ISPRS International Journal of Geo-Information

18 pages, 4342 KiB

Open AccessEditor’s ChoiceArticle

Analysis and Visualization of Vessels’ RElative MOtion (REMO)

by Hyowon Ban and Hye-jin Kim

ISPRS Int. J. Geo-Inf. 2023, 12(3), 115; https://doi.org/10.3390/ijgi12030115 - 08 Mar 2023

Cited by 2 | Viewed by 1535

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 [...] Read more.

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

(This article belongs to the Special Issue Cartography and Geomedia)

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20 pages, 17536 KiB

Open AccessEditor’s ChoiceArticle

Spatial Non-Stationarity of Influencing Factors of China’s County Economic Development Base on a Multiscale Geographically Weighted Regression Model

by Ziwei Huang, Shaoying Li, Yihuan Peng and Feng Gao

ISPRS Int. J. Geo-Inf. 2023, 12(3), 109; https://doi.org/10.3390/ijgi12030109 - 04 Mar 2023

Cited by 4 | Viewed by 2329

Abstract

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 [...] Read more.

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

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17 pages, 3127 KiB

Open AccessEditor’s ChoiceArticle

Classification of Spatial Objects with the Use of Graph Neural Networks

by Iwona Kaczmarek, Adam Iwaniak and Aleksandra Świetlicka

ISPRS Int. J. Geo-Inf. 2023, 12(3), 83; https://doi.org/10.3390/ijgi12030083 - 21 Feb 2023

Cited by 2 | Viewed by 1998

Abstract

Classification is one of the most-common machine learning tasks. In the field of GIS, deep-neural-network-based classification algorithms are mainly used in the field of remote sensing, for example for image classification. In the case of spatial data in the form of polygons or [...] Read more.

Classification is one of the most-common machine learning tasks. In the field of GIS, deep-neural-network-based classification algorithms are mainly used in the field of remote sensing, for example for image classification. In the case of spatial data in the form of polygons or lines, the representation of the data in the form of a graph enables the use of graph neural networks (GNNs) to classify spatial objects, taking into account their topology. In this article, a method for multi-class classification of spatial objects using GNNs is proposed. The method was compared to two others that are based solely on text classification or text classification and an adjacency matrix. The use case for the developed method was the classification of planning zones in local spatial development plans. The experiments indicated that information about the topology of objects has a significant impact on improving the classification results using GNNs. It is also important to take into account different input parameters, such as the document length, the form of the training data representation, or the network architecture used, in order to optimize the model. Full article

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25 pages, 21829 KiB

Open AccessEditor’s ChoiceArticle

BiodivAR: A Cartographic Authoring Tool for the Visualization of Geolocated Media in Augmented Reality

by Julien Mercier, Nicolas Chabloz, Gregory Dozot, Olivier Ertz, Erwan Bocher and Daniel Rappo

ISPRS Int. J. Geo-Inf. 2023, 12(2), 61; https://doi.org/10.3390/ijgi12020061 - 09 Feb 2023

Cited by 3 | Viewed by 2263

Abstract

Location-based augmented reality technology for real-world, outdoor experiences is rapidly gaining in popularity in a variety of fields such as engineering, education, and gaming. By anchoring medias to geographic coordinates, it is possible to design immersive experiences remotely, without necessitating an in-depth knowledge [...] Read more.

Location-based augmented reality technology for real-world, outdoor experiences is rapidly gaining in popularity in a variety of fields such as engineering, education, and gaming. By anchoring medias to geographic coordinates, it is possible to design immersive experiences remotely, without necessitating an in-depth knowledge of the context. However, the creation of such experiences typically requires complex programming tools that are beyond the reach of mainstream users. We introduce BiodivAR, a web cartographic tool for the authoring of location-based AR experiences. Developed using a user-centered design methodology and open-source interoperable web technologies, it is the second iteration of an effort that started in 2016. It is designed to meet needs defined through use cases co-designed with end users and enables the creation of custom geolocated points of interest. This approach enabled substantial progress over the previous iteration. Its reliance on geolocation data to anchor augmented objects relative to the user’s position poses a set of challenges: On mobile devices, GNSS accuracy typically lies between 1 m and 30 m. Due to its impact on the anchoring, this lack of accuracy can have deleterious effects on usability. We conducted a comparative user test using the application in combination with two different geolocation data types (GNSS versus RTK). While the test’s results are undergoing analysis, we hereby present a methodology for the assessment of our system’s usability based on the use of eye-tracking devices, geolocated traces and events, and usability questionnaires. Full article

(This article belongs to the Special Issue Cartography and Geomedia)

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20 pages, 8438 KiB

Open AccessEditor’s ChoiceArticle

Estimation of Travel Cost between Geographic Coordinates Using Artificial Neural Network: Potential Application in Vehicle Routing Problems

by Keyju Lee and Junjae Chae

ISPRS Int. J. Geo-Inf. 2023, 12(2), 57; https://doi.org/10.3390/ijgi12020057 - 08 Feb 2023

Cited by 2 | Viewed by 1429

Abstract

The vehicle routing problem (VRP) attempts to find optimal (minimum length) routes for a set of vehicles visiting a set of locations. Solving a VRP calls for a cost matrix between locations. The size of the matrix grows quadratically with an increasing number [...] Read more.

The vehicle routing problem (VRP) attempts to find optimal (minimum length) routes for a set of vehicles visiting a set of locations. Solving a VRP calls for a cost matrix between locations. The size of the matrix grows quadratically with an increasing number of locations, restricting large-sized VRPs from being solved in a reasonable amount of time. The time needed to obtain a cost matrix is expensive when routing engines are used, which solve shortest path problems in the back end. In fact, details on the shortest path are redundant; only distance or time values are necessary for VRPs. In this study, an artificial neural network (ANN) that receives two geo-coordinates as input and provides estimated cost (distance and time) as output is trained. The trained ANN model was able to estimate with a mean absolute percentage error of 7.68%, surpassing the quality of 13.2% with a simple regression model on Euclidean distance. The possibility of using a trained model in VRPs is examined with different implementation scenarios. The experimental results with VRPs confirm that using ANN estimation instead of Euclidean distance produces a better solution, which is verified to be statistically significant. The results also suggest that an ANN can be a better choice than routing engines when the trade-off between response time and solution quality is considered. Full article

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20 pages, 18367 KiB

Open AccessEditor’s ChoiceArticle

Revealing the Impact of COVID-19 on Urban Residential Travel Structure Based on Floating Car Trajectory Data: A Case Study of Nantong, China

by Fei Tao, Junjie Wu, Shuang Lin, Yaqiao Lv, Yu Wang and Tong Zhou

ISPRS Int. J. Geo-Inf. 2023, 12(2), 55; https://doi.org/10.3390/ijgi12020055 - 08 Feb 2023

Cited by 3 | Viewed by 1645

Abstract

The volume of residential travel with different purposes follows relatively stable patterns in a specific period and state; therefore, it can reflect the operating status of urban traffic and even indicate urban vitality. Recent research has focused on changes in the spatiotemporal characteristics [...] Read more.

The volume of residential travel with different purposes follows relatively stable patterns in a specific period and state; therefore, it can reflect the operating status of urban traffic and even indicate urban vitality. Recent research has focused on changes in the spatiotemporal characteristics of urban mobility affected by the pandemic but has rarely examined the impact of COVID-19 on the travel conditions and psychological needs of residents. To quantitatively assess travel characteristics during COVID-19, this paper proposed a method by which to determine the purpose of residential travel by combining urban functional areas (UFAs) based on machine learning. Then, the residential travel structure, which includes origin–destination (OD) points, residential travel flow, and the proportion of flows for different purposes, was established. Based on taxi trajectory data obtained during the epidemic in Nantong, China, the case study explores changes in travel flow characteristics under the framework of the residential travel structure. Through comparison of the number and spatial distribution of OD points in the residential travel structure, it is found that residential travel hotspots decreased significantly. The ratios of commuting and medical travel increased from 43.8% to 45.7% and 7.1% to 8.1%, respectively. Conversely, the ratios of other travel types all decreased sharply. Moreover, under Maslow’s hierarchy of needs model, further insights into the impacts of COVID-19 on changes in residential psychological needs are discussed in this paper. This work can provide a reference for decision makers to cope with the change in urban traffic during a public health emergency, which is beneficial to the sustainable healthy development of cities. Full article

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17 pages, 69177 KiB

Open AccessEditor’s ChoiceArticle

Crowd Density Estimation and Mapping Method Based on Surveillance Video and GIS

by Xingguo Zhang, Yinping Sun, Qize Li, Xiaodi Li and Xinyu Shi

ISPRS Int. J. Geo-Inf. 2023, 12(2), 56; https://doi.org/10.3390/ijgi12020056 - 08 Feb 2023

Cited by 4 | Viewed by 2441

Abstract

Aiming at the problem that the existing crowd counting methods cannot achieve accurate crowd counting and map visualization in a large scene, a crowd density estimation and mapping method based on surveillance video and GIS (CDEM-M) is proposed. Firstly, a crowd semantic segmentation [...] Read more.

Aiming at the problem that the existing crowd counting methods cannot achieve accurate crowd counting and map visualization in a large scene, a crowd density estimation and mapping method based on surveillance video and GIS (CDEM-M) is proposed. Firstly, a crowd semantic segmentation model (CSSM) and a crowd denoising model (CDM) suitable for high-altitude scenarios are constructed by transfer learning. Then, based on the homography matrix between the video and remote sensing image, the crowd areas in the video are projected to the map space. Finally, according to the distance from the crowd target to the camera, the camera inclination, and the area of the crowd polygon in the geographic space, a BP neural network for the crowd density estimation is constructed. The results show the following: (1) The test accuracy of the CSSM was 96.70%, and the classification accuracy of the CDM was 86.29%, which can achieve a high-precision crowd extraction in large scenes. (2) The BP neural network for the crowd density estimation was constructed, with an average error of 1.2 and a mean square error of 4.5. Compared to the density map method, the MAE and RMSE of the CDEM-M are reduced by 89.9 and 85.1, respectively, which is more suitable for a high-altitude camera. (3) The crowd polygons were filled with the corresponding number of points, and the symbol was a human icon. The crowd mapping and visual expression were realized. The CDEM-M can be used for crowd supervision in stations, shopping malls, and sports venues. Full article

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23 pages, 11883 KiB

Open AccessEditor’s ChoiceArticle

Implementation of GIS Tools in the Quality of Life Assessment of Czech Municipalities

by Karel Macků, Jaroslav Burian and Hynek Vodička

ISPRS Int. J. Geo-Inf. 2023, 12(2), 43; https://doi.org/10.3390/ijgi12020043 - 31 Jan 2023

Cited by 3 | Viewed by 1689

Abstract

Although quality of life is a phenomenon with a significant geographical component, its assessment is often only based on non-spatial statistical data. In Czechia, there are currently several assessments of quality of life at the level of municipalities, yet they do not consider [...] Read more.

