Submit to IJGI Review for IJGI Edit a Special Issue

Journal Browser

► Journal Browser

Special Issue "Urban Geospatial Analytics Based on Crowdsourced Data"

Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 26421

Share This Special Issue

Special Issue Editors

Dr. Hangbin Wu

E-Mail Website
Guest Editor

College of Surveying and Geoinformatics, Tongji University, Shanghai 200092, China
Interests: crowd dynamic monitoring; travel mode classification; urban sensing; point cloud

Dr. Tessio Novack

E-Mail Website
Guest Editor

Centre for Interdisciplinary Methodologies, University of Warwick, Coventry, UK
Interests: urban mobility; geo-visualisation; spatio-temporal modelling; critical GIS

Special Issue Information

Dear Colleagues,

In recent years, geospatial knowledge extraction from massive crowdsourced datasets has become one of the main foci of geographic information science. However, factors such as the multiplicity of data formats, the variable and uncertain data quality, the often flexible data contribution guidelines, as well as issues related to data accessibility and sampling, are persistent challenges that the geospatial community needs to cope with in order to develop efficient technical solutions and advance geospatial theories based on this type of data.

Supported by advancements in ubiquitous sensing and computing, information and communication technologies, and location-based services, crowdsourced data on human mobility practices and daily activities, as well as on the structure and form of geographical space, have been extensively generated. The potential for spatial knowledge extraction from such data is highly relevant, particularly for urban studies. Given the challenges mentioned above, though, advanced tools and novel approaches need to be developed to harness big crowdsourced geospatial datasets towards effective geospatial knowledge extraction. The outcomes of that effort shall, in different ways, benefit the general public, researchers, and governments alike.

The aim of this Special Issue is to present state-of-the-art research on methods, theories, applications, and services developed based on crowdsourced geospatial datasets. Due to the multidisciplinary nature of the topic, contributions may be within different fields of research, including GIS, volunteered geographic information, big spatial data analytics, geospatial artificial intelligence, computer vision, machine learning, urban analytics, and many others.

Contributions may be conventional research articles focusing on technical solutions or theoretical developments as well as literature reviews. Keywords summarizing the scope of the Special Issue include:

Urban mobility analyses;
Data quality management;
Data inequalities;
Data conflation;
Knowledge extraction.

Dr. Hangbin Wu
Dr. Tessio Novack
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

big spatial data
geospatial knowledge
spatial crowdsourcing
social media
trajectory analysis
task allocation
volunteered geographic information
spatiotemporal modeling
urban computing

Published Papers (23 papers)

Download All Papers

Research

Open AccessArticle

Spatiotemporal Patterns Evolution of Residential Areas and Transportation Facilities Based on Multi-Source Data: A Case Study of Xi’an, China

Xinyi Lai

and

Chao Gao

ISPRS Int. J. Geo-Inf. 2023, 12(6), 233; https://doi.org/10.3390/ijgi12060233 (registering DOI) - 06 Jun 2023

Viewed by 46

Abstract

The spatiotemporal patterns of residential and supporting service facilities are critical to effective urban planning. However, with growing urban sprawl and congestion, the spatial distribution patterns and evolutionary characteristics of these areas show significant uncertainty. This research was conducted for six phases from 2012 to 2022, incorporating datasets of point of interest (POI) data for residential areas and transportation facilities (RATFs) and OpenStreetMap (OSM) data. Using exploratory spatial data analysis (ESDA) and standard deviation ellipse, we investigated the spatiotemporal patterns and directional characteristics of RATFs in Xi’an, as well as their evolution and underlying causes. The analysis demonstrated that: (1) The spatial distribution of RATFs in Xi’an exhibits non-uniform and gradually evolving patterns, with significant spatial agglomeration characteristics over the past decade. Residential areas (RAs) exhibit a spatial autocorrelation that is high in the middle and low in the surrounding areas, while transportation facilities (TFs) exhibit spatial patterns that are high in the southern and low in the northern areas. (2) Overall, the number of RATFs has continued to increase, and they exhibit significant spatial autocorrelation. Specifically, the trend of RAs concentrating in the central city has become increasingly prominent, while TFs have expanded from the center to the north. (3) Furthermore, from the perspective of supply–demand matching, this study proposes targeted adjustment strategies for the distribution of RATFs. It provides significant references for the optimization of service facilities and provides new ideas and practical experience for urban spatial analysis methods based on multi-source data. Full article

(This article belongs to the Special Issue Urban Geospatial Analytics Based on Crowdsourced Data)

► Show Figures

Figure 1

Open AccessArticle

Verification of Geographic Laws Hidden in Textual Space and Analysis of Spatial Interaction Patterns of Information Flow

and

ISPRS Int. J. Geo-Inf. 2023, 12(6), 217; https://doi.org/10.3390/ijgi12060217 - 26 May 2023

Viewed by 405

Abstract

The rapid development of Internet technology has formed a huge virtual information space. In the information space, information flow has become a link of communication between objects. Information flow is an alternative or supplement to the traditional physical flow for the study of the spatial interaction of geographical entities. The research uses toponym co-occurrence and search index as information flow data, verifies the geographical laws hidden in the information space by spatial autocorrelation analysis and gravity model fitting, and analyzes the spatial interaction patterns of provinces in China in the information space by complex network analysis methods. The results show that: (1) information flow in the information space obeys Tobler’s first law of geography and Goodchild’s second law of geography. The spatial interaction represented by information flow has a distance decay effect. The best distance decay coefficients for toponym co-occurrence and the search index are 0.189 and 0.186, respectively. (2) The inter-provincial spatial interaction network of China shows a hierarchical pattern of the triangular primary network and diamond secondary network, and the ranking of provinces in the centrality analysis is basically stable, but the network hierarchy is deepening. The gravity center of spatial interaction is located in the east-central region of China. (3) The information flow-based interaction network is of higher asymmetry than the population mobility network, and its spatial structure is also obvious. This research provides a new idea for studying the spatial interaction of geographical entities in the physical world from the perspective of information flow. Full article

