMSE (2.89 µg/m${}^3$). This result, in comparison with the real data standard deviation and the models using only satellite data, represented an RMSE decrease of 55%. Future work will focus on replacing the use of meteorological ground sensors with only satellite-based data. Full article

The emergence of the third information wave makes extensive maps available to be generated by volunteered ways, never specially designed and generated by professional institutes alone. These large-scale images-based volunteered maps created by the public provide plentiful geographical information regarding a place while posing a challenge for recognizing the unstructured text in these maps for previous approaches to standard map text detection. Map text or map annotations denote the critical element of map content. To achieve the detection of unstructured map text, this paper proposed an integrated data-based and model-based transfer learning model, which mainly respectively included data augmentation techniques and adaptive fine-tuning, to reinforce the state-of-the-art CNNs by transferring the OCR knowledge for detecting the unstructured text units in volunteered maps. The experiment proved that our proposed framework can effectively reinforce the state-of-the-art CNN in detecting unstructured map text. We hope our research results can contribute to unstructured map text detection and recognition. Full article