ISPRS International Journal of Geo-Information

17 pages, 9586 KiB

Open AccessEditor’s ChoiceArticle

Digital Graphic Documentation and Architectural Heritage: Deformations in a 16th-Century Ceiling of the Pinelo Palace in Seville (Spain)

by Juan Francisco Reinoso-Gordo, Antonio Gámiz-Gordo and Pedro Barrero-Ortega

ISPRS Int. J. Geo-Inf. 2021, 10(2), 85; https://doi.org/10.3390/ijgi10020085 - 19 Feb 2021

Cited by 11 | Viewed by 4089

Abstract

Suitable graphic documentation is essential to ascertain and conserve architectural heritage. For the first time, accurate digital images are provided of a 16th-century wooden ceiling, composed of geometric interlacing patterns, in the Pinelo Palace in Seville. Today, this ceiling suffers from significant deformation. [...] Read more.

Suitable graphic documentation is essential to ascertain and conserve architectural heritage. For the first time, accurate digital images are provided of a 16th-century wooden ceiling, composed of geometric interlacing patterns, in the Pinelo Palace in Seville. Today, this ceiling suffers from significant deformation. Although there are many publications on the digital documentation of architectural heritage, no graphic studies on this type of deformed ceilings have been presented. This study starts by providing data on the palace history concerning the design of geometric interlacing patterns in carpentry according to the 1633 book by López de Arenas, and on the ceiling consolidation in the 20th century. Images were then obtained using two complementary procedures: from a 3D laser scanner, which offers metric data on deformations; and from photogrammetry, which facilitates the visualisation of details. In this way, this type of heritage is documented in an innovative graphic approach, which is essential for its conservation and/or restoration with scientific foundations and also to disseminate a reliable digital image of the most beautiful ceiling of this Renaissance palace in southern Europe. Full article

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

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20 pages, 3352 KiB

Open AccessEditor’s ChoiceArticle

Do Different Map Types Support Map Reading Equally? Comparing Choropleth, Graduated Symbols, and Isoline Maps for Map Use Tasks

by Katarzyna Słomska-Przech and Izabela Małgorzata Gołębiowska

ISPRS Int. J. Geo-Inf. 2021, 10(2), 69; https://doi.org/10.3390/ijgi10020069 - 10 Feb 2021

Cited by 15 | Viewed by 7137

Abstract

It is acknowledged that various types of thematic maps emphasize different aspects of mapped phenomena and thus support different map users’ tasks. To provide empirical evidence, a user study with 366 participants was carried out comparing three map types showing the same input [...] Read more.

It is acknowledged that various types of thematic maps emphasize different aspects of mapped phenomena and thus support different map users’ tasks. To provide empirical evidence, a user study with 366 participants was carried out comparing three map types showing the same input data. The aim of the study is to compare the effect of using choropleth, graduated symbols, and isoline maps to solve basic map user tasks. Three metrics were examined: two performance metrics (answer accuracy and time) and one subjective metric (difficulty). The results showed that the performance metrics differed between the analyzed map types, and better performances were recorded using the choropleth map. It was also proven that map users find the most commonly applied type of the map, choropleth map, as the easiest. In addition, the subjective metric matched the performance metrics. We conclude with the statement that the choropleth map can be a sufficient solution for solving various tasks. However, it should be remembered that making this type of map correctly may seem easy, but it is not. Moreover, we believe that the richness of thematic cartography should not be abandoned, and work should not be limited to one favorable map type only. Full article

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

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25 pages, 4285 KiB

Open AccessEditor’s ChoiceReview

Machine Learning Approaches to Bike-Sharing Systems: A Systematic Literature Review

by Vitória Albuquerque, Miguel Sales Dias and Fernando Bacao

ISPRS Int. J. Geo-Inf. 2021, 10(2), 62; https://doi.org/10.3390/ijgi10020062 - 2 Feb 2021

Cited by 37 | Viewed by 8866

Abstract

Cities are moving towards new mobility strategies to tackle smart cities’ challenges such as carbon emission reduction, urban transport multimodality and mitigation of pandemic hazards, emphasising on the implementation of shared modes, such as bike-sharing systems. This paper poses a research question and [...] Read more.

Cities are moving towards new mobility strategies to tackle smart cities’ challenges such as carbon emission reduction, urban transport multimodality and mitigation of pandemic hazards, emphasising on the implementation of shared modes, such as bike-sharing systems. This paper poses a research question and introduces a corresponding systematic literature review, focusing on machine learning techniques’ contributions applied to bike-sharing systems to improve cities’ mobility. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) method was adopted to identify specific factors that influence bike-sharing systems, resulting in an analysis of 35 papers published between 2015 and 2019, creating an outline for future research. By means of systematic literature review and bibliometric analysis, machine learning algorithms were identified in two groups: classification and prediction. Full article

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21 pages, 4047 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

A National Examination of the Spatial Extent and Similarity of Offenders’ Activity Spaces Using Police Data

by Sophie Curtis-Ham, Wim Bernasco, Oleg N. Medvedev and Devon L. L. Polaschek

ISPRS Int. J. Geo-Inf. 2021, 10(2), 47; https://doi.org/10.3390/ijgi10020047 - 23 Jan 2021

Cited by 14 | Viewed by 5421

Abstract

It is well established that offenders’ routine activity locations (nodes) shape their crime locations, but research examining the geography of offenders’ routine activity spaces has to date largely been limited to a few core nodes such as homes and prior offense locations, and [...] Read more.

It is well established that offenders’ routine activity locations (nodes) shape their crime locations, but research examining the geography of offenders’ routine activity spaces has to date largely been limited to a few core nodes such as homes and prior offense locations, and to small study areas. This paper explores the utility of police data to provide novel insights into the spatial extent of, and overlap between, individual offenders’ activity spaces. It includes a wider set of activity nodes (including relatives’ homes, schools, and non-crime incidents) and broadens the geographical scale to a national level, by comparison to previous studies. Using a police dataset including n = 60,229 burglary, robbery, and extra-familial sex offenders in New Zealand, a wide range of activity nodes were present for most burglary and robbery offenders, but fewer for sex offenders, reflecting sparser histories of police contact. In a novel test of the criminal profiling assumptions of homology and differentiation in a spatial context, we find that those who offend in nearby locations tend to share more activity space than those who offend further apart. However, in finding many offenders’ activity spaces span wide geographic distances, we highlight challenges for crime location choice research and geographic profiling practice. Full article

(This article belongs to the Special Issue Geographic Crime Analysis)

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30 pages, 11356 KiB

Open AccessEditor’s ChoiceArticle

Crowdsourcing without Data Bias: Building a Quality Assurance System for Air Pollution Symptom Mapping

by Marta Samulowska, Szymon Chmielewski, Edwin Raczko, Michał Lupa, Dorota Myszkowska and Bogdan Zagajewski

ISPRS Int. J. Geo-Inf. 2021, 10(2), 46; https://doi.org/10.3390/ijgi10020046 - 22 Jan 2021

Cited by 12 | Viewed by 5977

Abstract

Crowdsourcing is one of the spatial data sources, but due to its unstructured form, the quality of noisy crowd judgments is a challenge. In this study, we address the problem of detecting and removing crowdsourced data bias as a prerequisite for better-quality open-data [...] Read more.

Crowdsourcing is one of the spatial data sources, but due to its unstructured form, the quality of noisy crowd judgments is a challenge. In this study, we address the problem of detecting and removing crowdsourced data bias as a prerequisite for better-quality open-data output. This study aims to find the most robust data quality assurance system (QAs). To achieve this goal, we design logic-based QAs variants and test them on the air quality crowdsourcing database. By extending the paradigm of urban air pollution monitoring from particulate matter concentration levels to air-quality-related health symptom load, the study also builds a new perspective for citizen science (CS) air quality monitoring. The method includes the geospatial web (GeoWeb) platform as well as a QAs based on conditional statements. A four-month crowdsourcing campaign resulted in 1823 outdoor reports, with a rejection rate of up to 28%, depending on the applied. The focus of this study was not on digital sensors’ validation but on eliminating logically inconsistent surveys and technologically incorrect objects. As the QAs effectiveness may depend on the location and society structure, that opens up new cross-border opportunities for replication of the research in other geographical conditions. Full article

(This article belongs to the Special Issue Citizen Science and Geospatial Capacity Building)

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16 pages, 4611 KiB

Open AccessEditor’s ChoiceArticle

Using Restaurant POI Data to Explore Regional Structure of Food Culture Based on Cuisine Preference

by Shangjing Jiang, Haiping Zhang, Haoran Wang, Lei Zhou and Guoan Tang

ISPRS Int. J. Geo-Inf. 2021, 10(1), 38; https://doi.org/10.3390/ijgi10010038 - 18 Jan 2021

Cited by 17 | Viewed by 5632

Abstract

As a result of the influence of geographical environment and historical heritage, food preference has significant regional differentiation characteristics. However, the spatial structure of food culture represented by the cuisine culture at the regional level has not yet been explored from the perspective [...] Read more.

As a result of the influence of geographical environment and historical heritage, food preference has significant regional differentiation characteristics. However, the spatial structure of food culture represented by the cuisine culture at the regional level has not yet been explored from the perspective of geography. Cultural regionalization is an important way to analyze and understand the spatial structure of food culture. It is of great significance to deeply mine intra-regional homogeneity and scientifically cognize inter-regional cultural characteristics. This study aims to explore such patterns by focusing on the restaurants of the eight most famous cuisines in Mainland China. Initially, the density based geospatial hotspot detector method is proposed to analyze and mapping the spatial quantitative characteristics of the eight major cuisines. A heuristic method for geographical regionalization based on machine learning was used to analyze spatial distribution patterns in accordance with the proportion of these cuisines in each prefecture-level city. Results show that some types of single-category cuisines have a stronger spatial concentration effect in the present, whereas others have a strong diffusion trend. In the comprehensive analysis of multicategory cuisines, the eight major cuisines formed a new structure of geographical regionalization of Chinese cuisine culture. This study is helpful to understand regional structure characteristics of food preference, and the density-based hotspot detector proposed in this paper can also be used in the analysis of other type of point of interest (POI) data. Full article

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18 pages, 14253 KiB

Open AccessEditor’s ChoiceArticle

Incorporating Memory-Based Preferences and Point-of-Interest Stickiness into Recommendations in Location-Based Social Networks

by Hang Zhang, Mingxin Gan and Xi Sun

ISPRS Int. J. Geo-Inf. 2021, 10(1), 36; https://doi.org/10.3390/ijgi10010036 - 15 Jan 2021

Cited by 13 | Viewed by 3402

Abstract

In location-based social networks (LBSNs), point-of-interest (POI) recommendations facilitate access to information for people by recommending attractive locations they have not previously visited. Check-in data and various contextual factors are widely taken into consideration to obtain people’s preferences regarding POIs in existing POI [...] Read more.

