ISPRS Int. J. Geo-Inf., Volume 11, Issue 2 (February 2022) – 79 articles

In this article researchers examined the differences that may characterise selected groups of gamers with regard to age and time spent on playing a survival game, Valheim, confronted with their interpretation of map symbols used in the game. The Valheim video game, which was released in early 2021, is a survival game set in a gameplay world inspired by Norse mythology. The authors of the article noted that the age factor and gaming experience may have different results in terms of the interpretation of cartographic products. The obtained data came from an online questionnaire. In the statistical analysis the authors employed the Pearson’s chi-squared test and the Benjamini–Hochberg procedure to find statistically significant differences between selected groups of respondents and their subjective interpretation of map symbols. Statistical analysis showed several differences and interesting relationships in the interpretation of symbols in relation to the age of the players and in the interpretation of symbols in relation to the time spent in the game. For future research, it is worth continuing towards researching new video games with existing cartographic products in order to investigate how games and players influence the culture in which they participate. Full article

Semantic technologies have proven their relevance in facilitating the interpretation of Earth Observation (EO) data through formats such as RDF and reusable models, especially for the representation of space and time. While rasters are the usual data format for the results of image processing algorithms, a recurrent problem is transferring the pixel values of these rasters into features that make sense of the areas of interest on the Earth, thus facilitating the interpretation of their content. This paper addresses this issue through a semantic data integration process based on spatial and temporal properties. We propose (i) a modular and generic semantic model for the homogeneous representation of data qualifying a geographical area of interest thanks to territorial units (land parcels, administrative units, forest areas, etc.) that we define as divisions of a larger territory according to a criteria in relation with human activities; and (ii) a semantic extraction, transformation and load (ETL) process that builds on the model and the data extracted from rasters and that maps aggregated data to the corresponding unit areas. We evaluate our approach in terms of the (i) adaptability of the proposed model and pipeline to accommodate different use cases (vineyard and urban expansion monitoring), (ii) added value of the generated datasets to assist in decision making, and (iii) scalability of the approach. Full article

Agent-based modeling has the potential to deal with the ever-growing complexity of transport systems, including future disrupting mobility technologies and services, such as automated driving, Mobility as a Service, and micromobility. Although different software dedicated to the simulation of disaggregate travel demand have emerged, the amount of needed input data, in particular the characteristics of a synthetic population, is large and not commonly available, due to legit privacy concerns. In this paper, a methodology to spatially assign a synthetic population by exploiting only publicly available aggregate data is proposed, providing a systematic approach for an efficient treatment of the data needed for activity-based demand generation. The assignment of workplaces exploits aggregate statistics for economic activities and land use classifications to properly frame origins and destination dynamics. The methodology is validated in a case study for the city of Tallinn, Estonia, and the results show that, even with very limited data, the assignment produces reliable results up to a 500 × 500 m resolution, with an error at district level generally around 5%. Both the tools needed for spatial assignment and the resulting dataset are available as open source, so that they may be exploited by fellow researchers. Full article

The popularity of mobile devices with GPS capabilities, along with the worldwide adoption of social media, have created a rich source of text data combined with spatio-temporal information. Text data collected from location-based social networks can be used to gain space–time insights into human behavior and provide a view of time and space from the social media lens. From a data modeling perspective, text, time, and space have different scales and representation approaches; hence, it is not trivial to jointly represent them in a unified model. Existing approaches do not capture the sequential structure present in texts or the patterns that drive how text is generated considering the spatio-temporal context at different levels of granularity. In this work, we present a neural language model architecture that allows us to represent time and space as context for text generation at different granularities. We define the task of modeling text, timestamps, and geo-coordinates as a spatio-temporal conditioned language model task. This task definition allows us to employ the same evaluation methodology used in language modeling, which is a traditional natural language processing task that considers the sequential structure of texts. We conduct experiments over two datasets collected from location-based social networks, Twitter and Foursquare. Our experimental results show that each dataset has particular patterns for language generation under spatio-temporal conditions at different granularities. In addition, we present qualitative analyses to show how the proposed model can be used to characterize urban places. Full article

