ISPRS International Journal of Geo-Information

44 pages, 14019 KiB

Open AccessEditor’s ChoiceArticle

The City of Tomorrow from… the Data of Today

by Giorgio Agugiaro, Francisco Gabriel García González and Roberto Cavallo

ISPRS Int. J. Geo-Inf. 2020, 9(9), 554; https://doi.org/10.3390/ijgi9090554 - 16 Sep 2020

Cited by 17 | Viewed by 6097

In urban planning, a common unit of measure for housing density is the number of households per hectare. However, the actual size of the physical space occupied by a household, i.e., a dwelling, is seldom considered, neither in 2D nor in 3D. This [...] Read more.

In urban planning, a common unit of measure for housing density is the number of households per hectare. However, the actual size of the physical space occupied by a household, i.e., a dwelling, is seldom considered, neither in 2D nor in 3D. This article proposes a methodology to estimate the average size of a dwelling in existing urban areas from available open data, and to use it as one of the design parameters for new urban-development projects. The proposed unit of measure, called “living space”, includes outdoor and indoor spaces. The idea is to quantitatively analyze the city of today to help design the city of tomorrow. First, the “typical”-dwelling size and a series of Key Performance Indicators are computed for all neighborhoods from a semantic 3D city model and other spatial and non-spatial datasets. A limited number of neighborhoods is selected based on their similarities with the envisioned development plan. The size of the living space of the selected neighborhoods is successively used as a design parameter to support the computer-assisted generation of several design proposals. Each proposal can be exported, shared, and visualized online. As a test case, a to-be-planned neighborhood in Amsterdam, called “Sloterdijk One”, has been chosen. Full article

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

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19 pages, 7973 KiB

Open AccessEditor’s ChoiceArticle

Social Sensing for Urban Land Use Identification

by Adindha Surya Anugraha, Hone-Jay Chu and Muhammad Zeeshan Ali

ISPRS Int. J. Geo-Inf. 2020, 9(9), 550; https://doi.org/10.3390/ijgi9090550 - 15 Sep 2020

Cited by 15 | Viewed by 4317

Abstract

The utilization of urban land use maps can reveal the patterns of human behavior through the extraction of the socioeconomic and demographic characteristics of urban land use. Remote sensing that holds detailed and abundant information on spectral, textual, contextual, and spatial configurations is [...] Read more.

The utilization of urban land use maps can reveal the patterns of human behavior through the extraction of the socioeconomic and demographic characteristics of urban land use. Remote sensing that holds detailed and abundant information on spectral, textual, contextual, and spatial configurations is crucial to obtaining land use maps that reveal changes in the urban environment. However, social sensing is essential to revealing the socioeconomic and demographic characteristics of urban land use. This data mining approach is related to data cleaning/outlier removal and machine learning, and is used to achieve land use classification from remote and social sensing data. In bicycle and taxi density maps, the daytime destination and nighttime origin density reflects work-related land uses, including commercial and industrial areas. By contrast, the nighttime destination and daytime origin density pattern captures the pattern of residential areas. The accuracy assessment of land use classified maps shows that the integration of remote and social sensing, using the decision tree and random forest methods, yields accuracies of 83% and 86%, respectively. Thus, this approach facilitates an accurate urban land use classification. Urban land use identification can aid policy makers in linking human activities to the socioeconomic consequences of different urban land uses. Full article

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15 pages, 1623 KiB

Open AccessEditor’s ChoiceReview

GIS-Based Emotional Computing: A Review of Quantitative Approaches to Measure the Emotion Layer of Human–Environment Relationships

by Yingjing Huang, Teng Fei, Mei-Po Kwan, Yuhao Kang, Jun Li, Yizhuo Li, Xiang Li and Meng Bian

ISPRS Int. J. Geo-Inf. 2020, 9(9), 551; https://doi.org/10.3390/ijgi9090551 - 15 Sep 2020

Cited by 32 | Viewed by 7359

Abstract

In recent years, with the growing accessibility of abundant contextual emotion information, which is benefited by the numerous georeferenced user-generated content and the maturity of artificial intelligence (AI)-based emotional computing technics, the emotion layer of human–environment relationship is proposed for enriching traditional methods [...] Read more.

In recent years, with the growing accessibility of abundant contextual emotion information, which is benefited by the numerous georeferenced user-generated content and the maturity of artificial intelligence (AI)-based emotional computing technics, the emotion layer of human–environment relationship is proposed for enriching traditional methods of various related disciplines such as urban planning. This paper proposes the geographic information system (GIS)-based emotional computing concept, which is a novel framework for applying GIS methods to collective human emotion. The methodology presented in this paper consists of three key steps: (1) collecting georeferenced data containing emotion and environment information such as social media and official sites, (2) detecting emotions using AI-based emotional computing technics such as natural language processing (NLP) and computer vision (CV), and (3) visualizing and analyzing the spatiotemporal patterns with GIS tools. This methodology is a great synergy of multidisciplinary cutting-edge techniques, such as GIScience, sociology, and computer science. Moreover, it can effectively and deeply explore the connection between people and their surroundings with the help of GIS methods. Generally, the framework provides a standard workflow to calculate and analyze the new information layer for researchers, in which a measured human-centric perspective onto the environment is possible. Full article

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24 pages, 24446 KiB

Open AccessEditor’s ChoiceArticle

Drift Invariant Metric Quality Control of Construction Sites Using BIM and Point Cloud Data

by Maarten Bassier, Stan Vincke, Heinder De Winter and Maarten Vergauwen

ISPRS Int. J. Geo-Inf. 2020, 9(9), 545; https://doi.org/10.3390/ijgi9090545 - 14 Sep 2020

Cited by 29 | Viewed by 4557

Abstract

Construction site monitoring is currently performed through visual inspections and costly selective measurements. Due to the small overhead in construction projects, additional resources are scarce to frequently conduct a metric quality assessment of the constructed objects. However, contradictory, construction projects are characterised by [...] Read more.

Construction site monitoring is currently performed through visual inspections and costly selective measurements. Due to the small overhead in construction projects, additional resources are scarce to frequently conduct a metric quality assessment of the constructed objects. However, contradictory, construction projects are characterised by high failure costs which are often caused by erroneously constructed structural objects. With the upcoming use of periodic remote sensing during the different phases of the building process, new possibilities arise to advance from a selective quality analysis to an in-depth assessment of the full construction site. In this work, a novel methodology is presented to rapidly evaluate a large number of built objects on a construction site. Given a point cloud and a set of as-design BIM elements, our method evaluates the deviations between both datasets and computes the positioning errors of each object. Unlike the current state of the art, our method computes the error vectors regardless of drift, noise, clutter and (geo)referencing errors, leading to a better detection rate. The main contributions are the efficient matching of both datasets, the drift invariant metric evaluation and the intuitive visualisation of the results. The proposed analysis facilitates the identification of construction errors early on in the process, hence significantly lowering the failure costs. The application is embedded in native BIM software and visualises the objects by a simple color code, providing an intuitive indicator for the positioning accuracy of the built objects. Full article

(This article belongs to the Special Issue 3D Indoor Mapping and Modelling)

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21 pages, 9436 KiB

Open AccessEditor’s ChoiceArticle

Identification and Geographic Distribution of Accommodation and Catering Centers

by Ze Han and Wei Song

ISPRS Int. J. Geo-Inf. 2020, 9(9), 546; https://doi.org/10.3390/ijgi9090546 - 14 Sep 2020

Cited by 18 | Viewed by 4999

Abstract

As the most important manifestation of the activities of the life service industry, the reasonable layout of spatial agglomeration and dispersion of the accommodation and catering industry plays an important role in guiding the spatial structure of the urban industry and population. Applying [...] Read more.

As the most important manifestation of the activities of the life service industry, the reasonable layout of spatial agglomeration and dispersion of the accommodation and catering industry plays an important role in guiding the spatial structure of the urban industry and population. Applying the contour tree and location quotient index methods, based on points of interest (POI) data of the accommodation and catering industry in Beijing and on the identification of the spatial structure and cluster center of the accommodation and catering industry, we investigated the distribution and agglomeration characteristics of the urban accommodation and catering industry from the perspective of industrial spatial differentiation. The results show that: (1) the accommodation and catering industry in Beijing presents a polycentric agglomeration pattern in space, mainly distributed within a radius of 20 km from the city center and on a relatively large scale; areas beyond this distance contain isolated single cluster centers. (2) From the perspective of the industry, the cluster centers close to the core area of the city are characterized by the agglomeration of multiple advantageous industries, while those in the outer suburbs of the city are more prominent in a single industry. (3) From the perspective of the location quotient of cluster centers, the leisure catering industries are mainly located close to the urban centers. On the contrary, the cluster centers in the outer suburbs and counties are relatively small and dominated by restaurants and fast food industries. Commercial accommodation businesses are mainly distributed in the transportation hub centers and in entertainment and leisure areas. Full article

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

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20 pages, 988 KiB

Open AccessEditor’s ChoiceArticle

STS: Spatial–Temporal–Semantic Personalized Location Recommendation

by Wenchao Li, Xin Liu, Chenggang Yan, Guiguang Ding, Yaoqi Sun and Jiyong Zhang

ISPRS Int. J. Geo-Inf. 2020, 9(9), 538; https://doi.org/10.3390/ijgi9090538 - 8 Sep 2020

Cited by 10 | Viewed by 3213

Abstract

The rapidly growing location-based social network (LBSN) has become a promising platform for studying users’ mobility patterns. Many online applications can be built based on such studies, among which, recommending locations is of particular interest. Previous studies have shown the importance of spatial [...] Read more.

The rapidly growing location-based social network (LBSN) has become a promising platform for studying users’ mobility patterns. Many online applications can be built based on such studies, among which, recommending locations is of particular interest. Previous studies have shown the importance of spatial and temporal influences on location recommendation; however, most existing approaches build a universal spatial–temporal model for all users despite the fact that users always demonstrate heterogeneous check-in behavior patterns. In order to realize truly personalized location recommendations, we propose a Gaussian process based model for each user to systematically and non-linearly combine temporal and spatial information to predict the user’s displacement from their currently checked-in location to the next one. The locations whose distances to the user’s current checked-in location are the closest to the predicted displacement are recommended. We also propose an enhancement to take into account category information of locations for semantic-aware recommendation. A unified recommendation framework called spatial–temporal–semantic (STS) is introduced to combine displacement prediction and the semantic-aware enhancement to provide final top-N recommendation. Extensive experiments over real datasets show that the proposed STS framework significantly outperforms the state-of-the-art location recommendation models in terms of precision and mean reciprocal rank (MRR). Full article

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14 pages, 5336 KiB

Open AccessEditor’s ChoiceArticle

Modeling Diurnal Changes in Land Surface Temperature in Urban Areas under Cloudy Conditions

by Jaroslav Hofierka, Jozef Bogľarský, Štefan Kolečanský and Anastasia Enderova

ISPRS Int. J. Geo-Inf. 2020, 9(9), 534; https://doi.org/10.3390/ijgi9090534 - 7 Sep 2020

Cited by 9 | Viewed by 4042

Abstract

Land surface temperature (LST) in urban areas is a dynamic phenomenon affected by various factors such as solar irradiance, cloudiness, wind or urban morphology. The problem complexity requires a comprehensive geographic information system (GIS)-based approach. Our solution is based on solar radiation tools, [...] Read more.