Although quality of life is a phenomenon with a significant geographical component, its assessment is often only based on non-spatial statistical data. In Czechia, there are currently several assessments of quality of life at the level of municipalities, yet they do not consider the spatial aspect of the input indicators. This study uses the existing quality of life index compiled by the research agencies Median and the Aspen Institute, whose input indicators related to the accessibility of services and facilities have been redesigned to capture real-world phenomena more appropriately with GIS (Geographic Information Systems) tools using network analysis. In accordance with the original methodology, an adjusted index of quality of life was compiled. An update of indicators resulted in a more accurate description of quality of life. The differences between the original and the adjusted index were mainly seen in the areas around the larger cities, where quality of life has significantly risen. On the other hand, rural/rather rural areas experienced a slight decrease in quality of life with the change of inputs. The mapping of the resulting index documents the disparities in quality of life across Czechia and contributes to the discussions on the topic of quality of life in Czechia with new up-to-date reference data. Full article

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15 pages, 2804 KiB

Open AccessEditor’s ChoiceArticle

Imputation of Missing Parts in UAV Orthomosaics Using PlanetScope and Sentinel-2 Data: A Case Study in a Grass-Dominated Area

by Francisco R. da S. Pereira, Aliny A. Dos Reis, Rodrigo G. Freitas, Stanley R. de M. Oliveira, Lucas R. do Amaral, Gleyce K. D. A. Figueiredo, João F. G. Antunes, Rubens A. C. Lamparelli, Edemar Moro and Paulo S. G. Magalhães

ISPRS Int. J. Geo-Inf. 2023, 12(2), 41; https://doi.org/10.3390/ijgi12020041 - 28 Jan 2023

Cited by 1 | Viewed by 1895

Abstract

The recent advances in unmanned aerial vehicle (UAV)-based remote sensing systems have broadened the remote sensing applications for agriculture. Despite the great possibilities of using UAVs to monitor agricultural fields, specific problems related to missing parts in UAV orthomosaics due to drone flight [...] Read more.

The recent advances in unmanned aerial vehicle (UAV)-based remote sensing systems have broadened the remote sensing applications for agriculture. Despite the great possibilities of using UAVs to monitor agricultural fields, specific problems related to missing parts in UAV orthomosaics due to drone flight restrictions are common in agricultural monitoring, especially in large areas. In this study, we propose a methodological framework to impute missing parts of UAV orthomosaics using PlanetScope (PS) and Sentinel-2 (S2) data and the random forest (RF) algorithm of an integrated crop–livestock system (ICLS) covered by grass at the time. We validated the proposed framework by simulating and imputing artificial missing parts in a UAV orthomosaic and then comparing the original data with the model predictions. Spectral bands and the normalized difference vegetation index (NDVI) derived from PS, as well as S2 images (separately and combined), were used as predictor variables of the UAV spectral bands and NDVI in developing the RF-based imputation models. The proposed framework produces highly accurate results (RMSE = 6.77–17.33%) with a computationally efficient and robust machine-learning algorithm that leverages the wealth of empirical information present in optical satellite imagery (PS and S2) to impute up to 50% of missing parts in a UAV orthomosaic. Full article

(This article belongs to the Special Issue Geomatics in Forestry and Agriculture: New Advances and Perspectives)

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24 pages, 3974 KiB

Open AccessEditor’s ChoiceSystematic Review

Big Data Management Algorithms, Deep Learning-Based Object Detection Technologies, and Geospatial Simulation and Sensor Fusion Tools in the Internet of Robotic Things

by Mihai Andronie, George Lăzăroiu, Mariana Iatagan, Iulian Hurloiu, Roxana Ștefănescu, Adrian Dijmărescu and Irina Dijmărescu

ISPRS Int. J. Geo-Inf. 2023, 12(2), 35; https://doi.org/10.3390/ijgi12020035 - 21 Jan 2023

Cited by 53 | Viewed by 5322

Abstract

The objective of this systematic review was to analyze the recently published literature on the Internet of Robotic Things (IoRT) and integrate the insights it articulates on big data management algorithms, deep learning-based object detection technologies, and geospatial simulation and sensor fusion tools. [...] Read more.

The objective of this systematic review was to analyze the recently published literature on the Internet of Robotic Things (IoRT) and integrate the insights it articulates on big data management algorithms, deep learning-based object detection technologies, and geospatial simulation and sensor fusion tools. The research problems were whether computer vision techniques, geospatial data mining, simulation-based digital twins, and real-time monitoring technology optimize remote sensing robots. Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines were leveraged by a Shiny app to obtain the flow diagram comprising evidence-based collected and managed data (the search results and screening procedures). Throughout January and July 2022, a quantitative literature review of ProQuest, Scopus, and the Web of Science databases was performed, with search terms comprising “Internet of Robotic Things” + “big data management algorithms”, “deep learning-based object detection technologies”, and “geospatial simulation and sensor fusion tools”. As the analyzed research was published between 2017 and 2022, only 379 sources fulfilled the eligibility standards. A total of 105, chiefly empirical, sources have been selected after removing full-text papers that were out of scope, did not have sufficient details, or had limited rigor For screening and quality evaluation so as to attain sound outcomes and correlations, we deployed AMSTAR (Assessing the Methodological Quality of Systematic Reviews), AXIS (Appraisal tool for Cross-Sectional Studies), MMAT (Mixed Methods Appraisal Tool), and ROBIS (to assess bias risk in systematic reviews). Dimensions was leveraged as regards initial bibliometric mapping (data visualization) and VOSviewer was harnessed in terms of layout algorithms. Full article

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20 pages, 5433 KiB

Open AccessEditor’s ChoiceArticle

Multi-GPU-Parallel and Tile-Based Kernel Density Estimation for Large-Scale Spatial Point Pattern Analysis

by Guiming Zhang and Jin Xu

ISPRS Int. J. Geo-Inf. 2023, 12(2), 31; https://doi.org/10.3390/ijgi12020031 - 18 Jan 2023

Cited by 4 | Viewed by 1662

Abstract

Kernel density estimation (KDE) is a commonly used method for spatial point pattern analysis, but it is computationally demanding when analyzing large datasets. GPU-based parallel computing has been adopted to address such computational challenges. The existing GPU-parallel KDE method, however, utilizes only one [...] Read more.

Kernel density estimation (KDE) is a commonly used method for spatial point pattern analysis, but it is computationally demanding when analyzing large datasets. GPU-based parallel computing has been adopted to address such computational challenges. The existing GPU-parallel KDE method, however, utilizes only one GPU for parallel computing. Additionally, it assumes that the input data can be held in GPU memory all at once for computation, which is unrealistic when conducting KDE analysis over large geographic areas at high resolution. This study develops a multi-GPU-parallel and tile-based KDE algorithm to overcome these limitations. It exploits multiple GPUs to speedup complex KDE computation by distributing computation across GPUs, and approaches density estimation with a tile-based strategy to bypass the memory bottleneck. Experiment results show that the parallel KDE algorithm running on multiple GPUs achieves significant speedups over running on a single GPU, and higher speedups are achieved on KDE tasks of a larger problem size. The tile-based strategy renders it feasible to estimate high-resolution density surfaces over large areas even on GPUs with only limited memory. Multi-GPU parallel computing and tile-based density estimation, while incurring very little computational overhead, effectively enable conducting KDE for large-scale spatial point pattern analysis on geospatial big data. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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13 pages, 2209 KiB

Open AccessEditor’s ChoiceArticle

Automatic Clustering of Indoor Area Features in Shopping Malls

by Ziren Gao, Yi Shen, Jingsong Ma, Jie Shen and Jing Zheng

ISPRS Int. J. Geo-Inf. 2023, 12(1), 19; https://doi.org/10.3390/ijgi12010019 - 10 Jan 2023

Viewed by 1268

Abstract

The comprehensive expression of indoor maps directly affects the visualization effect of the map and the user’s map reading experience. Currently, only the points, lines, and polygons of outdoor maps are used as objects of cartographic generalization. Therefore, this study considers indoor map [...] Read more.

The comprehensive expression of indoor maps directly affects the visualization effect of the map and the user’s map reading experience. Currently, only the points, lines, and polygons of outdoor maps are used as objects of cartographic generalization. Therefore, this study considers indoor map area features as generalization objects and deems the automatic clustering of the indoor area features of shopping malls as the research goal. The approach is used to construct an encoder-decoder clustering model, where the encoder consists of a graph convolutional network and its variant models. The results show that the proposed model framework effectively extracts the area features suitable for the indoor space clustering of shopping malls and improves clustering efficacy. Specifically, the model with the Relational Graph Convolutional Network as the encoder demonstrated the best performance, time complexity, and accuracy of clustering results, with accuracy up to 95%. This study extends the research object of cartographic generalization to indoor maps, enabling the automatic clustering of indoor area features, and proposes a clustering model for the important indoor scene of shopping malls. This is valuable for scholars interested in the cartographic generalization of indoor maps. Full article

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23 pages, 5501 KiB

Open AccessEditor’s ChoiceArticle

Diagnosis and Planning Strategies for Quality of Urban Street Space Based on Street View Images

by Jiwu Wang, Yali Hu and Wuxihong Duolihong

ISPRS Int. J. Geo-Inf. 2023, 12(1), 15; https://doi.org/10.3390/ijgi12010015 - 07 Jan 2023

Cited by 3 | Viewed by 2866

Abstract

Under the background of stock planning, improving the quality of urban public space has become an important work of urban planning, design, and construction management. An accurate diagnosis of the spatial quality of streets and the effective implementation of street renewal planning play [...] Read more.

Under the background of stock planning, improving the quality of urban public space has become an important work of urban planning, design, and construction management. An accurate diagnosis of the spatial quality of streets and the effective implementation of street renewal planning play important roles in the high-quality development of urban spatial environments. However, traditional planning design and study methods, typically based on questionnaires, interviews, and on-site research, are inefficient and make it difficult to objectively and comprehensively grasp the overall construction characteristics and problems of urban street space in a large area, thus making it challenging to meet the needs of practical planning. Therefore, based on street view images, this study combined machine learning with an artificial audit to put forward a methodological framework for diagnosing the quality issues of street space. The Gongshu District of Hangzhou, China, was selected as a case study, and the diagnosis of quality problems for streets at different grades was achieved. The diagnosis results showed the current situation and problems of the selected area. Simultaneously, a series of targeted strategies for street spatial update planning was proposed to solve these problems. This diagnostic method, based on a combination of subjective and objective approaches, can be conducive to the precise and comprehensive identification of urban public spatial problems, which is expected to become an effective tool to assist in urban renewal and other planning decisions. Full article

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18 pages, 4374 KiB

Open AccessEditor’s ChoiceArticle

Spatial–Temporal Data Imputation Model of Traffic Passenger Flow Based on Grid Division

by Li Cai, Cong Sha, Jing He and Shaowen Yao

ISPRS Int. J. Geo-Inf. 2023, 12(1), 13; https://doi.org/10.3390/ijgi12010013 - 04 Jan 2023

Cited by 1 | Viewed by 1835

Abstract

Traffic flows (e.g., the traffic of vehicles, passengers, and bikes) aim to reveal traffic flow phenomena generated by traffic participants in traffic activities. Various studies of traffic flows rely heavily on high-quality traffic data. The taxi GPS trajectory data are location data that [...] Read more.