In location-based social networks (LBSNs), point-of-interest (POI) recommendations facilitate access to information for people by recommending attractive locations they have not previously visited. Check-in data and various contextual factors are widely taken into consideration to obtain people’s preferences regarding POIs in existing POI recommendation methods. In psychological effect-based POI recommendations, the memory-based attenuation of people’s preferences with respect to POIs, e.g., the fact that more attention is paid to POIs that were checked in to recently than those visited earlier, is emphasized. However, the memory effect only reflects the changes in an individual’s check-in trajectory and cannot discover the important POIs that dominate their mobility patterns, which are related to the repeat-visit frequency of an individual at a POI. To solve this problem, in this paper, we developed a novel POI recommendation framework using people’s memory-based preferences and POI stickiness, named U-CF-Memory-Stickiness. First, we used the memory-based preference-attenuation mechanism to emphasize personal psychological effects and memory-based preference evolution in human mobility patterns. Second, we took the visiting frequency of POIs into consideration and introduced the concept of POI stickiness to identify the important POIs that reflect the stable interests of an individual with respect to their mobility behavior decisions. Lastly, we incorporated the influence of both memory-based preferences and POI stickiness into a user-based collaborative filtering framework to improve the performance of POI recommendations. The results of the experiments we conducted on a real LBSN dataset demonstrated that our method outperformed other methods. Full article

(This article belongs to the Special Issue Geovisualization and Social Media)

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20 pages, 10533 KiB

Open AccessEditor’s ChoiceArticle

A Tourist Attraction Recommendation Model Fusing Spatial, Temporal, and Visual Embeddings for Flickr-Geotagged Photos

by Shanshan Han, Cuiming Liu, Keyun Chen, Dawei Gui and Qingyun Du

ISPRS Int. J. Geo-Inf. 2021, 10(1), 20; https://doi.org/10.3390/ijgi10010020 - 8 Jan 2021

Cited by 18 | Viewed by 4541

Abstract

The rapid development of social media data, including geotagged photos, has benefited the research of tourism geography; additionally, tourists’ increasing demand for personalized travel has encouraged more researchers to pay attention to tourism recommendation models. However, few studies have comprehensively considered the content [...] Read more.

The rapid development of social media data, including geotagged photos, has benefited the research of tourism geography; additionally, tourists’ increasing demand for personalized travel has encouraged more researchers to pay attention to tourism recommendation models. However, few studies have comprehensively considered the content and contextual information that may influence the recommendation accuracy, especially tourist attractions’ visual content due to redundant and noisy geotagged photos; therefore, we propose a tourist attraction recommendation model for Flickr-geotagged photos which fuses spatial, temporal, and visual embeddings (STVE). After spatial clustering and extracting visual embeddings of tourist attractions’ representative images, the spatial and temporal embeddings are modeled with the Word2Vec negative sampling strategy, and the visual embeddings are fused with Matrix Factorization and Bayesian Personalized Ranking. The combination of these two parts comprises our proposed STVE model. The experimental results demonstrate that our STVE model outperforms other baseline models. We also analyzed the parameter sensitivity and component performance to prove the performance superiority of our model. Full article

(This article belongs to the Special Issue Geovisualization and Social Media)

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17 pages, 4087 KiB

Open AccessEditor’s ChoiceArticle

Identifying Complex Junctions in a Road Network

by Jianting Yang, Kongyang Zhao, Muzi Li, Zhu Xu and Zhilin Li

ISPRS Int. J. Geo-Inf. 2021, 10(1), 4; https://doi.org/10.3390/ijgi10010004 - 24 Dec 2020

Cited by 10 | Viewed by 4393

Abstract

Automated generalization of road network data is of great concern to the map generalization community because of the importance of road data and the difficulty involved. Complex junctions are where roads meet and join in a complicated way and identifying them is a [...] Read more.

Automated generalization of road network data is of great concern to the map generalization community because of the importance of road data and the difficulty involved. Complex junctions are where roads meet and join in a complicated way and identifying them is a key issue in road network generalization. In addition to their structural complexity, complex junctions don’t have regular geometric boundary and their representation in spatial data is scale-dependent. All these together make them hard to identify. Existing methods use geometric and topological statistics to characterize and identify them, and are thus error-prone, scale-dependent and lack generality. More significantly, they cannot ensure the integrity of complex junctions. This study overcomes the obstacles by clarifying the topological boundary of a complex junction, which provides the basis for straightforward identification of them. Test results show the proposed method can find and isolate complex junctions in a road network with their integrity and is able to handle different road representations. The integral identification achieved can help to guarantee connectivity among roads when simplifying complex junctions, and greatly facilitate the geometric and semantic simplification of them. Full article

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16 pages, 5359 KiB

Open AccessEditor’s ChoiceArticle

Participatory Rural Spatial Planning Based on a Virtual Globe-Based 3D PGIS

by Linjun Yu, Xiaotong Zhang, Feng He, Yalan Liu and Dacheng Wang

ISPRS Int. J. Geo-Inf. 2020, 9(12), 763; https://doi.org/10.3390/ijgi9120763 - 21 Dec 2020

Cited by 14 | Viewed by 3652

Abstract

With the current spatial planning reform in China, public participation is becoming increasingly important in the success of rural spatial planning. However, engaging various stakeholders in spatial planning projects is difficult, mainly due to the lack of planning knowledge and computer skills. Therefore, [...] Read more.

With the current spatial planning reform in China, public participation is becoming increasingly important in the success of rural spatial planning. However, engaging various stakeholders in spatial planning projects is difficult, mainly due to the lack of planning knowledge and computer skills. Therefore, this paper discusses the development of a virtual globe-based 3D participatory geographic information system (PGIS) aiming to support public participation in the spatial planning process. The 3D PGIS-based rural planning approach was applied in the village of XiaFan, Ningbo, China. The results demonstrate that locals’ participation capacity was highly promoted, with their interest in 3D PGIS visualization being highly activated. The interactive landscape design tools allow stakeholders to present their own suggestions and designs, just like playing a computer game, thus improving their interactive planning abilities on-site. The scientific analysis tools allow planners to analyze and evaluate planning scenarios in different disciplines in real-time to quickly respond to suggestions from participants on-site. Functions and tools such as data management, marking, and highlighting were found to be useful for smoothing the interactions among planners and participants. In conclusion, virtual globe-based 3D PGIS highly supports the participatory rural landscape planning process and is potentially applicable to other regions. Full article

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16 pages, 4565 KiB

Open AccessEditor’s ChoiceArticle

The Land Use Mapping Techniques (Including the Areas Used by Pedestrians) Based on Low-Level Aerial Imagery

by Maciej Smaczyński, Beata Medyńska-Gulij and Łukasz Halik

ISPRS Int. J. Geo-Inf. 2020, 9(12), 754; https://doi.org/10.3390/ijgi9120754 - 16 Dec 2020

Cited by 10 | Viewed by 3900

Abstract

Traditionally, chorochromatic maps with a qualitative measurement level are used for land use presentations. Along with the use of UAV (Unmanned Aerial Vehicles), it became possible to register dynamic phenomena in a small space. We analyze the application of qualitative and quantitative mapping [...] Read more.

Traditionally, chorochromatic maps with a qualitative measurement level are used for land use presentations. Along with the use of UAV (Unmanned Aerial Vehicles), it became possible to register dynamic phenomena in a small space. We analyze the application of qualitative and quantitative mapping methods to visualize land use in a dynamic context thanks to cyclically obtained UAV imaging. The aim of the research is to produce thematic maps showing the actual land use of the small area urbanized by pedestrians. The research was based on low-level aerial imagery that recorded the movement of pedestrians in the research area. Additionally, based on the observation of pedestrian movement, researchers pointed out the areas of land that pedestrians used incorrectly. For this purpose, the author will present his own concept of the point-to-polygon transformation of pedestrians’ representation. The research was an opportunity to demonstrate suitable mapping techniques to effectively convey the information on land use by pedestrians. The results allowed the authors of this article to draw conclusions on the choice of suitable mapping techniques during the process of thematic land use map design and to specify further areas for research. Full article

(This article belongs to the Special Issue Multimedia Cartography)

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22 pages, 3274 KiB

Open AccessEditor’s ChoiceReview

Towards Self-Service GIS—Combining the Best of the Semantic Web and Web GIS

by Alexandra Rowland, Erwin Folmer and Wouter Beek

ISPRS Int. J. Geo-Inf. 2020, 9(12), 753; https://doi.org/10.3390/ijgi9120753 - 15 Dec 2020

Cited by 27 | Viewed by 6063

Abstract

The field of geographic information science has grown exponentially over the last few decades and, particularly within the context of the pervasiveness of the internet, bears witness to a rapid transition of its associated technologies from stand-alone systems to increasingly networked and distributed [...] Read more.

The field of geographic information science has grown exponentially over the last few decades and, particularly within the context of the pervasiveness of the internet, bears witness to a rapid transition of its associated technologies from stand-alone systems to increasingly networked and distributed systems as geospatial information becomes increasingly available online. With its long-standing history for innovation, the field has adopted many disruptive technologies from the fields of computer and information sciences through this transition towards web geographic information systems (GIS); most interestingly in the context of this research is the limited uptake of semantic web technologies by the field and its associated technologies, the lack of which has resulted in a technological disjoint between these fields. As the field seeks to make geospatial information more accessible to more users and in more contexts through ‘self-service’ applications, the use of these technologies is imperative to support the interoperability between distributed data sources. This paper aims to provide insight into what linked data tooling already exists, and based on the features of these, what may be possible for the achievement of self-service GIS. Findings include what visualisation, interactivity, analytics and usability features could be included in the realisation of self-service GIS, pointing to the opportunities that exist in bringing GIS technologies closer to the user. Full article

(This article belongs to the Special Issue Spatial Data Infrastructure for Distributed Management and Processing)

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18 pages, 2515 KiB

Open AccessEditor’s ChoiceArticle

Automatic Workflow for Roof Extraction and Generation of 3D CityGML Models from Low-Cost UAV Image-Derived Point Clouds

by Arnadi Murtiyoso, Mirza Veriandi, Deni Suwardhi, Budhy Soeksmantono and Agung Budi Harto

ISPRS Int. J. Geo-Inf. 2020, 9(12), 743; https://doi.org/10.3390/ijgi9120743 - 12 Dec 2020

Cited by 20 | Viewed by 5599

Abstract

Developments in UAV sensors and platforms in recent decades have stimulated an upsurge in its application for 3D mapping. The relatively low-cost nature of UAVs combined with the use of revolutionary photogrammetric algorithms, such as dense image matching, has made it a strong [...] Read more.

Developments in UAV sensors and platforms in recent decades have stimulated an upsurge in its application for 3D mapping. The relatively low-cost nature of UAVs combined with the use of revolutionary photogrammetric algorithms, such as dense image matching, has made it a strong competitor to aerial lidar mapping. However, in the context of 3D city mapping, further 3D modeling is required to generate 3D city models which is often performed manually using, e.g., photogrammetric stereoplotting. The aim of the paper was to try to implement an algorithmic approach to building point cloud segmentation, from which an automated workflow for the generation of roof planes will also be presented. 3D models of buildings are then created using the roofs’ planes as a base, therefore satisfying the requirements for a Level of Detail (LoD) 2 in the CityGML paradigm. Consequently, the paper attempts to create an automated workflow starting from UAV-derived point clouds to LoD 2-compatible 3D model. Results show that the rule-based segmentation approach presented in this paper works well with the additional advantage of instance segmentation and automatic semantic attribute annotation, while the 3D modeling algorithm performs well for low to medium complexity roofs. The proposed workflow can therefore be implemented for simple roofs with a relatively low number of planar surfaces. Furthermore, the automated approach to the 3D modeling process also helps to maintain the geometric requirements of CityGML such as 3D polygon coplanarity vis-à-vis manual stereoplotting. Full article

(This article belongs to the Special Issue Virtual 3D City Models)

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21 pages, 4134 KiB

Open AccessEditor’s ChoiceArticle

A Fuzzy Logic-Based Approach for Modelling Uncertainty in Open Geospatial Data on Landfill Suitability Analysis

by Neema Nicodemus Lyimo, Zhenfeng Shao, Ally Mgelwa Ally, Nana Yaw Danquah Twumasi, Orhan Altan and Camilius A. Sanga

ISPRS Int. J. Geo-Inf. 2020, 9(12), 737; https://doi.org/10.3390/ijgi9120737 - 9 Dec 2020

Cited by 14 | Viewed by 4248

Abstract

Besides OpenStreetMap (OSM), there are other local sources, such as open government data (OGD), that have the potential to enrich the modeling process with decision criteria that uniquely reflect some local patterns. However, both data are affected by uncertainty issues, which limits their [...] Read more.