Hydroelectric energy generates more than 50% of all renewable electricity in the world. The Amazon is home to a large part of these ventures, promoted as a strategy of energy independence in order to reduce greenhouse gas emissions in the countries of the region. However, these hydroelectric plants lead to changes in land cover, fragmentation, degradation, and loss of tropical forests. This article analyzes the spatial pattern of alterations in the land cover of the municipality of Goianésia do Pará, one of the seven municipalities affected by the artificial lake of the Tucuruí hydroelectric plant. This case study integrates remote sensing and landscape metrics to identify, quantify, and spatialize the loss of tropical forest within the municipality by using satellite images of the TM-Landsat 5, ETM+-Landsat 7 and OLI-Landsat 8 sensors. The results show that the average deforestation rates were high in the first two periods: 1984–1988 (23,101.2 ha per year) and 1988–1999 (13,428.6 ha per year). However, this rate drastically fell in the last period because, by 2010, more than 60% of the territory was already deforested, which shows the consolidation of the municipality’s deforestation process. Full article

Understanding the space-time dynamics of human activities is essential in studying human security issues such as climate change impacts, pandemic spreading, or urban sustainability. Geotagged social media posts provide an open and space-time continuous data source with user locations which is convenient for studying human movement. However, the reliability of Chinese geotagged social media data for representing human mobility remains unclear. This study compares human movement data derived from the posts of Sina Weibo, one of the largest social media software in China, and that of Baidu Qianxi, a high-resolution human movement dataset from ‘Baidu Map’, a popular location-based service in China with 1.3 billion users. Correlation analysis was conducted from multiple dimensions of time periods (weekly and monthly), geographic scales (cities and provinces), and flow directions (inflow and outflow), and a case study on COVID-19 transmission was further explored with such data. The result shows that Sina Weibo data can reveal similar patterns as that of Baidu Qianxi, and that the correlation is higher at the provincial level than at the city level and higher at the monthly scale than at the weekly scale. The study also revealed spatial variations in the degree of similarity between the two sources. Findings from this study reveal the values and properties and spatiotemporal heterogeneity of human mobility data extracted from Weibo tweets, providing a reference for the proper use of social media posts as the data sources for human mobility studies. Full article

Data obtained through Volunteered Geographical Information (VGI) have gradually been used to monitor and support planning mainly in urban contexts. Regarding recreational activities in peri-urban green and natural areas, VGI has been used to map, measure use intensity, profile users, and evaluate their preferences and motivations. Given their extensive use, it is now worthwhile to assess the value of VGI data to (1) compare recreational uses, profile users and map recreational activities in different contexts (metropolitan vs. rural areas), and (2) evaluate outdoor and adventure tourist products such as Grand Routes (GR). Data from former GPSies (AllTrails nowadays), one of the most popular web-share services, were used to assess recreational uses in Lisbon Metropolitan Area (LMA) and southwest Portugal (SWPT). A set of 22,031 tracks of “on foot” and “on wheels” activities, submitted by 3297 national and foreign users, covering 12 years, was analysed within a GIS modelling environment. Results indicate that, although there are many more submissions in the LMA, the influence of foreigners in the SWPT is higher (11% vs. 19%). The existing GR in SWPT concentrates the foreign use for hiking (71% of foreign vs. 28% of national users), demonstrating its attractiveness. For the favourite activity in both areas—Mountain biking—results show a higher spatial dispersion, yet part of the activity in SWPT still conforms to the GR (16% of foreign and 20% of national use). This study proves other applications for VGI, showing its usefulness for assessing recreational uses in both metropolitan and rural areas. Spatial knowledge about recreational uses is a valuable tool to evaluate and monitor such activities, and to know what users like to do, and where, and is also useful information when designing recreational products considering their tourist potential, thus adding value to these offers. Full article

Traffic accidents in urban areas lead to reduced quality of life and added pressure in the cities’ infra-structures. In the context of smart city data is becoming available that allows a deeper analysis of the phenomenon. We propose a data fusion process from different information sources like road accidents, weather conditions, local authority reports tools, traffic, fire brigade. These big data analytics allow the creation of knowledge for local municipalities using local data. Data visualizations allow big picture overview. This paper presents an approach to the geo-referenced accident-hotspots identification. Using ArcGIS Pro, we apply Kernel Density and Hot Spot Analysis (Getis-Ord Gi*) tools, identifying the existence of black spots in terms of location and context conditions, and evaluate the possible human, environmental and circumstantial factors that may influence the severity of accidents. The results were validated by an expert committee. This approach can be applied to other cites wherever this data is available. Full article