Traffic flows (e.g., the traffic of vehicles, passengers, and bikes) aim to reveal traffic flow phenomena generated by traffic participants in traffic activities. Various studies of traffic flows rely heavily on high-quality traffic data. The taxi GPS trajectory data are location data that include latitude, longitude, and time. These data are critical for traffic flow analysis, planning, infrastructure layout, and recommendations for urban residents. A city map can be divided into multiple grids according to the latitude and longitude coordinates, and traffic passenger flows data derived from taxi trajectory data can be extracted. However, random missing data occur due to weather and equipment failure. Therefore, the effective imputation of missing traffic flow data is a hot topic. This study proposes the spatio-temporal generative adversarial imputation net (ST-GAIN) model to solve the traffic passenger flows imputation. An adversarial game with multiple generators and one discriminator is established. The generator observes some components of the time-domain and regional traffic data vector extracted from the grid. It effectively imputes the missing values of the spatio-temporal traffic passenger flow data. The experimental data are accurate Kunming taxi trajectory data, and experimental results show that the proposed method outperforms five baseline methods regarding the imputation accuracy. It is significant and suggests the possibility of effectively applying the model to predict the passenger flows in some areas where traffic data cannot be collected for some reason or traffic data are randomly missing. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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25 pages, 29480 KiB

Open AccessEditor’s ChoiceArticle

A Fine-Grain Batching-Based Task Allocation Algorithm for Spatial Crowdsourcing

by Yuxin Jiao, Zhikun Lin, Long Yu and Xiaozhu Wu

ISPRS Int. J. Geo-Inf. 2022, 11(3), 203; https://doi.org/10.3390/ijgi11030203 - 17 Mar 2022

Cited by 4 | Viewed by 2515

Abstract

Task allocation is a critical issue of spatial crowdsourcing. Although the batching strategy performs better than the real-time matching mode, it still has the following two drawbacks: (1) Because the granularity of the batch size set obtained by batching is too coarse, it [...] Read more.

Task allocation is a critical issue of spatial crowdsourcing. Although the batching strategy performs better than the real-time matching mode, it still has the following two drawbacks: (1) Because the granularity of the batch size set obtained by batching is too coarse, it will result in poor matching accuracy. However, roughly designing the batch size for all possible delays will result in a large computational overhead. (2) Ignoring non-stationary factors will lead to a change in optimal batch size that cannot be found as soon as possible. Therefore, this paper proposes a fine-grained, batching-based task allocation algorithm (FGBTA), considering non-stationary setting. In the batch method, the algorithm first uses variable step size to allow for fine-grained exploration within the predicted value given by the multi-armed bandit (MAB) algorithm and uses the results of pseudo-matching to calculate the batch utility. Then, the batch size with higher utility is selected, and the exact maximum weight matching algorithm is used to obtain the allocation result within the batch. In order to cope with the non-stationary changes, we use the sliding window (SW) method to retain the latest batch utility and discard the historical information that is too far away, so as to finally achieve refined batching and adapt to temporal changes. In addition, we also take into account the benefits of requesters, workers, and the platform. Experiments on real data and synthetic data show that this method can accomplish the task assignment of spatial crowdsourcing effectively and can adapt to the non-stationary setting as soon as possible. This paper mainly focuses on the spatial crowdsourcing task of ride-hailing. Full article

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17 pages, 2311 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

The Governance of INSPIRE: Evaluating and Exploring Governance Scenarios for the European Spatial Data Infrastructure

by Jaap-Willem Sjoukema, Jalal Samia, Arnold K. Bregt and Joep Crompvoets

ISPRS Int. J. Geo-Inf. 2022, 11(2), 141; https://doi.org/10.3390/ijgi11020141 - 15 Feb 2022

Cited by 3 | Viewed by 3366

Abstract

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 [...] Read more.

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

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17 pages, 64925 KiB

Open AccessEditor’s ChoiceArticle

Identifying Urban Wetlands through Remote Sensing Scene Classification Using Deep Learning: A Case Study of Shenzhen, China

by Renfei Yang, Fang Luo, Fu Ren, Wenli Huang, Qianyi Li, Kaixuan Du and Dingdi Yuan

ISPRS Int. J. Geo-Inf. 2022, 11(2), 131; https://doi.org/10.3390/ijgi11020131 - 14 Feb 2022

Cited by 13 | Viewed by 3480

Abstract

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 [...] Read more.

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

(This article belongs to the Special Issue Deep Learning and Computer Vision for GeoInformation Sciences)

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30 pages, 9647 KiB

Open AccessEditor’s ChoiceArticle

Topographic Characteristics of Drainage Divides at the Mountain-Range Scale—A Review of DTM-Based Analytical Tools

by Kacper Jancewicz, Milena Różycka, Mariusz Szymanowski, Maciej Kryza and Piotr Migoń

ISPRS Int. J. Geo-Inf. 2022, 11(2), 116; https://doi.org/10.3390/ijgi11020116 - 06 Feb 2022

Cited by 4 | Viewed by 2574

Abstract

We review DTM-based measures that can be applied to study the main drainage divides of mountain ranges. Both measures proposed in the past and new or modified approaches are presented, in order to show an ensemble of tools and jointly discuss their information [...] Read more.

We review DTM-based measures that can be applied to study the main drainage divides of mountain ranges. Both measures proposed in the past and new or modified approaches are presented, in order to show an ensemble of tools and jointly discuss their information potential and problematic issues. The first group focuses on the main drainage divide (MDD) as a line running along the range and includes elevation profile, sinuosity, and orientation. The second one includes measures used to compare morphometric properties of two parts of the range, located on the opposite sides of the MDD, such as range asymmetry, morphometric properties of drainage basins, and the position of MDD versus maximum elevation within the range. In the third group, morphometric properties of the terrain immediately adjacent to the MDD are considered. These include properties of areas located far beyond the range symmetry line, topographic asymmetry, longitudinal stream profiles, and relief types derived from automatic landform classifications. The majority of these tools supports identification of sectors of the MDD, anomalous in terms of elevation, symmetry of the range, or the geomorphic context. All these measures were applied to the test area of the Sudetes range in Central Europe. Full article

(This article belongs to the Special Issue Geomorphometry and Terrain Analysis)

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24 pages, 4369 KiB

Open AccessEditor’s ChoiceArticle

GisGCN: A Visual Graph-Based Framework to Match Geographical Areas through Time

by Margarita Khokhlova, Nathalie Abadie, Valérie Gouet-Brunet and Liming Chen

ISPRS Int. J. Geo-Inf. 2022, 11(2), 97; https://doi.org/10.3390/ijgi11020097 - 29 Jan 2022

Cited by 1 | Viewed by 3382

Abstract

Historical visual sources are particularly useful for reconstructing the successive states of the territory in the past and for analysing its evolution. However, finding visual sources covering a given area within a large mass of archives can be very difficult if they are [...] Read more.

Historical visual sources are particularly useful for reconstructing the successive states of the territory in the past and for analysing its evolution. However, finding visual sources covering a given area within a large mass of archives can be very difficult if they are poorly documented. In the case of aerial photographs, most of the time, this task is carried out by solely relying on the visual content of the images. Convolutional Neural Networks are capable to capture the visual cues of the images and match them to each other given a sufficient amount of training data. However, over time and across seasons, the natural and man-made landscapes may evolve, making historical image-based retrieval a challenging task. We want to approach this cross-time aerial indexing and retrieval problem from a different novel point of view: by using geometrical and topological properties of geographic entities of the researched zone encoded as graph representations which are more robust to appearance changes than the pure image-based ones. Geographic entities in the vertical aerial images are thought of as nodes in a graph, linked to each other by edges representing their spatial relationships. To build such graphs, we propose to use instances from topographic vector databases and state-of-the-art spatial analysis methods. We demonstrate how these geospatial graphs can be successfully matched across time by means of the learned graph embedding. Full article

(This article belongs to the Special Issue Geographic Information Science (GIScience) and Geospatial Approaches for the Analysis of Historical Visual Sources and Cartographic Material)

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19 pages, 539 KiB

Open AccessEditor’s ChoiceArticle

Approaches for the Clustering of Geographic Metadata and the Automatic Detection of Quasi-Spatial Dataset Series

by Javier Lacasta, Francisco Javier Lopez-Pellicer, Javier Zarazaga-Soria, Rubén Béjar and Javier Nogueras-Iso

ISPRS Int. J. Geo-Inf. 2022, 11(2), 87; https://doi.org/10.3390/ijgi11020087 - 26 Jan 2022

Cited by 4 | Viewed by 2539

Abstract

The discrete representation of resources in geospatial catalogues affects their information retrieval performance. The performance could be improved by using automatically generated clusters of related resources, which we name quasi-spatial dataset series. This work evaluates whether a clustering process can create quasi-spatial dataset [...] Read more.

The discrete representation of resources in geospatial catalogues affects their information retrieval performance. The performance could be improved by using automatically generated clusters of related resources, which we name quasi-spatial dataset series. This work evaluates whether a clustering process can create quasi-spatial dataset series using only textual information from metadata elements. We assess the combination of different kinds of text cleaning approaches, word and sentence-embeddings representations (Word2Vec, GloVe, FastText, ELMo, Sentence BERT, and Universal Sentence Encoder), and clustering techniques (K-Means, DBSCAN, OPTICS, and agglomerative clustering) for the task. The results demonstrate that combining word-embeddings representations with an agglomerative-based clustering creates better quasi-spatial dataset series than the other approaches. In addition, we have found that the ELMo representation with agglomerative clustering produces good results without any preprocessing step for text cleaning. Full article

(This article belongs to the Special Issue Geospatial Metadata)

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12 pages, 22201 KiB

Open AccessEditor’s ChoiceArticle

The Influence of Landscape Structure on Wildlife–Vehicle Collisions: Geostatistical Analysis on Hot Spot and Habitat Proximity Relations

by Lina Galinskaitė, Alius Ulevičius, Vaidotas Valskys, Arūnas Samas, Peter E. Busher and Gytautas Ignatavičius

ISPRS Int. J. Geo-Inf. 2022, 11(1), 63; https://doi.org/10.3390/ijgi11010063 - 14 Jan 2022

Cited by 3 | Viewed by 2642

Abstract

Vehicle collisions with animals pose serious issues in countries with well-developed highway networks. Both expanding wildlife populations and the development of urbanised areas reduce the potential contact distance between wildlife species and vehicles. Many recent studies have been conducted to better understand the [...] Read more.