Besides OpenStreetMap (OSM), there are other local sources, such as open government data (OGD), that have the potential to enrich the modeling process with decision criteria that uniquely reflect some local patterns. However, both data are affected by uncertainty issues, which limits their usability. This work addresses the imprecisions on suitability layers generated from such data. The proposed method is founded on fuzzy logic theories. The model integrates OGD, OSM data and remote sensing products and generate reliable landfill suitability results. A comparison analysis demonstrates that the proposed method generates more accurate, representative and reliable suitability results than traditional methods. Furthermore, the method has facilitated the introduction of open government data for suitability studies, whose fusion improved estimations of population distribution and land-use mapping than solely relying on free remotely sensed images. The proposed method is applicable for preparing decision maps from open datasets that have undergone similar generalization procedures as the source of their uncertainty. The study provides evidence for the applicability of OGD and other related open data initiatives (ODIs) for land-use suitability studies, especially in developing countries. Full article

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22 pages, 5250 KiB

Open AccessEditor’s ChoiceArticle

Form Follows Content: An Empirical Study on Symbol-Content (In)Congruences in Thematic Maps

by Silvia Klettner

ISPRS Int. J. Geo-Inf. 2020, 9(12), 719; https://doi.org/10.3390/ijgi9120719 - 2 Dec 2020

Cited by 3 | Viewed by 3171

Abstract

Through signs and symbols, maps represent geographic space in a generalized and abstracted way. Cartographic research is, therefore, concerned with establishing a mutually shared set of signs and semiotic rules to communicate geospatial information successfully. While cartographers generally strive for cognitively congruent maps, [...] Read more.

Through signs and symbols, maps represent geographic space in a generalized and abstracted way. Cartographic research is, therefore, concerned with establishing a mutually shared set of signs and semiotic rules to communicate geospatial information successfully. While cartographers generally strive for cognitively congruent maps, empirical research has only started to explore the different facets and levels of correspondences between external cartographic representations and processes of human cognition. This research, therefore, draws attention to the principle of contextual congruence to study the correspondences between shape symbols and different geospatial content. An empirical study was carried out to explore the (in)congruence of cartographic point symbols with respect to positive, neutral, and negative geospatial topics in monothematic maps. In an online survey, 72 thematic maps (i.e., 12 map topics × 6 symbols) were evaluated by 116 participants in a between-groups design. The point symbols comprised five symmetric shapes (i.e., Circle, Triangle, Square, Rhomb, Star) and one Asymmetric Star shape. The study revealed detailed symbol-content congruences for each map topic as well as on an aggregated level, i.e., by positive, neutral, and negative topic clusters. Asymmetric Star symbols generally showed to be highly incongruent with positive and neutral topics, while highly congruent with negative map topics. Symmetric shapes, on the other hand, emerged to be of high congruence with positive and neutral map topics, whilst incongruent with negative topics. As the meaning of point symbols showed to be susceptible to context, the findings lead to the conclusion that cognitively congruent maps require profound context-specific considerations when designing and employing map symbols. Full article

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

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14 pages, 1200 KiB

Open AccessEditor’s ChoiceArticle

OurPlaces: Cross-Cultural Crowdsourcing Platform for Location Recommendation Services

by Luong Vuong Nguyen, Jason J. Jung and Myunggwon Hwang

ISPRS Int. J. Geo-Inf. 2020, 9(12), 711; https://doi.org/10.3390/ijgi9120711 - 27 Nov 2020

Cited by 18 | Viewed by 3346

Abstract

This paper presents a cross-cultural crowdsourcing platform, called OurPlaces, where people from different cultures can share their spatial experiences. We built a three-layered architecture composed of: (i) places (locations where people have visited); (

i i

) cognition (how people [...] Read more.

This paper presents a cross-cultural crowdsourcing platform, called OurPlaces, where people from different cultures can share their spatial experiences. We built a three-layered architecture composed of: (i) places (locations where people have visited); (

i i

) cognition (how people have experienced these places); and (

i i i

) users (those who have visited these places). Notably, cognition is represented as a paring of two similar places from different cultures (e.g., Versailles and Gyeongbokgung in France and Korea, respectively). As a case study, we applied the OurPlaces platform to a cross-cultural tourism recommendation system and conducted a simulation using a dataset collected from TripAdvisor. The tourist places were classified into four types (i.e., hotels, restaurants, shopping malls, and attractions). In addition, user feedback (e.g., ratings, rankings, and reviews) from various nationalities (assumed to be equivalent to cultures) was exploited to measure the similarities between tourism places and to generate a cognition layer on the platform. To demonstrate the effectiveness of the OurPlaces-based system, we compared it with a Pearson correlation-based system as a baseline. The experimental results show that the proposed system outperforms the baseline by 2.5% and 4.1% in the best case in terms of MAE and RMSE, respectively. Full article

(This article belongs to the Special Issue Intelligent Systems Based on Open and Crowdsourced Location Data)

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19 pages, 7012 KiB

Open AccessEditor’s ChoiceArticle

Network Characteristics and Vulnerability Analysis of Chinese Railway Network under Earthquake Disasters

by Lingzhi Yin and Yafei Wang

ISPRS Int. J. Geo-Inf. 2020, 9(12), 697; https://doi.org/10.3390/ijgi9120697 - 25 Nov 2020

Cited by 11 | Viewed by 2895

Abstract

The internal structure and operation rules of railway network have become increasingly complex along with the expansion of the network, putting a higher demand on the development of the railway and the reliability and adaptability of the railway under earthquake disasters. The theory [...] Read more.

The internal structure and operation rules of railway network have become increasingly complex along with the expansion of the network, putting a higher demand on the development of the railway and the reliability and adaptability of the railway under earthquake disasters. The theory and method concerning complex railway network can well capture the internal structure of railway facilities system and the relationship between subsystems. However, most of the research focuses on the vulnerability based on the logical network of railway, deviating from the actual spatial location of railway network. Additionally, only random attacks and deliberate attacks are factored in, ignoring the impact of earthquake disasters on actual railway lines. Therefore, this paper built a geographic railway network and analyzed topological structure of the network and its vulnerability under earthquake disasters. First, the geographic network of Chinese railway was built based on the methods of complex network, linear reference and dynamic segmentation. Second, the spatial distribution of railway network flow was analyzed by node degree, betweenness and clustering coefficient. Finally, the vulnerability of the geographic railway network in areas with high seismic hazards were assessed, aiming to improve the capacity to prevent and resist earthquake disasters. Full article

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16 pages, 7273 KiB

Open AccessEditor’s ChoiceArticle

Developing Versatile Graphic Map Load Metrics

by Radek Barvir and Vit Vozenilek

ISPRS Int. J. Geo-Inf. 2020, 9(12), 705; https://doi.org/10.3390/ijgi9120705 - 25 Nov 2020

Cited by 11 | Viewed by 3082

Abstract

Graphic map load is a property of a map quantifying the amount of map content. It indicates the visual complexity of the map and helps cartographers to adapt maps and other geospatial visualizations to accomplish their purpose. Generally, map design needs to enable [...] Read more.

Graphic map load is a property of a map quantifying the amount of map content. It indicates the visual complexity of the map and helps cartographers to adapt maps and other geospatial visualizations to accomplish their purpose. Generally, map design needs to enable the user to quickly, comprehensively, and intuitively obtain the relevant spatial information from a map. Especially, this applies in cases like crisis management, immunology and military. However, there are no widely applicable metrics to assess the complexity of cartographic products. This paper evaluates seven simple metrics for graphic map load calculation based on image analytics using the set of 50 various maps on an easily understandable scale of 0–100%. The metrics are compared to values of user-perceived map load survey joined by 62 respondents. All the suggested metrics are designed for calculation with easy-accessible software and therefore suitable for use in any user environment. Metrics utilizing the principle of edge detection have been found suitable for a diversity of geospatial visualizations providing the best results among other metrics. Full article

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

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21 pages, 18887 KiB

Open AccessEditor’s ChoiceArticle

Worldwide Detection of Informal Settlements via Topological Analysis of Crowdsourced Digital Maps

by Satej Soman, Anni Beukes, Cooper Nederhood, Nicholas Marchio and Luís M. A. Bettencourt

ISPRS Int. J. Geo-Inf. 2020, 9(11), 685; https://doi.org/10.3390/ijgi9110685 - 16 Nov 2020

Cited by 25 | Viewed by 12645

Abstract

The recent growth of high-resolution spatial data, especially in developing urban environments, is enabling new approaches to civic activism, urban planning and the provision of services necessary for sustainable development. A special area of great potential and urgent need deals with urban expansion [...] Read more.

The recent growth of high-resolution spatial data, especially in developing urban environments, is enabling new approaches to civic activism, urban planning and the provision of services necessary for sustainable development. A special area of great potential and urgent need deals with urban expansion through informal settlements (slums). These neighborhoods are too often characterized by a lack of connections, both physical and socioeconomic, with detrimental effects to residents and their cities. Here, we show how a scalable computational approach based on the topological properties of digital maps can identify local infrastructural deficits and propose context-appropriate minimal solutions. We analyze 1 terabyte of OpenStreetMap (OSM) crowdsourced data to create worldwide indices of street block accessibility and local cadastral maps and propose infrastructure extensions with a focus on 120 Low and Middle Income Countries (LMICs) in the Global South. We illustrate how the lack of physical accessibility can be identified in detail, how the complexity and costs of solutions can be assessed and how detailed spatial proposals are generated. We discuss how these diagnostics and solutions provide a multiscalar set of new capabilities—from individual neighborhoods to global regions—that can coordinate local community knowledge with political agency, technical capability, and further research. Full article

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

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17 pages, 3380 KiB

Open AccessEditor’s ChoiceArticle

Time-Series Clustering for Home Dwell Time during COVID-19: What Can We Learn from It?

by Xiao Huang, Zhenlong Li, Junyu Lu, Sicheng Wang, Hanxue Wei and Baixu Chen

ISPRS Int. J. Geo-Inf. 2020, 9(11), 675; https://doi.org/10.3390/ijgi9110675 - 13 Nov 2020

Cited by 52 | Viewed by 5845

Abstract

In this study, we investigate the potential driving factors that lead to the disparity in the time-series of home dwell time in a data-driven manner, aiming to provide fundamental knowledge that benefits policy-making for better mitigation strategies of future pandemics. Taking Metro Atlanta [...] Read more.

In this study, we investigate the potential driving factors that lead to the disparity in the time-series of home dwell time in a data-driven manner, aiming to provide fundamental knowledge that benefits policy-making for better mitigation strategies of future pandemics. Taking Metro Atlanta as a study case, we perform a trend-driven analysis by conducting Kmeans time-series clustering using fine-grained home dwell time records from SafeGraph. Furthermore, we apply ANOVA (Analysis of Variance) coupled with post-hoc Tukey’s test to assess the statistical difference in sixteen recoded demographic/socioeconomic variables (from ACS 2014–2018 estimates) among the identified time-series clusters. We find that demographic/socioeconomic variables can explain the disparity in home dwell time in response to the stay-at-home order, which potentially leads to disparate exposures to the risk from the COVID-19. The results further suggest that socially disadvantaged groups are less likely to follow the order to stay at home, pointing out the extensive gaps in the effectiveness of social distancing measures that exist between socially disadvantaged groups and others. Our study reveals that the long-standing inequity issue in the U.S. stands in the way of the effective implementation of social distancing measures. Full article

(This article belongs to the Special Issue GIScience for Risk Management in Big Data Era)

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22 pages, 5099 KiB

Open AccessEditor’s ChoiceArticle

Exploring Travel Patterns during the Holiday Season—A Case Study of Shenzhen Metro System During the Chinese Spring Festival

by Jianxiao Liu, Wenzhong Shi and Pengfei Chen

ISPRS Int. J. Geo-Inf. 2020, 9(11), 651; https://doi.org/10.3390/ijgi9110651 - 30 Oct 2020

Cited by 22 | Viewed by 4946

Abstract

Research has shown that the growing holiday travel demand in modern society has a significant influence on daily travel patterns. However, few studies have focused on the distinctness of travel patterns during a holiday season and as a specified case, travel behavior studies [...] Read more.

Research has shown that the growing holiday travel demand in modern society has a significant influence on daily travel patterns. However, few studies have focused on the distinctness of travel patterns during a holiday season and as a specified case, travel behavior studies of the Chinese Spring Festival (CSF) at the city level are even rarer. This paper adopts a text-mining model (latent Dirichlet allocation (LDA)) to explore the travel patterns and travel purposes during the CSF season in Shenzhen based on the metro smart card data (MSC) and the points of interest (POIs) data. The study aims to answer two questions—(1) how to use MSC and POIs inferring travel purpose at the metro station level without the socioeconomic backgrounds of the cardholders? (2) What are the overall inner-city mobility patterns and travel activities during the Spring Festival holiday-week? The results show that six features of the CSF travel behavior are found and nine (three broad categories) travel patterns and trip activities are inferred. The activities in which travelers engaged during the CSF season are mainly consumption-oriented events, visiting relatives and friends and traffic-oriented events. This study is beneficial to metro corporations (timetable management), business owners (promotion strategy), researchers (travelers’ social attribute inference) and decision-makers (examine public service). Full article

(This article belongs to the Special Issue Geospatial Methods in Social and Behavioral Sciences)

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21 pages, 3387 KiB

Open AccessEditor’s ChoiceArticle

Urban Population Distribution Mapping with Multisource Geospatial Data Based on Zonal Strategy

by Guanwei Zhao and Muzhuang Yang

ISPRS Int. J. Geo-Inf. 2020, 9(11), 654; https://doi.org/10.3390/ijgi9110654 - 30 Oct 2020

Cited by 12 | Viewed by 4525

Abstract

Mapping population distribution at fine resolutions with high accuracy is crucial to urban planning and management. This paper takes Guangzhou city as the study area, illustrates the gridded population distribution map by using machine learning methods based on zoning strategy with multisource geospatial [...] Read more.

Mapping population distribution at fine resolutions with high accuracy is crucial to urban planning and management. This paper takes Guangzhou city as the study area, illustrates the gridded population distribution map by using machine learning methods based on zoning strategy with multisource geospatial data such as night light remote sensing data, point of interest data, land use data, and so on. The street-level accuracy evaluation results show that the proposed approach achieved good overall accuracy, with determinant coefficient (R²) being 0.713 and root mean square error (RMSE) being 5512.9. Meanwhile, the goodness of fit for single linear regression (LR) model and random forest (RF) regression model are 0.0039 and 0.605, respectively. For dense area, the accuracy of the random forest model is better than the linear regression model, while for sparse area, the accuracy of the linear regression model is better than the random forest model. The results indicated that the proposed method has great potential in fine-scale population mapping. Therefore, it is advised that the zonal modeling strategy should be the primary choice for solving regional differences in the population distribution mapping research. Full article

(This article belongs to the Special Issue Measuring, Mapping, Modeling, and Visualization of Cities)

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28 pages, 6386 KiB

Open AccessEditor’s ChoiceArticle

Using Flickr Geotagged Photos to Estimate Visitor Trajectories in World Heritage Cities

by Antoni Domènech, Inmaculada Mohino and Borja Moya-Gómez

ISPRS Int. J. Geo-Inf. 2020, 9(11), 646; https://doi.org/10.3390/ijgi9110646 - 29 Oct 2020

Cited by 27 | Viewed by 7458

Abstract

World tourism dynamics are in constant change, as well as they are deeply shaping the trajectories of cities. The “call effect” for having the World Heritage status has boosted tourism in many cities. The large number of visitors and the side effects, such [...] Read more.

World tourism dynamics are in constant change, as well as they are deeply shaping the trajectories of cities. The “call effect” for having the World Heritage status has boosted tourism in many cities. The large number of visitors and the side effects, such as the overcrowding of central spaces, are arousing the need to develop and protect heritage assets. Hence, the analysis of tourist spatial behaviour is critical for tackling the needs of touristified cities correctly. In this article, individual visitor spatiotemporal trajectories are reconstructed along with the urban network using thousands of geotagged Flickr photos taken by visitors in the historic centre of the World Heritage City of Toledo (Spain). A process of trajectory reconstruction using advanced GIS techniques has been implemented. The spatial behaviour has been used to classify the tourist sites offered on the city’s official tourist map, as well as to identify the association with the land uses. Results bring new knowledge to understand visitor spatial behaviour and new visions about the influence of the urban environment and its uses on the visitor spatial behaviour. Our findings illustrate how tourist attractions and the location of mixed commercial and recreational uses shape the visitor spatial behaviour. Overflowed streets and shadow areas underexplored by visitors are pinpointed. Full article

(This article belongs to the Special Issue Geospatial Methods in Social and Behavioral Sciences)

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24 pages, 7796 KiB

Open AccessEditor’s ChoiceArticle

Personalized Legibility of an Indoor Environment for People with Motor Disabilities: A New Framework

by Ali Afghantoloee, Mir Abolfazl Mostafavi, Geoffrey Edwards and Amin Gharebaghi

ISPRS Int. J. Geo-Inf. 2020, 9(11), 649; https://doi.org/10.3390/ijgi9110649 - 29 Oct 2020

Cited by 4 | Viewed by 3057

Abstract

A mental map refers to the personalized representation of spatial knowledge in the human brain and is based on the perceptions, experiences, and interactions of people with their environment. For people with motor disabilities (PWMD) some perceptions and interactions with the environment during [...] Read more.

A mental map refers to the personalized representation of spatial knowledge in the human brain and is based on the perceptions, experiences, and interactions of people with their environment. For people with motor disabilities (PWMD) some perceptions and interactions with the environment during their mobility occur in different ways and consequently lead to different mental maps. For example, these people perceive and interact differently with elevators, escalators, and steps during their mobility. Hence, their perceptions of the level of complexity and the legibility of an environment may be different. Legibility of an environment is an indicator that measures the level of complexity and the ease of understanding of that environment by a person. In the literature, legibility is mostly estimated based on the environmental factors such as visibility, connectivity, and layout complexity for a given space. However, the role of personal factors (e.g., capacities) is rarely considered in the legibility assessment, which complicates its personalization. This paper aims at studying the influence of personal factors on the evaluation of the legibility of indoor environments for PWMD. In addition to the visibility, the connectivity, and the complexity of indoor environments, we also integrate the influence of the level of accessibility (i.e., presence of facilitators and obstacles) in the legibility assessment process. The Quebec City Convention Centre is selected as our study area and the legibility of this building is quantified. We show how the integration of the above-mentioned factors can influence the legibility for PWMD and hence their mobility performance in those environments. Full article

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28 pages, 26554 KiB

Open AccessEditor’s ChoiceArticle

A Feasibility Study of Map-Based Dashboard for Spatiotemporal Knowledge Acquisition and Analysis

by Chenyu Zuo, Linfang Ding and Liqiu Meng

ISPRS Int. J. Geo-Inf. 2020, 9(11), 636; https://doi.org/10.3390/ijgi9110636 - 27 Oct 2020

Cited by 12 | Viewed by 7728

Abstract

Map-based dashboards are among the most popular tools that support the viewing and understanding of a large amount of geo-data with complex relations. In spite of many existing design examples, little is known about their impacts on users and whether they match the [...] Read more.

Map-based dashboards are among the most popular tools that support the viewing and understanding of a large amount of geo-data with complex relations. In spite of many existing design examples, little is known about their impacts on users and whether they match the information demand and expectations of target users. The authors first designed a novel map-based dashboard to support their target users’ spatiotemporal knowledge acquisition and analysis, and then conducted an experiment to assess the feasibility of the proposed dashboard. The experiment consists of eye-tracking, benchmark tasks, and interviews. A total of 40 participants were recruited for the experiment. The results have verified the effectiveness and efficiency of the proposed map-based dashboard in supporting the given tasks. At the same time, the experiment has revealed a number of aspects for improvement related to the layout design, the labeling of multiple panels and the integration of visual analytical elements in map-based dashboards, as well as future user studies. Full article

(This article belongs to the Special Issue Multimedia Cartography)

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19 pages, 11680 KiB

Open AccessEditor’s ChoiceArticle

Spatiotemporal Patterns and Driving Factors on Crime Changing During Black Lives Matter Protests

by Zhiran Zhang, Dexuan Sha, Beidi Dong, Shiyang Ruan, Agen Qiu, Yun Li, Jiping Liu and Chaowei Yang

ISPRS Int. J. Geo-Inf. 2020, 9(11), 640; https://doi.org/10.3390/ijgi9110640 - 27 Oct 2020

Cited by 10 | Viewed by 5801

Abstract

The death of George Floyd has brought a new wave of 2020 Black Lives Matter (BLM) protests into U.S. cities. Protests happened in a few cities accompanied by reports of violence over the first few days. The protests appear to be related to [...] Read more.

The death of George Floyd has brought a new wave of 2020 Black Lives Matter (BLM) protests into U.S. cities. Protests happened in a few cities accompanied by reports of violence over the first few days. The protests appear to be related to rising crime. This study uses newly collected crime data in 50 U.S. cities/counties to explore the spatiotemporal crime changes under BLM protests and to estimate the driving factors of burglary induced by the BLM protest. Four spatial and statistic models were used, including the Average Nearest Neighbor (ANN), Hotspot Analysis, Least Absolute Shrinkage, and Selection Operator (LASSO), and Binary Logistic Regression. The results show that (1) crime, especially burglary, has risen sharply in a few cities/counties, yet heterogeneity exists across cities/counties; (2) the volume and spatial distribution of certain crime types changed under BLM protest, the activity of burglary clustered in certain regions during protests period; (3) education, race, demographic, and crime rate in 2019 are related with burglary changes during BLM protests. The findings from this study can provide valuable information for ensuring the capabilities of the police and governmental agencies to deal with the evolving crisis. Full article

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20 pages, 10319 KiB

Open AccessEditor’s ChoiceArticle

Investigating the Relationship between the Built Environment and Relative Risk of COVID-19 in Hong Kong

by Jianwei Huang, Mei-Po Kwan, Zihan Kan, Man Sing Wong, Coco Yin Tung Kwok and Xinyu Yu

ISPRS Int. J. Geo-Inf. 2020, 9(11), 624; https://doi.org/10.3390/ijgi9110624 - 25 Oct 2020

Cited by 81 | Viewed by 9480

Abstract

Understanding the relationship between the built environment and the risk of COVID-19 transmission is essential to respond to the pandemic. This study explores the relationship between the built environment and COVID-19 risk using the confirmed cases data collected in Hong Kong. Using the [...] Read more.

