ISPRS International Journal of Geo-Information

15 pages, 2624 KiB

Open AccessArticle

Integrative Analysis of Spatial Heterogeneity and Overdispersion of Crime with a Geographically Weighted Negative Binomial Model

by Jianguo Chen, Lin Liu, Luzi Xiao, Chong Xu and Dongping Long

ISPRS Int. J. Geo-Inf. 2020, 9(1), 60; https://doi.org/10.3390/ijgi9010060 - 20 Jan 2020

Cited by 23 | Viewed by 4001

Abstract

Negative binomial (NB) regression model has been used to analyze crime in previous studies. The disadvantage of the NB model is that it cannot deal with spatial effects. Therefore, spatial regression models, such as the geographically weighted Poisson regression (GWPR) model, were introduced [...] Read more.

Negative binomial (NB) regression model has been used to analyze crime in previous studies. The disadvantage of the NB model is that it cannot deal with spatial effects. Therefore, spatial regression models, such as the geographically weighted Poisson regression (GWPR) model, were introduced to address spatial heterogeneity in crime analysis. However, GWPR could not account for overdispersion, which is commonly observed in crime data. The geographically weighted negative binomial model (GWNBR) was adopted to address spatial heterogeneity and overdispersion simultaneously in crime analysis, based on a 3-year data set collected from ZG city, China, in this study. The count of residential burglaries was used as the dependent variable to calibrate the above models, and the results revealed that the GWPR and GWNBR models performed better than NB for reducing spatial dependency in the model residuals. GWNBR outperformed GWPR for incorporating overdispersion. Therefore, GWNBR was proven to be a promising tool for crime modeling. Full article

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21 pages, 389 KiB

Open AccessEditorial

Acknowledgement to Reviewers of IJGI in 2019

by IJGI Editorial Office

ISPRS Int. J. Geo-Inf. 2020, 9(1), 59; https://doi.org/10.3390/ijgi9010059 - 20 Jan 2020

Viewed by 2288

Abstract

The editorial team greatly appreciates the reviewers who have dedicated their considerable time and expertise to the journal’s rigorous editorial process over the past 12 months, regardless of whether the papers are finally published or not [...] Full article

28 pages, 9450 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Spaces in Spatial Science and Urban Applications—State of the Art Review

by Sisi Zlatanova, Jinjin Yan, Yijing Wang, Abdoulaye Diakité, Umit Isikdag, George Sithole and Jack Barton

ISPRS Int. J. Geo-Inf. 2020, 9(1), 58; https://doi.org/10.3390/ijgi9010058 - 20 Jan 2020

Cited by 36 | Viewed by 9971

Abstract

In spatial science and urban applications, “space" is presented by multiple disciplines as a notion referencing our living environment. Space is used as a general term to help understand particular characteristics of the environment. However, the definition and perception of space varies and [...] Read more.

In spatial science and urban applications, “space" is presented by multiple disciplines as a notion referencing our living environment. Space is used as a general term to help understand particular characteristics of the environment. However, the definition and perception of space varies and these variations have to be harmonised. For example, space may have diverse definitions and classification, the same environment may be abstracted/modelled by contradicting notions of space, which can lead to inconsistencies and misunderstandings. In this paper, we seek to investigate and document the state-of-the-art in the research of “space” regarding its definition, classification, modelling and utilization (2D/3D) in spatial sciences and urban applications. We focus on positioning, navigation, building micro-climate and thermal comfort, landscape, urban planning and design, urban heat island, interior design and planning, transportation and intelligent space. We review 147 research papers, technical reports and on-line resources. We compare the presented space concepts with respect to five criteria—classification, boundary, modelling components, use of standards and granularity. The review inventory is intended for both scientists and professionals in the spatial industry, such as companies, national mapping agencies and governments, and aim to provide a reference to better understand and employ the “space” while working across disciplines. Full article

(This article belongs to the Special Issue State-of-the-Art in Spatial Information Science)

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12 pages, 1695 KiB

Open AccessEditor’s ChoiceArticle

Linguistic Landscapes on Street-Level Images

by Seong-Yun Hong

ISPRS Int. J. Geo-Inf. 2020, 9(1), 57; https://doi.org/10.3390/ijgi9010057 - 20 Jan 2020

Cited by 12 | Viewed by 7201

Abstract

Linguistic landscape research focuses on relationships between written languages in public spaces and the sociodemographic structure of a city. While a great deal of work has been done on the evaluation of linguistic landscapes in different cities, most of the studies are based [...] Read more.

Linguistic landscape research focuses on relationships between written languages in public spaces and the sociodemographic structure of a city. While a great deal of work has been done on the evaluation of linguistic landscapes in different cities, most of the studies are based on ad-hoc interpretation of data collected from fieldwork. The purpose of this paper is to develop a new methodological framework that combines computer vision and machine learning techniques for assessing the diversity of languages from street-level images. As demonstrated with an analysis of a small Chinese community in Seoul, South Korea, the proposed approach can reveal the spatiotemporal pattern of linguistic variations effectively and provide insights into the demographic composition as well as social changes in the neighborhood. Although the method presented in this work is at a conceptual stage, it has the potential to open new opportunities to conduct linguistic landscape research at a large scale and in a reproducible manner. It is also capable of yielding a more objective description of a linguistic landscape than arbitrary classification and interpretation of on-site observations. The proposed approach can be a new direction for the study of linguistic landscapes that builds upon urban analytics methodology, and it will help both geographers and sociolinguists explore and understand our society. Full article

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

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21 pages, 5516 KiB

Open AccessEditor’s ChoiceArticle

Detecting Intra-Urban Housing Market Spillover through a Spatial Markov Chain Model

by Daijun Zhang, Xiaoqi Zhang, Yanqiao Zheng, Xinyue Ye, Shengwen Li and Qiwen Dai

ISPRS Int. J. Geo-Inf. 2020, 9(1), 56; https://doi.org/10.3390/ijgi9010056 - 19 Jan 2020

Cited by 6 | Viewed by 2879

Abstract

This study analyzed the spillovers among intra-urban housing submarkets in Beijing, China. Intra-urban spillover imposes a methodological challenge for housing studies from the spatial and temporal perspectives. Unlike the inter-urban spillover, the range of every submarket is not naturally defined; therefore, it is [...] Read more.