Vehicle collisions with animals pose serious issues in countries with well-developed highway networks. Both expanding wildlife populations and the development of urbanised areas reduce the potential contact distance between wildlife species and vehicles. Many recent studies have been conducted to better understand the factors that influence wildlife–vehicle collisions (WVCs) and provide mitigation methods. Most of these studies examined road density, traffic volume, seasonal fluctuations, etc. However, in analysing the distribution of WVC, few studies have considered a spatial and significant distance geostatistical analysis approach that includes how different land-use categories are associated with the distance to WVCs. Our study investigated the spatial distribution of agricultural land, meadows and pastures, forests, built-up areas, rivers, lakes, and ponds, to highlight the most dangerous sections of roadways where WVCs occur. We examined six potential ‘hot spot’ distances (5–10–25–50–100–200 m) to evaluate the role different landscape elements play in the occurrence of WVC. The near analysis tool showed that a distance of 10–25 m to different landscape elements provided the most sensitive results. Hot spots associated with agricultural land, forests, as well as meadows and pastures, peaked on roadways in close proximity (10 m), while hot spots associated with built-up areas, rivers, lakes, and ponds peaked on roadways farther (200 m) from these land-use types. We found that the order of habitat importance in WVC hot spots was agricultural land < forests < meadows and pastures < built-up areas < rivers < lakes and ponds. This methodological approach includes general hot-spot analysis as well as differentiated distance analysis which helps to better reveal the influence of landscape structure on WVCs. Full article

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13 pages, 2819 KiB

Open AccessEditor’s ChoiceArticle

Where Maps Lie: Visualization of Perceptual Fallacy in Choropleth Maps at Different Levels of Aggregation

by Giedrė Beconytė, Andrius Balčiūnas, Aurelija Šturaitė and Rita Viliuvienė

ISPRS Int. J. Geo-Inf. 2022, 11(1), 64; https://doi.org/10.3390/ijgi11010064 - 14 Jan 2022

Cited by 5 | Viewed by 3384

Abstract

This paper proposes a method for quantitative evaluation of perception deviations due to generalization in choropleth maps. The method proposed is based on comparison of class values assigned to different aggregation units chosen for representing the same dataset. It is illustrated by the [...] Read more.

This paper proposes a method for quantitative evaluation of perception deviations due to generalization in choropleth maps. The method proposed is based on comparison of class values assigned to different aggregation units chosen for representing the same dataset. It is illustrated by the results of application of the method to population density maps of Lithuania. Three spatial aggregation levels were chosen for comparison: the 1 × 1 km statistical grid, elderships (NUTS3), and municipalities (NUTS2). Differences in density class values between the reference grid map and the other two maps were calculated. It is demonstrated that a perceptual fallacy on the municipality level population map of Lithuania leads to a misinterpretation of data that makes such maps frankly useless. The eldership level map is, moreover, also largely misleading, especially in sparsely populated areas. The method proposed is easy to use and transferable to any other field where spatially aggregated data are mapped. It can be used for visual analysis of the degree to which a generalized choropleth map is liable to mislead the user in particular areas. Full article

(This article belongs to the Special Issue Cartographic Communication of Big Data)

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18 pages, 786 KiB

Open AccessEditor’s ChoiceArticle

Achieving ‘Active’ 30 Minute Cities: How Feasible Is It to Reach Work within 30 Minutes Using Active Transport Modes?

by Alan Both, Lucy Gunn, Carl Higgs, Melanie Davern, Afshin Jafari, Claire Boulange and Billie Giles-Corti

ISPRS Int. J. Geo-Inf. 2022, 11(1), 58; https://doi.org/10.3390/ijgi11010058 - 13 Jan 2022

Cited by 9 | Viewed by 5421

Abstract

Confronted with rapid urbanization, population growth, traffic congestion, and climate change, there is growing interest in creating cities that support active transport modes including walking, cycling, or public transport. The ‘30 minute city’, where employment is accessible within 30 min by active transport, [...] Read more.

Confronted with rapid urbanization, population growth, traffic congestion, and climate change, there is growing interest in creating cities that support active transport modes including walking, cycling, or public transport. The ‘30 minute city’, where employment is accessible within 30 min by active transport, is being pursued in some cities to reduce congestion and foster local living. This paper examines the spatial relationship between employment, the skills of residents, and transport opportunities, to answer three questions about Australia’s 21 largest cities: (1) What percentage of workers currently commute to their workplace within 30 min? (2) If workers were to shift to an active transport mode, what percent could reach their current workplace within 30 min? and (3) If it were possible to relocate workers closer to their employment or relocate employment closer to their home, what percentage could reach work within 30 min by each mode? Active transport usage in Australia is low, with public transport, walking, and cycling making up 16.8%, 2.8%, and 1.1% respectively of workers’ commutes. Cycling was found to have the most potential for achieving the 30 min city, with an estimated 29.5% of workers able to reach their current workplace were they to shift to cycling. This increased to 69.1% if workers were also willing and able to find a similar job closer to home, potentially reducing commuting by private motor vehicle from 79.3% to 30.9%. Full article

(This article belongs to the Special Issue Geo-Information Applications in Active Mobility and Health in Cities)

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29 pages, 5165 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Bridges and Barriers: An Exploration of Engagements of the Research Community with the OpenStreetMap Community

by A. Yair Grinberger, Marco Minghini, Godwin Yeboah, Levente Juhász and Peter Mooney

ISPRS Int. J. Geo-Inf. 2022, 11(1), 54; https://doi.org/10.3390/ijgi11010054 - 12 Jan 2022

Cited by 2 | Viewed by 3531

Abstract

The academic community frequently engages with OpenStreetMap (OSM) as a data source and research subject, acknowledging its complex and contextual nature. However, existing literature rarely considers the position of academic research in relation to the OSM community. In this paper we explore the [...] Read more.

The academic community frequently engages with OpenStreetMap (OSM) as a data source and research subject, acknowledging its complex and contextual nature. However, existing literature rarely considers the position of academic research in relation to the OSM community. In this paper we explore the extent and nature of engagement between the academic research community and the larger communities in OSM. An analysis of OSM-related publications from 2016 to 2019 and seven interviews conducted with members of one research group engaged in OSM-related research are described. The literature analysis seeks to uncover general engagement patterns while the interviews are used to identify possible causal structures explaining how these patterns may emerge within the context of a specific research group. Results indicate that academic papers generally show few signs of engagement and adopt data-oriented perspectives on the OSM project and product. The interviews expose that more complex perspectives and deeper engagement exist within the research group to which the interviewees belong, e.g., engaging in OSM mapping and direct interactions based on specific points-of-contact in the OSM community. Several conclusions and recommendations emerge, most notably: that every engagement with OSM includes an interpretive act which must be acknowledged and that the academic community should act to triangulate its interpretation of the data and OSM community by diversifying their engagement. This could be achieved through channels such as more direct interactions and inviting members of the OSM community to participate in the design and evaluation of research projects and programmes. Full article

(This article belongs to the Special Issue OpenStreetMap as A Multi-Disciplinary Nexus: Perspectives, Practices and Procedures)

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18 pages, 4016 KiB

Open AccessEditor’s ChoiceArticle

Development and Application of a QGIS-Based Model to Estimate Monthly Streamflow

by Hanyong Lee, Min Suh Chae, Jong-Yoon Park, Kyoung Jae Lim and Youn Shik Park

ISPRS Int. J. Geo-Inf. 2022, 11(1), 40; https://doi.org/10.3390/ijgi11010040 - 08 Jan 2022

Cited by 4 | Viewed by 2846

Abstract

Changes in rainfall pattern and land use have caused considerable impacts on the hydrological behavior of watersheds; a Long-Term Hydrologic Impact Analysis (L-THIA) model has been used to simulate such variations. The L-THIA model defines curve number according to the land use and [...] Read more.

Changes in rainfall pattern and land use have caused considerable impacts on the hydrological behavior of watersheds; a Long-Term Hydrologic Impact Analysis (L-THIA) model has been used to simulate such variations. The L-THIA model defines curve number according to the land use and hydrological soil group before calculating the direct runoff based on the amount of rainfall, making it a convenient method of analysis. Recently, a method was proposed to estimate baseflow using this model, which may be used to estimate the overall streamflow. Given that this model considers the spatial distribution of land use and hydrological soil groups and must use rainfall data at multiple positions, it requires the usage of a geographical information system (GIS). Therefore, a model that estimates streamflow using land use maps, hydrologic soil group maps, and rain gauge station maps in QGIS, a popular GIS software, was developed. This model was tested in 15 watersheds. Full article

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20 pages, 18905 KiB

Open AccessEditor’s ChoiceArticle

A Hierarchical Spatial Network Index for Arbitrarily Distributed Spatial Objects

by Xiangqiang Min, Dieter Pfoser, Andreas Züfle and Yehua Sheng

ISPRS Int. J. Geo-Inf. 2021, 10(12), 814; https://doi.org/10.3390/ijgi10120814 - 01 Dec 2021

Cited by 4 | Viewed by 2361

Abstract

The range query is one of the most important query types in spatial data processing. Geographic information systems use it to find spatial objects within a user-specified range, and it supports data mining tasks, such as density-based clustering. In many applications, ranges are [...] Read more.

The range query is one of the most important query types in spatial data processing. Geographic information systems use it to find spatial objects within a user-specified range, and it supports data mining tasks, such as density-based clustering. In many applications, ranges are not computed in unrestricted Euclidean space, but on a network. While the majority of access methods cannot trivially be extended to network space, existing network index structures partition the network space without considering the data distribution. This potentially results in inefficiency due to a very skewed node distribution. To improve range query processing on networks, this paper proposes a balanced Hierarchical Network index (HN-tree) to query spatial objects on networks. The main idea is to recursively partition the data on the network such that each partition has a similar number of spatial objects. Leveraging the HN-tree, we present an efficient range query algorithm, which is empirically evaluated using three different road networks and several baselines and state-of-the-art network indices. The experimental evaluation shows that the HN-tree substantially outperforms existing methods. Full article

(This article belongs to the Special Issue Geo-Enriched Data Modeling & Mining)

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16 pages, 1516 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Interactive Maps for the Production of Knowledge and the Promotion of Participation from the Perspective of Communication, Journalism, and Digital Humanities

by Pedro Molina Rodríguez-Navas, Johamna Muñoz Lalinde and Narcisa Medranda Morales

ISPRS Int. J. Geo-Inf. 2021, 10(11), 722; https://doi.org/10.3390/ijgi10110722 - 26 Oct 2021

Viewed by 2977

Abstract

New technologies have allowed traditional map production criteria to be modified or even subverted. Starting from the communication sciences—journalism in particular—and digital humanities via the history of communication, we show how to use interactive digital maps for the production and publication of knowledge [...] Read more.

