Special Issue "Geospatial Big Data and Urban Studies"

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

Deadline for manuscript submissions: 30 September 2018

Special Issue Editors

Guest Editor
Prof. Dr. Maria Antonia Brovelli

GeoLab, Department of Civil and Environmental Engineering, Politecnico di Milano, 20133 Milano, Italy
Website | E-Mail
Interests: geoinformatics; free and open source GIS; geospatial web; geo big data; geo crowdsourcing
Guest Editor
Mr. Hussein M. Abdulmuttalib

Senior GIS Specialist, GIS Department of Dubai Municipality, UAE
Website | E-Mail
Interests: GI-Science; geo basemap data/big data; data quality; remote sensing; OBIA; spatial environmental monitoring; smart 3D surfaces and urban designs
Guest Editor
Dr. Peter Mooney

Department of Computer Science, Maynooth University, Maynooth, Ireland
Website | E-Mail
Interests: volunteered geographic information (VGI); citizen science; geospatial data mining and knowledge extraction; free and open source software for geomatics (FOSS4G)
Guest Editor
Prof. Chris Pettit

City Futures Research Centre, Built Environment UNSW, Sydney, Australia
Website | E-Mail
Interests: informed urbanization; big data; city dashboards; urban modelling geographical visualisation; geodesign; GIS

Special Issue Information

Dear Colleagues,

The trends in global population growth by international organisations such as the United Nations indicate that the number of people living in cities and urban areas will increase dramatically on current figures in the next decade. These population growth challenges will require global societal responses, the results of which will determine the quality of life of billions of citizens. As it stands, humanity is already using the Earth's resources at an unsustainable rate and subsequently placing increasing pressures on the natural environment and its ecosystem upon which we so greatly depend. With these challenges comes unprecedented opportunities to collect spatial data and information about these cities and urban areas in order to address these problems. Big Data is an emerging research area which has immense potential to help society adapt to population increases and growing urbanization. Big Data is generated by a myriad of sources including satellites, in-situ sensor networks, sensing devices, Internet of Things systems and applications, the social Internet including social media, networking, etc. Indeed, more specifically, Geospatial Big Data refers to all of these data and information streams which contain specific spatial and location references. Citizens generate information and data which combines to make Geospatial Big Data in so many ways—passively (using apps, web sites, sensors, etc., automatically with little or no interaction) and actively with more interaction such as sharing GPS tracks, geolocating social media posts, contributing to Volunteered Geographic Information projects, etc. A massive challenge has arisen to elicit and extract knowledge and intelligence from this Geospatial Big Data in order to understand, predict and manage how cities and urban areas function, change and grow. Cities and urban areas themselves generate massive volumes of Geospatial Big Data directly and indirectly, passively and actively. However, few examples exist where these data resources have been efficiently explored and exploited in Urban and City Studies.

On the other hand, urban studies involve sophisticated factors which, once deployed, are expected to guide towards city smartness and urban smartness. These factors can be more effective using geospatial technology, and spatially enabled Big Data is definitely one of current and future trends of urban studies besides other implementation areas. Thus, considering environmental; physical; social; economic and other urban study factors which include but are not limited to location and geographic aspects such as lakes, seashores, hills, topography, geology and geomorphology, geographic needs, climate changes, current and future land use, socioeconomic level, future economic trends, say of citizens will require the involvement of Geospatial Big Data. Further, extracting valuable information from Geospatial Big Data using analytics, geo analytics spatial analysis, or perhaps the combination of both shall obligate the enforcement of quality dimensions of data and information. Those dimensions will increase the level of confidence while implementing it for urban development, urban planning and miscellaneous urban studies and city smartness and development. Dimensions such as data source reliability; data accuracy and precision as compared to the real values; data completeness and the effect of lacking part of the data on decision making; the uncertainty level of parts of Geospatial Big Data that is related to the fitness of use where the purpose of data extraction is to be deeply involved in the process; and other dimensions can be common in Big Data, etc. Thus, ideas and practical aspects of Geospatial reliability, enabling Big Data for urban studies, will be welcome herein.

This Special Issue is dedicated to exploring the methodologies and research techniques developed and delivered to tackle the challenges of Geospatial Big Data in order to understand how City Studies will benefit from it. We call for original papers which focus on all topics involving Geospatial Big Data and related City and Urban Studies.