This study analyzed the spillovers among intra-urban housing submarkets in Beijing, China. Intra-urban spillover imposes a methodological challenge for housing studies from the spatial and temporal perspectives. Unlike the inter-urban spillover, the range of every submarket is not naturally defined; therefore, it is impossible to evaluate the intra-urban spillover by standard time-series models. Instead, we formulated the spillover effect as a Markov chain procedure. The constrained clustering technique was applied to identify the submarkets as the hidden states of Markov chain and estimate the transition matrix. Using a day-by-day transaction dataset of second-hand apartments in Beijing during 2011–2017, we detected 16 submarkets/regions and the spillover effect among these regions. The highest transition probability appeared in the overlapped region of urban core and Tongzhou district. This observation reflects the impact of urban planning proposal initiated since early 2012. In addition to the policy consequences, we analyzed a variety of spillover “types” through regression analysis. The latter showed that the “ripple” form of spillover is not dominant at the intra-urban level. Other types, such as the spillover due to the existence of price depressed regions, play major roles. This observation reveals the complexity of intra-urban spillover dynamics and its distinct driving-force compared to the inter-urban spillover. Full article

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

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18 pages, 9813 KiB

Open AccessArticle

Multi-Parameter Estimation of Average Speed in Road Networks Using Fuzzy Control

by Johanna Guth, Sven Wursthorn and Sina Keller

ISPRS Int. J. Geo-Inf. 2020, 9(1), 55; https://doi.org/10.3390/ijgi9010055 - 17 Jan 2020

Cited by 7 | Viewed by 3213

Abstract

Average speed is crucial for calculating link travel time to find the fastest path in a road network. However, readily available data sources like OpenStreetMap (OSM) often lack information about the average speed of a road. However, OSM contains other road information which [...] Read more.

Average speed is crucial for calculating link travel time to find the fastest path in a road network. However, readily available data sources like OpenStreetMap (OSM) often lack information about the average speed of a road. However, OSM contains other road information which enables an estimation of average speed in rural regions. In this paper, we develop a Fuzzy Framework for Speed Estimation (Fuzzy-FSE) that employs fuzzy control to estimate average speed based on the parameters road class, road slope, road surface and link length. The OSM road network and, optionally, a digital elevation model (DEM) serve as free-to-use and worldwide available input data. The Fuzzy-FSE consists of two parts: (a) a rule and knowledge base which decides on the output membership functions and (b) multiple Fuzzy Control Systems which calculate the output average speeds. The Fuzzy-FSE is applied exemplary and evaluated for the BioBío and Maule region in central Chile and for the north of New South Wales in Australia. Results demonstrate that, even using only OSM data, the Fuzzy-FSE performs better than existing methods such as fixed speed profiles. Compared to these methods, the Fuzzy-FSE improves the speed estimation between 2% to 12%. In future work, we will investigate the potential of data-driven machine learning methods to estimate average speed. The applied datasets and the source code of the Fuzzy-FSE are available via GitHub. Full article

(This article belongs to the Special Issue Enhanced Modeling and Surveying Tools for Smart Cities)

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22 pages, 10420 KiB

Open AccessEditor’s ChoiceArticle

A Spatial Analytics Framework to Investigate Electric Power-Failure Events and Their Causes

by Vivian Sultan and Brian Hilton

ISPRS Int. J. Geo-Inf. 2020, 9(1), 54; https://doi.org/10.3390/ijgi9010054 - 16 Jan 2020

Cited by 13 | Viewed by 5417

Abstract

The U.S. electric-power infrastructure urgently needs renovation. Recent major power outages in California, New York, Texas, and Florida have highlighted U.S. electric-power unreliability. The media have discussed the U.S. aging power infrastructure and the Public Utilities Commission has demanded a comprehensive review of [...] Read more.

The U.S. electric-power infrastructure urgently needs renovation. Recent major power outages in California, New York, Texas, and Florida have highlighted U.S. electric-power unreliability. The media have discussed the U.S. aging power infrastructure and the Public Utilities Commission has demanded a comprehensive review of the causes of recent power outages. This paper explores geographic information systems (GIS) and a spatially enhanced predictive power-outage model to address: How may spatial analytics enhance our understanding of power outages? To answer this research question, we developed a spatial analysis framework that utilities can use to investigate power-failure events and their causes. Analysis revealed areas of statistically significant power outages due to multiple causes. This study’s GIS model can help to advance smart-grid reliability by, for example, elucidating power-failure root causes, defining a data-responsive blackout solution, or implementing a continuous monitoring and management solution. We unveil a novel use of spatial analytics to enhance power-outage understanding. Future work may involve connecting to virtually any type of streaming-data feed and transforming GIS applications into frontline decision applications, showing power-outage incidents as they occur. GIS can be a major resource for electronic-inspection systems to lower the duration of customer outages, improve crew response time, as well as reduce labor and overtime costs. Full article

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22 pages, 30142 KiB

Open AccessEditor’s ChoiceArticle

Evaluation of Augmented Reality-Based Building Diagnostics Using Third Person Perspective

by Fei Liu, Torsten Jonsson and Stefan Seipel

ISPRS Int. J. Geo-Inf. 2020, 9(1), 53; https://doi.org/10.3390/ijgi9010053 - 16 Jan 2020

Cited by 11 | Viewed by 3565

Abstract

Comprehensive user evaluations of outdoor augmented reality (AR) applications in the architecture, engineering, construction and facilities management (AEC/FM) industry are rarely reported in the literature. This paper presents an AR prototype system for infrared thermographic façade inspection and its evaluation. The system employs [...] Read more.