Drifting snow, the flow of dispersed snow particles near ground level under the action of wind, is a major form of snow damage. When drifting snow occurs on railways, highways, and other transportation lines, it seriously affects their operational safety and results in drifting snow disasters. Drifting snow disasters frequently occur in the high latitudes of northwest China. At present, most scholars are committed to studying the prevention and control measures of drifting snow, but the prerequisite for prevention is to effectively evaluate the susceptibility of drifting snow along railways and highways to identify areas with a high risk of occurrence. Taking the Xinjiang Afukuzhun Railway as an example, this study uses a geographic information system (GIS) combined with on-site monitoring and surveys to establish a drifting snow susceptibility evaluation index system. The drifting snow susceptibility index (DSSI) is calculated through the weight of an evidence (WOE) model, and a genetic algorithm backpropagation (GA-BP) algorithm is used to obtain optimised evaluation index weights to improve the accuracy of model evaluation. The results show that the accuracies of the WOE model, WOE backpropagation (WOE-BP) model, and weight of evidence genetic algorithm backpropagation (WOE-GA-BP) model are 0.747, 0.748, and 0.785, respectively, indicating that the method can be effectively applied to evaluate drifting snow susceptibility. Full article

The development of a European Spatial Data Infrastructure (SDI) officially started with the entry into force of the INSPIRE Directive in 2007. INSPIRE’s implementation phase should be completed by the European Union (EU) and its member states at the end of 2021: a pivotal point to evaluate INSPIRE’s current governance and explore future scenarios. First, INSPIRE’s governing system is evaluated through an online survey by its involved stakeholders. Second, these results are applied in an agent-based model to explore potential governance scenarios and strategies. The results show that strong aspects of INSPIRE’s governing system are the supported vision and its formal structures, such as standards, technology and roles and responsibilities. Weak aspects are the access to resources, especially budget and time resources, and data use. The agent-based simulations show that INSPIRE is probably more constrained by its budget resources than its current dominant hierarchical interaction mix, although a combination of adaptive governance and continuous budget proved the most sustainable governance scenario. Full article

Massive taxi trajectory data can be easily obtained in the era of big data, which is helpful to reveal the spatiotemporal information of human travel behavior but neglects activity semantics. The activity semantics reflect people’s daily activities and trip purposes, and lead to a deeper understanding of human travel patterns. Most existing literature analyses of activity semantics mainly focus on the characteristics of the destination. However, the movement from the origin to the destination can be represented as the flow. The flow can completely represent the activity semantic and describe the spatial interaction between the origin and the destination. Therefore, in this paper, we proposed a two-layer framework to infer the activity semantics of each taxi trip and generalized the similar activity semantic flow to reveal human travel patterns. We introduced the activity inference in the first layer by a combination of the improved Word2vec model and Bayesian rules-based visiting probability ranking. Then, a flow clustering method is used to uncover human travel behaviors based on the similarity of activity semantics and spatial distribution. A case study within the Fifth Ring Road in Beijing is adopted and the results show that our method is effective for taxi trip activity inference. Six activity semantics and four activity semantics are identified in origins and destinations, respectively. We also found that differences exist in the activity transitions from origins to destinations at distinct periods. The research results can inform the taxi travel demand and provide a scientific decision-making basis for taxi operation and transportation management. Full article

The Moulouya basin in Morocco is one of many river basins around the world that are regulated with physical flow control, a range of regulations and storage structures. The water budget of the basin is unbalanced; the available water resources are insufficient for agricultural productivity, nature conservation and ecosystem services. This study evaluates spatial and temporal distributions of actual evapotranspiration, groundwater recharge and surface runoff for the period 2000–2020 using the WetSpass-M model in the Moulouya basin, Morocco. The WetSpass-M model’s input data are created in grid maps with the ArcGIS tool. They include monthly meteorological parameters (e.g., temperature, wind speed, rainfall,), soil map, land cover, topography, slope and groundwater depth. A good correlation has been observed between the simulated groundwater recharge and base flow, with the value of R² = 0.98. The long-term spatial and temporal average annual precipitation of 298 mm is distributed as 45 mm (15.1%) groundwater recharge and 44 mm (14.8%) surface runoff, while 209 mm (70.1%) is lost through evapotranspiration. The simulated results showed that the average groundwater recharge of 15.1 mm (30%) falls during the summer and spring seasons, while the remaining 29.5 mm (70%) occurs during the winter and autumn seasons. Annually, 2430 million m³ of water recharges to the groundwater system from the rainfall for the entire basin. The study’s findings would help local stakeholders and policymakers in developing sustainable and effective management of available surface water and groundwater resources in the Moulouya basin. Full article