(This article belongs to the Special Issue Urban Geospatial Analytics Based on Crowdsourced Data)

► Show Figures

Figure 1

Open AccessArticle

Exploring Public Transportation Supply–Demand Structure of Beijing from the Perspective of Spatial Interaction Network

and

ISPRS Int. J. Geo-Inf. 2023, 12(6), 213; https://doi.org/10.3390/ijgi12060213 - 23 May 2023

Viewed by 411

Abstract

A comprehensive understanding of the relationship between public transportation supply and demand is crucial for the construction and sustainable development of urban transportation. Due to the spatial and networked nature of public transportation, revealing the spatial configuration and structural disparities between public transportation supply and demand networks (TSN and TDN) can provide significant insights into complex urban systems. In this study, we explored the spatial configuration and structural disparities between TSN and TDN in the complex urban environment of Beijing. By constructing subdistrict-scale TSN and TDN using urban public transportation operation data and mobile phone data, we analyzed the spatial characteristics and structural disparities of these networks from various dimensions, including global indicators, three centralities, and community structure, and measured the current public transportation supply and demand matching pattern in Beijing. Our findings revealed strong structural and geographic heterogeneities of TSN and TDN, with significant traffic supply–demand mismatch being observed in urban areas within the Sixth Ring Road. Moreover, based on the percentage results of supply–demand matching patterns, we identified that the current public transportation supply–demand balance in Beijing is approximately 64%, with around 18% of both excess and shortage of traffic supply. These results provide valuable insights into the structure and functioning of public transportation supply–demand networks for policymakers and urban planners; these can be used to facilitate the development of a sustainable urban transportation system. Full article

(This article belongs to the Special Issue Urban Geospatial Analytics Based on Crowdsourced Data)

► Show Figures

Figure 1

Open AccessArticle

Isolated or Colocated? Exploring the Spatio-Temporal Evolution Pattern and Influencing Factors of the Attractiveness of Residential Areas to Restaurants in the Central Urban Area

Ruien Tang

Guolin Hou

and

Rui Du

ISPRS Int. J. Geo-Inf. 2023, 12(5), 202; https://doi.org/10.3390/ijgi12050202 - 15 May 2023

Viewed by 472

Abstract

Catering and urban elements have a strong spatial association. The spatial clustering and dispersal patterns of catering can effectively influence cities’ economic and socio-spatial reconfiguration. This research first introduced the concept of the ARTR (the attractiveness of residential areas to restaurants) and measured its value as well as its spatial and temporal evolutionary patterns using global and local colocation quotients. The DBSCAN algorithm and spatial hot-spot analysis were used to analyze their spatial evolution patterns. On this basis, a multiscale geographically weighted regression (MGWR) model was used to analyze the scale of and spatial variation in the drivers. The results show that (1) Nanjing’s ARTR is at a low level, with the most significant decline in ARTR occurring from 2005 to 2020 for MRs and HRs, while LRs did not significantly respond to urban regeneration. (2) The spatial layout of the ARTR in Nanjing has gradually evolved from a circular structure to a semi-enclosed structure, and the circular structure has continued to expand outward. At the same time, the ARTR for different levels of catering shows a diverse distribution in the margins. (3) Urban expansion and regeneration have led to increasingly negative effects of the clustering level, commercial competition, economic level and neighborhood newness, while the density of the road network has been more stable. (4) The road network density has consistently remained a global influence. Commercial diversity has changed from a local factor to a global factor, while economic and locational factors have strongly spatially non-smooth relationships with the ARTR. The results of this study can provide a basis for a harmonious relationship between catering and residential areas in the context of urban expansion and regeneration. Full article

(This article belongs to the Special Issue Urban Geospatial Analytics Based on Crowdsourced Data)

► Show Figures

Figure 1

Open AccessArticle

A Deep Transfer Learning Toponym Extraction and Geospatial Clustering Framework for Investigating Scenic Spots as Cognitive Regions

and

ISPRS Int. J. Geo-Inf. 2023, 12(5), 196; https://doi.org/10.3390/ijgi12050196 - 12 May 2023

Viewed by 474

Abstract

In recent years, the Chinese tourism industry has developed rapidly, leading to significant changes in the relationship between people and space patterns in scenic regions. To attract more tourists, the surrounding environment of a scenic region is usually well developed, attracting a large number of human activities, which creates a cognitive range for the scenic region. From the perspective of tourism, tourists’ perceptions of the region in which tourist attractions are located in a city usually differ from the objective region of the scenic spots. Among them, social media serves as an important medium for tourists to share information about scenic spots and for potential tourists to learn scenic spot information, and it interacts to influence people’s perceptions of the destination image. Extracting the names of tourist attractions from social media data and exploring their spatial distribution patterns is the basis for research on the cognitive region of tourist attractions. This study takes Hangzhou, a well-known tourist city in China, as a case study to explore the human cognitive region of its popular scenic spots. First, we propose a Chinese tourist attraction name extraction model based on RoBERTa-BiLSTM-CRF to extract the names of tourist attractions from social media data. Then, we use a multi-distance spatial clustering method called Ripley’s K to filter the extracted tourist attraction names. Finally, we combine road network data and polygons generated using the chi-shape algorithm to construct the vague cognitive regions of each scenic spot. The results show that the classification indicators of our proposed tourist attraction name extraction model are significantly better than those of previous toponym extraction models and algorithms (precision = 0.7371, recall = 0.6926, F1 = 0.7141), and the extracted vague cognitive regions of tourist attractions also generally conform to people’s habitual cognition. Full article