Comprehensive user evaluations of outdoor augmented reality (AR) applications in the architecture, engineering, construction and facilities management (AEC/FM) industry are rarely reported in the literature. This paper presents an AR prototype system for infrared thermographic façade inspection and its evaluation. The system employs markerless tracking based on image registration using natural features and a third person perspective (TPP) augmented view displayed on a hand-held smart device. We focus on evaluating the system in user experiments with the task of designating positions of heat spots on an actual façade as if acquired through thermographic inspection. User and system performance were both assessed with respect to target designation errors. The main findings of this study show that positioning accuracy using this system is adequate for objects of the size of one decimeter. After ruling out the system inherent errors, which mainly stem from our application-specific image registration procedure, we find that errors due to a human’s limited visual-motoric and cognitive performance, which have a more general implication for using TPP AR for target designation, are only a few centimeters. Full article

(This article belongs to the Special Issue Advances in Augmented Reality and Virtual Reality for Smart Cities)

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18 pages, 6382 KiB

Open AccessCase Report

Developing Web-Based and Mobile-Based GIS for Places of Worship Information to Support Halal Tourism: A Case Study in Bukittinggi, Indonesia

by Surya Afnarius, Fajril Akbar and Fitri Yuliani

ISPRS Int. J. Geo-Inf. 2020, 9(1), 52; https://doi.org/10.3390/ijgi9010052 - 16 Jan 2020

Cited by 22 | Viewed by 7231

Abstract

Indonesia is an archipelago country in which the tourism sector plays a role as an economic locomotive. In 2016, Indonesia joined the World Halal Tourism Award (WHTA) and won 12 awards, three of which were won by West Sumatra. Bukittinggi is the principal [...] Read more.

Indonesia is an archipelago country in which the tourism sector plays a role as an economic locomotive. In 2016, Indonesia joined the World Halal Tourism Award (WHTA) and won 12 awards, three of which were won by West Sumatra. Bukittinggi is the principal city of tourism in West Sumatra. There are many halal hotels and restaurants and 190 mosques available in the city. Unfortunately, the information regarding the mosque locations is still inadequate. For this reason, this research was conducted in order to develop a web-based and mobile-based geographic information system (GIS) for places of worship information (GPWI) to make it easier for Muslim tourists to find mosques, and other tourism objects and facilities. This paper reports on the development of the GPWI. The development of the GPWI employed the waterfall method. The GPWI allowed tourists to find mosques based on specific criteria, whose output showed them the location, information, route, and local transportation available to get to the mosques as well as other tourism objects and facilities around the mosque. The GPWI was developed using Free Open Source Software (FOSS) PostgreSQL/PostGIS, PHP, JavaScript, and Basic4Android. The spatial-based database and programs that were used to develop this GPWI are the main contributions of this study. Based on the product evaluation, the GPWI successfully met the needs of Muslim tourists in finding mosques during their visits to Bukittinggi. Full article

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20 pages, 6653 KiB

Open AccessArticle

Integrated Visualization Approach for Real-Time and Dynamic Assessment of Storm Surge Disasters for China’s Seas

by Lin Zhou, Wei Hu, Zhen Jia, Xinfang Li, Yaru Li, Tianyun Su and Qingsheng Guo

ISPRS Int. J. Geo-Inf. 2020, 9(1), 51; https://doi.org/10.3390/ijgi9010051 - 15 Jan 2020

Cited by 4 | Viewed by 2435

Abstract

For improved prevention and reduction of marine disasters, China’s marine authorities and emergency response agencies require a solution that provides risk assessment, early warning, and decision-making support. This paper proposes a comprehensive approach to disaster assessment that involves automated long-term operation, a spatial [...] Read more.

For improved prevention and reduction of marine disasters, China’s marine authorities and emergency response agencies require a solution that provides risk assessment, early warning, and decision-making support. This paper proposes a comprehensive approach to disaster assessment that involves automated long-term operation, a spatial information visualization method and systematic integration. The proposed approach provides functions for numerical ocean models with forecast results, automated processing of massive data, multiple disaster/element coupled assessment, and multidimensional display and expression. With regard to storm surge disasters, the approach proposed in this paper adopts a four-tier structure and the functions of each tier are described separately. The original data are comprised of a combination of statistical analysis data and real-time data obtained from the unstructured grid Finite Volume Community Ocean Model. Automated data processing methods and assessment theories incorporating an indicator system and weighted parameters are used for the assessment. By applying 2D/3D visualization technology, assessment results are displayed via several modes for ease of operation and comprehension. The validity of the approach was verified by applying it to Typhoon Hato (No. 1713). Compared with the results of the post-disaster investigation, the assessment results of the proposed approach proved the reliability of the system. Full article

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21 pages, 6313 KiB

Open AccessArticle

Accuracy Improvement of Airborne Lidar Strip Adjustment by Using Height Data and Surface Feature Strength Information Derived from the Tensor Voting Algorithm

by Rey-Jer You and Chao-Liang Lee

ISPRS Int. J. Geo-Inf. 2020, 9(1), 50; https://doi.org/10.3390/ijgi9010050 - 15 Jan 2020

Cited by 4 | Viewed by 2268

Abstract

Light detection and ranging (Lidar) spatial coordinates, especially height data, and the intensity data of point clouds are often used for strip adjustment in airborne Lidar. However, inconsistency in the intensity data and then intensity gradient data because of the variations in the [...] Read more.

Light detection and ranging (Lidar) spatial coordinates, especially height data, and the intensity data of point clouds are often used for strip adjustment in airborne Lidar. However, inconsistency in the intensity data and then intensity gradient data because of the variations in the incidence and reflection angles in the scanning direction and sunlight incident in the same areas of different strips may cause problems in the Lidar strip adjustment process. Instead of the Lidar intensity, a new type of data, termed surface feature strength data derived by using the tensor voting method, were introduced into the strip adjustment process using the partial least squares method in this study. These data are consistent in the same regions of different strips, especially on the roofs of buildings. Our experimental results indicated a significant improvement in the accuracy of strip adjustment results when both height data and surface feature strength data were used. Full article

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31 pages, 2078 KiB

Open AccessReview

Strengthening Participation Using Interactive Planning Support Systems: A Systematic Review

by Johannes Flacke, Rehana Shrestha and Rosa Aguilar

ISPRS Int. J. Geo-Inf. 2020, 9(1), 49; https://doi.org/10.3390/ijgi9010049 - 15 Jan 2020

Cited by 31 | Viewed by 6121

Abstract

Interactive Planning Support Systems (PSS) implemented on a maptable are deemed suitable to support participatory planning processes. They are supposed to facilitate exchange of knowledge between stakeholders, consensus building among them, and group-learning processes. In this systematic review, based on 16 case studies [...] Read more.