New technologies have allowed traditional map production criteria to be modified or even subverted. Starting from the communication sciences—journalism in particular—and digital humanities via the history of communication, we show how to use interactive digital maps for the production and publication of knowledge through and/or for participation. Firstly, we establish the theoretical-conceptual framework necessary to base the practices, dividing the elements into three areas: interactive maps and knowledge production (decentralization, pluralization, reticularization, and humanization), maps as instruments to promote political and social participation (egalitarianism, horizontality, and criticism), and maps as instruments for the visualization of data that favors the user experience (interactivity, multimediality, reticularity of reading, and participation). Next, we present two cases that we developed to put into practice the theoretical concepts that we established: the Mapa Infoparticipa (Infoparticipa Map), which shows the results of the evaluation of the transparency of public administrations, and the Ciutadania Plural (Plural Citizenship) web platform for the production of social knowledge about the past and the present. This theoretical and practical model shows the possibilities of interactive maps as tools to promote political participation and as instruments for the construction of social knowledge in a collaborative, participatory, networked way. Full article

(This article belongs to the Special Issue Public Participation in 2021: New Forms, New Modes, New Questions?)

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19 pages, 6035 KiB

Open AccessEditor’s ChoiceArticle

Towards Managing Visual Pollution: A 3D Isovist and Voxel Approach to Advertisement Billboard Visual Impact Assessment

by Szymon Chmielewski

ISPRS Int. J. Geo-Inf. 2021, 10(10), 656; https://doi.org/10.3390/ijgi10100656 - 30 Sep 2021

Cited by 10 | Viewed by 3611

Abstract

Visual pollution (VP) is a visual landscape quality issue, and its most consistently recognized symptom is an excess of out of home advertising billboards (OOHb). However, the VP related research concerns landscape aesthetic and advertisement cultural context, leaving the impact of outdoor billboard [...] Read more.

Visual pollution (VP) is a visual landscape quality issue, and its most consistently recognized symptom is an excess of out of home advertising billboards (OOHb). However, the VP related research concerns landscape aesthetic and advertisement cultural context, leaving the impact of outdoor billboard infrastructure on landscape openness unanswered to date. This research aims to assess the visual impact of outdoor billboard infrastructure on landscape openness, precisely the visual volume—a key geometrical quality of a landscape. The method uses 3D isovists and voxels to calculate the visible and obstructed subsets of visible volume. Using two case studies (Lublin City, Poland) and 26 measurement points, it was found that OOHb decreased landscape openness by at least 4% of visible volume; however, the severe impact may concern up to 35% of visual volume. GIS scientists develop the proposed method for policy-makers, and urban planners end users. It is also the very first example of compiling 3D isovists and voxels in ArcGIS Pro software in an easy-to-replicate framework. The research results, accompanied by statistically significant proofs, explain the visual landscape’s fragility and contribute to understanding the VP phenomenon. Full article

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24 pages, 2429 KiB

Open AccessEditor’s ChoiceArticle

Geospatial Data Utilisation in National Disaster Management Frameworks and the Priorities of Multilateral Disaster Management Frameworks: Case Studies of India and Bulgaria

by Tarun Ghawana, Lyubka Pashova and Sisi Zlatanova

ISPRS Int. J. Geo-Inf. 2021, 10(9), 610; https://doi.org/10.3390/ijgi10090610 - 15 Sep 2021

Cited by 7 | Viewed by 4925

Abstract

Facing the increased frequency of disasters and resulting in massive damages, many countries have developed their frameworks for Disaster Risk Management (DRM). However, these frameworks may differ concerning legal, policy, planning and organisational arrangements. We argue that geospatial data is a crucial binding [...] Read more.

Facing the increased frequency of disasters and resulting in massive damages, many countries have developed their frameworks for Disaster Risk Management (DRM). However, these frameworks may differ concerning legal, policy, planning and organisational arrangements. We argue that geospatial data is a crucial binding element in each national framework for different stages of the disaster management cycle. The multilateral DRM frameworks, like the Sendai Framework 2015–2030 and the United Nations Committee of Experts on Global Geospatial Information Management (UNGGIM) Strategic Framework on Geospatial Information and Services for Disasters, provide the strategic direction, but they are too generic to compare geospatial data in national DRM frameworks. This study investigates the two frameworks and suggests criteria for evaluating the utilisation of geospatial data for DRM. The derived criteria are validated for the comparative analysis of India and Bulgaria’s National Disaster Management Frameworks. The validation proves that the criteria can be used for a general comparison across national DRM. Full article

(This article belongs to the Special Issue Disaster Management and Geospatial Information)

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19 pages, 16572 KiB

Open AccessEditor’s ChoiceArticle

Integration and Analysis of Multi-Modal Geospatial Secondary Data to Inform Management of at-Risk Archaeological Sites

by Rebecca Guiney, Elettra Santucci, Samuel Valman, Adam Booth, Andrew Birley, Ian Haynes, Stuart Marsh and Jon Mills

ISPRS Int. J. Geo-Inf. 2021, 10(9), 575; https://doi.org/10.3390/ijgi10090575 - 24 Aug 2021

Cited by 4 | Viewed by 3896

Abstract

Climate change poses an imminent physical risk to cultural heritage sites and their surrounding landscape through intensifying environmental processes such as damaging wetting and drying cycles that disrupt archaeological preservation conditions, and soil erosion which threatens to expose deposits and alter the archaeological [...] Read more.

Climate change poses an imminent physical risk to cultural heritage sites and their surrounding landscape through intensifying environmental processes such as damaging wetting and drying cycles that disrupt archaeological preservation conditions, and soil erosion which threatens to expose deposits and alter the archaeological context of sites. In the face of such threats, geospatial techniques such as GIS, remote sensing, and spatial modelling have proved invaluable tools for archaeological research and cultural heritage monitoring. This paper presents the application of secondary multi-source and multi-temporal geospatial data within a processing framework to provide a comprehensive assessment of geophysical risk to the Roman fort of Magna, Carvoran, UK. An investigation into the ancient hydraulic system at Magna was carried out with analysis of vegetation change over time, and spatio-temporal analysis of soil erosion risk at the site. Due to COVID-19 restrictions in place at the time of this study, these analyses were conducted using only secondary data with the aim to guide further archaeological research, and management and monitoring strategies for the stakeholders involved. Results guided inferences about the ancient hydraulic system, providing insights regarding how to better manage the site at Magna in the future. Analysis of soil erosion allowed the identification of hot spot areas, indicating a future increase in rates of erosion at Magna and suggesting a seasonal period of higher risk of degradation to the site. Results have proven that freely available multi-purpose national-scale datasets are sufficient to create meaningful insights into archaeological sites where physical access to the site is inhibited. This infers the potential to carry out preliminary risk assessment to inform future site management practices. Full article

(This article belongs to the Special Issue 3D Modeling and GIS for Historical Sites Reconstruction)

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24 pages, 4294 KiB

Open AccessEditor’s ChoiceArticle

Heat Maps: Perfect Maps for Quick Reading? Comparing Usability of Heat Maps with Different Levels of Generalization

by Katarzyna Słomska-Przech, Tomasz Panecki and Wojciech Pokojski

ISPRS Int. J. Geo-Inf. 2021, 10(8), 562; https://doi.org/10.3390/ijgi10080562 - 18 Aug 2021

Cited by 5 | Viewed by 6790

Abstract

Recently, due to Web 2.0 and neocartography, heat maps have become a popular map type for quick reading. Heat maps are graphical representations of geographic data density in the form of raster maps, elaborated by applying kernel density estimation with a given radius [...] Read more.

Recently, due to Web 2.0 and neocartography, heat maps have become a popular map type for quick reading. Heat maps are graphical representations of geographic data density in the form of raster maps, elaborated by applying kernel density estimation with a given radius on point- or linear-input data. The aim of this study was to compare the usability of heat maps with different levels of generalization (defined by radii of 10, 20, 30, and 40 pixels) for basic map user tasks. A user study with 412 participants (16–20 years old, high school students) was carried out in order to compare heat maps that showed the same input data. The study was conducted in schools during geography or IT lessons. Objective (the correctness of the answer, response times) and subjective (response time self-assessment, task difficulty, preferences) metrics were measured. The results show that the smaller radius resulted in the higher correctness of the answers. A larger radius did not result in faster response times. The participants perceived the more generalized maps as easier to use, although this result did not match the performance metrics. Overall, we believe that heat maps, in given circumstances and appropriate design settings, can be considered an efficient method for spatial data presentation. Full article

(This article belongs to the Special Issue Geovisualization and Map Design)

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28 pages, 3017 KiB

Open AccessEditor’s ChoiceArticle

The Role of Participatory Village Maps in Strengthening Public Participation Practice

by Aulia Akbar, Johannes Flacke, Javier Martinez and Martin F. A. M. van Maarseveen

ISPRS Int. J. Geo-Inf. 2021, 10(8), 512; https://doi.org/10.3390/ijgi10080512 - 29 Jul 2021

Cited by 5 | Viewed by 3825

Abstract

This study investigated the role of participatory village maps in strengthening the Musrenbang, an annual multi-stakeholder public consultation forum to discuss development issues and plans in Indonesia. We evaluated the Musrenbang in five villages in Deli Serdang District after conducting participatory mapping workshops [...] Read more.

This study investigated the role of participatory village maps in strengthening the Musrenbang, an annual multi-stakeholder public consultation forum to discuss development issues and plans in Indonesia. We evaluated the Musrenbang in five villages in Deli Serdang District after conducting participatory mapping workshops to produce village maps to inform the Musrenbang process. Our results show that communication between Musrenbang participants improved because the maps provided a clear definition of the village administrative area, geospatial data as resources for participation, transparency, and a dynamic deliberative process. Collaboration was also evident as the maps enabled participants to exchange knowledge, experience social learning, and have greater influence on the decision-making process. Despite the benefits, some issues impeded the optimal use of the village maps to support the participatory process in the Musrenbang. The maps could not completely overcome the power disparities between Musrenbang participants. Certain actors still dominated the implementation of the Musrenbang, making the deliberative process inaccessible to and less inclusive of some local stakeholders. Several improvements are urgently needed to optimise the use of participatory village maps and enhance Musrenbang implementation. Full article

(This article belongs to the Special Issue Public Participation in 2021: New Forms, New Modes, New Questions?)