The Special Issue will place emphasis on innovative research on Geospatial Big Data and approaches and methodologies to using this data. Example topics may include, but are not limited to, the following:

  • Data models for the representation of Geospatial Big Data
  • Geospatial Big Data analytics and knowledge discovery
  • Quality assessment of Geospatial Big Data
  • Examples of City and Urban Studies on Geospatial Big Data
  • City Analytics methods and applications
  • City Dashboards for spatial planning and citizen engagement
  • Urban data visualisation including virtual and augmented reality

Prof. Dr. Maria Antonia Brovelli
Mr. Hussein M. Abdulmuttalib
Dr. Peter Mooney
Prof. Chris Pettit
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 papers will be 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 1000 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

  • Urban Big data
  • Sensors/Internet of Things
  • Geo Crowdsourced Data
  • Social Web
  • Cities Studies
  • City analytics
  • Geographical visualisation
  • Virtual/Augmented reality
  • Dashboards
  • Urban Data Quality

Published Papers (5 papers)

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Research

Open AccessArticle Approach to Accelerating Dissolved Vector Buffer Generation in Distributed In-Memory Cluster Architecture
ISPRS Int. J. Geo-Inf. 2018, 7(1), 26; doi:10.3390/ijgi7010026
Received: 6 November 2017 / Revised: 9 January 2018 / Accepted: 11 January 2018 / Published: 15 January 2018
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Abstract
The buffer generation algorithm is a fundamental function in GIS, identifying areas of a given distance surrounding geographic features. Past research largely focused on buffer generation algorithms generated in a stand-alone environment. Moreover, dissolved buffer generation is data- and computing-intensive. In this scenario,
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The buffer generation algorithm is a fundamental function in GIS, identifying areas of a given distance surrounding geographic features. Past research largely focused on buffer generation algorithms generated in a stand-alone environment. Moreover, dissolved buffer generation is data- and computing-intensive. In this scenario, the improvement in the stand-alone environment is limited when considering large-scale mass vector data. Nevertheless, recent parallel dissolved vector buffer algorithms suffer from scalability problems, leaving room for further optimization. At present, the prevailing in-memory cluster-computing framework—Spark—provides promising efficiency for computing-intensive analysis; however, it has seldom been researched for buffer analysis. On this basis, we propose a cluster-computing-oriented parallel dissolved vector buffer generating algorithm, called the HPBM, that contains a Hilbert-space-filling-curve-based data partition method, a data skew and cross-boundary objects processing strategy, and a depth-given tree-like merging method. Experiments are conducted in both stand-alone and cluster environments using real-world vector data that include points and roads. Compared with some existing parallel buffer algorithms, as well as various popular GIS software, the HPBM achieves a performance gain of more than 50%. Full article
(This article belongs to the Special Issue Geospatial Big Data and Urban Studies)
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Open AccessArticle Optimizing Cruising Routes for Taxi Drivers Using a Spatio-Temporal Trajectory Model
ISPRS Int. J. Geo-Inf. 2017, 6(11), 373; doi:10.3390/ijgi6110373
Received: 26 September 2017 / Revised: 2 November 2017 / Accepted: 13 November 2017 / Published: 19 November 2017
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Abstract
Much of the taxi route-planning literature has focused on driver strategies for finding passengers and determining the hot spot pick-up locations using historical global positioning system (GPS) trajectories of taxis based on driver experience, distance from the passenger drop-off location to the next
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Much of the taxi route-planning literature has focused on driver strategies for finding passengers and determining the hot spot pick-up locations using historical global positioning system (GPS) trajectories of taxis based on driver experience, distance from the passenger drop-off location to the next passenger pick-up location and the waiting times at recommended locations for the next passenger. The present work, however, considers the average taxi travel speed mined from historical taxi GPS trajectory data and the allocation of cruising routes to more than one taxi driver in a small-scale region to neighboring pick-up locations. A spatio-temporal trajectory model with load balancing allocations is presented to not only explore pick-up/drop-off information but also provide taxi drivers with cruising routes to the recommended pick-up locations. In simulation experiments, our study shows that taxi drivers using cruising routes recommended by our spatio-temporal trajectory model can significantly reduce the average waiting time and travel less distance to quickly find their next passengers, and the load balancing strategy significantly alleviates road loads. These objective measures can help us better understand spatio-temporal traffic patterns and guide taxi navigation. Full article
(This article belongs to the Special Issue Geospatial Big Data and Urban Studies)
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Open AccessArticle Semantic-Geographic Trajectory Pattern Mining Based on a New Similarity Measurement
ISPRS Int. J. Geo-Inf. 2017, 6(7), 212; doi:10.3390/ijgi6070212
Received: 4 May 2017 / Revised: 2 July 2017 / Accepted: 5 July 2017 / Published: 14 July 2017
Cited by 1 | PDF Full-text (3124 KB) | HTML Full-text | XML Full-text
Abstract