(This article belongs to the Special Issue Urban Geospatial Analytics Based on Crowdsourced Data)

► Show Figures

Figure 1

Open AccessArticle

Linguistic Landscape of Arabs in New York City: Application of a Geosemiotics Analysis

Siham Mousa Alhaider

ISPRS Int. J. Geo-Inf. 2023, 12(5), 192; https://doi.org/10.3390/ijgi12050192 - 05 May 2023

Viewed by 574

Abstract

The investigation of linguistic landscapes (LL) among the Arab community in downtown Brooklyn, New York City, is an underserved public space in the literature. This research focused on social and commercial or ‘bottom-up signs’ in LL to understand their purpose, origin and target audience. Drawing upon discourse analysis, the study was conceptualized according to the principles of border theory and geosemiotics. The latter was used to analyze the data, which consisted of random photographs of shopfronts in Brooklyn taken with a digital camera during the summer of 2016. The three semiotic aggregates used for analysis consisted of interaction order, visual and place semiotics. The data analysis showed the multi-layered nature of LL in this urban community and the subjectiveness of spatial borders through a combination of text and symbolic imagery. The paper highlights the importance of commercial signs in the LL among ethnic minority communities. Full article

(This article belongs to the Special Issue Urban Geospatial Analytics Based on Crowdsourced Data)

► Show Figures

Figure 1

Open AccessArticle

Assessment of Perceived and Physical Walkability Using Street View Images and Deep Learning Technology

and

ISPRS Int. J. Geo-Inf. 2023, 12(5), 186; https://doi.org/10.3390/ijgi12050186 - 02 May 2023

Viewed by 1027

Abstract

As neighborhood walkability has gradually become an important topic in various fields, many cities around the world are promoting an eco-friendly and people-centered walking environment as a top priority in urban planning. The purpose of this study is to visualize physical and perceived walkability in detail and analyze the differences to prepare alternatives for improving the neighborhood’s walking environment. The study area is Jeonju City, one of the medium-sized cities in Korea. For the evaluation of perceived walkability, 196,624 street view images were crawled and 127,317 pairs of training datasets were constructed. After developing a convolutional neural network model, the scores of perceived walkability are predicted. For the evaluation of physical walkability, eight indicators are selected, and the score of overall physical walkability is calculated by combining the scores of the eight indicators. After that, the scores of perceived and physical walkability are visualized, and the difference between them is analyzed. This study is novel in three aspects. First, we develop a deep learning model that can improve the accuracy of perceived walkability using street view images, even in small and medium-sized cities. Second, in analyzing the characteristics of street view images, the possibilities and limitations of the semantic segmentation technique are confirmed. Third, the differences between perceived and physical walkability are analyzed in detail, and how the results of our study can be used to prepare alternatives for improving the walking environment is presented. Full article

(This article belongs to the Special Issue Urban Geospatial Analytics Based on Crowdsourced Data)

► Show Figures

Figure 1

Open AccessArticle

GIS Analysis of Adequate Accessibility to Public Transportation in Metropolitan Areas

and

ISPRS Int. J. Geo-Inf. 2023, 12(5), 180; https://doi.org/10.3390/ijgi12050180 - 25 Apr 2023

Viewed by 868

Abstract

The public transport system plays an important role in a city as it moves people from one place to another efficiently and economically. The public transport network must be organized in a way that will cover as many places and as much of the population as possible, and support the city’s growth. As one of Australia’s largest capital cities, Melbourne is growing and expanding its metropolitan area to reflect the growth in population and an increased number of activities. To date, little research has been conducted to determine the accessibility and adequacy of public transport taking into consideration the blank spot areas, the number of public transport options for each area, the population density within specific geographical areas, and other issues. In this study, a new measurement model is developed that examines public transport in residential areas and the extent to which it is adequate for the various local government areas (LGAs). An accessibility approach is adopted to evaluate the accessibility of different types of public transportation in residential areas in metropolitan Melbourne, Victoria, Australia. The results show that in most LGAs, the number of blank spots will decrease as the population density increases. This indicates that residents in lower-density areas will have less accessibility to public transportation. However, there is no indication that there is a greater level of services (such as more night-time and weekend public transportation services) in the high-density areas. This research is significant as it will point to and help to improve the areas with inadequate public transportation and other issues, taking into consideration their geographical locations and population density. Full article

(This article belongs to the Special Issue Urban Geospatial Analytics Based on Crowdsourced Data)

► Show Figures

Figure 1

Open AccessArticle

Measuring Traffic Congestion with Novel Metrics: A Case Study of Six U.S. Metropolitan Areas

and

ISPRS Int. J. Geo-Inf. 2023, 12(3), 130; https://doi.org/10.3390/ijgi12030130 - 20 Mar 2023

Viewed by 1076

Abstract

Quantifying traffic congestion is a critical task for transportation planning and research. Numerous metrics have been developed, mainly focusing on changes in vehicle speeds, their extents, and travel time. In this study, new metrics are presented using the Hägerstrand’s space-time cube that has been studied from time geography perspectives since the 1960s. Particularly, the product of distance and time, i.e., distanceTime, is proposed as a base metric to measure traffic congestion amounts. Using the base metric such as mileHours, metrics of weighted congestion and normalized congestion amounts were also developed. New metrics were applied to six metropolitan areas and their vicinities in the United States (Atlanta, Chicago, Washington, D.C. and Baltimore, Dallas and Fort Worth, Los Angeles, and New York), and congestion amounts were calculated and compared. The Google Traffic Layer API was used to obtain traffic congestion datasets for six months (April–September 2022), and GIS (geographic information systems) was used for delineating road features and traffic intensity levels. Among the six areas, New York and its vicinity showed the largest congestion when only heavy congestion was used. Los Angeles and its vicinity showed the largest congestion when all congestion levels were considered. This study shows that the proposed metrics are very effective in summarizing traffic amounts and broadly applicable for further analyses of traffic congestion phenomena by associating various other factors, such as weekdays, months, or gas prices. The new metrics developed in this research may help transportation researchers and practitioners by providing them with a set of metrics applicable to summarizing congestion amounts by synthesizing congestion intensity, extent, and duration. Full article