Interactive Planning Support Systems (PSS) implemented on a maptable are deemed suitable to support participatory planning processes. They are supposed to facilitate exchange of knowledge between stakeholders, consensus building among them, and group-learning processes. In this systematic review, based on 16 case studies using interactive PSS, we analyze how these have contributed to the goal of strengthening stakeholder participation. To this end, we first elicit details of the interactive PSS and the related participatory processes. In the second step, we analyze how and what the studies report, as the impacts on participation. Results show that tools and applications have become more sophisticated over time and goals of the studies changed from collaboratively designing interventions to observing and understanding how the application of such tools contributes to improved plan outcomes and group-based learning. All interactive PSS succeeded to facilitate intensive stakeholder collaboration. However, many studies lack a proper framework for investigating its impacts on participation and therefore assess these rather incidentally based on implicit assumptions. Thus, a significant outcome of this review is an evaluation framework, which allows the structural assessment of the impacts of interactive PSS on stakeholder participation. Full article

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

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17 pages, 7811 KiB

Open AccessArticle

Uncertainty Analysis of Remote Sensing Pretreatment for Biomass Estimation on Landsat OLI and Landsat ETM+

by Qi Zhang, Lihua Xu, Maozhen Zhang, Zhi Wang, Zhangfeng Gu, Yaqi Wu, Yijun Shi and Zhangwei Lu

ISPRS Int. J. Geo-Inf. 2020, 9(1), 48; https://doi.org/10.3390/ijgi9010048 - 15 Jan 2020

Cited by 4 | Viewed by 2575

Abstract

The accurate quantification of biomass helps to understand forest productivity and carbon cycling dynamics. Research on uncertainty during pretreatment is still lacking despite it being one of the major sources of uncertainty and an essential step in biomass estimation. In this study, we [...] Read more.

The accurate quantification of biomass helps to understand forest productivity and carbon cycling dynamics. Research on uncertainty during pretreatment is still lacking despite it being one of the major sources of uncertainty and an essential step in biomass estimation. In this study, we investigated pretreatment uncertainty and conducted a comparative study on the uncertainty of three optical imagery preprocessing stages (radiometric calibration, atmospheric and terrain correction) in biomass estimation. A combination of statistical models (random forest) and multisource data (Landsat enhanced thematic mapper plus (ETM+), Landsat operational land imager (OLI), national forest inventory (NFI)) was used to estimate forest biomass. Particularly, mean absolute error (MAE) and relative error (RE) were used to assess and quantify the uncertainty of each pretreatment, while the coefficient of determination (R²) was employed to evaluate the accuracy of the model. The results obtained show that random forest (RF) and 10-fold cross validation algorithms provided reliable accuracy for biomass estimation to better understand the uncertainty in pretreatments. In this study, there was a considerable uncertainty in biomass estimation using original OLI and ETM+ images from. Uncertainty was lower after data processing, emphasizing the importance of pretreatments for improving accuracy in biomass estimation. Further, the effects of three pretreatments on uncertainty of biomass estimation were objectively quantified. In this study (results of test sample), a 33.70% uncertainty was found in biomass estimation using original images from the OLI, and a 34.28% uncertainty in ETM+. Radiometric calibration slightly increased the uncertainty of biomass estimation (OLI increased by 1.38%, ETM+ increased by 2.08%). Moreover, atmospheric correction (5.56% for OLI, 4.41% for ETM+) and terrain correction (1.00% for OLI, 1.67% for ETM+) significantly reduced uncertainty for OLI and ETM+, respectively. This is an important development in the field of improving the accuracy of biomass estimation by remote sensing. Notably, the three pretreatments presented the same trend in uncertainty during biomass estimation using OLI and ETM+. This may exhibit the same effects in other optical images. This article aims to quantify uncertainty in pretreatment and to analyze the resultant effects to provide a theoretical basis for improving the accuracy of biomass estimation. Full article

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Graphical abstract

35 pages, 5205 KiB

Open AccessArticle

Multistage Cascade Predictor of Structural Elements Movement in the Deformation Analysis of Large Objects Based on Time Series Influencing Factors

by Adis Hamzic, Zikrija Avdagic and Ingmar Besic

ISPRS Int. J. Geo-Inf. 2020, 9(1), 47; https://doi.org/10.3390/ijgi9010047 - 15 Jan 2020

Cited by 3 | Viewed by 3142

Abstract

Hydropower dam displacement is influenced by various factors (dam ageing, reservoir water level, air, water, and concrete temperature), which cause complex nonlinear behaviour that is difficult to predict. Object deformation monitoring is a task of geodetic and civil engineers who use different instruments [...] Read more.

Hydropower dam displacement is influenced by various factors (dam ageing, reservoir water level, air, water, and concrete temperature), which cause complex nonlinear behaviour that is difficult to predict. Object deformation monitoring is a task of geodetic and civil engineers who use different instruments and methods for measurements. Only geodetic methods have been used for the object movement analysis in this research. Although the whole object is affected by the influencing factors, different parts of the object react differently. Hence, one model cannot describe behaviour of every part of the object precisely. In this research, a localised approach is presented—two individual models are developed for every point strategically placed on the object: one model for the analysis and prediction in the direction of the X axis and the other for the Y axis. Additionally, the prediction of horizontal dam movement is not performed directly from measured values of influencing factors, but from predicted values obtained by machine learning and statistical methods. The results of this research show that it is possible to perform accurate short-term time series dam movement prediction by using machine learning and statistical methods and that the only limiting factor for improving prediction length is accurate weather forecast. Full article

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23 pages, 10245 KiB

Open AccessEditor’s ChoiceArticle

An Efficient Staged Evacuation Planning Algorithm Applied to Multi-Exit Buildings

by Litao Han, Huan Guo, Haisi Zhang, Qiaoli Kong, Aiguo Zhang and Cheng Gong

ISPRS Int. J. Geo-Inf. 2020, 9(1), 46; https://doi.org/10.3390/ijgi9010046 - 15 Jan 2020

Cited by 17 | Viewed by 4049

Abstract

When the occupant density of buildings is large enough, evacuees are prone to congestion during emergency evacuation, which leads to the extension of the overall escape time. Especially for multi-exit buildings, it’s a challenging problem to afford an effective evacuation plan. In this [...] Read more.