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17 pages, 5235 KiB

Open AccessEditor’s ChoiceArticle

Efficient Interactive Tactile Maps: A Semi-Automated Workflow Using the TouchIt3D Technology and OpenStreetMap Data

by Radek Barvir, Alena Vondrakova and Jan Brus

ISPRS Int. J. Geo-Inf. 2021, 10(8), 505; https://doi.org/10.3390/ijgi10080505 - 27 Jul 2021

Cited by 9 | Viewed by 3970

Abstract

Despite the growing efficiency of the map-design process in general, tactile mapping has remained peripheral to mainstream cartography. For a specific group of people with visual impairment, however, tactile maps are the only effective way to obtain a complex idea about the geospatial [...] Read more.

Despite the growing efficiency of the map-design process in general, tactile mapping has remained peripheral to mainstream cartography. For a specific group of people with visual impairment, however, tactile maps are the only effective way to obtain a complex idea about the geospatial distribution of the surrounding world. As there are numerous specifics in creating these 3D maps and only a limited group of users, tactile products have usually been either very simple creations or, on the other hand, difficult and expensive to produce. Modern trends and progress in the availability of new technologies (e.g., 3D printing) bring new possibilities for keeping tactile map production both effective and up to date. Therefore, this paper aims to present a methodology to apply the TouchIt3D technology to link 3D-printed multi-material tactile maps with a mobile device. Utilizing this solution resulted in a set of interactive tactile maps following current trends of inclusive education. Using OpenStreetMap data together with a semi-automated workflow significantly lowered expenses compared to antecedent maps with similar functionality. A semi-automated workflow was designed, focusing on three use cases of independent movement: walking, using public transport, and tourism. Full article

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19 pages, 7387 KiB

Open AccessEditor’s ChoiceArticle

Subjectively Measured Streetscape Perceptions to Inform Urban Design Strategies for Shanghai

by Waishan Qiu, Wenjing Li, Xun Liu and Xiaokai Huang

ISPRS Int. J. Geo-Inf. 2021, 10(8), 493; https://doi.org/10.3390/ijgi10080493 - 21 Jul 2021

Cited by 38 | Viewed by 5775

Abstract

Recently, many new studies applying computer vision (CV) to street view imagery (SVI) datasets to objectively extract the view indices of various streetscape features such as trees to proxy urban scene qualities have emerged. However, human perception (e.g., imageability) have a subtle relationship [...] Read more.

Recently, many new studies applying computer vision (CV) to street view imagery (SVI) datasets to objectively extract the view indices of various streetscape features such as trees to proxy urban scene qualities have emerged. However, human perception (e.g., imageability) have a subtle relationship to visual elements that cannot be fully captured using view indices. Conversely, subjective measures using survey and interview data explain human behaviors more. However, the effectiveness of integrating subjective measures with SVI datasets has been less discussed. To address this, we integrated crowdsourcing, CV, and machine learning (ML) to subjectively measure four important perceptions suggested by classical urban design theory. We first collected ratings from experts on sample SVIs regarding these four qualities, which became the training labels. CV segmentation was applied to SVI samples extracting streetscape view indices as the explanatory variables. We then trained ML models and achieved high accuracy in predicting scores. We found a strong correlation between the predicted complexity score and the density of urban amenities and services points of interest (POI), which validates the effectiveness of subjective measures. In addition, to test the generalizability of the proposed framework as well as to inform urban renewal strategies, we compared the measured qualities in Pudong to other five urban cores that are renowned worldwide. Rather than predicting perceptual scores directly from generic image features using a convolution neural network, our approach follows what urban design theory has suggested and confirmed as various streetscape features affecting multi-dimensional human perceptions. Therefore, the results provide more interpretable and actionable implications for policymakers and city planners. Full article

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15 pages, 5623 KiB

Open AccessEditor’s ChoiceArticle

Automatic Road Extraction from Historical Maps Using Deep Learning Techniques: A Regional Case Study of Turkey in a German World War II Map

by Burak Ekim, Elif Sertel and M. Erdem Kabadayı

ISPRS Int. J. Geo-Inf. 2021, 10(8), 492; https://doi.org/10.3390/ijgi10080492 - 21 Jul 2021

Cited by 20 | Viewed by 4576

Abstract

Scanned historical maps are available from different sources in various scales and contents. Automatic geographical feature extraction from these historical maps is an essential task to derive valuable spatial information on the characteristics and distribution of transportation infrastructures and settlements and to conduct [...] Read more.

Scanned historical maps are available from different sources in various scales and contents. Automatic geographical feature extraction from these historical maps is an essential task to derive valuable spatial information on the characteristics and distribution of transportation infrastructures and settlements and to conduct quantitative and geometrical analysis. In this research, we used the Deutsche Heereskarte 1:200,000 Türkei (DHK 200 Turkey) maps as the base geoinformation source to construct the past transportation networks using the deep learning approach. Five different road types were digitized and labeled to be used as inputs for the proposed deep learning-based segmentation approach. We adapted U-Net++ and ResneXt50_32×4d architectures to produce multi-class segmentation masks and perform feature extraction to determine various road types accurately. We achieved remarkable results, with 98.73% overall accuracy, 41.99% intersection of union, and 46.61% F1 score values. The proposed method can be implemented in DHK maps of different countries to automatically extract different road types and used for transfer learning of different historical maps. Full article

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23 pages, 3749 KiB

Open AccessEditor’s ChoiceArticle

User-Centred Design of Multidisciplinary Spatial Data Platforms for Human-History Research

by Meeli Roose, Tua Nylén, Harri Tolvanen and Outi Vesakoski

ISPRS Int. J. Geo-Inf. 2021, 10(7), 467; https://doi.org/10.3390/ijgi10070467 - 08 Jul 2021

Cited by 4 | Viewed by 3879

Abstract

The role of open spatial data is growing in human-history research. Spatiality can be utilized to bring together and seamlessly examine data describing multiple aspects of human beings and their environment. Web-based spatial data platforms can create equal opportunities to view and access [...] Read more.

The role of open spatial data is growing in human-history research. Spatiality can be utilized to bring together and seamlessly examine data describing multiple aspects of human beings and their environment. Web-based spatial data platforms can create equal opportunities to view and access these data. In this paper, we aim at advancing the development of user-friendly spatial data platforms for multidisciplinary research. We conceptualize the building process of such a platform by systematically reviewing a diverse sample of historical spatial data platforms and by piloting a user-centered design process of a multidisciplinary spatial data platform. We outline (1) the expertise needed in organizing multidisciplinary spatial data sharing, (2) data types that platforms should be able to handle, (3) the most useful platform functionalities, and (4) the design process itself. We recommend that the initiative and subject expertise should come from the end-users, i.e., scholars of human history, and all key end-user types should be involved in the design process. We also highlight the importance of geographic expertise in the process, an important link between subject, spatial and technical viewpoints, for reaching a common understanding and common terminology. Based on the analyses, we identify key development goals for spatial data platforms, including full layer management functionalities. Moreover, we identify the main roles in the user-centered design process, main user types and suggest good practices including a multimodal design workshop. Full article

(This article belongs to the Special Issue Geospatial Open Systems)

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34 pages, 17827 KiB

Open AccessEditor’s ChoiceArticle

An “Animated Spatial Time Machine” in Co-Creation: Reconstructing History Using Gamification Integrated into 3D City Modelling, 4D Web and Transmedia Storytelling

by Mario Matthys, Laure De Cock, John Vermaut, Nico Van de Weghe and Philippe De Maeyer

ISPRS Int. J. Geo-Inf. 2021, 10(7), 460; https://doi.org/10.3390/ijgi10070460 - 06 Jul 2021

Cited by 18 | Viewed by 7347

Abstract

More and more digital 3D city models might evolve into spatiotemporal instruments with time as the 4th dimension. For digitizing the current situation, 3D scanning and photography are suitable tools. The spatial future could be integrated using 3D drawings by public space designers [...] Read more.

More and more digital 3D city models might evolve into spatiotemporal instruments with time as the 4th dimension. For digitizing the current situation, 3D scanning and photography are suitable tools. The spatial future could be integrated using 3D drawings by public space designers and architects. The digital spatial reconstruction of lost historical environments is more complex, expensive and rarely done. Three-dimensional co-creative digital drawing with citizens’ collaboration could be a solution. In 2016, the City of Ghent (Belgium) launched the “3D city game Ghent” project with time as one of the topics, focusing on the reconstruction of disappeared environments. Ghent inhabitants modelled in open-source 3D software and added animated 3D gamification and Transmedia Storytelling, resulting in a 4D web environment and VR/AR/XR applications. This study analyses this low-cost interdisciplinary 3D co-creative process and offers a framework to enable other cities and municipalities to realise a parallel virtual universe (an animated digital twin bringing the past to life). The result of this co-creation is the start of an “Animated Spatial Time Machine” (AniSTMa), a term that was, to the best of our knowledge, never used before. This research ultimately introduces a conceptual 4D space–time diagram with a relation between the current physical situation and a growing number of 3D animated models over time. Full article

(This article belongs to the Special Issue Crowdsourced Geographic Information in Citizen Science)

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16 pages, 7789 KiB

Open AccessEditor’s ChoiceArticle

Hydrological Modeling of Green Infrastructure to Quantify Its Effect on Flood Mitigation and Water Availability in the High School Watershed in Tucson, AZ

by Yoganand Korgaonkar, David Phillip Guertin, Thomas Meixner and David C Goodrich

ISPRS Int. J. Geo-Inf. 2021, 10(7), 443; https://doi.org/10.3390/ijgi10070443 - 29 Jun 2021

Cited by 4 | Viewed by 2930

Abstract

Green Infrastructure (GI) practices are being implemented in numerous cities to tackle stormwater management issues and achieve co-benefits such as mitigating heat island effects and air pollution, as well as water augmentation, health, and economic benefits. Tucson, Arizona is a fast-growing city in [...] Read more.