(This article belongs to the Special Issue Urban Geospatial Analytics Based on Crowdsourced Data)

► Show Figures

Figure 1

Open AccessArticle

Spatial Pattern Evolution and Influencing Factors of Tourism Flow in the Chengdu–Chongqing Economic Circle in China

Xuejun Chen

Yang Huang

and

Yuesheng Chen

ISPRS Int. J. Geo-Inf. 2023, 12(3), 121; https://doi.org/10.3390/ijgi12030121 - 09 Mar 2023

Viewed by 967

Abstract

Based on Ctrip’s ‘tourism digital footprint’, the spatial pattern of tourism flows in the Chengdu–Chongqing Economic Circle from 2018 to 2021 is explored, social network analysis and spatial visualisation of tourism information data are conducted, and factors affecting the network structure of tourism flows are analysed using linear weighted regression methods. The results show that tourism flows in the Chengdu–Chongqing Economic Circle show a significant ‘dual core’ polarisation effect. At the end of 2019, as a turning point, the density value of the tourism flow network shows an irregular inverted ‘U’ distribution. Kuanzhai Alley, Hong Ya Dong and Chunxi Road have irreplaceable competitive advantages in the tourism flow network. The density of highways, the number of star-rated hotels and the regional GDP per capita are positively correlated with the effective size of the structural hole of the administrative unit. Finally, based on the research results, countermeasures are proposed to optimise the tourism development of the Chengdu–Chongqing Economic Circle. Full article

(This article belongs to the Special Issue Urban Geospatial Analytics Based on Crowdsourced Data)

► Show Figures

Figure 1

Open AccessArticle

Sustainability Indicators and GIS as Land-Use Planning Instrument Tools for Urban Model Assessment

Montaña Jiménez-Espada

Francisco Manuel Martínez García

and

Rafael González-Escobar

ISPRS Int. J. Geo-Inf. 2023, 12(2), 42; https://doi.org/10.3390/ijgi12020042 - 30 Jan 2023

Viewed by 1400

Abstract

Among the priority concerns that figure in the public manager’s portfolio, the existing problems in cities when planning a more efficient management of urban space are well known. Within the wide range of reflections that local corporations consider, one of their main concerns is based on achieving a more livable city model, where the quality of life of its inhabitants is substantially improved and founded on sustainable development parameters. In view of these considerations, the purpose of this research is to establish an analysis of the formal relationship between urban sustainability and spatial morphology in a medium-sized Spanish city chosen as a pattern. The methodological process established combines the application of open data (from public administrations) with the calculation of urban sustainability indicators and GIS tools, with a particular focus at the neighborhood level. The results obtained at a global level throughout the city show that a large number of indicators including density, green areas, public facilities, public parking and cultural heritage elements are above the minimum standards required, which means that they comfortably meet the regulatory requirements and presumably present an adequate degree of sustainability. On the other hand, other indicators such as building compactness, urban land sponging and organic and recycling bins are below the minimum required standard. Considering the evaluation of the urban model obtained and, through the urban planning instruments set out in the law, the necessary corrective measures must be established to try to adapt the urban configuration to the objectives of sustainable development. It can be concluded that the implementation of urban sustainability indicators as a territorial planning tool linked to GIS tools would objectively facilitate the application of measures to promote the improvement of the citizens’ quality of life. However, the availability of open data sources must be taken into account as a prerequisite to develop the transformation into useful parameters for their practical application for citizens in urban environments. Full article

(This article belongs to the Special Issue Urban Geospatial Analytics Based on Crowdsourced Data)

► Show Figures

Figure 1

Open AccessArticle

Correlation of Road Network Structure and Urban Mobility Intensity: An Exploratory Study Using Geo-Tagged Tweets

Li Geng

and

Ke Zhang

ISPRS Int. J. Geo-Inf. 2023, 12(1), 7; https://doi.org/10.3390/ijgi12010007 - 28 Dec 2022

Viewed by 1485

Abstract

Urban planners have been long interested in understanding how urban structure and activities are mutually influenced. Human mobility and economic activities naturally drive the formation of road network structure and the accessibility of the latter shapes the patterns of movement flow across urban space. In this paper, we perform an exploratory study on the relationship between the street network structure and the intensity of human movement in urban areas. We focus on two cities and we utilize a dataset of geo-tagged tweets that can form a proxy to urban mobility and the corresponding street networks as obtained from OpenStreetMap. We apply three network centrality measures, including closeness, betweenness and straightness centrality, calculated at a global or local scale, as well as under mixed or individual transportation mode (e.g., driving, biking and walking) with its directional accessibility, to uncover the structural properties of urban street networks. We further design an urban area transition network and apply PageRank to capture the intensity of human mobility. Our correlation analysis indicates different centrality metrics have different levels of correlation with the intensity of human movement. The closeness centrality consistently shows the highest correlation (with a coefficient around

0.6

) with human movement intensity when calculated at a global scale, while straightness centrality often shows no correlation at the global scale or weaker correlation