Land surface temperature (LST) in urban areas is a dynamic phenomenon affected by various factors such as solar irradiance, cloudiness, wind or urban morphology. The problem complexity requires a comprehensive geographic information system (GIS)-based approach. Our solution is based on solar radiation tools, a high-resolution digital surface model of urban areas, spatially distributed data representing thermal properties of urban surfaces and meteorological conditions. The methodology is implemented in GRASS GIS using shell scripts. In these shell scripts, the r.sun solar radiation model was used to calculate the effective solar irradiance for selected time horizons during the day. The calculation accounts for attenuation of beam solar irradiance by clouds estimated by field measurements. The suggested algorithm accounts for heat storage in urban structures depending on their thermal properties and geometric configuration. Computed land surface temperature was validated using field measurements of LST in 10 locations within the study area. The study confirmed the applicability of our approach with an acceptable accuracy expressed by the root mean square error of 3.45 K. The proposed approach has the advantage of providing high spatial detail coupled with the flexibility of GIS to evaluate various geometrical and land surface properties for any daytime horizon. Full article

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22 pages, 26545 KiB

Open AccessEditor’s ChoiceArticle

Comparing Machine and Deep Learning Methods for Large 3D Heritage Semantic Segmentation

by Francesca Matrone, Eleonora Grilli, Massimo Martini, Marina Paolanti, Roberto Pierdicca and Fabio Remondino

ISPRS Int. J. Geo-Inf. 2020, 9(9), 535; https://doi.org/10.3390/ijgi9090535 - 7 Sep 2020

Cited by 116 | Viewed by 8245

Abstract

In recent years semantic segmentation of 3D point clouds has been an argument that involves different fields of application. Cultural heritage scenarios have become the subject of this study mainly thanks to the development of photogrammetry and laser scanning techniques. Classification algorithms based [...] Read more.

In recent years semantic segmentation of 3D point clouds has been an argument that involves different fields of application. Cultural heritage scenarios have become the subject of this study mainly thanks to the development of photogrammetry and laser scanning techniques. Classification algorithms based on machine and deep learning methods allow to process huge amounts of data as 3D point clouds. In this context, the aim of this paper is to make a comparison between machine and deep learning methods for large 3D cultural heritage classification. Then, considering the best performances of both techniques, it proposes an architecture named DGCNN-Mod+3Dfeat that combines the positive aspects and advantages of these two methodologies for semantic segmentation of cultural heritage point clouds. To demonstrate the validity of our idea, several experiments from the ArCH benchmark are reported and commented. Full article

(This article belongs to the Special Issue Machine Learning and Deep Learning in Cultural Heritage)

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18 pages, 6691 KiB

Open AccessEditor’s ChoiceArticle

Urban Green Plastic Cover Mapping Based on VHR Remote Sensing Images and a Deep Semi-Supervised Learning Framework

by Jiantao Liu, Quanlong Feng, Ying Wang, Bayartungalag Batsaikhan, Jianhua Gong, Yi Li, Chunting Liu and Yin Ma

ISPRS Int. J. Geo-Inf. 2020, 9(9), 527; https://doi.org/10.3390/ijgi9090527 - 2 Sep 2020

Cited by 15 | Viewed by 3860

Abstract

With the rapid process of both urban sprawl and urban renewal, large numbers of old buildings have been demolished in China, leading to wide spread construction sites, which could cause severe dust contamination. To alleviate the accompanied dust pollution, green plastic mulch has [...] Read more.

With the rapid process of both urban sprawl and urban renewal, large numbers of old buildings have been demolished in China, leading to wide spread construction sites, which could cause severe dust contamination. To alleviate the accompanied dust pollution, green plastic mulch has been widely used by local governments of China. Therefore, timely and accurate mapping of urban green plastic covered regions is of great significance to both urban environmental management and the understanding of urban growth status. However, the complex spatial patterns of the urban landscape make it challenging to accurately identify these areas of green plastic cover. To tackle this issue, we propose a deep semi-supervised learning framework for green plastic cover mapping using very high resolution (VHR) remote sensing imagery. Specifically, a multi-scale deformable convolution neural network (CNN) was exploited to learn representative and discriminative features under complex urban landscapes. Afterwards, a semi-supervised learning strategy was proposed to integrate the limited labeled data and massive unlabeled data for model co-training. Experimental results indicate that the proposed method could accurately identify green plastic-covered regions in Jinan with an overall accuracy (OA) of 91.63%. An ablation study indicated that, compared with supervised learning, the semi-supervised learning strategy in this study could increase the OA by 6.38%. Moreover, the multi-scale deformable CNN outperforms several classic CNN models in the computer vision field. The proposed method is the first attempt to map urban green plastic-covered regions based on deep learning, which could serve as a baseline and useful reference for future research. Full article

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

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19 pages, 6153 KiB

Open AccessEditor’s ChoiceArticle

A Comprehensive Measurement of Progress toward Local SDGs with Geospatial Information: Methodology and Lessons Learned

by Jun Chen, Shu Peng, Hao Chen, Xuesheng Zhao, Yuejing Ge and Zhilin Li

ISPRS Int. J. Geo-Inf. 2020, 9(9), 522; https://doi.org/10.3390/ijgi9090522 - 1 Sep 2020

Cited by 21 | Viewed by 5205

Abstract

The UN’s 2030 Agenda defined 17 Sustainable Development Goals (SDGs). In order to ensure the implementation of this global agenda, the UN proposed a systematic follow-up and review through indicator-based tracking and reporting of the progress with statistical and geospatial information toward SDGs [...] Read more.

The UN’s 2030 Agenda defined 17 Sustainable Development Goals (SDGs). In order to ensure the implementation of this global agenda, the UN proposed a systematic follow-up and review through indicator-based tracking and reporting of the progress with statistical and geospatial information toward SDGs at national, regional, and global levels. This has posed many technical and institutional challenges. Although international communities have devoted great attention to this hot topic, most of their work has focused on the conceptual design and preliminary testing. There are very few good practices for a comprehensive measurement and assessment of progress toward SDGs with the integration of statistical and geospatial information at national or local levels. This paper presents the methodology and results of a pioneer project which measured the progress toward SDGs at a local level in China (i.e., Deqing County) by integrating statistical and geospatial information. In this study, a number of technical/institutional issues have been tackled, such as the adoption of appropriate indicators at a local level, availability and acquisition of reliable data sets, and spatiotemporal analysis with a geographical perspective, interaction between SDGs and cross-sector coordination. The major conclusions are (a) the comprehensive progress toward SDGs in Deqing can be most appropriately measured and assessed by integrating geospatial and statistical information; (b) Deqing has made significant economic and social advances while maintaining a good ecological environment over the past few years. The results were released at the first United Nations World Geospatial Information Congress as a good practice and a live example to stimulate discussions. 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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20 pages, 1447 KiB

Open AccessEditor’s ChoiceArticle

An Overview of Social Media Apps and their Potential Role in Geospatial Research

by Innocensia Owuor and Hartwig H. Hochmair

ISPRS Int. J. Geo-Inf. 2020, 9(9), 526; https://doi.org/10.3390/ijgi9090526 - 1 Sep 2020

Cited by 25 | Viewed by 7690

Abstract

Social media apps provide analysts with a wide range of data to study behavioral aspects of our everyday lives and to answer societal questions. Although social media data analysis is booming, only a handful of prominent social media apps, such as Twitter, Foursquare/Swarm, [...] Read more.

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

(This article belongs to the Special Issue Advances in Social Network Analysis – Spatio-Temporal and Semantic Methods)

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17 pages, 3405 KiB

Open AccessEditor’s ChoiceArticle

School Commuting Mode Shift: A Scenario Analysis for Active School Commuting Using GIS and Online Map API

by Anqi Liu, Keone Kelobonye, Zhenqi Zhou, Qiuxia Xu, Zhen Xu and Lingyun Han

ISPRS Int. J. Geo-Inf. 2020, 9(9), 520; https://doi.org/10.3390/ijgi9090520 - 31 Aug 2020

Cited by 7 | Viewed by 4360

Abstract

Active school commuting provides a convenient opportunity to promote physical activity for children, while also reducing car dependence and its associated environmental impacts. School–home distance is a critical factor in school commuting mode choice, and longer distances have been proven to increase the [...] Read more.

Active school commuting provides a convenient opportunity to promote physical activity for children, while also reducing car dependence and its associated environmental impacts. School–home distance is a critical factor in school commuting mode choice, and longer distances have been proven to increase the likelihood of driving. In this study, we combine open-access data acquired from Baidu Map application programming interface (API) with GIS (Geographic Information System) technology to estimate the extent to which the present school–home distances can be reduced for public middle schools in Jianye District, Nanjing, China. Based on the policies for school planning and catchment allocation, we conduct a scenario analysis of school catchment reassignment whereby residences are reassigned to the nearest school. The results show that, despite the government’s ‘attending nearby school’ policy, some students in the study area are subjected to excess school–home distances, and the overall journey-to-school trips can be reduced by 20.07%, accounting for 330.8 km. This excess distance indicates the extent to which the need for vehicle travel can be potentially reduced in favor of active school commuting and a low-carbon lifestyle. Therefore, these findings provide important insights into school siting and school catchment assignment policies seeking to facilitate active school commuting, achieve educational spatial equity and reduce car dependence. Full article

(This article belongs to the Special Issue New Geospatial Science: Analytics and Management for Large Geospatial Datasets)

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24 pages, 6998 KiB

Open AccessEditor’s ChoiceArticle

Optimized Spatiotemporal Data Scheduling Based on Maximum Flow for Multilevel Visualization Tasks

by Qing Zhu, Meite Chen, Bin Feng, Yan Zhou, Maosu Li, Zhaowen Xu, Yulin Ding, Mingwei Liu, Wei Wang and Xiao Xie

ISPRS Int. J. Geo-Inf. 2020, 9(9), 518; https://doi.org/10.3390/ijgi9090518 - 28 Aug 2020

Cited by 4 | Viewed by 2959

Abstract

Massive spatiotemporal data scheduling in a cloud environment play a significant role in real-time visualization. Existing methods focus on preloading, prefetching, multithread processing and multilevel cache collaboration, which waste hardware resources and cannot fully meet the different scheduling requirements of diversified tasks. This [...] Read more.

Massive spatiotemporal data scheduling in a cloud environment play a significant role in real-time visualization. Existing methods focus on preloading, prefetching, multithread processing and multilevel cache collaboration, which waste hardware resources and cannot fully meet the different scheduling requirements of diversified tasks. This paper proposes an optimized spatiotemporal data scheduling method based on maximum flow for multilevel visualization tasks. First, the spatiotemporal data scheduling framework is designed based on the analysis of three levels of visualization tasks. Second, the maximum flow model is introduced to construct the spatiotemporal data scheduling topological network, and the calculation algorithm of the maximum data flow is presented in detail. Third, according to the change in the data access hotspot, the adaptive caching algorithm and maximum flow model parameter switching strategy are devised to achieve task-driven spatiotemporal data optimization scheduling. Compared with two typical methods of first come first serve (FCFS) and priority scheduling algorithm (PSA) by simulating visualization tasks at three levels, the proposed maximum flow scheduling (MFS) method has been proven to be more flexible and efficient in adjusting each spatiotemporal data flow type as needed, and the method realizes spatiotemporal data flow global optimization under limited hardware resources in the cloud environment. Full article

(This article belongs to the Special Issue New Geospatial Science: Analytics and Management for Large Geospatial Datasets)

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26 pages, 2639 KiB

Open AccessEditor’s ChoiceArticle

Knowing My Village from the Sky: A Collaborative Spatial Learning Framework to Integrate Spatial Knowledge of Stakeholders in Achieving Sustainable Development Goals

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

ISPRS Int. J. Geo-Inf. 2020, 9(9), 515; https://doi.org/10.3390/ijgi9090515 - 26 Aug 2020

Cited by 19 | Viewed by 4522

Abstract

Geospatial data is urgently needed in decision-making processes to achieve Sustainable Development Goals (SDGs) at global, national, regional and local scales. While the advancement of geo-technologies to obtain or produce geospatial data has become faster and more affordable, many countries in the global [...] Read more.