Maps are not just powerful tools to communicate spatial information; they also have significant educational potential to develop students’ knowledge, skills, and thinking. To fully exploit this potential, deep research is needed into map-use processes considering the variability of map types and the cognitive complexity of map operations. Whereas research on map reading is relatively common, the research into cognitively more demanding operations is lacking. Therefore, this study employed an eye-tracking experiment combined with a follow-up questionnaire with 20 upper-secondary students to examine the strategies students choose when analyzing general-reference maps. Specifically, attention is paid to the strategy repertoire, distribution, efficiency, and adaptiveness of strategy choice. Subsequently, the study is focused on students’ perception of strategies. According to the results, participants used a rich repertoire of strategies (although many of them unconsciously) and adapted the strategy choice to task demands. The solution efficiency varied among task demands, as did the efficiency of individual strategies and their combinations. The research design allowed a comparison with earlier studies on strategies for thematic map use. The results should be of interest to cartographers (to design effective educational tools) and educators (to educate map users complexly and effectively). Full article

The hospital accessibility of Acute Myocardial Infarction (AMI) emergency treatment is of great importance, not only for improving the survival rate of patients but also for protecting the basic human right to health care. Traditional AMI emergency treatment research often does not consider ways to shorten the travel time to hospitals for AMI patients and does not reflect the actual time it takes to travel to hospitals, which is critical to AMI emergency treatment. To avoid these shortcomings, this study proposes a method of accessibility measurement based on Web Mapping API (Application Programming Interface) to obtain travel time to hospitals during different periods, then calculated the AMI hospital accessibility based on these detailed data. This study considered the Shijingshan District, Beijing, China, as an empirical case. The study discovered significant differences in the temporal and spatial characteristics of the AMI hospital accessibility on weekdays and weekends. The analysis revealed that travel time to hospitals and traffic congestion are the two main factors affecting AMI hospital accessibility. The research results shed new light on the accessibility of urban medical facilities and provide a scientific basis with which local governments can optimize the spatial structure of medical facilities. Full article

A terrain skeleton determines the overall structure and characteristics of the terrain and indicates the presence of significant terrain features, such as ridges and valleys. It plays an important role in terrain representation and reconstruction, hydrological analysis of watersheds, and other scientific studies and engineering applications. Previous studies of terrain skeleton have been mostly focused on the extraction of terrain skeletons, ignoring their important effect on terrain analysis. Therefore, this work proposes a new terrain skeleton, which includes three types of terrain skeleton points and two types of terrain skeleton lines. The terrain control points are peak, saddle, and valley nodes, while the terrain skeleton lines are connection lines of peaks and saddles and connection lines of saddles and valley nodes. The terrain skeleton connects isolated terrain control points together. The data structure is designed, and three analysis indicators, namely, nearest-neighbor index, topological connectivity index and landscape shape index are selected. Results show that the three selected indicators can reflect the spatial structure of the terrain skeleton and describe the landform development to a certain extent. Different areas of the same landform, such as the two sample areas in Shenmu County, show variations. Full article

Environmental exposure of people plays an important role in assessing the quality of human life. The most existing methods that estimate the environmental exposure either focus on the individual level or do not consider human mobility. This paper adopts a vector field generated from the observed locations of human activities to model the environmental exposure at the population level. An improved vector-field-generation method was developed by considering people’s decision-making factors, and we proposed two indicators, i.e., the total exposure indicator (TEI) and the average exposure indicator (AEI), to assess various social groups’ environmental exposure. A case study about the risky environmental exposure of coronavirus disease 2019 (COVID-19) was conducted in Guangzhou, China. Over 900 participants with various socioeconomic backgrounds were involved in the questionnaire, and the survey-based activity locations were extracted to generate the vector field using the improved method. COVID-19 pandemic exposure (or risk) was estimated for different social groups. The findings show that people in the low-income group have an 8% to 10% higher risk than those in the high-income group. This new method of vector field may benefit geographers and urban researchers, as it provides opportunities to integrate human activities into the metrics of pandemic risk, spatial justice, and other environmental exposures. Full article

