Special Issue "Human-Centric Data Science for Urban Studies"

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

Deadline for manuscript submissions: 30 November 2018

Special Issue Editors

Guest Editor
Assist. Prof. Bernd Resch

1. Department of Geoinformatics, Paris-Lodron University of Salzburg, Austria
2. Center for Geographic Analysis, Harvard University, USA
Website | E-Mail
Interests: fusion of human and technical sensors; people as sensors and collective sensing (VGI); standardized geo-sensor webs; real-time; smart cities
Guest Editor
Assist. Prof. Michael Szell

Center for Network Science, Central European University (CEU), Hungary
Website | E-Mail
Interests: quantifying human behavior and social networks, in particular online and in cities; urban sustainability and human mobility; data visualization; data science; complex systems

Special Issue Information

Dear Colleagues,

In the past decade, the concept of smart cities has been greatly driven by the idea of an IT-infused city, that is, an urban system enriched with a number of different information technologies to support urban management and planning. However, most previous smart city research initiatives have promoted techno-positivistic approaches, which oftentimes do not account enough for the citizens' needs. Thus, this Special Issue focuses on a more human-centric view of smart cities. A variety of large-scale datasets, sensing technologies, geo-participation initiatives, collaborative mapping tools, and data science approaches have emerged that have the potential to help us in gaining a better understanding of urban processes and how to convert them into concrete urban planning and management actions. These new developments have led to a previously unknown situation in urban science, namely the transformation from data-scarce to data-rich research environments. To optimally leverage these new datasets and technologies, the GIScience community is currently developing innovative methods that go well beyond traditional geospatial analysis, including multidisciplinary approaches combining methods from GIScience, computer and data science, urban science, sociology, computational linguistics, complex systems and networks, a.o. This Special Issue encourages the submission of both basic research papers and application-oriented contributions in the area of urban data science, dedicating a particular focus to human-centric approaches.

DATA SOURCES

  • Human sensing technologies
  • Social media and VGI
  • Mobile phone networks
  • OSM and OGD
  • Participatory geo-technologies

METHODS

  • Spatio-temporal analysis of urban processes
  • Geo-infused self-learning systems and machine learning approaches
  • Statistical analysis of urban processes and structures
  • Sentiment analysis and emotion extraction
  • Dynamic, spatio-temporal geovisualisation
  • Multidisciplinary research (GIScience, computer and data science, urban science, sociology, computational linguistics, complex systems and networks, a.o.)

APPLICATION AREAS

  • Urban planning and management
  • Mobility and transportation
  • Wellbeing, quality of life and livability
  • Energy infrastructure planning and management

Assist. Prof. Bernd Resch
Assist. Prof. Michael Szell
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 (5 papers)

View options order results:
result details:
Displaying articles 1-5
Export citation of selected articles as:

Research

Open AccessArticle Journey-to-Crime Distances of Residential Burglars in China Disentangled: Origin and Destination Effects
ISPRS Int. J. Geo-Inf. 2018, 7(8), 325; https://doi.org/10.3390/ijgi7080325
Received: 25 June 2018 / Revised: 27 July 2018 / Accepted: 6 August 2018 / Published: 12 August 2018
PDF Full-text (1479 KB) | HTML Full-text | XML Full-text
Abstract
Research on journey-to-crime distance has revealed the importance of both the characteristics of the offender as well as those of target communities. However, the effect of the home community has so far been ignored. Besides, almost all journey-to-crime studies were done in Western
[...] Read more.
Research on journey-to-crime distance has revealed the importance of both the characteristics of the offender as well as those of target communities. However, the effect of the home community has so far been ignored. Besides, almost all journey-to-crime studies were done in Western societies, and little is known about how the distinct features of communities in major Chinese cities shape residential burglars’ travel patterns. To fill this gap, we apply a cross-classified multilevel regression model on data of 3763 burglary trips in ZG City, one of the bustling metropolises in China. This allows us to gain insight into how residential burglars’ journey-to-crime distances are shaped by their individual-level characteristics as well as those of their home and target communities. Results show that the characteristics of the home community have larger effects than those of target communities, while individual-level features are most influential. Older burglars travel over longer distances to commit their burglaries than the younger ones. Offenders who commit their burglaries in groups tend to travel further than solo offenders. Burglars who live in communities with a higher average rent, a denser road network and a higher percentage of local residents commit their burglaries at shorter distances. Communities with a denser road network attract burglars from a longer distance, whereas those with a higher percentage of local residents attract them from shorter by. Full article
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
Figures

Figure 1

Open AccessArticle Identifying Modes of Driving Railway Trains from GPS Trajectory Data: An Ensemble Classifier-Based Approach
ISPRS Int. J. Geo-Inf. 2018, 7(8), 308; https://doi.org/10.3390/ijgi7080308
Received: 18 June 2018 / Revised: 24 July 2018 / Accepted: 30 July 2018 / Published: 1 August 2018
PDF Full-text (4926 KB) | HTML Full-text | XML Full-text
Abstract
Recognizing Modes of Driving Railway Trains (MDRT) can help to solve railway freight transportation problems in driver behavior research, auto-driving system design and capacity utilization optimization. Previous studies have focused on analyses and applications of MDRT, but there is currently no approach to
[...] Read more.
Recognizing Modes of Driving Railway Trains (MDRT) can help to solve railway freight transportation problems in driver behavior research, auto-driving system design and capacity utilization optimization. Previous studies have focused on analyses and applications of MDRT, but there is currently no approach to automatically and effectively identify MDRT in the context of big data. In this study, we propose an integrated approach including data preprocessing, feature extraction, classifiers modeling, training and parameter tuning, and model evaluation to infer MDRT using GPS data. The highlights of this study are as follows: First, we propose methods for extracting Driving Segmented Standard Deviation Features (DSSDF) combined with classical features for the purpose of improving identification performances. Second, we find the most suitable classifier for identifying MDRT based on a comparison of performances of K-Nearest Neighbor, Support Vector Machines, AdaBoost, Random Forest, Gradient Boosting Decision Tree, and XGBoost. From the real-data experiment, we conclude that: (i) The ensemble classifier XGBoost produces the best performance with an accuracy of 92.70%; (ii) The group of DSSDF plays an important role in identifying MDRT with an accuracy improvement of 11.2% (using XGBoost). The proposed approach has been applied in capacity utilization optimization and new driver training for the Baoshen Railway. Full article
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
Figures

Figure 1

Open AccessArticle Grid-Based Crime Prediction Using Geographical Features
ISPRS Int. J. Geo-Inf. 2018, 7(8), 298; https://doi.org/10.3390/ijgi7080298
Received: 24 May 2018 / Revised: 21 June 2018 / Accepted: 23 July 2018 / Published: 25 July 2018
PDF Full-text (8960 KB) | HTML Full-text | XML Full-text
Abstract
Machine learning is useful for grid-based crime prediction. Many previous studies have examined factors including time, space, and type of crime, but the geographic characteristics of the grid are rarely discussed, leaving prediction models unable to predict crime displacement. This study incorporates the
[...] Read more.
Machine learning is useful for grid-based crime prediction. Many previous studies have examined factors including time, space, and type of crime, but the geographic characteristics of the grid are rarely discussed, leaving prediction models unable to predict crime displacement. This study incorporates the concept of a criminal environment in grid-based crime prediction modeling, and establishes a range of spatial-temporal features based on 84 types of geographic information by applying the Google Places API to theft data for Taoyuan City, Taiwan. The best model was found to be Deep Neural Networks, which outperforms the popular Random Decision Forest, Support Vector Machine, and K-Near Neighbor algorithms. After tuning, compared to our design’s baseline 11-month moving average, the F1 score improves about 7% on 100-by-100 grids. Experiments demonstrate the importance of the geographic feature design for improving performance and explanatory ability. In addition, testing for crime displacement also shows that our model design outperforms the baseline. Full article
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
Figures

Figure 1

Open AccessArticle A Smartphone-Based System for Outdoor Data Gathering Using a Wireless Beacon Network and GPS Data: From Cyber Spaces to Senseable Spaces
ISPRS Int. J. Geo-Inf. 2018, 7(5), 190; https://doi.org/10.3390/ijgi7050190
Received: 10 April 2018 / Revised: 11 May 2018 / Accepted: 12 May 2018 / Published: 15 May 2018
PDF Full-text (8326 KB) | HTML Full-text | XML Full-text
Abstract
Information and Communication Technologies (ICTs) and mobile devices are deeply influencing all facets of life, directly affecting the way people experience space and time. ICTs are also tools for supporting urban development, and they have also been adopted as equipment for furnishing public
[...] Read more.
Information and Communication Technologies (ICTs) and mobile devices are deeply influencing all facets of life, directly affecting the way people experience space and time. ICTs are also tools for supporting urban development, and they have also been adopted as equipment for furnishing public spaces. Hence, ICTs have created a new paradigm of hybrid space that can be defined as Senseable Spaces. Even if there are relevant cases where the adoption of ICT has made the use of public open spaces more “smart”, the interrelation and the recognition of added value need to be further developed. This is one of the motivations for the research presented in this paper. The main goal of the work reported here is the deployment of a system composed of three different connected elements (a real-world infrastructure, a data gathering system, and a data processing and analysis platform) for analysis of human behavior in the open space of Cardeto Park, in Ancona, Italy. For this purpose, and because of the complexity of this task, several actions have been carried out: the deployment of a complete real-world infrastructure in Cardeto Park, the implementation of an ad-hoc smartphone application for the gathering of participants’ data, and the development of a data pre-processing and analysis system for dealing with all the gathered data. A detailed description of these three aspects and the way in which they are connected to create a unique system is the main focus of this paper. Full article
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
Figures

Figure 1

Open AccessArticle Reliable Rescue Routing Optimization for Urban Emergency Logistics under Travel Time Uncertainty
ISPRS Int. J. Geo-Inf. 2018, 7(2), 77; https://doi.org/10.3390/ijgi7020077
Received: 20 December 2017 / Revised: 6 February 2018 / Accepted: 18 February 2018 / Published: 24 February 2018
PDF Full-text (3162 KB) | HTML Full-text | XML Full-text
Abstract
The reliability of rescue routes is critical for urban emergency logistics during disasters. However, studies on reliable rescue routing under stochastic networks are still rare. This paper proposes a multiobjective rescue routing model for urban emergency logistics under travel time reliability. A hybrid
[...] Read more.
The reliability of rescue routes is critical for urban emergency logistics during disasters. However, studies on reliable rescue routing under stochastic networks are still rare. This paper proposes a multiobjective rescue routing model for urban emergency logistics under travel time reliability. A hybrid metaheuristic integrating ant colony optimization (ACO) and tabu search (TS) was designed to solve the model. An experiment optimizing rescue routing plans under a real urban storm event, was carried out to validate the proposed model. The experimental results showed how our approach can improve rescue efficiency with high travel time reliability. Full article
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
Figures

Figure 1

Back to Top