When the occupant density of buildings is large enough, evacuees are prone to congestion during emergency evacuation, which leads to the extension of the overall escape time. Especially for multi-exit buildings, it’s a challenging problem to afford an effective evacuation plan. In this paper, a novel evacuation planning algorithm applied to multi-exit buildings is proposed, which is based on an indoor route network model. Firstly, evacuees are grouped by their location proximity, then all groups are approximately equally classified into several evacuation zones, each of which has only one safe exit. After that, all evacuation groups in the same zone are sorted by their shortest path length, then the time window of each evacuation group occupying the safe exit is calculated in turn. In the case of congestion at the safe exit, the departure time of each evacuation group is delayed in its arrival order. The objectives of the proposed algorithm include minimizing the total evacuation time of all evacuees, the travel time of each evacuee, avoiding traffic congestion, balancing traffic loads among different exits, and achieving high computational efficiency. Case studies are conducted to examine the performance of our algorithm. The influences of group number, group size, evacuation speed on the total evacuation time are discussed on a single-exit network, and that of partitioning methods and evacuation density on the performance and applicability in different congestion levels are also discussed on a multi-exit network. Results demonstrate that our algorithm has a higher efficiency and performs better for evacuations with a large occupant density. Full article

(This article belongs to the Special Issue Spatial Big Data, BIM and advanced GIS for Smart Transformation: City, Infrastructure and Construction)

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19 pages, 5297 KiB

Open AccessArticle

Differences in the Gaze Behaviours of Pedestrians Navigating between Regular and Irregular Road Patterns

by Bing Liu, Weihua Dong, Zhicheng Zhan, Shengkai Wang and Liqiu Meng

ISPRS Int. J. Geo-Inf. 2020, 9(1), 45; https://doi.org/10.3390/ijgi9010045 - 15 Jan 2020

Cited by 10 | Viewed by 2832

Abstract

While a road pattern influences wayfinding and navigation, its influence on the gaze behaviours of navigating pedestrians is not well documented. In this study, we compared gaze behaviour differences between regular and irregular road patterns using eye-tracking technology. Twenty-one participants performed orientation (ORI) [...] Read more.

While a road pattern influences wayfinding and navigation, its influence on the gaze behaviours of navigating pedestrians is not well documented. In this study, we compared gaze behaviour differences between regular and irregular road patterns using eye-tracking technology. Twenty-one participants performed orientation (ORI) and shortest route selection (SRS) tasks with both road patterns. We used accuracy of answers and response time to estimate overall performance and time to first fixation duration, average fixation duration, fixation count and fixation duration to estimate gaze behaviour. The results showed that participants performed better with better accuracy of answers using irregular road patterns. For both tasks and both road patterns, the Label areas of interest (AOIs) (including shops and signs) received quicker or greater attention. The road patterns influenced gaze behaviour for both Road AOIs and Label AOIs but exhibited a greater influence on Road AOIs in both tasks. In summary, for orientation and route selection, users are more likely to rely on labels, and roads with irregular patterns are important. These findings may serve as the anchor point for determining how people’s gaze behaviours differ depending on road pattern and indicate that labels and unique road patterns should be highlighted for better wayfinding and navigation. Full article

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18 pages, 2521 KiB

Open AccessArticle

Spatio-Temporal Prediction of the Epidemic Spread of Dangerous Pathogens Using Machine Learning Methods

by Wolfgang B. Hamer, Tim Birr, Joseph-Alexander Verreet, Rainer Duttmann and Holger Klink

ISPRS Int. J. Geo-Inf. 2020, 9(1), 44; https://doi.org/10.3390/ijgi9010044 - 15 Jan 2020

Cited by 16 | Viewed by 4424

Abstract

Real-time identification of the occurrence of dangerous pathogens is of crucial importance for the rapid execution of countermeasures. For this purpose, spatial and temporal predictions of the spread of such pathogens are indispensable. The R package papros developed by the authors offers an [...] Read more.

Real-time identification of the occurrence of dangerous pathogens is of crucial importance for the rapid execution of countermeasures. For this purpose, spatial and temporal predictions of the spread of such pathogens are indispensable. The R package papros developed by the authors offers an environment in which both spatial and temporal predictions can be made, based on local data using various deterministic, geostatistical regionalisation, and machine learning methods. The approach is presented using the example of a crops infection by fungal pathogens, which can substantially reduce the yield if not treated in good time. The situation is made more difficult by the fact that it is particularly difficult to predict the behaviour of wind-dispersed pathogens, such as powdery mildew (Blumeria graminis f. sp. tritici). To forecast pathogen development and spatial dispersal, a modelling process scheme was developed using the aforementioned R package, which combines regionalisation and machine learning techniques. It enables the prediction of the probability of yield- relevant infestation events for an entire federal state in northern Germany at a daily time scale. To run the models, weather and climate information are required, as is knowledge of the pathogen biology. Once fitted to the pathogen, only weather and climate information are necessary to predict such events, with an overall accuracy of 68% in the case of powdery mildew at a regional scale. Thereby, 91% of the observed powdery mildew events are predicted. Full article

(This article belongs to the Special Issue Spatial Data Science)

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14 pages, 1521 KiB

Open AccessArticle

Optimization-Based Construction of Quadrilateral Table Cartograms

by Ryo Inoue and Mao Li

ISPRS Int. J. Geo-Inf. 2020, 9(1), 43; https://doi.org/10.3390/ijgi9010043 - 14 Jan 2020

Cited by 2 | Viewed by 2212

Abstract

A quadrilateral table cartogram is a rectangle-shaped figure that visualizes table-form data; quadrilateral cells in a table cartogram are transformed to express the magnitude of positive weights by their areas, while maintaining the adjacency of cells in the original table. However, the previous [...] Read more.

A quadrilateral table cartogram is a rectangle-shaped figure that visualizes table-form data; quadrilateral cells in a table cartogram are transformed to express the magnitude of positive weights by their areas, while maintaining the adjacency of cells in the original table. However, the previous construction method is difficult to implement because it consists of multiple operations that do not have a unique solution and require complex settings to obtain the desired outputs. In this article, we propose a new construction for quadrilateral table cartograms by recasting the construction as an optimization problem. The proposed method is formulated as a simple minimization problem to achieve mathematical clarity. It can generate quadrilateral table cartograms with smaller deformation of rows and columns, thereby aiding readers to recognize the correspondence between table cartograms and original tables. In addition, we also propose a means of sorting rows and/or columns prior to the construction of table cartograms to reduce excess shape deformation. Applications of the proposed method confirm its capability to output table cartograms that clearly visualize the characteristics of datasets. Full article

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20 pages, 13940 KiB

Open AccessArticle

Where Urban Youth Work and Live: A Data-Driven Approach to Identify Urban Functional Areas at a Fine Scale

by Yiming Yan, Yuanyuan Wang, Zhenhong Du, Feng Zhang, Renyi Liu and Xinyue Ye

ISPRS Int. J. Geo-Inf. 2020, 9(1), 42; https://doi.org/10.3390/ijgi9010042 - 14 Jan 2020

Cited by 5 | Viewed by 3206

Abstract

As a major labor force of cities, young people provide a huge driving force for urban innovation and development, and contribute to urban industrial upgrading and restructuring. In addition, with the acceleration of urbanization in China, the young floating population has increased rapidly, [...] Read more.