Green Infrastructure (GI) practices are being implemented in numerous cities to tackle stormwater management issues and achieve co-benefits such as mitigating heat island effects and air pollution, as well as water augmentation, health, and economic benefits. Tucson, Arizona is a fast-growing city in the semiarid region of the southwest United States and provides a unique landscape in terms of urban hydrology and stormwater management, where stormwater is routed along the streets to the nearest ephemeral washes. Local organizations have implemented various GI practices, such as curb cuts, traffic chicanes, roof runoff harvesting, and retention basins, to capture the excess runoff and utilize it on-site. This study models the 3.31 km² High School watershed in central Tucson using the Automated Geospatial Watershed Assessment (AGWA) tool and the Kinematic Runoff and Erosion (KINEROS2) model. Each parcel in the watershed was individually represented using the KINEROS2 Urban element to simulate small-scale flow-on/flow-off processes. Seven different configurations of GI implementation were simulated using design storms, and we stochastically generated 20 years of precipitation data to understand the effects of GI implementation on flood mitigation and long-term water availability, respectively. The design storm analysis indicates that the configuration designed to mimic the current level of GI implementation, which includes 175 on-street basins and 37 roof runoff harvesting cisterns, has minimum (<2%) influence on runoff volume. Furthermore, the analysis showed that the current level of GI implementation caused an increase (<1%) in peak flows at the watershed outlet but predicted reduced on-street accumulated volumes (>25%) and increased water availability via GI capture and infiltration. When the GI implementation was increased by a factor of two and five, a larger reduction of peak flow (<8% and <22%, respectively) and volume (<3% and <8%, respectively) was simulated at the watershed outlet. The 20-year analysis showed that parcels with roof runoff harvesting cisterns were able to meet their landscape irrigation demands throughout the year, except for the dry months of May and June. Additionally, stormwater captured and infiltrated by the on-street basins could support xeric vegetation for most of the year, except June, where the water demand exceeded volume of water infiltrated in the basins. The current level of GI implementation in the High School watershed may not have significant large-scale impacts, but it provides numerous benefits at the parcel, street, and small neighborhood scales. Full article

(This article belongs to the Special Issue Advancements in Geospatial Planning and Assessment of Green Infrastructure in Cities of the Future)

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12 pages, 15071 KiB

Open AccessEditor’s ChoiceArticle

Exploring Allometric Scaling Relations between Fractal Dimensions of Metro Networks and Economic, Environmental and Social Indicators: A Case Study of 26 Cities in China

by Tian Lan, Qian Peng, Haoyu Wang, Xinyu Gong, Jing Li and Zhicheng Shi

ISPRS Int. J. Geo-Inf. 2021, 10(7), 429; https://doi.org/10.3390/ijgi10070429 - 23 Jun 2021

Cited by 3 | Viewed by 2095

Abstract

Allometric scaling originates in biology, where it refers to scaling relations between the size of a body part and the size of the whole body when an organism grows. In cities, various allometric relations have also been discovered, such as those between the [...] Read more.

Allometric scaling originates in biology, where it refers to scaling relations between the size of a body part and the size of the whole body when an organism grows. In cities, various allometric relations have also been discovered, such as those between the complexity of traffic networks and urban quantities. Metro networks are typical traffic networks in cities. However, whether allometric relations with metro networks exist is still uncertain. In this study, “fractal dimension” was employed as the complexity measure of metro networks, and potential allometric relations between fractal dimensions and urban indicators in 26 main cities in China were explored. It was found that fractal dimensions of metro networks had positive allometric relations with gross domestic product (GDP), population, particulate matter with a diameter less than 2.5 microns (PM_2.5), the road congestion index and the average price of second-hand housing (with Spearman’s R of 0.789, 0.806, 0.273, 0.625 and 0.335, respectively) but inverse allometric relations with sulfur dioxide (SO₂) and residential satisfaction (with Spearman’s R of −0.270 and −0.419, respectively). Such discoveries imply that allometric relations do exist with metro networks, which is helpful in deepening our understanding of how metro systems interact with urban quantities in the self-organized evolution of cities. Full article

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15 pages, 39210 KiB

Open AccessEditor’s ChoiceArticle

A Spatially Highly Resolved Ground Mounted and Rooftop Potential Analysis for Photovoltaics in Austria

by Christian Mikovits, Thomas Schauppenlehner, Patrick Scherhaufer, Johannes Schmidt, Lilia Schmalzl, Veronika Dworzak, Nina Hampl and Robert Gennaro Sposato

ISPRS Int. J. Geo-Inf. 2021, 10(6), 418; https://doi.org/10.3390/ijgi10060418 - 16 Jun 2021

Cited by 10 | Viewed by 5492

Abstract

Austria aims to meet 100% of its electricity demand from domestic renewable sources by 2030 which means, that an additional 27

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of renewable electricity generation are required, thereof 11

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from photovoltaic. While some [...] Read more.

Austria aims to meet 100% of its electricity demand from domestic renewable sources by 2030 which means, that an additional 27

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of renewable electricity generation are required, thereof 11

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from photovoltaic. While some federal states and municipalities released a solar rooftop cadastre, there is lacking knowledge on the estimation of the potential of both, ground mounted installations and rooftop modules, on a national level with a high spatial resolution. As a first, in this work data on agricultural land-use is combined with highly resolved data on buildings on a national level. Our results show significant differences between urban and rural areas, as well as between the Alpine regions and the Prealpine- and Easter Plain areas. Rooftop potential concentrates in the big urban areas, but also in densely populated areas in Lower- and Upper Austria, Styria and the Rhine valley of Vorarlberg. The ground mounted photovoltaic potential is highest in Eastern Austria. This potential is geographically consistent with the demand and allows for a production close to the consumer. In theory, the goal of meeting 11

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in 2030 can be achieved solely with the rooftop PV potential. However, considering the necessary installation efforts, the associated costs of small and dispersed production units and finally the inherent uncertainty with respect to the willingness of tens of thousands of individual households to install PV systems, installing the necessary solar PV on buildings alone is constrained. Full article

(This article belongs to the Collection Spatial and Temporal Modelling of Renewable Energy Systems)

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20 pages, 9736 KiB

Open AccessEditor’s ChoiceArticle

Emojis as Contextual Indicants in Location-Based Social Media Posts

by Eva Hauthal, Alexander Dunkel and Dirk Burghardt

ISPRS Int. J. Geo-Inf. 2021, 10(6), 407; https://doi.org/10.3390/ijgi10060407 - 12 Jun 2021

Cited by 3 | Viewed by 3178

Abstract

The presented study aims to investigate the relationship between the use of emojis in location-based social media and the location of the corresponding post in terms of perceived objects and conducted activities connected to this place. The basis for this is not a [...] Read more.

The presented study aims to investigate the relationship between the use of emojis in location-based social media and the location of the corresponding post in terms of perceived objects and conducted activities connected to this place. The basis for this is not a purely frequency-based assessment, but a specifically introduced measure called typicality. To evaluate the typicality measure and examine the assumption that emojis are contextual indicants, a dataset of worldwide geotagged posts from Instagram relating to sunset and sunrise events is used, converted to a privacy-aware version based on a Hyperloglog approach. Results suggest that emojis can often provide more nuanced information about user activities and the surrounding environment than is possible with hashtags. Thus, emojis may be suitable for identifying less obvious characteristics and the sense of a place. Emojis are already explored in research, but mainly for sentiment analysis, for semantic studies or as part of emoji prediction. In contrast, this work provides novel insights into the user’s spatial or activity context by applying the typicality measure and therefore considers emojis contextual indicants. Full article

(This article belongs to the Special Issue Social Computing for Geographic Information Science)

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31 pages, 9866 KiB

Open AccessEditor’s ChoiceArticle

Automatic Delineation of Urban Growth Boundaries Based on Topographic Data Using Germany as a Case Study

by Oliver Harig, Robert Hecht, Dirk Burghardt and Gotthard Meinel

ISPRS Int. J. Geo-Inf. 2021, 10(5), 353; https://doi.org/10.3390/ijgi10050353 - 20 May 2021

Cited by 14 | Viewed by 4162

Abstract

Urban Growth Boundary (UGB) is a growth management policy that designates specific areas where growth should be concentrated in order to avoid urban sprawl. The objective of such a boundary is to protect agricultural land, open spaces and the natural environment, as well [...] Read more.

Urban Growth Boundary (UGB) is a growth management policy that designates specific areas where growth should be concentrated in order to avoid urban sprawl. The objective of such a boundary is to protect agricultural land, open spaces and the natural environment, as well as to use existing infrastructure and public services more efficiently. Due to the inherent heterogeneity and complexity of settlements, UGBs in Germany are currently created manually by experts. Therefore, every dataset is linked to a specific area, investigation period and dedicated use. Clearly, up-to-date, homogeneous, meaningful and cost-efficient delineations created automatically are needed to avoid this reliance on manually or semi-automatically generated delineations. Here, we present an aggregative method to produce UGBs using building footprints and generally available topographic data as inputs. It was applied to study areas in Frankfurt/Main, the Hanover region and rural Brandenburg while taking full account of Germany’s planning and legal framework for spatial development. Our method is able to compensate for most of the weaknesses of available UGB data and to significantly raise the accuracy of UGBs in Germany. Therefore, it represents a valuable tool for generating basic data for future studies. Application elsewhere is also conceivable by regionalising the employed parameters. Full article

(This article belongs to the Special Issue Geo-Information Science in Planning and Development of Smart Cities)

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17 pages, 9393 KiB

Open AccessEditor’s ChoiceArticle

Coupling Historical Maps and LiDAR Data to Identify Man-Made Landforms in Urban Areas

by Martino Terrone, Pietro Piana, Guido Paliaga, Marco D’Orazi and Francesco Faccini

ISPRS Int. J. Geo-Inf. 2021, 10(5), 349; https://doi.org/10.3390/ijgi10050349 - 18 May 2021

Cited by 16 | Viewed by 3726

Abstract

In recent years, there has been growing interest in urban geomorphology both for its applications in terms of landscape planning, and its historical, cultural, and scientific interest. Due to recent urban growth, the identification of landforms in cities is difficult, particularly in Mediterranean [...] Read more.

In recent years, there has been growing interest in urban geomorphology both for its applications in terms of landscape planning, and its historical, cultural, and scientific interest. Due to recent urban growth, the identification of landforms in cities is difficult, particularly in Mediterranean and central European cities, characterized by more than 1000 years of urban stratification. By comparing and overlapping 19th-century cartography and modern topography from remote sensing data, this research aims to assess the morphological evolution of the city of Genoa (Liguria, NW Italy). The analysis focuses on a highly detailed 1:2’000 scale map produced by Eng. Ignazio Porro in the mid-19th century. The methodology, developed in QGIS, was applied on five case studies of both hillside and valley floor areas of the city of Genoa. Through map overlay and digitalization of elevation data and contour lines, it was possible to identify with great accuracy the most significant morphological transformations that have occurred in the city since the mid-19th century. In addition, the results were validated by direct observation and by drills data of the regional database. The results allowed the identification and quantification of the main anthropic landforms. The paper suggests that the same methodology can be applied to other historical urban contexts characterized by urban and architectural stratification. Full article

(This article belongs to the Special Issue Geographic Information Science (GIScience) and Geospatial Approaches for the Analysis of Historical Visual Sources and Cartographic Material)

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17 pages, 6084 KiB

Open AccessEditor’s ChoiceArticle

What Is the Shape of Geographical Time-Space? A Three-Dimensional Model Made of Curves and Cones

by Alain L’Hostis and Farouk Abdou

ISPRS Int. J. Geo-Inf. 2021, 10(5), 340; https://doi.org/10.3390/ijgi10050340 - 17 May 2021

Cited by 3 | Viewed by 3786

Abstract

Geographical time-spaces exhibit a series of properties, including space inversion, that turns any representation effort into a complex task. In order to improve the legibility of the representation and leveraging the advances of three-dimensional computer graphics, the aim of the study is to [...] Read more.