ρ \approx 0.4

at the local scale. The correlation levels further depend on the type of directional accessibility and of various types of transportation modes. Hence, the directionality and transportation mode, largely ignored in the analysis of road networks, are crucial. Furthermore, the strength of the correlation varies in the two cities examined, indicating potential differences in urban spatial structure and human mobility patterns. Full article

(This article belongs to the Special Issue Urban Geospatial Analytics Based on Crowdsourced Data)

► Show Figures

Figure 1

Open AccessArticle

High-Speed Railway Access Pattern and Spatial Overlap Characteristics of the Yellow River Basin Urban Agglomeration

Yajun Xiong

Hui Tang

and

Tao Xu

ISPRS Int. J. Geo-Inf. 2023, 12(1), 3; https://doi.org/10.3390/ijgi12010003 - 22 Dec 2022

Viewed by 1053

Abstract

With the rapid development of high-speed railway (HSR) transportation in China, its impact on regional spatial patterns and shaping has become increasingly significant. This study took seven urban agglomerations in the Yellow River Basin as the research object, using the 2 h HSR access time in the Yellow River Basin to comparatively analyze the differences in HSR access in the urban agglomeration in the Yellow River Basin, and using the 3 h HSR access to central cities as the background to conduct regional division and overlapping space identification through cross-regional economic links, before finally selecting the overlapping city of Changzhi for long-term space development strategic planning. The main conclusions were as follows: First, the low-value area of HSR travel time in the Yellow River Basin urban agglomerations was biased toward the center of the urban agglomerations, while the peripheral areas were relatively high-value travel traffic circles, and the HSR travel time showed a circular spatial pattern characteristic of continuous expansion from the center to the peripheral areas. Four urban agglomerations in the upper reaches of the city achieved a 2 h access pattern within the urban agglomeration, whereas three urban agglomerations in the middle and lower reaches of the city only reached the 2 h access level in the center. Second, the Yellow River Basin was divided into six community spaces using the SLPA model based on the economic linkage between the central city and other cities, which were filtered by the 3 h access time from the central city to each city for HSR travel. Three of the six communities produced overlapping spaces, i.e., Community 3 and Community 4 produced overlapping spaces containing Linfen, Community 3 and Community 5 produced overlapping spaces containing Changzhi, Handan, and Xingtai, and Community 4 and Community 5 produced overlapping spaces containing Yuncheng and Sanmenxia. Third, the overlapping space of Changzhi City was selected as a case study for a visionary strategic planning outlook. Combining the geographic location characteristics and future development opportunities of Changzhi, we can try to transform a pass-through node like Changzhi into a hub node in the future, strengthening the gateway status and expanding the hinterland. According to the results of the research and analysis, policymakers can try to implement the expansion and renovation of HSR trunk lines, break the transportation bottlenecks in less developed areas, improve the coverage of the HSR network, and establish a “cross-urban agglomeration” cooperation and coordination mechanism. Full article

(This article belongs to the Special Issue Urban Geospatial Analytics Based on Crowdsourced Data)

► Show Figures

Figure 1

Open AccessArticle

A Novel Approach Based on Machine Learning and Public Engagement to Predict Water-Scarcity Risk in Urban Areas

Sadeq Khaleefah Hanoon

Ahmad Fikri Abdullah

Helmi Z. M. Shafri

and

Aimrun Wayayok

ISPRS Int. J. Geo-Inf. 2022, 11(12), 606; https://doi.org/10.3390/ijgi11120606 - 04 Dec 2022

Cited by 2 | Viewed by 1502

Abstract

Climate change, population growth and urban sprawl have put a strain on water supplies across the world, making it difficult to meet water demand, especially in city regions where more than half of the world’s population now reside. Due to the complex urban fabric, conventional techniques should be developed to diagnose water shortage risk (WSR) by engaging crowdsourcing. This study aims to develop a novel approach based on public participation (PP) with a geographic information system coupled with machine learning (ML) in the urban water domain. The approach was used to detect (WSR) in two ways, namely, prediction using ML models directly and using the weighted linear combination (WLC) function in GIS. Five types of ML algorithm, namely, support vector machine (SVM), multilayer perceptron, K-nearest neighbour, random forest and naïve Bayes, were incorporated for this purpose. The Shapley additive explanation model was added to analyse the results. The Water Evolution and Planning system was also used to predict unmet water demand as a relevant criterion, which was aggregated with other criteria. The five algorithms that were used in this work indicated that diagnosing WSR using PP achieved good-to-perfect accuracy. In addition, the findings of the prediction process achieved high accuracy in the two proposed techniques. However, the weights of relevant criteria that were extracted by SVM achieved higher accuracy than the weights of the other four models. Furthermore, the average weights of the five models that were applied in the WLC technique increased the prediction accuracy of WSR. Although the uncertainty ratio was associated with the results, the novel approach interpreted the results clearly, supporting decision makers in the proactive exploration processes of urban WSR, to choose the appropriate alternatives at the right time. Full article

(This article belongs to the Special Issue Urban Geospatial Analytics Based on Crowdsourced Data)

► Show Figures

Figure 1

Open AccessFeature PaperArticle

Different Ways Ambient and Immobile Population Distributions Influence Urban Crime Patterns

Natalia Sypion-Dutkowska

Minxuan Lan

Marek Dutkowski

and

Victoria Williams

ISPRS Int. J. Geo-Inf. 2022, 11(12), 581; https://doi.org/10.3390/ijgi11120581 - 22 Nov 2022