Understanding the relationship between the built environment and the risk of COVID-19 transmission is essential to respond to the pandemic. This study explores the relationship between the built environment and COVID-19 risk using the confirmed cases data collected in Hong Kong. Using the information on the residential buildings and places visited for each case from the dataset, we assess the risk of COVID-19 and explore their geographic patterns at the level of Tertiary Planning Unit (TPU) based on incidence rate (R1) and venue density (R2). We then investigate the associations between several built-environment variables (e.g., nodal accessibility and green space density) and COVID-19 risk using global Poisson regression (GPR) and geographically weighted Poisson regression (GWPR) models. The results indicate that COVID-19 risk tends to be concentrated in particular areas of Hong Kong. Using the incidence rate as an indicator to assess COVID-19 risk may underestimate the risk of COVID-19 transmission in some suburban areas. The GPR and GWPR models suggest a close and spatially heterogeneous relationship between the selected built-environment variables and the risk of COVID-19 transmission. The study provides useful insights that support policymakers in responding to the COVID-19 pandemic and future epidemics. Full article

(This article belongs to the Special Issue Geospatial Methods in Social and Behavioral Sciences)

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12 pages, 12296 KiB

Open AccessEditor’s ChoiceArticle

Generation of Spatiotemporally Resolved Power Production Data of PV Systems in Germany

by Reinhold Lehneis, David Manske and Daniela Thrän

ISPRS Int. J. Geo-Inf. 2020, 9(11), 621; https://doi.org/10.3390/ijgi9110621 - 24 Oct 2020

Cited by 13 | Viewed by 4143

Abstract

Photovoltaics, as one of the most important renewable energies in Germany, have increased significantly in recent years and cover up to 50% of the German power provision on sunny days. To investigate the manifold effects of increasing renewables, spatiotemporally disaggregated data on the [...] Read more.

Photovoltaics, as one of the most important renewable energies in Germany, have increased significantly in recent years and cover up to 50% of the German power provision on sunny days. To investigate the manifold effects of increasing renewables, spatiotemporally disaggregated data on the power generation from photovoltaic (PV) systems are often mandatory. Due to strict data protection regulations, such information is not freely available for Germany. To close this gap, numerical simulations using publicly accessible plant and weather data can be applied to determine the required spatiotemporal electricity generation. For this, the sunlight-to-power conversion is modeled with the help of the open-access web tool of the Photovoltaic Geographical Information System (PVGIS). The presented simulations are carried out for the year 2016 and consider nearly 1.612 million PV systems in Germany, which have been aggregated into municipal areas before performing the calculations. The resulting hourly resolved time series of the entire plant ensemble are converted into a time series with daily resolution and compared with measured feed-in data to validate the numerical simulations that show a high degree of agreement. Such power production data can be used to monitor and optimize renewable energy systems on different spatiotemporal scales. Full article

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

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28 pages, 986 KiB

Open AccessEditor’s ChoiceArticle

A Flexible Framework for Covering and Partitioning Problems in Indoor Spaces

by Sung-Hwan Kim, Ki-Joune Li and Hwan-Gue Cho

ISPRS Int. J. Geo-Inf. 2020, 9(11), 618; https://doi.org/10.3390/ijgi9110618 - 23 Oct 2020

Cited by 4 | Viewed by 3130

Abstract

Utilizing indoor spaces has become important with the progress of localization and positioning technologies. Covering and partitioning problems play an important role in managing, indexing, and analyzing spatial data. In this paper, we propose a multi-stage framework for indoor space partitioning, each stage [...] Read more.

Utilizing indoor spaces has become important with the progress of localization and positioning technologies. Covering and partitioning problems play an important role in managing, indexing, and analyzing spatial data. In this paper, we propose a multi-stage framework for indoor space partitioning, each stage of which can be flexibly adjusted according to target applications. One of the main features of our framework is the parameterized constraint, which characterizes the properties and restrictions of unit geometries used for the covering and partitioning tasks formulated as the binary linear programs. It enables us to apply the proposed method to various problems by simply changing the constraint parameter. We present basic constraints that are widely used in many covering and partitioning problems regarding the indoor space applications along with several techniques that simplify the computation process. We apply it to particular applications, device placement and route planning problems, in order to give examples of the use of our framework in the perspective on how to design a constraint and how to use the resulting partitions. We also demonstrate the effectiveness with experimental results compared to baseline methods. Full article

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16 pages, 3179 KiB

Open AccessEditor’s ChoiceArticle

Spatial Knowledge Acquisition with Mobile Maps: Effects of Map Size on Users’ Wayfinding Performance with Interactive Interfaces

by Chien-Hsiung Chen and Xiao Li

ISPRS Int. J. Geo-Inf. 2020, 9(11), 614; https://doi.org/10.3390/ijgi9110614 - 22 Oct 2020

Cited by 7 | Viewed by 3386

Abstract

Restricted by the small screen size, it is challenging for users to obtain all the wayfinding content they need when utilizing mobile devices. This study investigated the effects of map size and interactive interface on users’ wayfinding performance and preference when using mobile [...] Read more.

Restricted by the small screen size, it is challenging for users to obtain all the wayfinding content they need when utilizing mobile devices. This study investigated the effects of map size and interactive interface on users’ wayfinding performance and preference when using mobile devices. Two types of interactive interfaces (i.e., panning and peephole interfaces) and three different map sizes (i.e., small, medium, and large) were examined. The experiment was a 2 × 3 between-subjects design. Sixty participants were invited to complete five wayfinding tasks (i.e., Euclidean distance judgment, route distance judgment, landmark recognition, map section rotation, and route recognition), a system usability scale (SUS) questionnaire, and the subjective preference questionnaire. The results showed that: (1) The participants’ wayfinding performance was affected by the map size and interactive interface; (2) the peephole interface was superior for the Euclidean distance judgment and the route recognition tasks; (3) it does not always take a significantly longer time to complete the task with the larger map when performing the map section rotation task with the panning interface; and (4) the usability scores of the peephole interface were considered above average, and it had a positive impact on the participants’ preferences. Full article

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16 pages, 3644 KiB

Open AccessEditor’s ChoiceArticle

How Urban Factors Affect the Spatiotemporal Distribution of Infectious Diseases in Addition to Intercity Population Movement in China

by Xinyi Niu, Yufeng Yue, Xingang Zhou and Xiaohu Zhang

ISPRS Int. J. Geo-Inf. 2020, 9(11), 615; https://doi.org/10.3390/ijgi9110615 - 22 Oct 2020

Cited by 25 | Viewed by 3958

Abstract

The outbreak of the 2019 novel coronavirus (COVID-19) has attracted global attention. During the Chinese New Year holiday, population outflow from Wuhan induced the spread of the epidemic to other cities in China. This study analyzed massive intercity movement data from Baidu and [...] Read more.

The outbreak of the 2019 novel coronavirus (COVID-19) has attracted global attention. During the Chinese New Year holiday, population outflow from Wuhan induced the spread of the epidemic to other cities in China. This study analyzed massive intercity movement data from Baidu and epidemic data to study how intercity population outflows affected the spatiotemporal spread of the epidemic. This study further investigated how urban factors influenced the spatiotemporal spread of COVID-19. The analysis indicates that intercity movement was an important factor in the spread of the epidemic in China, and the impact of intercity movement on the spread was heterogeneous across different classes of cities. The spread of the epidemic also varied among cities and was affected by urban factors including the total population, population density, and gross domestic product (GDP). The findings have implications for public health management. Mega-cities should consider tougher measures to contain the spread of the epidemic compared with other cities. It is of great significance for policymakers in any nation to assess the potential risk of epidemics and make cautious plans ahead of time. Full article

(This article belongs to the Special Issue Geospatial Methods in Social and Behavioral Sciences)

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23 pages, 7669 KiB

Open AccessEditor’s ChoiceArticle

A 3D Geodatabase for Urban Underground Infrastructures: Implementation and Application to Groundwater Management in Milan Metropolitan Area

by Davide Sartirana, Marco Rotiroti, Chiara Zanotti, Tullia Bonomi, Letizia Fumagalli and Mattia De Amicis

ISPRS Int. J. Geo-Inf. 2020, 9(10), 609; https://doi.org/10.3390/ijgi9100609 - 21 Oct 2020

Cited by 11 | Viewed by 4922

Abstract

The recent rapid increase in urbanization has led to the inclusion of underground spaces in urban planning policies. Among the main subsurface resources, a strong interaction between underground infrastructures and groundwater has emerged in many urban areas in the last few decades. Thus, [...] Read more.

The recent rapid increase in urbanization has led to the inclusion of underground spaces in urban planning policies. Among the main subsurface resources, a strong interaction between underground infrastructures and groundwater has emerged in many urban areas in the last few decades. Thus, listing the underground infrastructures is necessary to structure an urban conceptual model for groundwater management needs. Starting from a municipal cartography (Open Data), thus making the procedure replicable, a GIS methodology was proposed to gather all the underground infrastructures into an updatable 3D geodatabase (GDB) for the metropolitan city of Milan (Northern Italy). The underground volumes occupied by three categories of infrastructures were included in the GDB: (a) private car parks, (b) public car parks and (c) subway lines and stations. The application of the GDB allowed estimating the volumes lying below groundwater table in four periods, detected as groundwater minimums or maximums from the piezometric trend reconstructions. Due to groundwater rising or local hydrogeological conditions, the shallowest, non-waterproofed underground infrastructures were flooded in some periods considered. This was evaluated in a specific pilot area and qualitatively confirmed by local press and photographic documentation reviews. The methodology emerged as efficient for urban planning, particularly for urban conceptual models and groundwater management plans definition. Full article

(This article belongs to the Special Issue Measuring, Mapping, Modeling, and Visualization of Cities)

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14 pages, 4883 KiB

Open AccessEditor’s ChoiceArticle

Accurate Road Marking Detection from Noisy Point Clouds Acquired by Low-Cost Mobile LiDAR Systems

by Ronghao Yang, Qitao Li, Junxiang Tan, Shaoda Li and Xinyu Chen

ISPRS Int. J. Geo-Inf. 2020, 9(10), 608; https://doi.org/10.3390/ijgi9100608 - 20 Oct 2020

Cited by 28 | Viewed by 4693

Abstract

Road markings that provide instructions for unmanned driving are important elements in high-precision maps. In road information collection technology, multi-beam mobile LiDAR scanning (MLS) is currently adopted instead of traditional mono-beam LiDAR scanning because of the advantages of low cost and multiple fields [...] Read more.

Road markings that provide instructions for unmanned driving are important elements in high-precision maps. In road information collection technology, multi-beam mobile LiDAR scanning (MLS) is currently adopted instead of traditional mono-beam LiDAR scanning because of the advantages of low cost and multiple fields of view for multi-beam laser scanners; however, the intensity information scanned by multi-beam systems is noisy and current methods designed for road marking detection from mono-beam point clouds are of low accuracy. This paper presents an accurate algorithm for detecting road markings from noisy point clouds, where most nonroad points are removed and the remaining points are organized into a set of consecutive pseudo-scan lines for parallel and/or online processing. The road surface is precisely extracted by a moving fitting window filter from each pseudo-scan line, and a marker edge detector combining an intensity gradient with an intensity statistics histogram is presented for road marking detection. Quantitative results indicate that the proposed method achieves average recall, precision, and Matthews correlation coefficient (MCC) levels of 90%, 95%, and 92%, respectively, showing excellent performance for road marking detection from multi-beam scanning point clouds. Full article

(This article belongs to the Special Issue Measuring, Mapping, Modeling, and Visualization of Cities)

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19 pages, 5103 KiB

Open AccessEditor’s ChoiceArticle

Assessing Quality of Life Inequalities. A Geographical Approach

by Antigoni Faka

ISPRS Int. J. Geo-Inf. 2020, 9(10), 600; https://doi.org/10.3390/ijgi9100600 - 12 Oct 2020

Cited by 24 | Viewed by 6087

Abstract

This study proposes an integrated methodology for evaluating and mapping quality of life (QoL) and the quality of a place as residence area, at local level. The QoL assessment was based on the development of composite criteria, using geographical variables that evaluate QoL, [...] Read more.