Geospatial data is urgently needed in decision-making processes to achieve Sustainable Development Goals (SDGs) at global, national, regional and local scales. While the advancement of geo-technologies to obtain or produce geospatial data has become faster and more affordable, many countries in the global south still experience a geospatial data scarcity at the rural level due to complex geographical terrains, weak coordination among institutions and a lack of knowledge and technologies to produce visualised geospatial data like maps. We proposed a collaborative spatial learning framework that integrates the spatial knowledge of stakeholders to obtain geospatial data. By conducting participatory mapping workshops in three villages in the Deli Serdang district in Indonesia, we tested the framework in terms of facilitating communication and collaboration of the village stakeholders while also supporting knowledge co-production and social learning among them. Satellite images were used in digital and non-digital mapping workshops to support village stakeholders to produce proper village maps while fulfilling the SDGs’ emphasis to make geospatial data available through a participatory approach. Full article

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25 pages, 7982 KiB

Open AccessEditor’s ChoiceArticle

A New Algorithm for Calculating the Flow Path Curvature (C) from the Square-Grid Digital Elevation Model (DEM)

by Qianjiao Wu, Yumin Chen, Hongyan Zhou, Shujie Chen and Han Wang

ISPRS Int. J. Geo-Inf. 2020, 9(9), 510; https://doi.org/10.3390/ijgi9090510 - 24 Aug 2020

Cited by 3 | Viewed by 3130

Abstract

This paper proposes a flow-path-network-based (FPN-based) algorithm, constructed from a square-grid digital elevation model (DEM) to improve the simulation of the flow path curvature (C). First, the flow-path network model was utilized to obtain an FPN. Then, a flow-path-network-flow-path-curvature (FPN-C) algorithm [...] Read more.

This paper proposes a flow-path-network-based (FPN-based) algorithm, constructed from a square-grid digital elevation model (DEM) to improve the simulation of the flow path curvature (C). First, the flow-path network model was utilized to obtain an FPN. Then, a flow-path-network-flow-path-curvature (FPN-C) algorithm was proposed to estimate C from the FPN. The experiments consisted of two sections: (1) quantitatively evaluating the accuracy using 5 m DEMs generated from the mathematical ellipsoid and Gauss models, and (2) qualitatively assessing the accuracy using a 30 m DEM of a real-world complex region. The three algorithms proposed by Evans (1980), Zevenbergen and Throne (1987), and Shary (1995) were used to validate the accuracy of the new algorithm. The results demonstrate that the C value of the proposed algorithm was generally closer to the theoretical C value derived from two mathematical surfaces. The root mean standard error (RMSE) and mean absolute error (MAE) of the new method are 0.0014 and 0.0002 m, reduced by 42% and 82% of that of the third algorithm on the ellipsoid surface, respectively. The RMSE and MAE of the presented method are 0.0043 and 0.0025 m at best, reduced by up to 35% and 14% of that of the former two algorithms on the Gauss surface, respectively. The proposed algorithm generally produces better spatial distributions of C on different terrain surfaces. Full article

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18 pages, 3908 KiB

Open AccessEditor’s ChoiceArticle

OSMWatchman: Learning How to Detect Vandalized Contributions in OSM Using a Random Forest Classifier

by Quy Thy Truong, Guillaume Touya and Cyril de Runz

ISPRS Int. J. Geo-Inf. 2020, 9(9), 504; https://doi.org/10.3390/ijgi9090504 - 22 Aug 2020

Cited by 10 | Viewed by 3765

Abstract

Though Volunteered Geographic Information (VGI) has the advantage of providing free open spatial data, it is prone to vandalism, which may heavily decrease the quality of these data. Therefore, detecting vandalism in VGI may constitute a first way of assessing the data in [...] Read more.

Though Volunteered Geographic Information (VGI) has the advantage of providing free open spatial data, it is prone to vandalism, which may heavily decrease the quality of these data. Therefore, detecting vandalism in VGI may constitute a first way of assessing the data in order to improve their quality. This article explores the ability of supervised machine learning approaches to detect vandalism in OpenStreetMap (OSM) in an automated way. For this purpose, our work includes the construction of a corpus of vandalism data, given that no OSM vandalism corpus is available so far. Then, we investigate the ability of random forest methods to detect vandalism on the created corpus. Experimental results show that random forest classifiers perform well in detecting vandalism in the same geographical regions that were used for training the model and has more issues with vandalism detection in “unfamiliar regions”. Full article

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

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33 pages, 25960 KiB

Open AccessEditor’s ChoiceArticle

Tools for BIM-GIS Integration (IFC Georeferencing and Conversions): Results from the GeoBIM Benchmark 2019

by Francesca Noardo, Lars Harrie, Ken Arroyo Ohori, Filip Biljecki, Claire Ellul, Thomas Krijnen, Helen Eriksson, Dogus Guler, Dean Hintz, Mojgan A. Jadidi, Maria Pla, Santi Sanchez, Ville-Pekka Soini, Rudi Stouffs, Jernej Tekavec and Jantien Stoter

ISPRS Int. J. Geo-Inf. 2020, 9(9), 502; https://doi.org/10.3390/ijgi9090502 - 21 Aug 2020

Cited by 76 | Viewed by 11106

Abstract

The integration of 3D city models with Building Information Models (BIM), coined as GeoBIM, facilitates improved data support to several applications, e.g., 3D map updates, building permits issuing, detailed city analysis, infrastructure design, context-based building design, to name a few. To solve the [...] Read more.

The integration of 3D city models with Building Information Models (BIM), coined as GeoBIM, facilitates improved data support to several applications, e.g., 3D map updates, building permits issuing, detailed city analysis, infrastructure design, context-based building design, to name a few. To solve the integration, several issues need to be tackled and solved, i.e., harmonization of features, interoperability, format conversions, integration of procedures. The GeoBIM benchmark 2019, funded by ISPRS and EuroSDR, evaluated the state of implementation of tools addressing some of those issues. In particular, in the part of the benchmark described in this paper, the application of georeferencing to Industry Foundation Classes (IFC) models and making consistent conversions between 3D city models and BIM are investigated, considering the OGC CityGML and buildingSMART IFC as reference standards. In the benchmark, sample datasets in the two reference standards were provided. External volunteers were asked to describe and test georeferencing procedures for IFC models and conversion tools between CityGML and IFC. From the analysis of the delivered answers and processed datasets, it was possible to notice that while there are tools and procedures available to support georeferencing and data conversion, comprehensive definition of the requirements, clear rules to perform such two tasks, as well as solid technological solutions implementing them, are still lacking in functionalities. Those specific issues can be a sensible starting point for planning the next GeoBIM integration agendas. Full article

(This article belongs to the Special Issue Integration of BIM and GIS for Built Environment Applications)

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20 pages, 2471 KiB

Open AccessEditor’s ChoiceArticle

Semantic Integration of Raster Data for Earth Observation: An RDF Dataset of Territorial Unit Versions with their Land Cover

by Ba-Huy Tran, Nathalie Aussenac-Gilles, Catherine Comparot and Cassia Trojahn

ISPRS Int. J. Geo-Inf. 2020, 9(9), 503; https://doi.org/10.3390/ijgi9090503 - 21 Aug 2020

Cited by 21 | Viewed by 4098

Abstract

Semantic technologies are at the core of Earth Observation (EO) data integration, by providing an infrastructure based on RDF representation and ontologies. Because many EO data come in raster files, this paper addresses the integration of data calculated from rasters as a way [...] Read more.

Semantic technologies are at the core of Earth Observation (EO) data integration, by providing an infrastructure based on RDF representation and ontologies. Because many EO data come in raster files, this paper addresses the integration of data calculated from rasters as a way of qualifying geographic units through their spatio-temporal features. We propose (i) a modular ontology that contributes to the semantic and homogeneous description of spatio-temporal data to qualify predefined areas; (ii) a Semantic Extraction, Transformation, and Load (ETL) process, allowing us to extract data from rasters and to link them to the corresponding spatio-temporal units and features; and (iii) a resulting dataset that is published as an RDF triplestore, exposed through a SPARQL endpoint, and exploited by a semantic interface. We illustrate the integration process with raster files providing the land cover of a specific French winery geographic area, its administrative units, and their land registers over different periods. The results have been evaluated with regards to three use-cases exploiting these EO data: integration of time series observations; EO process guidance; and data cross-comparison. Full article

(This article belongs to the Special Issue Geographic Information Extraction and Retrieval)

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18 pages, 4687 KiB

Open AccessEditor’s ChoiceArticle

Development of a Novel Framework to Propose New Strategies for Automated External Defibrillators Deployment Targeting Residential Out-Of-Hospital Cardiac Arrests: Application to the City of Milan

by Gianquintieri Lorenzo, Brovelli Maria Antonia, Brambilla Piero Maria, Pagliosa Andrea, Villa Guido Francesco and Caiani Enrico Gianluca

ISPRS Int. J. Geo-Inf. 2020, 9(8), 491; https://doi.org/10.3390/ijgi9080491 - 17 Aug 2020

Cited by 9 | Viewed by 4075

Abstract

Public Access Defibrillation (PAD) is the leading strategy in reducing time to first defibrillation in cases of Out-Of-Hospital Cardiac Arrest (OHCA), but PAD programs are underperforming considering their potentiality. Our aim was to develop an analysis and optimization framework, exploiting georeferenced information processed [...] Read more.

Public Access Defibrillation (PAD) is the leading strategy in reducing time to first defibrillation in cases of Out-Of-Hospital Cardiac Arrest (OHCA), but PAD programs are underperforming considering their potentiality. Our aim was to develop an analysis and optimization framework, exploiting georeferenced information processed with Geographic Information Systems (GISs), specifically targeting residential OHCAs. The framework, based on an historical database of OHCAs, location of Automated External Defibrillators (AEDs), topographic and demographic information, proposes new strategies for AED deployment focusing on residential OHCAs, where performance assessment was evaluated using AEDs “catchment area” (area that can be reached within 6 min walk along streets). The proposed framework was applied to the city of Milan, Lombardy (Italy), considering the OHCA database of four years (2015–2018), including 8152 OHCA, of which 7179 (88.06%) occurred in residential locations. The proposed strategy for AEDs deployment resulted more effective compared to the existing distribution, with a significant improvement (from 41.77% to 73.33%) in OHCAs’ spatial coverage. Further improvements were simulated with different cost scenarios, resulting in more cost-efficient solutions. Results suggest that PAD programs, either in brand-new territories or in further improvements, could significantly benefit from a comprehensive planning, based on mathematical models for risk mapping and on geographical tools. Full article

(This article belongs to the Special Issue GIS in Healthcare)

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16 pages, 6387 KiB

Open AccessEditor’s ChoiceArticle

Index for the Consistent Measurement of Spatial Heterogeneity for Large-Scale Land Cover Datasets

by Jing Yu, Shu Peng, Weiwei Zhang and Shun Kang

ISPRS Int. J. Geo-Inf. 2020, 9(8), 483; https://doi.org/10.3390/ijgi9080483 - 11 Aug 2020

Cited by 5 | Viewed by 5982

Abstract

Recognizing land cover heterogeneity is essential for the assessment of spatial patterns to guide conservation planning. One of the top research priorities is the quantification of land cover heterogeneity using effective landscape metrics. However, due to the diversity of land cover types and [...] Read more.