Viewed by 965

Abstract

The article aims to propose a new way of estimating the ambient and immobile urban population using geotagged tweets and age structure, and to test how they are related to urban crime patterns. Using geotagged tweets and age structure data in 37 neighborhoods of Szczecin, Poland, we analyzed the following crime types that occurred during 2015–2017: burglary in commercial buildings, drug crime, fight and battery, property damage, and theft. Using negative binomial regression models, we found a positive correlation between the size of the ambient population and all investigated crime types. Additionally, neighborhoods with more immobile populations (younger than 16 or older than 65) tend to experience more commercial burglaries, but not other crime types. This may be related to the urban structure of Szczecin, Poland. Neighborhoods with higher rates of poverty and unemployment tend to experience more commercial burglaries, drug problems, property damage, and thefts. Additionally, the count of liquor stores is positively related to drug crime, fight-battery, and theft. This article suggests that the age structure of the population has an influence on the distribution of crime, thus it is necessary to tailor crime prevention strategies for different areas of the city. Full article

(This article belongs to the Special Issue Urban Geospatial Analytics Based on Crowdsourced Data)

► Show Figures

Figure 1

Open AccessArticle

Vulnerability Identification and Cascading Failure Spatiotemporal Patterns on Road Network under the Rainstorm Disaster

and

ISPRS Int. J. Geo-Inf. 2022, 11(11), 564; https://doi.org/10.3390/ijgi11110564 - 09 Nov 2022

Viewed by 1265

Abstract

Road vulnerability is crucial for enhancing the robustness of urban road networks and urban resilience. In medium or large cities, road failures in the face of unexpected events, such as heavy rainfall, can affect regional traffic efficiency and operational stability, which can cause high economic losses in severe cases. Conventional studies of road cascading failures under unexpected events focus on dynamic traffic flow, but the significant drop in traffic flow caused by urban flooding does not accurately reflect road load changes. Meanwhile, limited studies analyze the spatiotemporal pattern of cascading failure of urban road networks under real rainstorms and the correlation of this pattern with road vulnerability. In this study, road vulnerability is calculated using a network’s global efficiency measures to identify locations of high and low road vulnerability. Using the between centrality as a measure of road load, the spatiotemporal patterns of road network cascading failure during a real rainstorm are analyzed. The spatial association between road network vulnerability and cascading failure is then investigated. It has been determined that 90.09% of the roads in Zhengzhou city have a vulnerability of less than one, indicating a substantial degree of spatial heterogeneity. The vulnerability of roads adjacent to the city ring roads and city center is often lower, which has a significant impact on the global network’s efficiency. In contrast, road vulnerability is greater in areas located on the urban periphery, which has little effect on the global network’s efficiency. Five hot spots and three cold spots of road vulnerability are identified by using spatial autocorrelation analysis. The cascading failure of a road network exhibits varied associational characteristics in distinct clusters of road vulnerability. Road cascading failure has a very minor influence on the network in hot spots but is more likely to cause widespread traffic congestion or disruption in cold spots. These findings can help stakeholders adopt more targeted policies and strategies in urban planning and disaster emergency management to build more resilient cities and promote sustainable urban development. Full article

(This article belongs to the Special Issue Urban Geospatial Analytics Based on Crowdsourced Data)

► Show Figures

Figure 1

Open AccessArticle

Mining the Spatial Distribution Pattern of the Typical Fast-Food Industry Based on Point-of-Interest Data: The Case Study of Hangzhou, China

and

ISPRS Int. J. Geo-Inf. 2022, 11(11), 559; https://doi.org/10.3390/ijgi11110559 - 09 Nov 2022

Cited by 1 | Viewed by 1294

Abstract

There is a Chinese proverb which states “Where there are Shaxian Snacks, there are generally Lanzhou Ramen nearby”. This proverb reflects the characteristics of spatial clustering in the catering industry. Since the proverbs are rarely elucidated from the geospatial perspective, we aimed to explore the spatial clustering characteristics of the fast food industry from the perspective of geographical proximity and mutual attraction. Point-of-interest, OSM road network, population, and other types of data from the typical fast-food industry in Hangzhou were used as examples. The spatial pattern of the overall catering industry in Hangzhou was analyzed, while the spatial distribution of the four types of fast food selected in Hangzhou was identified and evaluated. The “core-edge” circle structure characteristics of Hangzhou’s catering industry were fitted by the inverse S function. The common location connection between the Western fast-food KFC and McDonald’s and the Chinese fast-food Lanzhou Ramen and Shaxian Snacks and the spatial aggregation were elucidated, being supported by correlation analysis. The degree of mutual attraction between the two was applied to express the spatial correlation. The analysis demonstrated that (1) the distribution of the catering industry in Hangzhou was northeast–southwest. The center of the catering industry in Hangzhou was located near the economic center of the main city rather than in the center of urban geography. (2) The four types of fast food were distributed in densely populated areas and exhibited an anti-S law, which first increased but then decreased as the distance from the center increased. Among these, the number of four typical fast foods was the highest within a distance of 4–10 km from the center. (3) It was concluded that 81.6% of KFCs had a McDonald’s nearby within 2500 m, and 68.5% of Shaxian Snacks had a Lanzhou Ramen nearby within 400 m. McDonald’s attractiveness to KFC was calculated as 0.928448. KFC’s attractiveness to McDonald’s was 0.908902. The attractiveness of the Shaxian Snacks to Lanzhou Ramen was 0.826835. The attractiveness of Lanzhou Ramen to Shaxian Snacks was 0.854509. McDonald’s was found to be dependent on KFC in the main urban area. Shaxian Snacks were strongly attributed to Lanzhou Ramen in commercial centers and streets, while Shaxian Snacks were distributed independently in the eastern Xiaoshan and Yuhang Districts. This study also helped us to optimize the spatial distribution of a typical fast-food industry, while providing case references and decision-making assistance with respect to the locations of catering industries. Full article