Geographical time-spaces exhibit a series of properties, including space inversion, that turns any representation effort into a complex task. In order to improve the legibility of the representation and leveraging the advances of three-dimensional computer graphics, the aim of the study is to propose a new method extending time-space relief cartography introduced by Mathis and L’Hostis. The novelty of the model resides in the use of cones to describing the terrestrial surface instead of graph faces, and in the use of curves instead of broken segments for edges. We implement the model on the Chinese space. The Chinese geographical time-space of reference year 2006 is produced by the combination and the confrontation of the fast air transport system and of the 7.5-times slower road transport system. Slower, short range flights are represented as curved lines above the earth surface with longer length than the geodesic, in order to account for a slower speed. The very steep slope of cones expresses the relative difficulty of crossing terrestrial time-space, as well as the comparably extreme efficiency of long-range flights for moving between cities. Finally, the whole image proposes a coherent representation of the geographical time-space where fast city-to-city transport is combined with slow terrestrial systems that allow one to reach any location. Full article

(This article belongs to the Special Issue Spatio-Temporal Models and Geo-Technologies)

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15 pages, 8845 KiB

Open AccessEditor’s ChoiceArticle

Vector Map Encryption Algorithm Based on Double Random Position Permutation Strategy

by Xiaolong Wang, Haowen Yan and Liming Zhang

ISPRS Int. J. Geo-Inf. 2021, 10(5), 311; https://doi.org/10.3390/ijgi10050311 - 07 May 2021

Cited by 8 | Viewed by 3092

Abstract

Encryption of vector maps, used for copyright protection, is of importance in the community of geographic information sciences. However, some studies adopt one-to-one mapping to scramble vertices and permutate the coordinates one by one according to the coordinate position in a plain map. [...] Read more.

Encryption of vector maps, used for copyright protection, is of importance in the community of geographic information sciences. However, some studies adopt one-to-one mapping to scramble vertices and permutate the coordinates one by one according to the coordinate position in a plain map. An attacker can easily obtain the key values by analyzing the relationship between the cipher vector map and the plain vector map, which will lead to the ineffectiveness of the scrambling operation. To solve the problem, a vector map encryption algorithm based on a double random position permutation strategy is proposed in this paper. First, the secret key sequence is generated using a four-dimensional quadratic autonomous hyperchaotic system. Then, all coordinates of the vector map are encrypted using the strategy of double random position permutation. Lastly, the encrypted coordinates are reorganized according to the vector map structure to obtain the cipher map. Experimental results show that: (1) one-to-one mapping between the plain vector map and cipher vector map is prevented from happening; (2) scrambling encryption between different map objects is achieved; (3) hackers cannot obtain the permutation key value by analyzing the pairs of the plain map and cipher map. Full article

(This article belongs to the Special Issue Cartographic Communication of Big Data)

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15 pages, 2522 KiB

Open AccessEditor’s ChoiceArticle

Context-Specific Point-of-Interest Recommendation Based on Popularity-Weighted Random Sampling and Factorization Machine

by Dongjin Yu, Yi Shen, Kaihui Xu and Yihang Xu

ISPRS Int. J. Geo-Inf. 2021, 10(4), 258; https://doi.org/10.3390/ijgi10040258 - 11 Apr 2021

Cited by 5 | Viewed by 2255

Abstract

Point-Of-Interest (POI) recommendation not only assists users to find their preferred places, but also helps businesses to attract potential customers. Recent studies have proposed many approaches to the POI recommendation. However, the lack of negative samples and the complexities of check-in contexts limit [...] Read more.

Point-Of-Interest (POI) recommendation not only assists users to find their preferred places, but also helps businesses to attract potential customers. Recent studies have proposed many approaches to the POI recommendation. However, the lack of negative samples and the complexities of check-in contexts limit their effectiveness significantly. This paper focuses on the problem of context-specific POI recommendation based on the check-in behaviors recorded by Location-Based Social Network (LBSN) services, which aims at recommending a list of POIs for a user to visit at a given context (such as time and weather). Specifically, a bidirectional influence correlativity metric is proposed to measure the semantic feature of user check-in behavior, and a contextual smoothing method to effectively alleviate the problem of data sparsity. In addition, the check-in probability is computed based on the geographical distance between the user’s home and the POI. Furthermore, to handle the problem of no negative feedback in LBSN, a weighted random sampling method is proposed based on contextual popularity. Finally, the recommendation results is obtained by utilizing Factorization Machine with Bayesian Personalized Ranking (BPR) loss. Experiments on a real dataset collected from Foursquare show that the proposed approach has better performance than others. Full article

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20 pages, 34596 KiB

Open AccessEditor’s ChoiceArticle

On the Use of ‘Glyphmaps’ for Analysing the Scale and Temporal Spread of COVID-19 Reported Cases

by Roger Beecham, Jason Dykes, Layik Hama and Nik Lomax

ISPRS Int. J. Geo-Inf. 2021, 10(4), 213; https://doi.org/10.3390/ijgi10040213 - 01 Apr 2021

Cited by 3 | Viewed by 3102

Abstract

Recent analysis of area-level COVID-19 cases data attempts to grapple with a challenge familiar to geovisualization: how to capture the development of the virus, whilst supporting analysis across geographic areas? We present several glyphmap designs for addressing this challenge applied to local authority [...] Read more.

Recent analysis of area-level COVID-19 cases data attempts to grapple with a challenge familiar to geovisualization: how to capture the development of the virus, whilst supporting analysis across geographic areas? We present several glyphmap designs for addressing this challenge applied to local authority data in England whereby charts displaying multiple aspects related to the pandemic are given a geographic arrangement. These graphics are visually complex, with clutter, occlusion and salience bias an inevitable consequence. We develop a framework for describing and validating the graphics against data and design requirements. Together with an observational data analysis, this framework is used to evaluate our designs, relating them to particular data analysis needs based on the usefulness of the structure they expose. Our designs, documented in an accompanying code repository, attend to common difficulties in geovisualization design and could transfer to contexts outside of the UK and to phenomena beyond the pandemic. Full article

(This article belongs to the Special Issue Geospatial Approaches for Understanding the Social, Economic and Environmental Impacts of COVID-19)

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19 pages, 4054 KiB

Open AccessEditor’s ChoiceArticle

Comparing World City Networks by Language: A Complex-Network Approach

by Wenjia Zhang, Jiancheng Zhu and Pu Zhao

ISPRS Int. J. Geo-Inf. 2021, 10(4), 219; https://doi.org/10.3390/ijgi10040219 - 01 Apr 2021

Cited by 6 | Viewed by 2563

Abstract

City networks are multiplex and diverse rather than being regarded as part of a single universal model that is valid worldwide. This study contributes to the debate on multiple globalizations by distinguishing multiscale structures of world city networks (WCNs) reflected in the Internet [...] Read more.

City networks are multiplex and diverse rather than being regarded as part of a single universal model that is valid worldwide. This study contributes to the debate on multiple globalizations by distinguishing multiscale structures of world city networks (WCNs) reflected in the Internet webpage content in English, German, and French. Using big data sets from web crawling, we adopted a complex-network approach with both macroscale and mesoscale analyses to compare global and grouping properties in varying WCNs, by using novel methods such as the weighted stochastic block model (WSBM). The results suggest that at the macro scale, the rankings of city centralities vary across languages due to the uneven geographic distribution of languages and the variant levels of globalization of cities perceived in different languages. At the meso scale, the WSBMs infer different grouping patterns in the WCNs by language, and the specific roles of many world cities vary with language. The probability-based comparative analyses reveal that the English WCN looks more globalized, while the French and German worlds appear more territorial. Using the mesoscale structure detected in the English WCN to comprehend the city networks in other languages may be biased. These findings demonstrate the importance of scrutinizing multiplex WCNs in different cultures and languages as well as discussing mesoscale structures in comparative WCN studies. Full article

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21 pages, 43310 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Empirical Insights from a Study on Outlier Preserving Value Generalization in Animated Choropleth Maps

by Christoph Traun, Manuela Larissa Schreyer and Gudrun Wallentin

ISPRS Int. J. Geo-Inf. 2021, 10(4), 208; https://doi.org/10.3390/ijgi10040208 - 01 Apr 2021

Cited by 7 | Viewed by 2263

Abstract

Time series animation of choropleth maps easily exceeds our perceptual limits. In this empirical research, we investigate the effect of local outlier preserving value generalization of animated choropleth maps on the ability to detect general trends and local deviations thereof. Comparing generalization in [...] Read more.

Time series animation of choropleth maps easily exceeds our perceptual limits. In this empirical research, we investigate the effect of local outlier preserving value generalization of animated choropleth maps on the ability to detect general trends and local deviations thereof. Comparing generalization in space, in time, and in a combination of both dimensions, value smoothing based on a first order spatial neighborhood facilitated the detection of local outliers best, followed by the spatiotemporal and temporal generalization variants. We did not find any evidence that value generalization helps in detecting global trends. Full article

(This article belongs to the Special Issue Multimedia Cartography)

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22 pages, 2898 KiB

Open AccessEditor’s ChoiceArticle

Beyond Objects in Space-Time: Towards a Movement Analysis Framework with ‘How’ and ‘Why’ Elements

by Saeed Rahimi, Antoni B. Moore and Peter A. Whigham

ISPRS Int. J. Geo-Inf. 2021, 10(3), 190; https://doi.org/10.3390/ijgi10030190 - 22 Mar 2021

Cited by 3 | Viewed by 2991

Abstract

Current spatiotemporal data has facilitated movement studies to shift objectives from descriptive models to explanations of the underlying causes of movement. From both a practical and theoretical standpoint, progress in developing approaches for these explanations should be founded on a conceptual model. This [...] Read more.

Current spatiotemporal data has facilitated movement studies to shift objectives from descriptive models to explanations of the underlying causes of movement. From both a practical and theoretical standpoint, progress in developing approaches for these explanations should be founded on a conceptual model. This paper presents such a model in which three conceptual levels of abstraction are proposed to frame an agent-based representation of movement decision-making processes: ‘attribute,’ ‘actor,’ and ‘autonomous agent’. These in combination with three temporal, spatial, and spatiotemporal general forms of observations distinguish nine (3 × 3) representation typologies of movement data within the agent framework. Thirdly, there are three levels of cognitive reasoning: ‘association,’ ‘intervention,’ and ‘counterfactual’. This makes for 27 possible types of operation embedded in a conceptual cube with the level of abstraction, type of observation, and degree of cognitive reasoning forming the three axes. The conceptual model is an arena where movement queries and the statement of relevant objectives takes place. An example implementation of a tightly constrained spatiotemporal scenario to ground the agent-structure was summarised. The platform has been well-defined so as to accommodate different tools and techniques to drive causal inference in computational movement analysis as an immediate future step. Full article

(This article belongs to the Special Issue Innovations in Agent-Based Modelling of Spatial Systems)

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