As a major labor force of cities, young people provide a huge driving force for urban innovation and development, and contribute to urban industrial upgrading and restructuring. In addition, with the acceleration of urbanization in China, the young floating population has increased rapidly, causing over-urbanization and creating certain social problems. It is important to analyze the demand of urban youth and promote their social integration. With the development of the mobile Internet and the improvement of the city express system, ordering food delivery has become a popular and convenient way to dine, especially in China. Food delivery data have a significant user attribute where the ages of most delivery customers are under 35 years old. In this paper, we introduce food delivery data as a new data source in urban functional zone detection and propose a time-series-based clustering approach to discover the urban hotspot areas of young people. The work and living areas were effectively identified according to the human behavioral characteristics of ordering food delivery. Furthermore, we analyzed the relationship between young people and the industry structure of Hangzhou and discovered that the geographical distribution of the identified work areas was similar to that of the Internet and e-commerce companies. The characteristics of the identified living areas were also analyzed in combination with the distribution of subway lines and residential communities, and it was found that the living areas were mainly distributed along subway lines and that urban villages appeared in the living hotspot regions, indicating that transportation and living cost were two important factors in the choice of residential location for young people. The findings of this paper can help urban industrial and residential planning and young population management. Full article

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

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21 pages, 7978 KiB

Open AccessArticle

Assessing Emergency Shelter Demand Using POI Data and Evacuation Simulation

by Wei Chen, Yao Fang, Qing Zhai, Wei Wang and Yijie Zhang

ISPRS Int. J. Geo-Inf. 2020, 9(1), 41; https://doi.org/10.3390/ijgi9010041 - 14 Jan 2020

Cited by 15 | Viewed by 3567

Abstract

Mapping the fine-scale spatial distribution of emergency shelter demand is crucial for shelter planning during disasters. To provide shelter for people within a reasonable evacuation distance under day and night disaster scenarios, we formed an approach for examining the distribution of day and [...] Read more.

Mapping the fine-scale spatial distribution of emergency shelter demand is crucial for shelter planning during disasters. To provide shelter for people within a reasonable evacuation distance under day and night disaster scenarios, we formed an approach for examining the distribution of day and night shelter demand at the plot-scale using point of interest (POI) data, and then analyzed the supply and demand status of shelters after an evacuation simulation built in Python programming language. Taking the downtown areas of Guangzhou, China as a case study, the results show that significant differences exist in the size and spatial distribution of shelter demand in daytime and nighttime, and the total demand is 7.929 million people, which is far larger than the resident population. The average evacuation time of all 16,883 routes is 12.6 min, and after the evacuation, 558 of 888 shelters exceed their capacity to varying degrees, accounting for 62.84% of the total, indicating that the shelters cannot completely receive the potential evacuees. The method proposed in this paper provides a direct quantitative basis for the number and size of new shelter resources being planned during urban renewal activities, and form a reference for land reuse and disaster prevention space organization in future urban planning. Full article

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18 pages, 15585 KiB

Open AccessEditor’s ChoiceArticle

Spatial Multi-Objective Land Use Optimization toward Livability Based on Boundary-Based Genetic Algorithm: A Case Study in Singapore

by Kai Cao, Muyang Liu, Shu Wang, Mengqi Liu, Wenting Zhang, Qiang Meng and Bo Huang

ISPRS Int. J. Geo-Inf. 2020, 9(1), 40; https://doi.org/10.3390/ijgi9010040 - 14 Jan 2020

Cited by 19 | Viewed by 4386

Abstract

In this research, the concept of livability has been quantitatively and comprehensively reviewed and interpreted to contribute to spatial multi-objective land use optimization modelling. In addition, a multi-objective land use optimization model was constructed using goal programming and a weighted-sum approach, followed by [...] Read more.

In this research, the concept of livability has been quantitatively and comprehensively reviewed and interpreted to contribute to spatial multi-objective land use optimization modelling. In addition, a multi-objective land use optimization model was constructed using goal programming and a weighted-sum approach, followed by a boundary-based genetic algorithm adapted to help address the spatial multi-objective land use optimization problem. Furthermore, the model is successfully and effectively applied to the case study in the Central Region of Queenstown Planning Area of Singapore towards livability. In the case study, the experiments based on equal weights and experiments based on different weights combination have been successfully conducted, which can demonstrate the effectiveness of the spatial multi-objective land use optimization model developed in this research as well as the robustness and reliability of computer-generated solutions. In addition, the comparison between the computer-generated solutions and the two real planned scenarios has also clearly demonstrated that our generated solutions are much better in terms of fitness values. Lastly, the limitation and future direction of this research have been discussed. Full article

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

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16 pages, 4221 KiB

Open AccessArticle

A GRID-Based Spatial Interpolation Method as a Tool Supporting Real Estate Market Analyses

by Agnieszka Szczepańska, Dariusz Gościewski and Małgorzata Gerus-Gościewska

ISPRS Int. J. Geo-Inf. 2020, 9(1), 39; https://doi.org/10.3390/ijgi9010039 - 14 Jan 2020

Cited by 6 | Viewed by 2714

Abstract

The spatial distribution of prices is closely linked with the urban real estate market. Property prices are one of the key indicators of economic activity because they influence economic decisions. Decision-makers and consumers often need information about the spatial distribution of prices, but [...] Read more.