(This article belongs to the Special Issue Urban Geospatial Analytics Based on Crowdsourced Data)

► Show Figures

Figure 1

Open AccessArticle

Evaluating Stable Matching Methods and Ridesharing Techniques in Optimizing Passenger Transportation Cost and Companionship

Elmer Magsino

Gerard Ryan Ching

Francis Miguel Espiritu

and

Kerwin Go

ISPRS Int. J. Geo-Inf. 2022, 11(11), 556; https://doi.org/10.3390/ijgi11110556 - 09 Nov 2022

Cited by 1 | Viewed by 1058

Abstract

In this work, we propose a Game Theory-based pricing solution to the ridesharing problem of taxi commuters that addresses the optimal selection of their travel companionship and effectively minimizes their cost. Two stable matching techniques are proposed in this study, namely: First-Come, First-Served (FCFS) and Best Time Sharing (BT). FCFS discovers pairs based on earliest time of pair occurrences, while BT prioritizes selecting pairs with high proportion of shared distance between passengers to the overall distance of their trips. We evaluate our methods through extensive simulations from empirical taxi trajectories from Jakarta, Singapore, and New York. Results in terms of post-stable matching, cost savings, successful matches, and total number of trips have been evaluated to gauge the performance with respect to the no ridesharing condition. BT outperformed FCFS in terms of generating more pairs with compatible routes. Additionally, in the New York dataset with high amount of trip density, BT has efficiently reduced the number of trips present at a given time. On the other hand, FCFS has been more effective in pairing trips for the Jakarta and Singapore datasets because of lower density due to limited number of trajectories. The Game Theory (GT) pricing model proved to generally be the most beneficial to the ride share’s cost savings, specifically leaning toward the passenger benefits. Analysis has shown that the stable matching algorithm reduced the overall number of trips while still adhering to the temporal frequency of trips within the dataset. Moreover, our developed Best Time Pairing and Game Theory Pricing methods served the most efficient based on passenger cost savings. Applying these stable matching algorithms will benefit more users and will encourage more ridesharing instances. Full article

(This article belongs to the Special Issue Urban Geospatial Analytics Based on Crowdsourced Data)

► Show Figures

Figure 1

Open AccessArticle

Extraction of Urban Built-Up Areas Based on Data Fusion: A Case Study of Zhengzhou, China

Yaping Chen

and

Jun Zhang

ISPRS Int. J. Geo-Inf. 2022, 11(10), 521; https://doi.org/10.3390/ijgi11100521 - 17 Oct 2022

Cited by 1 | Viewed by 949

Abstract

Urban built-up areas are not only the spatial carriers of urban activities but also the direct embodiment of urban expansion. Therefore, it is of great practical significance to accurately extract urban built-up areas to judge the process of urbanization. Previous studies that only used single-source nighttime light (NTL) data to extract urban built-up areas can no longer meet the needs of rapid urbanization development. Therefore, in this study, spatial location big data were first fused with NTL data, which effectively improved the accuracy of urban built-up area extraction. Then, a wavelet transform was used to fuse the data, and multiresolution segmentation was used to extract the urban built-up areas of Zhengzhou. The study results showed that the precision and kappa coefficient of urban built-up area extraction by single-source NTL data were 85.95% and 0.7089, respectively, while the precision and kappa coefficient of urban built-up area extraction by the fused data are 96.15% and 0.8454, respectively. Therefore, after data fusion of the NTL data and spatial location big data, the fused data compensated for the deficiency of single-source NTL data in extracting urban built-up areas and significantly improved the extraction accuracy. The data fusion method proposed in this study could extract urban built-up areas more conveniently and accurately, which has important practical value for urbanization monitoring and subsequent urban planning and construction. Full article

(This article belongs to the Special Issue Urban Geospatial Analytics Based on Crowdsourced Data)

► Show Figures

Figure 1

Open AccessArticle

Road Intersection Recognition via Combining Classification Model and Clustering Algorithm Based on GPS Data

and

ISPRS Int. J. Geo-Inf. 2022, 11(9), 487; https://doi.org/10.3390/ijgi11090487 - 14 Sep 2022

Cited by 3 | Viewed by 1413

Abstract

Road intersections are essential to road networks. How to precisely recognize road intersections based on GPS data is still challenging in intelligent transportation systems. Road intersection recognition involves detecting intersections and recognizing its scope. There are few works on intersections’ scope recognition. The existing methods always focus on road intersection detection. It includes two parts: one is selecting turning points from GPS data and extracting their geometric features, another is clustering them into center coordinates of road intersections. However, the accuracy of road intersection detection still has improvement room due to two drawbacks: (1) Besides geometric features, spatial features explored from GPS data and the interactions among all features are also important to represent intersections’ semantics more accurately, and (2) How to capture the points around intersections for clustering has great impact on the accuracy of intersection detection. To solve the preceding problems, we propose a novel approach for road intersection recognition via combining a classification model and clustering algorithm based on GPS data, which involves detecting the center coordinate and computing the radius of the intersection. Firstly, we distil geometric features and spatial features from historical GPS points. These features are inputted into the Extreme Deep Factorization Machine (xDeepFM) model which is applied for capturing the GPS points nearby road intersections. Secondly, the preceding points are clustered into center coordinates of road intersections by the Density-Based Spatial Clustering of Applications with Noise algorithm (DBSCAN). Thirdly, we present a new method of radius computing by integrating Delaunay triangulation with circle shape structure. Experiments are carried out on the GPS data of Chengdu, China. Compared with some state-of-the-art methods, our approach achieves higher accuracy on road intersection recognition based on GPS data. The precision, recall, and f-measure of our proposed center coordinates detection method are respectively 99.0%, 92.7%, and 95.8% when the matching area’s radius is 30 m. Moreover, the error of the proposed radius calculation method is less than 26.5%. Full article