Trajectory pattern mining is becoming increasingly popular because of the development of ubiquitous computing technology. Trajectory data contain abundant semantic and geographic information that reflects people’s movement patterns, i.e., who is performing a certain type of activity when and where. However, the variety
[...] Read more.
Trajectory pattern mining is becoming increasingly popular because of the development of ubiquitous computing technology. Trajectory data contain abundant semantic and geographic information that reflects people’s movement patterns, i.e., who is performing a certain type of activity when and where. However, the variety and complexity of people’s movement activity and the large size of trajectory datasets make it difficult to mine valuable trajectory patterns. Moreover, most existing trajectory similarity measurements only consider a portion of the information contained in trajectory data. The patterns obtained cannot be interpreted well in terms of both semantic meaning and geographic distributions. As a result, these patterns cannot be used accurately for recommendation systems or other applications. This paper introduces a novel concept of the semantic-geographic pattern that considers both semantic and geographic meaning simultaneously. A flexible density-based clustering algorithm with a new trajectory similarity measurement called semantic intensity is used to mine these semantic-geographic patterns. Comparative experiments on check-in data from the Sina Weibo service demonstrate that semantic intensity can effectively measure both semantic and geographic similarities among trajectories. The resulting patterns are more accurate and easy to interpret. Full article
(This article belongs to the Special Issue Geospatial Big Data and Urban Studies)
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Open AccessArticle A Visual Analysis Approach for Inferring Personal Job and Housing Locations Based on Public Bicycle Data
ISPRS Int. J. Geo-Inf. 2017, 6(7), 205; doi:10.3390/ijgi6070205
Received: 2 May 2017 / Revised: 22 June 2017 / Accepted: 29 June 2017 / Published: 7 July 2017
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Abstract
Information concerning the home and workplace of residents is the basis of analyzing the urban job-housing spatial relationship. Traditional methods conduct time-consuming user surveys to obtain personal job and housing location information. Some new methods define rules to detect personal places based on
[...] Read more.
Information concerning the home and workplace of residents is the basis of analyzing the urban job-housing spatial relationship. Traditional methods conduct time-consuming user surveys to obtain personal job and housing location information. Some new methods define rules to detect personal places based on human mobility data. However, because the travel patterns of residents are variable, simple rule-based methods are unable to generalize highly changing and complex travel modes. In this paper, we propose a visual analysis approach to assist the analyzer in inferring personal job and housing locations interactively based on public bicycle data. All users are first clustered to find potential commuting users. Then, several visual views are designed to find the key candidate stations for a specific user, and the visited temporal pattern of stations and the user’s hire behavior are analyzed, which helps with the inference of station semantic meanings. Finally, a number of users’ job and housing locations are detected by the analyzer and visualized. Our approach can manage the complex and diverse cycling habits of users. The effectiveness of the approach is shown through case studies based on a real-world public bicycle dataset. Full article
(This article belongs to the Special Issue Geospatial Big Data and Urban Studies)
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Open AccessArticle A New Look at Public Services Inequality: The Consistency of Neighborhood Context and Citizens’ Perception across Multiple Scales
ISPRS Int. J. Geo-Inf. 2017, 6(7), 200; doi:10.3390/ijgi6070200
Received: 2 May 2017 / Revised: 26 June 2017 / Accepted: 29 June 2017 / Published: 4 July 2017
Cited by 1 | PDF Full-text (1544 KB) | HTML Full-text | XML Full-text
Abstract
A challenge in regional inequality is to identify the relative influence of objective neighborhood context on subjective citizens’ attitudes and experiences of place. This paper first presents six groups of hierarchal neighborhoods in optimizing public service inequality (PSI) indicators based on census blocks
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A challenge in regional inequality is to identify the relative influence of objective neighborhood context on subjective citizens’ attitudes and experiences of place. This paper first presents six groups of hierarchal neighborhoods in optimizing public service inequality (PSI) indicators based on census blocks collected in Quito, Ecuador. Multilevel models were then applied to understand the relative influence of neighborhood-level PSI on citizens’ perceptions of place, including individual-level perceptions of neighborhood social cohesion and neighborhood safety, and self-perceived health status. Our results show that the internal variability of the individual perceptions that is explained by neighborhood context is strongly influenced by the scale of neighborhood units. A spatial consistency between objective neighborhood context and subjective individual perception of place plays a crucial role in propagating mixed-methods approaches (qualitative-quantitative) and improves the spatial interpretation of public services inequality. Neighborhood context and citizens’ perception of place should be integrated to investigate urban segregation, thereby providing insights into the underlying societal inequality phenomenon and quality of life. Full article
(This article belongs to the Special Issue Geospatial Big Data and Urban Studies)
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