Recognizing land cover heterogeneity is essential for the assessment of spatial patterns to guide conservation planning. One of the top research priorities is the quantification of land cover heterogeneity using effective landscape metrics. However, due to the diversity of land cover types and their varied distribution, a consistent, larger-scale, and standardized framework for heterogeneity information extraction from this complex perspective is still lacking. Consequently, we developed a new Land Cover Complexity Index (LCCI), which is based on information-theory. The LCCI contains two foundational aspects of heterogeneity, composition and configuration, thereby capturing more comprehensive information on land cover patterns than any single metric approach. In this study, we compare the performance of the LCCI with that of other landscape metrics at two different scales, and the results show that our newly developed indicator more accurately characterizes and distinguishes different land cover patterns. LCCI provides an alternative way to measure the spatial variation of land cover distribution. Classification maps of land cover heterogeneity generated using the LCCI provide valuable insights and implications for regional conservation planning. Thus, the LCCI is shown to be a consistent indicator for the quantification of land cover heterogeneity that functions in an adaptive way by simultaneously considering both composition and configuration. Full article

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

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26 pages, 6256 KiB

Open AccessEditor’s ChoiceArticle

A Framework Uniting Ontology-Based Geodata Integration and Geovisual Analytics

by Linfang Ding, Guohui Xiao, Diego Calvanese and Liqiu Meng

ISPRS Int. J. Geo-Inf. 2020, 9(8), 474; https://doi.org/10.3390/ijgi9080474 - 28 Jul 2020

Cited by 19 | Viewed by 6602

Abstract

In a variety of applications relying on geospatial data, getting insights into heterogeneous geodata sources is crucial for decision making, but often challenging. The reason is that it typically requires combining information coming from different sources via data integration techniques, and then making [...] Read more.

In a variety of applications relying on geospatial data, getting insights into heterogeneous geodata sources is crucial for decision making, but often challenging. The reason is that it typically requires combining information coming from different sources via data integration techniques, and then making sense out of the combined data via sophisticated analysis methods. To address this challenge we rely on two well-established research areas: data integration and geovisual analytics, and propose to adopt an ontology-based approach to decouple the challenges of data access and analytics. Our framework consists of two modules centered around an ontology: (1) an ontology-based data integration (OBDI) module, in which mappings specify the relationship between the underlying data and a domain ontology; (2) a geovisual analytics (GeoVA) module, designed for the exploration of the integrated data, by explicitly making use of standard ontologies. In this framework, ontologies play a central role by providing a coherent view over the heterogeneous data, and by acting as a mediator for visual analysis tasks. We test our framework in a scenario for the investigation of the spatiotemporal patterns of meteorological and traffic data from several open data sources. Initial studies show that our approach is feasible for the exploration and understanding of heterogeneous geospatial data. Full article

(This article belongs to the Special Issue On Denotation and Connotation in Web Semantics, Collaboration and Metadata)

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22 pages, 2838 KiB

Open AccessEditor’s ChoiceArticle

Measuring Community Disaster Resilience in the Conterminous Coastal United States

by Shaikh Abdullah Al Rifat and Weibo Liu

ISPRS Int. J. Geo-Inf. 2020, 9(8), 469; https://doi.org/10.3390/ijgi9080469 - 23 Jul 2020

Cited by 59 | Viewed by 8932

Abstract

In recent years, building resilient communities to disasters has become one of the core objectives in the field of disaster management globally. Despite being frequently targeted and severely impacted by disasters, the geographical extent in studying disaster resilience of the coastal communities of [...] Read more.

In recent years, building resilient communities to disasters has become one of the core objectives in the field of disaster management globally. Despite being frequently targeted and severely impacted by disasters, the geographical extent in studying disaster resilience of the coastal communities of the United States (US) has been limited. In this study, we developed a composite community disaster resilience index (CCDRI) for the coastal communities of the conterminous US that considers different dimensions of disaster resilience. The resilience variables used to construct the CCDRI were justified by examining their influence on disaster losses using ordinary least squares (OLS) and geographically weighted regression (GWR) models. Results suggest that the CCDRI score ranges from −12.73 (least resilient) to 8.69 (most resilient), and northeastern communities are comparatively more resilient than southeastern communities in the study area. Additionally, resilience components used in this study have statistically significant impact on minimizing disaster losses. The GWR model performs much better in explaining the variances while regressing the disaster property damage against the resilience components (explains 72% variance) than the OLS (explains 32% variance) suggesting that spatial variations of resilience components should be accounted for an effective disaster management program. Moreover, findings from this study could provide local emergency managers and decision-makers with unique insights for enhancing overall community resilience to disasters and minimizing disaster impacts in the study area. Full article

(This article belongs to the Special Issue Geomatic Applications to Coastal Research: Challenges and New Developments)

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19 pages, 8220 KiB

Open AccessEditor’s ChoiceArticle

An Open-Source Framework of Generating Network-Based Transit Catchment Areas by Walking

by Diao Lin, Ruoxin Zhu, Jian Yang and Liqiu Meng

ISPRS Int. J. Geo-Inf. 2020, 9(8), 467; https://doi.org/10.3390/ijgi9080467 - 22 Jul 2020

Cited by 7 | Viewed by 3489

Abstract

The transit catchment area is an important concept for public transport planning. This study proposes a methodological framework to generate network-based transit catchment areas by walking. Three components of the framework, namely subgraph construction, extended shortest path tree construction and contour generation are [...] Read more.

The transit catchment area is an important concept for public transport planning. This study proposes a methodological framework to generate network-based transit catchment areas by walking. Three components of the framework, namely subgraph construction, extended shortest path tree construction and contour generation are presented step by step. Methods on how to generalize the framework to the cases of the directed road network and non-point facilities are developed. The implementation of the framework is provided as an open-source project. Using metro stations in Shanghai as a case study, we illustrate the feasibility of the proposed framework. Experiments show that the proposed method generates catchment areas of high geospatial accuracy and significantly increases computational efficiency. The open-source program can be applied to support research related to transit catchment areas and has the potential to be extended to include more routing-related factors. Full article

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22 pages, 11839 KiB

Open AccessEditor’s ChoiceArticle

Post-Earthquake Recovery Phase Monitoring and Mapping Based on UAS Data

by Nikolaos Soulakellis, Christos Vasilakos, Stamatis Chatzistamatis, Dimitris Kavroudakis, Georgios Tataris, Ermioni-Eirini Papadopoulou, Apostolos Papakonstantinou, Olga Roussou and Themistoklis Kontos

ISPRS Int. J. Geo-Inf. 2020, 9(7), 447; https://doi.org/10.3390/ijgi9070447 - 17 Jul 2020

Cited by 14 | Viewed by 4155

Abstract

Geoinformatics plays an essential role during the recovery phase of a post-earthquake situation. The aim of this paper is to present the methodology followed and the results obtained by the utilization of Unmanned Aircraft Systems (UASs) 4K-video footage processing and the automation of [...] Read more.

Geoinformatics plays an essential role during the recovery phase of a post-earthquake situation. The aim of this paper is to present the methodology followed and the results obtained by the utilization of Unmanned Aircraft Systems (UASs) 4K-video footage processing and the automation of geo-information methods targeted at both monitoring the demolition process and mapping the demolished buildings. The field campaigns took place on the traditional settlement of Vrisa (Lesvos, Greece), which was heavily damaged by a strong earthquake (Mw=6.3) on June 12th, 2017. For this purpose, a flight campaign took place on 3rd February 2019 for collecting aerial 4K video footage using an Unmanned Aircraft. The Structure from Motion (SfM) method was applied on frames which derived from the 4K video footage, for producing accurate and very detailed 3D point clouds, as well as the Digital Surface Model (DSM) of the building stock of the Vrisa traditional settlement, twenty months after the earthquake. This dataset has been compared with the corresponding one which derived from 25th July 2017, a few days after the earthquake. Two algorithms have been developed for detecting the demolished buildings of the affected area, based on the DSMs and 3D point clouds, correspondingly. The results obtained have been tested through field studies and demonstrate that this methodology is feasible and effective in building demolition detection, giving very accurate results (97%) and, in parallel, is easily applicable and suit well for rapid demolition mapping during the recovery phase of a post-earthquake scenario. The significant advantage of the proposed methodology is its ability to provide reliable results in a very low cost and time-efficient way and to serve all stakeholders and national and local organizations that are responsible for post-earthquake management. Full article

(This article belongs to the Special Issue Geomatics and Geo-Information in Earthquake Studies)

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17 pages, 5758 KiB

Open AccessEditor’s ChoiceArticle

An Accurate Matching Method for Projecting Vector Data into Surveillance Video to Monitor and Protect Cultivated Land

by Zhenfeng Shao, Congmin Li, Deren Li, Orhan Altan, Lei Zhang and Lin Ding

ISPRS Int. J. Geo-Inf. 2020, 9(7), 448; https://doi.org/10.3390/ijgi9070448 - 17 Jul 2020

Cited by 51 | Viewed by 4313

Abstract

The integration of intelligent video surveillance and GIS (geograhical information system) data provides a new opportunity for monitoring and protecting cultivated land. For a GIS-based video monitoring system, the prerequisite is to align the GIS data with video image. However, existing methods or [...] Read more.

The integration of intelligent video surveillance and GIS (geograhical information system) data provides a new opportunity for monitoring and protecting cultivated land. For a GIS-based video monitoring system, the prerequisite is to align the GIS data with video image. However, existing methods or systems have their own shortcomings when implemented in monitoring cultivated land. To address this problem, this paper aims to propose an accurate matching method for projecting vector data into surveillance video, considering the topographic characteristics of cultivated land in plain area. Once an adequate number of control points are identified from 2D (two-dimensional) GIS data and the selected reference video image, the alignment of 2D GIS data and PTZ (pan-tilt-zoom) video frames can be realized by automatic feature matching method. Based on the alignment results, we can easily identify the occurrence of farmland destruction by visually inspecting the image content covering the 2D vector area. Furthermore, a prototype of intelligent surveillance video system for cultivated land is constructed and several experiments are conducted to validate the proposed approach. Experimental results show that the proposed alignment methods can achieve a high accuracy and satisfy the requirements of cultivated land monitoring. Full article

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21 pages, 5753 KiB

Open AccessEditor’s ChoiceArticle

High Resolution Viewscape Modeling Evaluated Through Immersive Virtual Environments

by Payam Tabrizian, Anna Petrasova, Perver K. Baran, Jelena Vukomanovic, Helena Mitasova and Ross K. Meentemeyer

ISPRS Int. J. Geo-Inf. 2020, 9(7), 445; https://doi.org/10.3390/ijgi9070445 - 17 Jul 2020

Cited by 8 | Viewed by 4411

Abstract

Visual characteristics of urban environments influence human perception and behavior, including choices for living, recreation and modes of transportation. Although geospatial visualizations hold great potential to better inform urban planning and design, computational methods are lacking to realistically measure and model urban and [...] Read more.