The spatial distribution of prices is closely linked with the urban real estate market. Property prices are one of the key indicators of economic activity because they influence economic decisions. Decision-makers and consumers often need information about the spatial distribution of prices, but spatial-temporal analyses of the real estate market are based on the prices quoted in different locations across years (epochs). Due to this idiosyncrasy, the resulting datasets are dispersed (different across years) and difficult to compare. For this reason, the existing interpolation methods are not always effective in analyses of the real estate market. A different approach to interpolating real estate prices that supports the generation of continuous interpolated surfaces while maintaining the values of measurement points is thus needed. This paper proposes a method for replacing dispersed spatial data with a regular GRID structure. The GRID structure covers the measured object with a regular network of nodes, which supports uniform interpolation at every point of the analyzed space and a comparison of interpolation models in successive epochs (years). The proposed method was tested on a selected object. The results indicate that the GRID structure can be used in analyses of highly complex real estate markets where input data are incomplete, irregular and dispersed. Full article

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17 pages, 6516 KiB

Open AccessArticle

Revealing the Correlation between Population Density and the Spatial Distribution of Urban Public Service Facilities with Mobile Phone Data

by Yi Shi, Junyan Yang and Peiyu Shen

ISPRS Int. J. Geo-Inf. 2020, 9(1), 38; https://doi.org/10.3390/ijgi9010038 - 13 Jan 2020

Cited by 31 | Viewed by 5699

Abstract

Some studies have confirmed the association between urban public services and population density; however, other studies using census data, for example, have arrived at the opposite conclusion. Mobile signaling data provide new technological tools to investigate the subject. Based on the data of [...] Read more.

Some studies have confirmed the association between urban public services and population density; however, other studies using census data, for example, have arrived at the opposite conclusion. Mobile signaling data provide new technological tools to investigate the subject. Based on the data of 20 million 2G mobile phone users in downtown Shanghai and the land use data of urban public service facilities, this study explores the spatiotemporal correlation between population density and public service facilities’ locations in downtown Shanghai and its variation laws. The correlation between individual population density at day vs. night and urban public service facilities distribution was also examined from a dynamic perspective. The results show a correlation between service facilities’ locations and urban population density at different times of the day. As a result, the average population density observed over a long period of time (day-time periodicity or longer) with census data or remote sensing data does not directly correlation with the distribution of public service facilities despite its correlation with public service facilities distribution. Among them, there is a significant spatial correlation between public service facilities and daytime population density and a significant spatial correlation between non-public service facilities and night-time population density. The spatial and temporal changes in the relationship between urban population density and service facilities is due to changing crowd behavior; however, the density of specific types of behavior is the real factor that affects the layout of urban public service facilities. The results show that mobile signaling data and land use data of service facilities are of great value for studying the spatiotemporal correlations between urban population density and service facilities. Full article

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21 pages, 2896 KiB

Open AccessEditor’s ChoiceArticle

The Role of Spatial Context Information in the Generalization of Geographic Information: Using Reducts to Indicate Relevant Attributes

by Anna Fiedukowicz

ISPRS Int. J. Geo-Inf. 2020, 9(1), 37; https://doi.org/10.3390/ijgi9010037 - 10 Jan 2020

Cited by 4 | Viewed by 3271

Abstract

Generalization of geographic information enables cognition and understanding not only of objects and phenomena located in space but also the relations and processes between them. The automation of this process requires formalization of cartographic knowledge, including information on the spatial context of objects. [...] Read more.

Generalization of geographic information enables cognition and understanding not only of objects and phenomena located in space but also the relations and processes between them. The automation of this process requires formalization of cartographic knowledge, including information on the spatial context of objects. However, the question remains which information is crucial to the decisions regarding the generalization (in this paper: selection) of objects. The article presents and compares the usability of three methods based on rough set theories (rough set theory, dominance-based rough set theory, fuzzy rough set theory) that facilitate the designation of the attributes relevant to a decision. The methods are using different types (levels of measurements) of attributes. The author determines reducts and their cores (common elements) that show the relevance of attributes stemming from the spatial context. The fuzzy rough set theory method proved the least useful, whereas the rough set theory and dominance-based rough set theory methods seem to be recommendable (depending on the governing level of measurement). Full article

(This article belongs to the Special Issue Map Generalization)

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18 pages, 3555 KiB

Open AccessArticle

A Data-Driven Framework for Walkability Measurement with Open Data: A Case Study of Triple Cities, New York

by Chengbin Deng, Xiaoyu Dong, Huihai Wang, Weiying Lin, Hao Wen, John Frazier, Hung Chak Ho and Louisa Holmes

ISPRS Int. J. Geo-Inf. 2020, 9(1), 36; https://doi.org/10.3390/ijgi9010036 - 09 Jan 2020

Cited by 13 | Viewed by 5188

Abstract

Walking is the most common, environment-friendly, and inexpensive type of physical activity. To perform in-depth walkability analysis, one option is to objectively evaluate different aspects of built environment related to walkability. In this study, we proposed a computational framework for walkability measurement using [...] Read more.

Walking is the most common, environment-friendly, and inexpensive type of physical activity. To perform in-depth walkability analysis, one option is to objectively evaluate different aspects of built environment related to walkability. In this study, we proposed a computational framework for walkability measurement using open data. Three major steps of this framework include the web scrapping of publicly available online data, determining varying weights of variables, and generating a synthetic walkability index. The results suggest three major conclusions. First, the proposed framework provides an explicit mechanism for walkability measurement. Second, the synthetic walkability index from this framework is comparable to Walk Score, and it tends to have a slightly higher sensitivity, especially in highly walkable areas in urban core. Third, this framework was effectively applied in a metropolitan area that contains three small cities that together represent a small, old shrinking region, which extends the topical area in the literature. This framework has the potential to quantify walkability in any city, especially cities with a small population where walkability has rarely been studied, or those having no quantification indicator. For such areas, researchers can calculate the synthetic walkability index based on this framework, to assist urban planners, community leaders, health officials, and policymakers in their practices to improve the walking environment of their communities. Full article

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

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2 pages, 180 KiB

Open AccessCorrection

Correction: Gu, Q., et al. Regionalization Analysis and Mapping for the Source and Sink of Tourist Flows. ISPRS Int. J. Geo-Inf. 2019, 8, 314

by Qiushi Gu, Haiping Zhang, Min Chen and Chongcheng Chen

ISPRS Int. J. Geo-Inf. 2020, 9(1), 35; https://doi.org/10.3390/ijgi9010035 - 07 Jan 2020

Viewed by 1818

Abstract

The authors wish to make the following corrections to their paper [...] Full article

21 pages, 10312 KiB

Open AccessArticle

Procedures for Condition Mapping Using 360° Images

by Luigi Barazzetti, Mattia Previtali and Marco Scaioni

ISPRS Int. J. Geo-Inf. 2020, 9(1), 34; https://doi.org/10.3390/ijgi9010034 - 07 Jan 2020

Cited by 15 | Viewed by 3156

Abstract

The identification of deterioration mechanisms and their monitoring over time is an essential phase for conservation. This work aimed at developing a novel approach for deterioration mapping and monitoring based on 360° images, which allows for simple and rapid data collection. The opportunity [...] Read more.