This study proposes an integrated methodology for evaluating and mapping quality of life (QoL) and the quality of a place as residence area, at local level. The QoL assessment was based on the development of composite criteria, using geographical variables that evaluate QoL, and geographic information systems. The composite criteria are related to the natural and the socioeconomic environment, the housing conditions, the infrastructure and services, and the cultural and recreational facilities. Each criterion was evaluated by a set of variables and each variable was weighted based on the residents’ preferences and the analytical hierarchy process. The criteria were also weighted and combined to assess overall QoL. The methodology was implemented in the Municipality of Katerini, Greece, and QoL mapping led to the zoning of the study area and the identification of areas with low and high QoL. The results revealed the highest level of overall QoL in three out of twenty-nine communities, which provide better housing conditions and access to public services and infrastructures, combining also qualitative natural environment, whereas five mountainous and remote communities scored the lowest level. Mapping QoL may support decision making strategies that target to improve human well-being, increase QoL levels and upgrade living conditions. Full article

(This article belongs to the Special Issue GIS-Based Analysis for Quality of Life and Environmental Monitoring)

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19 pages, 4739 KiB

Open AccessEditor’s ChoiceArticle

Concept and Evaluation of Heating Demand Prediction Based on 3D City Models and the CityGML Energy ADE—Case Study Helsinki

by Maxim Rossknecht and Enni Airaksinen

ISPRS Int. J. Geo-Inf. 2020, 9(10), 602; https://doi.org/10.3390/ijgi9100602 - 12 Oct 2020

Cited by 42 | Viewed by 5634

Abstract

This work presents a concept for heating demand and resulting CO

_{2}

emissions prediction based on a 3D city model in CityGML format in various scenarios under the consideration of a changing climate. In the case study of Helsinki, the Helsinki Energy and [...] Read more.

This work presents a concept for heating demand and resulting CO

_{2}

emissions prediction based on a 3D city model in CityGML format in various scenarios under the consideration of a changing climate. In the case study of Helsinki, the Helsinki Energy and Climate Atlas, that provides detailed information for individual buildings conducting the heating demand, is integrated into the 3D city model using the CityGML Energy Application Domain Extension (Energy ADE) to provide energy-relevant information based on a standardized data model stored in a CityGML database, called 3DCityDB. The simulation environment SimStadt is extended to retrieve the information stored within the Energy ADE schema, use it during simulations, and write simulation results back to the 3DCityDB. Due to climate change, a heating demand reduction of 4% per decade is predicted. By 2035, a reduction of 0.7 TWh is calculated in the normal and of 1.5 TWh in the advanced refurbishment scenario. Including the proposed improvements of the district heating network, heating CO

_{2}

emissions are predicted to be reduced by up to 82% by 2035 compared to 1990. The City of Helsinki’s assumed heating demand reduction through the modernization of 2.0 TWh/a by 2035 is not achieved with a 3% refurbishment rate. Furthermore, the reduction of CO

_{2}

emissions is mainly achieved through lower CO

_{2}

emission factors of the district heating network in Helsinki. Full article

(This article belongs to the Special Issue The Applications of 3D-City Models in Urban Studies)

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15 pages, 4010 KiB

Open AccessEditor’s ChoiceTechnical Note

PolySimp: A Tool for Polygon Simplification Based on the Underlying Scaling Hierarchy

by Ding Ma, Zhigang Zhao, Ye Zheng, Renzhong Guo and Wei Zhu

ISPRS Int. J. Geo-Inf. 2020, 9(10), 594; https://doi.org/10.3390/ijgi9100594 - 10 Oct 2020

Cited by 6 | Viewed by 4255

Abstract

Map generalization is a process of reducing the contents of a map or data to properly show a geographic feature(s) at a smaller extent. Over the past few years, the fractal way of thinking has emerged as a new paradigm for map generalization. [...] Read more.

Map generalization is a process of reducing the contents of a map or data to properly show a geographic feature(s) at a smaller extent. Over the past few years, the fractal way of thinking has emerged as a new paradigm for map generalization. A geographic feature can be deemed as a fractal given the perspective of scaling, as its rough, irregular, and unsmooth shape inherently holds a striking scaling hierarchy of far more small elements than large ones. The pattern of far more small things than large ones is a de facto heavy tailed distribution. In this paper, we apply the scaling hierarchy for map generalization to polygonal features. To do this, we firstly revisit the scaling hierarchy of a classic fractal: the Koch Snowflake. We then review previous work that used the Douglas–Peuker algorithm, which identifies characteristic points on a line to derive three types of measures that are long-tailed distributed: the baseline length (d), the perpendicular distance to the baseline (x), and the area formed by x and d (area). More importantly, we extend the usage of the three measures to other most popular cartographical generalization methods; i.e., the bend simplify method, Visvalingam–Whyatt method, and hierarchical decomposition method, each of which decomposes any polygon into a set of bends, triangles, or convex hulls as basic geometric units for simplification. The different levels of details of the polygon can then be derived by recursively selecting the head part of geometric units and omitting the tail part using head/tail breaks, which is a new classification scheme for data with a heavy-tailed distribution. Since there are currently few tools with which to readily conduct the polygon simplification from such a fractal perspective, we have developed PolySimp, a tool that integrates the mentioned four algorithms for polygon simplification based on its underlying scaling hierarchy. The British coastline was selected to demonstrate the tool’s usefulness. The developed tool can be expected to showcase the applicability of fractal way of thinking and contribute to the development of map generalization. Full article

(This article belongs to the Special Issue Geographic Complexity: Concepts, Theories, and Practices)

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16 pages, 42937 KiB

Open AccessEditor’s ChoiceArticle

A Smooth Transition Algorithm for Adjacent Panoramic Viewpoints Using Matched Delaunay Triangular Patches

by Pengcheng Zhao, Qingwu Hu, Zhixiong Tang and Mingyao Ai

ISPRS Int. J. Geo-Inf. 2020, 9(10), 596; https://doi.org/10.3390/ijgi9100596 - 10 Oct 2020

Cited by 5 | Viewed by 4412

Abstract

The unnatural panoramic image transition between two adjacent viewpoints reduces the immersion and interactive experiences of 360° panoramic walkthrough systems. In this paper, a dynamic panoramic image rendering and smooth transition algorithm for adjacent viewpoints is proposed. First, the feature points of adjacent [...] Read more.

The unnatural panoramic image transition between two adjacent viewpoints reduces the immersion and interactive experiences of 360° panoramic walkthrough systems. In this paper, a dynamic panoramic image rendering and smooth transition algorithm for adjacent viewpoints is proposed. First, the feature points of adjacent view images are extracted, a robust matching algorithm is used to establish adjacent point pairs, and the matching triangles are formed by using the homonymous points. Then, a dynamic transition model is formed by the simultaneous linear transitions of shape and texture for each control triangle. Finally, the smooth transition between adjacent viewpoints is implemented by overlaying the dynamic transition model with the 360° panoramic walkthrough scene. Experimental results show that this method has obvious advantages in visual representation with distinct visual movement. It can realize the smooth transition between two indoor panoramic stations with arbitrary station spacing, and its execution efficiency is up to 50 frames per second. It effectively enhances the interactivity and immersion of 360° panoramic walkthrough systems. Full article

(This article belongs to the Special Issue Measuring, Mapping, Modeling, and Visualization of Cities)

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34 pages, 9315 KiB

Open AccessEditor’s ChoiceArticle

A Comparative Study of Several Metaheuristic Algorithms to Optimize Monetary Incentive in Ridesharing Systems

by Fu-Shiung Hsieh

ISPRS Int. J. Geo-Inf. 2020, 9(10), 590; https://doi.org/10.3390/ijgi9100590 - 8 Oct 2020

Cited by 28 | Viewed by 3537

Abstract

The strong demand on human mobility leads to excessive numbers of cars and raises the problems of serious traffic congestion, large amounts of greenhouse gas emissions, air pollution and insufficient parking space in cities. Although ridesharing is a potential transport mode to solve [...] Read more.

The strong demand on human mobility leads to excessive numbers of cars and raises the problems of serious traffic congestion, large amounts of greenhouse gas emissions, air pollution and insufficient parking space in cities. Although ridesharing is a potential transport mode to solve the above problems through car-sharing, it is still not widely adopted. Most studies consider non-monetary incentive performance indices such as travel distance and successful matches in ridesharing systems. These performance indices fail to provide a strong incentive for ridesharing. The goal of this paper is to address this issue by proposing a monetary incentive performance indicator to improve the incentives for ridesharing. The objectives are to improve the incentive for ridesharing through a monetary incentive optimization problem formulation, development of a solution methodology and comparison of different solution algorithms. A non-linear integer programming optimization problem is formulated to optimize monetary incentive in ridesharing systems. Several discrete metaheuristic algorithms are developed to cope with computational complexity for solving the above problem. These include several discrete variants of particle swarm optimization algorithms, differential evolution algorithms and the firefly algorithm. The effectiveness of applying the above algorithms to solve the monetary incentive optimization problem is compared based on experimental results. Full article

(This article belongs to the Special Issue GIS in Sustainable Transportation)

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30 pages, 20417 KiB

Open AccessEditor’s ChoiceArticle

A Built Heritage Information System Based on Point Cloud Data: HIS-PC

by Florent Poux, Roland Billen, Jean-Paul Kasprzyk, Pierre-Henri Lefebvre and Pierre Hallot

ISPRS Int. J. Geo-Inf. 2020, 9(10), 588; https://doi.org/10.3390/ijgi9100588 - 7 Oct 2020

Cited by 19 | Viewed by 5184

Abstract

The digital management of an archaeological site requires to store, organise, access and represent all the information that is collected on the field. Heritage building information modelling, archaeological or heritage information systems now tend to propose a common framework where all the materials [...] Read more.

The digital management of an archaeological site requires to store, organise, access and represent all the information that is collected on the field. Heritage building information modelling, archaeological or heritage information systems now tend to propose a common framework where all the materials are managed from a central database and visualised through a 3D representation. In this research, we offer the development of a built heritage information system prototype based on a high-resolution 3D point cloud data set. The particularity of the approach is to consider a user-centred development methodology while avoiding meshing/down-sampling operations. The proposed system is initiated by a close collaboration between multi-modal users (managers, visitors, curators) and a development team (designers, developers, architects). The developed heritage information system permits the management of spatial and temporal information, including a wide range of semantics using relational along with NoSQL databases. The semantics used to describe the artifacts are subject to conceptual modelling. Finally, the system proposes a bi-directional communication with a 3D interface able to stream massive point clouds, which is a big step forward to provide a comprehensive site representation for stakeholders while minimising modelling costs. Full article

(This article belongs to the Special Issue BIM for Cultural Heritage (HBIM))

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15 pages, 2567 KiB

Open AccessEditor’s ChoiceArticle

Evaluation of the Space Syntax Measures Affecting Pedestrian Density through Ordinal Logistic Regression Analysis

by Özge Öztürk Hacar, Fatih Gülgen and Serdar Bilgi

ISPRS Int. J. Geo-Inf. 2020, 9(10), 589; https://doi.org/10.3390/ijgi9100589 - 7 Oct 2020

Cited by 13 | Viewed by 4865

Abstract

This paper examines the relationship between pedestrian density and space syntax measures in a university campus using ordinal logistic regression analysis. The pedestrian density assumed as the dependent variable of regression analysis was categorised in low, medium, and high classes by using Jenks [...] Read more.