Visual characteristics of urban environments influence human perception and behavior, including choices for living, recreation and modes of transportation. Although geospatial visualizations hold great potential to better inform urban planning and design, computational methods are lacking to realistically measure and model urban and parkland viewscapes at sufficiently fine-scale resolution. In this study, we develop and evaluate an integrative approach to measuring and modeling fine-scale viewscape characteristics of a mixed-use urban environment, a city park. Our viewscape approach improves the integration of geospatial and perception elicitation techniques by combining high-resolution lidar-based digital surface models, visual obstruction, and photorealistic immersive virtual environments (IVEs). We assessed the realism of our viewscape models by comparing metrics of viewscape composition and configuration to human subject evaluations of IVEs across multiple landscape settings. We found strongly significant correlations between viewscape metrics and participants’ perceptions of viewscape openness and naturalness, and moderately strong correlations with landscape complexity. These results suggest that lidar-enhanced viewscape models can adequately represent visual characteristics of fine-scale urban environments. Findings also indicate the existence of relationships between human perception and landscape pattern. Our approach allows urban planners and designers to model and virtually evaluate high-resolution viewscapes of urban parks and natural landscapes with fine-scale details never before demonstrated. Full article

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

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18 pages, 12046 KiB

Open AccessEditor’s ChoiceArticle

A Machine Learning Approach to Delineating Neighborhoods from Geocoded Appraisal Data

by Rao Hamza Ali, Josh Graves, Stanley Wu, Jenny Lee and Erik Linstead

ISPRS Int. J. Geo-Inf. 2020, 9(7), 451; https://doi.org/10.3390/ijgi9070451 - 17 Jul 2020

Cited by 3 | Viewed by 4487

Abstract

Identification of neighborhoods is an important, financially-driven topic in real estate. It is known that the real estate industry uses ZIP (postal) codes and Census tracts as a source of land demarcation to categorize properties with respect to their price. These demarcated boundaries [...] Read more.

Identification of neighborhoods is an important, financially-driven topic in real estate. It is known that the real estate industry uses ZIP (postal) codes and Census tracts as a source of land demarcation to categorize properties with respect to their price. These demarcated boundaries are static and are inflexible to the shift in the real estate market and fail to represent its dynamics, such as in the case of an up-and-coming residential project. Delineated neighborhoods are also used in socioeconomic and demographic analyses where statistics are computed at a neighborhood level. Current practices of delineating neighborhoods have mostly ignored the information that can be extracted from property appraisals. This paper demonstrates the potential of using only the distance between subjects and their comparable properties, identified in an appraisal, to delineate neighborhoods that are composed of properties with similar prices and features. Using spatial filters, we first identify regions with the most appraisal activity, and through the application of a spatial clustering algorithm, generate neighborhoods composed of properties sharing similar characteristics. Through an application of bootstrapped linear regression, we find that delineating neighborhoods using geolocation of subjects and comparable properties explains more variation in a property’s features, such as valuation, square footage, and price per square foot, than ZIP codes or Census tracts. We also discuss the ability of the neighborhoods to grow and shrink over the years, due to shifts in each housing submarket. Full article

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36 pages, 7836 KiB

Open AccessEditor’s ChoiceArticle

Measuring Accessibility to Various ASFs from Public Transit using Spatial Distance Measures in Indian Cities

by Pavan Teja Yenisetty and Pankaj Bahadure

ISPRS Int. J. Geo-Inf. 2020, 9(7), 446; https://doi.org/10.3390/ijgi9070446 - 17 Jul 2020

Cited by 12 | Viewed by 5267

Abstract

Nowadays, accessibility to facilities is one of the most discussed issues in sustainable urban planning. In the current research, two spatial distance accessibility measures were applied to evaluate the accessibility to amenities, services, and facilities (ASFs) from public transit (PT) by walking distance [...] Read more.

Nowadays, accessibility to facilities is one of the most discussed issues in sustainable urban planning. In the current research, two spatial distance accessibility measures were applied to evaluate the accessibility to amenities, services, and facilities (ASFs) from public transit (PT) by walking distance in six Indian cities. The first stage accounts for distance measures using the Euclidean distance with a new methodical approach derived from the built-up area with a spatial resolution of 30 m from Landsat data, and for the network distance method, the actual road distances using OpenStreetMap (OSM) for different threshold ranges of distances were derived. Meanwhile, in the second stage, indicators such as built-up area, network connectivity, and network density with the percentage of ASFs are evaluated and combined for normalization process for ranking the city. The present study assesses the accessibility to various ASFs from PT at city level and explores whether the actual road network access (by measuring distance) in Indian cities is contributing to a high level of accessibility. It adopts a unique approach using statistical tools while assessing both Euclidean and network distances. It models a framework for overall benchmarking in all six cities by ranking them for their accessibility. The results show various scenarios in terms of the rank of cities, which had been strongly affected by distance metrics (Euclidean vs. network) and thus emphasize the careful use of these measures as supporting tools for planning. This facilitates the identification of the local barriers and problems with network access that affect the actual distance. This unique approach can help policymakers to identify the gaps in PT coverage for reaching ASFs. Furthermore, it helps in crucial implementation by strategic planning that can be achieved using these distance criteria. Full article

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

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19 pages, 3164 KiB

Open AccessEditor’s ChoiceArticle

Capacitated Refuge Assignment for Speedy and Reliable Evacuation

by Takanori Hara, Masahiro Sasabe, Taiki Matsuda and Shoji Kasahara

ISPRS Int. J. Geo-Inf. 2020, 9(7), 442; https://doi.org/10.3390/ijgi9070442 - 16 Jul 2020

Cited by 5 | Viewed by 3127

Abstract

When a large-scale disaster occurs, each evacuee should move to an appropriate refuge in a speedy and safe manner. Most of the existing studies on the refuge assignment consider the speediness of evacuation and refuge capacity while the safety of evacuation is not [...] Read more.

When a large-scale disaster occurs, each evacuee should move to an appropriate refuge in a speedy and safe manner. Most of the existing studies on the refuge assignment consider the speediness of evacuation and refuge capacity while the safety of evacuation is not taken into account. In this paper, we propose a refuge assignment scheme that considers both the speediness and safety of evacuation under the refuge capacity constraint. We first formulate the refuge assignment problem as a two-step integer linear program (ILP). Since the two-step ILP requires route candidates between evacuees and their possible refuges, we further propose a speedy and reliable route selection scheme as an extension of the existing route selection scheme. Through numerical results using the actual data of Arako district of Nagoya city in Japan, we show that the proposed scheme can improve the average route reliability among evacuees by 13.6% while suppressing the increase of the average route length among evacuees by 7.3%, compared with the distance-based route selection and refuge assignment. In addition, we also reveal that the current refuge capacity is not enough to support speedy and reliable evacuation for the residents. Full article

(This article belongs to the Special Issue Geomatics and Geo-Information in Earthquake Studies)

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25 pages, 50699 KiB

Open AccessEditor’s ChoiceArticle

Multidimensional Visualization and Processing of Big Open Urban Geospatial Data on the Web

by Candan Eylül Kilsedar and Maria Antonia Brovelli

ISPRS Int. J. Geo-Inf. 2020, 9(7), 434; https://doi.org/10.3390/ijgi9070434 - 11 Jul 2020

Cited by 17 | Viewed by 12480

Abstract

The focus of this research is addressing a subset of the geovisualization (i.e., geographic visualization) challenges identified in the literature, namely multidimensional vector and raster geospatial data visualization. Moreover, the work implements an approach for multidimensional raster geospatial data processing. The results of [...] Read more.

The focus of this research is addressing a subset of the geovisualization (i.e., geographic visualization) challenges identified in the literature, namely multidimensional vector and raster geospatial data visualization. Moreover, the work implements an approach for multidimensional raster geospatial data processing. The results of this research are provided through a geoportal comprised of multiple applications that are related to 3D visualization of cities, ground deformation, land use and land cover and mobility. In a subset of the applications, the datasets handled are considered to be large in volume. The geospatial data were visualized on dynamic and interactive virtual globes to enable visual exploration. The geoportal is available on the web to enable cross-platform access to it. Furthermore, the geoportal was developed employing open standards, free and open source software (FOSS) and open data, most importantly to ensure interoperability and reduce the barriers to access it. The geoportal brings together various datasets, different both in terms of context and format employing numerous technologies. As a result, the existing web technologies for geovisualization and geospatial data processing were examined and exemplary and innovative software was developed to extend the state of the art. Full article

(This article belongs to the Special Issue Geodata Science and Spatial Analysis in Urban Studies)

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22 pages, 4697 KiB

Open AccessEditor’s ChoiceArticle

Analyzing Links between Spatio-Temporal Metrics of Built-Up Areas and Socio-Economic Indicators on a Semi-Global Scale

by Marta Sapena, Luis A. Ruiz and Hannes Taubenböck

ISPRS Int. J. Geo-Inf. 2020, 9(7), 436; https://doi.org/10.3390/ijgi9070436 - 11 Jul 2020

Cited by 10 | Viewed by 4941

Abstract

Manifold socio-economic processes shape the built and natural elements in urban areas. They thus influence both the living environment of urban dwellers and sustainability in many dimensions. Monitoring the development of the urban fabric and its relationships with socio-economic and environmental processes will [...] Read more.

Manifold socio-economic processes shape the built and natural elements in urban areas. They thus influence both the living environment of urban dwellers and sustainability in many dimensions. Monitoring the development of the urban fabric and its relationships with socio-economic and environmental processes will help to elucidate their linkages and, thus, aid in the development of new strategies for more sustainable development. In this study, we identified empirical and significant relationships between income, inequality, GDP, air pollution and employment indicators and their change over time with the spatial organization of the built and natural elements in functional urban areas. We were able to demonstrate this in 32 countries using spatio-temporal metrics, using geoinformation from databases available worldwide. We employed random forest regression, and we were able to explain 32% to 68% of the variability of socio-economic variables. This confirms that spatial patterns and their change are linked to socio-economic indicators. We also identified the spatio-temporal metrics that were more relevant in the models: we found that urban compactness, concentration degree, the dispersion index, the densification of built-up growth, accessibility and land-use/land-cover density and change could be used as proxies for some socio-economic indicators. This study is a first and fundamental step for the identification of such relationships at a global scale. The proposed methodology is highly versatile, the inclusion of new datasets is straightforward, and the increasing availability of multi-temporal geospatial and socio-economic databases is expected to empirically boost the study of these relationships from a multi-temporal perspective in the near future. Full article

(This article belongs to the Special Issue Geodata Science and Spatial Analysis in Urban Studies)

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26 pages, 10737 KiB

Open AccessEditor’s ChoiceArticle

Research on the Colors of Military Symbols in Digital Situation Maps Based on Event-Related Potential Technology

by Yafeng Niu, Guorui Ma, Wei Xue, Chengqi Xue, Tianyu Zhou, Yue Gao, Hongrui Zuo and Tao Jin

ISPRS Int. J. Geo-Inf. 2020, 9(7), 420; https://doi.org/10.3390/ijgi9070420 - 30 Jun 2020

Cited by 5 | Viewed by 6479

Abstract

Under the trend of increasingly informationalized military operations and the increasing maneuverability of combat units, military commanders have put forward higher requirements for the accuracy and promptness of information on battlefield situation maps. Based on the sea battlefield, this paper studies the pros [...] Read more.

Under the trend of increasingly informationalized military operations and the increasing maneuverability of combat units, military commanders have put forward higher requirements for the accuracy and promptness of information on battlefield situation maps. Based on the sea battlefield, this paper studies the pros and cons of the color matching of military symbols on sea situation maps. Fifteen colors, where each Hue had five colors, were chosen using the Munsell Color System according to Chroma axis and the Value axis on a span of 2 and 4. By collecting and analyzing the P300 EEG data, reaction time data, and accuracy data of 20 subjects, a better color matching selection of military symbols on pure color (L = 85, a = −10, and b = −23) sea situation maps is put forward, and the conclusions are as follows: (1) the different colors all cause the P300 component in EEG experiment. Among them, the P300 amplitude that is caused by military symbols with lower Chroma is smaller and the latency is shorter, indicating that the user experience and efficiency of low Chroma color symbols will be better than those with high Chroma color symbols. (2) High Value color map military symbols cause higher P300 amplitude and longer latency. According to the results above, this paper puts forward three optimized colors, namely, blue (L = 39, a = 20, and b = −49), green (L = 80, a = −72, and b = 72), and red (L = 20, a = 41, and b = 28). Additionally, three map interfaces were designed to confirm the validity of these colors. By means of applying the NASA-TLX (Task Load Index) scale to evaluate the task load of the confirmation interfaces, it can be concluded that these three optimized colors are preferred by users who are skilled in GIS and interface design. Therefore, the research conclusion of this paper can provide important reference values for military map design, which is helpful in shortening the identification and judgment time during the use of situation maps and it can improve users’ operation performance. Full article

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23 pages, 4154 KiB

Open AccessEditor’s ChoiceArticle

Numbers on Thematic Maps: Helpful Simplicity or Too Raw to Be Useful for Map Reading?

by Jolanta Korycka-Skorupa and Izabela Małgorzata Gołębiowska

ISPRS Int. J. Geo-Inf. 2020, 9(7), 415; https://doi.org/10.3390/ijgi9070415 - 28 Jun 2020

Cited by 12 | Viewed by 5017

Abstract

As the development of small-scale thematic cartography continues, there is a growing interest in simple graphic solutions, e.g., in the form of numerical values presented on maps to replace or complement well-established quantitative cartographic methods of presentation. Numbers on maps are used as [...] Read more.

As the development of small-scale thematic cartography continues, there is a growing interest in simple graphic solutions, e.g., in the form of numerical values presented on maps to replace or complement well-established quantitative cartographic methods of presentation. Numbers on maps are used as an independent form of data presentation or function as a supplement to the cartographic presentation, becoming a legend placed directly on the map. Despite the frequent use of numbers on maps, this relatively simple form of presentation has not been extensively empirically evaluated. This article presents the results of an empirical study aimed at comparing the usability of numbers on maps for the presentation of quantitative information to frequently used proportional symbols, for simple map-reading tasks. The study showed that the use of numbers on single-variable and two-variable maps results in a greater number of correct answers and also often an improved response time compared to the use of proportional symbols. Interestingly, the introduction of different sizes of numbers did not significantly affect their usability. Thus, it has been proven that—for some tasks—map users accept this bare-bones version of data presentation, often demonstrating a higher level of preference for it than for proportional symbols. Full article

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20 pages, 4400 KiB

Open AccessEditor’s ChoiceArticle

A Sightseeing Spot Recommendation System That Takes into Account the Visiting Frequency of Users

by Yudai Kato and Kayoko Yamamoto

ISPRS Int. J. Geo-Inf. 2020, 9(7), 411; https://doi.org/10.3390/ijgi9070411 - 27 Jun 2020

Cited by 15 | Viewed by 3836

Abstract

The present study aimed to design, develop, operate and evaluate a sightseeing spot recommendation system that can efficiently and usefully support tourists while considering their visiting frequencies. This system was developed by integrating social networking services (SNSs), Web-geographic information systems (GIS) and recommendation [...] Read more.

The present study aimed to design, develop, operate and evaluate a sightseeing spot recommendation system that can efficiently and usefully support tourists while considering their visiting frequencies. This system was developed by integrating social networking services (SNSs), Web-geographic information systems (GIS) and recommendation systems. The system recommends sightseeing spots to users with different visiting frequencies, adopting two recommendation methods (knowledge-based recommendation and collaborative recommendation methods). Additionally, the system was operated for six weeks in Kamakura City, Kanagawa Prefecture, Japan, and the total number of users was 61. Based on the results of the web questionnaire survey, the usefulness of the system when sightseeing was high, and the recommendation function of sightseeing spots, which is an original function, received mainly good ratings. From the results of the access analysis of users’ log data, the total number of sessions in this system was 329, 77% used mobile devices, and smartphones were used most frequently. Therefore, it is evident that the system was used by different types of devices just as it was designed for, and that the system was used according to the purpose of the present study, which is to support the sightseeing activities of users. Full article

(This article belongs to the Special Issue Multimedia Cartography)

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18 pages, 6859 KiB

Open AccessEditor’s ChoiceArticle

A Simplified Method of Cartographic Visualisation of Buildings’ Interiors (2D+) for Navigation Applications

by Dariusz Gotlib, Michał Wyszomirski and Miłosz Gnat

ISPRS Int. J. Geo-Inf. 2020, 9(6), 407; https://doi.org/10.3390/ijgi9060407 - 26 Jun 2020

Cited by 8 | Viewed by 3816

Abstract

This article proposes an original method of a coherent and simplified cartographic presentation of the interior of buildings called 2D+, which can be used in geoinformation applications that do not support an extensive three-dimensional visualisation or do not have access to a 3D [...] Read more.

This article proposes an original method of a coherent and simplified cartographic presentation of the interior of buildings called 2D+, which can be used in geoinformation applications that do not support an extensive three-dimensional visualisation or do not have access to a 3D model of the building. A simplified way of cartographic visualisation can be used primarily in indoor navigation systems and other location-based services (LBS) applications. It can also be useful in systems supporting facility management (FM) and various kinds of geographic information systems (GIS). On the one hand, it may increase an application’s efficiency; on the other, it may unify the method of visualisation in the absence of a building’s 3D model. Thanks to the proposed method, it is possible to achieve the same effect regardless of the data source used: Building Information Modelling (BIM), a Computer-aided Design (CAD) model, or traditional architectural and construction drawings. Such a solution may be part of a broader concept of a multi-scale presentation of buildings’ interiors. The article discusses the issues of visualising data and converting data to the appropriate coordinate system, as well as the properties of the application model of data. Full article

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

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21 pages, 55347 KiB

Open AccessEditor’s ChoiceArticle

Evaluating the Influence of Urban Morphology on Urban Wind Environment Based on Computational Fluid Dynamics Simulation

by Chia-An Ku and Hung-Kai Tsai

ISPRS Int. J. Geo-Inf. 2020, 9(6), 399; https://doi.org/10.3390/ijgi9060399 - 17 Jun 2020

Cited by 30 | Viewed by 5397

Abstract

Due to urbanization around the world, people living in urban areas have been suffering from a series of negative effects caused by changes in urban microclimate, especially when it comes to urban heat islands (UHIs). To mitigate UHIs, management of urban wind environments [...] Read more.

Due to urbanization around the world, people living in urban areas have been suffering from a series of negative effects caused by changes in urban microclimate, especially when it comes to urban heat islands (UHIs). To mitigate UHIs, management of urban wind environments is increasingly considered as a crucial part of the process. Computational fluid dynamics (CFD) simulation of wind fields has become a prevailing method to explore the relationship between morphological factors and wind environment. However, most studies are focused on building scale and fail to reflect the effects of comprehensive planning. In addition, the combined influence of different morphological factors on wind environment is rarely discussed. Therefore, this study tries to explore the relationship between urban morphology and wind environment in a new-town area. CFD method was applied to simulate the wind field, and 11 scenarios based on criteria according to existing literature, planning regulations and local characteristics were developed. The simulation results from different scenarios show that the impact of the five selected factors on wind speeds was non-linear, and the impact varied significantly among different areas of the study region. Simulation of the differences in regional wind speeds among different planning scenarios can provide strong decision-making support. Full article

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

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14 pages, 3900 KiB

Open AccessEditor’s ChoiceCommunication

Terrain Analysis in Google Earth Engine: A Method Adapted for High-Performance Global-Scale Analysis

by José Lucas Safanelli, Raul Roberto Poppiel, Luis Fernando Chimelo Ruiz, Benito Roberto Bonfatti, Fellipe Alcantara de Oliveira Mello, Rodnei Rizzo and José A. M. Demattê

ISPRS Int. J. Geo-Inf. 2020, 9(6), 400; https://doi.org/10.3390/ijgi9060400 - 17 Jun 2020

Cited by 62 | Viewed by 11577

Abstract

Terrain analysis is an important tool for modeling environmental systems. Aiming to use the cloud-based computing capabilities of Google Earth Engine (GEE), we customized an algorithm for calculating terrain attributes, such as slope, aspect, and curvatures, for different resolution and geographical extents. The [...] Read more.

Terrain analysis is an important tool for modeling environmental systems. Aiming to use the cloud-based computing capabilities of Google Earth Engine (GEE), we customized an algorithm for calculating terrain attributes, such as slope, aspect, and curvatures, for different resolution and geographical extents. The calculation method is based on geometry and elevation values estimated within a 3 × 3 spheroidal window, and it does not rely on projected elevation data. Thus, partial derivatives of terrain are calculated considering the great circle distances of reference nodes of the topographic surface. The algorithm was developed using the JavaScript programming interface of the online code editor of GEE and can be loaded as a custom package. The algorithm also provides an additional feature for making the visualization of terrain maps with a dynamic legend scale, which is useful for mapping different extents: from local to global. We compared the consistency of the proposed method with an available but limited terrain analysis tool of GEE, which resulted in a correlation of 0.89 and 0.96 for aspect and slope over a near-global scale, respectively. In addition to this, we compared the slope, aspect, horizontal, and vertical curvature of a reference site (Mount Ararat) to their equivalent attributes estimated on the System for Automated Geospatial Analysis (SAGA), which achieved a correlation between 0.96 and 0.98. The visual correspondence of TAGEE and SAGA confirms its potential for terrain analysis. The proposed algorithm can be useful for making terrain analysis scalable and adapted to customized needs, benefiting from the high-performance interface of GEE. Full article

(This article belongs to the Special Issue Big Data Computing for Geospatial Applications)

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21 pages, 6006 KiB

Open AccessEditor’s ChoiceArticle

National-Scale Landslide Susceptibility Mapping in Austria Using Fuzzy Best-Worst Multi-Criteria Decision-Making

by Meisam Moharrami, Amin Naboureh, Thimmaiah Gudiyangada Nachappa, Omid Ghorbanzadeh, Xudong Guan and Thomas Blaschke

ISPRS Int. J. Geo-Inf. 2020, 9(6), 393; https://doi.org/10.3390/ijgi9060393 - 16 Jun 2020

Cited by 28 | Viewed by 4559

Abstract

Landslides are one of the most detrimental geological disasters that intimidate human lives along with severe damages to infrastructures and they mostly occur in the mountainous regions across the globe. Landslide susceptibility mapping (LSM) serves as a key step in assessing potential areas [...] Read more.