The identification of deterioration mechanisms and their monitoring over time is an essential phase for conservation. This work aimed at developing a novel approach for deterioration mapping and monitoring based on 360° images, which allows for simple and rapid data collection. The opportunity to capture the whole scene around a 360° camera reduces the number of images needed in a condition mapping project, resulting in a powerful solution to document small and narrow spaces. The paper will describe the implemented workflow for deterioration mapping based on 360° images, which highlights pathologies on surfaces and quantitatively measures their extension. Such a result will be available as standard outputs as well as an innovative virtual environment for immersive visualization. The case of multi-temporal data acquisition will be considered and discussed as well. Multiple 360° images acquired at different epochs from slightly different points are co-registered to obtain pixel-to-pixel correspondence, providing a solution to quantify and track deterioration effects. Full article

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16 pages, 3207 KiB

Open AccessArticle

Urban Ecological Corridor Network Construction: An Integration of the Least Cost Path Model and the InVEST Model

by Yuhan Tang, Chi Gao and Xuefei Wu

ISPRS Int. J. Geo-Inf. 2020, 9(1), 33; https://doi.org/10.3390/ijgi9010033 - 06 Jan 2020

Cited by 47 | Viewed by 5845

Abstract

Under the background of urban expansion, ecological protection cannot be delayed. The construction of ecological networks is of considerable significance to ecosystem services. However, in the process of constructing a corridor network, there is no uniform standard for the selection of ecological sources [...] Read more.

Under the background of urban expansion, ecological protection cannot be delayed. The construction of ecological networks is of considerable significance to ecosystem services. However, in the process of constructing a corridor network, there is no uniform standard for the selection of ecological sources and the determination of cost factors. The InVEST model is an effective complement to ecosystem service assessment for sensitively measuring external threats and their threat intensity. Therefore, taking Wuhan as an example, we combined InVEST and the least cost path model (LCP) to construct a multi-target corridor network with comprehensive cost factors for birds and small terrestrial mammals. The results showed that: (1) The InVEST model provided a reliable basis for ecological source screening by demonstrating the distribution of habitat quality. (2) The corridor with a length of 12–25 km presented a “U” shape, and the impact of urbanization on small terrestrial mammals was more significant than that of birds. (3) The integrated network pattern proposed by the “point-line-plane” principle would provide a reference for urban ecological construction and sustainable development. Full article

(This article belongs to the Special Issue Application of GIS for Biodiversity Research)

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13 pages, 21335 KiB

Open AccessArticle

Statistical Correlation between Monthly Electric Power Consumption and VIIRS Nighttime Light

by Jintang Lin and Wenzhong Shi

ISPRS Int. J. Geo-Inf. 2020, 9(1), 32; https://doi.org/10.3390/ijgi9010032 - 05 Jan 2020

Cited by 18 | Viewed by 3085

Abstract

The nighttime light (NTL) imagery acquired from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) enables feasibility of investigating socioeconomic activities at monthly scale, compared with annual study using nighttime light data acquired from the Defense Meteorological Satellite Program/Operational Linescan System [...] Read more.

The nighttime light (NTL) imagery acquired from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) enables feasibility of investigating socioeconomic activities at monthly scale, compared with annual study using nighttime light data acquired from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS). This paper is the first attempt to discuss the quantitative correlation between monthly composite VIIRS DNB NTL data and monthly statistical data of electric power consumption (EPC), using 14 provinces of southern China as study area. Two types of regressions (linear regression and polynomial regression) and nine kinds of NTL with different treatments are employed and compared in experiments. The study demonstrates that: (1) polynomial regressions acquire higher reliability, whose average R square is 0.8816, compared with linear regressions, whose average R square is 0.8727; (2) regressions between denoised NTL with threshold of 0.3 nW/(cm²·sr) and EPC steadily exhibit the strongest reliability among the nine kinds of processed NTL data. In addition, the polynomial regressions for 12 months between denoised NTL with threshold of 0.3 nW/(cm²·sr) and EPC are constructed, whose average values of R square and mean absolute relative error are 0.8906 and 16.02%, respectively. These established optimal regression equations can be used to accurately estimate monthly EPC of each province, produce thematic maps of EPC, and analyze their spatial distribution characteristics. Full article

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17 pages, 4758 KiB

Open AccessArticle

Map Matching for Urban High-Sampling-Frequency GPS Trajectories

by Minshi Liu, Ling Zhang, Junlian Ge, Yi Long and Weitao Che

ISPRS Int. J. Geo-Inf. 2020, 9(1), 31; https://doi.org/10.3390/ijgi9010031 - 05 Jan 2020

Cited by 12 | Viewed by 2890

Abstract

As a fundamental component of trajectory processing and analysis, trajectory map-matching can be used for urban traffic management and tourism route planning, among other applications. While there are many trajectory map-matching methods, urban high-sampling-frequency GPS trajectory data still depend on simple geometric matching [...] Read more.

As a fundamental component of trajectory processing and analysis, trajectory map-matching can be used for urban traffic management and tourism route planning, among other applications. While there are many trajectory map-matching methods, urban high-sampling-frequency GPS trajectory data still depend on simple geometric matching methods, which can lead to mismatches when there are multiple trajectory points near one intersection. Therefore, this study proposed a novel segmented trajectory matching method in which trajectory points were separated into intersection and non-intersection trajectory points. Matching rules and processing methods dedicated to intersection trajectory points were developed, while a classic “Look-Ahead” matching method was applied to non-intersection trajectory points, thereby implementing map matching of the whole trajectory. Then, a comparative analysis between the proposed method and two other new related methods was conducted on trajectories with multiple sampling frequencies. The results indicate that the proposed method is not only competent for intersection matching with high-frequency trajectory data but also superior to two other methods in both matching efficiency and accuracy. Full article

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ISPRS Int. J. Geo-Inf., Volume 9, Issue 1 (January 2020) – 60 articles

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