Several studies have worked on co-clustering analysis of spatio-temporal data. However, most of them search for co-clusters with similar values and are unable to identify co-clusters with coherent trends, defined as exhibiting similar tendencies in the attributes. In this study, we present the Bregman co-clustering algorithm with minimum sum-squared residue (BCC_MSSR), which uses the residue to quantify coherent trends and enables the identification of co-clusters with coherent trends in geo-referenced time series. Dutch monthly temperatures over 20 years at 28 stations were used as the case study dataset. Station-clusters, month-clusters, and co-clusters in the BCC_MSSR results were showed and compared with co-clusters of similar values. A total of 112 co-clusters with different temperature variations were identified in the Results, and 16 representative co-clusters were illustrated, and seven types of coherent temperature trends were summarized: (1) increasing; (2) decreasing; (3) first increasing and then decreasing; (4) first decreasing and then increasing; (5) first increasing, then decreasing, and finally increasing; (6) first decreasing, then increasing, and finally decreasing; and (7) first decreasing, then increasing, decreasing, and finally increasing. Comparisons with co-clusters of similar values show that BCC_MSSR explored coherent spatio-temporal patterns in regions and certain time periods. However, the selection of the suitable co-clustering methods depends on the objective of specific tasks. Full article

Averaging GPS trajectories is needed in applications such as clustering and automatic extraction of road segments. Calculating mean for trajectories and other time series data is non-trivial and shown to be an NP-hard problem. medoid has therefore been widely used as a practical alternative and because of its (assumed) better noise tolerance. In this paper, we study the usefulness of the medoid to solve the averaging problem with ten different trajectory-similarity/-distance measures. Our results show that the accuracy of medoid depends mainly on the sample size. Compared to other averaging methods, the performance deteriorates especially when there are only few samples from which the medoid must be selected. Another weakness is that medoid inherits properties such as the sample frequency of the arbitrarily selected sample. The choice of the trajectory distance function becomes less significant. For practical applications, other averaging methods than medoid seem a better alternative for higher accuracy. Full article

Air pollutant monitoring is a basic issue in contemporary urban life. This paper describes an approach based on the diffused use of low-cost sensors that can be mounted on board urban vehicles for more abundant and distributed measures. The system exchanges data, exploiting a “Smart Road” infrastructure, with a central computing facility, the CIPCast platform, a GIS-based Decision Support System designed to perform real-time monitoring and interpolation of data with the aim of possibly issuing alarms with respect to different town areas. Experimental data gathering in the Rome urban area and subsequent processing results are presented. Algorithms for data fusion among different simulated monitoring systems and interpolation of data for a geographically denser map were utilised. Thus, in the framework of the Smart Road, protocols for data exchange were designed. Finally, air pollutant distribution maps were produced and integrated into the CIPCast platform. The feasibility of a full system architecture from the sensors to the real-time pollutant maps is shown. Full article

Urban wetlands provide cities with unique and valuable ecosystem services but are under great degradation pressure. Correctly identifying urban wetlands from remote sensing images is fundamental for developing appropriate management and protection plans. To overcome the semantic limitations of traditional pixel-level urban wetland classification techniques, we proposed an urban wetland identification framework based on an advanced scene-level classification scheme. First, the Sentinel-2 high-resolution multispectral image of Shenzhen was segmented into 320 m × 320 m square patches to generate sample datasets for classification. Next, twelve typical convolutional neural network (CNN) models were transformed for the comparison experiments. Finally, the model with the best performance was used to classify the wetland scenes in Shenzhen, and pattern and composition analyses were also implemented in the classification results. We found that the DenseNet121 model performed best in classifying urban wetland scenes, with overall accuracy (OA) and kappa values reaching 0.89 and 0.86, respectively. The analysis results revealed that the wetland scene in Shenzhen is generally balanced in the east–west direction. Among the wetland scenes, coastal open waters accounted for a relatively high proportion and showed an obvious southward pattern. The remaining swamp, marsh, tidal flat, and pond areas were scattered, accounting for only 4.64% of the total area of Shenzhen. For scattered and dynamic urban wetlands, we are the first to achieve scene-level classification with satisfactory results, thus providing a clearer and easier-to-understand reference for management and protection, which is of great significance for promoting harmony between humanity and ecosystems in cities. Full article