Landslides are one of the most detrimental geological disasters that intimidate human lives along with severe damages to infrastructures and they mostly occur in the mountainous regions across the globe. Landslide susceptibility mapping (LSM) serves as a key step in assessing potential areas that are prone to landslides and could have an impact on decreasing the possible damages. The application of the fuzzy best-worst multi-criteria decision-making (FBWM) method was applied for LSM in Austria. Further, the role of employing a few numbers of pairwise comparisons on LSM was investigated by comparing the FBWM and Fuzzy Analytical Hierarchical Process (FAHP). For this study, a wide range of data was sourced from the Geological Survey of Austria, the Austrian Land Information System, Humanitarian OpenStreetMap Team, and remotely sensed data were collected. We used nine conditioning factors that were based on the previous studies and geomorphological characteristics of Austria, such as elevation, slope, slope aspect, lithology, rainfall, land cover, distance to drainage, distance to roads, and distance to faults. Based on the evaluation of experts, the slope conditioning factor was chosen as the best criterion (highest impact on LSM) and the distance to roads was considered as the worst criterion (lowest impact on LSM). LSM was generated for the region based on the best and worst criterion. The findings show the robustness of FBWM in landslide susceptibility mapping. Additionally, using fewer pairwise comparisons revealed that the FBWM can obtain higher accuracy as compared to FAHP. The finding of this research can help authorities and decision-makers to provide effective strategies and plans for landslide prevention and mitigation at the national level. Full article

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29 pages, 10016 KiB

Open AccessEditor’s ChoiceArticle

Measuring Accessibility of Healthcare Facilities for Populations with Multiple Transportation Modes Considering Residential Transportation Mode Choice

by Xinxin Zhou, Zhaoyuan Yu, Linwang Yuan, Lei Wang and Changbin Wu

ISPRS Int. J. Geo-Inf. 2020, 9(6), 394; https://doi.org/10.3390/ijgi9060394 - 16 Jun 2020

Cited by 34 | Viewed by 6158

Abstract

Accessibility research of healthcare facilities is developing towards multiple transportation modes (MTM), which are influenced by residential transportation choices and preferences. Due to differences in travel impact factors such as traffic conditions, origin location, distance to the destination, and economic cost, residents’ daily [...] Read more.

Accessibility research of healthcare facilities is developing towards multiple transportation modes (MTM), which are influenced by residential transportation choices and preferences. Due to differences in travel impact factors such as traffic conditions, origin location, distance to the destination, and economic cost, residents’ daily travel presents different residential transportation mode choices (RTMC). The purpose of our study was to measure the spatial accessibility of healthcare facilities based on MTM considering RTMC (MTM-RTMC). We selected the gravity two-step floating catchment area method (G2SFCA) as a fundamental model. Through the single transportation mode (STM), MTM, and MTM-RTMC, three aspects used to illustrate and redesign the G2SFCA, we obtained the MTM-RTMC G2SFCA model that integrates RTMC probabilities and the travel friction coefficient. We selected Nanjing as the experimental area, used route planning data of four modes (including driving, walking, public transportation, and bicycling) from a web mapping platform, and applied the three models to pediatric clinic services to measure accessibility. The results show that the MTM-RTMC mechanism is to make up for the traditional estimation of accessibility, which loses sight of the influence of residential transportation choices. The MTM-RTMC mechanism that provides a more realistic and reliable way can generalize to major accessibility models and offers preferable guidance for policymakers. Full article

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

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15 pages, 1708 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

A Map Is a Living Structure with the Recurring Notion of Far More Smalls than Larges

by Bin Jiang and Terry Slocum

ISPRS Int. J. Geo-Inf. 2020, 9(6), 388; https://doi.org/10.3390/ijgi9060388 - 11 Jun 2020

Cited by 5 | Viewed by 5185

Abstract

The Earth’s surface or any territory is a coherent whole or subwhole, in which the notion of “far more small things than large ones” recurs at different levels of scale ranging from the smallest of a couple of meters to the largest of [...] Read more.

The Earth’s surface or any territory is a coherent whole or subwhole, in which the notion of “far more small things than large ones” recurs at different levels of scale ranging from the smallest of a couple of meters to the largest of the Earth’s surface or that of the territory. The coherent whole has the underlying character called wholeness or living structure, which is a physical phenomenon pervasively existing in our environment and can be defined mathematically under the new third view of space conceived and advocated by Christopher Alexander: space is neither lifeless nor neutral, but a living structure capable of being more alive or less alive. This paper argues that both the map and the territory are a living structure, and that it is the inherent hierarchy of “far more smalls than larges” that constitutes the foundation of maps and mapping. It is the underlying living structure of geographic space or geographic features that makes maps or mapping possible, i.e., larges to be retained, while smalls to be omitted in a recursive manner (Note: larges and smalls should be understood broadly and wisely, in terms of not only sizes, but also topological connectivity and semantic meaning). Thus, map making is largely an objective undertaking governed by the underlying living structure, and maps portray the truth of the living structure. Based on the notion of living structure, a map can be considered to be an iterative system, which means that the map is the map of the map of the map, and so on endlessly. The word endlessly means continuous map scales between two discrete ones, just as there are endless real numbers between 1 and 2. The iterated map system implies that each of the subsequent small-scale maps is a subset of the single large-scale map, not a simple subset but with various constraints to make all geographic features topologically correct. Full article

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25 pages, 10353 KiB

Open AccessEditor’s ChoiceArticle

by Xiaoyi Yuan, Andrew Crooks and Andreas Züfle

ISPRS Int. J. Geo-Inf. 2020, 9(6), 385; https://doi.org/10.3390/ijgi9060385 - 10 Jun 2020

Cited by 6 | Viewed by 4579

Abstract

The research presented in this paper proposes a thematic network approach to explore rich relationships between places. We connect places in networks through their thematic similarities by applying topic modeling to the textual volunteered geographic information (VGI) pertaining to the places. The network [...] Read more.

The research presented in this paper proposes a thematic network approach to explore rich relationships between places. We connect places in networks through their thematic similarities by applying topic modeling to the textual volunteered geographic information (VGI) pertaining to the places. The network approach enhances previous research involving place clustering using geo-textual information, which often simplifies relationships between places to be either in-cluster or out-of-cluster. To demonstrate our approach, we use as a case study in Manhattan (New York) that compares networks constructed from three different geo-textural data sources—TripAdvisor attraction reviews, TripAdvisor restaurant reviews, and Twitter data. The results showcase how the thematic similarity network approach enables us to conduct clustering analysis as well as node-to-node and node-to-cluster analysis, which is fruitful for understanding how places are connected through individuals’ experiences. Furthermore, by enriching the networks with geodemographic information as node attributes, we discovered that some low-income communities in Manhattan have distinctive restaurant cultures. Even though geolocated tweets are not always related to place they are posted from, our case study demonstrates that topic modeling is an efficient method to filter out the place-irrelevant tweets and therefore refining how of places can be studied. Full article

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

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18 pages, 3242 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Evaluating Temporal Approximation Methods Using Burglary Data

by Lukas Oswald and Michael Leitner

ISPRS Int. J. Geo-Inf. 2020, 9(6), 386; https://doi.org/10.3390/ijgi9060386 - 10 Jun 2020

Cited by 5 | Viewed by 4477

Abstract

Law enforcement is very interested in knowing when a crime has happened. Unfortunately, the occurrence time of a crime is often not exactly known. In such circumstances, estimating the most likely time that a crime has happened is crucial for spatio-temporal analysis. The [...] Read more.

Law enforcement is very interested in knowing when a crime has happened. Unfortunately, the occurrence time of a crime is often not exactly known. In such circumstances, estimating the most likely time that a crime has happened is crucial for spatio-temporal analysis. The main purpose of this research is to introduce two novel temporal approximation methods, termed retrospective temporal analysis (RTA) and extended retrospective temporal analysis (RTA_ext). Both methods are compared to six existing temporal approximation methods and subsequently evaluated in order to identify the method that can most accurately estimate the occurrence time of crimes. This research is conducted with 100,000+ burglary crimes from the city of Vienna, Austria provided by the Criminal Intelligence Service Austria, from 2009–2015. The RTA method assumes that crimes in the immediate past occur at very similar times as in the present and in the future. Historical crimes with accurately known time stamps can therefore be applied to estimate when crimes occur in the present/future. The RTA_ext method enhances one existing temporal approximation method, aoristic_ext, with probability values derived from historical crime data with accurately known time stamps. The results show that the RTA method performs superiorly to all other temporal approximation methods, including the novel RTA_ext method, in two out of the three crime types analyzed. Additionally, the RTA_ext method shows very good results that are similar to the best performing existing approximation methods. Full article

(This article belongs to the Special Issue Urban Crime Mapping and Analysis Using GIS)

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19 pages, 8572 KiB

Open AccessEditor’s ChoiceArticle

Machine Learning Generalisation across Different 3D Architectural Heritage

by Eleonora Grilli and Fabio Remondino

ISPRS Int. J. Geo-Inf. 2020, 9(6), 379; https://doi.org/10.3390/ijgi9060379 - 9 Jun 2020

Cited by 67 | Viewed by 8398

Abstract

The use of machine learning techniques for point cloud classification has been investigated extensively in the last decade in the geospatial community, while in the cultural heritage field it has only recently started to be explored. The high complexity and heterogeneity of 3D [...] Read more.

The use of machine learning techniques for point cloud classification has been investigated extensively in the last decade in the geospatial community, while in the cultural heritage field it has only recently started to be explored. The high complexity and heterogeneity of 3D heritage data, the diversity of the possible scenarios, and the different classification purposes that each case study might present, makes it difficult to realise a large training dataset for learning purposes. An important practical issue that has not been explored yet, is the application of a single machine learning model across large and different architectural datasets. This paper tackles this issue presenting a methodology able to successfully generalise to unseen scenarios a random forest model trained on a specific dataset. This is achieved looking for the best features suitable to identify the classes of interest (e.g., wall, windows, roof and columns). Full article

(This article belongs to the Special Issue Automatic Feature Recognition from Point Clouds)

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20 pages, 4725 KiB

Open AccessEditor’s ChoiceArticle

by Yufeng He, Yehua Sheng, Yunqing Jing, Yue Yin and Ahmad Hasnain

ISPRS Int. J. Geo-Inf. 2020, 9(6), 381; https://doi.org/10.3390/ijgi9060381 - 9 Jun 2020

Cited by 3 | Viewed by 3001

Abstract

Unstructured geo-text annotations volunteered by users of web map services enrich the basic geographic data. However, irrelevant geo-texts can be added to the web map, and these geo-texts reduce utility to users. Therefore, this study proposes a method to detect uncorrelated geo-text annotations [...] Read more.