This paper examines the relationship between pedestrian density and space syntax measures in a university campus using ordinal logistic regression analysis. The pedestrian density assumed as the dependent variable of regression analysis was categorised in low, medium, and high classes by using Jenks natural break classification. The data elements of groups were derived from pedestrian counts performed in 22 gates 132 times. The counting period grouped in nominal categories was assumed as an independent variable. Another independent was one of the 15 derived measures of axial analysis and visual graphic analysis. The statistically significant model results indicated that the integration of axial analysis was the most reasonable measure that explained the pedestrian density. Then, the changes in integration values of current and master plan datasets were analysed using paired sample t-test. The calculated p-value of t-test proved that the master plan would change the campus morphology for pedestrians. Full article

(This article belongs to the Special Issue Measuring, Mapping, Modeling, and Visualization of Cities)

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27 pages, 4612 KiB

Open AccessEditor’s ChoiceArticle

Supporting Policy Design for the Diffusion of Cleaner Technologies: A Spatial Empirical Agent-Based Model

by Caterina Caprioli, Marta Bottero and Elena De Angelis

ISPRS Int. J. Geo-Inf. 2020, 9(10), 581; https://doi.org/10.3390/ijgi9100581 - 1 Oct 2020

Cited by 15 | Viewed by 3939

Abstract

Renewable energy resources and energy-efficient technologies, as well as building retrofitting, are only some of the possible strategies that can achieve more sustainable cities and reduce greenhouse gas emissions. Subsidies and incentives are often provided by governments to increase the number of people [...] Read more.

Renewable energy resources and energy-efficient technologies, as well as building retrofitting, are only some of the possible strategies that can achieve more sustainable cities and reduce greenhouse gas emissions. Subsidies and incentives are often provided by governments to increase the number of people adopting these sustainable energy efficiency actions. However, actual sales of green products are currently not as high as would be desired. The present paper applies a hybrid agent-based model (ABM) integrated with a Geographic Information System (GIS) to simulate a complex socio-economic-architectural adaptive system to study the temporal diffusion and the willingness of inhabitants to adopt photovoltaic (PV) systems. The San Salvario neighborhood in Turin (Italy) is used as an exemplary case study for testing consumer behavior associated with this technology, integrating social network theories, opinion formation dynamics and an adaptation of the theory of planned behavior (TPB). Data/characteristics for both buildings and people are explicitly spatialized with the level of detail at the block scale. Particular attention is given to the comparison of the policy mix for supporting decision-makers and policymakers in the definition of the most efficient strategies for achieving a long-term vision of sustainable development. Both variables and outcomes accuracy of the model are validated with historical real-world data. Full article

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

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18 pages, 9387 KiB

Open AccessEditor’s ChoiceArticle

Improved Estimations of Nitrate and Sediment Concentrations Based on SWAT Simulations and Annual Updated Land Cover Products from a Deep Learning Classification Algorithm

by Nikiforos Samarinas, Nikolaos Tziolas and George Zalidis

ISPRS Int. J. Geo-Inf. 2020, 9(10), 576; https://doi.org/10.3390/ijgi9100576 - 30 Sep 2020

Cited by 11 | Viewed by 3593

Abstract

The agricultural sector and natural resources are heavily interdependent, comprising a coherent but complex system. The soil and water assessment tool (SWAT) is widely used in assessing these interdependencies for regional watershed management. However, long-term simulations of agricultural watersheds are considered as not [...] Read more.

The agricultural sector and natural resources are heavily interdependent, comprising a coherent but complex system. The soil and water assessment tool (SWAT) is widely used in assessing these interdependencies for regional watershed management. However, long-term simulations of agricultural watersheds are considered as not realistic since they have often been performed assuming constant land use over time and are based on the coarse resolution of the existing global or national data. This work presents the first insights of the synergy among SWAT model and deep learning classification algorithms to provide annually updated and realistic model’s parameterization and simulations. The proposed hybrid modelling approach couples the physical process SWAT model with the versatility of Earth observation data-driven non-linear deep learning algorithms for land use classification (Overall Accuracy (OA) = 79.58% and Kappa = 0.79), giving a strong advantage to decision makers for efficient management planning. A validation case at an agricultural watershed located in Northern Greece is provided to demonstrate their synergistic use to estimate nitrate and sediment concentrations that load in Zazari Lake. The SWAT model has been implemented under two different simulations; one with the use of a static coarse land use map and the other with the use of the annual updated land use maps for three consecutive years (2017–2019). The results indicate that the land use changes affect the final estimations resulting to an enhanced prediction performance of 1% and 2% for sediment and nitrate, respectively, when the annual land use maps are incorporated into SWAT simulations. In this context, a hybrid approach could further contribute to addressing challenges and support a data-centric scheme for informed decision making with regard to environmental and agricultural issues on the river basin scale. Full article

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17 pages, 8014 KiB

Open AccessEditor’s ChoiceArticle

Quantitative Evaluation of Spatial Differentiation for Public Open Spaces in Urban Built-Up Areas by Assessing SDG 11.7: A Case of Deqing County

by Qiang Chen, Mingyi Du, Qianhao Cheng and Changfeng Jing

ISPRS Int. J. Geo-Inf. 2020, 9(10), 575; https://doi.org/10.3390/ijgi9100575 - 30 Sep 2020

Cited by 17 | Viewed by 3750

Abstract

Urban public open spaces refer to open space between architectural structures in a city or urban agglomeration that is open for urban residents to conduct public exchanges and hold various activities. Sustainable Development Goal (SDG) 11.7 in the 2030 UN Agenda for Sustainable [...] Read more.

Urban public open spaces refer to open space between architectural structures in a city or urban agglomeration that is open for urban residents to conduct public exchanges and hold various activities. Sustainable Development Goal (SDG) 11.7 in the 2030 UN Agenda for Sustainable Development clearly states that the distribution characteristics of public open spaces are important indicators to measure the sustainable development of urban ecological society. In 2018, in order to implement the sustainable development agenda, China offered the example of Deqing to the world. Therefore, taking Deqing as an example, this paper uses geographic statistics and spatial analysis methods to quantitatively evaluate and visualize public open spaces in the built area in 2016 and analyzes the spatial pattern and relationship of the population. The results show that the public open spaces in the built-up area of Deqing have typical global and local spatial autocorrelation. The spatial pattern shows obvious differences in different parts of the built area and attributes of public open spaces. According to the results of correlation analysis, it can be seen that the decentralized characteristics of public open spaces have a significant relationship with the population agglomeration, and this correlation is also related to the types of public open spaces. The assessment results by SDG 11.7.1 indicate that the public open spaces in the built-up area of Deqing conform to the living needs of residents on the whole and have a humanized space design and good accessibility. However, the per capita public open spaces of towns and villages outside the built area are relatively low, and there is an imbalance in public open spaces. Therefore, more attention should be paid to constructing urban public open spaces fairly. Full article

(This article belongs to the Special Issue Measuring and Monitoring Progress towards SDGs by Integrating Geospatial and Statistical Information)

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26 pages, 44915 KiB

Open AccessEditor’s ChoiceArticle

Emergency Department Overcrowding: A Retrospective Spatial Analysis and the Geocoding of Accesses. A Pilot Study in Rome

by Cristiano Pesaresi, Giuseppe Migliara, Davide Pavia and Corrado De Vito

ISPRS Int. J. Geo-Inf. 2020, 9(10), 579; https://doi.org/10.3390/ijgi9100579 - 30 Sep 2020

Cited by 6 | Viewed by 6818

Abstract

The overcrowding of first aid facilities creates considerable hardship and problems which have repercussions on patients’ wellbeing, the time needed for a diagnosis, and on the quality of the assistance. The basic objective of this contribution, based on the data collected by the [...] Read more.

The overcrowding of first aid facilities creates considerable hardship and problems which have repercussions on patients’ wellbeing, the time needed for a diagnosis, and on the quality of the assistance. The basic objective of this contribution, based on the data collected by the Hospital Policlinico Umberto I in Rome (Lazio region, Italy), is to carry out a territorial screening of the municipality using GIS applications and spatial analyses aimed at reducing—in terms of triage—code white (inappropriate) attendances, after having identified the areas of greatest provenance of improperly used emergency room access. Working in a GIS environment and using functions for geocoding, we have tested an experimental model aimed at giving a close-up geographical-sanitary look at the situation: recognizing the territorial sectors in Rome which contribute to amplifying the Policlinico Umberto I emergency room overcrowding; leading up to an improvement of the situation; promoting greater awareness and knowledge of the services available on the territory, a closer relationship between patient and regular doctor (general practitioner, GP) or Local Healthcare Unit and a more efficient functioning of the emergency room. In particular, we have elaborated a “source” map from which derive all the others and it is a dot map on which all the codes white have been geolocalized on a satellite image through geocoding. We have produced three sets made up of three digital cartographic elaborations each, constructed on the census sections, the census areas and the sub-municipal areas, according to data aggregation, for absolute and relative values, and using different templates. Finally, following the same methodology and steps, we elaborated another dot map about all the codes red to provide another kind of information and input for social utility. In the near future, this system could be tested on a platform that spatially analyzes the emergency department (ED) accesses in near-real-time in order to facilitate the identification of critical territorial issues and intervene in a shorter time to regulate the influx of patients to the ED. Full article

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

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20 pages, 2407 KiB

Open AccessEditor’s ChoiceArticle

Evaluating the Performance of Three Popular Web Mapping Libraries: A Case Study Using Argentina’s Life Quality Index

by Alejandro Zunino, Guillermo Velázquez, Juan Pablo Celemín, Cristian Mateos, Matías Hirsch and Juan Manuel Rodriguez

ISPRS Int. J. Geo-Inf. 2020, 9(10), 563; https://doi.org/10.3390/ijgi9100563 - 29 Sep 2020

Cited by 11 | Viewed by 7855

Abstract

Recent Web technologies such as HTML5, JavaScript, and WebGL have enabled powerful and highly dynamic Web mapping applications executing on standard Web browsers. Despite the complexity for developing such applications has been greatly reduced by Web mapping libraries, developers face many choices to [...] Read more.