One of the core tasks in digital soil mapping (DSM) studies is the estimation of the spatial distribution of different soil variables. In addition, however, assessing the uncertainty of these estimations is equally important, something that a lot of current DSM studies lack. Machine learning (ML) methods are increasingly used in this scientific field, the majority of which do not have intrinsic uncertainty estimation capabilities. A solution to this is the use of specific ML methods that provide advanced prediction capabilities, along with innate uncertainty estimation metrics, like Quantile Regression Forests (QRF). In the current paper, the prediction and the uncertainty capabilities of QRF, Random Forests (RF) and geostatistical methods were assessed. It was confirmed that QRF exhibited outstanding results at predicting soil organic matter (OM) in the study area. In particular, R² was much higher than the geostatistical methods, signifying that more variation is explained by the specific model. Moreover, its uncertainty capabilities as presented in the uncertainty maps, shows that it can also provide a good estimation of the uncertainty with distinct representation of the local variation in specific parts of the area, something that is considered a significant advantage, especially for decision support purposes. Full article

Geographically weighted regression (GWR) is an effective method for detecting spatial non-stationary features based on the hypothesis of proximity correlation. In reality, especially in the social and economic fields, research objects not only have spatial non-stationary characteristics, but also spatial discrete heterogeneity characteristics. Therefore, how to improve the accuracy of GWR estimation in this case is worth studying. In this paper, a regionally geographically weighted regression (RGWR) is proposed. Using incoming dummy variables, the zoning discrimination is added to the spatial kernel function of GWR, the spatial kernel function is modified, the spatial weight is optimized, and the influence of “near heterogeneous” observation points is reduced. In this paper, the residential sale price in Wuhan City is taken as an example in the analysis of three aspects: model performance, fitting effect and influencing factors. The results show that the introduction of a zoning dummy variables can significantly improve the model accuracy of a fixed bandwidth and adaptive bandwidth. Under a fixed bandwidth, compared with the GWR model, RGWR increases R² and R²adj from 0.6776 and 0.6732 to 0.777 and 0.7746, respectively, and the Akaike information criterion, corrected (AICc) standard decreases by 37.4006 compared with GWR, which proves the effectiveness of the method. Full article

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. Full article

The modes of interaction (e.g., mouse and touch) between maps and users affect the effectiveness and efficiency of transmitting cartographic information. Recent advances in eye tracking technology have made eye trackers lighter, cheaper and more accurate, broadening the potential to interact with maps via gaze. In this study, we focused exclusively on using gaze to choose map features (i.e., points, polylines and polygons) via the select operation, a fundamental action preceding other operations in map interactions. We adopted an approach based on the dwell time and buffer size to address the low spatial accuracy and Midas touch problem in gaze-based interactions and to determine the most suitable dwell time and buffer size for the gaze-based selection of map features. We conducted an experiment in which 38 participants completed a series of map feature selection tasks via gaze. We compared the participants’ performance (efficiency and accuracy) between different combinations of dwell times (200 ms, 600 ms and 1000 ms) and buffer sizes (point: 1°, 1.5°, and 2°; polyline: 0.5°, 0.7° and 1°). The results confirmed that a larger buffer size raised efficiency but reduced accuracy, whereas a longer dwell time lowered efficiency but enhanced accuracy. Specifically, we found that a 600 ms dwell time was more efficient in selecting map features than 200 ms and 1000 ms but was less accurate than 1000 ms. However, 600 ms was considered to be more appropriate than 1000 ms because a longer dwell time has a higher risk of causing visual fatigue. Therefore, 600 ms supports a better balance between accuracy and efficiency. Additionally, we found that buffer sizes of 1.5° and 0.7° were more efficient and more accurate than other sizes for selecting points and polylines, respectively. Our results provide important empirical evidence for choosing the most appropriate dwell times and buffer sizes for gaze-based map interactions. Full article