Unstructured geo-text annotations volunteered by users of web map services enrich the basic geographic data. However, irrelevant geo-texts can be added to the web map, and these geo-texts reduce utility to users. Therefore, this study proposes a method to detect uncorrelated geo-text annotations based on Voronoi k-order neighborhood partition and auto-correlation statistical models. On the basis of the geo-text classification and semantic vector transformation, a quantitative description method for spatial autocorrelation was established by the Voronoi weighting method of inverse vicinity distance. The Voronoi k-order neighborhood self-growth strategy was used to detect the minimum convergence neighborhood for spatial autocorrelation. The Pearson method was used to calculate the correlation degree of the geo-text in the convergence region and then deduce the type of geo-text to be filtered. Experimental results showed that for given geo-text types in the study region, the proposed method effectively calculated the correlation between new geo-texts and the convergence region, providing an effective suggestion for preventing uncorrelated geo-text from uploading to the web map environment. Full article

(This article belongs to the Special Issue Advances in Computational Approaches for Spatial Analysis and Modeling)

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19 pages, 13149 KiB

Open AccessEditor’s ChoiceArticle

Flight Planning for LiDAR-Based UAS Mapping Applications

by Bashar Alsadik and Fabio Remondino

ISPRS Int. J. Geo-Inf. 2020, 9(6), 378; https://doi.org/10.3390/ijgi9060378 - 8 Jun 2020

Cited by 24 | Viewed by 8850

Abstract

In the last two decades, unmanned aircraft systems (UAS) were successfully used in different environments for diverse applications like territorial mapping, heritage 3D documentation, as built surveys, construction monitoring, solar panel placement and assessment, road inspections, etc. These applications were correlated to the [...] Read more.

In the last two decades, unmanned aircraft systems (UAS) were successfully used in different environments for diverse applications like territorial mapping, heritage 3D documentation, as built surveys, construction monitoring, solar panel placement and assessment, road inspections, etc. These applications were correlated to the onboard sensors like RGB cameras, multi-spectral cameras, thermal sensors, panoramic cameras, or LiDARs. According to the different onboard sensors, a different mission plan is required to satisfy the characteristics of the sensor and the project aims. For UAS LiDAR-based mapping missions, requirements for the flight planning are different with respect to conventional UAS image-based flight plans because of different reasons related to the LiDAR scanning mechanism, scanning range, output scanning rate, field of view (FOV), rotation speed, etc. Although flight planning for image-based UAS missions is a well-known and solved problem, flight planning for a LiDAR-based UAS mapping is still an open research topic that needs further investigations. The article presents the developments of a LiDAR-based UAS flight planning tool, tested with simulations in real scenarios. The flight planning simulations considered an UAS platform equipped, alternatively, with three low-cost multi-beam LiDARs, namely Quanergy M8, Velodyne VLP-16, and the Ouster OS-1-16. The specific characteristics of the three sensors were used to plan flights and acquired dense point clouds. Comparisons and analyses of the results showed clear relationships between point density, flying speeds, and flying heights. Full article

(This article belongs to the Special Issue UAV in Smart City and Smart Region)

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18 pages, 13389 KiB

Open AccessEditor’s ChoiceArticle

A Change of Theme: The Role of Generalization in Thematic Mapping

by Paulo Raposo, Guillaume Touya and Pia Bereuter

ISPRS Int. J. Geo-Inf. 2020, 9(6), 371; https://doi.org/10.3390/ijgi9060371 - 4 Jun 2020

Cited by 11 | Viewed by 6964

Abstract

Cartographic generalization research has focused almost exclusively in recent years on topographic mapping, and has thereby gained an incorrect reputation for having to do only with reference or positional data. The generalization research community needs to broaden its scope to include thematic cartography [...] Read more.

Cartographic generalization research has focused almost exclusively in recent years on topographic mapping, and has thereby gained an incorrect reputation for having to do only with reference or positional data. The generalization research community needs to broaden its scope to include thematic cartography and geovisualization. Generalization is not new to these areas of cartography, and has in fact always been involved in thematic geographic visualization, despite rarely being acknowledged. We illustrate this involvement with several examples of famous, public-audience thematic maps, noting the generalization procedures involved in drawing each, both across their basemap and thematic layers. We also consider, for each map example we note, which generalization operators were crucial to the formation of the map’s thematic message. The many incremental gains made by the cartographic generalization research community while treating reference data can be brought to bear on thematic cartography in the same way they were used implicitly on the well-known thematic maps we highlight here as examples. Full article

(This article belongs to the Special Issue Map Generalization)

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20 pages, 9411 KiB

Open AccessEditor’s ChoiceArticle

Developing Shopping and Dining Walking Indices Using POIs and Remote Sensing Data

by Yingbin Deng, Yingwei Yan, Yichun Xie, Jianhui Xu, Hao Jiang, Renrong Chen and Runnan Tan

ISPRS Int. J. Geo-Inf. 2020, 9(6), 366; https://doi.org/10.3390/ijgi9060366 - 2 Jun 2020

Cited by 4 | Viewed by 3666

Abstract

Walking is one of the most commonly promoted traveling methods and is garnering increasing attention. Many indices/scores have been developed by scholars to measure the walkability in a local community. However, most existing walking indices/scores involve urban planning-oriented, local service-oriented, regional accessibility-oriented, and [...] Read more.

Walking is one of the most commonly promoted traveling methods and is garnering increasing attention. Many indices/scores have been developed by scholars to measure the walkability in a local community. However, most existing walking indices/scores involve urban planning-oriented, local service-oriented, regional accessibility-oriented, and physical activity-oriented walkability assessments. Since shopping and dining are two major leisure activities in our daily lives, more attention should be given to the shopping or dining-oriented walking environment. Therefore, we developed two additional walking indices that focus on shopping or dining. The point of interest (POI), vegetation coverage, water coverage, distance to bus/subway station, and land surface temperature were employed to construct walking indices based on 50-m street segments. Then, walking index values were categorized into seven recommendation levels. The field verification illustrates that the proposed walking indices can accurately represent the walking environment for shopping and dining. The results in this study could provide references for citizens seeking to engage in activities of shopping and dining with a good walking environment. Full article

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17 pages, 2780 KiB

Open AccessEditor’s ChoiceArticle

Meet the Virtual Jeju Dol Harubang—The Mixed VR/AR Application for Cultural Immersion in Korea’s Main Heritage

by Kwanghee Jung, Vinh T. Nguyen, Diana Piscarac and Seung-Chul Yoo

ISPRS Int. J. Geo-Inf. 2020, 9(6), 367; https://doi.org/10.3390/ijgi9060367 - 2 Jun 2020

Cited by 62 | Viewed by 8834

Abstract

Jeju Island comes second to only Seoul as Korea’s most visited destination, yet most visitors do not have the chance to go beyond brief visits and immerse themselves in the island’s history and cultural heritage. This project introduces the cultural heritage of Jeju [...] Read more.

Jeju Island comes second to only Seoul as Korea’s most visited destination, yet most visitors do not have the chance to go beyond brief visits and immerse themselves in the island’s history and cultural heritage. This project introduces the cultural heritage of Jeju Island to visitors through virtual reality/augmented reality (VR/AR) model visualization technology, namely JejuView, which provides an intuitive way to experience cultural heritage sites on the island. The proposed VR/AR application is designed to introduce a series of heritage spots on Jeju Island through (i) a printed Jeju map with embedded QR code markers that enable viewers to experience the locations without being present at the site, (ii) a mobile device with WebGL supported browser which allows 3D content to be rendered, and (iii) an AR library (A-Frame.io) that enables enthusiasts to recreate similar work. To test the effectiveness of the proposed VR/AR application, the authors conducted an experiment with 251 participants to test the research model based on the technology acceptance model (TAM) and employed generalized structured component analysis (GSCA) for the analysis. Results show that when using sensory new media such as VR/AR, consumers are more focused on the hedonic value than on the utilitarian value of the information. In conclusion, the proposed VR/AR application is complementary to existing studies and provides significant support to researchers, engineers, and designers developing VR/AR technologies for use in cultural education and tourism marketing. Full article

(This article belongs to the Special Issue Smart Tourism: A GIS-Based Approach)

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24 pages, 6068 KiB

Open AccessEditor’s ChoiceReview

Issues of Healthcare Planning and GIS: A Review

by Bandar Fuad Khashoggi and Abdulkader Murad

ISPRS Int. J. Geo-Inf. 2020, 9(6), 352; https://doi.org/10.3390/ijgi9060352 - 27 May 2020

Cited by 55 | Viewed by 17956

Abstract

Introduction: For the past 2400 years, the spatial relationship between health and location has been a concern for researchers. Studies have been conducted for decades to understand such a relationship, which has led to the identification of a number of healthcare planning issues. [...] Read more.

Introduction: For the past 2400 years, the spatial relationship between health and location has been a concern for researchers. Studies have been conducted for decades to understand such a relationship, which has led to the identification of a number of healthcare planning issues. Geographic Information Systems (GIS) technology has contributed to addressing such issues by applying analytical approaches at the level of epidemiological surveillance and evaluating the spatial inequality of access to healthcare. Consequently, the importance of reviewing healthcare planning issues and recognition of the role of GIS are integral to relevant studies. Such research will contribute to increasing the understanding of how to apply analytical approaches for dealing with healthcare planning issues using GIS. Methods: This paper aims to provide an examination of healthcare planning issues and focuses on reviewing the potential of GIS in dealing with such issues by applying analytical approaches. The method of a typical literature review was used through collecting data from various studies selected based on temporal and descriptive considerations. Results: Researchers have focused on developing and applying analytical approaches using GIS to support two important aspects of healthcare planning: first, epidemic surveillance and modeling, despite a lack of health information and its management, and, second, evaluating the spatial inequality of access to healthcare in order to determine the optimum distribution of health resources. Conclusion: GIS is an effective tool to support spatial decision-making in public health through applying the evolving analytical approaches to dealing with healthcare planning issues. This requires a literature review before preparing relevant studies, particularly because of the continuous development of GIS technologies. Full article

(This article belongs to the Special Issue GIS in Healthcare)

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23 pages, 17246 KiB

Open AccessEditor’s ChoiceArticle

Constructing Geospatial Concept Graphs from Tagged Images for Geo-Aware Fine-Grained Image Recognition

by Naoko Nitta, Kazuaki Nakamura and Noboru Babaguchi

ISPRS Int. J. Geo-Inf. 2020, 9(6), 354; https://doi.org/10.3390/ijgi9060354 - 27 May 2020

Cited by 5 | Viewed by 3000

Abstract

While visual appearances play a main role in recognizing the concepts captured in images, additional information can provide complementary information for fine-grained image recognition, where concepts with similar visual appearances such as species of birds need to be distinguished. Especially for recognizing geospatial [...] Read more.

While visual appearances play a main role in recognizing the concepts captured in images, additional information can provide complementary information for fine-grained image recognition, where concepts with similar visual appearances such as species of birds need to be distinguished. Especially for recognizing geospatial concepts, which are observed only at specific places, geographical locations of the images can improve the recognition accuracy. However, such geo-aware fine-grained image recognition requires prior information about the visual and geospatial features of each concept or the training data composed of high-quality images for each concept associated with correct geographical locations. By using a large number of images photographed in various places and described with textual tags which can be collected from image sharing services such as Flickr, this paper proposes a method for constructing a geospatial concept graph which contains the necessary prior information for realizing the geo-aware fine-grained image recognition, such as a set of visually recognizable fine-grained geospatial concepts, their visual and geospatial features, and the coarse-grained representative visual concepts whose visual features can be transferred to several fine-grained geospatial concepts. Leveraging the information from the images captured by many people can automatically extract diverse types of geospatial concepts with proper features for realizing efficient and effective geo-aware fine-grained image recognition. Full article

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