Recent Web technologies such as HTML5, JavaScript, and WebGL have enabled powerful and highly dynamic Web mapping applications executing on standard Web browsers. Despite the complexity for developing such applications has been greatly reduced by Web mapping libraries, developers face many choices to achieve optimal performance and network usage. This scenario is even more complex when considering different representations of geographical data (raster, raw data or vector) and variety of devices (tablets, smartphones, and personal computers). This paper compares the performance and network usage of three popular JavaScript Web mapping libraries for implementing a Web map using different representations for geodata, and executing on different devices. In the experiments, Mapbox GL JS achieved the best overall performance on mid and high end devices for displaying raster or vector maps, while OpenLayers was the best for raster maps on all devices. Vector-based maps are a safe bet for new Web maps, since performance is on par with raster maps on mid-end smartphones, with significant less network bandwidth requirements. Full article

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22 pages, 3891 KiB

Open AccessEditor’s ChoiceArticle

Monitoring Wildfires in the Northeastern Peruvian Amazon Using Landsat-8 and Sentinel-2 Imagery in the GEE Platform

by Elgar Barboza Castillo, Efrain Y. Turpo Cayo, Cláudia Maria de Almeida, Rolando Salas López, Nilton B. Rojas Briceño, Jhonsy Omar Silva López, Miguel Ángel Barrena Gurbillón, Manuel Oliva and Raul Espinoza-Villar

ISPRS Int. J. Geo-Inf. 2020, 9(10), 564; https://doi.org/10.3390/ijgi9100564 - 29 Sep 2020

Cited by 61 | Viewed by 9507

Abstract

During the latest decades, the Amazon has experienced a great loss of vegetation cover, in many cases as a direct consequence of wildfires, which became a problem at local, national, and global scales, leading to economic, social, and environmental impacts. Hence, this study [...] Read more.

During the latest decades, the Amazon has experienced a great loss of vegetation cover, in many cases as a direct consequence of wildfires, which became a problem at local, national, and global scales, leading to economic, social, and environmental impacts. Hence, this study is committed to developing a routine for monitoring fires in the vegetation cover relying on recent multitemporal data (2017–2019) of Landsat-8 and Sentinel-2 imagery using the cloud-based Google Earth Engine (GEE) platform. In order to assess the burnt areas (BA), spectral indices were employed, such as the Normalized Burn Ratio (NBR), Normalized Burn Ratio 2 (NBR2), and Mid-Infrared Burn Index (MIRBI). All these indices were applied for BA assessment according to appropriate thresholds. Additionally, to reduce confusion between burnt areas and other land cover classes, further indices were used, like those considering the temporal differences between pre and post-fire conditions: differential Mid-Infrared Burn Index (dMIRBI), differential Normalized Burn Ratio (dNBR), differential Normalized Burn Ratio 2 (dNBR2), and differential Near-Infrared (dNIR). The calculated BA by Sentinel-2 was larger during the three-year investigation span (16.55, 78.50, and 67.19 km²) and of greater detail (detected small areas) than the BA extracted by Landsat-8 (16.39, 6.24, and 32.93 km²). The routine for monitoring wildfires presented in this work is based on a sequence of decision rules. This enables the detection and monitoring of burnt vegetation cover and has been originally applied to an experiment in the northeastern Peruvian Amazon. The results obtained by the two satellites imagery are compared in terms of accuracy metrics and level of detail (size of BA patches). The accuracy for Landsat-8 and Sentinel-2 in 2017, 2018, and 2019 varied from 82.7–91.4% to 94.5–98.5%, respectively. Full article

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20 pages, 8298 KiB

Open AccessEditor’s ChoiceArticle

A Method for 3D Reconstruction of the Ming and Qing Official-Style Roof Using a Decorative Components Template Library

by Pengpeng Huo, Miaole Hou, Youqiang Dong, Aiqun Li, Yuhang Ji and Songnian Li

ISPRS Int. J. Geo-Inf. 2020, 9(10), 570; https://doi.org/10.3390/ijgi9100570 - 29 Sep 2020

Cited by 13 | Viewed by 5320

Abstract

The ancient roof decorative components of the official-style architectures from the Ming and Qing dynasties in China hold both physical and symbolic significance. These roof structures are the essential objects in three-dimensional (3D) modeling of ancient architectures for traditional Chinese cultural preservation. Although [...] Read more.

The ancient roof decorative components of the official-style architectures from the Ming and Qing dynasties in China hold both physical and symbolic significance. These roof structures are the essential objects in three-dimensional (3D) modeling of ancient architectures for traditional Chinese cultural preservation. Although ancient architectures can be surveyed by a 3D laser scanner, the complex geometry and diverse pattern of their roof decorative components make the 3D point cloud reconstruction challenging, or at some points, nearly impossible in a fully automated manner. In this paper, we propose a method to ensure that the 3D shape of each roof decorative component is accurately modeled. First, we establish a decorative components template library (or “template library” in short hereafter), which is the first of its kind for the roofs of Ming and Qing official-style architectures. The process of establishing the decorative components template library begins with a remote collection of survey data using a terrestrial laser scanner and digital camera. The next stage involves the design and construction of different 3D decorative components in the template library with reference to the manuscripts written in the Ming and Qing dynasties’ architectural pattern books. With the point cloud data collected on any Ming and Qing official-style architecture, we further propose a geo-registration mechanism to search for an optimal fitting of the decorative components from the template library on the collected point cloud automatically. Based on the experimental results, the accuracy of point cloud registration yields less than 0.02 m, which meets the accuracy of the 3D model at LoD 300 level. Time consumption is less than 5s and stable, for large volume computing capacity has good robustness. The proposed strategy provides a new way for the 3D modeling of large and clustered historical architectures, particularly with complex structures. Full article

(This article belongs to the Special Issue BIM for Cultural Heritage (HBIM))

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31 pages, 18687 KiB

Open AccessEditor’s ChoiceArticle

An Application of Sentinel-1, Sentinel-2, and GNSS Data for Landslide Susceptibility Mapping

by Omid Ghorbanzadeh, Khalil Didehban, Hamid Rasouli, Khalil Valizadeh Kamran, Bakhtiar Feizizadeh and Thomas Blaschke

ISPRS Int. J. Geo-Inf. 2020, 9(10), 561; https://doi.org/10.3390/ijgi9100561 - 27 Sep 2020

Cited by 17 | Viewed by 5085

Abstract

In this study, we used Sentinel-1 and Sentinel-2 data to delineate post-earthquake landslides within an object-based image analysis (OBIA). We used our resulting landslide inventory map for training the data-driven model of the frequency ratio (FR) for landslide susceptibility modelling and mapping considering [...] Read more.

In this study, we used Sentinel-1 and Sentinel-2 data to delineate post-earthquake landslides within an object-based image analysis (OBIA). We used our resulting landslide inventory map for training the data-driven model of the frequency ratio (FR) for landslide susceptibility modelling and mapping considering eleven conditioning factors of soil type, slope angle, distance to roads, distance to rivers, rainfall, normalised difference vegetation index (NDVI), aspect, altitude, distance to faults, land cover, and lithology. A fuzzy analytic hierarchy process (FAHP) also was used for the susceptibility mapping using expert knowledge. Then, we integrated the data-driven model of the FR with the knowledge-based model of the FAHP to reduce the associated uncertainty in each approach. We validated our resulting landslide inventory map based on 30% of the global positioning system (GPS) points of an extensive field survey in the study area. The remaining 70% of the GPS points were used to validate the performance of the applied models and the resulting landslide susceptibility maps using the receiver operating characteristic (ROC) curves. Our resulting landslide inventory map got a precision of 94% and the AUCs (area under the curve) of the susceptibility maps showed 83%, 89%, and 96% for the F-AHP, FR, and the integrated model, respectively. The introduced methodology in this study can be used in the application of remote sensing data for landslide inventory and susceptibility mapping in other areas where earthquakes are considered as the main landslide-triggered factor. Full article

(This article belongs to the Special Issue Multi-Hazard Spatial Modelling and Mapping)

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23 pages, 49398 KiB

Open AccessEditor’s ChoiceArticle

Glacial Lakes Mapping Using Multi Satellite PlanetScope Imagery and Deep Learning

by Nida Qayyum, Sajid Ghuffar, Hafiz Mughees Ahmad, Adeel Yousaf and Imran Shahid

ISPRS Int. J. Geo-Inf. 2020, 9(10), 560; https://doi.org/10.3390/ijgi9100560 - 25 Sep 2020

Cited by 59 | Viewed by 9281

Abstract

Glacial lakes mapping using satellite remote sensing data are important for studying the effects of climate change as well as for the mitigation and risk assessment of a Glacial Lake Outburst Flood (GLOF). The 3U cubesat constellation of Planet Labs offers the capability [...] Read more.

Glacial lakes mapping using satellite remote sensing data are important for studying the effects of climate change as well as for the mitigation and risk assessment of a Glacial Lake Outburst Flood (GLOF). The 3U cubesat constellation of Planet Labs offers the capability of imaging the whole Earth landmass everyday at 3–4 m spatial resolution. The higher spatial, as well as temporal resolution of PlanetScope imagery in comparison with Landsat-8 and Sentinel-2, makes it a valuable data source for monitoring the glacial lakes. Therefore, this paper explores the potential of the PlanetScope imagery for glacial lakes mapping with a focus on the Hindu Kush, Karakoram and Himalaya (HKKH) region. Though the revisit time of the PlanetScope imagery is short, courtesy of 130+ small satellites, this imagery contains only four bands and the imaging sensors in these small satellites exhibit varying spectral responses as well as lower dynamic range. Furthermore, the presence of cast shadows in the mountainous regions and varying spectral signature of the water pixels due to differences in composition, turbidity and depth makes it challenging to automatically and reliably extract surface water in PlanetScope imagery. Keeping in view these challenges, this work uses state of the art deep learning models for pixel-wise classification of PlanetScope imagery into the water and background pixels and compares the results with Random Forest and Support Vector Machine classifiers. The deep learning model is based on the popular U-Net architecture. We evaluate U-Net architecture similar to the original U-Net as well as a U-Net with a pre-trained EfficientNet backbone. In order to train the deep neural network, ground truth data are generated by manual digitization of the surface water in PlanetScope imagery with the aid of Very High Resolution Satellite (VHRS) imagery. The created dataset consists of more than 5000 water bodies having an area of approx.

71 {km}^{2}

in eight different sites in the HKKH region. The evaluation of the test data show that the U-Net with EfficientNet backbone achieved the highest F1 Score of 0.936. A visual comparison with the existing glacial lake inventories is then performed over the Baltoro glacier in the Karakoram range. The results show that the deep learning model detected significantly more lakes than the existing inventories, which have been derived from Landsat OLI imagery. The trained model is further evaluated on the time series PlanetScope imagery of two glacial lakes, which have resulted in an outburst flood. The output of the U-Net is also compared with the GLakeMap data. The results show that the higher spatial and temporal resolution of PlanetScope imagery is a significant advantage in the context of glacial lakes mapping and monitoring. Full article

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23 pages, 4038 KiB

Open AccessEditor’s ChoiceArticle

Visit Probability in Space–Time Prisms Based on Binomial Random Walk

by Deepak Elias and Bart Kuijpers

ISPRS Int. J. Geo-Inf. 2020, 9(9), 555; https://doi.org/10.3390/ijgi9090555 - 18 Sep 2020

Cited by 3 | Viewed by 3373

Abstract

Space–time prisms are used to model the uncertainty of space–time locations of moving objects between (for instance, GPS-measured) sample points. However, not all space–time points in a prism are equally likely and we propose a simple, formal model for the so-called “visit probability” [...] Read more.

Space–time prisms are used to model the uncertainty of space–time locations of moving objects between (for instance, GPS-measured) sample points. However, not all space–time points in a prism are equally likely and we propose a simple, formal model for the so-called “visit probability” of space–time points within prisms. The proposed mathematical framework is based on a binomial random walk within one- and two-dimensional space–time prisms. Without making any assumptions on the random walks (we do not impose any distribution nor introduce any bias towards the second anchor point), we arrive at the conclusion that binomial random walk-based visit probability in space–time prisms corresponds to a hypergeometric distribution. Full article

(This article belongs to the Special Issue Human Dynamics Research in the Age of Smart and Intelligent Systems)

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