Special Issue "Algorithms and Techniques in Urban Monitoring"

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

Deadline for manuscript submissions: 31 August 2019.

Special Issue Editor

Guest Editor
Assoc. Prof. Seth Spielman

Associate Professor of University of Colorado at Boulder, Boulder, United States
Website | E-Mail
Interests: Maps; Cities; Demographics; Statistics; Machine Learning

Special Issue Information

Dear Colleagues,

Location aware mobile computing, and services that leverage such resources, have created a tectonic shift in the urban data landscape. More data than ever is available to urban scientists. This new abundance of data, coupled with recent advances in machine learning, has enormous potential to transform our understanding of cities. However, this nexus of new data and methods is complex, and its potential to yield significant scientific and policy insights remains largely unrealized.

This Special Issue focuses on “Algorithms and Techniques for Urban Monitoring”, our goal is to coalesce a set of new ways to observe urban processes. Of specific interest are methods that generate new ways to "see" the past, present, and future state of a city. That is, what can we learn using novel forms of data and machine learning, alone or in concert, that we could not have known using traditional sources of data and/or methods. The words "see” and "monitoring" are deliberately broad. We are interested in both directly observed physical phenomena (like travel, urban growth, and energy consumption) and indirectly observed social processes (such as gentrification, inequality, and well-being).

These new forms of data and methods raise significant questions about individual privacy, the privatization of data resources, the use of algorithms to make and/or inform policy. Data is increasingly politicized; this is true of both traditional state sponsored sources of data and the data collected global technology companies. Where relevant we encourage authors to situate their work within these broader social trends and discuss any tensions that emerge between the technical methods/models and their application in urban governance and management.

Expressions of interest and/or questions are welcomed via email to [email protected]. The journal and the editors are committed to a rapid and thorough review process, papers will be published on a rolling basis.

Assoc. Prof. Seth Spielman
Guest Editor

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 (4 papers)

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Research

Open AccessArticle
Assessing Spatial Information Themes in the Spatial Information Infrastructure for Participatory Urban Planning Monitoring: Indonesian Cities
ISPRS Int. J. Geo-Inf. 2019, 8(7), 305; https://doi.org/10.3390/ijgi8070305
Received: 22 May 2019 / Revised: 3 July 2019 / Accepted: 12 July 2019 / Published: 17 July 2019
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Abstract
Most urban planning monitoring activities were designed to monitor implementation of aggregated sectors from different initiatives into practical and measurable indicators. Today, cities utilize spatial information in monitoring and evaluating urban planning implementation for not only national or local goals but also for [...] Read more.
Most urban planning monitoring activities were designed to monitor implementation of aggregated sectors from different initiatives into practical and measurable indicators. Today, cities utilize spatial information in monitoring and evaluating urban planning implementation for not only national or local goals but also for the 2030 Agenda of Sustainable Development Goals (SDGs). Modern cities adopt Participatory Geographic Information System (PGIS) initiative for their urban planning monitoring. Cities provide spatial information and online tools for citizens to participate. However, the selection of spatial information services for participants is made from producers’ perception and often disregards requirements from the regulation, functionalities, and broader user’s perception. By providing appropriate spatial information, the quality of participatory urban monitoring can be improved. This study presents a method for selecting appropriate spatial information for urban planning monitoring. It considers regulation, urban planning, and spatial science theories, as well as citizens’ requirements, to support participatory urban planning monitoring as a way to ensure the success of providing near real-time urban information to planners and decision-makers. Full article
(This article belongs to the Special Issue Algorithms and Techniques in Urban Monitoring)
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Graphical abstract

Open AccessArticle
Detecting Urban Polycentric Structure from POI Data
ISPRS Int. J. Geo-Inf. 2019, 8(6), 283; https://doi.org/10.3390/ijgi8060283
Received: 30 April 2019 / Revised: 3 June 2019 / Accepted: 15 June 2019 / Published: 17 June 2019
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Abstract
It is meaningful to analyze urban spatial structure by identifying urban subcenters, and many methods of doing so have been proposed in the published literature. Although these methods are widely applied, they exhibit obvious shortcomings that limit their further application. Therefore, it is [...] Read more.
It is meaningful to analyze urban spatial structure by identifying urban subcenters, and many methods of doing so have been proposed in the published literature. Although these methods are widely applied, they exhibit obvious shortcomings that limit their further application. Therefore, it is of great value to propose a new urban subcenter identification method that can overcome these shortcomings. In this paper, we propose the density contour tree (DCT) method for detecting urban polycentric structures and their spatial distributions. Conceptually, this method is based on an analogy between urban spatial structure and terrain. The point-of-interest (POI) density is visualized as a continuous mathematical surface representing the urban terrain. Peaks represent the regions of the most frequent human activity, valleys represent regions with small population densities in the city, and slopes represent spatial changes in urban land-use intensity. Using this method, we have detected the urban “polycentric” structure of Beijing and determined the corresponding spatial relationships. In addition, several important properties of the urban centers have been identified. For example, Beijing has a typical urban polycentric structure with an urban center area accounting for 5.9% of the total urban area, and most of the urban centers in Beijing serve comprehensive functions. In general, the method and the results can serve as references for the later research on analyzing urban structure. Full article
(This article belongs to the Special Issue Algorithms and Techniques in Urban Monitoring)
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Open AccessArticle
An Urban Road-Traffic Commuting Dynamics Study Based on Hotspot Clustering and a New Proposed Urban Commuting Electrostatics Model
ISPRS Int. J. Geo-Inf. 2019, 8(4), 190; https://doi.org/10.3390/ijgi8040190
Received: 20 February 2019 / Revised: 1 April 2019 / Accepted: 9 April 2019 / Published: 11 April 2019
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Abstract
With the recent rapid development of cities, the dynamics of urban road-traffic commuting are becoming more and more complex. In this research, we study urban road-traffic commuting dynamics based on clustering analysis and a new proposed urban commuting electrostatics model. As a case [...] Read more.
With the recent rapid development of cities, the dynamics of urban road-traffic commuting are becoming more and more complex. In this research, we study urban road-traffic commuting dynamics based on clustering analysis and a new proposed urban commuting electrostatics model. As a case study, we investigate the characteristics of urban road-traffic commuting dynamics during the morning rush hour in Beijing, China, using over 1.3 million Global Positioning System (GPS) data records of vehicle trajectories. The hotspot clusters are identified using clustering analysis, after which the urban commuting electric field is simulated based on an urban commuting electrostatics model. The results show that the areas with high electric field intensity tend to have slow traffic, and also that the vehicles in most areas tend to head in the same direction as the electric field. The results above verify the validity of the model, in that the electric field intensity can reflect the traffic pressure of an area, and that the direction of the electric field can reflect the traffic direction in that area. This new proposed urban commuting electrostatics model helps greatly in understanding urban road-traffic commuting dynamics and has broad applicability for the optimization of urban and traffic system planning. Full article
(This article belongs to the Special Issue Algorithms and Techniques in Urban Monitoring)
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Open AccessArticle
Dynamic Land Cover Mapping of Urbanized Cities with Landsat 8 Multi-temporal Images: Comparative Evaluation of Classification Algorithms and Dimension Reduction Methods
ISPRS Int. J. Geo-Inf. 2019, 8(3), 139; https://doi.org/10.3390/ijgi8030139
Received: 30 January 2019 / Revised: 3 March 2019 / Accepted: 11 March 2019 / Published: 13 March 2019
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Abstract
Uncontrolled and continuous urbanization is an important problem in the metropolitan cities of developing countries. Urbanization progress that occurs due to population expansion and migration results in important changes in the land cover characteristics of a city. These changes mostly affect natural habitats [...] Read more.
Uncontrolled and continuous urbanization is an important problem in the metropolitan cities of developing countries. Urbanization progress that occurs due to population expansion and migration results in important changes in the land cover characteristics of a city. These changes mostly affect natural habitats and the ecosystem in a negative manner. Hence, urbanization-related changes should be monitored regularly, and land cover maps should be updated to reflect the current situation. This research presents a comparative evaluation of two classification algorithms, pixel-based support vector machine (SVM) classification and decision-tree-oriented geographic object-based image analysis (GEOBIA) classification, in producing a dynamic land cover map of the Istanbul metropolitan city in Turkey between 2013 and 2017 using Landsat 8 Operational Land Imager (OLI) multi-temporal satellite images. Additionally, the efficiencies of the two data dimension reduction methods are evaluated as part of this research. For dimension reduction, built-up index (BUI) and principal component analysis (PCA) data were calculated for five images during the mentioned period, and the classification algorithms were applied on data stacks for each dimension reduction method. The classification results indicate that the GEOBIA classification of the BUI data set provided the highest accuracy, with a 91.60% overall accuracy and 0.91 kappa value. This combination was followed by the GEOBIA classification of the PCA data set, which highlights the overall efficiency of the GEOBIA over the SVM method. On the other hand, the BUI data set provided more reliable and consistent results for urban expansion classes due to representing physical responses of the surface when compared to the data set of the PCA, which is a spectral transformation method. Full article
(This article belongs to the Special Issue Algorithms and Techniques in Urban Monitoring)
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