(This article belongs to the Special Issue Urban Geospatial Analytics Based on Crowdsourced Data)

► Show Figures

Figure 1

Open AccessArticle

Exploring the Inter-Monthly Dynamic Patterns of Chinese Urban Spatial Interaction Networks Based on Baidu Migration Data

and

ISPRS Int. J. Geo-Inf. 2022, 11(9), 486; https://doi.org/10.3390/ijgi11090486 - 14 Sep 2022

Cited by 2 | Viewed by 1327

Abstract

The rapid development of the economy promotes the increasing of interactions between cities and forms complex networks. Many scholars have explored the structural characteristics of urban spatial interaction networks in China and have conducted spatio-temporal analyzes. However, scholars have mainly focused on the perspective of static networks and have not understood the dynamic spatial interaction patterns of Chinese cities. Therefore, this paper proposes a research framework to explore the urban dynamic spatial interaction patterns. Firstly, we establish a dynamic urban spatial interaction network according to monthly migration data. Then, the dynamic community detection algorithm, combined with the Louvain and Jaccard matching method, is used to obtain urban communities and their dynamic events. We construct event vectors for each urban community and use hierarchical clustering to cluster event vectors to obtain different types of spatial interaction patterns. Finally, we divide the urban dynamic interaction into three urban spatial interaction modes: fixed spatial interaction pattern, long-term spatial interaction pattern, and short-term spatial interaction pattern. According to the results, we find that the cities in well-developed areas (eastern China) and under-developed areas (northwestern China) mostly show fixed spatial interaction patterns and long-term spatial interaction patterns, while the cities in moderately developed areas (central and western China) often show short-term spatial interaction patterns. The research results and conclusions of this paper reveal the inter-monthly urban spatial interaction patterns in China, provide theoretical support for the policy making and development planning of urban agglomeration construction, and contribute to the coordinated development of national and regional cities. Full article

(This article belongs to the Special Issue Urban Geospatial Analytics Based on Crowdsourced Data)

► Show Figures

Figure 1

Open AccessArticle

Exploring Spatial Features of Population Activities and Functional Facilities in Rail Transit Station Realm Based on Real-Time Positioning Data: A Case of Xi’an Metro Line 2

and

ISPRS Int. J. Geo-Inf. 2022, 11(9), 485; https://doi.org/10.3390/ijgi11090485 - 14 Sep 2022

Viewed by 1455

Abstract

The rail transit station realm is an important urban spatial node that carries various behavioral activities and multiple functions. In order to accurately identify the spatial and temporal distribution of population activities and functional facilities in the rail transit station realm and understand the dynamic influence relationship between them, this paper takes four different types of stations of Xi’an Metro Line 2 as the research object, using real-time positioning data to represent population activities and points of interest (POIs) to represent functional facilities. An analytical framework combining the spatial point pattern identification technique and ordinary least squares (OLS) regression model is proposed. The results show that (1) there is spatial and temporal heterogeneity in the population activities in the rail transit station realm; the density distribution of population activities in different time periods shows the characteristic of clustering within 500 m of the station, regardless of working days or off days; (2) the distribution of shopping service POI, catering service POI, and living service POI in different station realms shows the feature of clustering around the stations; (3) the catering POI, living POI, shopping POI and transportation POI have positive attraction to population activities in different time periods; the constructed OLS model can basically explain the influence relationship between various functional facilities and population activities in all time periods. The conclusions can help city managers understand the spatial and temporal distribution and intrinsic mechanisms of population activities and functional facilities from a microscopic perspective and provide an effective decision-making basis for optimizing the allocation of functional resources in the station realm. Full article

(This article belongs to the Special Issue Urban Geospatial Analytics Based on Crowdsourced Data)

► Show Figures

Figure 1

Open AccessArticle

Identification of Urban Agglomeration Spatial Range Based on Social and Remote-Sensing Data—For Evaluating Development Level of Urban Agglomeration

Shuai Zhang

and

Hua Wei

ISPRS Int. J. Geo-Inf. 2022, 11(8), 456; https://doi.org/10.3390/ijgi11080456 - 21 Aug 2022

Cited by 2 | Viewed by 1385

Abstract

The accurate identification of urban agglomeration spatial area is helpful in understanding the internal spatial relationship under urban expansion and in evaluating the development level of urban agglomeration. Previous studies on the identification of spatial areas often ignore the functional distribution and development of urban agglomerations by only using nighttime light data (NTL). In this study, a new method is firstly proposed to identify the accurate spatial area of urban agglomerations by fusing night light data (NTL) and point of interest data (POI); then an object-oriented method is used by this study to identify the spatial area, finally the identification results obtained by different data are verified. The results show that the accuracy identified by NTL data is 82.90% with the Kappa coefficient of 0.6563, the accuracy identified by POI data is 81.90% with the Kappa coefficient of 0.6441, and the accuracy after data fusion is 90.70%, with the Kappa coefficient of 0.8123. The fusion of these two kinds of data has higher accuracy in identifying the spatial area of urban agglomeration, which can play a more important role in evaluating the development level of urban agglomeration; this study proposes a feasible method and path for urban agglomeration spatial area identification, which is not only helpful to optimize the spatial structure of urban agglomeration, but also to formulate the spatial development policy of urban agglomeration. Full article

(This article belongs to the Special Issue Urban Geospatial Analytics Based on Crowdsourced Data)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Special Issue "Urban Geospatial Analytics Based on Crowdsourced Data"

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (23 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI