Special Issue "Big Data for Urban Informatics and Earth Observation"

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

Deadline for manuscript submissions: closed (30 April 2016)

Special Issue Editors

Guest Editor
Dr. Jamal Jokar Arsanjani

Geographic Information Science, Department of Planning and Development, Aalborg University Copenhagen, A.C. Meyers Vænge 15, DK-2450 Copenhagen, Denmark
Website | E-Mail
Interests: volunteered geographic information (VGI); big (geo) data; crowdsourced mapping; citizen science; geocomputation; digital earth; remote sensing and spatio-temporal monitoring of environment; data fusion; (geo)data quality
Guest Editor
Prof. Ming-Hsiang (Ming) Tsou

Geography Department, San Diego State University, 5500 Campanile Drive, San Diego, CA 92182-4493, USA
Director, The Center for Human Dynamics in the Mobile Age, 5500 Campanile Drive, San Diego, CA 92182-4493, USA
Website | E-Mail
Phone: 6195940205
Interests: Human Dynamics; Social Media; Big Data; Visualization; Internet Mapping; Web GIS; Mobile GIS; Cartography, and K-12 GISeducation

Special Issue Information

Dear Colleagues,

The recent availability of big data linked to geographical locations has provided the GIScientists and Remote Sensing scholars a new era of research in for Urban Informatics and Earth Observation. Big data era is far beyond storage and access and by making sense of it, hidden interconnected patterns of data and information can be revealed for the sake of for Urban Informatics and Earth Observation. The unique structure of big data requires new algorithms and methods for exploiting value-added information in various applications, including smart cities, land use change, urban planning, transportation, and biodiversity. On the other hand, the advanced development of spatio-temporal analysis algorithms and techniques has provided a unique opportunity for discovering uncovered information at (near) real time scale.

This Special Issue is dedicated to address the contributions of big data to assist in urban informatics and Earth observation and how it can help advancing geoinformatics-dependent applications, by improving the spatiality and temporality of traditional datasets in geoinformatics and remote sensing applications. Topics of interest include, but are not limited to:

  • Innovative tools and toolkits for processing big (geo)data relevant to Earth observation
  • Big (geo)data mining, data analytics and visualization for urban informatics
  • Big (geo)data: Analysis and visualization for urban land use and land cover
  • (Near) real time data-intensive applications for transportation.
  • Models, algorithms, and methods for big data understanding, mining, and integration
  • Case studies of big data including navigation, public health, 3D modeling, business planning, transportation, urban management, environmental monitoring, and smart cities
  • Earth observation big data: Algorithms and applications
  • Volunteered/contributed geographic information and crowdsourcing
  • Digital Earth and augmented reality
  • Indoor and outdoor navigation and routing
  • Social media analysis and human dynamics
  • Real-time disaster and risk management
  • Socio-economic analysis, assessment, and modeling

Dr. Jamal Jokar Arsanjani
Prof. Ming-Hsiang (Ming) Tsou
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.

Published Papers (6 papers)

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Research

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Open AccessArticle Assessing Patient bypass Behavior Using Taxi Trip Origin–Destination (OD) Data
ISPRS Int. J. Geo-Inf. 2016, 5(9), 157; https://doi.org/10.3390/ijgi5090157
Received: 10 June 2016 / Revised: 21 August 2016 / Accepted: 24 August 2016 / Published: 1 September 2016
Cited by 3 | PDF Full-text (6861 KB) | HTML Full-text | XML Full-text
Abstract
Many patients prefer to use the best hospitals even if there are one or more other hospitals closer to their homes; this behavior is called “hospital bypass behavior”. Because this behavior can be problematic in urban areas, it is important that it be
[...] Read more.
Many patients prefer to use the best hospitals even if there are one or more other hospitals closer to their homes; this behavior is called “hospital bypass behavior”. Because this behavior can be problematic in urban areas, it is important that it be reduced. In this paper, the taxi GPS data of Beijing and Suzhou were used to measure hospital bypass behavior. The “bypass behavior index” (BBI) represents the bypass behavior for each hospital. The results indicated that the mean hospital bypass trip distance value ranges from 5.988 km to 9.754 km in Beijing and from 4.168 km to 10.283 km in Suzhou. In general, the bypass shares of both areas show a gradually increasing trend. The following hospitals exhibited significant patient bypass behavior: the 301 Hospital, Beijing Children’s Hospital, the Second Affiliated Hospital of Soochow University and the Suzhou Hospital of Traditional Chinese Medicine. The hospitals’ reputation, transport accessibility and spatial distribution were found to be the main factors affecting patient bypass behavior. Although the hospital bypass phenomena generally appeared to be more pronounced in Beijing, the bypass trip distances between hospitals were found to be more significant in Suzhou. Full article
(This article belongs to the Special Issue Big Data for Urban Informatics and Earth Observation)
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Open AccessArticle A New Approach to Urban Road Extraction Using High-Resolution Aerial Image
ISPRS Int. J. Geo-Inf. 2016, 5(7), 114; https://doi.org/10.3390/ijgi5070114
Received: 24 March 2016 / Revised: 2 July 2016 / Accepted: 8 July 2016 / Published: 13 July 2016
Cited by 5 | PDF Full-text (5207 KB) | HTML Full-text | XML Full-text
Abstract
Road information is fundamental not only in the military field but also common daily living. Automatic road extraction from a remote sensing images can provide references for city planning as well as transportation database and map updating. However, owing to the spectral similarity
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Road information is fundamental not only in the military field but also common daily living. Automatic road extraction from a remote sensing images can provide references for city planning as well as transportation database and map updating. However, owing to the spectral similarity between roads and impervious structures, the current methods solely using spectral characteristics are often ineffective. By contrast, the detailed information discernible from the high-resolution aerial images enables road extraction with spatial texture features. In this study, a knowledge-based method is established and proposed; this method incorporates the spatial texture feature into urban road extraction. The spatial texture feature is initially extracted by the local Moran’s I, and the derived texture is added to the spectral bands of image for image segmentation. Subsequently, features like brightness, standard deviation, rectangularity, aspect ratio, and area are selected to form the hypothesis and verification model based on road knowledge. Finally, roads are extracted by applying the hypothesis and verification model and are post-processed based on the mathematical morphology. The newly proposed method is evaluated by conducting two experiments. Results show that the completeness, correctness, and quality of the results could reach approximately 94%, 90% and 86% respectively, indicating that the proposed method is effective for urban road extraction. Full article
(This article belongs to the Special Issue Big Data for Urban Informatics and Earth Observation)
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Open AccessArticle Comparative Perspective of Human Behavior Patterns to Uncover Ownership Bias among Mobile Phone Users
ISPRS Int. J. Geo-Inf. 2016, 5(6), 85; https://doi.org/10.3390/ijgi5060085
Received: 12 January 2016 / Revised: 4 May 2016 / Accepted: 30 May 2016 / Published: 6 June 2016
Cited by 3 | PDF Full-text (1740 KB) | HTML Full-text | XML Full-text
Abstract
With the rapid spread of mobile devices, call detail records (CDRs) from mobile phones provide more opportunities to incorporate dynamic aspects of human mobility in addressing societal issues. However, it has been increasingly observed that CDR data are not always representative of the
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With the rapid spread of mobile devices, call detail records (CDRs) from mobile phones provide more opportunities to incorporate dynamic aspects of human mobility in addressing societal issues. However, it has been increasingly observed that CDR data are not always representative of the population under study because it only includes device users alone. To understand the discrepancy between the population captured by CDRs and the general population, we profile principal populations of CDRs by analyzing routines based on time spent at key locations and compare these data with those of the general population. We employ a topic model to estimate typical routines of mobile phone users using CDRs as topics. The routines are extracted from field survey data and compared between those of the general population and mobile phone users. We found that there are two main population groups of mobile phone users in Dhaka: males engaged in an income-generating activity at a specific location other than home and females performing household tasks and spending most of their time at home. We determine that CDRs tend to omit students, who form a significant component of the Dhaka population. Full article
(This article belongs to the Special Issue Big Data for Urban Informatics and Earth Observation)
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Open AccessArticle A Framework for Data-Centric Analysis of Mapping Activity in the Context of Volunteered Geographic Information
ISPRS Int. J. Geo-Inf. 2016, 5(3), 37; https://doi.org/10.3390/ijgi5030037
Received: 5 November 2015 / Revised: 8 February 2016 / Accepted: 22 February 2016 / Published: 15 March 2016
Cited by 9 | PDF Full-text (4466 KB) | HTML Full-text | XML Full-text
Abstract
Over the last decade, volunteered geographic information (VGI) has become established as one of the most relevant geographic data sources in terms of worldwide coverage, representation of local knowledge and open data policies. Beside the data itself, data about community activity provides valuable
[...] Read more.
Over the last decade, volunteered geographic information (VGI) has become established as one of the most relevant geographic data sources in terms of worldwide coverage, representation of local knowledge and open data policies. Beside the data itself, data about community activity provides valuable insights into the mapping progress which can be useful for estimating data quality, understanding the activity of VGI communities or predicting future developments. This work proposes a conceptual as well as technical framework for structuring and analyzing mapping activity building on the concepts of activity theory. Taking OpenStreetMap as an example, the work outlines the necessary steps for converting database changes into user- and feature-centered operations and higher-level actions acting as a universal scheme for arbitrary spatio-temporal analyses of mapping activities. Different examples from continent to region and city-scale analyses demonstrate the practicability of the approach. Instead of focusing on the interpretation of specific analysis results, the work contributes on a meta-level by addressing several conceptual and technical questions with respect to the overall process of analyzing VGI community activity. Full article
(This article belongs to the Special Issue Big Data for Urban Informatics and Earth Observation)
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Open AccessArticle Generating Heat Maps of Popular Routes Online from Massive Mobile Sports Tracking Application Data in Milliseconds While Respecting Privacy
ISPRS Int. J. Geo-Inf. 2015, 4(4), 1813-1826; https://doi.org/10.3390/ijgi4041813
Received: 15 May 2015 / Revised: 14 August 2015 / Accepted: 11 September 2015 / Published: 24 September 2015
Cited by 9 | PDF Full-text (1379 KB) | HTML Full-text | XML Full-text
Abstract
The breakthrough of GPS-equipped smartphones has enabled the collection of track data from human mobility on massive scales that can be used in route recommendation, urban planning and traffic management. In this work we present a fast map server that can generate and
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The breakthrough of GPS-equipped smartphones has enabled the collection of track data from human mobility on massive scales that can be used in route recommendation, urban planning and traffic management. In this work we present a fast map server that can generate and visualize heat maps of popular routes online from massive sports track data based on client preferences, e.g., running routes lasting less than an hour. The heat maps shown respect user privacy by not showing routes with less than a predefined number of different users, for instance five. The results are represented to the client using a dynamic tile layer. The current implementation uses data collected by the Sports Tracker mobile application with over 800,000 different tracks and 2.8 billion GPS data points. Stress tests indicate that the server can handle hundreds of simultaneous client requests in a single server configuration. Full article
(This article belongs to the Special Issue Big Data for Urban Informatics and Earth Observation)
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Review