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. Full article

With the rapidly increasing house prices in the Netherlands, there is a growing need for more localised value predictions for mortgage collaterals within the financial sector. Many existing studies focus on modelling house prices for an individual city; however, these models are often not interesting for mortgage lenders with assets spread out all over the country. That is why, with the current abundance of national geospatial datasets, this paper implements and compares three hedonic pricing models (linear regression, geographically weighted regression, and extreme gradient boosting—XGBoost) to model real estate appraisals values for five large municipalities in different parts of the Netherlands. The appraisal values used to train the model are provided by Stater N.V., which is the largest mortgage service provider in the Netherlands. Out of the three implemented models, the XGBoost model has the highest accuracy. XGBoost can explain 83% of the variance with an RMSE of €65,312, an MAE of €43,625, and an MAPE of 6.35% across the five municipalities. The two most important variables in the model are the total living area and taxation value, which were taken from publicly available datasets. Furthermore, a comparison is made between indexation and XGBoost, which shows that the XGBoost model is able to more accurately predict the appraisal values of different types of houses. The remaining unexplained variance is most probably caused by the lack of good indicators for the condition of the house. Overall, this paper highlights the benefits of open geospatial datasets to build a national real estate appraisal model. Full article

Ensuring that commuting distance remains within a certain range has important effect of residents’ quality of life. Although many studies have investigated the relationship between the built environment and residents’ commuting distance, limited evidence has been provided of the impact of job location. As such, in this study, we used data from the Wuhan Metropolitan Development Area in China and applied Bayesian linear regression (BLR) models to examine the impact of the built environment at both residential and job locations on commuting distances for residents. Our findings showed that, for residential locations, the residential density, land use mix, number of intersections, parking service level, and number of companies have a significant negative effect on commuting distance, whereas the plot ratio, distance to sub-employment centers, number of metro stations, and number of bus stops have a significant positive effect on commuting distance. For employment locations, land use mix, parking service level, and number of companies have a significant negative effect on commuting distance, whereas job density, number of intersections, distance to sub-employment centers, number of metro stations, and number of bus stops have a significant positive effect on commuting distance. By describing the influence of the built environment at both residential and job locations on commuting distance, our findings are conducive to the optimization of land use and the formulation of related policies to reduce commuting distance, which has a positive effect on improving residents’ quality of life and reducing energy emissions and air pollution. Full article

This paper considers k-farthest neighbor (kFN) join queries in spatial networks where the distance between two points is the length of the shortest path connecting them. Given a positive integer k, a set of query points Q, and a set of data points P, the kFN join query retrieves the k data points farthest from each query point in Q. There are many real-life applications using kFN join queries, including artificial intelligence, computational geometry, information retrieval, and pattern recognition. However, the solutions based on the Euclidean distance or nearest neighbor search are not suitable for our purpose due to the difference in the problem definition. Therefore, this paper proposes a cluster nested loop join (CNLJ) algorithm, which clusters query points (data points) into query clusters (data clusters) and reduces the number of kFN queries required to perform the kFN join. An empirical study was performed using real-life roadmaps to confirm the superiority and scalability of the CNLJ algorithm compared to the conventional solutions in various conditions. Full article

COVID-19 has had a huge impact on many industries around the world. Internationally-funded enterprises have been greatly affected by COVID-19 prevention and control measures, such as border controls. However, few studies have examined the impact of COVID-19 on internationally-funded enterprises. To this end, this paper considered 12 of China’s industrial parks situated in Southeast Asia, while comparing the operation status before and after the outbreak of COVID-19 based on remote sensing of nighttime lights (NTL). The NTL is generally used as a proxy for economic activity. First, six parameters were proposed to quantify and monitor the operation status based on NTL data. Subsequently, these parameters were calculated for the parks and for 10 km buffer zones surrounding them to analyze the differences in operating conditions. The results showed that (1) despite the negative impact of COVID-19, 9 out of the 12 parks had a mean NTL greater than 1, indicating that these parks are in better operating condition in 2020 than 2019; (2) 7 out of the 10 km buffer zones around the parks showed a decline in mean NTL. Only three parks showed a decline in mean NTL. The impact of COVID-19 on surrounding areas was greater than the impact on parks. Full article

The European Programme Copernicus, one of the principal sources of free and open Earth Observation (EO) data, intends to sustain social and economic advancements to the European Union. To this end, User Uptake initiatives have been undertaken to increase Copernicus awareness, dissemination, and competencies, thus supporting the development of downstream applications. As part of the activities performed in the EO-UPTAKE project, we illustrate a set of application scenario workflows exemplifying usage practices of the data and tools available in the Copernicus ecosystem. Through the know-how gained in the design and development of the application scenarios and the bibliographic analysis on downstream applications, we discuss a series of practical recommendations to promote the use of Copernicus resources towards a wider audience of end-users boosting the development of new EO applications along with some advice to data providers to improve their publication practices. Full article