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Open AccessReview Review of Forty Years of Technological Changes in Geomatics toward the Big Data Paradigm
ISPRS Int. J. Geo-Inf. 2016, 5(9), 155; https://doi.org/10.3390/ijgi5090155
Received: 29 April 2016 / Revised: 8 August 2016 / Accepted: 19 August 2016 / Published: 29 August 2016
Cited by 7 | PDF Full-text (3110 KB) | HTML Full-text | XML Full-text
Abstract
Looking back at the last four decades, the technologies that have been developed for Earth observation and mapping can shed a light on the technologies that are trending today and on their challenges. Forty years ago, the first digital pictures decided the fate
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Looking back at the last four decades, the technologies that have been developed for Earth observation and mapping can shed a light on the technologies that are trending today and on their challenges. Forty years ago, the first digital pictures decided the fate of remote sensing, photogrammetric engineering, GIS, or, for short: of geomatics. This sudden wave of volumes of data triggered the research in fields that Big Data is plowing today: this paper will examine this transition. First, a rapid survey of the technology through the succession of selected terms, will help identify two main periods in the last four decades. Spatial information appears in 1970 with the preparation of Landsat, and Big Data appears in 2010. The method for exploring geomatics’ contribution to Big Data, is to examine each of the “Vs” that are used today to characterize the latter: volume, velocity, variety, visualization, value, veracity, validity, and variability. Geomatics has been confronted to each of these facets during the period. The discussion compares the answers offered early by geomatics, with the situation in Big Data today. Over a very large range of issues, from signal processing to the semantics of information, geomatics has made contributions to many data models and algorithms. Big Data now enables geographic information to be disseminated much more widely, and to benefit from new information sources, expanding through the Internet of Things towards a future Digital Earth. Some of the lessons learned during the four decades of geomatics can also be lessons for Big Data today, and for the future of geomatics. Full article
(This article belongs to the Special Issue Big Data for Urban Informatics and Earth Observation)
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